Predicting the Effect of Hydro-Climatic and Land-Use Dynamic Variables on Watershed Health Status

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To conduct this study, first, key characteristics were extracted based on human, climatic, and hydrological factors for all three indicators of pressure, state, and response. Then, the watershed health index was calculated for the current conditions. After that, watershed health was predicted based on dynamic hydro-climatic and land-use variables for the 10 and 20 years ahead. The health assessment and zoning of the Safa-Roud Watershed showed that the average value and standard deviation of the current pressure index were equal to 0.573 and 0.185, respectively. The lowest value of this index was around 0.290 and related to sub-watershed 5, and the highest value was around 0.840 and related to sub-watershed 11. The initial evaluation of the classification indicated the prevalence of moderate and high-pressure conditions with a range of about 79%. Finally, the physical factors of sub-watersheds (time of concentration with 15.72%) had the most minor role. In general, among the criteria used to calculate the pressure index in the current period, human factors and climatic factors showed the highest percentage of participation in determining the pressure index. The quantification of the current watershed health status and the 10- and 20-year forecast periods showed that the values of the watershed health index were similar. However, the changes in the health index in the sub-watersheds at the beginning of the study period ranged from relatively unhealthy favorable conditions to moderately positive and moderately negative conditions. Integrated Watershed Management (IWM) Pressure-Status-Response (PSR) approach Watershed Adaptive Management (WAM) Watershed health Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Determining the degree of watershed health is one of the most effective components of Integrated Watershed Management (IWM) (Alilou et al. 2019 ; Gatgash and Sadeghi 2024 ). One of the most critical and essential parts of sustainable watershed management is the awareness of watershed health (Yu et al. 2013 ; Sadeghi et al. 2019b ; Salehpour Jam et al. 2021 ; Duan et al. 2022 ; Ebrahimi Gatgash and Sadeghi 2023 ). To evaluate watershed health, critical criteria and indicators such as human activities, climatic, hydrological, geological, soil, and vegetation factors representing the current health conditions have been introduced and developed. Applying these criteria alone cannot represent the health status of the studied ecosystems (Jabbar and Grote, 2020 ; S. H. Sadeghi et al., 2019, 2022 ). Based on this, a combination of criteria has been taken into consideration, but the way of conjunction and integrating these criteria and indicators is different depending on the study conditions and is considered one of the primary challenges in assessing the health of water and soil resources and ultimately ecosystems (Hook et al. 2014 ; Vollmer et al. 2018 ; Angerer et al. 2023 ; Hughes et al. 2023 ). Many of the characteristics affecting watershed health are static and constant over time or their changes, which reveal the relative value of these variables next to each other (Phaneuf et al. 2008 ; Murphy 2020 ; Zeraatpisheh et al. 2022 ). However, characteristics such as hydro-climatic variables and land-use are among the essential and influential criteria on watershed health, which are necessary to analyze or predict future changes and investigate their effect on watershed health (Ervinia et al. 2019 ; Singh et al. 2021 ). Also, the analysis of time series usually pursues the two goals of understanding or modeling the stochastic process that leads to observing the series and predicting the future values of the series based on its past (Chatfield 2013 ). In this regard, the investigation of various dynamic characteristics (hydro-climatic variables and land-use) about the effect of predicted values of future changes on the ecosystem's health can play a very influential role in IWM and optimal decision-making (Huang et al. 2017 ; Ngonzo Luwesi et al. 2017 ; Azam et al. 2021 ). The health assessment is based on possible hydro-climatic and land-use characteristics, which deal with temporal analysis of changes, and static characteristics is based on watershed characteristics, which deal with spatial analysis of health status (Deshmukh and Singh 2016 ; Ervinia et al. 2019 ; Venkatesh et al. 2020 ; Cui et al. 2021 ). The characteristics related to the physical criteria are generally constant. However, hydro-climatic characteristics and land-use change are among the possible characteristics that can have an influential role on watershed health in different periods (Murphy 2020 ; Singh et al. 2021 ; Mojtahedi et al. 2022 ). Therefore, the analysis of these characteristics and their effect on the watershed health status, the prediction of possible future changes, and its comparison with the current health status are among the expected goals of the researchers, after which appropriate management should be applied (Suehring 2017 ; Jabbar and Grote 2020 ; Lee et al. 2021 ; Tankpa et al. 2021 ). Researchers have conducted several studies related to watershed health assessment using different conceptual models, including the studies of (Dai et al. 2007 ; Hazbavi, Baartman, et al. 2018; Hazbavi, Keesstra et al. 2018; Hazbavi, Sadeghi, et al. 2018; Hazbavi and Sadeghi 2017 ; Liang et al. 2010 ; Yu et al. 2013 ). Regarding the ecosystem's health using different physical, chemical, biological, and hydrological indicators, various studies have been conducted can be referred to the studies of (Cook et al. 2015 ; Sanchez et al. 2015 ; Hoque et al. 2016 ; Mallya et al. 2018 ; Alilou et al. 2019 ). Regarding the application of hydrological and ecological indicators to assess the river and watershed health, we can refer to the studies of (Liu et al. 2006 ; Taylor et al. 2013 ; Woznicki et al. 2015 ; Gonzales-Inca et al. 2016 ; Rahman et al. 2017 ; Vollmer et al. 2018 ) Also (Zhou et al. 2013 ; Yang et al. 2015 ; Ahn and Kim 2017a ; Liu and Hao 2017 ; Wang et al. 2019a ) evaluated watershed and ecosystem health using the Pressure-State-Response (PSR) model. In research in China (Sun et al. 2019 ), ecosystem health for wetlands (Mallya et al. 2018 ), evaluation of multi-purpose watershed health measures from water quality measures (Rani et al. 2020 ), review of ecosystem health and dynamics (Ahn and Kim 2019 ), watershed health, vulnerability and restoration potential, (Zhang et al. 2020 ), water cycle health status assessment, (Liu et al. 2022 ) in Tianjin, China, comprehensive study of ecological security patterns, (Duan et al. 2022 ), studied the health status in Chaohu Lake watershed using integrated multi-statistical analysis and Driving Forces-Pressures-State-Impacts-Responses (DPSIR) framework. Summarizing the research showed that various studies have been conducted on the issue of conceptual models of the health assessment at the watershed scale and the study of the effect of different characteristics on the health assessment, which was mainly limited to examining the current health status in a watershed. This is even though the investigation of the watershed health status in the future based on the dynamic hydro-climatic change and land-use characteristics that play an essential role in the watershed health status has yet to be reported so far. Also, although many studies have been conducted in predicting hydro-climatic variables using time series and predicting future land-use using the Markov chain method, investigating the effect of these variables on health status is one of the research gaps. In addition to determining the current health status, the effect of dynamic variables on the health status and future predictions was obtained, and a management strategy was applied for the studied watershed. The poor condition of water and soil resources in Iran's watersheds and the lack of credit resources in the implementation of water and soil protection programs, the need for spatial prioritization in order to determine critical sub-watersheds based on the expected key characteristics in determining the health degree are among the most important goals. It means effective management in the matter of protecting water and soil resources. The Safa-Roud Watershed is one of the few with a wide range of land-uses and the influence of human societies on it. Due to its altitude and climate conditions, investigating the effect of hydro-climatic variables and land-use is more tangible. Therefore, the current study was carried out in the Safa-Roud Watershed as one of the research priorities of the General Directorate of Natural Resources and Watershed Management of Mazandaran province. Therefore, this study was conducted with the objectives of predicting the effect of changing hydro-climatic variables on the future health status of the Safa-Roud Watershed, predicting the effect of land-use change on the future health status of the watershed, and the role of hydro-climatic and land-use variables in the spatial prioritization of sub-watersheds based on watershed health index. 2. Materials and methods 2.1 Description of the study area The Safa-Roud Watershed, with an area of 13726 ha, is located in the west of Mazandaran province and the south of Ramsar city. This area is roughly circumscribed by a rectangle at 50°37' and 50°25' N and 36°54' and 36°48' E. The maximum height of the watershed is 3562 meters, and the minimum height at the outlet of the watershed is 33 meters above sea level. Also, the weighted average height is 1457 meters, and the weighted average slope is 50.04%. The average annual rainfall is estimated at 810 mm. The entire watershed under study has been divided into 11 sub-watersheds based on the level, location of hydrometric stations, topography, drainage network, and research objectives of the study area in order to maintain a relative balance in the extent and distribution of sub-watersheds (Salehi et al. 2019 ). The location of the Safa-Roud Watershed and the distribution and location of its sub-watersheds are presented in Fig. 1 . Also, in Table 1 , the specifications of meteorological and hydrometric stations of the region were mentioned. In Table 2 , some physiographic and topographic characteristics are presented separately for each sub-watershed. Table 1 Characteristics of rain and river gauge stations, the Safa-Roud Watershed, Iran Station name Station type Elevation (m) UTM Coordinate system X Y Ramsar Airport Synoptic -20 471848 4084336 Ramsar-Safa-Roud Rain gauge 100 467189 4085464 Ab Madani Nidasht Rain gauge 190 461926 4082597 Javaherdeh Rain gauge 1940 453234 4079054 Gavrmak Rain gauge 300 465997 4084904 Zarodak Rain gauge 1340 464363 4083854 Ramsar-Safa-Roud Hydrometric station 100 466928 4085363 Gavrmak Hydrometric station 300 466093 4084763 Javaherdeh Hydrometric station 1300 455922 4078436 Mazuben Hydrometric station 1245 456307 4079590 Zarodak-Modkoh Hydrometric station 1340 455286 4078596 Table 2 Some physiographic and topographic characteristics, the Safa-Roud Watershed, Mazandaran province, Iran Sub-watershed A (ha) Sl (%) Tc (hr) T (C) P (mm) D (m 3 .s − 1 ) Sub-1 1308 41.8 0.65 13.9 921 0.32 Sub-2 488 42.7 0.42 11.5 812 0.08 Sub-3 1979 51.2 0.51 10.3 783 0.32 Sub-4 1635 49.2 0.38 9.0 755 0.17 Sub-5 799 44.8 0.42 8.4 744 0.10 Sub-6 1247 54.1 0.50 8.3 742 0.16 Sub-7 730 55.0 0.35 10.2 780 0.07 Sub-8 387 55.0 0.25 11.3 807 0.06 Sub-9 223 55.0 0.20 11.7 821 0.03 Sub-10 1266 53.2 0.47 11.4 812 0.20 Sub-11 3667 48.4 0.79 13.9 924 0.60 Total 13726 50.04 1.48 11 810 2.265 * A (Area); Sl (Weighted average slope); Tc (Time of concentration); T (Average annual temperature); P (Average annual precipitation); D (Average discharge) Figure 1 . Table 1 . Table 2 . 2.2 Data sources and analytics In order to carry out this study, a digital topographic map with a scale of 1:20,000 of the study area, along with other digital layers such as the drainage network, the network of communication roads, etc., was obtained from the Iran National Cartographic Center, and the topography and physiographic characteristics of the study area were derived within the ArcGIS 10.4 software. Also, a digital geological map with a scale of 1:100,000 was obtained from the Iran Geological Organization, and the extent of geological formations was processed. Geological, soil, land, and hydrological characteristics of the study area were extracted from the information archive of the General Directorate of Natural Resources and Watershed Management of Mazandaran province. The hydro-climatic data of meteorological and hydrometric stations were also obtained from the Iran Water Resources Management Company. The land-use map was prepared in two stages between 1994 and 2021 using the archive of land-use maps of the Iran Natural Resources and Watershed Management Organization. 2.3 Research methodology 2.3.1 Quantification of static criteria in watershed health assessment To conduct this study, first, the studied area was divided into working units under the name of sub-watershed based on the expected goals (Ebrahimi Gatgash and Sadeghi 2023 ). Statistics, data, and layers of physiography and topography, meteorology and climate, geology, soil science, hydrology, and land-use/cover were then extracted for the entire study area and each land unit. In the next step, key characteristics were extracted based on human, climatic, and hydrological factors for all three indicators of pressure, state, and response. In this regard, some static criteria such as area, average slope, time of concentration, total length of streamflow, drainage density, and adjusted slope of streamflow were extracted (Ahn and Kim 2017b ; Dongare et al. 2022 ). Also, during the field visits and the reports in the information archive of the General Directorate of Natural Resources and Watershed Management of Mazandaran province, the criteria of disturbance in the drainage network, places of accumulation and burial of garbage, the number of fish breeding ponds, susceptible lands based on mass movement, road density, extent of encroached land, sediment yield and retention and population characteristics were also extracted for the entire area and each of the sub-watersheds. From the Google Earth Engine system, the ratio of roads road to total roads, the average Mann-Kendall statistic for monthly soil moisture changes, the average Mann-Kendall statistic for monthly and annual vegetation cover changes, Shannon diversity index, forestry indices, nature indices orientation was extracted (Landier et al. 2016 ; Roy 2019 ; Senanayake 2023 ). 2.3.2 quantification of dynamic criteria in watershed health assessment Some criteria, such as hydro-climatic varia land-use changes and the appearance of vegetation, were considered as dynamic variables to predict the watershed health status in the next 10 and 20 years (Ervinia et al. 2019 ; Foroumandi et al. 2022 ; Mojtahedi et al. 2022 ). To extract dynamic hydro-climatic characteristics, monthly data of hydro-climatic variables such as precipitation, temperature, evapotranspiration, and average discharge of meteorological and hydrometric station was collected (Lyu et al. 2015 ; Eshetu 2020 ). Then, time series modeling was done for 10 and 20 years ahead. For this purpose, first, the monthly data of the mentioned variables during the statistical period were arranged in the order of their occurrence. The trend of the data was then examined using nonparametric Mann-Kendall test (Kendall, 1975). Finally, modeling the time series of the mentioned variables based on periodic, random, seasonal changes and jumps, which include the calculation of autocorrelation in different delays (Ma et al. 2018 ), the calculation of partial autocorrelation in different delays (PACF) and the implementation of autoregressive and moving average (ARMA), autoregressive integrated moving average (ARIMA) models, and finally with the intervention of periodicity, seasonal autoregressive integrated moving average (SARIMA) with 120 months (10 years) and 240 months ahead (Adekola 2019; Dastorani et al. 2020 ; Kahraman 2022 ). 2.3.3 Land-use forecasting process for future periods At first, using the final maps produced in the transfer potential stage and checking the number of changes and developments in each of the land-uses and using the Markov chain, the land-use map of 2021 to check the validation of the model was selected (Iacono et al. 2015 ; Rimal et al. 2018 ; Rahnama 2021 ). To measure the accuracy of the land-use map predicted using the Markov chain for 2021, first, the land-use map was prepared by object-oriented classification, and then the predicted map was analyzed by visual methods and error matrix (Gharaibeh et al. 2020 ; Tariq and Mumtaz 2023 ). To form the error matrix in the land-use map prepared from the classification of the satellite image, it was considered as the actual image, and the overall coefficient Kappa for the predicted land-use map was obtained using the Validate function (Sankarrao et al. 2021 ). The Kappa coefficient is a valuable coefficient to reveal the accuracy of the produced map without considering a unique and utterly random method in monitoring classification. 2.3.4 Application of PSR conceptual model and quantification of watershed health index After extracting the desired criteria, the state of the health index was determined using the PSR model for the present using static and dynamic criteria and in the future, about 10- and 20-year forecasts of dynamic hydro-climatic variables and land-use change (Singh and Sinha 2021 ; Chamani et al. 2022 ). In this regard, static and stable criteria were used in the current conditions in the model, and then, according to the purpose of the study, hydro-climatic and land-use criteria with different 10- and 20-year predicted scenarios were also used in the implementation model and the results obtained in the health assessment. After collecting the information and calculating the selected criteria for evaluating the watershed health, due to the difference in the data and the difference in the units of the criteria, standardization was done. Standardization of criteria was calculated in two categories. To standardize the criteria with positive and negative meanings and effects on watershed health, Eqs. ( 1 ) and ( 2 ) were used respectively (Hazbavi and Sadeghi 2017 ; Liu and Hao 2017 ; Hazbavi et al. 2018b ; Sadeghi et al. 2019a ) $${\text{X}}_{\text{s}}=\frac{{\text{X}}_{\text{i}}-{\text{X}}_{\text{m}\text{i}\text{n}}}{{\text{X}}_{\text{m}\text{a}\text{x}}-{\text{X}}_{\text{m}\text{i}\text{n}}}$$ 1 $${\text{X}}_{\text{s}}=\frac{{\text{X}}_{\text{m}\text{a}\text{x}}-{\text{X}}_{\text{i}}}{{\text{X}}_{\text{m}\text{a}\text{x}}-{\text{X}}_{\text{m}\text{i}\text{n}}}$$ 2 where X s , X i , X min , and X max express the standardized, actual, minimum, and maximum values of the desired criterion, respectively. Then, pressure, state, and response indicators were also calculated based on the arithmetic mean of the standardized values. Finally, to determine the final health status of the studied watershed, the geometric mean of the pressure, state, and response indicators for each of the sub-watersheds were used according to Eq. ( 3 ) (Hazbavi et al. 2018b ; Mallya et al. 2018 ; Sadeghi et al. 2019a ; Gatgash and Sadeghi 2024 ) $$Geometric average={\left[\prod _{\text{n}=1}^{\text{k}}{\text{X}}_{\text{n}}\right]}^{\frac{1}{\text{k}}}$$ 3 where \(\prod _{\text{n}=1}^{\text{k}}{\text{X}}_{\text{n}}\) and k are equal to the product of indices and the number of indices, respectively. In the following, according to the regression analysis, the effect of each of the criteria in the calculation of pressure, state, and response indicators, as well as the final state of watershed health, was evaluated (Chamani et al., 2022 ; Chamani, Vafakhah, et al. 2023; Hazbavi and Sadeghi 2017 ; S. H. R. Sadeghi et al. 2019). Thus, each sub-watershed was proportional to the value of PSR conceptual model indicators as well as watershed health status in one of the five categories: healthy (0.81-1.00), relatively healthy (0.61–0.80), moderate (0.41–0.60), relatively unhealthy (0.21–0.40) and unhealthy (0.00-0.20) (Hazbavi et al. 2018b ; Ervinia et al. 2019 ). Also, to check the health classification of the study watershed and to set the stage for a more comprehensive management of existing sub-watersheds, the main classes were divided into two subclasses with positive and negative trends as necessary (Sadeghi et al., 2019b ). Finally, the health zoning map was prepared using ArcGIS 10.4 software. 3. Results The results related to the selection and compilation of factors, criteria, and primary indicators for watershed health assessment in the study area are presented in Table 3 . Maps related to land-use for 1994 and 2021 are presented in Fig. 2 . Also, the maps related to the prediction of land-use based on the Markov chain for the next 10- and 20 years were presented in Fig. 3 . Table 3 Selected primary factors, criteria, and indicators in the watershed health assessment, the Safa-Roud Watershed, Mazandaran province, Iran Indicator Factor Criterion Source or method of calculation Pressure Hydrology Annual average discharge (m 3 .s − 1 ) Statistical analysis of hydrometric stations Average slope (%) Slope map Time of concentration (hr) Digital data analysis Human Relative size of residential land (ha) Land-use map 2020 Climatic Average annual precipitation (mm) Statistics of rain gauge stations Average annual temperature (C) Average annual Evapotranspiration (mm) Standardized Precipitation Index (SPI) State Hydrology Average annual runoff coefficient Statistics of River Gauge Stations Runoff depth The ratio of lands participating in the production of runoff and flood to the total area The ratio of participating lands in each sub-watershed to the total area of ​​the watershed Discharge characteristic maximum (m 3 .s − 1 ) Statistics of hydrometric stations Discharge characteristic minimum (m 3 .s − 1 ) Statistics of hydrometric stations Human Naturalism evaluation index Land-use map 2020 The ratio of the area of ​​residential land to the area of ​​the watershed Climatic The ratio of average annual precipitation to average annual evaporation and transpiration Statistical analysis of river gauge stations Response Human Changing the face of the land Land-use maps, land surface measurements, and field observations Table 3 . Figure 2 . Figure 3 . Also, the classification of sub-watersheds related to the Safa-Roud Watershed, Mazandaran, based on the watershed health index was included in Table 4 . The classification results of the study sub-watersheds based on the watershed health index for the next 10- and 20-year were presented in Tables 5 and 6 , respectively. Table 4 Watershed health index status for current conditions, the Safa-Roud Watershed, Iran Sub-watershed P S R Health Health Classification Sub-classification Health Sub-1 0.78 0.31 0.45 0.48 Medium Medium Negative Sub-2 0.35 0.50 0.48 0.44 Medium Medium Negative Sub-3 0.59 0.44 0.70 0.56 Medium Medium Positive Sub-4 0.36 0.38 0.55 0.42 Medium Medium Negative Sub-5 0.29 0.36 0.77 0.43 Medium Medium Negative Sub-6 0.45 0.20 0.57 0.37 Relatively unhealthy Relatively unhealthy positive Sub-7 0.58 0.43 0.66 0.55 Medium Medium Positive Sub-8 0.69 0.59 0.51 0.59 Medium Medium Positive Sub-9 0.69 0.35 0.43 0.47 Medium Medium Negative Sub-10 0.69 0.35 0.37 0.45 Medium Medium Negative Sub-11 0.84 0.46 0.32 0.50 Medium Medium Negative * P (Pressure); S (State); R (Response) Table 5 Watershed health index status for 10-year prediction, the Safa-Roud Watershed, Iran Sub-watershed P S R Health Health Classification Sub-classification Health Sub-1 0.76 0.31 0.47 0.48 Medium Medium Negative Sub-2 0.35 0.48 0.48 0.43 Medium Medium Negative Sub-3 0.58 0.43 0.70 0.56 Medium Medium Positive Sub-4 0.36 0.37 0.53 0.413 Medium Medium Negative Sub-5 0.29 0.35 0.75 0.43 Medium Medium Negative Sub-6 0.45 0.20 0.55 0.37 Relatively unhealthy Relatively unhealthy positive Sub-7 0.58 0.42 0.70 0.56 Medium Medium Positive Sub-8 0.68 0.58 0.50 0.59 Medium Medium Positive Sub-9 0.68 0.35 0.43 0.47 Medium Medium Negative Sub-10 0.68 0.35 0.36 0.44 Medium Medium Negative Sub-11 0.83 0.46 0.35 0.51 Medium Medium Negative *P (Pressure); S (State); R (Response) Table 6 Watershed health index status for 20-year prediction, the Safa-Roud Watershed, Iran Sub-watershed P S R Health Health Classification Sub-classification Health Sub-1 0.75 0.30 0.46 0.47 Medium Medium Negative Sub-2 0.34 0.47 0.48 0.42 Medium Medium Negative Sub-3 0.58 0.42 0.71 0.56 Medium Medium Positive Sub-4 0.36 0.36 0.55 0.413 Medium Medium Negative Sub-5 0.29 0.35 0.75 0.42 Medium Medium Negative Sub-6 0.45 0.20 0.54 0.36 Relatively unhealthy Relatively unhealthy positive Sub-7 0.57 0.42 0.70 0.55 Medium Medium Positive Sub-8 0.68 0.58 0.51 0.58 Medium Medium Positive Sub-9 0.67 0.34 0.43 0.46 Medium Medium Negative Sub-10 0.68 0.34 0.36 0.44 Medium Medium Negative Sub-11 0.81 0.46 0.35 0.51 Medium Medium Negative * P (Pressure); S (State); R (Response) Table 4 . Table 5 . Table 6 . Zoning maps of pressure, state, and response indicators based on watershed health index in each sub-watershed for all three studied periods were included in Figs. 4 – 6 , respectively. The final maps related to the classification of study watershed health in each sub-watershed for 2021, 2023, and 2042 were also included in Fig. 7 . Also, the results related to the occupancy rate of each indicator related to pressure, state, and response variables in the current conditions, 10- and 20-year forecast, were presented in Table 7 . Table 7 Occupied percentage of PSR and watershed health indices in current conditions and 10- and 20-year forecast PSR Pressure State Response Health index Class Occupied percentage Class Occupied percentage Class Occupied percentage Class Occupied percentage 2021 High 0.0 Unfavorable 0.0 High 0.0 Unhealthy 0.0 Relatively high 21.3 Favorable low 47.2 Relatively high 35.9 Relatively unhealthy 9.1 Medium 28.8 Favorable Medium 52.8 Medium 38.5 Medium 90.9 Relatively low 23.2 Relatively favorable 0.0 Relatively low 25.6 Relatively healthy 0.0 Low 26.7 Favorable 0.0 Low 0.0 Healthy 0.0 2032 High 0.0 Unfavorable 9.1 High 0.0 Unhealthy 0.0 Relatively high 21.3 Favorable low 38.1 Relatively high 35.9 Relatively unhealthy 9.1 Medium 28.8 Favorable Medium 52.8 Medium 38.5 Medium 90.9 Relatively low 23.2 Relatively favorable 0.0 Relatively low 25.6 Relatively healthy 0.0 Low 26.7 Favorable 0.0 Low 0.0 Healthy 0.0 2042 High 0.0 Unfavorable 9.1 High 0.0 Unhealthy 0.0 Relatively high 21.3 Favorable low 38.1 Relatively high 35.9 Relatively unhealthy 9.1 Medium 28.8 Favorable Medium 52.8 Medium 38.5 Medium 90.9 Relatively low 23.2 Relatively favorable 0.0 Relatively low 25.6 Relatively healthy 0.0 Low 26.7 Favorable 0.0 Low 0.0 Healthy 0.0 Figure 4 . Figure 5 . Figure 6 . Figure 7 . Table 7 . 4. Discussion The successful application of the PSR approach in the assessment of watershed health has also been confirmed in the research (Hazbavi and Sadeghi, 2017 ; Q. Wang et al., 2019). The health assessment and zoning of the Safa-Roud Watershed, based on various tables and figures, showed that the average value and standard deviation of the current pressure index were equal to 0.573 and 0.185, respectively. The lowest value of this index was around 0.290 and related to sub-watershed 5, and the highest value was around 0.840 and related to sub-watershed 11. During this period, the pressure index was classed in high, relatively high, moderate, relatively low, and low categories of 26.70, 23.20, 28.90, 21.30, and 0.00 percent of the entire watershed, respectively. The initial evaluation of the classification indicated the prevalence of moderate and high-pressure conditions with a range of about 79% (Chamani, Sadeghi, et al., 2023; Ebrahimi Gatgash and Sadeghi, 2023 ). In general, among the criteria used to calculate the pressure index in the current period, human factors and climatic factors showed the highest percentage of participation in determining the pressure index (Hazbavi et al., 2020 ). Finally, the physical factors of sub-watersheds (time of concentration with 15.72%) had the most minor role. From the obtained results, the central pressures on the studied ecosystems were caused by human factors, with a participation percentage of about 50%. The average value and standard deviation of the pressure index 10 and 20 years after the effect of dynamic hydro-climatic variables and land-use with a decreasing trend equal to 0.568 and 0.181, respectively, for forecasting 10- and 20-year were 0.562 and 0.177. The lowest value of this index in the 10-year forecast was 0.290 and corresponds to sub-watershed 5, and the highest value was 0.83 and corresponds to sub-watershed 11. The extent of the watershed based on the pressure index during the 10-year forecast period was classified as high, relatively high, moderate, relatively low, and low, respectively, 26.68, 23.15, 28.9, 21.3, and 0.00 percent of the entire watershed. This index was also placed in high, relatively high, moderate, relatively low, and low categories during the 20-year forecast period, 26.7, 23.2, 28.9, 21.3, and 0.00 percent of the total watershed, respectively. The preliminary evaluation of the classification carried out for the 10- and 20-year forecast period indicated the predominance of moderate and high-pressure conditions with a range of about 79%. In general, among the criteria used to calculate the pressure index in the period of 10 and 20 years ahead, human factors played the most important role, followed by climatic factors and physical factors of sub-watersheds (Kambombe 2018 ; Sadeghi et al. 2019a ). From the obtained results, it can be concluded that the significant pressures on the studied ecosystems were mainly caused by human factors, with a participation percentage of more than 50%, and it indicated an increase in pressure on the health of the ecosystems compared to the current conditions. Complementary investigations of health assessment and zoning of the Safa-Roud Watershed regarding the response index analysis showed that the average value and standard deviation were equal to 0.527 and 0.139, respectively. The lowest value of this index was about 0.32 and related to sub-watershed 11, and the highest value was about 0.77 and related to sub-watershed 5. The initial evaluation of the performed classification indicated the predominance of relatively high and moderate response conditions with a range of more than 64% (Hazbavi and Sadeghi 2017 ; Hazbavi et al. 2018b ; Chamani et al. 2022 ). From the obtained results, the main variables affecting the response of the study watershed in the mentioned period were related to the comparative and relative role of the natural factors governing the region and, of course, in interaction with human activities. The lowest value of this index in both forecast periods was about 0.35 and related to sub-watershed 11, and the highest value was about 0.75 and related to sub-watershed 5. The scope of the watershed based on the response index during two forecast periods of 10- and 20-year, like the current period, covers 25.5, 38.6, and 35.9 percent of the entire watershed in relatively high, moderate, and relatively low categories, respectively (Park et al. 2011 ). In the current period, sub-watersheds 6 and 8, with health indices of 0.37 and 0.59, were selected as the unhealthiest and the healthiest sub-watersheds in the study area. However, in the 10-year forecast period, the same sub-watersheds with the determined health index values could be predicted without change. For the 20-year forecast period, sub-watersheds 6 and 8 were selected as the unhealthiest and healthy sub-watersheds with a slight decrease of 0.36 and 0.58, respectively (Ahn and Kim 2017b ; Duan et al. 2022 ; Ebrahimi Gatgash and Sadeghi 2023 ). Based on this and considering the weight share of each of the sub-watersheds, the health index of the whole watershed and the conditions governing them for the present and the two forecast periods were 0.478, 0.479, and 0.476, respectively. It is interesting to note that the lowest and highest coefficient of variation in the current period was related to the current condition and pressure index with values of 25.868 and 32.241%, respectively, and the average condition was related to the response index with the value of 26.425%. The comparative results of the indicators affecting the health of the study watershed in the current period and the 10 and 20-year forecast showed that in the first period, the pressure, state, and response indicators with weighted average values of 0.622, 0.497, and 0.395, respectively (Verdonschot et al. 2013 ; Neri et al. 2016 ). Also, for the 10- and 20-year forecast periods, the pressure, state, and response indicators with weighted average values of 0.615, 0.390, and 0.505 for the 10-year forecast, and the mentioned indicators with the values of 0.607 0, 0.385, and 0.506 in the 20-year forecast period were influential in determining the health status of the study watershed. The quantification of the current watershed health status and the 10- and 20-year forecast periods showed that the values of the watershed health index were similar. However, the changes in the health index in the sub-watersheds at the beginning of the study period ranged from relatively unhealthy favorable conditions to moderately positive and moderately negative conditions (Hazbavi et al. 2018b ; Singh 2022 ; Gatgash and Sadeghi 2024 ). However, at the end of the 10- and 20-year forecast periods, while the numerical values of the health degree have decreased, there were no significant changes in the scope of the classification above, and only in sub-watershed 11, with a slight increase in the degree of health, there was a change from the current average negative to average favorable conditions for the 10- and 20-year forecast conditions. 5. Conclusion In the watershed health assessment, in addition to the physical characteristics and current conditions governing the watershed, the investigation of various dynamic hydro-climatic characteristics and land-use can play a very influential role in optimal decision-making. Therefore, this study was conducted with the objectives of predicting the effect of changing hydro-climatic variables on the future health status of the Safa-Roud Watershed, predicting the effect of land-use change on the future health status of the watershed, and the role of hydro-climatic and land-use variables in the spatial prioritization of sub-watersheds based on watershed health index. Also, the value of the watershed health index at present and its forecast during two periods of 10- and 20-year were successfully determined. The obtained results, despite the concrete changes of hydro-climatic variables in forecast periods and insignificant changes in land-use, indicated a slight decrease in health status in most sub-watersheds. In this study, changing the role and importance of variables affecting each of the study indicators and, finally, watershed health from primarily natural and inherent factors to human-oriented characteristics resulting from human interferences were among the prominent evaluation profiles. Investigations showed that most of the pressures on the studied ecosystems were caused by human factors. As a result, most of the variables affecting the response of the watershed in two forecast periods, such as the current period, were entirely of the type of human intervention activities. The obtained results emphasized not only the relative difference in the value of the desired indicators but also the difference in the type and amount of effect of different factors on the whole watershed and the difference in the condition of the studied sub-watersheds against the pressures. So, the natural factors have shown their functional difference in determining the state, pressure, response indicators, and, finally, the health status of sub-watersheds. The role of human variables in the current period and both forecast periods has often shown a significant effect on the determination of pressure, response, and state indicators and, finally, the health of different sectors. This issue indicates the high risk of sub-watersheds with an unfavorable condition and their placement in an unhealthy condition in the not-so-distant future. For future studies, in addition to using comprehensive and complete data, optimal multi-criteria decision-making methods and deep learning algorithms should be used to quantify the watershed health index. Declarations Author Contributions Mohammad Rasoul Rajabi acquired the data, performed the analysis and wrote the manuscript and discussion;, Mehdi Vafakhah and Seyed Hamidreza Sadeghi provided technical sights, as well as edited, restructured, and professionally optimized the manuscript. All authors discussed the results and edited the manuscript. All authors have read and agreed to the published version of the manuscript. Funding This work is based upon research funded by Iran National Science Foundation (INSF) under project No.4006075. Availability of Data and Materials We have no permission to release data and codes. Ethical Approval We confirm that this manuscript has not been published elsewhere and is not under consideration by another journal. 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J Environ Manage 128:642–654 Cite Share Download PDF Status: Published Journal Publication published 27 Jun, 2024 Read the published version in Environmental Science and Pollution Research → Version 1 posted Reviewers agreed at journal 18 Jan, 2024 Reviewers invited by journal 18 Jan, 2024 Editor assigned by journal 05 Dec, 2023 First submitted to journal 28 Nov, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3636356","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268141567,"identity":"d9c9cfba-bb56-47e1-9ed8-4ea308226b1e","order_by":0,"name":"Mohammadrasoul 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1","display":"","copyAsset":false,"role":"figure","size":271892,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical location of the studied area, the Safa-Roud Watershed, Mazandaran province, Iran\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3636356/v1/211f6f7a24fb88fe315f2ecf.jpeg"},{"id":50024125,"identity":"526ed719-868e-41d4-b1ad-c69df77f1847","added_by":"auto","created_at":"2024-01-23 09:34:57","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":440881,"visible":true,"origin":"","legend":"\u003cp\u003eLand-use map for 1994 and 2022, the Safa-Roud Watershed, Iran\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3636356/v1/38a82d2fd09425864b90ee37.jpeg"},{"id":50024126,"identity":"e89c69fe-aebc-4510-9bcc-98f348c6dcb7","added_by":"auto","created_at":"2024-01-23 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ahead.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3636356/v1/23298188976419a9f98af604.jpeg"},{"id":50023754,"identity":"91c36f34-8d90-4e72-b437-f28c05b5243e","added_by":"auto","created_at":"2024-01-23 09:26:57","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":404524,"visible":true,"origin":"","legend":"\u003cp\u003eZoning map of state index in each sub-watershed based on watershed health index for current conditions and 10 and 20 years ahead.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3636356/v1/188502b9b1187b104cdb325c.jpeg"},{"id":50023755,"identity":"c27b7e16-0b8f-44cc-8bcb-d634366e9e99","added_by":"auto","created_at":"2024-01-23 09:26:58","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":415309,"visible":true,"origin":"","legend":"\u003cp\u003eZoning map of response index in each sub-watershed based on watershed health index for current conditions and 10 and 20 years ahead.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3636356/v1/656c16ec276cdd620cc37887.jpeg"},{"id":50023750,"identity":"ba7cec8e-91f7-4be8-ae75-ef2086230dcb","added_by":"auto","created_at":"2024-01-23 09:26:57","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":421350,"visible":true,"origin":"","legend":"\u003cp\u003eZoning map of watershed health index in each sub-watershed for current conditions and 10 and 20 years ahead\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3636356/v1/98da42ee63ce7b24aae903ff.jpeg"},{"id":60380112,"identity":"d709e42e-02e0-4a9b-80f5-ee216c75fa15","added_by":"auto","created_at":"2024-07-16 07:07:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3719971,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3636356/v1/2832c7a9-bffe-4e9e-bf8c-ebd1c4dda46a.pdf"}],"financialInterests":"","formattedTitle":"Predicting the Effect of Hydro-Climatic and Land-Use Dynamic Variables on Watershed Health Status","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDetermining the degree of watershed health is one of the most effective components of Integrated Watershed Management (IWM) (Alilou et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gatgash and Sadeghi \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). One of the most critical and essential parts of sustainable watershed management is the awareness of watershed health (Yu et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sadeghi et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e; Salehpour Jam et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Duan et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ebrahimi Gatgash and Sadeghi \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To evaluate watershed health, critical criteria and indicators such as human activities, climatic, hydrological, geological, soil, and vegetation factors representing the current health conditions have been introduced and developed. Applying these criteria alone cannot represent the health status of the studied ecosystems (Jabbar and Grote, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; S. H. Sadeghi et al., 2019, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Based on this, a combination of criteria has been taken into consideration, but the way of conjunction and integrating these criteria and indicators is different depending on the study conditions and is considered one of the primary challenges in assessing the health of water and soil resources and ultimately ecosystems (Hook et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Vollmer et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Angerer et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hughes et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany of the characteristics affecting watershed health are static and constant over time or their changes, which reveal the relative value of these variables next to each other (Phaneuf et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Murphy \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zeraatpisheh et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, characteristics such as hydro-climatic variables and land-use are among the essential and influential criteria on watershed health, which are necessary to analyze or predict future changes and investigate their effect on watershed health (Ervinia et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Singh et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Also, the analysis of time series usually pursues the two goals of understanding or modeling the stochastic process that leads to observing the series and predicting the future values of the series based on its past (Chatfield \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In this regard, the investigation of various dynamic characteristics (hydro-climatic variables and land-use) about the effect of predicted values of future changes on the ecosystem's health can play a very influential role in IWM and optimal decision-making (Huang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ngonzo Luwesi et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Azam et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe health assessment is based on possible hydro-climatic and land-use characteristics, which deal with temporal analysis of changes, and static characteristics is based on watershed characteristics, which deal with spatial analysis of health status (Deshmukh and Singh \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ervinia et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Venkatesh et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cui et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The characteristics related to the physical criteria are generally constant. However, hydro-climatic characteristics and land-use change are among the possible characteristics that can have an influential role on watershed health in different periods (Murphy \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Singh et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mojtahedi et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, the analysis of these characteristics and their effect on the watershed health status, the prediction of possible future changes, and its comparison with the current health status are among the expected goals of the researchers, after which appropriate management should be applied (Suehring \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jabbar and Grote \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lee et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tankpa et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearchers have conducted several studies related to watershed health assessment using different conceptual models, including the studies of (Dai et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Hazbavi, Baartman, et al. 2018; Hazbavi, Keesstra et al. 2018; Hazbavi, Sadeghi, et al. 2018; Hazbavi and Sadeghi \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Regarding the ecosystem's health using different physical, chemical, biological, and hydrological indicators, various studies have been conducted can be referred to the studies of (Cook et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sanchez et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hoque et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mallya et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Alilou et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding the application of hydrological and ecological indicators to assess the river and watershed health, we can refer to the studies of (Liu et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Taylor et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Woznicki et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gonzales-Inca et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rahman et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vollmer et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eAlso (Zhou et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ahn and Kim \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e; Liu and Hao \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e) evaluated watershed and ecosystem health using the Pressure-State-Response (PSR) model. In research in China (Sun et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), ecosystem health for wetlands (Mallya et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), evaluation of multi-purpose watershed health measures from water quality measures (Rani et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), review of ecosystem health and dynamics (Ahn and Kim \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), watershed health, vulnerability and restoration potential, (Zhang et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), water cycle health status assessment, (Liu et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in Tianjin, China, comprehensive study of ecological security patterns, (Duan et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), studied the health status in Chaohu Lake watershed using integrated multi-statistical analysis and Driving Forces-Pressures-State-Impacts-Responses (DPSIR) framework.\u003c/p\u003e \u003cp\u003eSummarizing the research showed that various studies have been conducted on the issue of conceptual models of the health assessment at the watershed scale and the study of the effect of different characteristics on the health assessment, which was mainly limited to examining the current health status in a watershed. This is even though the investigation of the watershed health status in the future based on the dynamic hydro-climatic change and land-use characteristics that play an essential role in the watershed health status has yet to be reported so far. Also, although many studies have been conducted in predicting hydro-climatic variables using time series and predicting future land-use using the Markov chain method, investigating the effect of these variables on health status is one of the research gaps. In addition to determining the current health status, the effect of dynamic variables on the health status and future predictions was obtained, and a management strategy was applied for the studied watershed. The poor condition of water and soil resources in Iran's watersheds and the lack of credit resources in the implementation of water and soil protection programs, the need for spatial prioritization in order to determine critical sub-watersheds based on the expected key characteristics in determining the health degree are among the most important goals. It means effective management in the matter of protecting water and soil resources. The Safa-Roud Watershed is one of the few with a wide range of land-uses and the influence of human societies on it. Due to its altitude and climate conditions, investigating the effect of hydro-climatic variables and land-use is more tangible. Therefore, the current study was carried out in the Safa-Roud Watershed as one of the research priorities of the General Directorate of Natural Resources and Watershed Management of Mazandaran province. Therefore, this study was conducted with the objectives of predicting the effect of changing hydro-climatic variables on the future health status of the Safa-Roud Watershed, predicting the effect of land-use change on the future health status of the watershed, and the role of hydro-climatic and land-use variables in the spatial prioritization of sub-watersheds based on watershed health index.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Description of the study area\u003c/h2\u003e \u003cp\u003eThe Safa-Roud Watershed, with an area of 13726 ha, is located in the west of Mazandaran province and the south of Ramsar city. This area is roughly circumscribed by a rectangle at 50\u0026deg;37' and 50\u0026deg;25' N and 36\u0026deg;54' and 36\u0026deg;48' E. The maximum height of the watershed is 3562 meters, and the minimum height at the outlet of the watershed is 33 meters above sea level. Also, the weighted average height is 1457 meters, and the weighted average slope is 50.04%. The average annual rainfall is estimated at 810 mm. The entire watershed under study has been divided into 11 sub-watersheds based on the level, location of hydrometric stations, topography, drainage network, and research objectives of the study area in order to maintain a relative balance in the extent and distribution of sub-watersheds (Salehi et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe location of the Safa-Roud Watershed and the distribution and location of its sub-watersheds are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Also, in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the specifications of meteorological and hydrometric stations of the region were mentioned. In Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, some physiographic and topographic characteristics are presented separately for each sub-watershed.\u003c/p\u003e \u003cp\u003e \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\u003eCharacteristics of rain and river gauge stations, the Safa-Roud Watershed, Iran\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=\"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=\"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\u003eStation name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStation type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eElevation (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eUTM Coordinate system\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRamsar Airport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSynoptic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e471848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4084336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRamsar-Safa-Roud\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRain gauge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e467189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4085464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAb Madani Nidasht\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRain gauge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e461926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4082597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJavaherdeh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRain gauge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e453234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4079054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGavrmak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRain gauge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e465997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4084904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZarodak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRain gauge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e464363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4083854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRamsar-Safa-Roud\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydrometric station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e466928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4085363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGavrmak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydrometric station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e466093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4084763\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJavaherdeh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydrometric station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e455922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4078436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMazuben\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydrometric station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e456307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4079590\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZarodak-Modkoh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydrometric station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e455286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4078596\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\u003eSome physiographic and topographic characteristics, the Safa-Roud Watershed, Mazandaran province, Iran\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-watershed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSl (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTc (hr)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT (C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eD (m\u003csup\u003e3\u003c/sup\u003e.s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSub-1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSub-2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSub-3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSub-4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSub-5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSub-6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSub-7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSub-8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSub-9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSub-10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSub-11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e* A (Area); Sl (Weighted average slope); Tc (Time of concentration); T (Average annual temperature); P (Average annual precipitation); D (Average discharge)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data sources and analytics\u003c/h2\u003e \u003cp\u003eIn order to carry out this study, a digital topographic map with a scale of 1:20,000 of the study area, along with other digital layers such as the drainage network, the network of communication roads, etc., was obtained from the Iran National Cartographic Center, and the topography and physiographic characteristics of the study area were derived within the ArcGIS 10.4 software. Also, a digital geological map with a scale of 1:100,000 was obtained from the Iran Geological Organization, and the extent of geological formations was processed. Geological, soil, land, and hydrological characteristics of the study area were extracted from the information archive of the General Directorate of Natural Resources and Watershed Management of Mazandaran province. The hydro-climatic data of meteorological and hydrometric stations were also obtained from the Iran Water Resources Management Company. The land-use map was prepared in two stages between 1994 and 2021 using the archive of land-use maps of the Iran Natural Resources and Watershed Management Organization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Research methodology\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Quantification of static criteria in watershed health assessment\u003c/h2\u003e \u003cp\u003eTo conduct this study, first, the studied area was divided into working units under the name of sub-watershed based on the expected goals (Ebrahimi Gatgash and Sadeghi \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Statistics, data, and layers of physiography and topography, meteorology and climate, geology, soil science, hydrology, and land-use/cover were then extracted for the entire study area and each land unit. In the next step, key characteristics were extracted based on human, climatic, and hydrological factors for all three indicators of pressure, state, and response. In this regard, some static criteria such as area, average slope, time of concentration, total length of streamflow, drainage density, and adjusted slope of streamflow were extracted (Ahn and Kim \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e; Dongare et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlso, during the field visits and the reports in the information archive of the General Directorate of Natural Resources and Watershed Management of Mazandaran province, the criteria of disturbance in the drainage network, places of accumulation and burial of garbage, the number of fish breeding ponds, susceptible lands based on mass movement, road density, extent of encroached land, sediment yield and retention and population characteristics were also extracted for the entire area and each of the sub-watersheds. From the Google Earth Engine system, the ratio of roads road to total roads, the average Mann-Kendall statistic for monthly soil moisture changes, the average Mann-Kendall statistic for monthly and annual vegetation cover changes, Shannon diversity index, forestry indices, nature indices orientation was extracted (Landier et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Roy \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Senanayake \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 quantification of dynamic criteria in watershed health assessment\u003c/h2\u003e \u003cp\u003eSome criteria, such as hydro-climatic varia land-use changes and the appearance of vegetation, were considered as dynamic variables to predict the watershed health status in the next 10 and 20 years (Ervinia et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Foroumandi et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mojtahedi et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To extract dynamic hydro-climatic characteristics, monthly data of hydro-climatic variables such as precipitation, temperature, evapotranspiration, and average discharge of meteorological and hydrometric station was collected (Lyu et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Eshetu \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Then, time series modeling was done for 10 and 20 years ahead. For this purpose, first, the monthly data of the mentioned variables during the statistical period were arranged in the order of their occurrence. The trend of the data was then examined using nonparametric Mann-Kendall test (Kendall, 1975). Finally, modeling the time series of the mentioned variables based on periodic, random, seasonal changes and jumps, which include the calculation of autocorrelation in different delays (Ma et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the calculation of partial autocorrelation in different delays (PACF) and the implementation of autoregressive and moving average (ARMA), autoregressive integrated moving average (ARIMA) models, and finally with the intervention of periodicity, seasonal autoregressive integrated moving average (SARIMA) with 120 months (10 years) and 240 months ahead (Adekola 2019; Dastorani et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kahraman \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Land-use forecasting process for future periods\u003c/h2\u003e \u003cp\u003eAt first, using the final maps produced in the transfer potential stage and checking the number of changes and developments in each of the land-uses and using the Markov chain, the land-use map of 2021 to check the validation of the model was selected (Iacono et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rimal et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rahnama \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To measure the accuracy of the land-use map predicted using the Markov chain for 2021, first, the land-use map was prepared by object-oriented classification, and then the predicted map was analyzed by visual methods and error matrix (Gharaibeh et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tariq and Mumtaz \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To form the error matrix in the land-use map prepared from the classification of the satellite image, it was considered as the actual image, and the overall coefficient Kappa for the predicted land-use map was obtained using the Validate function (Sankarrao et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Kappa coefficient is a valuable coefficient to reveal the accuracy of the produced map without considering a unique and utterly random method in monitoring classification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Application of PSR conceptual model and quantification of watershed health index\u003c/h2\u003e \u003cp\u003eAfter extracting the desired criteria, the state of the health index was determined using the PSR model for the present using static and dynamic criteria and in the future, about 10- and 20-year forecasts of dynamic hydro-climatic variables and land-use change (Singh and Sinha \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chamani et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this regard, static and stable criteria were used in the current conditions in the model, and then, according to the purpose of the study, hydro-climatic and land-use criteria with different 10- and 20-year predicted scenarios were also used in the implementation model and the results obtained in the health assessment. After collecting the information and calculating the selected criteria for evaluating the watershed health, due to the difference in the data and the difference in the units of the criteria, standardization was done. Standardization of criteria was calculated in two categories. To standardize the criteria with positive and negative meanings and effects on watershed health, Eqs.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and (\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were used respectively (Hazbavi and Sadeghi \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu and Hao \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hazbavi et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e; Sadeghi et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e)\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${\\text{X}}_{\\text{s}}=\\frac{{\\text{X}}_{\\text{i}}-{\\text{X}}_{\\text{m}\\text{i}\\text{n}}}{{\\text{X}}_{\\text{m}\\text{a}\\text{x}}-{\\text{X}}_{\\text{m}\\text{i}\\text{n}}}$$\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$${\\text{X}}_{\\text{s}}=\\frac{{\\text{X}}_{\\text{m}\\text{a}\\text{x}}-{\\text{X}}_{\\text{i}}}{{\\text{X}}_{\\text{m}\\text{a}\\text{x}}-{\\text{X}}_{\\text{m}\\text{i}\\text{n}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003emin\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e express the standardized, actual, minimum, and maximum values of the desired criterion, respectively. Then, pressure, state, and response indicators were also calculated based on the arithmetic mean of the standardized values. Finally, to determine the final health status of the studied watershed, the geometric mean of the pressure, state, and response indicators for each of the sub-watersheds were used according to Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) (Hazbavi et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e; Mallya et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sadeghi et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e; Gatgash and Sadeghi \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$Geometric average={\\left[\\prod _{\\text{n}=1}^{\\text{k}}{\\text{X}}_{\\text{n}}\\right]}^{\\frac{1}{\\text{k}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\prod _{\\text{n}=1}^{\\text{k}}{\\text{X}}_{\\text{n}}\\)\u003c/span\u003e\u003c/span\u003e and k are equal to the product of indices and the number of indices, respectively. In the following, according to the regression analysis, the effect of each of the criteria in the calculation of pressure, state, and response indicators, as well as the final state of watershed health, was evaluated (Chamani et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chamani, Vafakhah, et al. 2023; Hazbavi and Sadeghi \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; S. H. R. Sadeghi et al. 2019). Thus, each sub-watershed was proportional to the value of PSR conceptual model indicators as well as watershed health status in one of the five categories: healthy (0.81-1.00), relatively healthy (0.61\u0026ndash;0.80), moderate (0.41\u0026ndash;0.60), relatively unhealthy (0.21\u0026ndash;0.40) and unhealthy (0.00-0.20) (Hazbavi et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e; Ervinia et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Also, to check the health classification of the study watershed and to set the stage for a more comprehensive management of existing sub-watersheds, the main classes were divided into two subclasses with positive and negative trends as necessary (Sadeghi et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e). Finally, the health zoning map was prepared using ArcGIS 10.4 software.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe results related to the selection and compilation of factors, criteria, and primary indicators for watershed health assessment in the study area are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Maps related to land-use for 1994 and 2021 are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Also, the maps related to the prediction of land-use based on the Markov chain for the next 10- and 20 years were presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\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\u003eSelected primary factors, criteria, and indicators in the watershed health assessment, the Safa-Roud Watershed, Mazandaran province, Iran\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=\"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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCriterion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource or method of calculation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003ePressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHydrology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual average discharge (m\u003csup\u003e3\u003c/sup\u003e.s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistical analysis of hydrometric stations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage slope (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSlope map\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTime of concentration (hr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigital data analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRelative size of residential land (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLand-use map 2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eClimatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage annual precipitation (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eStatistics of rain gauge stations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage annual temperature (C)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage annual Evapotranspiration (mm)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandardized Precipitation Index (SPI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eState\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eHydrology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage annual runoff coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistics of River Gauge Stations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRunoff depth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe ratio of lands participating in the production of runoff and flood to the total area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe ratio of participating lands in each sub-watershed to the total area of ​​the watershed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDischarge characteristic maximum (m\u003csup\u003e3\u003c/sup\u003e.s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistics of hydrometric stations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDischarge characteristic minimum (m\u003csup\u003e3\u003c/sup\u003e.s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistics of hydrometric stations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNaturalism evaluation index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLand-use map 2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe ratio of the area of ​​residential land to the area of ​​the watershed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClimatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe ratio of average annual precipitation to average annual evaporation and transpiration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistical analysis of river gauge stations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChanging the face of the land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLand-use maps, land surface measurements, and field observations\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 \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAlso, the classification of sub-watersheds related to the Safa-Roud Watershed, Mazandaran, based on the watershed health index was included in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The classification results of the study sub-watersheds based on the watershed health index for the next 10- and 20-year were presented in Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, respectively.\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\u003eWatershed health index status for current conditions, the Safa-Roud Watershed, Iran\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-watershed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHealth Classification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSub-classification Health\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Positive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelatively unhealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRelatively unhealthy positive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Positive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Positive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e* P (Pressure); S (State); R (Response)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWatershed health index status for 10-year prediction, the Safa-Roud Watershed, Iran\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-watershed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHealth Classification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSub-classification Health\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Positive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\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\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelatively unhealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRelatively unhealthy positive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Positive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Positive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*P (Pressure); S (State); R (Response)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWatershed health index status for 20-year prediction, the Safa-Roud Watershed, Iran\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-watershed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHealth Classification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSub-classification Health\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Positive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\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\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelatively unhealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRelatively unhealthy positive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Positive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Positive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e* P (Pressure); S (State); R (Response)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eZoning maps of pressure, state, and response indicators based on watershed health index in each sub-watershed for all three studied periods were included in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, respectively. The final maps related to the classification of study watershed health in each sub-watershed for 2021, 2023, and 2042 were also included in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Also, the results related to the occupancy rate of each indicator related to pressure, state, and response variables in the current conditions, 10- and 20-year forecast, were presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOccupied percentage of PSR and watershed health indices in current conditions and 10- and 20-year forecast\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eState\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eResponse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eHealth index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOccupied percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOccupied percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOccupied percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOccupied percentage\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnfavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnhealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelatively high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFavorable low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelatively high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRelatively unhealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFavorable Medium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e90.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelatively low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelatively favorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelatively low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRelatively healthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e2032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnfavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnhealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelatively high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFavorable low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelatively high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRelatively unhealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFavorable Medium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e90.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelatively low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelatively favorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelatively low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRelatively healthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e2042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnfavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnhealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelatively high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFavorable low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelatively high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRelatively unhealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFavorable Medium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e90.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelatively low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelatively favorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelatively low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRelatively healthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0\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\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe successful application of the PSR approach in the assessment of watershed health has also been confirmed in the research (Hazbavi and Sadeghi, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Q. Wang et al., 2019). The health assessment and zoning of the Safa-Roud Watershed, based on various tables and figures, showed that the average value and standard deviation of the current pressure index were equal to 0.573 and 0.185, respectively. The lowest value of this index was around 0.290 and related to sub-watershed 5, and the highest value was around 0.840 and related to sub-watershed 11. During this period, the pressure index was classed in high, relatively high, moderate, relatively low, and low categories of 26.70, 23.20, 28.90, 21.30, and 0.00 percent of the entire watershed, respectively. The initial evaluation of the classification indicated the prevalence of moderate and high-pressure conditions with a range of about 79% (Chamani, Sadeghi, et al., 2023; Ebrahimi Gatgash and Sadeghi, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In general, among the criteria used to calculate the pressure index in the current period, human factors and climatic factors showed the highest percentage of participation in determining the pressure index (Hazbavi et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Finally, the physical factors of sub-watersheds (time of concentration with 15.72%) had the most minor role. From the obtained results, the central pressures on the studied ecosystems were caused by human factors, with a participation percentage of about 50%. The average value and standard deviation of the pressure index 10 and 20 years after the effect of dynamic hydro-climatic variables and land-use with a decreasing trend equal to 0.568 and 0.181, respectively, for forecasting 10- and 20-year were 0.562 and 0.177. The lowest value of this index in the 10-year forecast was 0.290 and corresponds to sub-watershed 5, and the highest value was 0.83 and corresponds to sub-watershed 11.\u003c/p\u003e \u003cp\u003eThe extent of the watershed based on the pressure index during the 10-year forecast period was classified as high, relatively high, moderate, relatively low, and low, respectively, 26.68, 23.15, 28.9, 21.3, and 0.00 percent of the entire watershed. This index was also placed in high, relatively high, moderate, relatively low, and low categories during the 20-year forecast period, 26.7, 23.2, 28.9, 21.3, and 0.00 percent of the total watershed, respectively. The preliminary evaluation of the classification carried out for the 10- and 20-year forecast period indicated the predominance of moderate and high-pressure conditions with a range of about 79%. In general, among the criteria used to calculate the pressure index in the period of 10 and 20 years ahead, human factors played the most important role, followed by climatic factors and physical factors of sub-watersheds (Kambombe \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sadeghi et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e). From the obtained results, it can be concluded that the significant pressures on the studied ecosystems were mainly caused by human factors, with a participation percentage of more than 50%, and it indicated an increase in pressure on the health of the ecosystems compared to the current conditions.\u003c/p\u003e \u003cp\u003eComplementary investigations of health assessment and zoning of the Safa-Roud Watershed regarding the response index analysis showed that the average value and standard deviation were equal to 0.527 and 0.139, respectively. The lowest value of this index was about 0.32 and related to sub-watershed 11, and the highest value was about 0.77 and related to sub-watershed 5. The initial evaluation of the performed classification indicated the predominance of relatively high and moderate response conditions with a range of more than 64% (Hazbavi and Sadeghi \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hazbavi et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e; Chamani et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). From the obtained results, the main variables affecting the response of the study watershed in the mentioned period were related to the comparative and relative role of the natural factors governing the region and, of course, in interaction with human activities. The lowest value of this index in both forecast periods was about 0.35 and related to sub-watershed 11, and the highest value was about 0.75 and related to sub-watershed 5. The scope of the watershed based on the response index during two forecast periods of 10- and 20-year, like the current period, covers 25.5, 38.6, and 35.9 percent of the entire watershed in relatively high, moderate, and relatively low categories, respectively (Park et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the current period, sub-watersheds 6 and 8, with health indices of 0.37 and 0.59, were selected as the unhealthiest and the healthiest sub-watersheds in the study area. However, in the 10-year forecast period, the same sub-watersheds with the determined health index values could be predicted without change. For the 20-year forecast period, sub-watersheds 6 and 8 were selected as the unhealthiest and healthy sub-watersheds with a slight decrease of 0.36 and 0.58, respectively (Ahn and Kim \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e; Duan et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ebrahimi Gatgash and Sadeghi \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Based on this and considering the weight share of each of the sub-watersheds, the health index of the whole watershed and the conditions governing them for the present and the two forecast periods were 0.478, 0.479, and 0.476, respectively. It is interesting to note that the lowest and highest coefficient of variation in the current period was related to the current condition and pressure index with values of 25.868 and 32.241%, respectively, and the average condition was related to the response index with the value of 26.425%. The comparative results of the indicators affecting the health of the study watershed in the current period and the 10 and 20-year forecast showed that in the first period, the pressure, state, and response indicators with weighted average values of 0.622, 0.497, and 0.395, respectively (Verdonschot et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Neri et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Also, for the 10- and 20-year forecast periods, the pressure, state, and response indicators with weighted average values of 0.615, 0.390, and 0.505 for the 10-year forecast, and the mentioned indicators with the values of 0.607 0, 0.385, and 0.506 in the 20-year forecast period were influential in determining the health status of the study watershed.\u003c/p\u003e \u003cp\u003eThe quantification of the current watershed health status and the 10- and 20-year forecast periods showed that the values of the watershed health index were similar. However, the changes in the health index in the sub-watersheds at the beginning of the study period ranged from relatively unhealthy favorable conditions to moderately positive and moderately negative conditions (Hazbavi et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e; Singh \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gatgash and Sadeghi \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, at the end of the 10- and 20-year forecast periods, while the numerical values of the health degree have decreased, there were no significant changes in the scope of the classification above, and only in sub-watershed 11, with a slight increase in the degree of health, there was a change from the current average negative to average favorable conditions for the 10- and 20-year forecast conditions.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn the watershed health assessment, in addition to the physical characteristics and current conditions governing the watershed, the investigation of various dynamic hydro-climatic characteristics and land-use can play a very influential role in optimal decision-making. Therefore, this study was conducted with the objectives of predicting the effect of changing hydro-climatic variables on the future health status of the Safa-Roud Watershed, predicting the effect of land-use change on the future health status of the watershed, and the role of hydro-climatic and land-use variables in the spatial prioritization of sub-watersheds based on watershed health index. Also, the value of the watershed health index at present and its forecast during two periods of 10- and 20-year were successfully determined. The obtained results, despite the concrete changes of hydro-climatic variables in forecast periods and insignificant changes in land-use, indicated a slight decrease in health status in most sub-watersheds.\u003c/p\u003e \u003cp\u003eIn this study, changing the role and importance of variables affecting each of the study indicators and, finally, watershed health from primarily natural and inherent factors to human-oriented characteristics resulting from human interferences were among the prominent evaluation profiles. Investigations showed that most of the pressures on the studied ecosystems were caused by human factors. As a result, most of the variables affecting the response of the watershed in two forecast periods, such as the current period, were entirely of the type of human intervention activities. The obtained results emphasized not only the relative difference in the value of the desired indicators but also the difference in the type and amount of effect of different factors on the whole watershed and the difference in the condition of the studied sub-watersheds against the pressures. So, the natural factors have shown their functional difference in determining the state, pressure, response indicators, and, finally, the health status of sub-watersheds. The role of human variables in the current period and both forecast periods has often shown a significant effect on the determination of pressure, response, and state indicators and, finally, the health of different sectors. This issue indicates the high risk of sub-watersheds with an unfavorable condition and their placement in an unhealthy condition in the not-so-distant future. For future studies, in addition to using comprehensive and complete data, optimal multi-criteria decision-making methods and deep learning algorithms should be used to quantify the watershed health index.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMohammad Rasoul Rajabi acquired the data, performed the analysis and wrote the manuscript and discussion;, Mehdi Vafakhah and Seyed Hamidreza Sadeghi provided technical sights, as well as edited, restructured, and professionally optimized the manuscript. All authors discussed the results and edited the manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis work is based upon research funded by Iran National Science Foundation (INSF) under project No.4006075.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e We have no permission to release data and codes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e We confirm that this manuscript has not been published elsewhere and is not under consideration by another journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e All authors have participated the manuscript and agree with submission to Environmental Science and Pollution Research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e All authors have approved the publication of this manuscript in the Environmental Science and Pollution Research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAhn SR, Kim SJ (2017a) Assessment of integrated watershed health based on the natural environment, hydrology, water quality, and aquatic ecology. 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J Environ Manage 128:642\u0026ndash;654\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Integrated Watershed Management (IWM), Pressure-Status-Response (PSR) approach, Watershed Adaptive Management (WAM), Watershed health","lastPublishedDoi":"10.21203/rs.3.rs-3636356/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3636356/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study was conducted with the objectives of predicting the effect of changing hydro-climatic variables, predicting the effect of land-use change on the future health status of the Safa-Roud Watershed, and the role of hydro-climatic and land-use variables in the spatial prioritization of sub-watersheds based on watershed health index. To conduct this study, first, key characteristics were extracted based on human, climatic, and hydrological factors for all three indicators of pressure, state, and response. Then, the watershed health index was calculated for the current conditions. After that, watershed health was predicted based on dynamic hydro-climatic and land-use variables for the 10 and 20 years ahead. The health assessment and zoning of the Safa-Roud Watershed showed that the average value and standard deviation of the current pressure index were equal to 0.573 and 0.185, respectively. The lowest value of this index was around 0.290 and related to sub-watershed 5, and the highest value was around 0.840 and related to sub-watershed 11. The initial evaluation of the classification indicated the prevalence of moderate and high-pressure conditions with a range of about 79%. Finally, the physical factors of sub-watersheds (time of concentration with 15.72%) had the most minor role. In general, among the criteria used to calculate the pressure index in the current period, human factors and climatic factors showed the highest percentage of participation in determining the pressure index. The quantification of the current watershed health status and the 10- and 20-year forecast periods showed that the values of the watershed health index were similar. However, the changes in the health index in the sub-watersheds at the beginning of the study period ranged from relatively unhealthy favorable conditions to moderately positive and moderately negative conditions.\u003c/p\u003e","manuscriptTitle":"Predicting the Effect of Hydro-Climatic and Land-Use Dynamic Variables on Watershed Health Status","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-23 09:26:52","doi":"10.21203/rs.3.rs-3636356/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-01-18T23:07:07+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-18T22:13:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-06T04:29:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2023-11-29T00:41:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"642ea3dc-f658-4cd0-b655-d56a354c580a","owner":[],"postedDate":"January 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-07-16T06:34:52+00:00","versionOfRecord":{"articleIdentity":"rs-3636356","link":"https://doi.org/10.1007/s11356-024-34071-6","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2024-06-27 06:34:52","publishedOnDateReadable":"June 27th, 2024"},"versionCreatedAt":"2024-01-23 09:26:52","video":"","vorDoi":"10.1007/s11356-024-34071-6","vorDoiUrl":"https://doi.org/10.1007/s11356-024-34071-6","workflowStages":[]},"version":"v1","identity":"rs-3636356","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3636356","identity":"rs-3636356","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}