Assessing Independent and Combined Effects of Land Use and Climate Change on Basin Runoff: A Remote Sensing, Statistical and Hydrological Modeling Approach

Assessing Independent and Combined Effects of Land Use and Climate Change on Basin Runoff: A Remote Sensing, Statistical and Hydrological Modeling Approach | 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 Research Article Assessing Independent and Combined Effects of Land Use and Climate Change on Basin Runoff: A Remote Sensing, Statistical and Hydrological Modeling Approach shahla tavangar, Hamidreza Moradi, Alireza Massah Bavani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7103894/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The key focus of this study is the use of future climate and land use data obtained from appropriate projection models to assess long-term annual streamflow changes in a basin located in northern Iran. Future climate projections were derived from the CanESM2 model under two Representative Concentration Pathways (RCP2.6 and RCP8.5), using the SDSM downscaling model for the mid- and end-21st century. The future land use map for the year 2050 was obtained from the Land Use Modeler (LCM). Streamflow under projected land use change (LUC) and climate change (CC) scenarios was simulated using the Soil and Water Assessment Tool (SWAT). The climate change evaluation indicates that precipitation will increase (up to 24%) in winter but decrease (up to -37%) in spring, summer, and autumn (except December). Additionally, temperature will rise in all months of the year. The effects of climate change on the Nekarood Basin are expected to increase streamflow in winter and decrease it in spring (except April), summer, and autumn (except December). The streamflow simulation results under the influence of land use change show that peak flow values will increase, while base flow will decrease. The combined effects of LUC and CC are projected to intensify future streamflow responses, with decreases of -2.9%, -8.3%, -8.1%, and − 9.2% in mid-century/RCP2.6, mid-century/RCP8.5, end-century/RCP2.6, and end-century/RCP8.5, respectively. A specific finding of this study is that the annual variations in streamflow are strongly influenced by climate in the basin. SWAT SDSM LCM RCP Scenario Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction The global warming is a reality, and the human influence is more important cause. Climate change especially in case of being in conflict with environment is considered as a critical factor in the prediction and evaluation of sustainable management watershed (Kim et al. 2013 ). The climate change result alter the seasonal and annual characteristics streamflow and intensification of the hydrologic cycle through changes in precipitation amount/intensity and temperature (Murray-Hudson, Wolski and Ringrose, 2006 ; Novotny and Stefan, 2007 ; Heathwaite, 2010 ). In most of studies, the climate change only have simulated. Whereas, rapid population growth, urbanization, deforestation drove LU/LC change are additional change for sustainable water resource management. The replacement of forest area with impervious surface areas changed the hydrologic fluxes of a drainage basin with important consequences on the basin resilience to flooding (Napoli et al. 2017 ). Having most effect on the environment, Combining climate changes and land uses are too noticeable. However several studies in different regions have analyzed separate consequences climate change (Abdo et al., 2009 ; Beyene et al., 2010 ; Taye et al., 2011 ; Hadgu, Tesfaye and Mamo, 2015 ; Gizaw and Gan, 2017 ) or LU/LC change on water resources of basin (Hurkmans et al., 2009 ; Tomer and Schilling, 2009 ; Tekleab et al., 2014 ; Zhang et al., 2016 ; Welde and Gebremariam, 2017 ), it cannot completely achieve change-effect results of water resources. Also, most of these studies exploit hydrological simulation models using data provided by climate change or land use future scenarios (Chung et al., 2011 ; Wijesekara et al., 2012 ), while only few studies investigated land use/climate change impacts on water resources on actual data like historical land maps, downscaling model and terrestrial data have been used in climate change. Anyway, policy maker of basin areas can be helped by assessing the impact of rainfall/ temperature and land use changes in future on runoff generation, as well as for planning land policy to prevent and mitigate negative impact like soil erosion and floods. Study Area As illustrated in Fig. (1), Nekarood watershed is located in Mazandaran province, north of Iran. Nekarood area is 185432 ha (Fig. 1 ). The rainfall decreases from west to east, however temperature increases in this direction (Tavangar et al. 2021 ). In addition, the study area, annual precipitation and temperature are 600 mm and 17 ○ c, respectively. Basin has moderate and humid climate. Maximum and minimum precipitation occur in autumn and summer, respectively (Ghanbarpour et al., 2014 ). The average annual runoff was 6m 3 /s during 1981–2013. Statistics indicate that the flood in the summers of 1999 was the most severe floods in the last five decades. While the rise in frequency and severity of the floods has been suggested as a consequence of climate change and intensified land-use and land-cover changes in the agriculture developed in the last half century. The changes in land use are often slow in the high attitude of Nekarood basin compared to flood plains because of irregular topography and relatively low anthropogenic influence. 2. Materials and methods 2. Materials and methods: 2.1. Required Data: 2.1.1. Satellite Images and LU/LC : Three images of Landsat in 1986 (TM), 2001 (ETM+), and 2016 (OLI) were downloaded from U.S Geological Survey (USGS) Centre for Earth Resources Observation and Science (EROS) ( https://earthexplorer.usgs.gov/ ). hybrid classification technique with supervised method is used for modeling land use maps by the aid of imagery satellite (Halder et al., 2011 ; Jensen and Lulla, 1987 ). 2.1.2. Meteorological data : The historical daily data for maximum temperature (Tmax), minimum temperature (Tmin), perception, relative humidity, wind speed and solar radiation - required for the SWAT and SDSM models- were obtained from the Iran Meteorological Department (IMD) for the period of 1961–2000. Additionally, daily data from the synoptic station of Amir Abad, Dashtenaz Sari and Gharakhil, covering the period 1995 and 2015 were used for WGN makers 4.1. 2.1.3. Discharge data : To calibration and validation the SWAT models, Monthly discharge data from the Abloo and Golvard were used. The observed data were divided into a calibration period (1983–2007) and validation period (2008–2013). 2.1.4. Spatial Data : The 1:250,000 scale State Soil Geographic data included in the SWAT database were obtained from the Department of Natural Resources and Watershed of Mazandaran. A 10-meter Digital Elevation Model (DEM) was extracted from the dataset provided by the National Cartographic Center of Iran. The flowchart of the methodology used in this study is shown in Fig. (2). 2.2. SDSM: 3.2.1. Preparation of observation/large-scale data: SDSM, being a decision support tool based on linear regression technique, is used in this study. The model combines weather generator and the multiple linear regression. Predictors were selected by assessing correlation, partial correlation and scatter plots between the predictors and the predictands (Wilby et al., 2002 ). Given the large-scale of the NCEP model, the selected base period should be between 1961–2000. The nearest synoptic stations with adequate historical records are Babolsar, Gharakhil and Gorgan. 2.2.2. Select of predictands suitable Two types of daily predictors datasets required for this study were obtained from a Canadian website ( http://www.cics.uvic.ca/scenarios/sdsm/select.cgi ): (a) the 26 predictors of the National Center of Environmental Prediction (NCEP) for the period of 1961–2000; and (b) the 26 predictors from the CanESM2 model for the RCP2.6 and 8.5 scenarios, covering the period 1961–2100. These datasets were specially required for SDSM processing. 2.2.3. Assessing the accuracy of SDSM The NCEP and CanESM2 predictors were normalized using the mean and standard deviation from the 1961–2000 period (CCCSN, 2012). After validation of model, the accuracy of the SDSM model in downscaling of NCEP and CanESM2 data were assessed between 1991–2000 period. In next step, model accuracy evaluation, he model's accuracy was evaluated using the Mean Absolute Error (MAE) and Mean Bias Error (MBE), as defined in Equations ( 1 ) and ( 2 ). $$\:\text{M}\text{A}\text{E}=\frac{\sum\:_{\text{i}=1}^{\text{n}}\left|{\text{O}}_{\text{i}}-{\text{S}}_{\text{i}}\right|}{\text{n}}$$ 1 $$\:\text{M}\text{B}\text{E}=\frac{{\sum\:}_{\text{i}=1}^{\text{n}}\left({\text{O}}_{\text{i}}-{\text{S}}_{\text{i}}\right)}{\text{n}}$$ 2 Where MAE is Mean Absolute Error, MBE is Mean Bias Error, Si is model simulations output, Oi is observed values, i is month of year and n is data number. 2.3. Unsupervised classification: The Supports Vector Machine (SVM) algorithm was used to classify land use for the years 1984, 2001 and 2016 using ENVI (The Environment for Visualizing Images) version 4.7. Five land use classes were identified: Agriculture, Forest, Bare land, Residential, and Rangeland. A total of 2,904 ground-truth training samples were collected through GPS and field surveys. These samples were then used to assemble training datasets for each land use category. To classification assessments were used Kappa (K) and overall accuracy (Ov.) coefficients (Srivastava et al., 2012 ; Yousefi et al., 2015 ). 2.4. Calibration and prediction of LCM: The Land change modeler was used to predict future land use change based on the classified outputs of three satellite images. Model calibration was performed using the classified land use layers from 1984 and 2001, while the 2016 classified land use layer was used to validate the simulated 2016 map. In modeling Land use changes in time interval, a number of transition probabilities have to be developed for each direction of change (Weng 2002 ). The change analysis provides a rapid assessment of quantitative changes, including gains and losses across land use categories. In the transition sub-model step, a detailed list of all minor to major land use transitions that occurred between 1984 and 2001 is generated. Constraints or drivers are a criterion that either raise or diminish from the suitability of a specific alternative for the land use activity under consideration. These criteria, often distance-based, indicate the degree of suitability for land use change and are incorporated into the Land Change Modeler (LCM) as raster datasets. In the present study, the selected drivers influencing land use transitions include elevation, slope, distance to residential areas, distance to forest areas, distance to agricultural land, distance to rangelands, distance to major roads, and distance to fluvial streams. To create transition susceptibility maps in separate sub-models used a MLP neural network (Sangermano et al., 2012 ; Lin et al., 2014 ). The total five transitions that have been selected are forest to residential, forest to agriculture, agriculture to residential and range land to agriculture, rangeland to residential. The results of the MLP indicated an overall accuracy of > 90% and a skill measure of > 0.88 was attained in predicting land cover in the period 1984–2001 in all sub models. Transition potential modeling framework supported by stochastic Markov-chain technique (Wu et al., 2006 ; Zhou and Liebhold, 1995 ). Markov prediction to 2016 based on land use and land cover maps of 1984 and 2001. To forecasting land use and planning scenario maps for the Nekarood watershed, a multilayer perceptron neural network integrated with Markov chain modeling, as implemented in Idrisi's Land Change Modeler (LCM), was used. Using the 2016 land use map as the base, along with transition potential maps and a transition probability matrix, future land use for the year 2050 was predicted through hard prediction based on multi-objective land allocation (Oñate-Valdivieso and Bosque Sendra 2010 ). 2.5. Run SWAT: The Soil and Water Assessment Tool (SWAT) is one of such physically based hydrological models generally used for quantifying and quantity the impacts of land use and climate changes on hydrological processes from watershed scales to global scales (Schilling et al., 2008 ; Ma et al., 2009 ; Tomer and Schilling, 2009 ; Karlsson et al., 2016 ; Ahiablame et al., 2017 ). Various components of SWAT involve hydrology, weather, soil characteristics, plant growth, pesticides, land management and nutrients (Manoj Jha, Philip Gassman, and Jeffrey Arnold 2007). In the simulation process, the watershed is divided into several sub-basins. Each sub-basin is further subdivided into smaller units called Hydrological Response Units (HRUs). These HRUs are defined as homogeneous spatial units with similar geomorphological and hydrological characteristics (Flügel 1995 ). Surface runoff for each HRU was estimated using the Curve Number method developed by the USDA Soil Conservation Service (1972). A 'warm-up' period is required in SWAT to ensure the model adequately reflects real-world basin hydrology (Wilson and Weng 2011 ). Calibration and validation of theSWAT by time series data of stream flow be performed by algorithm of sequential uncertainty fitting (Abbaspour, Johnson, and van Genuchten 2004 ) that is a semi inverse automated modelling tool. Since a number of iterations are needed to reach a better model Performance, the iteration process was done using the SUFI2-algoritm. Moreover, calibrating and adjusting of model input parameters are modified to achieve the most compatible between simulated data and observation data. The strong agreement between observed and monthly-simulated streamflow values during the 1983–2013, indicated that calibrated model, with its optimized parameter ranges, is suitable for assessing streamflow responses to land use change (LUC) and climate change (CC) in the Nekarood basin. Model performance was evaluated using several statistical indices, including the Nash–Sutcliffe efficiency coefficient (NSE), the coefficient of determination (R²), the P-factor, and the R-factor. The objective functions NSE and R² were calculated as shown in Equations ( 3 ) and ( 4 ), respectively. $$\:NS=1-\frac{{\sum\:}_{\:i}{\left({Q}_{m}-{Q}_{s}\right)}^{2}}{\sum\:_{i}{\left({Q}_{mi}-{\stackrel{-}{Q}}_{mi}\right)}^{2}}$$ 3 $$\:{R}^{2}=\frac{{\left[{\sum\:}_{i}\left({Q}_{mi}-{\stackrel{-}{Q}}_{m}\right)\left({Q}_{si}-{\stackrel{-}{Q}}_{S}\right)\right]}^{2}}{\sum\:_{i}{{\left({Q}_{mi}-{\stackrel{-}{Q}}_{m}\right)}^{2}-\sum\:_{i}\left({Q}_{si}-{\stackrel{-}{Q}}_{s}\right)}^{2}}$$ 4 Where, \(\:{\stackrel{-}{Q}}_{m}\) is mean of observed stream flow, \(\:{\stackrel{-}{Q}}_{s}\) is mean of simulated streamflow, \(\:{Q}_{si}\) is simulated streamflow, \(\:{Q}_{mi}\:\) is observed stream flow in simulation. 3. Results and Discussion 3.1. Calibration and Validation of SWAT: The model was calibrated to streamflow for the baseline period using 11 parameters (Table 1 ). Table 1 presents the results of parameter sensitivity analysis for stream flow, including the parameter range, sensitivity rankings, and optimal values for the Nekarood basin. The most sensitive parameter was the Precipitation Lapse Rate (PLAPS), with a best value of 0.19, followed by the Average Slope Length (SLSUBBSN) with a best value of 62.9, the Baseflow Alpha Factor (ALPHA_BF) with a best value of 0.08, and the SCS Runoff Curve Number (CN2) with a best value of 62.4, along with other parameters. This resulted in R² and NSE values greater than 0.5 for monthly streamflow, along with acceptable P-factor and R-factor values during both the calibration and validation periods (Table 2 , Fig. 3 ). These performance statistics were considered satisfactory for modeling streamflow using SWAT in the Nekarood watershed and, therefore, reliable for reconstructing the natural streamflow (Tavangar et al., 2018). According to Figure (3), there is no clear agreement between the peak values of observed and simulated streamflow during the calibration and validation periods at the Golvard station. This discrepancy may be attributed to temporal variability in precipitation, errors in input data, inaccuracies in the measured discharge values, or a combination of these factors (Nie et al. 2011 ). In general, the differences in model performance between the calibration and validation periods were small in terms of NSE, R², P-factor, and R-factor, indicating no evidence of model overfitting. However, since the focus of this study was on changes in mean long-term streamflow, calibrating to improve peak flow values is unlikely to significantly affect the overall comparison of future scenarios relative to the baseline. Overall, the model demonstrated strong performance within the study domain, making it a reliable tool for reconstructing natural streamflow. Table 1 Model parameters and their values used in SWAT_CUP Parameter name Definition Station Sensitivity rating Initial range Best value V_PLAPS.sub Precipitation lapse rate Ablo 1 -0.52 to 0.53 0.19 V_SLSUBBSN.hru Average slope length Ablo 2 6.51 to 68.84 62.92 V_ALPHA_BF.gw Baseflow alpha factor (days) Ablo 3 0 to 0.10 0.08 V_CN2.mgt SCS runoff curve number Ablo 4 53/49 تا 0/88 49.53 to 88.0 62.42 R_SOL_K.sol Saturated hydraulic conductivity Golvard 5 -0.22 to 0.93 0.34 V_CN2.mgt SCS runoff curve number Golvard 6 30.45 to 53.49 33.61 R_SOL_AWC.sol Available water capacity of the soil layer Golvard 7 0.37 to 1.08 0.59 V_PLAPS.sub Precipitation lapse rate Golvard 8 -304.6 to 31.8 -154.23 R_SOL_BD.sol Moist bulk density Ablo 9 -0.35 to 0.19 -0.30 V_DEEPST.gw Initial depth of water in the deep aquifer (mm) Golvard 10 208.1 to 504.9 226.8 V_GWQMN.gw Treshold depth of water in the shallow aquifer required for return flow to occur (mm) Ablo 11 700 to 1000 878.5 Table 2 Model results in calibration/validation statistics for the Nekarood basin Station Period NS R 2 r-factor p-factor Golvard Calibration 0.68 0.72 0.47 0.76 Validation 0.63 0.65 0.65 0.91 Abloo Calibration 0.70 0.75 0.40 0.70 Validation 0.65 0.68 0.70 0.87 3.2. Simulation basin stream flow in baseline: The calibrated model was used to simulate streamflow under baseline conditions for the period 1981–2007, while the period 2008–2013 was used for model validation (Table 2 ). 3.3. Simulation of land use 2050: The land use map prepared using the SVM method for 1986, 2001, and 2016 are shown in Fig. 4 . The image classification results indicate that the generated land use maps achieved acceptable accuracy. The land use map for 2050 was predicted using the LCM model. The spatial distribution of land use in the Nekarood watershed for 2050 is presented in Fig. 5 . According to the LCM model results, residential, agricultural, and rangeland areas are projected to increase by 41% (114 ha), 19% (4110 ha), and 4.8% (3938 ha), respectively. Conversely, forest areas are projected to decline by 12.1% (8161 ha), while bare land is expected to remain virtually unchanged (≈ 0.0%) by 2050 compared to 2016. The growth of the urban population in the Nekarood district is attributed to internal factors such as economic and social conditions. Finally, a comparison of land use maps from 1984, 2001, 2016, and 2050 indicates that significant changes have occurred across land use classes (Fig. 4 and Fig. 5 ). 3.4. Simulation of stream flow under land use change scenarios/current climate condition: The conversion of forest areas to residential, agricultural, and other land uses has led to an increase in streamflow during the wet season (Table 3 ). Streamflow simulations based on projected land use changes indicate an increase in annual streamflow from 6.0 to 6.8 m³/s (a 13% rise) by 2050. This increase is attributed to land use changes. Peak flow in 2050 is projected to be higher than that during the baseline period, while base flow is expected to decrease under future land use conditions. The results indicate that land surfaces covered by impermeable materials, such as buildings and roads, pose a major threat to streamflow. The findings also show an increase in peak flow due to the impacts of land use and land cover changes (Sajikumar and Remya 2015 ); (Du et al. 2012 ). The expansion of agricultural lands and urban areas is expected to increase the Curve Number (CN) and reduce evapotranspiration (ET) in the future, leading to higher water demand (Memarian et al. 2014 ). Previous studies have also reported reductions in streamflow under dry conditions, primarily due to decreased infiltration of precipitation resulting from urban expansion and the increase in impervious surface areas (Paul and Meyer 2001 ). Table 3 The percent change in month stream flow under land use change in the future (2050) compared to base line (2016) Month Discharge (m3/s) 2016 2050 Changes (Percent) Jan 5.35 5.70 + 6.49 Feb 7.45 7.65 + 2.64 Mar 9.00 9.45 + 5.00 Apr 8.35 7.96 -4.68 May 5.36 5.73 + 6.85 Jun 3.25 2.95 -9.23 Jul 1.85 1.60 -13.51 Aug 1.09 0.99 -9.41 Sep 0.97 0.88 -9.63 Oct 1.67 1.72 + 2.99 Nov 3.48 3.65 + 5.00 Dec 5.15 5.37 + 4.18 3.5. Prediction temperature and precipitation in future periods: Figure (6) to (8) show the percentage changes in precipitation and mean temperature (average of maximum and minimum temperatures) for the mid- and end-century periods under RCP2.6 and RCP8.5 scenarios for Babolsar, Gharakhil, and Gorgan, respectively. The figures reveal that, under both the pessimistic (RCP8.5) and optimistic (RCP2.6) scenarios, monthly precipitation is projected to increase in late autumn and winter, while decreasing in spring and summer, indicating significant inter-annual variability. The average temperature is expected to follow an increasing trend. According to the results, the projected changes in precipitation and temperature under RCP8.5 for the 2061–2080 period are at least slightly higher across all stations compared to those under RCP2.6 for the 2021–2040 period. Specifically, compared to the baseline period, annual precipitation is projected to decrease by -2.1 to -6.6% in Babolsar, from − 0.7% to -4% in Gharakhil and from − 3.2% to -4.8% in Gorgan station (Fig. 9 ). In contrast, the annual mean temperature increased significantly across all three weather stations. This finding is consistent with previous studies in other humid regions, which forecast reductions in annual precipitation under climate change scenarios (Bangash et al. , 2013; Khoi and Suetsugi, 2014; Lu et al. , Serpa et al., 2015 ). According to results of Fig. (6), inter-annual changes in precipitation and temperature at the Babolsar station are projected to range from − 29.4–45% and from 2.5–8.5%, respectively. The most significant decrease in rainfall is expected in July, while the greatest increase is projected in December, both occurring in the end-century period (2061–2080) under the RCP8.5 scenario. Monthly temperatures are projected to increase consistently throughout the year. According to the climate change projections for the Gharakhil station (Fig. 7 ) both climatic scenarios predict a decrease in precipitation during May, June, July, August, and September, and an increase during January, February, October, and November. Precipitation is projected to increase in March under the RCP8.5 scenario and in April under the RCP2.6 scenario during the mid-century period (2046–2065), while in other periods, these months are expected to experience a decrease in precipitation under both scenarios. Additionally, temperatures are projected to rise in all months of the year during both the mid-century (2046–2065) and end-century (2080–2099) periods under both climatic scenarios. Changes in precipitation and temperature at the Gorgan station are shown in Fig. (8). The results indicate an overall increase in temperature throughout the study periods, with the RCP8.5 scenario projecting slightly higher temperatures than RCP2.6. Similarly, an increase in precipitation is forecasted for the winter months, consistent with findings from previous studies (Nunes, Seixas and Pacheco, 2008 ; (Li et al. 2012 ). As shown in the results fig of (6) to (8), although there are some differences in individual monthly precipitation and temperature values, the overall pattern of change is consistent across the three stations. This similarity may be attributed to the fact that, under all future climate scenarios, the projected changes at each station follow the same directional trend when compared to the baseline period. 3.6. Simulation of streamflow under climate change scenarios /current land use: Changes in streamflow due to climate change are projected to vary under both RCP2.6 and RCP8.5 scenarios during the mid-century and end-century periods. Under RCP2.6, annual streamflow is expected to decrease by -4.1% in the mid-century and by -9.1% in the end-century period. Under the more extreme RCP8.5 scenario, streamflow is projected to decline by -10.6% and − 21.6% during the mid- and end-century periods, respectively. The most significant reductions in streamflow are observed under the RCP8.5 scenario in the end-century period. The climate change projections indicate an increase in precipitation during winter and a decrease during spring, summer, and autumn (except for December), along with a general rise in temperature. These changes are more pronounced in the end-century period under the RCP8.5 scenario. Therefore, a reduction in annual streamflow and base flow due to climate change is not unexpected. Also, according to the results of figures (10) to (13), climate change in Nekarood basin causing will increase stream flow in winter. Streamflow is projected to decrease during spring (except April), summer, and autumn (except December), which can be attributed to reduced precipitation, increased temperatures, and consequently higher evapotranspiration. These results are consistent with findings from previous studies, which indicate that reductions in precipitation are generally associated with decreased streamflow (Kalogeropoulos and Chalkias, 2013 ; Zabaleta et al., 2014 ; Serpa et al., 2015 ). These changes occur because hydrological characteristics are directly and indirectly influenced by precipitation and temperature. By comparing Figures Fig. (6) to (8) and (10) to (13), it is evident that increases in rainfall and temperature during January, February, March, and December are associated with increased streamflow. In contrast, the reductions in streamflow observed in May, June, July, August, September, October, and November correspond to decreased rainfall and elevated temperatures during these months. Interestingly, despite a decrease in rainfall and an increase in temperature in April, streamflow rises. This anomaly can be attributed to increased winter precipitation and higher annual temperatures, which enhance snowmelt in late winter, subsequently increasing streamflow in April. Overall, the average annual streamflow decreases as temperature rises and precipitation declines. These findings are consistent with previous studies in Pennsylvania (Chang, Evans, and Easterling 2001 ) and in eastern Massachusetts (Tu 2009 ), which also reported reductions in streamflow during spring and summer months in certain watersheds. (Franczyk and Chang, 2009 ; Praskievicz and Chang, 2011 ; Kim et al., 2013 ) noted that the streamflow decrease during the summer and increase during the winter. The results presented in figure (10) show that the most significant increase and decrease in stream flow are projected to occur in Jan (+ 5%) and Sep (-12%), respectively, due to climate change during the mid-century period under RCP2.6 scenario. Based on Fig. (11), the greatest increase in streamflow due to climate change in the end-century period under the RCP2.6 scenario is projected to occur in January (+ 6.6%), while the most significant decrease is expected in September (− 17%). Based on Fig. (12), the most significant effects of climate change on stream flow during the mid-century period under RCP8.5 are from (14%) in Dec to (-18%) in Sep. Also in Fig. (13) shows most increase and decrease of stream flow in end-century period under RCP 8.5 will occur in Dec (16%) and Jul (-25.9%), respectively. It is clearly evident from the four figures that both RCP scenarios, across both time periods, lead to a reduction in total streamflow. 3.7. Simulation of stream flow under combined land use and climate change scenarios: The impacts of climate change under RCP2.6 and RCP8.5 scenarios for two future periods were simulated while keeping land use constant at baseline conditions. Both base flow and annual streamflow in the basin are projected to decrease under these scenarios. The predicted reduction in streamflow is expected to have a significant impact on the basin’s future hydrological state. Additionally, simulated land use maps for the year 2050 were used to estimate the potential effects of land use change on streamflow at the basin outlet. The results suggest that land use change will further intensify the reduction in base flow and contribute to an increase in peak flow. In this step, the effects of simultaneous changes in land use and climate on basin streamflow were examined. To assess these impacts, the SWAT model was run using various combinations of land use and climate change scenarios, and the results were compared with the streamflow simulation under the baseline scenario (Table 4 ). The combined impacts of land use and climate change under the RCP2.6 scenario are projected to result in the greatest streamflow decreases of − 12.2% in August and − 32.4% in July, and the greatest increases of 12.9% and 16.4% in December during the periods 2041–2060 and 2061–2080, respectively. Similarly, under the RCP8.5 scenario, the most significant decreases are expected in July (− 29.7% and − 32.4%) and the greatest increases in December (20.2% and 43.5%) during the mid- and end-century periods, respectively. The results indicate that changes in streamflow are more pronounced in the end-century period than in the mid-century, and the RCP8.5 scenario has a more substantial impact than RCP2.6. Although simulated land use changes for future scenarios do affect streamflow, their influence is relatively minor compared to the effects of climate change. Table 4 Percent change of monthly stream flow in Nekarood basin under simultaneous effects of climate change and land use in future Month RCP2.6 RCP8.5 Mid of century End of century Mid of century End of century Jun + 7.9 + 11.1 + 13.9 + 19.5 Feb + 6.0 + 10.0 + 11.5 + 14.0 Mar + 13.1 + 4.0 + 4.4 + 10.6 Apr -1.2 -5.7 -7.4 -13.1 May -8.0 -11.9 -15.8 -20.1 Jun -7.6 -25.5 -27.3 -34.1 Jul -7.0 -32.4 -29.7 -40.5 Aug -12.2 -13.0 -14.9 -22.2 Sep -10.6 -17.8 -14.7 -19.9 Oct -9.5 -19.7 -25.1 -31.1 Nov -7.9 -13.6 -13.1 -19.1 Dec + 12.9 + 16.4 + 20.2 + 43.5 Thus, the streamflow of the Nekarood Basin is more sensitive to climate change than to land use change. However, since climate and land use changes occur concurrently, the impact of land use change should not be overlooked (Shrestha et al. 2018 ). According to the results of this section, the pattern of streamflow changes caused by land use change alone is similar to that caused by climate change alone in most months. Consequently, when climate change and land development occur concurrently, their combined effects tend to amplify streamflow changes in the majority of months (Tu 2009 ). Furthermore, annual streamflow is sensitive to both climate and land use changes. Since the directions of change caused by climate change alone and land use change alone are often opposite, their concurrent occurrence tends to reduce the overall impact. In other words, the effects of climate change and land use change on annual streamflow may partially offset each other. 4. Conclusion This study simulated the individual and combined impacts of climate and land use change on streamflow in the Nekarood Basin. The land use analysis indicated a reduction in base flow, along with increases in peak flow and annual streamflow, compared to the baseline period. Climate change simulations indicated an increase in future temperatures and a decrease in annual rainfall across the basin compared to the baseline period, under both climate scenarios and for all projected time periods. According to both climate scenarios, streamflow is projected to decrease during early autumn and the dry seasons, while increasing in late autumn (December) and winter. These findings suggest that climate change alone has a significant impact on streamflow patterns in the basin. A comparison of the separate effects of land use and climate change on streamflow indicated that climate change has a more significant impact. The combined effects of future climate and land use changes further intensified the individual impacts of each. The results of this study suggest that changes in temperature and precipitation have substantial effects on the hydrological conditions of the Nekarood Basin. These effects are further complicated by the expansion of agricultural and residential areas and the reduction of forest cover. As a result, the risk of hydrological crises, including floods and droughts, is expected to increase. The quantitative insights provided by this study can support informed decision-making for hydrological management and natural resource conservation in the Nekarood watershed. Declarations Finding : This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Competing Interests The authors declare that they have no competing interests. Author Contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Shahla Tavangar , Hamidreza Moradi and Alireza Massah Bavani .The first draft of the manuscript was written by Shahla Tavangar and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. References Abbaspour KC, Johnson CA, van Genuchten MT (2004) Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone J 3(4):1340 Abdo KS, Fiseha BM, Rientjes THM, Gieske ASM, Haile AT (2009) Assessment of climate change impacts on the hydrology of Gilgel Abay catchment in Lake Tana Basin, Ethiopia. Hydrol Process 23(26):3661–3669. https://doi.org/10.1002/hyp.7363 Ahiablame L, Sheshukov AY, Rahmani V, Moriasi D (2017) Annual baseflow variations as influenced by climate variability and agricultural land use change in the Missouri River Basin. J Hydrol 551:188–202 Beyene T, Lettenmaier DP, Kabat P (2010) Hydrologic impacts of climate change on the Nile River Basin: Implications of the 2007 IPCC scenarios. Clim Change 100(3):433–461. https://doi.org/10.1007/s10584-009-9693-0 Chang H, Evans BM, Easterling DR (2001) The effects of climate change on stream flow and nutrient loading. J Am Water Resour Assoc 37(4):973–985. https://doi.org/10.1111/j.1752-1688.2001.tb05526.x Chung ES, Park K, Lee KS (2011) The relative impacts of climate change and urbanization on the hydrological response of a Korean urban watershed. Hydrol Process 25(4):544–560. https://doi.org/10.1002/hyp.7781 Du Y, Liu R, Chen Y, Han Z, Liu X (2012) Comparison between CBR and CA methods for estimating land use change in Dongguan, China. J Geog Sci 22(4):716–736. https://doi.org/10.1007/s11442-012-0958-6 Flügel WA (1995) Delineating hydrological response units by geographical information system analyses for regional hydrological modelling using PRMS/MMS in the drainage basin of the River Bröl. Ger Hydrol Processes 9(3–4):423–436 Franczyk J, Chang H (2009) The effects of climate change and urbanization on the runoff of the Rock Creek Basin in the Portland Metropolitan Area, Oregon, USA. Hydrol Process 23(6):805–815 Ghanbarpour MR, Mohseni Saravi M, Salimi S (2014) Floodplain inundation analysis combined with contingent valuation: Implications for sustainable flood risk management. Water Resour Manage 28(9):2491–2505 Gizaw MS, Gan TY (2017) Impact of climate change and El Niño episodes on droughts in Sub-Saharan Africa. Clim Dyn 49(1–2):665–682. https://doi.org/10.1007/s00382-016-3366-2 Hadgu G, Tesfaye K, Mamo G (2015) Analysis of climate change in northern Ethiopia: Implications for agricultural production. Theoret Appl Climatol 121(3–4):733–747. https://doi.org/10.1007/s00704-014-1261-5 Halder A, Ghosh A, Ghosh S (2011) Supervised and unsupervised landuse map generation from remotely sensed images using ant based systems. Appl Soft Comput 11(8):5770–5781 Heathwaite AL (2010) Multiple stressors on water availability at global to catchment scales: Understanding human impact on nutrient cycles to protect water quality and water availability in the long term. Freshw Biol 55(S1):241–257. https://doi.org/10.1111/j.1365-2427.2009.02368.x Hurkmans RTWL, Terink W, Uijlenhoet R, Moors EJ, Troch PA, Versteeg K (2009) Effects of land use changes on streamflow generation in the Rhine Basin. Water Resour Res 45(6):W06405 Jensen JR, Lulla K (1987) Introductory digital image processing: A remote sensing perspective. Geocarto Int 2(1):65–65 Kalogeropoulos K, Chalkias C (2013) Modelling the impacts of climate change on surface runoff in small Mediterranean catchments: Empirical evidence from Greece. Water Environ J 27(4):505–513. https://doi.org/10.1111/j.1747-6593.2012.00369.x Karlsson IB, Sonnenborg TO, Seaby LP, Refsgaard JC (2016) Combined effects of climate models, hydrological model structures and land use scenarios on hydrological impacts of climate change. J Hydrol 535:301–317 Kim J, Choi J, Choi C, Park S (2013) Impacts of changes in climate and land use/land cover under IPCC RCP scenarios on streamflow in the Hoeya River Basin, Korea. Sci Total Environ, 452–453, 181–195 Li S, Li X, Chen Y, Zhang S, Fan Y (2012) Spatial analysis of the driving factors of grassland degradation under conditions of climate change and intensive use in Inner Mongolia, China. Reg Envriron Chang 12(3):461–474. https://doi.org/10.1007/s10113-011-0264-3 Lin Y, Deng X, Li X, Ma E (2014) Comparison of multinomial logistic regression and logistic regression: Which is more efficient in allocating land use. Front Earth Sci 8(4):512–523. https://doi.org/10.1007/s11707-014-0426-y Ma X, Wang Y, Dong G, Zhao X (2009) Response of hydrological processes to land-cover and climate changes in Kejie Watershed, South-West China. Hydrol Process 23(8):1179–1191. https://doi.org/10.1002/hyp.7233 Jha M, Gassman P, Arnold J (2007) Water quality modeling for the Raccoon River Watershed using SWAT. Trans ASABE 50(2):479–493 Memarian H, Balasundram SK, Abdullah R, Sood AM (2014) Modélisation hydrologique de scénarios d’utilisation des sols en zone tropicale à l’aide du modèle SWAT. Hydrol Sci J 59(10):1808–1829 Murray-Hudson M, Wolski P, Ringrose S (2006) Scenarios of the impact of local and upstream changes in climate and water use on hydro-ecology in the Okavango Delta, Botswana. J Hydrol 331(1–2):73–84 Napoli N, Strollo R, Paladini A, Russo S (2017) Mechanisms of diabetes mellitus-induced bone fragility. Nat Reviews Endocrinol 13(4):208–219. https://doi.org/10.1038/nrendo.2016.153 Nie W, Yuan Y, Kepner WG, Jackson M, Erickson C (2011) Assessing impacts of land use and land cover changes on hydrology for the Upper San Pedro Watershed. J Hydrol 407(1–4):105–114. https://doi.org/10.1016/j.jhydrol.2011.07.012 Novotny EV, Stefan HG (2007) Stream flow in Minnesota: Indicator of climate change. J Hydrol 334(3–4):319–333 Nunes JP, Seixas J, Pacheco NR (2008) Vulnerability of water resources, vegetation productivity and soil erosion to climate change in Mediterranean watersheds. Hydrol Process 22(16):3115–3134. https://doi.org/10.1002/hyp.6897 Oñate-Valdivieso F, Bosque Sendra J (2010) Application of GIS and remote sensing techniques in generation of land use scenarios for hydrological modeling. J Hydrol 395(3–4):256–263 Paul MJ, Meyer JL (2001) Streams in the urban landscape. Annu Rev Ecol Syst 32:333–365 Praskievicz S, Chang H (2011) Impacts of climate change and urban development on water resources in the Tualatin River Basin, Oregon. Ann Assoc Am Geogr 101(2):249–271. https://doi.org/10.1080/00045608.2010.544934 Sajikumar N, Remya RS (2015) Impact of land cover and land use change on runoff characteristics. J Environ Manage 161:460–468 Sangermano F, Toledano J, Eastman R (2012) Land cover change in the Bolivian Amazon and its implications for REDD + and endemic biodiversity. Landscape Ecol 27(4):571–584. https://doi.org/10.1007/s10980-012-9710-y Schilling KE, Jha MK, Zhang YK, Gassman PW, Wolter CF (2008) Impact of land use and land cover change on the water balance of a large agricultural watershed: Historical effects and future directions. Water Resour Res, 45(7), W00A09. Serpa D, Nunes JP, Keizer JJ, Santos JM, Abrantes N (2015) Impacts of climate and land use changes on the hydrological and erosion processes of two contrasting Mediterranean catchments. Sci Total Environ 538:64–77 Shrestha S, Bhatta B, Shrestha M, Shrestha PK (2018) Integrated assessment of the climate and land use change impact on hydrology and water quality in the Songkhram River Basin, Thailand. Sci Total Environ 643:1610–1622 Srivastava PK, Han D, Rico-Ramirez MA, Islam T (2012) Selection of classification techniques for land use/land cover change investigation. Adv Space Res 50(9):1250–1265 Tavangar S, Moradi H, Bavani M, A., and, Gholamalifard M (2021) A futuristic survey of the effects of LU/LC change on stream flow by CA–Markov model: A case of the Nekarood Watershed. Iran Geocarto Int 36(10):1100–1116. https://doi.org/10.1080/10106049.2019.1633419 Taye MT, Ntegeka V, Ogiramoi NP, Willems P (2011) Assessment of climate change impact on hydrological extremes in two source regions of the Nile River Basin. Hydrol Earth Syst Sci 15(1):209–222 Tekleab S, Mohamed Y, Uhlenbrook S, Wenninger J (2014) Hydrologic responses to land cover change: The case of Jedeb mesoscale catchment, Abay/Upper Blue Nile Basin, Ethiopia. Hydrol Process 28(20):5149–5161. https://doi.org/10.1002/hyp.9998 Tomer MD, Schilling KE (2009) A simple approach to distinguish land-use and climate-change effects on watershed hydrology. J Hydrol 376(1–2):24–33 Tu J (2009) Combined impact of climate and land use changes on streamflow and water quality in Eastern Massachusetts. USA J Hydrology 379(3–4):268–283 Welde K, Gebremariam B (2017) Effect of land use land cover dynamics on hydrological response of watershed: Case study of Tekeze Dam Watershed, Northern Ethiopia. Int Soil Water Conserv Res 5(1):1–16 Weng Q (2002) Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. J Environ Manage 64(3):273–284 Wijesekara GN, Gupta A, Valeo C, Hasnain S, Marceau D (2012) Assessing the impact of future land-use changes on hydrological processes in the Elbow River Watershed in Southern Alberta, Canada. J Hydrol 412–413:220–232 Wilby RL, Dawson CW, Barrow EM (2002) SDSM - A decision support tool for the assessment of regional climate change impacts. Environ Model Softw 17(2):145–157 Wilson CO, Weng Q (2011) Simulating the impacts of future land use and climate changes on surface water quality in the Des Plaines River Watershed, Chicago Metropolitan Statistical Area, and Illinois. Sci Total Environ 409(20):4387–4405 Wu Q, Li H, Wang R, Paulussen J (2006) Monitoring and predicting land use change in Beijing using remote sensing and GIS. Landsc Urban Plann 78(4):322–333 Yousefi S, Farajzadeh M, Roshani M (2015) Comparison of different algorithms for land use mapping in dry climate using satellite images: A case study of the central regions of Iran. Desert 20(1):1–10 Zabaleta A, Meaurio M, Ruiz E, Antigüedad I (2014) Simulation climate change impact on runoff and sediment yield in a small watershed in the Basque Country, Northern Spain. J Environ Qual 43(1):235–245. https://doi.org/10.2134/jeq2012.0209 Zhang L, Nan Z, Xu Y, Li S (2016) Hydrological impacts of land use change and climate variability in the headwater region of the Heihe River Basin, Northwest China. PLoS ONE, 11(6), e0158394 Zhou G, Liebhold AM (1995) Forecasting the spatial dynamics of gypsy moth outbreaks using cellular transition models. Landscape Ecol 10(3):177–189. https://doi.org/10.1007/BF00133030 Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-7103894","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492065310,"identity":"91d8b453-b30a-42c7-bc97-f9a8a466fd18","order_by":0,"name":"shahla 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University","correspondingAuthor":false,"prefix":"","firstName":"Hamidreza","middleName":"","lastName":"Moradi","suffix":""},{"id":492065312,"identity":"31912770-fe53-406a-8ccb-feb22d44e521","order_by":2,"name":"Alireza Massah Bavani","email":"","orcid":"","institution":"University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Alireza","middleName":"Massah","lastName":"Bavani","suffix":""}],"badges":[],"createdAt":"2025-07-11 18:18:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7103894/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7103894/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87915558,"identity":"299340b8-3d12-427c-aee2-e8cecb429d3d","added_by":"auto","created_at":"2025-07-30 10:52:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1039882,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the study area\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7103894/v1/4d9a0e11b5c1b6f85697fd4f.png"},{"id":87916357,"identity":"f9237066-7c3a-4445-b7fe-7270c82d2412","added_by":"auto","created_at":"2025-07-30 11:00:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76908,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the methodology used in this study\u003c/p\u003e","description":"","filename":"image2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7103894/v1/256fa82ef423cee939329803.jpg"},{"id":87917513,"identity":"cc4486ff-3eef-4be1-9ed7-b215093d71c3","added_by":"auto","created_at":"2025-07-30 11:16:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85843,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of simulated and observed streamflow at Abloo (a) and Golvard(b)\u003c/p\u003e","description":"","filename":"image3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7103894/v1/0ca75fc3bc31881cbbe85ec0.jpg"},{"id":87916365,"identity":"d593bb61-9cad-4689-9082-7eb28511b88f","added_by":"auto","created_at":"2025-07-30 11:00:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1623164,"visible":true,"origin":"","legend":"\u003cp\u003eLand use maps for 1984, 2001 and 2016\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7103894/v1/6e613e5a6c7afc79734bb47e.png"},{"id":87916360,"identity":"a2096bc2-c834-4044-808f-1206979d29dd","added_by":"auto","created_at":"2025-07-30 11:00:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":460837,"visible":true,"origin":"","legend":"\u003cp\u003eSimulated land use map of Nekarood watershed in 2050 using LCM\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7103894/v1/fad7fab31a35c47f37749c78.png"},{"id":87915568,"identity":"92632447-85e4-4046-a09a-74e23fa361bf","added_by":"auto","created_at":"2025-07-30 10:52:23","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":52267,"visible":true,"origin":"","legend":"\u003cp\u003eAmount of change in monthly a) precipitation and b) temperature in the near and far future periods under RCP2.6 and RCP 8.5 at Babolsar station\u003c/p\u003e","description":"","filename":"image6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7103894/v1/0b37588641d316625c1d813d.jpg"},{"id":87916761,"identity":"0a974252-aae4-410a-87fa-7079b9394ecd","added_by":"auto","created_at":"2025-07-30 11:08:24","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":51392,"visible":true,"origin":"","legend":"\u003cp\u003eAmount of change in monthly a) precipitation and b) temperature in the near and far future periods under RCP2.6 and RCP8.5 at Gharakhil station\u003c/p\u003e","description":"","filename":"image7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7103894/v1/8287750a7e8ca0c813f9fae3.jpg"},{"id":87915564,"identity":"796d8664-f9e8-42de-969b-9f4e06a7976a","added_by":"auto","created_at":"2025-07-30 10:52:23","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":50897,"visible":true,"origin":"","legend":"\u003cp\u003eAmount of change in monthly a) precipitation and b) temperature in the near and far future periods under RCP2.6 and RCP8.5 at Gorgan station\u003c/p\u003e","description":"","filename":"image8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7103894/v1/fc5047106e3295592663cd58.jpg"},{"id":87916759,"identity":"9543dff9-f9e0-4ca2-9bbd-2f7b77cd5d3d","added_by":"auto","created_at":"2025-07-30 11:08:23","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":42484,"visible":true,"origin":"","legend":"\u003cp\u003eChange of annual stream flow simulated in Babolsar, Gharakhil and Gorgan station in future periods under climatic scenarios\u003c/p\u003e","description":"","filename":"image9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7103894/v1/5915e2ca0bf17703e33bfddb.jpg"},{"id":87915578,"identity":"5b6d2608-27c8-46c6-b05f-4c2f8594fece","added_by":"auto","created_at":"2025-07-30 10:52:23","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":42484,"visible":true,"origin":"","legend":"\u003cp\u003eAverage monthly stream flow for the base line scenario and in middle- century under RCP2.6\u003c/p\u003e","description":"","filename":"image10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7103894/v1/c4fea15e1acdb481e15a037b.jpg"},{"id":87915586,"identity":"d305d872-3551-4b48-9958-165f5ae6c9ce","added_by":"auto","created_at":"2025-07-30 10:52:24","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":44084,"visible":true,"origin":"","legend":"\u003cp\u003eAverage monthly stream flow for the base line scenario and in end- century under RCP 2.6 in Nekarood basin\u003c/p\u003e","description":"","filename":"image11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7103894/v1/3fb917a13eebb17eba9309e9.jpg"},{"id":87915573,"identity":"8a74a842-1ce2-4c2b-ada7-c7b25d7e1f0a","added_by":"auto","created_at":"2025-07-30 10:52:23","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":44038,"visible":true,"origin":"","legend":"\u003cp\u003eAverage monthly stream flow for the base line scenario and in middle- century under RCP 8.5\u003c/p\u003e","description":"","filename":"image12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7103894/v1/0df28b862d562a1722d9408d.jpg"},{"id":87916367,"identity":"4c168ef1-284c-42c2-b7fe-b80e0d5a818f","added_by":"auto","created_at":"2025-07-30 11:00:23","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":41963,"visible":true,"origin":"","legend":"\u003cp\u003eAverage monthly stream flow for the base line scenario and in end- century under RCP 8.5 in Nekarood basin\u003c/p\u003e","description":"","filename":"image13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7103894/v1/2f8d6b6645eb38d1dcf430e3.jpg"},{"id":90331380,"identity":"79108b00-e902-4c8d-a0ce-e0e191dae89c","added_by":"auto","created_at":"2025-09-01 13:12:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4564481,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7103894/v1/6e1a9398-37d0-4e47-a296-01f21e56a384.pdf"}],"financialInterests":"","formattedTitle":"Assessing Independent and Combined Effects of Land Use and Climate Change on Basin Runoff: A Remote Sensing, Statistical and Hydrological Modeling Approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe global warming is a reality, and the human influence is more important cause. Climate change especially in case of being in conflict with environment is considered as a critical factor in the prediction and evaluation of sustainable management watershed (Kim et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe climate change result alter the seasonal and annual characteristics streamflow and intensification of the hydrologic cycle through changes in precipitation amount/intensity and temperature (Murray-Hudson, Wolski and Ringrose, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Novotny and Stefan, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Heathwaite, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In most of studies, the climate change only have simulated. Whereas, rapid population growth, urbanization, deforestation drove LU/LC change are additional change for sustainable water resource management. The replacement of forest area with impervious surface areas changed the hydrologic fluxes of a drainage basin with important consequences on the basin resilience to flooding (Napoli et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHaving most effect on the environment, Combining climate changes and land uses are too noticeable. However several studies in different regions have analyzed separate consequences climate change (Abdo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Beyene et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Taye et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hadgu, Tesfaye and Mamo, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gizaw and Gan, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) or LU/LC change on water resources of basin (Hurkmans et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Tomer and Schilling, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Tekleab et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Welde and Gebremariam, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), it cannot completely achieve change-effect results of water resources. Also, most of these studies exploit hydrological simulation models using data provided by climate change or land use future scenarios (Chung et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wijesekara et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), while only few studies investigated land use/climate change impacts on water resources on actual data like historical land maps, downscaling model and terrestrial data have been used in climate change. Anyway, policy maker of basin areas can be helped by assessing the impact of rainfall/ temperature and land use changes in future on runoff generation, as well as for planning land policy to prevent and mitigate negative impact like soil erosion and floods.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cb\u003eStudy Area\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs illustrated in Fig.\u0026nbsp;(1), Nekarood watershed is located in Mazandaran province, north of Iran. Nekarood area is 185432 ha (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The rainfall decreases from west to east, however temperature increases in this direction (Tavangar et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, the study area, annual precipitation and temperature are 600 mm and 17\u003csup\u003e○\u003c/sup\u003ec, respectively. Basin has moderate and humid climate. Maximum and minimum precipitation occur in autumn and summer, respectively (Ghanbarpour et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The average annual runoff was 6m\u003csup\u003e3\u003c/sup\u003e/s during 1981\u0026ndash;2013. Statistics indicate that the flood in the summers of 1999 was the most severe floods in the last five decades. While the rise in frequency and severity of the floods has been suggested as a consequence of climate change and intensified land-use and land-cover changes in the agriculture developed in the last half century. The changes in land use are often slow in the high attitude of Nekarood basin compared to flood plains because of irregular topography and relatively low anthropogenic influence.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv class=\"Heading\"\u003e\u003cb\u003e2.\u003c/b\u003e Materials and methods:\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Required Data:\u003c/h2\u003e\u003cp\u003e\u003cb\u003e2.1.1. Satellite Images and LU/LC\u003c/b\u003e: Three images of Landsat in 1986 (TM), 2001 (ETM+), and 2016 (OLI) were downloaded from U.S Geological Survey (USGS) Centre for Earth Resources Observation and Science (EROS) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov/\u003c/span\u003e\u003cspan address=\"https://earthexplorer.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ehybrid classification technique with supervised method is used for modeling land use maps by the aid of imagery satellite (Halder et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Jensen and Lulla, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003e2.1.2. Meteorological data\u003c/b\u003e: The historical daily data for maximum temperature (Tmax), minimum temperature (Tmin), perception, relative humidity, wind speed and solar radiation - required for the SWAT and SDSM models- were obtained from the Iran Meteorological Department (IMD) for the period of 1961\u0026ndash;2000. Additionally, daily data from the synoptic station of Amir Abad, Dashtenaz Sari and Gharakhil, covering the period 1995 and 2015 were used for WGN makers 4.1.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003e2.1.3. Discharge data\u003c/b\u003e: To calibration and validation the SWAT models, Monthly discharge data from the Abloo and Golvard were used. The observed data were divided into a calibration period (1983\u0026ndash;2007) and validation period (2008\u0026ndash;2013).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003e2.1.4. Spatial Data\u003c/b\u003e: The 1:250,000 scale State Soil Geographic data included in the SWAT database were obtained from the Department of Natural Resources and Watershed of Mazandaran. A 10-meter Digital Elevation Model (DEM) was extracted from the dataset provided by the National Cartographic Center of Iran. The flowchart of the methodology used in this study is shown in Fig.\u0026nbsp;(2).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. SDSM:\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1. Preparation of observation/large-scale data:\u003c/h2\u003e\u003cp\u003eSDSM, being a decision support tool based on linear regression technique, is used in this study. The model combines weather generator and the multiple linear regression. Predictors were selected by assessing correlation, partial correlation and scatter plots between the predictors and the predictands (Wilby et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Given the large-scale of the NCEP model, the selected base period should be between 1961\u0026ndash;2000. The nearest synoptic stations with adequate historical records are Babolsar, Gharakhil and Gorgan.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2. Select of predictands suitable\u003c/h2\u003e\u003cp\u003eTwo types of daily predictors datasets required for this study were obtained from a Canadian website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cics.uvic.ca/scenarios/sdsm/select.cgi\u003c/span\u003e\u003cspan address=\"http://www.cics.uvic.ca/scenarios/sdsm/select.cgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e): (a) the 26 predictors of the National Center of Environmental Prediction (NCEP) for the period of 1961\u0026ndash;2000; and (b) the 26 predictors from the CanESM2 model for the RCP2.6 and 8.5 scenarios, covering the period 1961\u0026ndash;2100. These datasets were specially required for SDSM processing.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3. Assessing the accuracy of SDSM\u003c/h2\u003e\u003cp\u003eThe NCEP and CanESM2 predictors were normalized using the mean and standard deviation from the 1961\u0026ndash;2000 period (CCCSN, 2012). After validation of model, the accuracy of the SDSM model in downscaling of NCEP and CanESM2 data were assessed between 1991\u0026ndash;2000 period. In next step, model accuracy evaluation, he model's accuracy was evaluated using the Mean Absolute Error (MAE) and Mean Bias Error (MBE), as defined in Equations (\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and (\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{M}\\text{A}\\text{E}=\\frac{\\sum\\:_{\\text{i}=1}^{\\text{n}}\\left|{\\text{O}}_{\\text{i}}-{\\text{S}}_{\\text{i}}\\right|}{\\text{n}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\text{M}\\text{B}\\text{E}=\\frac{{\\sum\\:}_{\\text{i}=1}^{\\text{n}}\\left({\\text{O}}_{\\text{i}}-{\\text{S}}_{\\text{i}}\\right)}{\\text{n}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere MAE is Mean Absolute Error, MBE is Mean Bias Error, Si is model simulations output, Oi is observed values, i is month of year and n is data number.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Unsupervised classification:\u003c/h2\u003e\u003cp\u003eThe Supports Vector Machine (SVM) algorithm was used to classify land use for the years 1984, 2001 and 2016 using ENVI (The Environment for Visualizing Images) version 4.7. Five land use classes were identified: Agriculture, Forest, Bare land, Residential, and Rangeland. A total of 2,904 ground-truth training samples were collected through GPS and field surveys. These samples were then used to assemble training datasets for each land use category. To classification assessments were used Kappa (K) and overall accuracy (Ov.) coefficients (Srivastava et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Yousefi et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Calibration and prediction of LCM:\u003c/h2\u003e\u003cp\u003eThe Land change modeler was used to predict future land use change based on the classified outputs of three satellite images. Model calibration was performed using the classified land use layers from 1984 and 2001, while the 2016 classified land use layer was used to validate the simulated 2016 map.\u003c/p\u003e\u003cp\u003eIn modeling Land use changes in time interval, a number of transition probabilities have to be developed for each direction of change (Weng \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The change analysis provides a rapid assessment of quantitative changes, including gains and losses across land use categories. In the transition sub-model step, a detailed list of all minor to major land use transitions that occurred between 1984 and 2001 is generated.\u003c/p\u003e\u003cp\u003eConstraints or drivers are a criterion that either raise or diminish from the suitability of a specific alternative for the land use activity under consideration. These criteria, often distance-based, indicate the degree of suitability for land use change and are incorporated into the Land Change Modeler (LCM) as raster datasets. In the present study, the selected drivers influencing land use transitions include elevation, slope, distance to residential areas, distance to forest areas, distance to agricultural land, distance to rangelands, distance to major roads, and distance to fluvial streams. To create transition susceptibility maps in separate sub-models used a MLP neural network (Sangermano et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The total five transitions that have been selected are forest to residential, forest to agriculture, agriculture to residential and range land to agriculture, rangeland to residential. The results of the MLP indicated an overall accuracy of \u0026gt;\u0026thinsp;90% and a skill measure of \u0026gt;\u0026thinsp;0.88 was attained in predicting land cover in the period 1984\u0026ndash;2001 in all sub models. Transition potential modeling framework supported by stochastic Markov-chain technique (Wu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zhou and Liebhold, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Markov prediction to 2016 based on land use and land cover maps of 1984 and 2001.\u003c/p\u003e\u003cp\u003eTo forecasting land use and planning scenario maps for the Nekarood watershed, a multilayer perceptron neural network integrated with Markov chain modeling, as implemented in Idrisi's Land Change Modeler (LCM), was used. Using the 2016 land use map as the base, along with transition potential maps and a transition probability matrix, future land use for the year 2050 was predicted through hard prediction based on multi-objective land allocation (O\u0026ntilde;ate-Valdivieso and Bosque Sendra \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Run SWAT:\u003c/h2\u003e\u003cp\u003eThe Soil and Water Assessment Tool (SWAT) is one of such physically based hydrological models generally used for quantifying and quantity the impacts of land use and climate changes on hydrological processes from watershed scales to global scales (Schilling et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Tomer and Schilling, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Karlsson et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ahiablame et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Various components of SWAT involve hydrology, weather, soil characteristics, plant growth, pesticides, land management and nutrients (Manoj Jha, Philip Gassman, and Jeffrey Arnold 2007).\u003c/p\u003e\u003cp\u003eIn the simulation process, the watershed is divided into several sub-basins. Each sub-basin is further subdivided into smaller units called Hydrological Response Units (HRUs). These HRUs are defined as homogeneous spatial units with similar geomorphological and hydrological characteristics (Fl\u0026uuml;gel \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Surface runoff for each HRU was estimated using the Curve Number method developed by the USDA Soil Conservation Service (1972). A 'warm-up' period is required in SWAT to ensure the model adequately reflects real-world basin hydrology (Wilson and Weng \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Calibration and validation of theSWAT by time series data of stream flow be performed by algorithm of sequential uncertainty fitting (Abbaspour, Johnson, and van Genuchten \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) that is a semi inverse automated modelling tool. Since a number of iterations are needed to reach a better model Performance, the iteration process was done using the SUFI2-algoritm. Moreover, calibrating and adjusting of model input parameters are modified to achieve the most compatible between simulated data and observation data.\u003c/p\u003e\u003cp\u003eThe strong agreement between observed and monthly-simulated streamflow values during the 1983\u0026ndash;2013, indicated that calibrated model, with its optimized parameter ranges, is suitable for assessing streamflow responses to land use change (LUC) and climate change (CC) in the Nekarood basin.\u003c/p\u003e\u003cp\u003eModel performance was evaluated using several statistical indices, including the Nash\u0026ndash;Sutcliffe efficiency coefficient (NSE), the coefficient of determination (R\u0026sup2;), the P-factor, and the R-factor. The objective functions NSE and R\u0026sup2; were calculated as shown in Equations (\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and (\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), respectively.\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:NS=1-\\frac{{\\sum\\:}_{\\:i}{\\left({Q}_{m}-{Q}_{s}\\right)}^{2}}{\\sum\\:_{i}{\\left({Q}_{mi}-{\\stackrel{-}{Q}}_{mi}\\right)}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{R}^{2}=\\frac{{\\left[{\\sum\\:}_{i}\\left({Q}_{mi}-{\\stackrel{-}{Q}}_{m}\\right)\\left({Q}_{si}-{\\stackrel{-}{Q}}_{S}\\right)\\right]}^{2}}{\\sum\\:_{i}{{\\left({Q}_{mi}-{\\stackrel{-}{Q}}_{m}\\right)}^{2}-\\sum\\:_{i}\\left({Q}_{si}-{\\stackrel{-}{Q}}_{s}\\right)}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{-}{Q}}_{m}\\)\u003c/span\u003e\u003c/span\u003eis mean of observed stream flow, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{-}{Q}}_{s}\\)\u003c/span\u003e\u003c/span\u003e is mean of simulated streamflow, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{si}\\)\u003c/span\u003e\u003c/span\u003e is simulated streamflow, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{mi}\\:\\)\u003c/span\u003e\u003c/span\u003eis observed stream flow in simulation.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Calibration and Validation of SWAT:\u003c/h2\u003e\u003cp\u003eThe model was calibrated to streamflow for the baseline period using 11 parameters (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the results of parameter sensitivity analysis for stream flow, including the parameter range, sensitivity rankings, and optimal values for the Nekarood basin. The most sensitive parameter was the Precipitation Lapse Rate (PLAPS), with a best value of 0.19, followed by the Average Slope Length (SLSUBBSN) with a best value of 62.9, the Baseflow Alpha Factor (ALPHA_BF) with a best value of 0.08, and the SCS Runoff Curve Number (CN2) with a best value of 62.4, along with other parameters.\u003c/p\u003e\u003cp\u003eThis resulted in R\u0026sup2; and NSE values greater than 0.5 for monthly streamflow, along with acceptable P-factor and R-factor values during both the calibration and validation periods (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These performance statistics were considered satisfactory for modeling streamflow using SWAT in the Nekarood watershed and, therefore, reliable for reconstructing the natural streamflow (Tavangar et al., 2018). According to Figure (3), there is no clear agreement between the peak values of observed and simulated streamflow during the calibration and validation periods at the Golvard station. This discrepancy may be attributed to temporal variability in precipitation, errors in input data, inaccuracies in the measured discharge values, or a combination of these factors (Nie et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In general, the differences in model performance between the calibration and validation periods were small in terms of NSE, R\u0026sup2;, P-factor, and R-factor, indicating no evidence of model overfitting. However, since the focus of this study was on changes in mean long-term streamflow, calibrating to improve peak flow values is unlikely to significantly affect the overall comparison of future scenarios relative to the baseline. Overall, the model demonstrated strong performance within the study domain, making it a reliable tool for reconstructing natural streamflow.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel parameters and their values used in SWAT_CUP\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDefinition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSensitivity rating\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInitial range\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBest value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eV_PLAPS.sub\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrecipitation lapse rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAblo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.52 to 0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eV_SLSUBBSN.hru\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage slope length\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAblo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.51 to 68.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e62.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eV_ALPHA_BF.gw\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBaseflow alpha factor (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAblo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 to 0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eV_CN2.mgt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSCS runoff curve number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAblo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53/49 تا 0/88\u003c/p\u003e\u003cp\u003e49.53 to 88.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e62.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR_SOL_K.sol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSaturated hydraulic conductivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGolvard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.22 to 0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eV_CN2.mgt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSCS runoff curve number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGolvard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.45 to 53.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR_SOL_AWC.sol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAvailable water capacity of the soil layer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGolvard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.37 to 1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eV_PLAPS.sub\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrecipitation lapse rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGolvard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-304.6 to 31.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-154.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR_SOL_BD.sol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMoist bulk density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAblo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.35 to 0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eV_DEEPST.gw\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInitial depth of water in the deep aquifer (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGolvard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e208.1 to 504.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e226.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eV_GWQMN.gw\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTreshold depth of water in the shallow aquifer required for return flow to occur (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAblo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e700 to 1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e878.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel results in calibration/validation statistics for the Nekarood basin\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeriod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003er-factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-factor\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGolvard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCalibration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValidation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAbloo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCalibration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValidation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Simulation basin stream flow in baseline:\u003c/h2\u003e\u003cp\u003eThe calibrated model was used to simulate streamflow under baseline conditions for the period 1981\u0026ndash;2007, while the period 2008\u0026ndash;2013 was used for model validation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Simulation of land use 2050:\u003c/h2\u003e\u003cp\u003eThe land use map prepared using the SVM method for 1986, 2001, and 2016 are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The image classification results indicate that the generated land use maps achieved acceptable accuracy. The land use map for 2050 was predicted using the LCM model. The spatial distribution of land use in the Nekarood watershed for 2050 is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. According to the LCM model results, residential, agricultural, and rangeland areas are projected to increase by 41% (114 ha), 19% (4110 ha), and 4.8% (3938 ha), respectively. Conversely, forest areas are projected to decline by 12.1% (8161 ha), while bare land is expected to remain virtually unchanged (\u0026asymp;\u0026thinsp;0.0%) by 2050 compared to 2016. The growth of the urban population in the Nekarood district is attributed to internal factors such as economic and social conditions. Finally, a comparison of land use maps from 1984, 2001, 2016, and 2050 indicates that significant changes have occurred across land use classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Simulation of stream flow under land use change scenarios/current climate condition:\u003c/h2\u003e\u003cp\u003eThe conversion of forest areas to residential, agricultural, and other land uses has led to an increase in streamflow during the wet season (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Streamflow simulations based on projected land use changes indicate an increase in annual streamflow from 6.0 to 6.8 m\u0026sup3;/s (a 13% rise) by 2050. This increase is attributed to land use changes. Peak flow in 2050 is projected to be higher than that during the baseline period, while base flow is expected to decrease under future land use conditions. The results indicate that land surfaces covered by impermeable materials, such as buildings and roads, pose a major threat to streamflow. The findings also show an increase in peak flow due to the impacts of land use and land cover changes (Sajikumar and Remya \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); (Du et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The expansion of agricultural lands and urban areas is expected to increase the Curve Number (CN) and reduce evapotranspiration (ET) in the future, leading to higher water demand (Memarian et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Previous studies have also reported reductions in streamflow under dry conditions, primarily due to decreased infiltration of precipitation resulting from urban expansion and the increase in impervious surface areas (Paul and Meyer \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe percent change in month stream flow under land use change in the future (2050) compared to base line (2016)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMonth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eDischarge (m3/s)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2050\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChanges (Percent)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;6.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;2.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;5.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;6.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-9.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJul\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-13.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAug\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-9.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-9.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOct\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;2.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNov\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;5.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDec\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;4.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Prediction temperature and precipitation in future periods:\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;(6) to (8) show the percentage changes in precipitation and mean temperature (average of maximum and minimum temperatures) for the mid- and end-century periods under RCP2.6 and RCP8.5 scenarios for Babolsar, Gharakhil, and Gorgan, respectively. The figures reveal that, under both the pessimistic (RCP8.5) and optimistic (RCP2.6) scenarios, monthly precipitation is projected to increase in late autumn and winter, while decreasing in spring and summer, indicating significant inter-annual variability. The average temperature is expected to follow an increasing trend. According to the results, the projected changes in precipitation and temperature under RCP8.5 for the 2061\u0026ndash;2080 period are at least slightly higher across all stations compared to those under RCP2.6 for the 2021\u0026ndash;2040 period. Specifically, compared to the baseline period, annual precipitation is projected to decrease by -2.1 to -6.6% in Babolsar, from \u0026minus;\u0026thinsp;0.7% to -4% in Gharakhil and from \u0026minus;\u0026thinsp;3.2% to -4.8% in Gorgan station (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). In contrast, the annual mean temperature increased significantly across all three weather stations. This finding is consistent with previous studies in other humid regions, which forecast reductions in annual precipitation under climate change scenarios (Bangash \u003cem\u003eet al.\u003c/em\u003e, 2013; Khoi and Suetsugi, 2014; Lu \u003cem\u003eet al.\u003c/em\u003e, Serpa et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAccording to results of Fig.\u0026nbsp;(6), inter-annual changes in precipitation and temperature at the Babolsar station are projected to range from \u0026minus;\u0026thinsp;29.4\u0026ndash;45% and from 2.5\u0026ndash;8.5%, respectively. The most significant decrease in rainfall is expected in July, while the greatest increase is projected in December, both occurring in the end-century period (2061\u0026ndash;2080) under the RCP8.5 scenario. Monthly temperatures are projected to increase consistently throughout the year.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAccording to the climate change projections for the Gharakhil station (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) both climatic scenarios predict a decrease in precipitation during May, June, July, August, and September, and an increase during January, February, October, and November. Precipitation is projected to increase in March under the RCP8.5 scenario and in April under the RCP2.6 scenario during the mid-century period (2046\u0026ndash;2065), while in other periods, these months are expected to experience a decrease in precipitation under both scenarios. Additionally, temperatures are projected to rise in all months of the year during both the mid-century (2046\u0026ndash;2065) and end-century (2080\u0026ndash;2099) periods under both climatic scenarios.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eChanges in precipitation and temperature at the Gorgan station are shown in Fig.\u0026nbsp;(8). The results indicate an overall increase in temperature throughout the study periods, with the RCP8.5 scenario projecting slightly higher temperatures than RCP2.6. Similarly, an increase in precipitation is forecasted for the winter months, consistent with findings from previous studies (Nunes, Seixas and Pacheco, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; (Li et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs shown in the results fig of (6) to (8), although there are some differences in individual monthly precipitation and temperature values, the overall pattern of change is consistent across the three stations. This similarity may be attributed to the fact that, under all future climate scenarios, the projected changes at each station follow the same directional trend when compared to the baseline period.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Simulation of streamflow under climate change scenarios /current land use:\u003c/h2\u003e\u003cp\u003eChanges in streamflow due to climate change are projected to vary under both RCP2.6 and RCP8.5 scenarios during the mid-century and end-century periods. Under RCP2.6, annual streamflow is expected to decrease by -4.1% in the mid-century and by -9.1% in the end-century period. Under the more extreme RCP8.5 scenario, streamflow is projected to decline by -10.6% and \u0026minus;\u0026thinsp;21.6% during the mid- and end-century periods, respectively. The most significant reductions in streamflow are observed under the RCP8.5 scenario in the end-century period.\u003c/p\u003e\u003cp\u003eThe climate change projections indicate an increase in precipitation during winter and a decrease during spring, summer, and autumn (except for December), along with a general rise in temperature. These changes are more pronounced in the end-century period under the RCP8.5 scenario. Therefore, a reduction in annual streamflow and base flow due to climate change is not unexpected. Also, according to the results of figures (10) to (13), climate change in Nekarood basin causing will increase stream flow in winter. Streamflow is projected to decrease during spring (except April), summer, and autumn (except December), which can be attributed to reduced precipitation, increased temperatures, and consequently higher evapotranspiration. These results are consistent with findings from previous studies, which indicate that reductions in precipitation are generally associated with decreased streamflow (Kalogeropoulos and Chalkias, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zabaleta et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Serpa et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These changes occur because hydrological characteristics are directly and indirectly influenced by precipitation and temperature.\u003c/p\u003e\u003cp\u003eBy comparing Figures Fig.\u0026nbsp;(6) to (8) and (10) to (13), it is evident that increases in rainfall and temperature during January, February, March, and December are associated with increased streamflow. In contrast, the reductions in streamflow observed in May, June, July, August, September, October, and November correspond to decreased rainfall and elevated temperatures during these months. Interestingly, despite a decrease in rainfall and an increase in temperature in April, streamflow rises. This anomaly can be attributed to increased winter precipitation and higher annual temperatures, which enhance snowmelt in late winter, subsequently increasing streamflow in April. Overall, the average annual streamflow decreases as temperature rises and precipitation declines. These findings are consistent with previous studies in Pennsylvania (Chang, Evans, and Easterling \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and in eastern Massachusetts (Tu \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), which also reported reductions in streamflow during spring and summer months in certain watersheds. (Franczyk and Chang, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Praskievicz and Chang, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) noted that the streamflow decrease during the summer and increase during the winter.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe results presented in figure (10) show that the most significant increase and decrease in stream flow are projected to occur in Jan (+\u0026thinsp;5%) and Sep (-12%), respectively, due to climate change during the mid-century period under RCP2.6 scenario.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on Fig.\u0026nbsp;(11), the greatest increase in streamflow due to climate change in the end-century period under the RCP2.6 scenario is projected to occur in January (+\u0026thinsp;6.6%), while the most significant decrease is expected in September (\u0026minus;\u0026thinsp;17%).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on Fig.\u0026nbsp;(12), the most significant effects of climate change on stream flow during the mid-century period under RCP8.5 are from (14%) in Dec to (-18%) in Sep. Also in Fig.\u0026nbsp;(13) shows most increase and decrease of stream flow in end-century period under RCP 8.5 will occur in Dec (16%) and Jul (-25.9%), respectively.\u003c/p\u003e\u003cp\u003eIt is clearly evident from the four figures that both RCP scenarios, across both time periods, lead to a reduction in total streamflow.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.7. Simulation of stream flow under combined land use and climate change scenarios:\u003c/h2\u003e\u003cp\u003eThe impacts of climate change under RCP2.6 and RCP8.5 scenarios for two future periods were simulated while keeping land use constant at baseline conditions. Both base flow and annual streamflow in the basin are projected to decrease under these scenarios. The predicted reduction in streamflow is expected to have a significant impact on the basin\u0026rsquo;s future hydrological state. Additionally, simulated land use maps for the year 2050 were used to estimate the potential effects of land use change on streamflow at the basin outlet. The results suggest that land use change will further intensify the reduction in base flow and contribute to an increase in peak flow. In this step, the effects of simultaneous changes in land use and climate on basin streamflow were examined. To assess these impacts, the SWAT model was run using various combinations of land use and climate change scenarios, and the results were compared with the streamflow simulation under the baseline scenario (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The combined impacts of land use and climate change under the RCP2.6 scenario are projected to result in the greatest streamflow decreases of \u0026minus;\u0026thinsp;12.2% in August and \u0026minus;\u0026thinsp;32.4% in July, and the greatest increases of 12.9% and 16.4% in December during the periods 2041\u0026ndash;2060 and 2061\u0026ndash;2080, respectively. Similarly, under the RCP8.5 scenario, the most significant decreases are expected in July (\u0026minus;\u0026thinsp;29.7% and \u0026minus;\u0026thinsp;32.4%) and the greatest increases in December (20.2% and 43.5%) during the mid- and end-century periods, respectively. The results indicate that changes in streamflow are more pronounced in the end-century period than in the mid-century, and the RCP8.5 scenario has a more substantial impact than RCP2.6. Although simulated land use changes for future scenarios do affect streamflow, their influence is relatively minor compared to the effects of climate change.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePercent change of monthly stream flow in Nekarood basin under simultaneous effects of climate change and land use in future\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMonth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eRCP2.6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eRCP8.5\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMid of century\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEnd of century\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMid of century\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnd of century\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;11.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;13.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;19.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;10.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;14.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;13.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;10.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-13.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-8.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-11.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-15.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-20.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-25.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-27.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-34.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJul\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-32.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-29.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-40.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAug\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-12.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-13.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-14.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-22.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-17.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-14.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-19.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOct\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-9.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-19.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-25.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-31.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNov\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-13.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-13.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-19.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDec\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;12.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;16.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;20.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;43.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThus, the streamflow of the Nekarood Basin is more sensitive to climate change than to land use change. However, since climate and land use changes occur concurrently, the impact of land use change should not be overlooked (Shrestha et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). According to the results of this section, the pattern of streamflow changes caused by land use change alone is similar to that caused by climate change alone in most months. Consequently, when climate change and land development occur concurrently, their combined effects tend to amplify streamflow changes in the majority of months (Tu \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Furthermore, annual streamflow is sensitive to both climate and land use changes. Since the directions of change caused by climate change alone and land use change alone are often opposite, their concurrent occurrence tends to reduce the overall impact. In other words, the effects of climate change and land use change on annual streamflow may partially offset each other.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study simulated the individual and combined impacts of climate and land use change on streamflow in the Nekarood Basin. The land use analysis indicated a reduction in base flow, along with increases in peak flow and annual streamflow, compared to the baseline period.\u003c/p\u003e\u003cp\u003eClimate change simulations indicated an increase in future temperatures and a decrease in annual rainfall across the basin compared to the baseline period, under both climate scenarios and for all projected time periods. According to both climate scenarios, streamflow is projected to decrease during early autumn and the dry seasons, while increasing in late autumn (December) and winter. These findings suggest that climate change alone has a significant impact on streamflow patterns in the basin. A comparison of the separate effects of land use and climate change on streamflow indicated that climate change has a more significant impact. The combined effects of future climate and land use changes further intensified the individual impacts of each. The results of this study suggest that changes in temperature and precipitation have substantial effects on the hydrological conditions of the Nekarood Basin. These effects are further complicated by the expansion of agricultural and residential areas and the reduction of forest cover. As a result, the risk of hydrological crises, including floods and droughts, is expected to increase. The quantitative insights provided by this study can support informed decision-making for hydrological management and natural resource conservation in the Nekarood watershed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cb\u003eFinding\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contributions:\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by \u003cb\u003eShahla Tavangar\u003c/b\u003e, \u003cb\u003eHamidreza Moradi\u003c/b\u003e and \u003cb\u003eAlireza Massah Bavani\u003c/b\u003e .The first draft of the manuscript was written by Shahla Tavangar and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbaspour KC, Johnson CA, van Genuchten MT (2004) Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone J 3(4):1340\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdo KS, Fiseha BM, Rientjes THM, Gieske ASM, Haile AT (2009) Assessment of climate change impacts on the hydrology of Gilgel Abay catchment in Lake Tana Basin, Ethiopia. Hydrol Process 23(26):3661\u0026ndash;3669. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/hyp.7363\u003c/span\u003e\u003cspan address=\"10.1002/hyp.7363\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhiablame L, Sheshukov AY, Rahmani V, Moriasi D (2017) Annual baseflow variations as influenced by climate variability and agricultural land use change in the Missouri River Basin. J Hydrol 551:188\u0026ndash;202\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeyene T, Lettenmaier DP, Kabat P (2010) Hydrologic impacts of climate change on the Nile River Basin: Implications of the 2007 IPCC scenarios. Clim Change 100(3):433\u0026ndash;461. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10584-009-9693-0\u003c/span\u003e\u003cspan address=\"10.1007/s10584-009-9693-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChang H, Evans BM, Easterling DR (2001) The effects of climate change on stream flow and nutrient loading. J Am Water Resour Assoc 37(4):973\u0026ndash;985. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1752-1688.2001.tb05526.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1752-1688.2001.tb05526.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChung ES, Park K, Lee KS (2011) The relative impacts of climate change and urbanization on the hydrological response of a Korean urban watershed. Hydrol Process 25(4):544\u0026ndash;560. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/hyp.7781\u003c/span\u003e\u003cspan address=\"10.1002/hyp.7781\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDu Y, Liu R, Chen Y, Han Z, Liu X (2012) Comparison between CBR and CA methods for estimating land use change in Dongguan, China. J Geog Sci 22(4):716\u0026ndash;736. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11442-012-0958-6\u003c/span\u003e\u003cspan address=\"10.1007/s11442-012-0958-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFl\u0026uuml;gel WA (1995) Delineating hydrological response units by geographical information system analyses for regional hydrological modelling using PRMS/MMS in the drainage basin of the River Br\u0026ouml;l. Ger Hydrol Processes 9(3\u0026ndash;4):423\u0026ndash;436\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFranczyk J, Chang H (2009) The effects of climate change and urbanization on the runoff of the Rock Creek Basin in the Portland Metropolitan Area, Oregon, USA. Hydrol Process 23(6):805\u0026ndash;815\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhanbarpour MR, Mohseni Saravi M, Salimi S (2014) Floodplain inundation analysis combined with contingent valuation: Implications for sustainable flood risk management. Water Resour Manage 28(9):2491\u0026ndash;2505\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGizaw MS, Gan TY (2017) Impact of climate change and El Ni\u0026ntilde;o episodes on droughts in Sub-Saharan Africa. Clim Dyn 49(1\u0026ndash;2):665\u0026ndash;682. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00382-016-3366-2\u003c/span\u003e\u003cspan address=\"10.1007/s00382-016-3366-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHadgu G, Tesfaye K, Mamo G (2015) Analysis of climate change in northern Ethiopia: Implications for agricultural production. Theoret Appl Climatol 121(3\u0026ndash;4):733\u0026ndash;747. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00704-014-1261-5\u003c/span\u003e\u003cspan address=\"10.1007/s00704-014-1261-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHalder A, Ghosh A, Ghosh S (2011) Supervised and unsupervised landuse map generation from remotely sensed images using ant based systems. Appl Soft Comput 11(8):5770\u0026ndash;5781\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeathwaite AL (2010) Multiple stressors on water availability at global to catchment scales: Understanding human impact on nutrient cycles to protect water quality and water availability in the long term. Freshw Biol 55(S1):241\u0026ndash;257. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2427.2009.02368.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2427.2009.02368.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHurkmans RTWL, Terink W, Uijlenhoet R, Moors EJ, Troch PA, Versteeg K (2009) Effects of land use changes on streamflow generation in the Rhine Basin. Water Resour Res 45(6):W06405\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJensen JR, Lulla K (1987) Introductory digital image processing: A remote sensing perspective. Geocarto Int 2(1):65\u0026ndash;65\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKalogeropoulos K, Chalkias C (2013) Modelling the impacts of climate change on surface runoff in small Mediterranean catchments: Empirical evidence from Greece. Water Environ J 27(4):505\u0026ndash;513. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1747-6593.2012.00369.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1747-6593.2012.00369.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarlsson IB, Sonnenborg TO, Seaby LP, Refsgaard JC (2016) Combined effects of climate models, hydrological model structures and land use scenarios on hydrological impacts of climate change. J Hydrol 535:301\u0026ndash;317\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim J, Choi J, Choi C, Park S (2013) Impacts of changes in climate and land use/land cover under IPCC RCP scenarios on streamflow in the Hoeya River Basin, Korea. Sci Total Environ, 452\u0026ndash;453, 181\u0026ndash;195\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi S, Li X, Chen Y, Zhang S, Fan Y (2012) Spatial analysis of the driving factors of grassland degradation under conditions of climate change and intensive use in Inner Mongolia, China. Reg Envriron Chang 12(3):461\u0026ndash;474. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10113-011-0264-3\u003c/span\u003e\u003cspan address=\"10.1007/s10113-011-0264-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin Y, Deng X, Li X, Ma E (2014) Comparison of multinomial logistic regression and logistic regression: Which is more efficient in allocating land use. Front Earth Sci 8(4):512\u0026ndash;523. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11707-014-0426-y\u003c/span\u003e\u003cspan address=\"10.1007/s11707-014-0426-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa X, Wang Y, Dong G, Zhao X (2009) Response of hydrological processes to land-cover and climate changes in Kejie Watershed, South-West China. Hydrol Process 23(8):1179\u0026ndash;1191. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/hyp.7233\u003c/span\u003e\u003cspan address=\"10.1002/hyp.7233\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJha M, Gassman P, Arnold J (2007) Water quality modeling for the Raccoon River Watershed using SWAT. Trans ASABE 50(2):479\u0026ndash;493\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMemarian H, Balasundram SK, Abdullah R, Sood AM (2014) Mod\u0026eacute;lisation hydrologique de sc\u0026eacute;narios d\u0026rsquo;utilisation des sols en zone tropicale \u0026agrave; l\u0026rsquo;aide du mod\u0026egrave;le SWAT. Hydrol Sci J 59(10):1808\u0026ndash;1829\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurray-Hudson M, Wolski P, Ringrose S (2006) Scenarios of the impact of local and upstream changes in climate and water use on hydro-ecology in the Okavango Delta, Botswana. J Hydrol 331(1\u0026ndash;2):73\u0026ndash;84\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNapoli N, Strollo R, Paladini A, Russo S (2017) Mechanisms of diabetes mellitus-induced bone fragility. Nat Reviews Endocrinol 13(4):208\u0026ndash;219. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrendo.2016.153\u003c/span\u003e\u003cspan address=\"10.1038/nrendo.2016.153\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNie W, Yuan Y, Kepner WG, Jackson M, Erickson C (2011) Assessing impacts of land use and land cover changes on hydrology for the Upper San Pedro Watershed. J Hydrol 407(1\u0026ndash;4):105\u0026ndash;114. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhydrol.2011.07.012\u003c/span\u003e\u003cspan address=\"10.1016/j.jhydrol.2011.07.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNovotny EV, Stefan HG (2007) Stream flow in Minnesota: Indicator of climate change. J Hydrol 334(3\u0026ndash;4):319\u0026ndash;333\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNunes JP, Seixas J, Pacheco NR (2008) Vulnerability of water resources, vegetation productivity and soil erosion to climate change in Mediterranean watersheds. Hydrol Process 22(16):3115\u0026ndash;3134. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/hyp.6897\u003c/span\u003e\u003cspan address=\"10.1002/hyp.6897\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO\u0026ntilde;ate-Valdivieso F, Bosque Sendra J (2010) Application of GIS and remote sensing techniques in generation of land use scenarios for hydrological modeling. J Hydrol 395(3\u0026ndash;4):256\u0026ndash;263\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaul MJ, Meyer JL (2001) Streams in the urban landscape. Annu Rev Ecol Syst 32:333\u0026ndash;365\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePraskievicz S, Chang H (2011) Impacts of climate change and urban development on water resources in the Tualatin River Basin, Oregon. Ann Assoc Am Geogr 101(2):249\u0026ndash;271. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00045608.2010.544934\u003c/span\u003e\u003cspan address=\"10.1080/00045608.2010.544934\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSajikumar N, Remya RS (2015) Impact of land cover and land use change on runoff characteristics. J Environ Manage 161:460\u0026ndash;468\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSangermano F, Toledano J, Eastman R (2012) Land cover change in the Bolivian Amazon and its implications for REDD\u0026thinsp;+\u0026thinsp;and endemic biodiversity. Landscape Ecol 27(4):571\u0026ndash;584. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10980-012-9710-y\u003c/span\u003e\u003cspan address=\"10.1007/s10980-012-9710-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchilling KE, Jha MK, Zhang YK, Gassman PW, Wolter CF (2008) Impact of land use and land cover change on the water balance of a large agricultural watershed: Historical effects and future directions. Water Resour Res, 45(7), W00A09.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSerpa D, Nunes JP, Keizer JJ, Santos JM, Abrantes N (2015) Impacts of climate and land use changes on the hydrological and erosion processes of two contrasting Mediterranean catchments. Sci Total Environ 538:64\u0026ndash;77\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShrestha S, Bhatta B, Shrestha M, Shrestha PK (2018) Integrated assessment of the climate and land use change impact on hydrology and water quality in the Songkhram River Basin, Thailand. Sci Total Environ 643:1610\u0026ndash;1622\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSrivastava PK, Han D, Rico-Ramirez MA, Islam T (2012) Selection of classification techniques for land use/land cover change investigation. Adv Space Res 50(9):1250\u0026ndash;1265\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTavangar S, Moradi H, Bavani M, A., and, Gholamalifard M (2021) A futuristic survey of the effects of LU/LC change on stream flow by CA\u0026ndash;Markov model: A case of the Nekarood Watershed. Iran Geocarto Int 36(10):1100\u0026ndash;1116. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10106049.2019.1633419\u003c/span\u003e\u003cspan address=\"10.1080/10106049.2019.1633419\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaye MT, Ntegeka V, Ogiramoi NP, Willems P (2011) Assessment of climate change impact on hydrological extremes in two source regions of the Nile River Basin. Hydrol Earth Syst Sci 15(1):209\u0026ndash;222\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTekleab S, Mohamed Y, Uhlenbrook S, Wenninger J (2014) Hydrologic responses to land cover change: The case of Jedeb mesoscale catchment, Abay/Upper Blue Nile Basin, Ethiopia. Hydrol Process 28(20):5149\u0026ndash;5161. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/hyp.9998\u003c/span\u003e\u003cspan address=\"10.1002/hyp.9998\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTomer MD, Schilling KE (2009) A simple approach to distinguish land-use and climate-change effects on watershed hydrology. J Hydrol 376(1\u0026ndash;2):24\u0026ndash;33\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTu J (2009) Combined impact of climate and land use changes on streamflow and water quality in Eastern Massachusetts. USA J Hydrology 379(3\u0026ndash;4):268\u0026ndash;283\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWelde K, Gebremariam B (2017) Effect of land use land cover dynamics on hydrological response of watershed: Case study of Tekeze Dam Watershed, Northern Ethiopia. Int Soil Water Conserv Res 5(1):1\u0026ndash;16\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeng Q (2002) Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. J Environ Manage 64(3):273\u0026ndash;284\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWijesekara GN, Gupta A, Valeo C, Hasnain S, Marceau D (2012) Assessing the impact of future land-use changes on hydrological processes in the Elbow River Watershed in Southern Alberta, Canada. J Hydrol 412\u0026ndash;413:220\u0026ndash;232\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilby RL, Dawson CW, Barrow EM (2002) SDSM - A decision support tool for the assessment of regional climate change impacts. Environ Model Softw 17(2):145\u0026ndash;157\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilson CO, Weng Q (2011) Simulating the impacts of future land use and climate changes on surface water quality in the Des Plaines River Watershed, Chicago Metropolitan Statistical Area, and Illinois. Sci Total Environ 409(20):4387\u0026ndash;4405\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu Q, Li H, Wang R, Paulussen J (2006) Monitoring and predicting land use change in Beijing using remote sensing and GIS. Landsc Urban Plann 78(4):322\u0026ndash;333\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYousefi S, Farajzadeh M, Roshani M (2015) Comparison of different algorithms for land use mapping in dry climate using satellite images: A case study of the central regions of Iran. Desert 20(1):1\u0026ndash;10\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZabaleta A, Meaurio M, Ruiz E, Antig\u0026uuml;edad I (2014) Simulation climate change impact on runoff and sediment yield in a small watershed in the Basque Country, Northern Spain. J Environ Qual 43(1):235\u0026ndash;245. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2134/jeq2012.0209\u003c/span\u003e\u003cspan address=\"10.2134/jeq2012.0209\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang L, Nan Z, Xu Y, Li S (2016) Hydrological impacts of land use change and climate variability in the headwater region of the Heihe River Basin, Northwest China. PLoS ONE, 11(6), e0158394\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou G, Liebhold AM (1995) Forecasting the spatial dynamics of gypsy moth outbreaks using cellular transition models. Landscape Ecol 10(3):177\u0026ndash;189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF00133030\u003c/span\u003e\u003cspan address=\"10.1007/BF00133030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SWAT, SDSM, LCM, RCP Scenario","lastPublishedDoi":"10.21203/rs.3.rs-7103894/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7103894/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe key focus of this study is the use of future climate and land use data obtained from appropriate projection models to assess long-term annual streamflow changes in a basin located in northern Iran. Future climate projections were derived from the CanESM2 model under two Representative Concentration Pathways (RCP2.6 and RCP8.5), using the SDSM downscaling model for the mid- and end-21st century. The future land use map for the year 2050 was obtained from the Land Use Modeler (LCM). Streamflow under projected land use change (LUC) and climate change (CC) scenarios was simulated using the Soil and Water Assessment Tool (SWAT). The climate change evaluation indicates that precipitation will increase (up to 24%) in winter but decrease (up to -37%) in spring, summer, and autumn (except December). Additionally, temperature will rise in all months of the year. The effects of climate change on the Nekarood Basin are expected to increase streamflow in winter and decrease it in spring (except April), summer, and autumn (except December). The streamflow simulation results under the influence of land use change show that peak flow values will increase, while base flow will decrease. The combined effects of LUC and CC are projected to intensify future streamflow responses, with decreases of -2.9%, -8.3%, -8.1%, and \u0026minus;\u0026thinsp;9.2% in mid-century/RCP2.6, mid-century/RCP8.5, end-century/RCP2.6, and end-century/RCP8.5, respectively. A specific finding of this study is that the annual variations in streamflow are strongly influenced by climate in the basin.\u003c/p\u003e","manuscriptTitle":"Assessing Independent and Combined Effects of Land Use and Climate Change on Basin Runoff: A Remote Sensing, Statistical and Hydrological Modeling Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 10:52:18","doi":"10.21203/rs.3.rs-7103894/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5b0c1d00-fd24-4b36-b033-aa8529e84d24","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T13:04:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-30 10:52:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7103894","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7103894","identity":"rs-7103894","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}