Enhanced surface-groundwater interaction modeling in the middle and lower reaches of the Songhua River Basin using a coupled SWAT-MODFLOW model

Enhanced surface-groundwater interaction modeling in the middle and lower reaches of the Songhua River Basin using a coupled SWAT-MODFLOW model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Enhanced surface-groundwater interaction modeling in the middle and lower reaches of the Songhua River Basin using a coupled SWAT-MODFLOW model xiao Yang, Chang-Lei Dai, Jian-yu Jing, Geng-wei Liu, Qing Ru, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5310099/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 15 You are reading this latest preprint version Abstract The management of groundwater resources and the rehabilitation of groundwater levels in the middle and lower portions of the Songhua River basin have consistently garnered significant attention in our country. The SWAT-MODLFOW model, developed on the QSWATMOD2 platform, was calibrated and validated utilizing river runoff and groundwater observation data to precisely illustrate the transformation relationship across various spatial and temporal scales in the middle and lower reaches of the Songhua River basin, characterized by numerous agricultural and irrigation zones with frequent surface water conversion. The water cycle process in the middle and lower sections of the Songhua River basin is simulated and studied based on this foundation.The results show: (1) The SWAT-MODLFOW coupling model has a good simulation effect, and the simulation effect of menstrual flow in the periodic and verification periods is R 2 ≥0.86, NSE≥0.87, R 2 ≥0.76, NSE≥0.77, respectively. The simulated groundwater level and the actual error value are within 0.6m, and the R 2 in the periodic and verification periods are 0.97 and 0.98, respectively. The simulation results of the model are satisfactory and meet the requirements of scientific research. (2) The groundwater in the study area generally decreases in the direction of west-north to northeast, and in the direction of east-south to north, and the groundwater level is affected by precipitation. Jiamusi, Fujin and Tongjiang, three major cities in the study area, are selected for characteristic study, and the lag time of their groundwater level to precipitation is about 10.56d, 10.58d and 3.15d. (3) The river channels of surface water recharge groundwater occupy 41.75% of the total length of Jiamusi - Tongjiang section of Songhua River, and the annual average recharge accounts for 50.84% of the total exchange water; On the seasonal scale, the maximum recharge value of each river section appeared in August, and the minimum recharge value appeared in April. On an annual scale, the maximum recharge occurred in 2009 and the minimum in 2014. The supply of groundwater to surface water fluctuates obviously, with seasonal variation ranging from -52% to 55% and inter-annual variation ranging from -35% to 52%. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology SWAT-MODFLOW coupling model Wavelet analysis Surface water-groundwater conversion relationship Groundwater level prediction The middle and lower reaches of Songhua River basin Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Water is the essence of life, the cornerstone of production and ecology, and a vital natural resource[1]. Currently, China's water resources confront significant problems, including limited total water availability and severe water pollution. As the social economy rapidly develops, the demand for water resources is steadily rising[2]. The middle and lower portions of the Songhua River, specifically the Jiamusi-Tongjiang basin, are significant locations where the groundwater contribution from rivers constitutes over fifty percent of the natural supply in China's Sanjiang Plain[3]. Nonetheless, the swift economic advancement over the past three decades has led to the over-cultivation of paddy fields and the imprudent management of water resources[4], resulting in significant environmental and ecological issues, including the depletion of groundwater levels and land subsidence, which have emerged as critical impediments to the sustainable development of the economy and society. The advancements in agriculture and the grain industry in this region are attributable to the water transport provided by numerous inland rivers, particularly the main channel of the Songhua River. The river starts in the top mountainous region, where numerous tributaries disperse in the middle and lower piedmont plain, and the conversion between surface water and groundwater is common[5]. Consequently, examining the relationship between surface water and groundwater conversion in the middle and lower portions of the Songhua River basin is crucial for ecological preservation, judicious water resource allocation, and the sustainable development of the basin. The techniques employed to examine the interplay between surface water and groundwater transformation encompass hydrological data analysis [6], isotope tracer methodology [7], and hydrological modeling approaches [8]. Notably, the distributed hydrological modeling method [9,10] effectively captures the temporal and spatial variability of the basin and holds significant applications in water cycle simulation. Generally, dispersed hydrological models primarily emphasize the characterisation of surface water circulation, while the characterization of groundwater in the corresponding regions is significantly oversimplified [11]. Researchers have increasingly acknowledged the significance of modeling surface water and groundwater collectively in regions where the interaction between the two is intricate, leading to the development of the coupled surface water-groundwater model[12~14]. Currently, pertinent researchers both domestically and internationally have conducted more comprehensive research on the coupling model. Based on several model coupling methodologies [15]. Coupling models can be classified into three categories: Loosely connected models, exemplified by the four-water conversion model [16,17], which is predicated on the principles of water circulation and balance within the basin, streamline the interactions and conversions between surface and groundwater models, thereby exhibiting some limitations. The SWAT model employs a semi-loosely coupled approach, utilizing SWAT-modflow [18,19]. However, its groundwater calculation module lacks precision in simulating groundwater processes. Consequently, the established MODFLOW model is implemented to supplant the original groundwater module, enhancing the accuracy of groundwater simulations. Tightly coupled models, such as MIKE-SHE[20], possess comprehensive surface water and groundwater modules and exhibit great computational accuracy; nevertheless, the extensive fine data required for modeling constrains their practical implementation. The semi-loosely coupled model is often utilized due to its significant flexibility in modeling and superior simulation accuracy [21]. Kim[22] and Sophodeous[23] validated the SWATMOD coupling model in a natural watershed, yielding excellent simulation results. To enhance the spatial scale and resolve the calculation unit discrepancy in the coupling process of SWAT and MODFLOW, Liu Luguang et al. [24] modified the SWAT source code and developed the SWATMOD coupling model for irrigation districts to address the issue of calculation unit mismatch. Simultaneously, it advocates for the implementation of the SWATMOD coupling model from natural river basins to regions characterized by frequent human activity. Aliyari et al. [25] confirmed the feasibility of the SWAT-MODFLOW coupling model in a substantial agricultural and urban mixed watershed. The results indicate that the coupled model enhances the accuracy of the groundwater model in comparison to the conventional groundwater model. Bailey[26] developed SWATMOD-PREP using the Python programming language to streamline the coupling process. Utilizing the QGIS platform, Park[27] built the QSWATMOD2 plug-in in Python, enhancing the development of coupling models, operational efficiency, result visualization, and other facets. In conclusion, the SWATMOD coupling model is highly effective for simulating the interaction between regional surface water and groundwater in the middle and lower portions of the Songhua River. This paper seeks to investigate the interaction between surface water and groundwater transformation in the middle and lower parts of the Songhua River basin, drawing on the aforementioned research background and prior model design expertise. The database necessary for the SWAT model is developed by gathering soil, land use, and meteorological data from the study area, while parameter sensitivity analysis, calibration, and validation of the model are conducted using the measured runoff data from the Tongjiang hydrology station in the study area. The MODFLOW model is developed using the MODFLOW module in GMS software, based on the gathered hydrogeological data, groundwater extraction volumes from various irrigation regions in the research region, and mechanical well data. The SWAT-MODFLOW coupling model is ultimately developed using the QSWATMOD2 plug-in within the QGIS platform. Parameter calibration and study of groundwater equilibrium were conducted. The methodological pathway of this study is illustrated in Figure 1 below. Study area profile The central and lower sections of the Songhua River are situated in the eastern region of Heilongjiang Province, China. It is a plain created by the combined influence of the Jiamusi-Tongjiang segment of the Songhua River and its tributaries. It is situated between 46°45'5"N and 47°38'17"N, and between 130°18'47"E and 132°32'07"E, encompassing the cities of Jiamusi, Fujin, and Tongjiang. Huachuan and Suibin are two county-level cities comprising Zhenxing Township, Jianguo Town, Fengle Town, Xingan Township, Yongan Township, Xincheng Town, Donghe Township, Shangshangji Township, and Suidong Township, encompassing nearly 150 villages and 15 irrigation zones, with a total area of approximately 6,720 km²[28]. The topography of the basin is predominantly low and level, primarily ranging from 50 to 80 meters. The topography is elevated in the northwest and southwest, while it is depressed in the northeast, resulting in an overall land gradient of 0.10‰. The southern region of the middle and lower portions of the Songhua River basin comprises the alluvial plain of the Qixing River and the Naoli River, elevated on both sides; to the east lies the flat terrain of the Qihulin flood area, while to the west is the rugged mountainous region. The Amur River basin is located to the north, as illustrated in Figure 2. The principal river in the research area is the Jiamusi-Tongjiang segment of the Songhua River's main course. The Songhua River rises in the Tianchi and Greater Khingan Mountains of the Changbai range, extending 939 kilometers in its main stream and encompassing a basin area of 565,900 square kilometers. The river in the research region measures 209 km in length[29]. The primary tributaries in the research area are Wutong River, Anbang River, Dulu River, and Meandering River, among others, with their sources predominantly located in hilly regions. The tributaries of the Songhua River system exhibit steep gradients and swift currents in the upper reaches, significant mountain flooding, and constricted river channels in the middle and lower reaches, resulting in inadequate flood discharge and limited river expansion. The research area is situated on the eastern periphery of the mid-latitude Asian continent, characterized by a moderate continental monsoon climate, with an average annual temperature ranging from 1 to 4℃ and four distinct seasons. The spring is brief and blustery, the summer is humid and rainy, with an average temperature in July reaching 25℃. The autumn is short and experiences a rapid drop in temperature, while the winter is prolonged, cold, and arid, with an average temperature in the coldest month (January) falling below -18℃. The initial frost occurs in mid to late September, the final frost in mid to early May, resulting in a frost-free duration of approximately 120 to 140 days. The yearly daylight duration is from 2400 to 2500 hours, the freezing season extends from November to March annually, and the greatest freezing depth is between 1.6 and 2.2 meters [30]. The precipitation data from the Jiamusi meteorological station indicates that the annual average precipitation ranged from 425.5 mm to 688 mm, exhibiting a gradual upward trend from 2008 to 2016, with the majority occurring in July and August, comprising approximately 65% of the annual total. Evaporation data in the study area typically varied from 600mm to 843mm, declining from 2008 to 2010, and stayed comparatively steady until October 2016 [31]. The middle and lower reaches of the Songhua River basin exhibit several soil types, with meadow soil and white pulpy soil being the most prevalent, while black soil possesses the highest fertility. The black soil in the basin is predominantly located in the mountain front region, with a thickness exceeding 30 cm and an average organic matter level of 5-6%. Meadow soil is predominantly found in the floodplains of both small and big rivers within the basin [32]. The 2022 remote sensing land use data published by the National Academy of Space and Space Sciences indicates that the watershed primarily comprises paddy fields, dry fields, marshlands, forests, grasslands, and areas designated for human habitation and building [33]. Paddy and dry fields predominantly characterize the landscape, with land use primarily dedicated to agriculture. The stratigraphic structure of the studied area is intricate, with the basin, created by neotectonic activity, representing a depression near the northern terminus of the second uplift zone of the Neocayathia tectonic system. Tectonically, it is part of the Tongjiang inland fault depression and constitutes a significant subsidence zone from the middle to the Cenozoic age. The middle and lower reaches of the Songhua River basin are situated on a substrate consisting of pre-Paleozoic metamorphic rocks, as well as Paleozoic and Mesozoic volcanic sedimentary rocks. The basin created by the Tertiary depression has the traits of ongoing, extensive, intermittent subsidence. The lithology of the formation in the research area predominantly consists of Quaternary strata, primarily consisting of sand and gravel, with a thickness ranging from 100 to 200 meters in most regions. The riverbed and floodplain of the main stream of the Songhua River and its tributaries are predominantly comprised of Holocene (Q4) deposits, specifically a thin layer of yellow clay and sub-clay, together with sand and gravel in the lower section of the basin. The terrace of the interriver zone in the basin predominantly consists of Upper Pleistocene (Q3) yellow-brown sand and gravel, with discontinuous sub-clay above. The flat region corresponds to the Middle Pleistocene (Q2), characterized by gray-brown and gray-black silty sand, sand, and gravel, with the substrate partially mixed with sub-clay and silty sub-clay. In contrast, the overall basin floor belongs to the Lower Pleistocene (Q1), comprising yellow-green and gray-green medium sand, fine sand, silty sand, and gravel [34]. 1. METHODS 1.1 Construction of SWAT model 1.1.1 Introduction to the SWAT model The ArcSWAT model [35] is a long-term distributed hydrological model for river basins, created by the Agricultural Research Institute of the United States Department of Agriculture during the mid to late 1990s. This model has been enhanced at a fundamental level regarding its physical process compared to earlier versions. It can not only characterize hydrological situations by linear regression but also comprehensively account for various processes of surface water bodies in its characterisation. The model may replicate several physical processes, including water migration, sediment transport, and crop growth, by incorporating pertinent data regarding production and surface confluence. Furthermore, the model possesses the subsequent characteristics: The model's code accessibility facilitates its utilization by researchers. Secondly, the model regularizes input data, accommodates diverse data formats and sources, and exhibits excellent computational efficiency, enabling rapid completion of extensive data processing and operations. Moreover, the model is reasonably straightforward to utilize, exhibits great computational precision, and can yield superior simulation outcomes. Nonetheless, it is important to note that the simulation accuracy of the groundwater process is significantly inadequate; therefore, if this model is to be utilized in groundwater applications, further enhancements and optimizations are necessary. The hydrological processes encompassed in the SWAT model primarily consist of atmospheric precipitation, evapotranspiration, soil flow, surface runoff, and river network confluence, among others. Table 1 shows the relevant governing equations of the model. Table 1 Introduction to the principle and formula of SWAT model Formula introduction Formula Formula symbol introduction Water balance equation of SWAT model: Where: \(\:{\text{S}\text{W}}_{\text{t}}\) is soil moisture content (mm), \(\:\text{t}\) is time (days); \(\:{\text{s}\text{w}}_{0}\) is the initial soil water content (mm) on day i; \(\:{\text{R}}_{\text{d}\text{a}\text{y}}\) is precipitation (mm); \(\:{\text{Q}}_{\text{s}\text{u}\text{r}\text{f}}\) is the surface runoff of day i (mm); \(\:{\text{E}}_{\text{a}}\) is the evaporation amount of day i (mm); \(\:{\text{Q}}_{\text{s}\text{e}\text{e}\text{p}}\) is the amount of water (mm) in the enshrouding zone reached through the soil profile on day i; \(\:{\text{Q}}_{\text{g}\text{w}}\) is the regression flow on day i (mm). In this study, the SCS runoff curve equation is applied to calculate the surface runoff of the basin. SCS curve equation method formula is shown in the right table: Where: \(\:\text{P}\) precipitation (mm); \(\:{\text{I}}_{\text{a}}\) is the precipitation loss before surface runoff (mm), \(\:{\text{I}}_{\text{a}}\) =2S; \(\:\text{S}\) is the maximum possible retention of the basin (mm), S=25400/CN-254(CN value can be obtained according to the combination of soil type, land use type and vegetation cover type); Evapotranspiration in SWAT simulation mainly includes water surface evaporation, soil water evaporation and plant evapotranspiration. In addition, measured daily potential evaporation data can also be used. The actual evaporation calculation includes canopy interception evaporation E can, vegetation transpiration Et and soil water evaporation. First, it is assumed that the canopy trapped water is evaporated as much as possible. When all the canopy trapped water is evaporated, the remaining evaporation comes from vegetation transpiration and soil evaporation. The main calculation equation is shown in the table on the right: Where, \(\:{\text{E}}_{\text{a}}\) is the canopy water evaporation (mm); \(\:{\Delta\:}{\text{W}}_{\text{i}\text{n}\text{t}}\) is the variation of water storage during the canopy period (mm); \(\:{\text{E}}_{\text{t}}\) is the maximum daily transpiration (mm); \(\:{\text{E}}_{0}^{{\prime\:}}\) is the remaining potential evaporation after canopy water evaporation (mm); \(\:\text{L}\text{A}\text{I}\) is the leaf area index; \(\:{\text{E}}_{\text{s}\text{o}\text{i}\text{l},\text{z}}\) is the evaporation water requirement at Z depth (mm); Z is the depth of the soil below the surface (mm); \(\:{\text{E}}_{\text{S}}^{{\prime\:}{\prime\:}}\) is the maximum soil water evaporation (mm). Soil water can be absorbed by plants, and can also be recharged to groundwater and/or form a soil flow. The critical condition of soil flow is that soil water content exceeds field water capacity. The flow in soil simulated by SWAT mainly includes uniform flow in small pores and preferential flow (or pipe flow) in large pores, and the calculation of preferential flow is selected according to the actual situation. The formula for calculating lateral flow in soil is as follows: \(\:{Q}_{int}=0.024\times\:\left(\frac{2\times\:S{W}_{ly,excess}\bullet\:{K}_{sat}\bullet\:slp}{{\varphi\:}_{d}\bullet\:{L}_{ℎill}}\right)\) Where: \(\:\text{S}{\text{W}}_{\text{l}\text{y},\text{e}\text{x}\text{c}\text{e}\text{s}\text{s}}\) is the amount of water that can flow out of the soil saturated area (mm); \(\:{\text{K}}_{\text{s}\text{a}\text{t}}\) is soil saturated water conductivity (mm/h); \(\:\text{s}\text{l}\text{p}\) is the slope; \(\:{\text{L}}_{\text{h}\text{i}\text{l}\text{l}}\) is the slope length; \(\:{{\upvarphi\:}}_{\text{d}}\) is the difference between total soil porosity and the field water capacity and porosity achieved by soil moisture content. Transport loss will regulate runoff and peak discharge in the basin. The formula in the right table is the formula of transport loss: \(\:{t}_{loss}={K}_{cℎ}\bullet\:TT\bullet\:{P}_{cℎ}\bullet\:{L}_{cℎ}\) Where: \(\:{\text{t}}_{\text{l}\text{o}\text{s}\text{s}}\) is river water loss (m3); \(\:\text{T}\text{T}\) the time required for upstream water to downstream water (hr); \(\:{\text{K}}_{\text{c}\text{h}}\) is the effective water conductivity of the river (mm/hr); \(\:{\text{P}}_{\text{c}\text{h}}\) is wet circumference (Km); \(\:{\text{L}}_{\text{c}\text{h}}\) the length of the river is (Km). 1.1.2 SWAT model data preparation and database construction The fundamental data necessary for SWAT model development can be categorized into two types: geographical data and attribute data. Spatial data primarily encompass digital elevation models, soil distribution maps, and land use distribution maps. Attribute data consist of meteorological and hydrological data.The data formats required for this survey are shown in Table 2 . Table 2 Basic geographic data required for constructing SWAT model in the study area Data type Data source Digital Elevation Model (DEM) NASA Earth Science data website( https://nasadaacs.eos.nasa.gov/ ) Soil type and attribute list HWSD data downloaded from the National Tibetan Plateau Scientific Data Center (World Soil Database)(data.tpdc.ac.cn) Land type use data Institute of Aerospace Information Innovation, Chinese Academy of Sciences Meteorological data CMADS (V1.1) downloaded by the National Tibetan Plateau Scientific Data Center(data.tpdc.ac.cn) Runoff data Tongjiang city hydrology station This study utilized DEM data obtained from the NASA Earth science data website, employing DEN topographic data with 30-meter precision, and standardized spatial and projection coordinates via ArcGIS 10.6. The acquired data are presented in Fig. 3 a below. The delineation of sub-basins is the preliminary phase of SWAT model development, resulting in the creation of 32 sub-basins for the middle and lower reaches of the Songhua River basin (see to Fig. 3 b below). The soil data utilized in the development of the SWAT model soil database for this study was sourced from the Harmonized World Soil Database, obtained from the National Tibetan Plateau Scientific Data Center. This data comprises essential input parameters for the SWAT model, including the soil type distribution map, soil index table, and soil physical property file [36]. Subsequent to projection and clipping, the soil type distribution map is juxtaposed with the soil physical property file, and the soil types exhibiting identical physical properties are reclassified to produce the soil type distribution map illustrated in Fig. 3 c below. The classification outcomes were segmented into eight soil groups. This study details the calculation of soil data parameters for the SWAT model using SPAW [37](Soil Profile Water Transfer) software, where the carbon content in the soil layer must be transformed into organic mass before being entered into the SPAW software for computation. Furthermore, to streamline the computation of USLE-K parameters inside the model, the quantities of clay loam and clay in the database were determined using the substitution formula suggested by Williams [38]. The precise values for soil layer 1 and soil layer 2 from the final calculations are shown in Table 3 , while the corresponding explanations of the soil physical coefficients in Table 3 are detailed in Table 4 . Table 3 Soil coefficient and level calculated by SPAW Coefficient Soil type SOL_BD1 SOL_AWC1 SOL_K1 SOL_CBN1 SOL_BD2 SOL_AWC2 SOL_K2 SOL_CBN2 Hierarchy FLc 1.53 0.14 9.32 0.6 1.48 0.14 12.65 0.4 L-L LPe 1.55 0.1 9.36 1.13 0 0 0 0 L PHh 1.37 0.14 14.24 1.95 1.52 0.13 8.22 0.67 L-L GLm 1.41 0.14 13.58 1.65 1.5 0.13 5.2 0.69 L-CL HSs 1.14 0.13 13.65 39.4 1.18 0.14 22.43 38.46 CL-SaCL ATc 0.98 0.18 44.52 1.12 1.49 0.14 8.94 0.82 SIL-L LVh 1.52 0.13 9.33 0.74 1.52 0.13 4.11 0.36 L-CL CMe 1.49 0.13 10.27 1 1.55 0.12 5.70 0.37 L-L WATER 1.72 0 260 0 0 0 0 0 Table 4 Related descriptions of soil coefficients involved in the calculation of SPAW Coefficient Description Coefficient Description SOL_BD weight of dried soil, comprising soil particles and intergranular pores, per unit volume. It stands for the moist bulk density of soil (SOILdensity). CLAY Clay content, %wt, refers to soil particles < 0.002mm in diameter. SOL_AWC Indicates the effective water content of soil layer, in mm/mm. SILT SILT1 refers to the loam content of the soil (%wt), that is, the percentage by weight of soil particles between 0.002 and 0.05mm in diameter. SOL_CBN Organic carbon content (%wt) of the soil layer. SAND Sand content, %wt, refers to particles with diameters between 0.05 and 2.0mm; SOL_K Saturated water conductivity/saturated hydraulic conductivity, mm/hr; ROCK Gravel content, %wt, refers to particles with a diameter greater than 2mm; SOL_ZMS Represents the maximum root depth of the soil profile, mm. USLE_K Erodibility factor The land use data is derived from the 2022 global land cover dataset with a 30-meter resolution, published by the Academy of Aerospace Information Innovation of the Chinese Academy of Sciences. The land use types in the study region were divided into six categories: cultivated land, forest land, grassland, water bodies, urban and rural areas, industrial and mining land, residential land, and unused land (refer to Fig. 3 d below). This paper primarily utilizes daily meteorological data encompassing precipitation, temperature, relative humidity, solar radiation, and wind speed. The meteorological data utilized were CMADSV1.1 datasets obtained from the National Tibetan Plateau Scientific Data Center [39]. The duration spans from 2008 to 2016, thereby satisfying the temporal criteria for the model's operation. This database is among the most extensively utilized meteorological datasets for the SWAT model. This database satisfies the accuracy criteria for the model's final output outcomes, following extensive utilization by numerous scholars [40]. 1.1.3 Sensitivity coefficient analysis and model calibration and verification The runoff simulation of the SWAT model involves numerous factors. To diminish the intricacy of parameter calibration, SWAT-CUP software is employed to assess the sensitivity of model parameters and identify those that significantly influence the model's simulation outcomes. This research employs P-value and t-statistic to assess parameter sensitivity. The t-statistic indicates the sensitivity of the parameter, whereas the p-value denotes the significance degree of that sensitivity. A greater absolute value of the T-value correlates with a p-value approaching 0, signifying an elevated significance level of the parameter's sensitivity. This study utilizes the SUFI2 algorithm, characterized by low computational accuracy yet high efficiency, in conjunction with SWAT-CUP software—a calibration uncertainty program appropriate for basins exhibiting relatively uncomplicated runoff variations—based on runoff data from Jiamusi City's Tong Jiang Hydrological Station collected between 2008 and 2016[41 ~ 42]. Twenty-two parameters closely associated with runoff were chosen for sensitivity analysis. The iterations were established at 500 each run, and the sensitivity analysis utilized global sensitivity analysis. The ultimate outcome is depicted in Fig. 4 . Reinsert the revised parameters into the model, revise the worksheet, and execute the validation once more. The findings of the runoff rate determination and verification are presented in Fig. 5 . The experimental results indicate that the runoff simulation of the Tongjiang hydrological station is optimal, with a rate period of R 2 > 0.87 and NSE > 0.76, and a verification period of R 2 > 0.81 and NSE > 0.76. 1.2 Construction of MODFLOW model 1.2.1 Introduction to MODFLOW model The concept of groundwater numerical modeling is derived from Darcy's Law, introduced by the renowned French engineer Darcy in 1856. Consequently, with advancements in groundwater numerical simulation theory and the evolution of computer software, groundwater numerical simulation software based on computer programs is becoming increasingly sophisticated. Currently, the primary numerical simulation approaches for groundwater are the finite difference method and the finite element method, with the former being extensively utilized by researchers. Currently, widely utilized modeling software includes Visual MODFLOW, FEFLOW, GMS, among others. Researchers domestically and internationally have employed several iterations of MODFLOW software [43]to undertake extensive studies in groundwater science. This work utilized the MODFLOW module inside GMS software, characterized by its user-friendly interface and effective 3D visualization, to construct a groundwater flow model for the middle and lower portions of the Songhua River basin [44]. The flow model program utilized in this instance is the MODFLOW-2005 version. The U.S. Geological Survey designed the program to enhance the management of unconfined aquifers. This module is an independent program intended to address issues related to nonlinear, non-confined aquifers involving dry and wet conditions. The fundamental governing equation of MODFLOW is as follows: $$\:\frac{\partial\:}{\partial\:x}\left({K}_{xx}\frac{\partial\:ℎ}{\partial\:x}\right)+\frac{\partial\:}{\partial\:y}\left({K}_{yy}\frac{\partial\:ℎ}{\partial\:y}\right)+\frac{\partial\:}{\partial\:z}\left({K}_{zz}\frac{\partial\:ℎ}{\partial\:z}\right)-W={S}_{s}\frac{\partial\:ℎ}{\partial\:t}$$ Where: \(\:{K}_{x}\text{、}{K}_{y}\) 、 \(\:{K}_{z}\) is the permeability coefficient (m/d) along the x, y and z axes; \(\:ℎ\) is the water head (m), \(\:W\) is the underground water source and sink (m/d), including precipitation infiltration recharge, irrigation return water, diving evaporation, mechanical well production, aquifer and river exchange water, diving and confined water exchange water; Up to unit volume flow through medium and isotropic soil in non-equilibrium state; \(\:{S}_{s}\) is the specific water storage coefficient of porous medium; \(\:t\) is time (d). 1.2.2 Conceptual model of hydrogeology The primary recharge sources of the groundwater system in the middle and lower portions of the Songhua River basin include piedmont lateral recharge, precipitation recharge, river lateral recharge, and irrigation area regression recharge. The drainage mostly comprises industrial and agricultural water, as well as groundwater evaporation. The extensive irrigation area in the studied region results in significant agricultural water use. The chosen simulation range for the study area aligns with the SWAT model. The submersible aquifers in the research area predominantly consist of Quaternary Holocene sand and gravel, with a thickness ranging from 100 to 150 meters in most regions. The riverbed and floodplain of the main stream of the Songhua River and its tributaries are predominantly comprised of Holocene (Q4) deposits, specifically a thin layer of yellow clay and sub-clay, together with sand and gravel in the lower basin. The terrace of the interriver zone in the basin predominantly consists of Upper Pleistocene (Q3) yellow-brown sand and gravel, with discontinuous sub-clay above. The flat region corresponds to the Middle Pleistocene (Q2), characterized by gray-brown, gray-black silty sand, sand, and sand gravel, with the lower section being a mixture of sub-clay and silty sub-clay. The basin's general base consists of Lower Pleistocene (Q1) deposits, characterized by yellow-green, gray-green medium sand, fine sand, silty sand, and sand gravel. This simulation treats the pore water of the entire Quaternary unconsolidated sediments as a singular aquifer. The model's roof elevation is derived from the interpolation of 30m precision DEM elevation data, while the upper boundary is determined using the approximate aquifer thickness documented in the hydrogeological data for the study area. Figure 6 a below illustrates the geological profile of the research region. Based on prior experience and pertinent hydrogeological data from Sanjiang Plain, the study region is initially categorized into five zones and further subdivided into seven zones according to the model's layer count. Refer to Fig. 6 b and ascertain the initial value (see Table 5 ). Moreover, a significant hydraulic connection exists between the northwestern and southern peripheries of the study area and the basin within it, with the northwestern and southern mountains receiving piedmont lateral recharge; thus, they are collectively classified as lateral inflow boundaries. The northern section of the study area represents the convergence of the Heilongjiang basin and the Songhua River basin, which aligns approximately parallel to the isowater line, thus categorized as the lateral inflow boundary. Conversely, the eastern boundary is classified as the zero flow boundary due to the minimal vertical flow observed. The western boundary of the study area features numerous outflowing tributaries, including the Wutong River and Anbang River, hence it is classified as a continuous head boundary. 1.2.3 Space-time dispersion and initial condition determination of the model Grid Division: The area delineated by SWAT served as the research domain for MODFLOW, encompassing an effective calculation area of 10,788.1 km². This research area was segmented into 1,000 m × 1,000 m square grids, resulting in a division into 168 rows, 198 columns, and 3 layers, comprising a total of 99,792 effective grids. The initial water level of the model was established using the iso-water level of the middle and lower sections of the Songhua River basin on January 31, 2008 (refer to Fig. 6 c). The simulation period was designated from January 2008 to December 2018, with each month serving as the stress period. Table 5 Initial value range of model geological parameters partition number Initial range of permeability coefficient(m/d) Initial value range of water supply degree Ⅰ 20 ~ 25 0.1 ~ 0.2 Ⅱ 15 ~ 20 0.15 ~ 0.20 Ⅲ 15 ~ 20 0.10 ~ 0.15 Ⅳ 1 ~ 5 0 ~ 0.1 Ⅴ 15 ~ 20 0.1 ~ 0.2 ⅰ 20.0 ~ 25.0 0.001 ~ 0.002 ⅱ 10.0 ~ 15.0 0.01 ~ 0.02 ⅲ 15.0 ~ 20.0 0.01 ~ 0.02 ⅳ 10.0 ~ 15.0 0.001 ~ 0.002 ⅴ 20.0 ~ 25.0 0.01 ~ 0.02 ⅵ 18.0 ~ 20.0 0.001 ~ 0.002 ⅶ 15.0 ~ 20.0 0.001 ~ 0.002 1.3 Construction of SAWT-MODFLOW coupling model 1.3.1 Introduction to SAWT-MODFLOW coupling model The SWATMOD coupling model integrates the strengths of the SWAT model for surface water analysis and the MODFLOW model for groundwater analysis. It can analyze the alterations in water resource utilization and agricultural production resulting from irrigation, fertilization, tillage, and other interventions, as well as assess the variations in surface water resources and the comprehensive dynamic changes in groundwater levels across each study area. The precision of assessing the correlation between surface water and groundwater in a region is enhanced. This study utilizes the mapping relationship between the hydrological response unit of the SWAT basic computing unit and the basic computing unit of MODFLOW, established via the QGIS platform, to transfer the soil seepage flow calculated by SWAT software to the MODFLOW grid, assigning values to each grid as RCH recharge packets. The subsurface water quantity computed by MODFLOW is conveyed to the SWAT sub-watershed via a mapping connection. 1.3.2 Calibration and verification of SAWT-MODFLOW coupling model The calibration of the SWAT-MODFLOW coupling model primarily consists of two components: the calibration of the surface SWAT model and the calibration of the subsurface MODFLOW model. Five highly sensitive characteristics were chosen for the calibration of the SWAT model. The runoff data from Jiamusi Hydrologic Station, collected between 2008 and 2016, was utilized for calibration purposes. The linear regression coefficient R² and the Nash-Sutcliffe efficiency coefficient (NSE) were chosen to assess the model's simulation performance. The R 2 and NSE values for rate periodicity, as depicted in Fig. 7 a, were found to be 0.86 and 0.87 respectively. During the verification period, an R 2 value of 0.77 and an NSE value of 0.75 were obtained, indicating a satisfactory simulation performance.. The MODFLOW model calibration involved selecting the permeability coefficient for parameter calibration, utilizing measured groundwater levels from January 2008 to December 2012, and employing measured water levels from January 2012 to December 2016 for parameter verification, ultimately yielding the optimal parameters presented in Table 6 . In addition, according to the regression analysis of the simulated water level and the measured water level shown in Fig. 7 b, the period R² is 0.98, and the verified R² is 0.97, indicating that the simulation results of the groundwater level by the model are in good agreement with the measured data, indicating that the model better meets the standards of scientific research. Table 6 Final values of geological parameters partition number Value of the permeability coefficient(m/d) Initial value of water supply Ⅰ 22 0.18 Ⅱ 16 0.13 Ⅲ 16 0.12 Ⅳ 2 0 ~ 0.1 Ⅴ 17 0.15 ⅰ 23.0 0.0014 ⅱ 13.0 0.009 ⅲ 15 0.009 ⅳ 14 0.008 ⅴ 23 0.0014 ⅵ 17.0 0.0011 ⅶ 17.0 0.0011 2. RESULTS 2.1 Analysis of influencing factors of groundwater level Surface water and groundwater constitute an interconnected hydrologic continuum in nature. The intricate and variable interplay among them influences the hydrologic cycle and water balance assessment in the basin. Precipitation and evaporation are the primary sources of groundwater replenishment. The lag effect of precipitation recharge and the impact of evaporation on groundwater levels govern the fluctuations of the water table. This chapter employs cross wavelet transform [45~57] to elucidate the interrelationship between groundwater levels and precipitation and evaporation. The cross wavelet transform of groundwater levels (refer to FIG. 8C) and precipitation data (refer to FIG. 8B) for Jimusi, Fujin, and Tongjiang was conducted using MATLAB software. FIG. 8 (A) illustrates the results, with red and blue denoting the peak and valley values of energy density, respectively, thereby demonstrating the location and dynamic properties of the time-frequency transition of the predominant wave group. The enclosed region delineated by the thick black solid line is depicted by a red noise test at a 95% confidence level, while the envelope formed by the thin black solid line signifies the cone of wavelet effect (COI). Figure 8A(a) illustrates that the elevated energy regions of groundwater levels and precipitation in Jiamusi City predominantly occur between 10 to 15 months. These findings passed the red noise test at a 95% confidence level from 2008 to 2016, indicating a significant correlation between the two variables, with the primary resonance period approximately 1 year. Figure 8A(b) illustrates that the groundwater level and precipitation in Fujin City successfully passed the red noise test at the 95% confidence level from 2008 to 2016, indicating that the majority of time periods met the significance criterion, with a predominant resonance period of approximately 1 year over this interval. Figure 8A(c) illustrates that the groundwater level and precipitation in Tongjiang City successfully passed the red noise test at a 95% confidence level from 2009 to 2014 and throughout 2016, with significant results for the majority of time periods, indicating a primary resonance period of approximately one month during this interval. Furthermore, the phase relationship is quantified using the mean phase angle of a significance test beyond the coherence of interest, followed by an analysis of the delay characteristics between the two time series. The cross-phase of time series between precipitation and groundwater levels for considerable periods outside the cone of influence can be derived by integrating Figure 8A. The lag period of groundwater levels in Jiamusi City, Fujin City, and Tongjiang City in relation to precipitation is approximately 10.56 days, 10.58 days, and 3.15 days, respectively. The wavelet analysis of evaporation (refer to Figure 8B) and groundwater levels throughout the three regions indicates that evaporation exhibits a coherent period of 10 to 13 months concerning groundwater levels, with analogous results observed at other sites. Evaporation exhibits a robust correlation with groundwater at each station, and this correlation is generally consistent; the impact of evaporation on groundwater levels across the stations is analogous. The variation in groundwater level across the entire study area is quite consistent. Aside from the weak correlation between groundwater levels in the northwestern region of the study area and other regions, the water level disparity peaks at approximately 30 meters. The temporal variation trend of groundwater levels is markedly distinct, while the correlation among the central, northern, eastern, and northeastern plains of the study area is strong, with minimal fluctuations in spatial scale at any given time. The highest variation of the water table is within 15 meters. The correlation between rainfall and evaporation concerning the groundwater table of each sub-basin indicates that precipitation exerts a greater influence on the fluctuations of the groundwater table than evaporation. Furthermore, the changes in the groundwater table in sub-basins 1, 2, 3, and 11, as well as sub-basins 16 to 20, are significantly influenced by both precipitation and evaporation, with a P value approaching 0.05. The fluctuation pattern of groundwater levels between 4 to 10 and 22 to 32 in sub-basins is minimally influenced, with a P value approaching 0.3, as illustrated in FIG. 8d for further details. 2.2 Analysis of the transformation relationship between surface water and groundwater To enable the analysis of the spatial variation of surface water-groundwater conversion in the main stream of the Songhua River, this study segmented the river, as extracted by the SWAT-MODFLOW model, into 11 sections and illustrated the surface water-groundwater conversion relationships in the study area using distinct colors, as depicted in Figure 9. The link between surface water and groundwater conversion exhibits significant regional heterogeneity, with the blue part of the river indicating that the prevailing tendency throughout the years in river reaches is surface water recharging groundwater. The yellow part of the river signifies that the interaction between surface water and groundwater has been often altered throughout the years, with a modest trend towards groundwater recharging surface water. The dark blue area signifies that the prevailing tendency of surface water to groundwater conversion in the river segment throughout the years is surface water recharge, and this pattern is markedly evident. In the entire basin, the river channel responsible for surface water recharging groundwater constitutes 41.75% of the total length, predominantly occurring in river segments 1, 5, 6, 8, and 9, with an annual average recharge of 3.60×10 7 m³/d. It constitutes 50.84% of the average yearly total water exchange. Groundwater recharge to surface water occurs mostly in river sections 2, 3, 4, 7, 10, and 11, with an average annual groundwater discharge of 5.02×107 m³/d, or 58.25% of the average annual total water exchange. To investigate the seasonal fluctuation of the surface water-groundwater conversion relationship in the basin's rivers and channels, January, March, July, and October are designated to represent winter, spring, summer, and fall, respectively, as illustrated in Figure 10. Seasonally, the conversion relationship and water volume of each river reach exhibit significant variations due to the presence of numerous river reaches in the studied area. Consequently, three river segments—1, 7, and 5—exhibiting significant alterations, extended lengths, and distinctive geographical positions are chosen for research. The annual average monthly recharge of surface water to groundwater in reach 1 during winter, spring, summer, and autumn is 2.8×10 7 m³/d, 2.6×10 7 m³/d, 7.8×10 7 m³/d, and 3.0×10 7 m³/d, respectively. The annual average monthly discharge of groundwater to surface water in reach 7 for winter, spring, summer, and autumn was 6.5×10 4 m³/d, 6.0×10 4 m³/d, 1.5×10 5 m³/d, and 8.1×10 4 m³/d, respectively. In section 5, where surface water predominantly contributes to groundwater recharge across several seasons, the average monthly recharge rates are as follows: 2.5×10 4 m³ in autumn, 2.7×10 4 m³/d in spring, 2.8×10 4 m³/d in winter, and 6.5×10 4 m³/d in summer. The investigation of the aforementioned 11 river segments reveals characteristics that align with the seasonal fluctuations of precipitation in the Songhua River basin. Furthermore, the monthly average surface-groundwater recharge in the Songhua River Basin from 2008 to 2016 indicates significant seasonal variability, with groundwater recharge to surface water not falling below 4.0×10 4 m³/d, and the average fluctuation range for each river reach is approximately -52% to 55%. The recharge of surface water to groundwater exhibits significant seasonal variability, peaking in summer, particularly in July, and reaching its nadir during the winter and spring exchange period, typically between December and April. This mostly pertains to the significant seasonal fluctuations in water quality within the basin and the frigid winter environment in northeastern China. Figure 11 illustrates that the interannual variability of the surface water-groundwater conversion relationship in the research area indicates a significant fluctuation in the recharge volume of surface water to groundwater, with a range of -37% to 63%. From 2008 to 2009, there was a rising trend; from 2009 to 2014, a general declining trend prevailed; and from 2014 to 2016, there was a modest increase. This pertains to the augmentation of precipitation in the Songhua River basin. The range of fluctuation in groundwater recharge to surface water is between -35% and 52%, and this variance is directly influenced by precipitation. The pattern mirrors that of river recharge to groundwater, exhibiting frequent variations in each river segment; yet, overall, the entire basin reflects a tendency of surface water transitioning to groundwater. 3. DISCUSSION 3.1 Genetic analysis of surface-groundwater conversion relationship in Songhua River Basin The SWAT-MODFLOW coupling model indicates that the relationship between surface water and groundwater conversion occurs frequently throughout the year, with prolonged groundwater recharge predominantly occurring from October to April of the subsequent year. The overall trend of surface water to groundwater conversion in the study area indicates that surface water recharges groundwater. The primary reason is that the Songhua River basin is situated in northeastern China and influenced by the temperate monsoon climate. The temperature variation among the four seasons is significant, and the substantial increase in river runoff during seasonal transitions leads to a considerable fluctuation in groundwater supply from the river. The Songhua River basin exhibits the fourth highest porosity, with sand coverage encompassing 75%, and demonstrates significant water seepage capacity. Consequently, river recharge of groundwater markedly escalates with seasonal variations [58]. Groundwater recharge to surface water is influenced by seasonal variations; however, the inertia is substantial, resulting in the formation of groundwater recharge rivers in the Songhua River basin during winter and spring, while summer and autumn typically experience flood conditions. Locally, the surface water-groundwater conversion mode of the river (sections 10 and 11) in the northwest of the study area is characterized by groundwater recharge from surface water, primarily due to the river's location in a mountainous region, where the geology influences recharge efficiency. Secondly, the region is situated between Hegang City and Luobei County. The Jiangluo, Fengxiang, Wutonghe, Puyang, and Suibin irrigation areas exhibit significant agricultural water extraction, which indirectly influences the relationship between surface water and groundwater conversion [59]. Furthermore, section 1 experiences continuous surface water replenishment due to substantial groundwater availability throughout the year. Besides its geographical position at the basin's ultimate outlet, the upstream region of this section is situated in Fujin City, Jiamusi City, which hosts numerous water storage projects; consequently, the river is influenced by factors such as the operation of reservoir gates and flood discharges throughout the year [60]. Over the past eight to nine years, the trend has shifted towards the recharge of groundwater by surface water. The primary reason is that the river is a lowland river adjacent to mountainous terrain, and its unique geographical position allows the river segment to remain unaffected by the recharge process impeded by the mountain geology, while also benefiting from lateral recharge from the foothill water. Moreover, Section 8 is part of the downstream confluence of mountainous rivers (Section 10, Section 11), which also exerts an indirect influence on the conversion between surface water and groundwater. In the central region of the basin, the conversion of surface water to groundwater occurs frequently, and the trend remains ambiguous due to geological conditions, water extraction in the irrigation zone, climate change, and minimal topographical variation in the area. In this study, despite the differing conversion trends of river segments 2 to 7, the disparity between recharge and excretion is minimal, with the conversion relationship primarily influenced by seasonal precipitation. The influence of other natural factors is comparatively minimal. 3.2 Analysis of differences with previous research results The investigation of the relationship between surface water and groundwater in the Songhua River Basin has consistently been a significant subject, with numerous scholars conducting extensive research on it since 2011. In 2011, scholar Zhou Yubo[61] utilized numerical simulation methods to quantify the exchange of river and groundwater in the Yilan to Fujin section of the Songhua River, assessing the conversion of groundwater and surface water during three representative years characterized by abundance, normalcy, and drought. The comparison of the research findings with those of this paper indicated minimal disparity between the results acquired during the study years and those presented herein. Nevertheless, numerous ambiguous variables existed in his research. The model's accuracy in river level and meteorological data was compromised by the technological and data limitations of that period. Consequently, this study integrated a more advanced model coupling method informed by prior experience. The SWAT-MODFlow coupling model was employed, incorporating the SWAT module into the original GMS model, which thoroughly accounted for land use, soil type, and climate change in the study area, while addressing the issue of low numerical simulation accuracy attributed to the technological limitations of that period. Comparing the research findings of Tian Haoran [62] from 2012 revealed minimal differences in the conversion volume of the upper reaches of the Songhua River, whereas significant discrepancies were observed in the lower reaches. The primary reason is that since 2013, Fujin City has effectively adjusted the water intake for local agriculture and enhanced the restoration efforts for the water level of the Songhua River. The secondary reason is that, despite the scholar employing various methodologies, including the surface water balance method, groundwater balance method, and GMS numerical simulation, to thoroughly assess the surface water to groundwater conversion volume of the Songhua River, the integration of multiple methods enhanced the precision of the paper's calculation results. Nevertheless, traditional calculation methods yield relative errors in summarizing the intricate surface water infiltration process, and the aggregation of these errors results in diminished calculation outcomes in the downstream catchment area. In comparison to the findings of scholar Zhang Bing [63] in 2015, this study reveals a low accuracy in the calculation results regarding the conversion amount of a single river reach. The disparity arises from the application of the isotope tracer method to investigate the spatial relationship between surface water and groundwater in the Second Songhua River. In comparison to the model method, this method provides a more precise depiction of the water flow seepage process. The experimental procedure of this method is complex, necessitating several steps including isotope labeling, sample preparation, and radioactive detection. Each link must be meticulously managed and regulated to guarantee the precision and dependability of experimental outcomes. Moreover, the expense associated with this method is comparatively elevated for analyzing the trend of surface water-groundwater conversion in the Songhua River basin. This method examines the conversion relationship between surface water and groundwater in the Songhua River by analyzing a single river section, focusing solely on the primary segment of the river network. This approach fails to provide a comprehensive trend characterization and neglects surface dynamics in favor of point-specific accuracy. This study, building on prior research, conducted a comprehensive analysis of the transformation dynamics of the Songhua River by segmenting it into 11 distinct river segments within the middle and lower reaches of the basin. Each segment was analyzed individually to attain comprehensive research outcomes, resulting in a low accuracy of conversion for individual river segments. 3.3 Research uncertainty analysis This study remains fraught with uncertainties, necessitating a substantial amount of high-quality data, including terrain, soil, meteorological, and hydrological information, to construct and operate the coupling model. The acquisition and processing of data can be challenging, particularly in regions with insufficient monitoring data. The groundwater level monitoring data in the study area is limited for various reasons, and this paper utilizes 11 groundwater level observation wells for calibration, resulting in unavoidable discrepancies between the model simulation outcomes and the actual conditions. Furthermore, the establishment of the MODFLOW model encountered inaccuracies in the simulation results due to the inability to acquire real-time water withdrawal data for each irrigation district in the study area, leading to the use of annual average water withdrawal figures for surface mining instead of real-time data. Furthermore, when configuring the SWAT module of the coupled model, it is permissible to input land use data from only one year, which is relatively straightforward. However, changes in land use significantly influence hydrology and non-point source pollution simulations. Particularly during the processes of flow, sediment, and non-point source pollution simulation, it is essential to dynamically update land use data to recalibrate the threshold value. To enhance the simulation accuracy of the model regarding land use change. Consequently, dynamic land use data must be incorporated into the model [64]. The Songnen Plain and Sanjiang Plain in northeastern China are the areas with the highest agricultural grain production in the country[65]. The topography in this area is intricate, featuring diverse land use and soil classifications. Nonetheless, the existing SWAT model is inadequate in handling high-precision and multi-transform terrain data, leading to inaccurate simulations of groundwater supply, waste, and pollution. A significant error exists in the shallow groundwater storage results [66]. It should be enhanced according to the planting configuration. The accurate planting structure data, derived from the integration of high-resolution drone imagery, remote sensing images, and ground-measured data, serves as the input land use data for the SWAT model. This approach aims to enhance the simulation of the migration and transformation processes of agricultural non-point source pollution, thereby improving the accuracy of watershed runoff simulations. 4. CONCLUSIONS 1. The SWAT-MODFLOW coupling model has good applicability in Songhua River Basin, and the simulated monthly runoff and groundwater level are in good agreement with the actual observed data. 2. For the whole study area, the correlation between the groundwater level in the northwest of the study area and other areas is weak, and the difference of the water level is about 30m at the highest, and the variation trend of the groundwater level over time is quite different. The correlation between the central, northern, eastern and northeastern plains of the study area is high, and the difference of spatial-temporal scale is not significant, and the maximum difference of groundwater level is less than 15m. Jiamusi City, Fujin City and Tongjiang City, which are geographically distributed in the study area, were selected to study the degree of influence of precipitation on the groundwater table. The lag time of precipitation on the groundwater table was about 10.56d, 10.58d and 3.15d. 3. Spatially, the recharge of surface water to groundwater in the Songhua River Basin from 2008 to 2016 mainly occurred in river segments 1, 5, 6, 8 and 9, while the recharge of groundwater to surface water was concentrated in river segments 2, 3, 4, 7, 10 and 11. 4. At the inter-annual scale, the recharge from surface water to groundwater varies significantly under the influence of precipitation, with a range of -37%~63%, while the recharge from groundwater to surface water is relatively stable, with a range of -35%~52%. 5. On the seasonal scale, combined with the analysis of 11 river segments, this feature is consistent with the seasonal variation of precipitation in the Songhua River Basin. The amount of groundwater recharge to surface water varies greatly with the seasons, not less than 4.0×104m3/d, and the average variation range of each river reach is about -52% ~ 55%. The recharge of surface water to groundwater shows obvious seasonal variability, with the highest value in summer and the peak value in July, and the lowest value in winter and spring exchange season, and the lowest value usually appears between December and March to April. Declarations Author Contribution Y.X .is responsible for writing documents, building models and proposing methodsJ.J.Y and L.G.W .are responsible for the guidance of data visualizationR.Q. is responsible for data processingL.J.J. and L.P.X .were responsible for building the auxiliary modelsD.C.L. is responsible for providing project funding and reviewing the final manuscript Data Availability Data availability statementThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Sun tienui. Global water resource situation highlights the intensification of climate change [N]. Guangming Daily,2024-10-16(012).DOI:10.28273/n.cnki.ngmrb.2024.004829 Zhang Jianyun, Wang Jianhua, He Guohua, et al. Discussion on some problems of water peak in China [J]. Advances in Water Science, 2019,35(01):1-10.DOI:10.14042/j.cnki.32.1309.2024.01.001. Wu Zexin, Wu Yao, Yu Yexiang, et al. Groundwater environment change and its controlling factors in Songhua River Basin, Sanjiang Plain [J]. Journal of Hydropower and Energy,2024,42(09):38-42.DOI:10.20040/j.cnki.1000-7709.2024.20241012. Yang Derui, Du Bingzhao, Huang Liqun, et al. Strengthening groundwater management to promote high-quality development. China Water Resources,2021,(07):1-4. Shu Longcang, Yin Xiaoran, Yuan Yajie, et al. Spatial and temporal variation of water exchange between river and groundwater in typical areas of Sanjiang Plain [J]. Journal of Hydraulic Engineering, 2019,52(10):1151-1162.DOI:10.13243/j.cnki.slxb.20210509. Hao Aibing, Li Yamin, Zheng Yuejun, et al. A case study on hydrogeological conditions analysis using groundwater level monitoring data: Nanqu Depression in Kuitun River Basin, Xinjiang [J]. Hydrogeology Engineering Geology,2008,(04):27-30. Peng Hongming, Xu Weilin, He Qing, et al. Hydrogeochemical and isotopic characteristics of the middle and upper reaches of the Buha River Basin [J]. Arid Land Research,2015,32(05):1032-1038.DOI:10.13866/j.azr.2015.05.28. Wu Xianfeng, Liu Changming. Progress in hydrological models of river basins [J]. Progress in Geography,2002,(04):341-348. Lai Zhengqing, Li Shuo, Li Chenggang, et al. Improvement and application of SWAT model in the middle and upper reaches of Heihe River Basin [J]. Journal of Natural Resources,2013,28(08):1404-1413. Kim Nam Won, Chung II Moon, Won Yoo Seung et al. Development and application of the integrated SWAT –MODFLOW model[J]. Journal of Hydrology, 2008, 356(1-2): 1-16 Wang Zhonggen, Zhu Xinjun, Li Wei, et al. Coupling simulation of surface water and groundwater in Haihe River Basin [J]. Progress in Geography,2011,30(11):1345-1353. Zhang Jing, Ross Mark, Trout Ken. Certification tests of MODFLOW implementation in the Integrated HydrologicModel[J]. Journal of Hydrologic Engineering, 2014, 19(3): 643-648. Carroll Rosemary W H, Huntington Justin L, Snyder Keirith A et al. Evaluating mountain meadow groundwater response to Pinyon‐Juniper and temperature in a great basin watershed[J]. Ecohydrology, 2017, 10(1): e1792. Yifru Bisrat Ayalew, Chung II-Moon, Kim Min-Gyu et al. Assessment of groundwater recharge in Agro-Urban Watersheds using Integrated SWAT-MODFLOW Model[J]. Sustainability, 2020, 12(16): 6593. Wang Rui, Wang Zhonggen, Xia Jun. Research progress of coupling model of surface water and groundwater [J]. Progress in Geography,2008,(04):37-41. Cong Zhentao, Yang Jing, Lei Huimin, et al. Simulation of four-water conversion model in Weishan irrigation district [J]. Yellow River,2011,33(03):70-72. Wang Chengli, Jiang Renfei, Ruan Benqing, et al. Water consumption calculation model of irrigation area based on four-water conversion [J]. Journal of Hydraulic Engineering,2009,40(10):1196-1203. Wang Zhonggen, Zhu Xinjun, Li Wei, et al. Coupling simulation of surface water and groundwater in Haihe River Basin [J]. Progress in Geography,2011,30(11):1345-1353. SEMIROMI M T, KOCH M. Analysis of spatio-temporal variability of surface-groundwater interactions in the Gharehsoo river basin, Iran, using a coupled SWAT- MODFLOW model [J]. Environmental earth sciences, 2019, 78(6): 201.1-.21. Huang Chuanji, Li Chunguang, Jing Hefang, et al. Effects of land use and precipitation change on groundwater level around the Yellow River in Zhongning Region of Ningxia based on MIKE SHE model [J]. Water and Electricity Energy Science,202,40(01):14-17+111. SABZZADEH I, SHOURIAN M. Maximizing crops yield net benefit in a groundwater-irrigated plain constrained to aquifer stable depletion using a coupled PSO-SWAT-MODFLOW hydro-agronomic model [J]. Journal of cleaner production, 2020, 262(Jul.20): 121349.1-.13. KIM N, CHUNG I, WON Y, et al. Development and application of the integrated SWAT-MODFLOW model [J]. Journal of Hydrology, 2008, 356(1/2): 1-16. PERKINS S P, SOPHOCLEOUS M. Development of a Comprehensive Watershed Model Applied to Study Stream Yield under Drought Conditions [J]. 1 Research Assistant, Kansas Geological Survey, The University of Kansas, 1930 Constant Ave, Lawrence, KS 66047 E-mail: perkins@kgsukansedu 2 Senior Scientist (corresponding author), Kansas Geological Survey, The University of Kansas, 1, 1999, 37(3):418-426. Liu Luguang, Cui Yuanlai. Construction of surface water-groundwater coupling model in irrigation area [J]. Journal of Hydraulic Engineering, 2012, 43(07): DOI:826-33.10.13243/j.cnki.slxb.2012.07.012 ALIYARI F, BAILEY R T, TASDIGHI A, et al. Coupled SWAT-MODFLOW model for large-scale mixed agro-urban river basins [J]. Environmental modelling & software, 2019, 115(3): 200-10. Rathjens, Hendrik, Bailey, et al. SWATMOD-PREP: GRAPHICAL USER INTERFACE FOR PREPARING COUPLED SWAT-MODFLOW SIMULATIONS[J]. Journal of the American Water Resources Association, 2017, 53(2):400-410. PARK S, NIELSEN A, BAILEY R T, et al. A QGIS-based graphical user interface for application and evaluation of SWAT-MODFLOW models [J]. Environmental modelling & software, 2019, 111(Jan.): 493-7. Wu Yuebin, Jia Yanhong, Fan Baoshan. Analysis of bedmaking flow in the middle and lower reaches of Songhua River [J]. Journal of Harbin Institute of Technology,2008,(06):880-883. Sun Teng, Wang Sicong, Chen Changchun, et al. View review and related thoughts on the determination of Songhua River's source [J]. Water Science, Technology and Economy,2024,30(09):1-5. Yang F, Chen J, Tian B X, et al. Spatial and temporal variations of temperature in Sanjiang Plain during 1961-2011. Farm Staff,2020,(06):145+163. Li Lin, Zhang Zhenqing. Spatial and temporal variation of precipitation in Sanjiang Plain [J]. China Comprehensive Utilization of Resources,2023,41(08):40-42. Yang Yi-xun, Jiang Xiao-Xu, Li Ming-sheng, et al. Spatial variation of soil organic carbon content and density in Sanjiang Plain [J]. Chinese Journal of Soil Science, 2019,53(06):1313-1319.DOI:10.19336/j.cnki.trtb.2021101804 Liang Yukai. Vertical distribution characteristics of soil organic carbon in different land use types in Sanjiang Plain [J]. Jilin Water Resources,2024,(09):32-35+40.DOI:10.15920/j.cnki.22-1179/tv.2024.09.006. Zhao Sijia. Research on groundwater exploitation control scheme based on appropriate groundwater level in Sanjiang Plain [D]. Jilin University,2023.DOI:10.27162/d.cnki.gjlin.2023.001581. ARNOLD J G,SRINIVASAN R,MUTTIAH R S,et al.Large area hydrologic modeling and assessment. I. Model development [J].Journal of the American Water Resources Association (USA),1998,1:73-89. Li Feng, Hu Tiesong, Huang Huajin. Research on the principle, structure and application of SWAT model [J]. China Rural Water Resources and Hydropower,2008,(03):24-28. Xu Atong, Mu Zhenwei. Construction of SWAT model database in Upper reaches of Kelan River Basin [J]. Yangtze River Technology and Economics,2021,5(S1):114-116.DOI:10.19679/j.cnki.cjjsjj.2021.0738. Zhu Yifan. Study on water resource vulnerability in Songhua River Basin under changing environment [D]. Jilin University,2024.DOI:10.27162/d.cnki.gjlin.2023.006002 Chen Kai, Wang Liquan, Liu Yan, et al. Applicability evaluation of CMADS dataset in Hulan River Basin [J]. Journal of Irrigation and Drainage, 2019,43(01):60-68.DOI:10.13522/j.cnki.ggps.2023215 Gu Xichen, Xiao Senyuan, Yang Guang, et al. Simulation of hydrological processes in Manas River Basin based on CMADS and SWAT model [J]. Journal of Water Resources and Water Engineering, 21,32(02):116-123. Wan Hao, Dong Xiaohua, Peng Tao, et al. Research on runoff simulation of the East branch Basin of Huangbai River based on SWAT model and SUFI-2 algorithm [J]. China Rural Water Resources and Hydropower,2018,(12):94-100. Zhang Li, Liu Ye, CAI Duoduo, et al. Comparison and applicability of SWAT model parameter automatic calibration methods: A case study of the middle and upper reaches of Jinghe River [J]. China Rural Water Resources and Hydropower,2016,(11):76-81. Fioreze Mariele, Mancuso Malva Andrea. MODFLOW and MODPATH for hydrodynamic simulation of porous media in horizontal subsurface flow constructed wetlands: A tool for design criteria[J]. Ecological Engineering, 2019, 130: 45-52. Wang Shiqin, Shao Jingli, Song Xianfang, et al. Application of groundwater model MODFLOW and GIS to groundwater resource evaluation in North China Plain [J]. Geographical Research,2007,(05):975-983. TORRENCE C,COMPO G P.A practical guide to wavelet analysis[J], Bulletin of the American Meteorological Society,1998,79(1):61－78． GRINSTED A, MOORE J C, JEVREJEVA S. Application of the cross wavelet transform and wavelet coherence to geophysical time series[J]. Nonlinear Processes in Geophysics,2004,11(5/6):561－566. NETO O P,PINTO I R C A,PINTO O J.The relationship between thunderstorm and solar activity for Brazil from 1951 to 2009[J].Journal of Atmospheric and Solar-Terrestrial Physics,2013,98:12－21. SHINE J M,HANDOJOSENO A M A,NGUYEN T N,et al.Abnormal patterns of theta frequency oscillations during the temporal evolution of freezing of gait in Parkinson ’ s disease[J]. Clinical Neurophysiology,2014,125(3):569－576. LABAT D,ABABOU R,MANGIN A. Wavelet analysis in karstic hydrology. 2nd part: rainfall-runoff cross-wavelet analysis[J]. Academie des Sciences,1999,329(12):881－887. ADAMOWSKI J, CHAN H F.A wavelet neural network conjunction model for groundwater level forecasting[J].Journal of Hydrology,2011,407(1/4):28－40. Qi xiaofan, Yang lizhi, Han ye, et al. Cross wavelet analysis of groundwater level dynamics and its response to precipitation in Jinan Spring Basin [J]. Advance in Earth Science,2012,27(9): 969-978. Qi xiaofan, Li wenpeng, Yang lizhi, et al. Annual time-lag analysis of groundwater level dynamic response to precipitation in Jinan Baiquan spring Basin [J]. Earth and Environment,2015,43(6): 619-627.DOI:10.14050/j.cnki.1672-9250.2015.06.004 Qi Xiaofan, Wang Yushan, Yang Lizhi, et al. Time-lag difference of groundwater level response to precipitation in Jinan karst spring in recent 50 years [J]. Carsologica Sinica,2016,35(4):384-393. TREMBLAY L,LAROCQUE M,ANCTIL F,et al.Teleconnections and interannual variability in Canadian groundwater levels[J].J Hydrol,2011,410(3/4):178－188． KUSS A J M,GURDAK J J. Groundwater level response in U S principal aquifers to ENSO,NAO,PDO,and AMO[J].J Hydrol,2014,519(B):1939－1952. Qi Xiaofan, Li Wenpeng, Li Haitao, et al. Remote correlation of groundwater level, precipitation and temperature with large-scale meteorological models in Jinan Karst spring region [J]. Hydrogeology Engineering Geology,2015,42(6):18-28.DOI:10.16030/j.cnki.issn.1000-3665.2015.06.04 Qi Xiaofan, Li Wenpeng, Li Haitao, et al. Multi-scale teleconnection analysis of meteorological elements and global atmospheric circulation characteristics in Heihe River Basin [J]. Arid Land Geography,2017,40(3):564-572.DOI:10.13826/j.cnki.cn65-1103/x.2017.03.010 Liu Guodong, Dai Huimin, Yang Ze, et al. Study on spatiotemporal variation and influencing factors of soil carbon pool in Sanjiang Plain [J]. Geoscience, 2019,35(02):443-454.DOI:10.19657/j.geoscience.1000-8527.2021.02.14. Xing Z X, Zhang Han, Ji Y, et al. Analysis of precipitation and its characteristics in typical irrigation area of Sanjiang Plain in recent 58 years [J]. Journal of Shenyang Agricultural University,2018,49(05):513-521. Liu Yuanyuan, Cui Wei. Supplementary and improvement of spatial data of important levee projects in the main stream of Songhua River Basin [J]. Northeast Water Resources and Hydropower,2021,39(08):68-70.DOI:10.14124/j.cnki.dbslsd22-1097.2021.08.025. Zhou Yubo, Xiao Changlai, Xu Mengyao. Study on surface water and groundwater conversion in the middle reaches of Songhua River [J]. Yellow River,2011,33(04):43-45. Tian Haoran, Xiao Changlai, Xu Mengyao. Study on the interconversion relationship between surface water and groundwater in the lower Jiamusi main stream of Songhua River [J]. Water saving Irrigation,2012,(05):26-28. Zhang Bing, Song Xianfang, Zhang Yinghua, et al. Relationship between surface water and groundwater in the Second Songhua River Basin [J]. Advances in Water Science,2014,25(03):336-347.DOI:10.14042/j.cnki.32.1309.2014.03.004. Li leifang, Chen jieying, Chen lei, et al. Improvement of SWAT model reservoir module and simulation of nitrogen and phosphorus flux in watershed [J]. Chinese Journal of Environmental Sciences,2023,43(07):427-439.DOI:10.13671/j.hjkxxb.2023.0061 Cui Huqun, Wu Tingwen, Liu Jiangtao, et al. Effect of succession process on groundwater in marsh wetland and farmland in Sanjiang Plain [J]. Hydrogeology and Engineering Geology,2023,50(06):51-58.DOI:10.16030/j.cnki.issn.1000-3665.202306022 Zhang Dong, Zhang Wanchang, Zhu Zhu, et al. Research on improvement and application of the hydrophysical model of distributed SWAT basin [J]. Scientia Geographica Sinica,2005,(04):52-58. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Dec, 2024 Reviews received at journal 02 Dec, 2024 Reviewers agreed at journal 26 Nov, 2024 Reviewers agreed at journal 25 Nov, 2024 Reviews received at journal 23 Nov, 2024 Reviews received at journal 23 Nov, 2024 Reviews received at journal 19 Nov, 2024 Reviewers agreed at journal 14 Nov, 2024 Reviewers agreed at journal 14 Nov, 2024 Reviewers agreed at journal 14 Nov, 2024 Reviewers invited by journal 14 Nov, 2024 Editor assigned by journal 14 Nov, 2024 Editor invited by journal 11 Nov, 2024 Submission checks completed at journal 07 Nov, 2024 First submitted to journal 22 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5310099","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":381832499,"identity":"c4308903-b69f-4929-a3f3-0bb6461cdc9d","order_by":0,"name":"xiao Yang","email":"","orcid":"","institution":"Heilongjiang University","correspondingAuthor":false,"prefix":"","firstName":"xiao","middleName":"","lastName":"Yang","suffix":""},{"id":381832500,"identity":"f16a8562-9c6b-4d9b-92a5-f3d4e00a7887","order_by":1,"name":"Chang-Lei Dai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIie3PsQrCMBCA4StCXGK7pgj6Cn0Aqa9yQdBJcHRwUALnIDr7GD5CNaBLxbWDg1ncRRBcxAq6tnETzL9chvs4AuBy/WKJN668pg/e5ITD1heEgaeiU9q1IQBvAiw0tCkX/k5ProPRMaaqoiGyBILpDAtJmEpVX27PHeJrypAfQaT7VSGJEkkVznSHCZkTcYZI9EvIweTkkZOmoQFG2oJk+ZUa6ZgJjwDRgoSZUfXaQiPjUglMurz0L/6hZ678pttBdWcu90erEUznxeSTHL8f3Gr9Vdt60+Vyuf6vJ0yGTfLdXXWdAAAAAElFTkSuQmCC","orcid":"","institution":"Heilongjiang University","correspondingAuthor":true,"prefix":"","firstName":"Chang-Lei","middleName":"","lastName":"Dai","suffix":""},{"id":381832501,"identity":"af364d45-a61a-49cc-8e8c-20df64cd9d63","order_by":2,"name":"Jian-yu Jing","email":"","orcid":"","institution":"Heilongjiang University","correspondingAuthor":false,"prefix":"","firstName":"Jian-yu","middleName":"","lastName":"Jing","suffix":""},{"id":381832502,"identity":"61aa9bc2-085a-4dcc-b3ba-ba90b8dc1971","order_by":3,"name":"Geng-wei Liu","email":"","orcid":"","institution":"Heilongjiang University","correspondingAuthor":false,"prefix":"","firstName":"Geng-wei","middleName":"","lastName":"Liu","suffix":""},{"id":381832503,"identity":"6789c015-173e-4343-a08f-87a3fbb3ba62","order_by":4,"name":"Qing Ru","email":"","orcid":"","institution":"Heilongjiang University","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Ru","suffix":""},{"id":381832504,"identity":"2d68d615-e509-4d31-92ab-b489a0ce0e84","order_by":5,"name":"Jia-jun Li","email":"","orcid":"","institution":"Heilongjiang University","correspondingAuthor":false,"prefix":"","firstName":"Jia-jun","middleName":"","lastName":"Li","suffix":""},{"id":381832505,"identity":"d0650e0b-eb40-4b69-aed1-29a5ca6f80eb","order_by":6,"name":"Peixian Liu","email":"","orcid":"","institution":"Heilongjiang University","correspondingAuthor":false,"prefix":"","firstName":"Peixian","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-10-22 08:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5310099/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5310099/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-92801-3","type":"published","date":"2025-03-08T15:58:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69777300,"identity":"6bd691d3-d52a-4ef0-bf0c-a8c5e5bd5491","added_by":"auto","created_at":"2024-11-25 07:35:09","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":121196,"visible":true,"origin":"","legend":"\u003cp\u003eTechnology roadmap\u003c/p\u003e","description":"","filename":"1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5310099/v1/8c455353eeea3199e4c90bc3.jpeg"},{"id":69777093,"identity":"b0e3b7ab-fdad-43e7-b0e8-2976a92c7aff","added_by":"auto","created_at":"2024-11-25 07:27:08","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56831,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area map\u003c/p\u003e","description":"","filename":"2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5310099/v1/5df51d544d78f646668616a6.jpeg"},{"id":69777095,"identity":"804a1c74-fd02-482d-95ff-0608c63c8235","added_by":"auto","created_at":"2024-11-25 07:27:09","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":243607,"visible":true,"origin":"","legend":"\u003cp\u003eSWAT model input and output data\u003c/p\u003e\n\u003cp\u003eNote: Figure 3a:The geographic elevation data processed by Arcgis. Figure 3b:Each subwatershed of the study area is divided by SWAT model. Figure 3c:Soil type data obtained after reclassification on the basis of original soil type data. Figure 3d:Land use data obtained after reclassification on the basis of original land use data. Figure 3e:HRU data graph simulated by SWAT model\u003c/p\u003e","description":"","filename":"3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5310099/v1/a6f042d2560247ed7483c0cc.jpeg"},{"id":69778760,"identity":"5f6050f2-b512-4d49-8bc3-a289f9674ac8","added_by":"auto","created_at":"2024-11-25 07:43:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":173749,"visible":true,"origin":"","legend":"\u003cp\u003eResults of global sensitivity analysis\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5310099/v1/8b83cc1c5c8590ce37064c67.png"},{"id":69777301,"identity":"21b73986-034e-4dde-9e21-21a017f32931","added_by":"auto","created_at":"2024-11-25 07:35:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":123289,"visible":true,"origin":"","legend":"\u003cp\u003eDetermination and verification of the runoff rate of the model (the longitudinal coordinate indicates the runoff unit: m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5310099/v1/5436e4d2bdd7a75005fe6379.png"},{"id":69777098,"identity":"1fac08ea-bd48-4212-b834-86c44fbef9ca","added_by":"auto","created_at":"2024-11-25 07:27:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":546758,"visible":true,"origin":"","legend":"\u003cp\u003eHydrogeological generalization map of the study area\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5310099/v1/1655bb4a8fc44afb97e7dc6a.png"},{"id":69778759,"identity":"03b6c36a-9e16-478f-b3eb-8747fec7cab1","added_by":"auto","created_at":"2024-11-25 07:43:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":305846,"visible":true,"origin":"","legend":"\u003cp\u003eSWAT-MODFLOW calibration verification results\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5310099/v1/87c08044e232c1db199b4c17.png"},{"id":69777100,"identity":"30117705-2c0a-4549-955c-07d68c4897c9","added_by":"auto","created_at":"2024-11-25 07:27:09","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":922831,"visible":true,"origin":"","legend":"\u003cp\u003eCross wavelet transform of precipitation, evaporation and groundwater level and their correlation heat map\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e The direction of the arrows in the figure reflects the phase relationship between groundwater level and precipitation. The arrows from left to right indicate that the two are in phase, and the arrows from right to left indicate that the two are in reverse phase. The vertical downward means that the wavelet transform of precipitation advances the groundwater level 1/4 cycle, and the vertical upward means that the precipitation advances the groundwater level 3/4 cycle. Other meanings of colors and symbols are described in the text.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5310099/v1/c728d4c60be087fd5fdc8d96.png"},{"id":69777103,"identity":"191e36ab-f206-448c-8e44-0d789673c1bf","added_by":"auto","created_at":"2024-11-25 07:27:09","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":192861,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution characteristics of surface water-groundwater conversion relationship in the study area\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5310099/v1/a80db2e4354de016128577f7.png"},{"id":69778893,"identity":"4bfa4906-1789-4ff5-be4d-b08d79e01bf1","added_by":"auto","created_at":"2024-11-25 07:51:09","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":193079,"visible":true,"origin":"","legend":"\u003cp\u003eAverage monthly conversion volume of surface water to groundwater from 2008 to 2016 (unit: ×10\u003csup\u003e6\u003c/sup\u003em\u003csup\u003e3\u003c/sup\u003e/d)\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-5310099/v1/590b8185b089f08116d3920e.png"},{"id":69777303,"identity":"0c87aa52-3ad7-4323-b363-fea71b7f7e2a","added_by":"auto","created_at":"2024-11-25 07:35:09","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":626568,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual seasonal conversion of surface water to groundwater from 2008 to 2016\u003c/p\u003e","description":"","filename":"11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5310099/v1/9c5aa8544f14cc502e1586fe.jpeg"},{"id":78191537,"identity":"cb122816-afd5-4044-ab99-fea4288fca15","added_by":"auto","created_at":"2025-03-10 20:07:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4473189,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5310099/v1/cbec19a1-1a3f-4f3a-b2d5-9be23402db64.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhanced surface-groundwater interaction modeling in the middle and lower reaches of the Songhua River Basin using a coupled SWAT-MODFLOW model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWater is the essence of life, the cornerstone of production and ecology, and a vital natural resource[1]. Currently, China\u0026apos;s water resources confront significant problems, including limited total water availability and severe water pollution. As the social economy rapidly develops, the demand for water resources is steadily rising[2]. The middle and lower portions of the Songhua River, specifically the Jiamusi-Tongjiang basin, are significant locations where the groundwater contribution from rivers constitutes over fifty percent of the natural supply in China\u0026apos;s Sanjiang Plain[3]. Nonetheless, the swift economic advancement over the past three decades has led to the over-cultivation of paddy fields and the imprudent management of water resources[4], resulting in significant environmental and ecological issues, including the depletion of groundwater levels and land subsidence, which have emerged as critical impediments to the sustainable development of the economy and society. The advancements in agriculture and the grain industry in this region are attributable to the water transport provided by numerous inland rivers, particularly the main channel of the Songhua River. The river starts in the top mountainous region, where numerous tributaries disperse in the middle and lower piedmont plain, and the conversion between surface water and groundwater is common[5]. Consequently, examining the relationship between surface water and groundwater conversion in the middle and lower portions of the Songhua River basin is crucial for ecological preservation, judicious water resource allocation, and the sustainable development of the basin.\u003c/p\u003e\n\u003cp\u003eThe techniques employed to examine the interplay between surface water and groundwater transformation encompass hydrological data analysis [6], isotope tracer methodology [7], and hydrological modeling approaches [8]. Notably, the distributed hydrological modeling method [9,10] effectively captures the temporal and spatial variability of the basin and holds significant applications in water cycle simulation. Generally, dispersed hydrological models primarily emphasize the characterisation of surface water circulation, while the characterization of groundwater in the corresponding regions is significantly oversimplified [11]. Researchers have increasingly acknowledged the significance of modeling surface water and groundwater collectively in regions where the interaction between the two is intricate, leading to the development of the coupled surface water-groundwater model[12~14].\u003c/p\u003e\n\u003cp\u003eCurrently, pertinent researchers both domestically and internationally have conducted more comprehensive research on the coupling model. Based on several model coupling methodologies [15]. Coupling models can be classified into three categories: Loosely connected models, exemplified by the four-water conversion model [16,17], which is predicated on the principles of water circulation and balance within the basin, streamline the interactions and conversions between surface and groundwater models, thereby exhibiting some limitations. The SWAT model employs a semi-loosely coupled approach, utilizing SWAT-modflow [18,19]. However, its groundwater calculation module lacks precision in simulating groundwater processes. Consequently, the established MODFLOW model is implemented to supplant the original groundwater module, enhancing the accuracy of groundwater simulations. Tightly coupled models, such as MIKE-SHE[20], possess comprehensive surface water and groundwater modules and exhibit great computational accuracy; nevertheless, the extensive fine data required for modeling constrains their practical implementation. The semi-loosely coupled model is often utilized due to its significant flexibility in modeling and superior simulation accuracy [21]. Kim[22] and Sophodeous[23] validated the SWATMOD coupling model in a natural watershed, yielding excellent simulation results. To enhance the spatial scale and resolve the calculation unit discrepancy in the coupling process of SWAT and MODFLOW, Liu Luguang et al. [24] modified the SWAT source code and developed the SWATMOD coupling model for irrigation districts to address the issue of calculation unit mismatch. Simultaneously, it advocates for the implementation of the SWATMOD coupling model from natural river basins to regions characterized by frequent human activity. Aliyari et al. [25] confirmed the feasibility of the SWAT-MODFLOW coupling model in a substantial agricultural and urban mixed watershed. The results indicate that the coupled model enhances the accuracy of the groundwater model in comparison to the conventional groundwater model. Bailey[26] developed SWATMOD-PREP using the Python programming language to streamline the coupling process. Utilizing the QGIS platform, Park[27] built the QSWATMOD2 plug-in in Python, enhancing the development of coupling models, operational efficiency, result visualization, and other facets. In conclusion, the SWATMOD coupling model is highly effective for simulating the interaction between regional surface water and groundwater in the middle and lower portions of the Songhua River.\u003cbr\u003e\u0026nbsp;This paper seeks to investigate the interaction between surface water and groundwater transformation in the middle and lower parts of the Songhua River basin, drawing on the aforementioned research background and prior model design expertise. The database necessary for the SWAT model is developed by gathering soil, land use, and meteorological data from the study area, while parameter sensitivity analysis, calibration, and validation of the model are conducted using the measured runoff data from the Tongjiang hydrology station in the study area. The MODFLOW model is developed using the MODFLOW module in GMS software, based on the gathered hydrogeological data, groundwater extraction volumes from various irrigation regions in the research region, and mechanical well data. The SWAT-MODFLOW coupling model is ultimately developed using the QSWATMOD2 plug-in within the QGIS platform. Parameter calibration and study of groundwater equilibrium were conducted. The methodological pathway of this study is illustrated in Figure 1 below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy area profile\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe central and lower sections of the Songhua River are situated in the eastern region of Heilongjiang Province, China. It is a plain created by the combined influence of the Jiamusi-Tongjiang segment of the Songhua River and its tributaries. It is situated between 46\u0026deg;45\u0026apos;5\u0026quot;N and 47\u0026deg;38\u0026apos;17\u0026quot;N, and between 130\u0026deg;18\u0026apos;47\u0026quot;E and 132\u0026deg;32\u0026apos;07\u0026quot;E, encompassing the cities of Jiamusi, Fujin, and Tongjiang. Huachuan and Suibin are two county-level cities comprising Zhenxing Township, Jianguo Town, Fengle Town, Xingan Township, Yongan Township, Xincheng Town, Donghe Township, Shangshangji Township, and Suidong Township, encompassing nearly 150 villages and 15 irrigation zones, with a total area of approximately 6,720 km\u0026sup2;[28]. The topography of the basin is predominantly low and level, primarily ranging from 50 to 80 meters. The topography is elevated in the northwest and southwest, while it is depressed in the northeast, resulting in an overall land gradient of 0.10\u0026permil;. The southern region of the middle and lower portions of the Songhua River basin comprises the alluvial plain of the Qixing River and the Naoli River, elevated on both sides; to the east lies the flat terrain of the Qihulin flood area, while to the west is the rugged mountainous region. The Amur River basin is located to the north, as illustrated in Figure 2.\u003c/p\u003e\n\u003cp\u003eThe principal river in the research area is the Jiamusi-Tongjiang segment of the Songhua River\u0026apos;s main course. The Songhua River rises in the Tianchi and Greater Khingan Mountains of the Changbai range, extending 939 kilometers in its main stream and encompassing a basin area of 565,900 square kilometers. The river in the research region measures 209 km in length[29]. The primary tributaries in the research area are Wutong River, Anbang River, Dulu River, and Meandering River, among others, with their sources predominantly located in hilly regions. The tributaries of the Songhua River system exhibit steep gradients and swift currents in the upper reaches, significant mountain flooding, and constricted river channels in the middle and lower reaches, resulting in inadequate flood discharge and limited river expansion.\u003c/p\u003e\n\u003cp\u003eThe research area is situated on the eastern periphery of the mid-latitude Asian continent, characterized by a moderate continental monsoon climate, with an average annual temperature ranging from 1 to 4℃ and four distinct seasons. The spring is brief and blustery, the summer is humid and rainy, with an average temperature in July reaching 25℃. The autumn is short and experiences a rapid drop in temperature, while the winter is prolonged, cold, and arid, with an average temperature in the coldest month (January) falling below -18℃. The initial frost occurs in mid to late September, the final frost in mid to early May, resulting in a frost-free duration of approximately 120 to 140 days. The yearly daylight duration is from 2400 to 2500 hours, the freezing season extends from November to March annually, and the greatest freezing depth is between 1.6 and 2.2 meters [30]. The precipitation data from the Jiamusi meteorological station indicates that the annual average precipitation ranged from 425.5 mm to 688 mm, exhibiting a gradual upward trend from 2008 to 2016, with the majority occurring in July and August, comprising approximately 65% of the annual total. Evaporation data in the study area typically varied from 600mm to 843mm, declining from 2008 to 2010, and stayed comparatively steady until October 2016 [31].\u003c/p\u003e\n\u003cp\u003eThe middle and lower reaches of the Songhua River basin exhibit several soil types, with meadow soil and white pulpy soil being the most prevalent, while black soil possesses the highest fertility. The black soil in the basin is predominantly located in the mountain front region, with a thickness exceeding 30 cm and an average organic matter level of 5-6%. Meadow soil is predominantly found in the floodplains of both small and big rivers within the basin [32]. The 2022 remote sensing land use data published by the National Academy of Space and Space Sciences indicates that the watershed primarily comprises paddy fields, dry fields, marshlands, forests, grasslands, and areas designated for human habitation and building [33]. Paddy and dry fields predominantly characterize the landscape, with land use primarily dedicated to agriculture. The stratigraphic structure of the studied area is intricate, with the basin, created by neotectonic activity, representing a depression near the northern terminus of the second uplift zone of the Neocayathia tectonic system. Tectonically, it is part of the Tongjiang inland fault depression and constitutes a significant subsidence zone from the middle to the Cenozoic age. The middle and lower reaches of the Songhua River basin are situated on a substrate consisting of pre-Paleozoic metamorphic rocks, as well as Paleozoic and Mesozoic volcanic sedimentary rocks. The basin created by the Tertiary depression has the traits of ongoing, extensive, intermittent subsidence. The lithology of the formation in the research area predominantly consists of Quaternary strata, primarily consisting of sand and gravel, with a thickness ranging from 100 to 200 meters in most regions. The riverbed and floodplain of the main stream of the Songhua River and its tributaries are predominantly comprised of Holocene (Q4) deposits, specifically a thin layer of yellow clay and sub-clay, together with sand and gravel in the lower section of the basin. The terrace of the interriver zone in the basin predominantly consists of Upper Pleistocene (Q3) yellow-brown sand and gravel, with discontinuous sub-clay above. The flat region corresponds to the Middle Pleistocene (Q2), characterized by gray-brown and gray-black silty sand, sand, and gravel, with the substrate partially mixed with sub-clay and silty sub-clay. In contrast, the overall basin floor belongs to the Lower Pleistocene (Q1), comprising yellow-green and gray-green medium sand, fine sand, silty sand, and gravel [34].\u003c/p\u003e"},{"header":"1. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e1.1 Construction of SWAT model\u003c/h2\u003e\n \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\n \u003ch2\u003e1.1.1 Introduction to the SWAT model\u003c/h2\u003e\n \u003cp\u003eThe ArcSWAT model [35] is a long-term distributed hydrological model for river basins, created by the Agricultural Research Institute of the United States Department of Agriculture during the mid to late 1990s. This model has been enhanced at a fundamental level regarding its physical process compared to earlier versions. It can not only characterize hydrological situations by linear regression but also comprehensively account for various processes of surface water bodies in its characterisation. The model may replicate several physical processes, including water migration, sediment transport, and crop growth, by incorporating pertinent data regarding production and surface confluence. Furthermore, the model possesses the subsequent characteristics: The model\u0026apos;s code accessibility facilitates its utilization by researchers. Secondly, the model regularizes input data, accommodates diverse data formats and sources, and exhibits excellent computational efficiency, enabling rapid completion of extensive data processing and operations. Moreover, the model is reasonably straightforward to utilize, exhibits great computational precision, and can yield superior simulation outcomes. Nonetheless, it is important to note that the simulation accuracy of the groundwater process is significantly inadequate; therefore, if this model is to be utilized in groundwater applications, further enhancements and optimizations are necessary. The hydrological processes encompassed in the SWAT model primarily consist of atmospheric precipitation, evapotranspiration, soil flow, surface runoff, and river network confluence, among others. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the relevant governing equations of the model.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIntroduction to the principle and formula of SWAT model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFormula introduction\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFormula\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFormula symbol introduction\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater balance equation of SWAT model:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhere:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}\\text{W}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is soil moisture content (mm), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{t}\\)\u003c/span\u003e\u003c/span\u003e is time (days);\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{s}\\text{w}}_{0}\\)\u003c/span\u003e\u003c/span\u003e is the initial soil water content (mm) on day i;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{R}}_{\\text{d}\\text{a}\\text{y}}\\)\u003c/span\u003e\u003c/span\u003e is precipitation (mm);\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Q}}_{\\text{s}\\text{u}\\text{r}\\text{f}}\\)\u003c/span\u003e\u003c/span\u003e is the surface runoff of day i (mm);\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{E}}_{\\text{a}}\\)\u003c/span\u003e\u003c/span\u003e is the evaporation amount of day i (mm);\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Q}}_{\\text{s}\\text{e}\\text{e}\\text{p}}\\)\u003c/span\u003e\u003c/span\u003e is the amount of water (mm) in the enshrouding zone reached through the soil profile on day i;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Q}}_{\\text{g}\\text{w}}\\)\u003c/span\u003e\u003c/span\u003e is the regression flow on day i (mm).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIn this study, the SCS runoff curve equation is applied to calculate the surface runoff of the basin. SCS curve equation method formula is shown in the right table:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhere:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{P}\\)\u003c/span\u003e\u003c/span\u003e precipitation (mm);\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{I}}_{\\text{a}}\\)\u003c/span\u003e\u003c/span\u003e is the precipitation loss before surface runoff (mm),\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{I}}_{\\text{a}}\\)\u003c/span\u003e\u003c/span\u003e=2S;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{S}\\)\u003c/span\u003e\u003c/span\u003e is the maximum possible retention of the basin (mm), S=25400/CN-254(CN value can be obtained according to the combination of soil type, land use type and vegetation cover type);\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEvapotranspiration in SWAT simulation mainly includes water surface evaporation, soil water evaporation and plant evapotranspiration. In addition, measured daily potential evaporation data can also be used. The actual evaporation calculation includes canopy interception evaporation E can, vegetation transpiration Et and soil water evaporation. First, it is assumed that the canopy trapped water is evaporated as much as possible. When all the canopy trapped water is evaporated, the remaining evaporation comes from vegetation transpiration and soil evaporation. The main calculation equation is shown in the table on the right:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhere,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{E}}_{\\text{a}}\\)\u003c/span\u003e\u003c/span\u003e is the canopy water evaporation (mm);\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}{\\text{W}}_{\\text{i}\\text{n}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is the variation of water storage during the canopy period (mm);\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{E}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is the maximum daily transpiration (mm);\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{E}}_{0}^{{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e is the remaining potential evaporation after canopy water evaporation (mm);\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{L}\\text{A}\\text{I}\\)\u003c/span\u003e\u003c/span\u003e is the leaf area index;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{E}}_{\\text{s}\\text{o}\\text{i}\\text{l},\\text{z}}\\)\u003c/span\u003e\u003c/span\u003e is the evaporation water requirement at Z depth (mm); Z is the depth of the soil below the surface (mm);\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{E}}_{\\text{S}}^{{\\prime\\:}{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e is the maximum soil water evaporation (mm).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoil water can be absorbed by plants, and can also be recharged to groundwater and/or form a soil flow. The critical condition of soil flow is that soil water content exceeds field water capacity. The flow in soil simulated by SWAT mainly includes uniform flow in small pores and preferential flow (or pipe flow) in large pores, and the calculation of preferential flow is selected according to the actual situation. The formula for calculating lateral flow in soil is as follows:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{int}=0.024\\times\\:\\left(\\frac{2\\times\\:S{W}_{ly,excess}\\bullet\\:{K}_{sat}\\bullet\\:slp}{{\\varphi\\:}_{d}\\bullet\\:{L}_{ℎill}}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhere:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{S}{\\text{W}}_{\\text{l}\\text{y},\\text{e}\\text{x}\\text{c}\\text{e}\\text{s}\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e is the amount of water that can flow out of the soil saturated area (mm);\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{K}}_{\\text{s}\\text{a}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is soil saturated water conductivity (mm/h);\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{s}\\text{l}\\text{p}\\)\u003c/span\u003e\u003c/span\u003e is the slope;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{L}}_{\\text{h}\\text{i}\\text{l}\\text{l}}\\)\u003c/span\u003e\u003c/span\u003e is the slope length;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\upvarphi\\:}}_{\\text{d}}\\)\u003c/span\u003e\u003c/span\u003e is the difference between total soil porosity and the field water capacity and porosity achieved by soil moisture content.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransport loss will regulate runoff and peak discharge in the basin. The formula in the right table is the formula of transport loss:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}_{loss}={K}_{cℎ}\\bullet\\:TT\\bullet\\:{P}_{cℎ}\\bullet\\:{L}_{cℎ}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhere:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{t}}_{\\text{l}\\text{o}\\text{s}\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e is river water loss (m3); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{T}\\text{T}\\)\u003c/span\u003e\u003c/span\u003e the time required for upstream water to downstream water (hr);\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{K}}_{\\text{c}\\text{h}}\\)\u003c/span\u003e\u003c/span\u003e is the effective water conductivity of the river (mm/hr);\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}}_{\\text{c}\\text{h}}\\)\u003c/span\u003e\u003c/span\u003e is wet circumference (Km);\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{L}}_{\\text{c}\\text{h}}\\)\u003c/span\u003e\u003c/span\u003e the length of the river is (Km).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003e1.1.2 SWAT model data preparation and database construction\u003c/h3\u003e\n\u003cp\u003eThe fundamental data necessary for SWAT model development can be categorized into two types: geographical data and attribute data. Spatial data primarily encompass digital elevation models, soil distribution maps, and land use distribution maps. Attribute data consist of meteorological and hydrological data.The data formats required for this survey are shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBasic geographic data required for constructing SWAT model in the study area\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eData type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eData source\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigital Elevation Model (DEM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNASA Earth Science data website(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nasadaacs.eos.nasa.gov/\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoil type and attribute list\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHWSD data downloaded from the National Tibetan Plateau Scientific Data Center (World Soil Database)(data.tpdc.ac.cn)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand type use data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInstitute of Aerospace Information Innovation, Chinese Academy of Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeteorological data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCMADS (V1.1) downloaded by the National Tibetan Plateau Scientific Data Center(data.tpdc.ac.cn)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRunoff data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTongjiang city hydrology station\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized DEM data obtained from the NASA Earth science data website, employing DEN topographic data with 30-meter precision, and standardized spatial and projection coordinates via ArcGIS 10.6. The acquired data are presented in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea below.\u003c/p\u003e\n\u003cp\u003eThe delineation of sub-basins is the preliminary phase of SWAT model development, resulting in the creation of 32 sub-basins for the middle and lower reaches of the Songhua River basin (see to Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb below).\u003c/p\u003e\n\u003cp\u003eThe soil data utilized in the development of the SWAT model soil database for this study was sourced from the Harmonized World Soil Database, obtained from the National Tibetan Plateau Scientific Data Center. This data comprises essential input parameters for the SWAT model, including the soil type distribution map, soil index table, and soil physical property file [36]. Subsequent to projection and clipping, the soil type distribution map is juxtaposed with the soil physical property file, and the soil types exhibiting identical physical properties are reclassified to produce the soil type distribution map illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec below. The classification outcomes were segmented into eight soil groups. This study details the calculation of soil data parameters for the SWAT model using SPAW [37](Soil Profile Water Transfer) software, where the carbon content in the soil layer must be transformed into organic mass before being entered into the SPAW software for computation. Furthermore, to streamline the computation of USLE-K parameters inside the model, the quantities of clay loam and clay in the database were determined using the substitution formula suggested by Williams [38]. The precise values for soil layer 1 and soil layer 2 from the final calculations are shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, while the corresponding explanations of the soil physical coefficients in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e are detailed in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSoil coefficient and level calculated by SPAW\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Coefficient\u003c/p\u003e\n \u003cp\u003eSoil type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSOL_BD1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSOL_AWC1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSOL_K1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSOL_CBN1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSOL_BD2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSOL_AWC2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSOL_K2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSOL_CBN2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHierarchy\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL-L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLPe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL-L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL-CL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHSs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCL-SaCL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIL-L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL-CL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCMe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL-L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWATER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRelated descriptions of soil coefficients involved in the calculation of SPAW\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSOL_BD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eweight of dried soil, comprising soil particles and intergranular pores, per unit volume. It stands for the moist bulk density of soil (SOILdensity).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCLAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClay content, %wt, refers to soil particles\u0026thinsp;\u0026lt;\u0026thinsp;0.002mm in diameter.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSOL_AWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndicates the effective water content of soil layer, in mm/mm.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSILT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSILT1 refers to the loam content of the soil (%wt), that is, the percentage by weight of soil particles between 0.002 and 0.05mm in diameter.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSOL_CBN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrganic carbon content (%wt) of the soil layer.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSand content, %wt, refers to particles with diameters between 0.05 and 2.0mm;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSOL_K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSaturated water conductivity/saturated hydraulic conductivity, mm/hr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGravel content, %wt, refers to particles with a diameter greater than 2mm;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSOL_ZMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRepresents the maximum root depth of the soil profile, mm.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSLE_K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eErodibility factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe land use data is derived from the 2022 global land cover dataset with a 30-meter resolution, published by the Academy of Aerospace Information Innovation of the Chinese Academy of Sciences. The land use types in the study region were divided into six categories: cultivated land, forest land, grassland, water bodies, urban and rural areas, industrial and mining land, residential land, and unused land (refer to Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed below).\u003c/p\u003e\n\u003cp\u003eThis paper primarily utilizes daily meteorological data encompassing precipitation, temperature, relative humidity, solar radiation, and wind speed. The meteorological data utilized were CMADSV1.1 datasets obtained from the National Tibetan Plateau Scientific Data Center [39]. The duration spans from 2008 to 2016, thereby satisfying the temporal criteria for the model\u0026apos;s operation. This database is among the most extensively utilized meteorological datasets for the SWAT model. This database satisfies the accuracy criteria for the model\u0026apos;s final output outcomes, following extensive utilization by numerous scholars [40].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1.3 Sensitivity coefficient analysis and model calibration and verification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe runoff simulation of the SWAT model involves numerous factors. To diminish the intricacy of parameter calibration, SWAT-CUP software is employed to assess the sensitivity of model parameters and identify those that significantly influence the model\u0026apos;s simulation outcomes. This research employs P-value and t-statistic to assess parameter sensitivity. The t-statistic indicates the sensitivity of the parameter, whereas the p-value denotes the significance degree of that sensitivity. A greater absolute value of the T-value correlates with a p-value approaching 0, signifying an elevated significance level of the parameter\u0026apos;s sensitivity.\u003c/p\u003e\n\u003cp\u003eThis study utilizes the SUFI2 algorithm, characterized by low computational accuracy yet high efficiency, in conjunction with SWAT-CUP software\u0026mdash;a calibration uncertainty program appropriate for basins exhibiting relatively uncomplicated runoff variations\u0026mdash;based on runoff data from Jiamusi City\u0026apos;s Tong Jiang Hydrological Station collected between 2008 and 2016[41\u0026thinsp;~\u0026thinsp;42]. Twenty-two parameters closely associated with runoff were chosen for sensitivity analysis. The iterations were established at 500 each run, and the sensitivity analysis utilized global sensitivity analysis. The ultimate outcome is depicted in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eReinsert the revised parameters into the model, revise the worksheet, and execute the validation once more. The findings of the runoff rate determination and verification are presented in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The experimental results indicate that the runoff simulation of the Tongjiang hydrological station is optimal, with a rate period of R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.87 and NSE\u0026thinsp;\u0026gt;\u0026thinsp;0.76, and a verification period of R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.81 and NSE\u0026thinsp;\u0026gt;\u0026thinsp;0.76.\u003c/p\u003e\n\u003ch2\u003e1.2 Construction of MODFLOW model\u003c/h2\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e1.2.1 Introduction to MODFLOW model\u003c/h2\u003e\n \u003cp\u003eThe concept of groundwater numerical modeling is derived from Darcy\u0026apos;s Law, introduced by the renowned French engineer Darcy in 1856. Consequently, with advancements in groundwater numerical simulation theory and the evolution of computer software, groundwater numerical simulation software based on computer programs is becoming increasingly sophisticated. Currently, the primary numerical simulation approaches for groundwater are the finite difference method and the finite element method, with the former being extensively utilized by researchers. Currently, widely utilized modeling software includes Visual MODFLOW, FEFLOW, GMS, among others. Researchers domestically and internationally have employed several iterations of MODFLOW software [43]to undertake extensive studies in groundwater science.\u003c/p\u003e\n \u003cp\u003eThis work utilized the MODFLOW module inside GMS software, characterized by its user-friendly interface and effective 3D visualization, to construct a groundwater flow model for the middle and lower portions of the Songhua River basin [44]. The flow model program utilized in this instance is the MODFLOW-2005 version. The U.S. Geological Survey designed the program to enhance the management of unconfined aquifers. This module is an independent program intended to address issues related to nonlinear, non-confined aquifers involving dry and wet conditions. The fundamental governing equation of MODFLOW is as follows:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:\\frac{\\partial\\:}{\\partial\\:x}\\left({K}_{xx}\\frac{\\partial\\:ℎ}{\\partial\\:x}\\right)+\\frac{\\partial\\:}{\\partial\\:y}\\left({K}_{yy}\\frac{\\partial\\:ℎ}{\\partial\\:y}\\right)+\\frac{\\partial\\:}{\\partial\\:z}\\left({K}_{zz}\\frac{\\partial\\:ℎ}{\\partial\\:z}\\right)-W={S}_{s}\\frac{\\partial\\:ℎ}{\\partial\\:t}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{K}_{x}\\text{、}{K}_{y}\\)\u003c/span\u003e\u003c/span\u003e、\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{K}_{z}\\)\u003c/span\u003e\u003c/span\u003e is the permeability coefficient (m/d) along the x, y and z axes;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ℎ\\)\u003c/span\u003e\u003c/span\u003e is the water head (m),\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:W\\)\u003c/span\u003e\u003c/span\u003e is the underground water source and sink (m/d), including precipitation infiltration recharge, irrigation return water, diving evaporation, mechanical well production, aquifer and river exchange water, diving and confined water exchange water; Up to unit volume flow through medium and isotropic soil in non-equilibrium state;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{s}\\)\u003c/span\u003e\u003c/span\u003e is the specific water storage coefficient of porous medium;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e is time (d).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e1.2.2 Conceptual model of hydrogeology\u003c/h3\u003e\n\u003cp\u003eThe primary recharge sources of the groundwater system in the middle and lower portions of the Songhua River basin include piedmont lateral recharge, precipitation recharge, river lateral recharge, and irrigation area regression recharge. The drainage mostly comprises industrial and agricultural water, as well as groundwater evaporation. The extensive irrigation area in the studied region results in significant agricultural water use.\u003c/p\u003e\n\u003cp\u003eThe chosen simulation range for the study area aligns with the SWAT model. The submersible aquifers in the research area predominantly consist of Quaternary Holocene sand and gravel, with a thickness ranging from 100 to 150 meters in most regions. The riverbed and floodplain of the main stream of the Songhua River and its tributaries are predominantly comprised of Holocene (Q4) deposits, specifically a thin layer of yellow clay and sub-clay, together with sand and gravel in the lower basin. The terrace of the interriver zone in the basin predominantly consists of Upper Pleistocene (Q3) yellow-brown sand and gravel, with discontinuous sub-clay above. The flat region corresponds to the Middle Pleistocene (Q2), characterized by gray-brown, gray-black silty sand, sand, and sand gravel, with the lower section being a mixture of sub-clay and silty sub-clay. The basin\u0026apos;s general base consists of Lower Pleistocene (Q1) deposits, characterized by yellow-green, gray-green medium sand, fine sand, silty sand, and sand gravel. This simulation treats the pore water of the entire Quaternary unconsolidated sediments as a singular aquifer. The model\u0026apos;s roof elevation is derived from the interpolation of 30m precision DEM elevation data, while the upper boundary is determined using the approximate aquifer thickness documented in the hydrogeological data for the study area. Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea below illustrates the geological profile of the research region.\u003c/p\u003e\n\u003cp\u003eBased on prior experience and pertinent hydrogeological data from Sanjiang Plain, the study region is initially categorized into five zones and further subdivided into seven zones according to the model\u0026apos;s layer count. Refer to Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb and ascertain the initial value (see Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Moreover, a significant hydraulic connection exists between the northwestern and southern peripheries of the study area and the basin within it, with the northwestern and southern mountains receiving piedmont lateral recharge; thus, they are collectively classified as lateral inflow boundaries. The northern section of the study area represents the convergence of the Heilongjiang basin and the Songhua River basin, which aligns approximately parallel to the isowater line, thus categorized as the lateral inflow boundary. Conversely, the eastern boundary is classified as the zero flow boundary due to the minimal vertical flow observed. The western boundary of the study area features numerous outflowing tributaries, including the Wutong River and Anbang River, hence it is classified as a continuous head boundary.\u003c/p\u003e\n\u003ch3\u003e1.2.3 Space-time dispersion and initial condition determination of the model\u003c/h3\u003e\n\u003cp\u003eGrid Division: The area delineated by SWAT served as the research domain for MODFLOW, encompassing an effective calculation area of 10,788.1 km\u0026sup2;. This research area was segmented into 1,000 m \u0026times; 1,000 m square grids, resulting in a division into 168 rows, 198 columns, and 3 layers, comprising a total of 99,792 effective grids. The initial water level of the model was established using the iso-water level of the middle and lower sections of the Songhua River basin on January 31, 2008 (refer to Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ec). The simulation period was designated from January 2008 to December 2018, with each month serving as the stress period.\u003c/p\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInitial value range of model geological parameters\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epartition number\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInitial range of permeability coefficient(m/d)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInitial value range of water supply degree\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅠ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026thinsp;~\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u0026thinsp;~\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u0026thinsp;~\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u0026thinsp;~\u0026thinsp;0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u0026thinsp;~\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u0026thinsp;~\u0026thinsp;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅣ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026thinsp;~\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u0026thinsp;~\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅤ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u0026thinsp;~\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u0026thinsp;~\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅰ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.0\u0026thinsp;~\u0026thinsp;25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u0026thinsp;~\u0026thinsp;0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅱ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0\u0026thinsp;~\u0026thinsp;15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u0026thinsp;~\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅲ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.0\u0026thinsp;~\u0026thinsp;20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u0026thinsp;~\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅳ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0\u0026thinsp;~\u0026thinsp;15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u0026thinsp;~\u0026thinsp;0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅴ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.0\u0026thinsp;~\u0026thinsp;25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u0026thinsp;~\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅵ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.0\u0026thinsp;~\u0026thinsp;20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u0026thinsp;~\u0026thinsp;0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅶ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.0\u0026thinsp;~\u0026thinsp;20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u0026thinsp;~\u0026thinsp;0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ch2\u003e1.3 Construction of SAWT-MODFLOW coupling model\u003c/h2\u003e\n\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e1.3.1 Introduction to SAWT-MODFLOW coupling model\u003c/h2\u003e\n \u003cp\u003eThe SWATMOD coupling model integrates the strengths of the SWAT model for surface water analysis and the MODFLOW model for groundwater analysis. It can analyze the alterations in water resource utilization and agricultural production resulting from irrigation, fertilization, tillage, and other interventions, as well as assess the variations in surface water resources and the comprehensive dynamic changes in groundwater levels across each study area. The precision of assessing the correlation between surface water and groundwater in a region is enhanced.\u003c/p\u003e\n \u003cp\u003eThis study utilizes the mapping relationship between the hydrological response unit of the SWAT basic computing unit and the basic computing unit of MODFLOW, established via the QGIS platform, to transfer the soil seepage flow calculated by SWAT software to the MODFLOW grid, assigning values to each grid as RCH recharge packets. The subsurface water quantity computed by MODFLOW is conveyed to the SWAT sub-watershed via a mapping connection.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e1.3.2 Calibration and verification of SAWT-MODFLOW coupling model\u003c/h2\u003e\n \u003cp\u003eThe calibration of the SWAT-MODFLOW coupling model primarily consists of two components: the calibration of the surface SWAT model and the calibration of the subsurface MODFLOW model. Five highly sensitive characteristics were chosen for the calibration of the SWAT model. The runoff data from Jiamusi Hydrologic Station, collected between 2008 and 2016, was utilized for calibration purposes. The linear regression coefficient R\u0026sup2; and the Nash-Sutcliffe efficiency coefficient (NSE) were chosen to assess the model\u0026apos;s simulation performance. The R\u003csup\u003e2\u003c/sup\u003e and NSE values for rate periodicity, as depicted in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea, were found to be 0.86 and 0.87 respectively. During the verification period, an R\u003csup\u003e2\u003c/sup\u003e value of 0.77 and an NSE value of 0.75 were obtained, indicating a satisfactory simulation performance.. The MODFLOW model calibration involved selecting the permeability coefficient for parameter calibration, utilizing measured groundwater levels from January 2008 to December 2012, and employing measured water levels from January 2012 to December 2016 for parameter verification, ultimately yielding the optimal parameters presented in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. In addition, according to the regression analysis of the simulated water level and the measured water level shown in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eb, the period R\u0026sup2; is 0.98, and the verified R\u0026sup2; is 0.97, indicating that the simulation results of the groundwater level by the model are in good agreement with the measured data, indicating that the model better meets the standards of scientific research.\u003c/p\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFinal values of geological parameters\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epartition number\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue of the permeability coefficient(m/d)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInitial value of water supply\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅠ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅣ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u0026thinsp;~\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅤ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅰ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅱ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅲ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅳ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅴ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅵ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅶ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"2. RESULTS","content":"\u003cp\u003e\u003cstrong\u003e2.1 Analysis of influencing factors of groundwater level\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurface water and groundwater constitute an interconnected hydrologic continuum in nature. The intricate and variable interplay among them influences the hydrologic cycle and water balance assessment in the basin. Precipitation and evaporation are the primary sources of groundwater replenishment. The lag effect of precipitation recharge and the impact of evaporation on groundwater levels govern the fluctuations of the water table. This chapter employs cross wavelet transform [45~57] to elucidate the interrelationship between groundwater levels and precipitation and evaporation.\u003cbr\u003e The cross wavelet transform of groundwater levels (refer to FIG. 8C) and precipitation data (refer to FIG. 8B) for Jimusi, Fujin, and Tongjiang was conducted using MATLAB software. FIG. 8 (A) illustrates the results, with red and blue denoting the peak and valley values of energy density, respectively, thereby demonstrating the location and dynamic properties of the time-frequency transition of the predominant wave group. The enclosed region delineated by the thick black solid line is depicted by a red noise test at a 95% confidence level, while the envelope formed by the thin black solid line signifies the cone of wavelet effect (COI). Figure 8A(a) illustrates that the elevated energy regions of groundwater levels and precipitation in Jiamusi City predominantly occur between 10 to 15 months. These findings passed the red noise test at a 95% confidence level from 2008 to 2016, indicating a significant correlation between the two variables, with the primary resonance period approximately 1 year. Figure 8A(b) illustrates that the groundwater level and precipitation in Fujin City successfully passed the red noise test at the 95% confidence level from 2008 to 2016, indicating that the majority of time periods met the significance criterion, with a predominant resonance period of approximately 1 year over this interval. Figure 8A(c) illustrates that the groundwater level and precipitation in Tongjiang City successfully passed the red noise test at a 95% confidence level from 2009 to 2014 and throughout 2016, with significant results for the majority of time periods, indicating a primary resonance period of approximately one month during this interval. Furthermore, the phase relationship is quantified using the mean phase angle of a significance test beyond the coherence of interest, followed by an analysis of the delay characteristics between the two time series. The cross-phase of time series between precipitation and groundwater levels for considerable periods outside the cone of influence can be derived by integrating Figure 8A. The lag period of groundwater levels in Jiamusi City, Fujin City, and Tongjiang City in relation to precipitation is approximately 10.56 days, 10.58 days, and 3.15 days, respectively. The wavelet analysis of evaporation (refer to Figure 8B) and groundwater levels throughout the three regions indicates that evaporation exhibits a coherent period of 10 to 13 months concerning groundwater levels, with analogous results observed at other sites. Evaporation exhibits a robust correlation with groundwater at each station, and this correlation is generally consistent; the impact of evaporation on groundwater levels across the stations is analogous.\u003cbr\u003e The variation in groundwater level across the entire study area is quite consistent. Aside from the weak correlation between groundwater levels in the northwestern region of the study area and other regions, the water level disparity peaks at approximately 30 meters. The temporal variation trend of groundwater levels is markedly distinct, while the correlation among the central, northern, eastern, and northeastern plains of the study area is strong, with minimal fluctuations in spatial scale at any given time. The highest variation of the water table is within 15 meters. The correlation between rainfall and evaporation concerning the groundwater table of each sub-basin indicates that precipitation exerts a greater influence on the fluctuations of the groundwater table than evaporation. Furthermore, the changes in the groundwater table in sub-basins 1, 2, 3, and 11, as well as sub-basins 16 to 20, are significantly influenced by both precipitation and evaporation, with a P value approaching 0.05. The fluctuation pattern of groundwater levels between 4 to 10 and 22 to 32 in sub-basins is minimally influenced, with a P value approaching 0.3, as illustrated in FIG. 8d for further details.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Analysis of the transformation relationship between surface water and groundwater\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo enable the analysis of the spatial variation of surface water-groundwater conversion in the main stream of the Songhua River, this study segmented the river, as extracted by the SWAT-MODFLOW model, into 11 sections and illustrated the surface water-groundwater conversion relationships in the study area using distinct colors, as depicted in Figure 9. The link between surface water and groundwater conversion exhibits significant regional heterogeneity, with the blue part of the river indicating that the prevailing tendency throughout the years in river reaches is surface water recharging groundwater. The yellow part of the river signifies that the interaction between surface water and groundwater has been often altered throughout the years, with a modest trend towards groundwater recharging surface water. The dark blue area signifies that the prevailing tendency of surface water to groundwater conversion in the river segment throughout the years is surface water recharge, and this pattern is markedly evident. In the entire basin, the river channel responsible for surface water recharging groundwater constitutes 41.75% of the total length, predominantly occurring in river segments 1, 5, 6, 8, and 9, with an annual average recharge of 3.60\u0026times;10\u003csup\u003e7\u003c/sup\u003e m\u0026sup3;/d. It constitutes 50.84% of the average yearly total water exchange. Groundwater recharge to surface water occurs mostly in river sections 2, 3, 4, 7, 10, and 11, with an average annual groundwater discharge of 5.02\u0026times;107 m\u0026sup3;/d, or 58.25% of the average annual total water exchange.\u003c/p\u003e\n\u003cp\u003eTo investigate the seasonal fluctuation of the surface water-groundwater conversion relationship in the basin\u0026apos;s rivers and channels, January, March, July, and October are designated to represent winter, spring, summer, and fall, respectively, as illustrated in Figure 10. Seasonally, the conversion relationship and water volume of each river reach exhibit significant variations due to the presence of numerous river reaches in the studied area. Consequently, three river segments\u0026mdash;1, 7, and 5\u0026mdash;exhibiting significant alterations, extended lengths, and distinctive geographical positions are chosen for research. The annual average monthly recharge of surface water to groundwater in reach 1 during winter, spring, summer, and autumn is 2.8\u0026times;10\u003csup\u003e7\u003c/sup\u003e m\u0026sup3;/d, 2.6\u0026times;10\u003csup\u003e7\u003c/sup\u003e m\u0026sup3;/d, 7.8\u0026times;10\u003csup\u003e7\u003c/sup\u003e m\u0026sup3;/d, and 3.0\u0026times;10\u003csup\u003e7\u003c/sup\u003e m\u0026sup3;/d, respectively. The annual average monthly discharge of groundwater to surface water in reach 7 for winter, spring, summer, and autumn was 6.5\u0026times;10\u003csup\u003e4\u003c/sup\u003e m\u0026sup3;/d, 6.0\u0026times;10\u003csup\u003e4\u003c/sup\u003e m\u0026sup3;/d, 1.5\u0026times;10\u003csup\u003e5\u003c/sup\u003e m\u0026sup3;/d, and 8.1\u0026times;10\u003csup\u003e4\u003c/sup\u003e m\u0026sup3;/d, respectively. In section 5, where surface water predominantly contributes to groundwater recharge across several seasons, the average monthly recharge rates are as follows: 2.5\u0026times;10\u003csup\u003e4\u003c/sup\u003e m\u0026sup3; in autumn, 2.7\u0026times;10\u003csup\u003e4\u003c/sup\u003e m\u0026sup3;/d in spring, 2.8\u0026times;10\u003csup\u003e4\u003c/sup\u003e m\u0026sup3;/d in winter, and 6.5\u0026times;10\u003csup\u003e4\u003c/sup\u003e m\u0026sup3;/d in summer. The investigation of the aforementioned 11 river segments reveals characteristics that align with the seasonal fluctuations of precipitation in the Songhua River basin. Furthermore, the monthly average surface-groundwater recharge in the Songhua River Basin from 2008 to 2016 indicates significant seasonal variability, with groundwater recharge to surface water not falling below 4.0\u0026times;10\u003csup\u003e4\u003c/sup\u003e m\u0026sup3;/d, and the average fluctuation range for each river reach is approximately -52% to 55%. The recharge of surface water to groundwater exhibits significant seasonal variability, peaking in summer, particularly in July, and reaching its nadir during the winter and spring exchange period, typically between December and April. This mostly pertains to the significant seasonal fluctuations in water quality within the basin and the frigid winter environment in northeastern China. Figure 11 illustrates that the interannual variability of the surface water-groundwater conversion relationship in the research area indicates a significant fluctuation in the recharge volume of surface water to groundwater, with a range of -37% to 63%. From 2008 to 2009, there was a rising trend; from 2009 to 2014, a general declining trend prevailed; and from 2014 to 2016, there was a modest increase. This pertains to the augmentation of precipitation in the Songhua River basin. The range of fluctuation in groundwater recharge to surface water is between -35% and 52%, and this variance is directly influenced by precipitation. The pattern mirrors that of river recharge to groundwater, exhibiting frequent variations in each river segment; yet, overall, the entire basin reflects a tendency of surface water transitioning to groundwater.\u003c/p\u003e"},{"header":"3. DISCUSSION","content":"\u003cp\u003e\u003cstrong\u003e3.1 Genetic analysis of surface-groundwater conversion relationship in Songhua River Basin\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SWAT-MODFLOW coupling model indicates that the relationship between surface water and groundwater conversion occurs frequently throughout the year, with prolonged groundwater recharge predominantly occurring from October to April of the subsequent year. The overall trend of surface water to groundwater conversion in the study area indicates that surface water recharges groundwater. The primary reason is that the Songhua River basin is situated in northeastern China and influenced by the temperate monsoon climate. The temperature variation among the four seasons is significant, and the substantial increase in river runoff during seasonal transitions leads to a considerable fluctuation in groundwater supply from the river. The Songhua River basin exhibits the fourth highest porosity, with sand coverage encompassing 75%, and demonstrates significant water seepage capacity. Consequently, river recharge of groundwater markedly escalates with seasonal variations [58]. Groundwater recharge to surface water is influenced by seasonal variations; however, the inertia is substantial, resulting in the formation of groundwater recharge rivers in the Songhua River basin during winter and spring, while summer and autumn typically experience flood conditions. Locally, the surface water-groundwater conversion mode of the river (sections 10 and 11) in the northwest of the study area is characterized by groundwater recharge from surface water, primarily due to the river\u0026apos;s location in a mountainous region, where the geology influences recharge efficiency. Secondly, the region is situated between Hegang City and Luobei County. The Jiangluo, Fengxiang, Wutonghe, Puyang, and Suibin irrigation areas exhibit significant agricultural water extraction, which indirectly influences the relationship between surface water and groundwater conversion [59]. Furthermore, section 1 experiences continuous surface water replenishment due to substantial groundwater availability throughout the year. Besides its geographical position at the basin\u0026apos;s ultimate outlet, the upstream region of this section is situated in Fujin City, Jiamusi City, which hosts numerous water storage projects; consequently, the river is influenced by factors such as the operation of reservoir gates and flood discharges throughout the year [60]. Over the past eight to nine years, the trend has shifted towards the recharge of groundwater by surface water. The primary reason is that the river is a lowland river adjacent to mountainous terrain, and its unique geographical position allows the river segment to remain unaffected by the recharge process impeded by the mountain geology, while also benefiting from lateral recharge from the foothill water. Moreover, Section 8 is part of the downstream confluence of mountainous rivers (Section 10, Section 11), which also exerts an indirect influence on the conversion between surface water and groundwater. In the central region of the basin, the conversion of surface water to groundwater occurs frequently, and the trend remains ambiguous due to geological conditions, water extraction in the irrigation zone, climate change, and minimal topographical variation in the area. In this study, despite the differing conversion trends of river segments 2 to 7, the disparity between recharge and excretion is minimal, with the conversion relationship primarily influenced by seasonal precipitation. The influence of other natural factors is comparatively minimal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Analysis of differences with previous research results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe investigation of the relationship between surface water and groundwater in the Songhua River Basin has consistently been a significant subject, with numerous scholars conducting extensive research on it since 2011. In 2011, scholar Zhou Yubo[61] utilized numerical simulation methods to quantify the exchange of river and groundwater in the Yilan to Fujin section of the Songhua River, assessing the conversion of groundwater and surface water during three representative years characterized by abundance, normalcy, and drought. The comparison of the research findings with those of this paper indicated minimal disparity between the results acquired during the study years and those presented herein. Nevertheless, numerous ambiguous variables existed in his research. The model\u0026apos;s accuracy in river level and meteorological data was compromised by the technological and data limitations of that period. Consequently, this study integrated a more advanced model coupling method informed by prior experience. The SWAT-MODFlow coupling model was employed, incorporating the SWAT module into the original GMS model, which thoroughly accounted for land use, soil type, and climate change in the study area, while addressing the issue of low numerical simulation accuracy attributed to the technological limitations of that period. Comparing the research findings of Tian Haoran [62] from 2012 revealed minimal differences in the conversion volume of the upper reaches of the Songhua River, whereas significant discrepancies were observed in the lower reaches. The primary reason is that since 2013, Fujin City has effectively adjusted the water intake for local agriculture and enhanced the restoration efforts for the water level of the Songhua River. The secondary reason is that, despite the scholar employing various methodologies, including the surface water balance method, groundwater balance method, and GMS numerical simulation, to thoroughly assess the surface water to groundwater conversion volume of the Songhua River, the integration of multiple methods enhanced the precision of the paper\u0026apos;s calculation results. Nevertheless, traditional calculation methods yield relative errors in summarizing the intricate surface water infiltration process, and the aggregation of these errors results in diminished calculation outcomes in the downstream catchment area. In comparison to the findings of scholar Zhang Bing [63] in 2015, this study reveals a low accuracy in the calculation results regarding the conversion amount of a single river reach. The disparity arises from the application of the isotope tracer method to investigate the spatial relationship between surface water and groundwater in the Second Songhua River. In comparison to the model method, this method provides a more precise depiction of the water flow seepage process. The experimental procedure of this method is complex, necessitating several steps including isotope labeling, sample preparation, and radioactive detection. Each link must be meticulously managed and regulated to guarantee the precision and dependability of experimental outcomes. Moreover, the expense associated with this method is comparatively elevated for analyzing the trend of surface water-groundwater conversion in the Songhua River basin. This method examines the conversion relationship between surface water and groundwater in the Songhua River by analyzing a single river section, focusing solely on the primary segment of the river network. This approach fails to provide a comprehensive trend characterization and neglects surface dynamics in favor of point-specific accuracy. This study, building on prior research, conducted a comprehensive analysis of the transformation dynamics of the Songhua River by segmenting it into 11 distinct river segments within the middle and lower reaches of the basin. Each segment was analyzed individually to attain comprehensive research outcomes, resulting in a low accuracy of conversion for individual river segments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Research uncertainty analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study remains fraught with uncertainties, necessitating a substantial amount of high-quality data, including terrain, soil, meteorological, and hydrological information, to construct and operate the coupling model. The acquisition and processing of data can be challenging, particularly in regions with insufficient monitoring data. The groundwater level monitoring data in the study area is limited for various reasons, and this paper utilizes 11 groundwater level observation wells for calibration, resulting in unavoidable discrepancies between the model simulation outcomes and the actual conditions. Furthermore, the establishment of the MODFLOW model encountered inaccuracies in the simulation results due to the inability to acquire real-time water withdrawal data for each irrigation district in the study area, leading to the use of annual average water withdrawal figures for surface mining instead of real-time data. Furthermore, when configuring the SWAT module of the coupled model, it is permissible to input land use data from only one year, which is relatively straightforward. However, changes in land use significantly influence hydrology and non-point source pollution simulations. Particularly during the processes of flow, sediment, and non-point source pollution simulation, it is essential to dynamically update land use data to recalibrate the threshold value. To enhance the simulation accuracy of the model regarding land use change. Consequently, dynamic land use data must be incorporated into the model [64]. The Songnen Plain and Sanjiang Plain in northeastern China are the areas with the highest agricultural grain production in the country[65]. The topography in this area is intricate, featuring diverse land use and soil classifications. Nonetheless, the existing SWAT model is inadequate in handling high-precision and multi-transform terrain data, leading to inaccurate simulations of groundwater supply, waste, and pollution. A significant error exists in the shallow groundwater storage results [66]. It should be enhanced according to the planting configuration. The accurate planting structure data, derived from the integration of high-resolution drone imagery, remote sensing images, and ground-measured data, serves as the input land use data for the SWAT model. This approach aims to enhance the simulation of the migration and transformation processes of agricultural non-point source pollution, thereby improving the accuracy of watershed runoff simulations.\u003c/p\u003e"},{"header":"4. CONCLUSIONS","content":"\u003cp\u003e1. The SWAT-MODFLOW coupling model has good applicability in Songhua River Basin, and the simulated monthly runoff and groundwater level are in good agreement with the actual observed data.\u003c/p\u003e\n\u003cp\u003e2. For the whole study area, the correlation between the groundwater level in the northwest of the study area and other areas is weak, and the difference of the water level is about 30m at the highest, and the variation trend of the groundwater level over time is quite different. The correlation between the central, northern, eastern and northeastern plains of the study area is high, and the difference of spatial-temporal scale is not significant, and the maximum difference of groundwater level is less than 15m. Jiamusi City, Fujin City and Tongjiang City, which are geographically distributed in the study area, were selected to study the degree of influence of precipitation on the groundwater table. The lag time of precipitation on the groundwater table was about 10.56d, 10.58d and 3.15d.\u003c/p\u003e\n\u003cp\u003e3. Spatially, the recharge of surface water to groundwater in the Songhua River Basin from 2008 to 2016 mainly occurred in river segments 1, 5, 6, 8 and 9, while the recharge of groundwater to surface water was concentrated in river segments 2, 3, 4, 7, 10 and 11.\u003c/p\u003e\n\u003cp\u003e4. At the inter-annual scale, the recharge from surface water to groundwater varies significantly under the influence of precipitation, with a range of -37%~63%, while the recharge from groundwater to surface water is relatively stable, with a range of -35%~52%.\u003c/p\u003e\n\u003cp\u003e5. On the seasonal scale, combined with the analysis of 11 river segments, this feature is consistent with the seasonal variation of precipitation in the Songhua River Basin. The amount of groundwater recharge to surface water varies greatly with the seasons, not less than 4.0\u0026times;104m3/d, and the average variation range of each river reach is about -52% ~ 55%. The recharge of surface water to groundwater shows obvious seasonal variability, with the highest value in summer and the peak value in July, and the lowest value in winter and spring exchange season, and the lowest value usually appears between December and March to April.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY.X .is responsible for writing documents, building models and proposing methodsJ.J.Y and L.G.W .are responsible for the guidance of data visualizationR.Q. is responsible for data processingL.J.J. and L.P.X .were responsible for building the auxiliary modelsD.C.L. is responsible for providing project funding and reviewing the final manuscript\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData availability statementThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSun tienui. Global water resource situation highlights the intensification of climate change [N]. Guangming Daily,2024-10-16(012).DOI:10.28273/n.cnki.ngmrb.2024.004829\u003c/li\u003e\n \u003cli\u003eZhang Jianyun, Wang Jianhua, He Guohua, et al. Discussion on some problems of water peak in China [J]. Advances in Water Science, 2019,35(01):1-10.DOI:10.14042/j.cnki.32.1309.2024.01.001.\u003c/li\u003e\n \u003cli\u003eWu Zexin, Wu Yao, Yu Yexiang, et al. Groundwater environment change and its controlling factors in Songhua River Basin, Sanjiang Plain [J]. Journal of Hydropower and Energy,2024,42(09):38-42.DOI:10.20040/j.cnki.1000-7709.2024.20241012.\u003c/li\u003e\n \u003cli\u003eYang Derui, Du Bingzhao, Huang Liqun, et al. Strengthening groundwater management to promote high-quality development. China Water Resources,2021,(07):1-4.\u003c/li\u003e\n \u003cli\u003eShu Longcang, Yin Xiaoran, Yuan Yajie, et al. Spatial and temporal variation of water exchange between river and groundwater in typical areas of Sanjiang Plain [J]. Journal of Hydraulic Engineering, 2019,52(10):1151-1162.DOI:10.13243/j.cnki.slxb.20210509.\u003c/li\u003e\n \u003cli\u003eHao Aibing, Li Yamin, Zheng Yuejun, et al. A case study on hydrogeological conditions analysis using groundwater level monitoring data: Nanqu Depression in Kuitun River Basin, Xinjiang [J]. Hydrogeology Engineering Geology,2008,(04):27-30.\u003c/li\u003e\n \u003cli\u003ePeng Hongming, Xu Weilin, He Qing, et al. Hydrogeochemical and isotopic characteristics of the middle and upper reaches of the Buha River Basin [J]. Arid Land Research,2015,32(05):1032-1038.DOI:10.13866/j.azr.2015.05.28.\u003c/li\u003e\n \u003cli\u003eWu Xianfeng, Liu Changming. Progress in hydrological models of river basins [J]. Progress in Geography,2002,(04):341-348.\u003c/li\u003e\n \u003cli\u003eLai Zhengqing, Li Shuo, Li Chenggang, et al. Improvement and application of SWAT model in the middle and upper reaches of Heihe River Basin [J]. Journal of Natural Resources,2013,28(08):1404-1413.\u003c/li\u003e\n \u003cli\u003eKim Nam Won, Chung II Moon, Won Yoo Seung et al. Development and application of the integrated SWAT \u0026ndash;MODFLOW model[J]. Journal of Hydrology, 2008, 356(1-2): 1-16\u003c/li\u003e\n \u003cli\u003eWang Zhonggen, Zhu Xinjun, Li Wei, et al. Coupling simulation of surface water and groundwater in Haihe River Basin [J]. Progress in Geography,2011,30(11):1345-1353.\u003c/li\u003e\n \u003cli\u003eZhang Jing, Ross Mark, Trout Ken. Certification tests of MODFLOW implementation in the Integrated HydrologicModel[J]. Journal of Hydrologic Engineering, 2014, 19(3): 643-648.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCarroll Rosemary W H, Huntington Justin L, Snyder Keirith A et al. Evaluating mountain meadow groundwater response to Pinyon‐Juniper and temperature in a great basin watershed[J]. Ecohydrology, 2017, 10(1): e1792.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYifru Bisrat Ayalew, Chung II-Moon, Kim Min-Gyu et al. Assessment of groundwater recharge in Agro-Urban Watersheds using Integrated SWAT-MODFLOW Model[J]. Sustainability, 2020, 12(16): 6593.\u003c/li\u003e\n \u003cli\u003eWang Rui, Wang Zhonggen, Xia Jun. Research progress of coupling model of surface water and groundwater [J]. Progress in Geography,2008,(04):37-41.\u003c/li\u003e\n \u003cli\u003eCong Zhentao, Yang Jing, Lei Huimin, et al. Simulation of four-water conversion model in Weishan irrigation district [J]. Yellow River,2011,33(03):70-72.\u003c/li\u003e\n \u003cli\u003eWang Chengli, Jiang Renfei, Ruan Benqing, et al. Water consumption calculation model of irrigation area based on four-water conversion [J]. Journal of Hydraulic Engineering,2009,40(10):1196-1203.\u003c/li\u003e\n \u003cli\u003eWang Zhonggen, Zhu Xinjun, Li Wei, et al. Coupling simulation of surface water and groundwater in Haihe River Basin [J]. Progress in Geography,2011,30(11):1345-1353.\u003c/li\u003e\n \u003cli\u003eSEMIROMI M T, KOCH M. Analysis of spatio-temporal variability of surface-groundwater interactions in the Gharehsoo river basin, Iran, using a coupled SWAT- MODFLOW model [J]. Environmental earth sciences, 2019, 78(6): 201.1-.21.\u003c/li\u003e\n \u003cli\u003eHuang Chuanji, Li Chunguang, Jing Hefang, et al. Effects of land use and precipitation change on groundwater level around the Yellow River in Zhongning Region of Ningxia based on MIKE SHE model [J]. Water and Electricity Energy Science,202,40(01):14-17+111.\u003c/li\u003e\n \u003cli\u003eSABZZADEH I, SHOURIAN M. Maximizing crops yield net benefit in a groundwater-irrigated plain constrained to aquifer stable depletion using a coupled PSO-SWAT-MODFLOW hydro-agronomic model [J]. Journal of cleaner production, 2020, 262(Jul.20): 121349.1-.13.\u003c/li\u003e\n \u003cli\u003eKIM N, CHUNG I, WON Y, et al. Development and application of the integrated SWAT-MODFLOW model [J]. Journal of Hydrology, 2008, 356(1/2): 1-16.\u003c/li\u003e\n \u003cli\u003ePERKINS S P, SOPHOCLEOUS M. Development of a Comprehensive Watershed Model Applied to Study Stream Yield under Drought Conditions [J]. 1 Research Assistant, Kansas Geological Survey, The University of Kansas, 1930 Constant Ave, Lawrence, KS 66047 E-mail: perkins@kgsukansedu 2 Senior Scientist (corresponding author), Kansas Geological Survey, The University of Kansas, 1, 1999, 37(3):418-426.\u003c/li\u003e\n \u003cli\u003eLiu Luguang, Cui Yuanlai. Construction of surface water-groundwater coupling model in irrigation area [J]. Journal of Hydraulic Engineering, 2012, 43(07): DOI:826-33.10.13243/j.cnki.slxb.2012.07.012\u003c/li\u003e\n \u003cli\u003eALIYARI F, BAILEY R T, TASDIGHI A, et al. Coupled SWAT-MODFLOW model for large-scale mixed agro-urban river basins [J]. Environmental modelling \u0026amp; software, 2019, 115(3): 200-10.\u003c/li\u003e\n \u003cli\u003eRathjens, Hendrik, Bailey, et al. SWATMOD-PREP: GRAPHICAL USER INTERFACE FOR PREPARING COUPLED SWAT-MODFLOW SIMULATIONS[J]. Journal of the American Water Resources Association, 2017, 53(2):400-410.\u003c/li\u003e\n \u003cli\u003ePARK S, NIELSEN A, BAILEY R T, et al. A QGIS-based graphical user interface for application and evaluation of SWAT-MODFLOW models [J]. Environmental modelling \u0026amp; software, 2019, 111(Jan.): 493-7.\u003c/li\u003e\n \u003cli\u003eWu Yuebin, Jia Yanhong, Fan Baoshan. Analysis of bedmaking flow in the middle and lower reaches of Songhua River [J]. Journal of Harbin Institute of Technology,2008,(06):880-883.\u003c/li\u003e\n \u003cli\u003eSun Teng, Wang Sicong, Chen Changchun, et al. View review and related thoughts on the determination of Songhua River\u0026apos;s source [J]. Water Science, Technology and Economy,2024,30(09):1-5.\u003c/li\u003e\n \u003cli\u003eYang F, Chen J, Tian B X, et al. Spatial and temporal variations of temperature in Sanjiang Plain during 1961-2011. Farm Staff,2020,(06):145+163.\u003c/li\u003e\n \u003cli\u003eLi Lin, Zhang Zhenqing. Spatial and temporal variation of precipitation in Sanjiang Plain [J]. China Comprehensive Utilization of Resources,2023,41(08):40-42.\u003c/li\u003e\n \u003cli\u003eYang Yi-xun, Jiang Xiao-Xu, Li Ming-sheng, et al. Spatial variation of soil organic carbon content and density in Sanjiang Plain [J]. Chinese Journal of Soil Science, 2019,53(06):1313-1319.DOI:10.19336/j.cnki.trtb.2021101804\u003c/li\u003e\n \u003cli\u003eLiang Yukai. Vertical distribution characteristics of soil organic carbon in different land use types in Sanjiang Plain [J]. Jilin Water Resources,2024,(09):32-35+40.DOI:10.15920/j.cnki.22-1179/tv.2024.09.006.\u003c/li\u003e\n \u003cli\u003eZhao Sijia. Research on groundwater exploitation control scheme based on appropriate groundwater level in Sanjiang Plain [D]. Jilin University,2023.DOI:10.27162/d.cnki.gjlin.2023.001581.\u003c/li\u003e\n \u003cli\u003eARNOLD J G,SRINIVASAN R,MUTTIAH R S,et al.Large area hydrologic modeling and assessment. I. Model development [J].Journal of the American Water Resources Association (USA),1998,1:73-89.\u003c/li\u003e\n \u003cli\u003eLi Feng, Hu Tiesong, Huang Huajin. Research on the principle, structure and application of SWAT model [J]. China Rural Water Resources and Hydropower,2008,(03):24-28.\u003c/li\u003e\n \u003cli\u003eXu Atong, Mu Zhenwei. Construction of SWAT model database in Upper reaches of Kelan River Basin [J]. Yangtze River Technology and Economics,2021,5(S1):114-116.DOI:10.19679/j.cnki.cjjsjj.2021.0738.\u003c/li\u003e\n \u003cli\u003eZhu Yifan. Study on water resource vulnerability in Songhua River Basin under changing environment [D]. Jilin University,2024.DOI:10.27162/d.cnki.gjlin.2023.006002\u003c/li\u003e\n \u003cli\u003eChen Kai, Wang Liquan, Liu Yan, et al. Applicability evaluation of CMADS dataset in Hulan River Basin [J]. Journal of Irrigation and Drainage, 2019,43(01):60-68.DOI:10.13522/j.cnki.ggps.2023215\u003c/li\u003e\n \u003cli\u003eGu Xichen, Xiao Senyuan, Yang Guang, et al. Simulation of hydrological processes in Manas River Basin based on CMADS and SWAT model [J]. Journal of Water Resources and Water Engineering, 21,32(02):116-123.\u003c/li\u003e\n \u003cli\u003eWan Hao, Dong Xiaohua, Peng Tao, et al. Research on runoff simulation of the East branch Basin of Huangbai River based on SWAT model and SUFI-2 algorithm [J]. China Rural Water Resources and Hydropower,2018,(12):94-100.\u003c/li\u003e\n \u003cli\u003eZhang Li, Liu Ye, CAI Duoduo, et al. Comparison and applicability of SWAT model parameter automatic calibration methods: A case study of the middle and upper reaches of Jinghe River [J]. China Rural Water Resources and Hydropower,2016,(11):76-81.\u003c/li\u003e\n \u003cli\u003eFioreze Mariele, Mancuso Malva Andrea. MODFLOW and MODPATH for hydrodynamic simulation of porous media in horizontal subsurface flow constructed wetlands: A tool for design criteria[J]. Ecological Engineering, 2019, 130: 45-52.\u003c/li\u003e\n \u003cli\u003eWang Shiqin, Shao Jingli, Song Xianfang, et al. Application of groundwater model MODFLOW and GIS to groundwater resource evaluation in North China Plain [J]. Geographical Research,2007,(05):975-983.\u003c/li\u003e\n \u003cli\u003eTORRENCE C,COMPO G P.A practical guide to wavelet analysis[J], Bulletin of the American Meteorological Society,1998,79(1):61－78．\u003c/li\u003e\n \u003cli\u003eGRINSTED A, MOORE J C, JEVREJEVA S. Application of the cross wavelet transform and wavelet coherence to geophysical time series[J]. Nonlinear Processes in Geophysics,2004,11(5/6):561－566.\u003c/li\u003e\n \u003cli\u003eNETO O P,PINTO I R C A,PINTO O J.The relationship between thunderstorm and solar activity for Brazil from 1951 to 2009[J].Journal of Atmospheric and Solar-Terrestrial Physics,2013,98:12－21.\u003c/li\u003e\n \u003cli\u003eSHINE J M,HANDOJOSENO A M A,NGUYEN T N,et al.Abnormal patterns of theta frequency oscillations during the temporal evolution of freezing of gait in Parkinson \u0026rsquo; s disease[J]. Clinical Neurophysiology,2014,125(3):569－576.\u003c/li\u003e\n \u003cli\u003eLABAT D,ABABOU R,MANGIN A. Wavelet analysis in karstic hydrology. 2nd part: rainfall-runoff cross-wavelet analysis[J]. Academie des Sciences,1999,329(12):881－887.\u003c/li\u003e\n \u003cli\u003eADAMOWSKI J, CHAN H F.A wavelet neural network conjunction model for groundwater level forecasting[J].Journal of Hydrology,2011,407(1/4):28－40.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eQi xiaofan, Yang lizhi, Han ye, et al. Cross wavelet analysis of groundwater level dynamics and its response to precipitation in Jinan Spring Basin [J]. Advance in Earth Science,2012,27(9): 969-978.\u003c/li\u003e\n \u003cli\u003eQi xiaofan, Li wenpeng, Yang lizhi, et al. Annual time-lag analysis of groundwater level dynamic response to precipitation in Jinan Baiquan spring Basin [J]. Earth and Environment,2015,43(6): 619-627.DOI:10.14050/j.cnki.1672-9250.2015.06.004\u003c/li\u003e\n \u003cli\u003eQi Xiaofan, Wang Yushan, Yang Lizhi, et al. Time-lag difference of groundwater level response to precipitation in Jinan karst spring in recent 50 years [J]. Carsologica Sinica,2016,35(4):384-393.\u003c/li\u003e\n \u003cli\u003eTREMBLAY L,LAROCQUE M,ANCTIL F,et al.Teleconnections and interannual variability in Canadian groundwater levels[J].J Hydrol,2011,410(3/4):178－188．\u003c/li\u003e\n \u003cli\u003eKUSS A J M,GURDAK J J. Groundwater level response in U S principal aquifers to ENSO,NAO,PDO,and AMO[J].J Hydrol,2014,519(B):1939－1952.\u003c/li\u003e\n \u003cli\u003eQi Xiaofan, Li Wenpeng, Li Haitao, et al. Remote correlation of groundwater level, precipitation and temperature with large-scale meteorological models in Jinan Karst spring region [J]. Hydrogeology Engineering Geology,2015,42(6):18-28.DOI:10.16030/j.cnki.issn.1000-3665.2015.06.04\u003c/li\u003e\n \u003cli\u003eQi Xiaofan, Li Wenpeng, Li Haitao, et al. Multi-scale teleconnection analysis of meteorological elements and global atmospheric circulation characteristics in Heihe River Basin [J]. Arid Land Geography,2017,40(3):564-572.DOI:10.13826/j.cnki.cn65-1103/x.2017.03.010\u003c/li\u003e\n \u003cli\u003eLiu Guodong, Dai Huimin, Yang Ze, et al. Study on spatiotemporal variation and influencing factors of soil carbon pool in Sanjiang Plain [J]. Geoscience, 2019,35(02):443-454.DOI:10.19657/j.geoscience.1000-8527.2021.02.14.\u003c/li\u003e\n \u003cli\u003eXing Z X, Zhang Han, Ji Y, et al. Analysis of precipitation and its characteristics in typical irrigation area of Sanjiang Plain in recent 58 years [J]. Journal of Shenyang Agricultural University,2018,49(05):513-521.\u003c/li\u003e\n \u003cli\u003eLiu Yuanyuan, Cui Wei. Supplementary and improvement of spatial data of important levee projects in the main stream of Songhua River Basin [J]. Northeast Water Resources and Hydropower,2021,39(08):68-70.DOI:10.14124/j.cnki.dbslsd22-1097.2021.08.025.\u003c/li\u003e\n \u003cli\u003eZhou Yubo, Xiao Changlai, Xu Mengyao. Study on surface water and groundwater conversion in the middle reaches of Songhua River [J]. Yellow River,2011,33(04):43-45.\u003c/li\u003e\n \u003cli\u003eTian Haoran, Xiao Changlai, Xu Mengyao. Study on the interconversion relationship between surface water and groundwater in the lower Jiamusi main stream of Songhua River [J]. Water saving Irrigation,2012,(05):26-28.\u003c/li\u003e\n \u003cli\u003eZhang Bing, Song Xianfang, Zhang Yinghua, et al. Relationship between surface water and groundwater in the Second Songhua River Basin [J]. Advances in Water Science,2014,25(03):336-347.DOI:10.14042/j.cnki.32.1309.2014.03.004.\u003c/li\u003e\n \u003cli\u003eLi leifang, Chen jieying, Chen lei, et al. Improvement of SWAT model reservoir module and simulation of nitrogen and phosphorus flux in watershed [J]. Chinese Journal of Environmental Sciences,2023,43(07):427-439.DOI:10.13671/j.hjkxxb.2023.0061\u003c/li\u003e\n \u003cli\u003eCui Huqun, Wu Tingwen, Liu Jiangtao, et al. Effect of succession process on groundwater in marsh wetland and farmland in Sanjiang Plain [J]. Hydrogeology and Engineering Geology,2023,50(06):51-58.DOI:10.16030/j.cnki.issn.1000-3665.202306022\u003c/li\u003e\n \u003cli\u003eZhang Dong, Zhang Wanchang, Zhu Zhu, et al. Research on improvement and application of the hydrophysical model of distributed SWAT basin [J]. Scientia Geographica Sinica,2005,(04):52-58.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"SWAT-MODFLOW coupling model, Wavelet analysis, Surface water-groundwater conversion relationship, Groundwater level prediction, The middle and lower reaches of Songhua River basin","lastPublishedDoi":"10.21203/rs.3.rs-5310099/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5310099/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe management of groundwater resources and the rehabilitation of groundwater levels in the middle and lower portions of the Songhua River basin have consistently garnered significant attention in our country. The SWAT-MODLFOW model, developed on the QSWATMOD2 platform, was calibrated and validated utilizing river runoff and groundwater observation data to precisely illustrate the transformation relationship across various spatial and temporal scales in the middle and lower reaches of the Songhua River basin, characterized by numerous agricultural and irrigation zones with frequent surface water conversion. The water cycle process in the middle and lower sections of the Songhua River basin is simulated and studied based on this foundation.The results show: (1) The SWAT-MODLFOW coupling model has a good simulation effect, and the simulation effect of menstrual flow in the periodic and verification periods is R\u003csup\u003e2\u003c/sup\u003e≥0.86, NSE≥0.87, R\u003csup\u003e2\u003c/sup\u003e≥0.76, NSE≥0.77, respectively. The simulated groundwater level and the actual error value are within 0.6m, and the R\u003csup\u003e2\u003c/sup\u003e in the periodic and verification periods are 0.97 and 0.98, respectively. The simulation results of the model are satisfactory and meet the requirements of scientific research. (2) The groundwater in the study area generally decreases in the direction of west-north to northeast, and in the direction of east-south to north, and the groundwater level is affected by precipitation. Jiamusi, Fujin and Tongjiang, three major cities in the study area, are selected for characteristic study, and the lag time of their groundwater level to precipitation is about 10.56d, 10.58d and 3.15d. (3) The river channels of surface water recharge groundwater occupy 41.75% of the total length of Jiamusi - Tongjiang section of Songhua River, and the annual average recharge accounts for 50.84% of the total exchange water; On the seasonal scale, the maximum recharge value of each river section appeared in August, and the minimum recharge value appeared in April. On an annual scale, the maximum recharge occurred in 2009 and the minimum in 2014. The supply of groundwater to surface water fluctuates obviously, with seasonal variation ranging from -52% to 55% and inter-annual variation ranging from -35% to 52%.\u003c/p\u003e","manuscriptTitle":"Enhanced surface-groundwater interaction modeling in the middle and lower reaches of the Songhua River Basin using a coupled SWAT-MODFLOW model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-25 07:27:04","doi":"10.21203/rs.3.rs-5310099/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-03T05:19:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-02T12:50:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99289308112857012795286752877246227209","date":"2024-11-27T04:34:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100757971996949251132483314105339320201","date":"2024-11-26T02:27:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-24T03:44:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-23T08:13:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-20T01:10:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287633384040525218734239781672129639686","date":"2024-11-15T01:52:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178096226320495627534551230918179758768","date":"2024-11-15T01:38:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5422388587261071824291403894132770465","date":"2024-11-14T12:48:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-14T11:57:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-14T11:14:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-12T04:51:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-07T07:00:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-22T08:47:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d001cecf-a2ef-46f3-85c5-67c63c4e5f3e","owner":[],"postedDate":"November 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40667391,"name":"Earth and environmental sciences/Environmental sciences"},{"id":40667392,"name":"Earth and environmental sciences/Hydrology"}],"tags":[],"updatedAt":"2025-03-10T20:06:55+00:00","versionOfRecord":{"articleIdentity":"rs-5310099","link":"https://doi.org/10.1038/s41598-025-92801-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-03-08 15:58:50","publishedOnDateReadable":"March 8th, 2025"},"versionCreatedAt":"2024-11-25 07:27:04","video":"","vorDoi":"10.1038/s41598-025-92801-3","vorDoiUrl":"https://doi.org/10.1038/s41598-025-92801-3","workflowStages":[]},"version":"v1","identity":"rs-5310099","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5310099","identity":"rs-5310099","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}