Comprehensive assessment and scenario simulation for the future of the hydrological processes in Dez river basin, Iran

Climate change is one of the leading factors that directly affect hydrological processes in large basins. This study assesses the impacts of climate change on streamflow, sediment and crop yield, actual evapotranspiration (AET), and water budget. In addition, the effects of land use and land cover (LULC) alteration with climate change on streamflow and sediment yield have been evaluated in the Dez river basin in the southwest of Iran. Five General Circulation Models (GCMs) based on two scenarios, Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 for the near period (2021– 2040) are considered. Hydrological simulation is performed using the Soil and Water assessment tool (SWAT) with good performance in the calibration (1990 to 2010) and validation (2010 to 2017) periods. The precipitation and temperature projected show a major upward trend related to the base period. The results showed that climate change increases the runoff and sediments. In addition, results projected that garden crop yields would increase while agricultural crop yields would decrease. In addition, AET will face a slight decline of about 2%–6%. Combined LULC and climate change scenarios showed that with amplification of orchards areas, sediment load would decrease.


INTRODUCTION
Universal climate change is one of the leading factors that directly affect hydrological processes (Zhang et al. ).
In this regard, global warming is recognized as a significant issue for climate change during the coming century (Chien et al. ). Possible impacts of changes in climate including temperature and rainfall have caused variations in hydrological processes such as evapotranspiration, surface runoff, timing, and magnitude of streamflow, and flood events (Neupane & Kumar ). Since these impacts are expected to have diverse influences across a region, different spatial and temporal distributions are created for water resources components. Furthermore, the studies show that variation in precipitation patterns plays a vital role in streamflow trends and sediment in various regions across the United States (Novotny & Stefan ). Moreover, temperature variation and wind speed affect evaporation and transpiration sub-processes, which directly have an influence on the surface and subsurface water budgets (Schmid et  Accurate hydrological simulation of a basin needs developed models to consider a wide range of detailed information including the list of cultivated crops and orchards, irrigation schedules, fertilization and harvesting operations and so on (Eini ). This detailed information, which constitutes the inputs for distributed simulation models like Soil and Water Assessment Tool (SWAT), significantly describes percolation and evapotranspiration. A common belief is that without an accurate and complete calibration and validation of a model for local conditions of the system, no additional useful analyses in respect of the model estimates are reliable (Smarzyńska & Miatkowski ).
Researchers have shown that the effects of climate change on various agricultural products will not have a predictable trend due to the type of product, conditions of the case study, and climate scenarios (Shahvari et al. ). In some studies, increased crop yields have been reported and, in others, a drop in crop yields has been reported (Boonwichai et al. ; Kolberg et al. ). The result of a study in 10 largest producing countries showed that compared to present conditions, a group of 11 crop models found a rise in yield loss risk of 12%, 6.3%, 19.4% and 16.1% for wheat, corn, rice and soybeans by 2100, respectively (Leng & Hall ).  The aim of the current study was to evaluate the impacts of climate change on streamflow, sediment yield, crop yield, actual evapotranspiration, and water budget and the effect of LULC alteration on streamflow and sediment yield in the Dez river basin in the southwest of Iran. The novelty of this study is to set up a comprehensive model for multifunctional calibration of hydrological processes. Similar to other water-limited regions of the Middle East, food and water security are the main concerns in Iran (Ashraf et al. ). The study area is very susceptible to climate change impacts because of its high dependency on climate-sensitive agriculture (Ashraf et al. ). Moreover, the basin plays an undeniable role in the economic sector, where supplying agricultural water and energy production are the main concerns. According to the mentioned studies, although these scenarios have been employed in a few studies of basins throughout the world, reasonable outcomes have been reported. The results of this study will provide reliable guidelines, ensuring sustained water accessibility by changing climate and land use, for policymakers and water resources authorities within the study area.

CASE STUDY
The basin of the Dez river is a sub-basin of the Karoon Basin and is located in a more significant division in the Persian Gulf basin (Figure 1). Total surface area of the Dez watershed is approximately 17,320 km 2 and its perimeter is  Table 3 shows the land use area (ha) and percent in Dez river basin. Texture of the soil ranges between loam, sandy loam, sandy clay loam and loamy coarse sand. The topographical elevation varies between 728 m to 2,543 m with an average elevation of roughly 1,300 m, and average slope of 51% (Noori et al. ). According to the discharge stations, about 50% of the Dez river discharge at the Talezang station is related to the Bakhtiari subbasin and 42% of it is from the Sezar subbasin. The long-term trend of hydrological 185 events along with 290 flood events in the Dez river basin has been evaluated for a 50-year period . Results indicate that the peak flows of 17 flood events (52% of the total floods) were over 2,900 m 3 /s from 1991 to 2005, while these values were normal from 1955 to 1990 (Samadi et al. ). Average precipitation in the study area is 700 mm with maximum and minimum temperatures of 27 C and 12 C, respectively. We used weather data (precipitation (mm), maximum and minimum temperature ( C)) and discharge data (Sezar, Bakhtiari, Dam, and Talezang discharge stations) for the calibration (1990 to 2010) and validation (2010 to 2017) periods.

MATERIALS AND METHODS
The analyses were conducted in five steps (Figure 2   SED j,k ¼ 11:8 (Q j,k q j,k A j,k ) 0:56 K j,k C j,k P j,k LS j,k CFRG j,k (1) In Equation (1), Q j,k denotes the volume of surface runoff related with the HRU (m 3 ), q j,k is the peak runoff rate (m 3 /s), K j,k is the soil erodibility factor, C j,k is the crop management factor, P j,k is the conservation practice factor, LS j,k is the topographic factor, and CFRG j,k is the coarse fragment factor. The complete description of the MUSLE is well recognized and utilized worldwide for investigation of water erosion (Djebou ).
In this study, ArcSWAT2012 is used as a visual interface to prepare a SWAT model (

Management data
The type of agricultural products, the amount of irrigation according to location changes of agricultural products in the basin, irrigation efficiency, amount of urea fertilizer, plant growth period, and the range of yield changes in different areas of the basin are presented in Table 1. According to the available information, irrigation took place at 10-day intervals based on the plant's water requirement; fertilization was performed during the first irrigation.  Table 2 shows the percentage of water available in each agricultural product. In Equation (2), the calculation of the crop yield (ton/ha) of agricultural products is shown. Table 3 shows the land use area (ha) and percent in the Dez river basin: objective function was assigned, and the software returned the range of predicted uncertainty within the 95% of the best simulations. Furthermore, to compare the performance of models, the statistical indices of NSE (Equation (3)) and R 2 (Equation (4)) were used.  Table 5 shows changes in precipitation, and also maximum and minimum temperatures. For coupling of five AOGCMs, an average of monthly changes was used.
The resolution of the GCMs is too wide for a local appraisement; hence, downscaling was performed using SWAT. The change factor downscaling method was performed to change the observed daily average temperature and precipitation utilizing Equations (5) and (6)

NSE
- number of the grid cells: The percentage of precipitation changes under each of the scenarios and the maximum and minimum increase in temperature of each of the scenarios are presented in Figure 3. The highest drop in rainfall is predicted in the RCP4.5 scenario (24% decrease) in August, and the highest increase is expected to be in July under the RCP8.5 scenario.
Furthermore, in scenario RCP8.5, the maximum temperature shows an increase of 3 C in the summer months (June-August). Table 5 is the combination of (ensemble) variations of the five selected models which display precipitation values relative to the base period and the absolute changes in temperature.

Water budget and land use land cover changes
Hydrological assessments within a watershed scale need a reliable water budget for management and evaluation of the future. Based on this concept, water budget components were calculated in the form of a historical period and future.
Precipitation, snowmelt, actual and potential evapotranspiration, shallow aquifer recharge, and deep aquifer recharge were then calculated.
The average long-term rainfall in this watershed is about 683 mm, which is known as the input of the hydrological cycle. Other components of the hydrological cycle are infiltration 87 mm (13%), runoff 184 mm (27%) and AET 412 mm (60%) ( Table 6).
To study and evaluate the effects of the LULC extreme change, three land use change scenarios were assigned for the developed model. The scenarios are: (i) change of crops (spring wheat, tomato, and winter wheat) to orchards   In this research, the SWAT model showed reasonable performance in runoff and sediment calibration and validation in the entire discharge stations. Table 7 shows calibrated parameters values and their sensitive ranking.
The lowest P-value with the highest absolute value of t-Stat indicates the most sensitive parameter and vice versa.
According to the results, CN2.mgt, TLAPS.sub, and GW_REVAP.gw were recognized as the most sensitive parameters for runoff simulation in this watershed. Also, in As mentioned earlier, the parameters given in Table 7 were used in the simulation of sediment. The results of statistical indices indicated that the developed SWAT model for the study area has a high accuracy in both calibration and validation phases. In accordance with the obtained results in Figure 5 and Table 9   In Figure 6   does not have the ability to simulate and understand many of these factors. In addition, the simulation of climate change effects on agricultural products with the SWAT model in a basin in China was investigated in a study conducted by Niu et al. (). In this study, the main products of the region such as corn, spring wheat, spring barley, and spring canola-polish were investigated. The simulated agricultural products showed high correlation     an increase of about 3.5 degrees of melting temperature, this will happen earlier with more intense evapotranspiration.
As sediment load is soluble in the river, sediment load changes will change with runoff. However, it worth mentioning that either sediment producing sources or sediment active factors (including crop type, soil, and topography and so on) did not change with climate change.
By increasing runoff in winter times within the RCP4.5 and RCP 8.5 scenarios, sediment load will increase about 13%-19% in February. Likewise, in the spring and summer, the mentioned changes will have increasing trends. For instance, sediment load will be increased under RCP scenarios around 30%-37%; 42%-49%; 54%-62%; respectively at July, August, and September. The latter occurred because of runoff increasing during these months. However, in the autumn, no reasonable significant changes will occur in sediment load, of course except the one in November by À12% decrease.
In general, it can be said that average runoff in the coming period will rise from 208 cubic meters per second in the baseline period to about 228 cubic meters per second in both scenarios (about 9% increase in each scenario). The results also showed that runoff variations in each scenario would be similar to each other, with a slight increase in the RCP8.5 scenario. Similar to these variations, sediment has also increased by 10% in each scenario.  () estimated that under RCP scenarios, significant changes will not occur in runoff in the upcoming periods.

Effect of climate change on crop yield and AET
Due to the effects of temperature increase and changes in precipitation pattern, the yield of agricultural products has reduced and the yield of horticultural products was slightly  studies under SRES scenarios, we will face a declining trend in agricultural products in the future, while changes in horticultural products will relatively increase.

Effect of climate change on the water budget
After the hydrological simulation of the basin in the base and future periods, the water budget of the watershed was calculated. The parameters of precipitation, surface runoff, infiltration into the aquifer and AET have been calculated from the components of discharge in the catchment area.
In the future, the changes will show a slight increase of about 5 mm in precipitation in both scenarios. In addition, the infiltration, which is a function of snow and snowmelt, will also be reduced from 11% to 9%. In each scenario, a surface runoff will be increased in the range of 26% to 29%. In addition, AET will increase relative to the base period (63% to 62%) but portion of AET will decrease relative to other water budget components (Table 12).
In an investigation conducted by Tan  Considering the major crops used in the model, the first scenario assumed that spring wheat and tomato have changed into apple and almond trees. In the second scenario, it is assumed that green grasslands, which contain grassy plants in spring and summer and contain about 58 percent of the area, have become bare lands. In the third scenario, assumption is that spring wheat (irrigated crop) has been changed to winter wheat (rainfed farming).
According to Figure 8, under the first scenario, LULC change in the SWAT model shows a reduction in runoff in comparison with changes in the existing LULC of the watershed. These changes occurred due to increased irrigation from the river, because the water requirement for the orchard's trees is more than the other crops in the region. For instance, major changes (about 5%) happened in May to July in RCP 8.5. In addition, sediment variations in this scenario has already shown a decrease in most months of the year. Under RCP 8.5 and RCP 4.5, sediment load has decreased by around 14% in summer.
In RCP scenarios, runoff has increased, in a range of 13%-19% under the second scenario of LULC changes.
The increase happened compared to the base period due to replacing the water-repellent and water-consuming areas (i.e. plants and shrubs) by bare lands. Furthermore, sediment variations in this scenario have been shown to rise in most months of the year so that, under RCP 8.5 and RCP 4.5, the sediment load has increased by around 13% in summer.
Considering the change of spring wheat to winter wheat (rainfed) under the third scenario of the study, area has not been changed significantly relative to the assigned climate scenarios. LULC changes on base period indicate that monthly runoff has been affected in most of the year. The latter occurred due to the irrigation reduction in this scenario, which led to increasing transpiration, evaporation, vegetation in winter and spring as well. Because the planting date for winter wheat is about four months earlier than spring wheat, during this time it decreases due to plant growth as well as changes in the surface cover of the runoff area. In addition, sediment in this scenario has been shown to rise in most months of the year. Under RCP 8.5 and RCP 4.5, sediment load has been increased by around 19% in summer.
In Figure 8, the runoff and sediment changes are shown relative to the base period. Moreover, all LULC scenarios that had been applied under climate change are given in appendices B and C.
In the study by Ngo et al. (), it was determined that, under the impact of increase in agricultural lands, the expansion of urban areas will increase runoff and sediment; in addition, increase in forest cover and implementation of soil conservation scenarios will reduce runoff and sediment.

CONCLUSION
In his study, the SWAT model was utilized to simulate the Dez river dam basin in Iran. In general, the climate change scenarios (RCP4.5, RCP8.5) did not predict unfavorable situations in the future.
The model indicated a high capability to simulate the runoff and sediment, (average accuracy of 0.75). The results of the climate change scenarios showed that climate change increases the runoff values, for example, an increase from 154 m 3 /s to 205 and 199 m 3 /s was projected for runoff, and an increase from 13 to 19% was projected for sediments.
Also, agricultural yield changes under the influence of two scenarios showed that horticultural crop yields would increase while agricultural crop yields would decrease. Furthermore, the AET will face a slight decline of 2% to 6%.
Results showed that, in the future, the watershed will face a slight increase in runoff and AET. Combined LULC and climate change scenarios showed that with amplification of orchards, sediment load would decrease.
In this research, multisite downscaling method in the SWAT model was applied. We would like to suggest that researchers use different multisite downscaling methods, like quantile mapping and bias correction. In addition, in the current research, it was not possible to add groundwater simulation. So, it is highly recommended to use SWAT-MODFLOW to simulate the interaction between groundwater and surface water.

DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.