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Presenting the Spatio-Temporal Model for Predicting and Determining Permissible Land Use Changes Based on Drinking Water Quality Standards: A Case Study of Northern Iran
Presenting the Spatio-Temporal Model for Predicting and Determining Permissible Land Use Changes...
Allahdad, Zahra;Malmasi, Saeed;Montazeralzohour, Morvarid;Sadeghi, Seyed Mohammad Moein;Khabbazan, Mohammad M.
resources Article Presenting the Spatio-Temporal Model for Predicting and Determining Permissible Land Use Changes Based on Drinking Water Quality Standards: A Case Study of Northern Iran 1 1 2 3,4 Zahra Allahdad , Saeed Malmasi , Morvarid Montazeralzohour , Seyed Mohammad Moein Sadeghi 5,6, and Mohammad M. Khabbazan Natural Resources and Environmental Engineering, Faculty of Marine Science and Technology, North Tehran Branch, Islamic Azad University, Tehran 1651153311, Iran Faculty of Applied Science and Technology, Institute of Technology & Advanced Learning, Humber College, Toronto, ON M9W 5L7, Canada Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, 500123 Brasov, Romania School of Forest, Fisheries and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USA Workgroup for Economic and Infrastructure Policy (WIP), Technical University of Berlin (TU Berlin), Strasse des 17. Juni 135, 10623 Berlin, Germany Research Unit Sustainability and Global Change (FNU), University of Hamburg, Grindelberg 5, 20144 Hamburg, Germany * Correspondence: firstname.lastname@example.org Abstract: Quantifying the effect of non-point source pollution from different land use types (e.g., agri- cultural lands, pastures, orchards, and urban areas) on stream water quality is critical in determining Citation: Allahdad, Z.; Malmasi, S.; the extent and type of land use. The relationship between surface water quality as the primary source Montazeralzohour, M.; of drinking water and land use patterns in suburban areas with an accelerated pace of industrial Sadeghi, S.M.M.; Khabbazan, M.M. development and progressive growth of population has drawn much attention recently. This study Presenting the Spatio-Temporal aims to determine the type and portion of the land use changes over three-time intervals from 2000 Model for Predicting and to 2015 in the Jajrood River Catchment (Tehran metropolis, north of Iran). We used satellite images Determining Permissible Land Use of Landsat TM and ETM for 2005, 2010, and 2015 to analyze land use changes as a spatiotemporal Changes Based on Drinking Water model. According to the image processing and analysis, we classiﬁed the land uses of the study area Quality Standards: A Case Study of into irrigated farming, orchards, pastures, and residential areas. In addition, we used temporal data Northern Iran. Resources 2022, 11, 103. https://doi.org/10.3390/ from sampling stations to identify the relationship between land use and water quality based on a resources11110103 multivariate regression model. The analysis shows a signiﬁcant correlation between the type and + + + 2 extent of land use and water quality parameters, including pH, Na , Ca , Mg , Cl , SO , NO , 4 3 Academic Editor: Monica Pinardi and TDS. Pastures and residential areas had the highest impact on water quality parameters among Received: 6 August 2022 all land use types. Besides, we have used the regression analysis results to determine the maximum Accepted: 3 November 2022 permissible areas of each land use type. Consequently, effective management strategies such as land Published: 11 November 2022 use optimization in catchment scale for this catchment and similar areas will help to consciously Publisher’s Note: MDPI stays neutral protect and manage the quality of drinking water resources. with regard to jurisdictional claims in published maps and institutional afﬁl- Keywords: catchment; Iran; Jajrood River; spatiotemporal model; surface water quality iations. 1. Introduction Copyright: © 2022 by the authors. Reduction in the water quality and pollution caused by human activities and spa- Licensee MDPI, Basel, Switzerland. tiotemporal changes of various factors, such as land use in the catchment, has become a This article is an open access article global crisis [1–3]. Water resources can be affected by climate and land use changes [3–5]. distributed under the terms and Land use changes at the catchment scale are among the most crucial factors inﬂuencing conditions of the Creative Commons river water quality [6,7], which emitted various pollutions through runoff into the river ’s Attribution (CC BY) license (https:// water [8,9]. The United Nations (UN)  states that 80% of the diseases in developing creativecommons.org/licenses/by/ 4.0/). countries are transmitted by water. For example, contaminated water and poor sanitation Resources 2022, 11, 103. https://doi.org/10.3390/resources11110103 https://www.mdpi.com/journal/resources Resources 2022, 11, 103 2 of 14 are linked to the transmission of illnesses, including polio, cholera, diarrhea, dysentery, typhoid, and hepatitis A . Therefore, land use changes must be controlled to properly manage the water resources in the river catchment . Having access to previously recorded data can help researchers achieve statistical parameters to predict the possible effects in the future [12,13]. Because water quality is a worldwide concern, water quality assessments are being widely investigated [9,14–16]. In this regard, analyzing changes in the drainage catchments is essential in developing effective management strategies to preserve water resources [17–19]. Most water pollution issues are due to the population pressure and the intensiﬁcation of economic activities in the drainage catchments, which has led to changing land use patterns [8,20,21]. As an example, the result of a study in China, Dongjiang River catchment, was accompanied by the investigation of the effects of changing patterns of land use on the quality of water in the base ﬂow of the river and attempted to use multivariate statistical models . An understanding of river hydromorphology and chemistry is essential for effective river management. It is, however, necessary to have a monitoring program that provides a representative and reliable estimate of river waters’ quality due to temporal variations. Liu et al.  evaluated the effects of riparian land use patterns on the summertime water quality in various rivers in Shanghai, China. Their findings imply a tenuous rela- tionship between anthropogenic activities and water quality because green and residential spaces were found to be closer to those analyzed rivers than industrial and commercial land types. Accordingly, literature reviews have indicated that the water quality in the base flow strongly depends on the characteristics of the location and different land use types in the catchment [6,16,23]. Land use planning for water-quality security can be shown by exam- ining the connections between land use, landscape design, and river water quality . In developing countries (like Iran), some poor and remote areas without hydrometric station records make it challenging to quantify water quality. To the best of the authors’ knowledge, no attempts were made to evaluate permissible land use changes based on drinking water quality in a semiarid region of the world. In the Tehran metropolis (Iran) and surrounding areas, the Jajrood river is one of the primary sources of drinking water  and a water quality indicator in the management strategies of river water quality in terms of standards. Since the land use type can have either positive or negative impacts on the river water quality, it is necessary to deﬁne the role and contribution of each land use type on water quality and the authorized threshold to transform a maximum allocation of land use, especially in cases that the water supply goes through storage or use of direct transmission of rivers (e.g., Jajrood River) for drinking. The primary sources of water pollutants are divided into two groups: (a) point source pollution and (b) non-point source pollution [26,27]. Therefore, as the factors of production are identiﬁable, the type, amount, and location of pollutants emission from point sources such as factories, farms, and aquaculture to rural or urban runoff surfaces (such as a river or a dam) are actually can be controlled and harnessed by several ways . However, this issue does not apply to non-point sources of pollutants, including the areas under different land uses such as forest, grassland, agriculture and the like, usually due to the uncertainty of the type, amount, location and pollutants entrance to runoff surface ﬂows and even groundwater resources. Due to rainfall events in different land uses, contaminations are carried by surface runoffs and even sub-surface ﬂows and enter into centered surface ﬂows in streams and rivers . Given the history of research and the existence of water stress in the world, especially in countries suffering from drought, such as Iran, proper management of water resources through the management of land around them, which directly affects water quantity and quality, is essential . It is worth mentioning that the applied model and methods in this research are used in a different type of water resources research; Rostammiri et al.  analyzed the qualitative changes of groundwater resources via a spatial–temporal model. Their ﬁndings demonstrated that the extent of the earth’s surface has altered through time, with a rise in urban land and a decline in agricultural and bare lands . Therefore, by Resources 2022, 11, 103 3 of 14 managing land uses in the catchment area, rivers can be protected, which are an essential source of drinking water supply and effective in the sustainability of a community. Hence, this research aimed to use the spatiotemporal analysis and multivariate regression models to predict and determine the relationship between land use changes on river water quality at the catchment level. 2. Materials and Methods 2.1. Study Area The studied area is a part of the Jajrood River catchment (latitude of 35.46 to 36.30 N and longitude of 51.24 to 51.50 E) up to the input of the Latyan dam in the northeast of the metropolis of Tehran, capital of Iran (Table 1 and Figure 1). The average annual rainfall in the catchment is 711 mm, and the average annual temperature is 26 °C. Melting snow in the mountains of central Alborz increases the ﬂow of the Jajrood River. The maximum and minimum precipitations in this catchment are in November (69.8 mm) and July (11.2 mm), respectively . The highest and lowest temperatures are 42 °C in summer and 30 °C in winter . According to the classiﬁcation of the De Martonne index, the climate of the study area changes from downstream to upstream of the catchment, from a cold semi-arid to a very humid cold climate. Most of the winds in the region are southwest or south. From the morphological point of view, the Jajroud catchment is located in a mountain unit and has water erosion. The predominant soil texture in the area is clay-sand type . The study area includes some faults and folds to the south [35,36]. The soil of this catchment consists of alluvial sediments, gravel, and sand. The highest altitude of the Jajrood catchment is 4000 m above sea level . The main activities of agriculture and horticulture are located in the river ’s riparian zone and several villages and cities along the way [32,34]. Figure 1. Location of the Jajrood catchment, and water quality sampling stations (1. Rooteh; 2. Mey- goon; 3. Ahar; 4. Central Latyan). Resources 2022, 11, 103 4 of 14 Table 1. The sub-catchments extent. Sub Catchments Area (ha) Rooteh 15,579.7 Meygoon 7124.4 Ahar 9266.8 Central Latyan 13,420.1 Total 45,390.9 2.2. Water Quality Data and Land Use Status Due to the extent of the studied catchment and the different characteristics of each sub-catchment, this study was carried out using information from four hydrometric stations and measuring the water quality. The available annual average data from four sampling stations (Figure 1) from 2000 to 2015 (as a three-time intervals average in 5 years) were obtained from the Water Resources Research Center (TAMAB) . These stations are located in densely populated areas and farms to monitor the water quality. Among the water quality parameters, according to Iran’s National Standard for Drinking Water Quality + + (ISIRI 2009)  and the adequacy of available and reliable data, parameters of pH, Na , Ca , + 2 Mg , Cl , SO , NO , and TDS were selected. After ﬁeld sampling, to avoid microbial 4 3 degradation, the samples were held at 4 °C in a refrigerator without acid preservation. The parameter of pH was measured using a Hach HQ40d portable meter (Düsseldorf, Germany). For the rest of the parameters, we used the APHA manual (1992)  to analyze the water samples in the Landlaboratory. Land use data in Jajrood catchment, including orchards, pastures, residential areas, and irrigated farming, has been processed and reviewed using the periodic method of remotely measuring using available satellite images of Landsat TM/ETM in three-time intervals of 2005, 2010, and 2015 with Arc GIS software (Ver. 10.3; ESRI, Redlands, CA, USA) . All images were classiﬁed into the four above-mentioned land use classes. Cloud-free images were chosen during the summer season (i.e., July to August), when vegetation is at its peak productivity. We used images from the same season to minimize variations in reﬂectance between land use classes . The percentage of land use of the available areas in the catchment scale is used to determine the permissible limit of use based on the drinking water quality in this river. The catchment area of the Jajroud River, which is mainly the habitat of Ovis gmelini and Capra aegagrus, does not have a favorable vegetation cover due to the excessive use of cattle ranchers, but at high altitudes, the vegetation cover includes Pistacia at- lantica, Amygdalus orientalis, and Astragalus Sp. Most orchards in the region have apple trees . Irrigated farming lands cultivate some crops such as Solanum melongena L., Solanum tuberosum L., Lycopersicum esculentum L., Cucumis sativus L., Allium cepa L., and Triticum aestivum L. The orchards of this catchment have various types of fruits such as Prunud domestica L., Armeniaca bulgar L., Prunus armeniaca L., Prunus cerasus L., and Pearpyrus communis L. . A heightened resident population on the edge of the river is a risk to the hygienic and environmental state that has direct and indirect effects on water quality. There are cesspools for residential areas’ sewage discharge. However, topographic situations consist of a high slope of the ground, and the rock beds under the residential areas on the river ’s edge cause a division of sewage from underground layers to reach the river. All of the residential areas have been forced to use septic tanks for discharging their sewage, but it requires continuous oversight to reach a proper performance . 2.3. Data Analysis This research uses descriptive statistics to analyze land use characteristics and river water quality parameters. To test water quality variables and land use parameters, we have conducted an analysis of variance; we have used SPSS software (Ver. 24.0) . We have performed the Pearson correlation analysis to examine the relationship between different land use types and water quality variables at a signiﬁcant level of 0.05 and Resources 2022, 11, 103 5 of 14 0.01. Then, a multivariate regression model was used to determine this relationship type. The water quality was considered as a dependent variable to evaluate the effects of land use changes in the catchment. To determine the best model for predicting each variable, we compared the regression equation with R² and indicated that the amount of change in the dependent variable could be described by changes in the independent variables. The relationships between spatiotemporal variables (land use pattern types) and the use of drinking water quality parameters based on the national standard of Iran have been determined by solving a multivariate regression model. This way, we determined the maximum permitted area of land use, causing no pollution and no changes in the river ’s water quality in the spatiotemporal range. Sensitivity analysis is primarily conducted to ascertain how the change in model variables impacts the model output [45,46]. Sensitivity is often assessed by a relatively small change in a parameter from its prediction . The constant amount of 9% was considered as the change in the regression model variables (i.e., residential area and pastures) to do sensitivity analysis for this study. Overall, 9% was added to them in 2015 to assess how sensitive water quality measures were to changes in the pasture. The residential neighborhood was left unchanged at the same time. The parameters for determining water quality were then determined using the updated land use percentages. A similar process was used for the sensitive residential area change. 3. Results 3.1. Descriptive Analysis Table 2 shows that the mean value of physicochemical parameters of water quality in three-time intervals in the area in the second period increased, but this amount except pH and NO decreased in the third period. Therefore, based on Iran’s National Standard for Drinking Water Quality (ISIRI 2009) , all quantified parameters are within permissible limits. Table 2. Mean of physicochemical parameters of water quality in three-time intervals. + + + 2 TDS Na Mg Ca SO Cl NO 4 3 Sampling Stations Time Intervals (Year) pH (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) 2005–2000 8.04 144.18 0.19 0.73 1.58 0.56 0.15 4.40 Rooteh 2010–2005 7.91 152.81 0.16 0.65 1.96 0.59 0.22 3.80 2015–2010 8.19 147.52 0.23 0.67 1.79 0.62 0.22 4.32 2005–2000 7.80 317.93 1.25 1.71 2.28 1.46 0.72 4.00 Meygoon 2010–2005 7.85 383.97 1.67 1.68 3.35 1.97 1.16 4.60 2015–2010 7.86 319.96 1.17 1.62 2.78 1.49 0.81 4.44 2005–2000 7.78 3136.77 32.15 9.61 9.46 29.83 17.00 3.40 Ahar 2010–2005 7.61 4912.89 45.34 15.28 9.93 35.73 27.44 3.60 2015–2010 7.64 2172.50 17.88 6.63 7.73 16.37 10.16 4.44 2005–2000 7.85 216.13 0.47 0.86 2.35 0.78 0.46 3.14 Central Latyan 2010–2005 7.86 196.26 0.37 0.76 2.38 0.75 0.33 4.40 2015–2010 8.07 206.46 0.56 0.83 2.35 0.89 0.46 5.10 2005–2000 7.87 953.75 8.52 3.23 3.92 8.16 4.58 3.74 Jajrood Catchment 2010–2005 7.81 1411.48 11.89 4.59 4.40 9.76 7.29 4.10 2015–2010 7.94 711.61 4.96 2.44 3.66 4.84 2.91 4.58 The results show that most of the spatial and environmental changes in the catch- ment area of the Jajrood River have taken place upstream of this catchment (Tables 2 and 3; Figure 2). According to the land use position results at the three-time points based on ETM and TM Landsat images and their comparison with field measurement results, there are four different types of land use in the study area: orchards, irrigated farming, pastures, and residen- tial areas. During the 15 years (2000 to 2015), the land use of the study area was determined Resources 2022, 11, 103 6 of 14 and explained (Figure 2). According to the area statistics data in Table 3 and Figure 2, it is apparent that in all three periods, the extent of orchards and irrigated farming lands decreased, and the extent of pastures and residential areas increased. In fact, irrigated farming lands had the least land use in all three-time points, 0.3% in 2005 vs. 0.2% in 2015. Orchards in 2005 included 9.0% (equals 4087.2 ha) of the area, and in 2015, it included 6.3% of the area (2844.8 ha). During the study period, pastures occupy most of the area, covering over 90% of it, so animals like rams, sheep, and goats farm there. From 2005 to 2015, the above-mentioned land use was increased with a certain trend. In 2005, it included 89.4% equals 40,574.1 ha of the area and in 2015, it included 90.3% of the area, 40,996.4 ha. Additionally, residential areas land use has been increasing, so that in 2005 was 1.3% equals 602.65 ha and it got 3.2% (1474.6 ha) in 2015. Therefore, periodical land use changes as spatial and temporal variables have signiﬁcant, meaningful, and often temporary effects on water quality that directly depend on the size and location of sub-catchments as independent ecosystems (Figure 2). Figure 2. Land use map for the Jajrood catchment during study period. Resources 2022, 11, 103 7 of 14 Table 3. Periodic comparison of land use change in the Jajrood catchment and sub-catchments. Year The Trend of Changes in Sub-Catchments Land Use 2005 2010 2015 Land Use Area (ha) Area (ha) Area (%) Area (ha) Area (%) Area (ha) Area (%) 2005–2010 2010–2015 Orchard 471.8 3 513 3.3 479 3.1 Increasing 41.2 Decreasing 34.0 Irrigated Farming 33.3 0.2 2.5 0 70.7 0.5 Decreasing 30.9 Increasing 68.2 Rooteh Pastures 15,048.9 96.6 15,000.1 96.3 14,854.4 95.3 Decreasing 48.8 Decreasing 145.7 Residential Areas 25.6 0.2 64.1 0.4 175.6 1.1 Increasing 38.5 Increasing 111.5 Sum 15,579.7 100 15,579.7 100 15,579.7 100 - - Orchard 1192.7 16.7 881.7 12.4 495.5 7 Decreasing 311.0 Decreasing 386.3 Irrigated Farming 16.9 0.2 5 0.1 3.4 0 Decreasing 11.9 Decreasing 1.6 Meygoon Pastures 5564.5 78.2 5567.6 78.1 5514.7 77.4 Decreasing 3.0 Decreasing 52.8 Residential Areas 350.3 4.9 670.1 9.4 1110.8 15.6 Increasing 319.9 Increasing 440.7 Sum 7124.4 100 7124.4 100 7124.4 100 - - Orchard 463.4 5 574.2 6.2 781.4 8.4 Increasing 110.8 Increasing 207.2 Irrigated Farming 0 0 157.3 1.7 0 0 Decreasing 157.3 Decreasing 157.3 Ahar Pastures 8787.9 94.8 8522.4 92 8367.5 90.3 Decreasing 265.5 Decreasing 154.9 Residential Areas 15.4 0.2 12.9 0.1 117.9 1.3 Decreasing 2.6 Increasing 105.1 Sum 9266.8 100 9266.8 100 9266.8 100 - - Orchard 1959.2 15 1350 10 1089 8 Decreasing 609.3 Decreasing 261.0 Irrigated Farming 76.8 1 3 0 1 0 Decreasing 73.8 Decreasing 2.0 Central Latyan Pastures 11,272.8 83 11,969.9 89 12,059.9 90 Increasing 697.1 Increasing 89.9 Residential Areas 111.3 1 97.2 1 270.2 2 Decreasing 14.1 Increasing 173.0 Sum 13,420.1 100 13,420.1 100 13,420.1 100 - - Orchard 4087.2 9 3318.9 7.3 2844.8 6.3 Decreasing 768.3 Decreasing 474.1 Irrigated Farming 127 0.3 167.8 0.4 75.1 0.2 Increasing 40.8 Decreasing 92.7 Jajrood Catchment Pastures 40,574.1 89.4 41,060 90.5 40,996.4 90.3 Increasing 485.8 Decreasing 63.6 Residential Areas 602.7 1.3 844.3 1.9 1474.6 3.2 Increasing 241.6 Increasing 630.3 Sum 45,390.9 100 45,390.9 100 45,390.9 100 - - Resources 2022, 11, 103 8 of 14 3.2. Regression Results We have developed multivariate regression models to establish the maximum permissible land use types based on simulating the interaction between spatiotemporal variables and water quality measurements. The maximum recommended area of each type of land use that affects water quality has been estimated using information from Iran’s National Standard for Drinking Water Quality (ISIRI 2009)  (see Tables 4 and 5). Our simulations have shown that the most critical link between various land uses has been found. The land use changes in three levels in time alignment with existing satellite imagery indicated a correlation and a strong correlation between land use and water quality parameters. Our analysis shows that the relationship between land use and water quality parameters is discernible. Since our model shows the most signiﬁcant relationship between pastures and resi- dential areas, there is no possibility of changing the area and reducing the land share at the area’s level to prevent social challenges such as social conﬂicts due to the replacement of residential areas. Therefore, the existing situation must be maintained to preserve the river ’s water quality, and only in this situation will there be a possibility to change the use of the pasture. This way, the pasture can be converted to other uses, such as orchards and irrigated farming, as these uses do not affect water quality at the main catchment. Table 4. Multivariate regression model in Jajrood catchment. Multivariate Regression Model Independent Variables * Dependent Variable R p-Value pH = 16.758 + 0.101 PA + 0.085 RA PA, RA pH 0.884 0.012 TDS = 57,018.252 + 654.820 PA 437.620 RA PA, RA TDS 0.836 0.018 Cl = 346.607 + 3.968 PA 2.759 RA PA, RA Cl 0.812 0.023 2 2 SO = 277.802 + 3.246 PA 3.285 RA PA, RA SO 0.809 0.023 4 4 NO = 7.145 + 0.116 PA + 0.387 RA PA, RA NO 0.766 0.035 3 3 + + Na = 314.598 + 3.627 PA 2.950 RA PA, RA Na 0.790 0.025 + + Mg = 171.392 + 1.973 PA 1.350 RA PA, RA Mg 0.774 0.033 + + Ca = 57.077 + 0.689 PA 0.463 RA PA, RA Ca 0.800 0.024 * (PA) Pasture; (RA) Residential Area. Table 5. Maximum permissible land use types in Jajrood catchment. Permissible Area Permissible Area Permissible Area of Permissible Area of Water Quality Permissible Multivariate Regression Model of Pasture (ha) of Pasture (%) Residential Areas (ha) Residential Areas (%) Limit (mg/ l) 1471.3 9.92 79.5 10.34 6.5–9 pH = 16.758 + 0.101 PA + 0.085 RA 1074.8 7.25 86.92 11.3 1500 TDS = 57,018.252 + 654.820 PA 437.620 RA 1213.5 8.19 11.23 1.46 400 Cl = 346.607 + 3.968 PA 2.759 RA 1691.7 11.41 34.22 4.45 400 SO = 277.802 + 3.246 PA - 3.285 RA 5412.2 36.51 34.2 4.45 50 NO = 7.145 + 0.116 PA + 0.387 RA 1341.2 9.05 73.85 9.6 200 Na = 314.598 + 3.627 PA 2.950 RA 1111.1 7.49 516.1 67.11 30 Mg = 171.392 + 1.973 PA - 1.350 RA 1509.2 10.18 481.95 62.67 300 Ca = 57.077 + 0.689 PA 0.463 RA 3.3. Sensitivity Analysis of Land Use Change and Water Quality The results of the sensitivity test and its analysis show that the use of the pasture has dramatically increased in relation to the change of the TDS parameter and increase, which indicates its greater sensitivity (Table 6). Based on this, the following scenarios can be predicted: A- With the increase in pastures and residential areas extent in the study catchment area, the TDS values of the river water will exceed the permissible limit. B- The increase of other drinking water quality parameters in this catchment area occurs when the extent of residential areas increases. Resources 2022, 11, 103 9 of 14 Table 6. Sensitivity analysis of regression model in Jajrood catchment. Values of Water Quality Parameters Values of Water Quality Parameters Water Quality Parameters for Residential Areas Change (mg/L) for Pastures Area Change (mg/L) pH 8.7 7.0 TDS 3139.4 6408.5 Cl 21.4 37.4 SO 22.5 34.8 NO 8.0 3.1 Na 22.5 35.0 Mg 9.4 19.6 Ca 0.4 9.7 4. Discussion The results conﬁrm the validity of the results of the research conducted by Donohue et al.  and Huang et al. , indicating the importance and effectiveness of the two components of the characteristics and the precipitation in accordance with the location of their catchment on the one hand and the type, extent, and status of land use change in a location and time on the other hand. The results of this study are in the context of the previous literature reviews. It has been suggested that reducing the surface waters quality has an irrefutable relationship with the expansion of rural areas, agricultural, industrial, and tourism activities at the upper catchment of rivers supplying dams (e.g., Baoying and Yuanqing , Józwiakowski et al. , Bahroun and Chaib , Wang and Kalin , Rimba et al. ). A 283% increase in stream nitrate was observed as a result of con- version from forests to agricultural lands . According to Baoying and Yuanqing , an enormous development in tourist infrastructure will negatively impact water quality. Brontowiyono et al.  showed that different land uses in Indonesia signiﬁcantly correlate with contaminant sources. In addition, all parameters of the study showed an increase in the water quality trend based on concentration values. Eighty-seven percent of urban land use causes signiﬁcant water pollution, according to Camara et al. . Moreover, as demonstrated by Park et al. , riparian land use types inﬂuence stream-based biological communities more than riparian land use patterns. Different levels of tourism-related water quality interference were reported by Wang et al. , mostly in terms of the makeup of the bacterial community. The reductions in the surface waters quality are mainly caused by the destruction of the catchment’s ecological balance, environmental pollution, and intensiﬁcation of water quality reduction in reservoir dams. In principle, there is no doubt that dealing with these challenges requires an overall view and multidisciplinary approach to manage the land in the area and the entire catchment of dams . Due to land use change, land degradation may affect soil degradation, affecting the biogeochemical cycles [60–62]. The consensus of the experts has been involved in the impact of actions and activities of the human factor, particularly land use changes, by converting natural areas such as meadows, pastures, and forests . Various land use types have remarkable impacts on ecohydrological processes, biogeochemical cycles, pollution generation, and transport on surface water [64,65]. For example, agricultural land can increase the concentration of non-point source pollution in adjacent areas of rivers by applying fertilizers and pesticides [66,67]. Most fertilizers used in agricultural land cover are not absorbed by plants; instead, they can build up in soils, volatilize and release gases into the atmosphere, or wash into streams or groundwater supplies . Urban areas impact water quality because of high pollu- tant discharge, increasing suspended solids, nutrients, and metals in surface waters . While increasing bare land areas, deforestation will decrease water storage capacity, rainfall interception loss, and soil and water conservation of the forest canopy . Deforesta- tion also increases the runoff and sediment volume, affecting the pollutant load . In Resources 2022, 11, 103 10 of 14 other words, due to reduced rain retention capacity, runoff and erosion are dramatically increased . According to Bu et al. , the forest land was the most appropriate land use to improve river water quality in China. In another study, Naﬁ’Shehab et al.  concluded that the un-fragmented forest can improve water quality and reduce pollutants’ release. A study by Huang et al.  has found that residential growth is also associated with an increase in domestic sewage discharge, which can reduce the quality of water by signiﬁcantly adding nutrients to it. Petersen et al.  investigated how variations in land use affected the quality of surface water and came to the conclusion that there is a strong link between land use and water quality. With the above description, non-point sources pollutions, considering water quality degradation, include any water quality degradation that reduces its value for humans and nature; it can be concluded that non-point quality reducing sources consist of various independent variables such as spatial, temporal and spatiotemporal, in which the land use type as an independent spatiotemporal variable is of particular importance. However, due to the heterogeneity between the spatial characteristics of the surface runoff production zones (catchment as independent natural ecosystems), it is necessary, depending on the spatial and temporal conditions and even the climatic, socio-economic, cultural, and environmental characteristics, the abstract analysis is performed, and regional application models are presented. Our sensitivity test results showed that pasture land use has dramatically increased the change in the TDS parameter. This ﬁnding is in line with other researchers [75–78]. Similar to our ﬁnding, Adeola Fashae et al.  showed a remarkable variation of TDS in surface water across the land use types, with the residential areas having the greatest TDS. There are a number of sociocultural factors responsible for high TDS levels in residential areas, including the inappropriate disposal of household wa- ter into water channels, excessive fertilizer use by farmers on ﬂoodplains, and uncontrolled efﬂuent discharges. It should be noted that a majority of TDS is composed of inorganic salts (e.g., sulfates, chlorides, bicarbonates, carbonates, magnesium, sodium, potassium, and phosphates) . Furthermore, Lee et al. , based on their research ﬁndings, emphasized that de- pending on the spatial characteristics of the catchment and different land use types in different intervals in their range, water quality changes in the rivers, hence because of different environmental characteristics of each catchment and the heterogeneity of spa- tiotemporal variations in them, the results cannot be the same. In other words, the results are always relative. In addition, we found that multivariate linear regression models provided simple but useful analytical methods for predicting water quality in various land uses. For routine water quality monitoring in river basins, these models can be used to select a few appropriate parameters to minimize management costs. This ﬁnding is in line with previous research . Access to water microbiological data and other physical and chemical parameters was not possible in this study (ﬁrst limitation). Therefore, we suggest that future studies include microbiological indicators in addition to chemical parameters because the primary cause of human sickness is connected to microbiological water pollution. Although multivariate linear regression model is an effective approach for identifying land use change and surface water quality, this model does not appear to quantitatively estimate the contribution of respective land use intensity on the surface water quality because they are based on mechanistic relationships between sources and receptors (second limitation). Hence, we suggested that future research will focus on understanding the exact mechanisms of the effect of land use intensity on surface water quality by adopting an alternative “sources-receptors model”. 5. Conclusions We concluded that in the catchment of the rivers such as the studied area, the change in water quality is a function of the type and extent of land use in spatiotemporal areas Resources 2022, 11, 103 11 of 14 of different catchments, but the effect of land use change is not the same on changing the amount and the type of water quality parameters. Our ﬁndings revealed that the indepen- dent variables inﬂuencing water quality are spatial variables with almost constant values. These independent variables include slope, aspect, elevation, geological characteristics and formations, soil properties, and geomorphology. These variables are predominant in permanent rivers like the Jajrood River. However, the results show that the types and extents of land use as spatiotemporal variables resulting from human activities signiﬁcantly impact water quality. In this regard, pastures and residential areas had the highest impact on water quality parameters among all land use types. In addition, we presents a model to determine the maximum permissible areas of each land use type to develop effective management strategies for this catchment. To decrease and manage water stress or scarcity, we suggested that all countries, especially in arid and semiarid climate zones, should identify the effects of different land uses to evaluate cause-effect responses to land use changes. Author Contributions: Conceptualization, Z.A., S.M. and M.M.K.; methodology, Z.A., S.M., M.M. and M.M.K.; analysis: Z.A., S.M.M.S.; visualization, Z.A. and M.M.K.; writing—original draft preparation, Z.A., M.M. and M.M.K.; writing—review and editing, M.M.K., Z.A. and S.M.M.S.; All authors have read and agreed to the published version of the manuscript. Funding: We acknowledge support by the German Research Foundation and the Open Access Publication Fund of TU Berlin. Data Availability Statement: The available annual average data from four sampling stations from 2000 to 2015 were obtained from the Water Resources Research Center (TAMAB). The data can be requested by contacting Zahra Allahdad; email@example.com. Acknowledgments: Seyed Mohammad Moein Sadeghi’s research at the Transilvania University of Brasov, Romania, has been supported by the program entitled “Transilvania Fellowship for Postdoctoral Research/Young Researchers”. The usual disclaimer applies. Conﬂicts of Interest: The authors declare no conﬂict of interest. References 1. Duan, W.; He, B.; Nover, D.; Yang, G.; Chen, W.; Meng, H.; Zou, S.; Liu, C. Water quality assessment and pollution source identiﬁcation of the eastern Poyang Lake Basin using multivariate statistical methods. Sustainability 2016, 8, 133. [CrossRef] 2. Ni, X.; Parajuli, P.B.; Ouyang, Y.; Dash, P.; Siegert, C. Assessing land use change impact on stream discharge and stream water quality in an agricultural watershed. Catena 2021, 198, 105055. 3. Esfandeh, S.; Danehkar, A.; Salmanmahiny, A.; Sadeghi, S.M.M.; Marcu, M.V. Climate Change Risk of Urban Growth and Land Use/Land Cover Conversion: An In-Depth Review of the Recent Research in Iran. Sustainability 2021, 14, 338. [CrossRef] 4. Tong, S.T.; Sun, Y.; Ranatunga, T.; He, J.; Yang, Y.J. Predicting plausible impacts of sets of climate and land use change scenarios on water resources. Appl. Geogr. 2012, 32, 477–489. [CrossRef] 5. Daba, M.H.; You, S. Quantitatively assessing the future land-use/land-cover changes and their driving factors in the upper stream of the Awash River based on the CA–markov model and their implications for water resources management. Sustainability 2022, 14, 1538. [CrossRef] 6. Ding, J.; Jiang, Y.; Fu, L.; Liu, Q.; Peng, Q.; Kang, M. Impacts of land use on surface water quality in a subtropical River Basin: a case study of the Dongjiang River Basin, Southeastern China. Water 2015, 7, 4427–4445. [CrossRef] 7. Wijesiri, B.; Deilami, K.; Goonetilleke, A. Evaluating the relationship between temporal changes in land use and resulting water quality. Environ. Pollut. 2018, 234, 480–486. [CrossRef] 8. Tong, S.T.; Chen, W. Modeling the relationship between land use and surface water quality. J. Environ. Manag. 2002, 66, 377–393. [CrossRef] 9. Chotpantarat, S.; Boonkaewwan, S. Impacts of land-use changes on watershed discharge and water quality in a large intensive agricultural area in Thailand. Hydrol. Sci. J. 2018, 63, 1386–1407. [CrossRef] 10. United Nations (UN). ‘Water-Related Diseases Responsible for 80 per Cent of All Illnesses, Deaths in Developing World’, Says Secretary-General in Environment Day Message. Available online: https://press.un.org/en/2003/sgsm8707.doc.htm (accessed on 8 October 2022). 11. Kapembo, M.L.; Mukeba, F.B.; Sivalingam, P.; Mukoko, J.B.; Bokolo, M.K.; Mulaji, C.K.; Mpiana, P.T.; Poté, J.W. Survey of water supply and assessment of groundwater quality in the suburban communes of Selembao and Kimbanseke, Kinshasa in Democratic Republic of the Congo. Sustain. Water Resour. Manag. 2022, 8, 1–13. Resources 2022, 11, 103 12 of 14 12. Morote, Á.F.; Olcina, J.; Hernández, M. The use of non-conventional water resources as a means of adaptation to drought and climate change in Semi-Arid Regions: South-Eastern Spain. Water 2019, 11, 93. [CrossRef] 13. Sun, Z.; Long, D.; Yang, W.; Li, X.; Pan, Y. Reconstruction of GRACE data on changes in total water storage over the global land surface and 60 basins. Water Resour. Res. 2020, 56, e2019WR026250. [CrossRef] 14. Olsen, R.L.; Chappell, R.W.; Loftis, J.C. Water quality sample collection, data treatment and results presentation for principal components analysis–literature review and Illinois River watershed case study. Water Res. 2012, 46, 3110–3122. [CrossRef] [PubMed] 15. Montazeralzohour, M.; Ziyarani, E.; Malmasi, S.; Rafati, M. River water quality assessment using WRASTIC and organizing methods: A case study in three subwatersheds of Karaj River (Varangeh Rud, Doab, and Varian). In Proceedings of the 4th International Congress of Developing Agriculture, Natural Resources, Environment and Tourism of Iran, Tabriz, Iran, 21 January 2019. 16. de Mello, K.; Taniwaki, R.H.; de Paula, F.R.; Valente, R.A.; Randhir, T.O.; Macedo, D.R.; Leal, C.G.; Rodrigues, C.B.; Hughes, R.M. Multiscale land use impacts on water quality: Assessment, planning, and future perspectives in Brazil. J. Environ. Manag. 2020, 270, 110879. [CrossRef] 17. Hu, H.B.; Liu, H.Y.; Hao, J.F.; An, J. Analysis of Land Use Change Characteristics Based on Remote Sensing and Gis in the Jiuxiang River Watershed . Int. J. Smart Sens. Intell. Syst. 2012, 5, 811–823. [CrossRef] 18. Rawat, J.; Kumar, M. Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. Egypt. J. Remote Sens. Space Sci. 2015, 18, 77–84. [CrossRef] 19. Hua, A.K. Land use land cover changes in detection of water quality: A study based on remote sensing and multivariate statistics. J. Environ. Public Health 2017, 7515130. [CrossRef] 20. Du Plessis, A.; Harmse, T.; Ahmed, F. Quantifying and predicting the water quality associated with land cover change: a case study of the Blesbok Spruit Catchment, South Africa. Water 2014, 6, 2946–2968. [CrossRef] 21. Li, Y.; Bi, Y.; Mi, W.; Xie, S.; Ji, L. Land-use change caused by anthropogenic activities increase ﬂuoride and arsenic pollution in groundwater and human health risk. J. Hazard. Mater. 2021, 406, 124337. [CrossRef] 22. Liu, J.; Cheng, F.; Zhu, Y.; Zhang, Q.; Song, Q.; Cui, X. Urban Land-Use Type Inﬂuences Summertime Water Quality in Small-and Medium-Sized Urban Rivers: A Case Study in Shanghai, China. Land 2022, 11, 511. [CrossRef] 23. Vrebos, D.; Beauchard, O.; Meire, P. The impact of land use and spatial mediated processes on the water quality in a river system. Sci. Total. Environ. 2017, 601, 365–373. [CrossRef] [PubMed] 24. Zhang, F.; Chen, Y.; Wang, W.; Jim, C.Y.; Zhang, Z.; Tan, M.L.; Liu, C.; Chan, N.W.; Wang, D.; Wang, Z.; et al. Impact of land-use/land-cover and landscape pattern on seasonal in-stream water quality in small watersheds. J. Clean. Prod. 2022, 357, 131907. [CrossRef] 25. Hosseinabadi, F.; Hashemi, S.H.; Abdoli, A.; Mehrjo, F. Development of multimetric index based on benthic macroinvertebrate for water quality assessment of Jajrood River in Iran. Casp. J. Environ. Sci. 2022, 20, 77–88. 26. Su, F.; Kaplan, D.; Li, L.; Li, H.; Song, F.; Liu, H. Identifying and classifying pollution hotspots to guide watershed management in a large multiuse watershed. Int. J. Environ. Res. Public Health 2017, 14, 260. [CrossRef] [PubMed] 27. Xue, J.; Wang, Q.; Zhang, M. A review of non-point source water pollution modeling for the urban–rural transitional areas of China: Research status and prospect. Sci. Total. Environ. 2022, 826, 154146. [CrossRef] 28. Karamouz, M.; Teymoori, J.; Olyaei, M. A Spatial Non-Stationary Based Site Selection of Artiﬁcial Groundwater Recharge: A Case Study for Semi-Arid Regions. Water Resour. Manag. 2021, 35, 963–978. [CrossRef] 29. Kawagoshi, Y.; Suenaga, Y.; Chi, N.L.; Hama, T.; Ito, H.; Van Duc, L. Understanding nitrate contamination based on the relationship between changes in groundwater levels and changes in water quality with precipitation ﬂuctuations. Sci. Total. Environ. 2019, 657, 146–153. [CrossRef] 30. Hakimi, L.; Sadeghi, S.M.M.; Van Stan, J.T.; Pypker, T.G.; Khosropour, E. Management of pomegranate (Punica granatum) orchards alters the supply and pathway of rain water reaching soils in an arid agricultural landscape. Agric. Ecosyst. Environ. 2018, 259, 77–85. [CrossRef] 31. Rostammiri, A.; Malmasi, S.; Yosefvand, F.; Hoseini, S.A.; Etminan, A. Presenting the spatial–temporal model for assessing and predicting qualitative changes of the groundwater resources in Shahriar, Tehran, Iran. Environ. Monit. Assess. 2022, 194, 1–12. [CrossRef] 32. Razmkhah, H.; Abrishamchi, A.; Torkian, A. Evaluation of spatial and temporal variation in water quality by pattern recognition techniques: A case study on Jajrood River (Tehran, Iran). J. Environ. Manag. 2010, 91, 852–860. [CrossRef] 33. Shayeghi, M.; Vatandoost, H.; Paksa, A.; Amin, M.; Youseﬁ, H.; Rahimi, S.; Abbasi, M.; Akhavan, A.A. Identiﬁcation of common aquatic insects of Jajroud river. J. Entomol. Zool Stud. 2017, 5, 1433–1436. 34. Allahdad, Z.; Arjmandi, R.; Hassani, A.; Vafaeinezhad, A.; Malmasi, S. Estimation of drinking water quality parameters changes in response to land use changes. Bulg. Chem. Commun. 2017, 49, 335 – 340. 35. Souri, L. Evaluation Factors Affecting of Vulner Ability the Quality of Groundwater and Drinking Wells in Pardis. Master ’s Thesis, Tehran University, Tehran, Iran, 2017. 36. Givi, M.; Jahangiri-Rad, M.; Tashauoei, H. Assessment of Groundwater Quality in the Jajrood River Basin, Tehran, Iran: A Coupled Physicochemical and Hydrogeochemical Study. J. Adv. Environ. Health Res. 2021, 9, 237–254. [CrossRef] 37. Water Resources Research Center (TAMAB). Available online: https://www.wrm.ir/?l=EN (accessed on 30 July 2022). 38. Institute of Standards & Industrial Research of Iran (ISIRI). Available online: http://www.isiri.com/ (accessed on 30 July 2022). Resources 2022, 11, 103 13 of 14 39. Apha, A. WEF, Standard Methods for the Examination of Water and Wastewater, 18th ed.; American Public Health Association: Washington, DC, USA, 1992. 40. Menon, S. ArcGIS 10.3: The Next Generation of GIS Is Here. Available online: https://www.esri.com/arcgis-blog/products/3d- gis/3d-gis/arcgis-10-3-the-next-generation-of-gis-is-here/ (accessed on 30 July 2022). 41. Nasiri, V.; Deljouei, A.; Moradi, F.; Sadeghi, S.M.M.; Borz, S.A. Land Use and Land Cover Mapping Using Sentinel-2, Landsat-8 Satellite Images, and Google Earth Engine: A Comparison of Two Composition Methods. Remote Sens. 2022, 14, 1977. [CrossRef] 42. Ghalegolab, B.A.; Khoshbakht, K.; Tabrizi, L.; Davari, A.; Vaisi, H. A Comparative Assessment of Agrobiodiversity Indices in Farms, Gardens and Home Gardens (Case Study: of Jajrood Basin); AGROECOLOGY-Boomshenasi Keshavarzi: Mashhad, Iran, 2013. . 43. Mirzaei, M.; Hasanian, H. Quality Evaluation of Jajrood River (IRAN) by Quality Indices Methods. Adv. Mater. Res. 2013, 650, 652–657. [CrossRef] 44. IBM SPSS Statistics 24. 2021. Available online: https://www.ibm.com/support/pages/downloading-ibm-spss-statistics-24 (accessed on 30 July 2022). 45. Attarod, P.; Kheirkhah, F.; Khalighi Sigaroodi, S.; Sadeghi, S. Sensitivity of Reference Evapotranspiration to Glob al Warming in the Caspian Region, North of Iran. J. Agr. Sci. Tech. 2015, 17, 869–883. 46. Shah, M.I.; Javed, M.F.; Abunama, T. Proposed formulation of surface water quality and modelling using gene expression, machine learning, and regression techniques. Environ. Sci. Pollut. Res. 2021, 28, 13202–13220. [CrossRef] 47. Donohue, I.; McGarrigle, M.L.; Mills, P. Linking catchment characteristics and water chemistry with the ecological status of Irish rivers. Water Res. 2006, 40, 91–98. [CrossRef] 48. Huang, J.; Li, Q.; Pontius, R.G.; Klemas, V.; Hong, H. Detecting the dynamic linkage between landscape characteristics and water quality in a subtropical coastal watershed, Southeast China. Environ. Manag. 2013, 51, 32–44. [CrossRef] 49. Baoying, N.; Yuanqing, H. Tourism development and water pollution: Case study in Lijiang Ancient Town. China Popul. Resour. Environ. 2007, 17, 123–127. [CrossRef] 50. Józwiakowski, ´ K.; Listosz, A.; Gizinska-Górna, ´ M.; Pytka, A.; Marzec, M.; Sosnowska, B.; Kowalczyk-Jusko, ´ A.; Grzywna, A.; Mazur, A.; Obroslak, ´ R. Effect of Anthropogenic Pollutants on the Quality of Surface Waters Aand Groundwaters in the Catchment Basin of Lake Bialskie. J. Ecol. Eng. 2016, 17, 154–162. [CrossRef] 51. Bahroun, S.; Chaib, W. The quality of surface waters of the dam reservoir Mexa, Northeast of Algeria. J. Water Land Dev. 2017, 34, 11–19. [CrossRef] 52. Wang, R.; Kalin, L. Combined and synergistic effects of climate change and urbanization on water quality in the Wolf Bay watershed, southern Alabama. J. Environ. Sci. 2018, 64, 107–121. [CrossRef] [PubMed] 53. Rimba, A.B.; Mohan, G.; Chapagain, S.K.; Arumansawang, A.; Payus, C.; Fukushi, K.; Osawa, T.; Avtar, R.; et al. Impact of population growth and land use and land cover (LULC) changes on water quality in tourism-dependent economies using a geographically weighted regression approach. Environ. Sci. Pollut. Res. 2021, 28, 25920–25938. [CrossRef] [PubMed] 54. Schilling, K.E.; Spooner, J. Effects of watershed-scale land use change on stream nitrate concentrations. J. Environ. Qual. 2006, 35, 2132–2145. [CrossRef] 55. Brontowiyono, W.; Asmara, A.A.; Jana, R.; Yulianto, A.; Rahmawati, S. Land-Use Impact on Water Quality of the Opak Sub-Watershed, Yogyakarta, Indonesia. Sustainability 2022, 14, 4346. [CrossRef] 56. Camara, M.; Jamil, N.R.; Abdullah, A.F.B. Impact of land uses on water quality in Malaysia: a review. Ecol. Process. 2019, 8, 1–10. [CrossRef] 57. Park, S.R.; Kim, S.; Lee, S.W. Evaluating the Relationships between Riparian land cover characteristics and biological integrity of streams using Random Forest algorithms. Int. J. Environ. Res. Public Health 2021, 18, 3182. [CrossRef] 58. Wang, B.; Zheng, X.; Zhang, H.; Xiao, F.; Gu, H.; Zhang, K.; He, Z.; Liu, X.; Yan, Q. Bacterial community responses to tourism development in the Xixi National Wetland Park, China. Sci. Total. Environ. 2020, 720, 137570. [CrossRef] 59. Winton, R.S.; Teodoru, C.R.; Calamita, E.; Kleinschroth, F.; Banda, K.; Nyambe, I.; Wehrli, B. Anthropogenic inﬂuences on Zambian water quality: hydropower and land-use change. Environ. Sci. Process. Impacts 2021, 23, 981–994. [CrossRef] 60. Keesstra, S.D.; Geissen, V.; Mosse, K.; Piiranen, S.; Scudiero, E.; Leistra, M.; van Schaik, L. Soil as a ﬁlter for groundwater quality. Curr. Opin. Environ. Sustain. 2012, 4, 507–516. [CrossRef] 61. Pérez-Fernández, M.A.; Calvo-Magro, E.; Valentine, A. Beneﬁts of the symbiotic association of shrubby legumes for the rehabilitation of degraded soils under Mediterranean climatic conditions. Land Degrad. Dev. 2016, 27, 395–405. [CrossRef] 62. Tanaka, Y.; Minggat, E.; Roseli, W. The impact of tropical land-use change on downstream riverine and estuarine water properties and biogeochemical cycles: A review. Ecol. Process. 2021, 10, 1–21. [CrossRef] 63. Gong, X.; Bian, J.; Wang, Y.; Jia, Z.; Wan, H. Evaluating and predicting the effects of land use changes on water quality using SWAT and CA–Markov models. Water Resour. Manag. 2019, 33, 4923–4938. [CrossRef] 64. Luo, Z.; Shao, Q.; Zuo, Q.; Cui, Y. Impact of land use and urbanization on river water quality and ecology in a dam dominated basin. J. Hydrol. 2020, 584, 124655. [CrossRef] 65. Rahbarisisakht, S.; Moayeri, M.H.; Hayati, E.; Sadeghi, S.M.M.; Kepfer-Rojas, S.; Pahlavani, M.H.; Kappel Schmidt, I.; Borz, S.A. Changes in Soil’s Chemical and Biochemical Properties Induced by Road Geometry in the Hyrcanian Temperate Forests. Forests 2021, 12, 1805. [CrossRef] 66. Wu, L.; Qi, T.; Zhang, J. Spatiotemporal variations of adsorbed nonpoint source nitrogen pollution in a highly erodible Loess Plateau watershed. Pol. J. Environ. Stud. 2017, 26, 1343–1352. [CrossRef] Resources 2022, 11, 103 14 of 14 67. Zhan, J.; Chu, X.; Li, Z.; Jia, S.; Wang, G. Incorporating ecosystem services into agricultural management based on land use/cover change in Northeastern China. Technol. Forecast. Soc. Chang. 2019, 144, 401–411. [CrossRef] 68. Rocha, K.F.; Mariano, E.; Grassmann, C.S.; Trivelin, P.C.; Rosolem, C.A. Fate of 15N fertilizer applied to maize in rotation with tropical forage grasses. Field Crop. Res. 2019, 238, 35–44. [CrossRef] 69. Sadeghi, S.M.M.; Gordon, D.A.; Van Stan, J.T., II. A global synthesis of throughfall and stemﬂow hydrometeorology. In Precipitation Partitioning by Vegetation; Springer: Berlin/Heidelberg, Germany, 2020; pp. 49–70. 70. Anand, J.; Gosain, A.K.; Khosa, R. Prediction of land use changes based on Land Change Modeler and attribution of changes in the water balance of Ganga basin to land use change using the SWAT model. Sci. Total. Environ. 2018, 644, 503–519. [CrossRef] 71. Kiage, L.M.; Douglas, P. Linkages between land cover change, lake shrinkage, and sublacustrine inﬂuence determined from remote sensing of select Rift Valley Lakes in Kenya. Sci. Total. Environ. 2020, 709, 136022. [CrossRef] [PubMed] 72. Bu, H.; Meng, W.; Zhang, Y.; Wan, J. Relationships between land use patterns and water quality in the Taizi River basin, China. Ecol. Indic. 2014, 41, 187–197. [CrossRef] 73. Naﬁ’Shehab, Z.; Jamil, N.R.; Aris, A.Z.; Shaﬁe, N.S. Spatial variation impact of landscape patterns and land use on water quality across an urbanized watershed in Bentong, Malaysia. Ecol. Indic. 2021, 122, 107254. 74. Petersen, C.R.; Jovanovic, N.Z.; Le Maitre, D.C.; Grenfell, M. Effects of land use change on streamﬂow and stream water quality of a coastal catchment. Water SA 2017, 43, 139–152. [CrossRef] 75. El-Gammal, M.; Ibrahim, M.; Gad, A.; El-Zeiny, A. Integration of lab analyses and GIS techniques for assessment of some physical and chemical characteristics in different water bodies, Damietta coastal region, Egypt. Mansoura J. Environ. Sci. 2015, 44, 257–284. 76. Alnagaawy, A.; Sherif, M.; Mohammed, G.; Shehata, A. Impact of industrial pollutants on some water quality parameters of Edku, Mariout lakes and the Nile River. Int. J. Environ. 2018, 7, 1–15. 77. Kadir, A.; Ahmed, Z.; Uddin, M.M.; Xie, Z.; Kumar, P. Integrated Approach to Quantify the Impact of Land Use and Land Cover Changes on Water Quality of Surma River, Sylhet, Bangladesh. Water 2021, 14, 17. [CrossRef] 78. Putri, A.; Jana, R.; Florensia, A.; Asmara, A.; Yulianto, A.; Brontowiyono, W. A Spatiotemporal Analysis of Water Quality and Land Use in Tambayakbayan River, Yogyakarta; IOP Conference Series: Earth and Environmental Science; IOP Publishing: Bristol, UK, 2021; Volume 933, p. 012045. 79. Adeola Fashae, O.; Abiola Ayorinde, H.; Oludapo Olusola, A.; Oluseyi Obateru, R. Landuse and surface water quality in an emerging urban city. Appl. Water Sci. 2019, 9, 1–12. [CrossRef] 80. Maurya, P.K.; Ali, S.A.; Alharbi, R.S.; Yadav, K.K.; Alfaisal, F.M.; Ahmad, A.; Ditthakit, P.; Prasad, S.; Jung, Y.K.; Jeon, B.H. Impacts of Land Use Change on Water Quality Index in the Upper Ganges River near Haridwar, Uttarakhand: A GIS-Based Analysis. Water 2021, 13, 3572. [CrossRef] 81. Lee, S.W.; Hwang, S.J.; Lee, S.B.; Hwang, H.S.; Sung, H.C. Landscape ecological approach to the relationships of land use patterns in watersheds to water quality characteristics. Landsc. Urban Plan. 2009, 92, 80–89. [CrossRef] 82. Attua, E.M.; Ayamga, J.; Pabi, O. Relating land use and land cover to surface water quality in the Densu River basin, Ghana. Int. J. River Basin Manag. 2014, 12, 57–68. [CrossRef]
Multidisciplinary Digital Publishing Institute
Presenting the Spatio-Temporal Model for Predicting and Determining Permissible Land Use Changes Based on Drinking Water Quality Standards: A Case Study of Northern Iran
Sadeghi, Seyed Mohammad Moein
Khabbazan, Mohammad M.
, Volume 11 (11) –
Nov 11, 2022
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