Evaluation of Interaction between Aquifer and river Using Integrated SWAT-MODFLOW-NWT Model (Case study: Mahabad plain)

Surface and groundwater dynamically interact at different spatial or temporal scales within a plain. Accurate estimation of water balance components is an important simulation of such interactions. Despite the rapid expansion of numerical models over the past two decades, there is still room for improvement for comprehensive and integrated assessment as well as management of surface and groundwater resources. In particular, the use of coupled surface and groundwater models is important to connect both surface and groundwater, and for proper representation of the water balance in the unsaturated root zone. The results of various studies suggest that the combination of SWAT and MODFLOW models can satisfactorily simulate the interaction between surface and groundwater at different spatial and temporal dimensions (Sophocleous and Perkins, 2000; Sun and Cornish, 2005; Bejranonda et al., 2007). Indeed, if both models are used simultaneously, not only the limitations of the two individual models can be improved, but also the temporal-spatial properties of the target area can be adequately reflected (Kim et al., 2008; Park and Bailey, 2017; Wei et al., 2018). Specifically in the Urmia Lake Basin, which has been severely affected by indiscriminate exploitation of water resources, these models can be used to maximize the supply of Urmia Lake based on the pattern of supplying irrigation needs from integrated water sources. This requires the interaction of surface and groundwater resources in different locations of plains and aquifers to be simulated and predicted based on different shares of agricultural water supply from integrated water sources.The main purpose of this study was to evaluate the interaction between ground and surface water in Mahabad plain using the coupled SWAT-MODFLOW-NWT model as a comprehensive and integrated model. The main challenge in this study is the interaction and monitoring of water table adjacent to the surface water bodies

Climate change or irrigated agriculture – what drives the water level decline of Lake Urmia

Lake Urmia is one of the largest hypersaline lakes on earth with a unique biodiversity. Over the past two decades the lake water level declined dramatically, threatening the functionality of the lake’s ecosystems. There is a controversial debate about the reasons for this decline, with either mismanagement of the water resources, or climatic changes assumed to be the main cause. In this study we quantified the water budget components of Lake Urmia and analyzed their temporal evolution and interplay over the last five decades. With this we can show that variations of Lake Urmia’s water level during the analyzed period were mainly triggered by climatic changes. However, under the current climatic conditions agricultural water extraction volumes are significant compared to the remaining surface water inflow volumes. Changes in agricultural water withdrawal would have a significant impact on the lake volume and could either stabilize the lake, or lead to its complete collapse

Is WaPOR precipitation data reliable over Iran?

&amp;lt;p&amp;gt;As a result of satellite observations, ground observations, and data assimilation, global precipitation datasets have been developed for regions like Iran, where ground observations are limited. This study presents a comprehensive evaluation of WaPOR precipitation dataset over Iran at daily time scale. We considered a period of three years from 2019 to the end of 2021 and 394 synoptic rain gauges are used for the assessment. Daily WaPOR precipitation data at 250m scale downloaded and compared pixel-to-point with in-situ data. In addition, the WaPOR data and stations data were compared based on time classification (seasonal), location in the main catchment basins of Iran, and elevation above sea level. Calculating MSE, R score, RMSE and MSLE between real data(stations) and predict data(Wapor) shows some important result: 1. From the time point of view, WaPOR has best performance in summer (MSE = 4.94 and MSLE = 0.16) 2. Location, the best performance is related to stations of the catchment areas of the eastern part of Iran (Qaraqom basin with MSE = 11.9 and eastern border basin with MSE = 6.26) and the worst performance is related to the catchment area of the Caspian Sea (Mazandaran Sea basin with MSE = 64.06). 3. For analyzing the effect of elevation on precipitation, we divided the stations into 5 groups with an interval of approximately 600 meters (according to the lowest and highest elevation, which is -25 meters and 2965 meters). The best performance is related to stations with an altitude between 572 and 1170 meters (MSE = 21.61) and the worst is related to stations with an altitude between 1768 and 2366 meters (MSE = 43.79). 4. Moreover, on average for each station, in the three years of study (1096 days), we have 166 days (with standard deviation 119 days) that station has recorded precipitation but WaPOR dataset didn&amp;amp;#8217;t represent any record, so it&amp;amp;#8217;s not appropriate for daily hydrological models. 5. The difference between the three-year precipitation total at the station and the WaPOR precipitation total is 449.6 mm on average (with standard deviation 724.5).&amp;lt;/p&amp;gt;</jats:p

A Method to Estimate Surface Soil Moisture and Map the Irrigated Cropland Area Using Sentinel-1 and Sentinel-2 Data

Considering variations in surface soil moisture (SSM) is essential in improving crop yield and irrigation scheduling. Today, most remotely sensed soil moisture products have difficulties in resolving irrigation signals at the plot scale. This study aims to use Sentinel-1 radar backscatter and Sentinel-2 multispectral imagery to estimate SSM at high spatial (10 m) and temporal resolution (at least 5 days) over an agricultural domain. Three supervised machine learning algorithms, multilayer perceptron (MLP), a convolutional neural network (CNN), and linear regression models, were trained to estimate changes in SSM based on the variation in surface reflectance and backscatter over five different crops. Results showed that CNN is the best algorithm as it understands spatial relations and better represents two-dimensional images. Estimated values for SSM were in agreement with in-situ measurements regardless of the crop type, with RMSE=0.0292 (cm3/cm3) and R2=0.92 for the Sentinel-2 derived SSM and RMSE=0.0317 (cm3/cm3) and R2=0.84 for the Sentinel-1 soil moisture data. Moreover, a time series of estimated SSM based on Sentinel-1 (SSM-S1), Sentinel-2 (SSM-S2), and SSM derived from SMAP-Sentinel1 was compared. The developed SSM data showed a significantly higher mean SSM state over irrigated agriculture relative to the rainfed cropland area during the irrigation season. The multiple comparisons (fisher LSD) were tested and found that these two groups are different (pvalue=0.035 in 95% confidence interval). Therefore, by employing the maximum likelihood classification on the SSM data, we managed to map the irrigated agriculture. The overall accuracy of this unsupervised classification is 77%, with a kappa coefficient of 65%.</jats:p

Siting Detention Basins Using SWMM and Spatial Multi-Criteria Decision Making

Detention basins are one of the structural measures for floodwater control in urban environments. They are effective tools in flood mitigation, but some studies have shown that they may aggravate the condition if not properly sited. This study presents an innovative approach which directly incorporates hydrologic-hydraulic modeling results to the site selection procedure for flood control detention basins. Darakeh Catchment located in Tehran is selected as the case study. Hydrologic, physiographic, and economic parameters are considered as siting criteria. SWMM model is employed for simulating hydrologic-hydraulic processes and evaluating the current drainage network against low-frequent storms. Modeling results, including flooded junctions and the flow hydrographs, are used as input parameters to the spatial decision making framework. The framework employs Analytical Hierarchy Process (AHP) as the decision making structure and geographic information system (GIS) as the spatial analyst tool. The output is a raster map which shows each cell potential for the placement of the detention basin. The proposed approach aims to improve the siting procedure based on these measures and other BMPs in an urban environment

System Dynamics Modeling of Multipurpose Reservoir Operation

System dynamics, a feedback – based object – oriented simulation approach, not only represents complex dynamic systemic systems in a realistic way but also allows the involvement of end users in model development to increase their confidence in modeling process. The increased speed of model development, the possibility of group model development, the effective communication of model results, and the trust developed in the model due to user participation are the main strengths of this approach. The ease of model modification in response to changes in the system and the ability to perform sensitivity analysis make this approach more attractive compared with systems analysis techniques for modeling water management systems. In this study, a system dynamics model was developed for the Zayandehrud basin in central Iran. This model contains river basin, dam reservoir, plains, irrigation systems, and groundwater. Current operation rule is conjunctive use of ground and surface water. Allocation factor for each irrigation system is computed based on the feedback from groundwater storage in its zone. Deficit water is extracted from groundwater.The results show that applying better rules can not only satisfy all demands such as Gawkhuni swamp environmental demand, but it can also  prevent groundwater level drawdown in future