SWAT-MODSIM-PSO optimization of multi-crop planning in the Karkheh River Basin, Iran, under the impacts of climate change

Fereidoon, Majid; Koch, Manfred

doi:10.1016/j.scitotenv.2018.02.234

Cited by 64 publications

(34 citation statements)

References 49 publications

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“…The SWAT model is a semi-distributed hydrological model that operates on a daily time-step and was developed to assess the impact of water flow, agricultural management practices, sediment, and nutrient transport simulation in large complex river basins under different hydrologic, geologic, and climatic conditions [6]. SWAT is one of the most widely used hydrological model and has found countless applications all across the world, with a focus on the simulation of regional hydrological impact of climate change [47][48][49]. In the SWAT model, a catchment or basin is divided into a number of sub-basins (=198 in the present basin), which are then, based on the soil type, land-use, and, optionally, slope characteristics, further subdivided into so-called hydrologic response units (HRUs) with identical characteristic of some of these properties.…”

Section: Hydrological Model Swat and Its Implementation To Krb Basinmentioning

confidence: 99%

“…Hence, the groundwater parameters RCHRG_DP, GWQMN, SHALLST, and ALPHA_BF, followed by soil parameter SOL_BD, are identified to be more sensitive in the low-elevation (southern) parts of KRB than in the northern part (with higher elevations) [49]. It should be noted that the Curve Number CN at moisture condition II (CN2) is found to be the most sensitive parameter for the watershed as a whole [45].…”

Section: Swat-calibration Validation and Sensitivity Analysis Of Stmentioning

confidence: 99%

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Predicting Rainfall and Runoff Through Satellite Soil Moisture Data and SWAT Modelling for a Poorly Gauged Basin in Iran

Fereidoon

Koch

Brocca

2019

Water

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Hydrological models are widely used for many purposes in water sector projects, including streamflow prediction and flood risk assessment. Among the input data used in such hydrological models, the spatial-temporal variability of rainfall datasets has a significant role on the final discharge estimation. Therefore, accurate measurements of rainfall are vital. On the other hand, ground-based measurement networks, mainly in developing countries, are either nonexistent or too sparse to capture rainfall accurately. In addition to in-situ rainfall datasets, satellite-derived rainfall products are currently available globally with high spatial and temporal resolution. An innovative approach called SM2RAIN that estimates rainfall from soil moisture data has been applied successfully to various regions. In this study, first, soil moisture content derived from the Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) is used as input into the SM2RAIN algorithm to estimate daily rainfall (SM2R-AMSRE) at different sites in the Karkheh river basin (KRB), southwest Iran. Second, the SWAT (Soil and Water Assessment Tool) hydrological model was applied to simulate runoff using both ground-based observed rainfall and SM2R-AMSRE rainfall as input. The results reveal that the SM2R-AMSRE rainfall data are, in most cases, in good agreement with ground-based rainfall, with correlations R ranging between 0.58 and 0.88, though there is some underestimation of the observed rainfall due to soil moisture saturation not accounted for in the SM2RAIN equation. The subsequent SWAT-simulated monthly runoff from SM2R-AMSRE rainfall data (SWAT-SM2R-AMSRE) reproduces the observations at the six gauging stations (with coefficient of determination, R² > 0.71 and NSE > 0.56), though with slightly worse performances in terms of bias (Bias) and root-mean-square error (RMSE) and, again, some systematic flow underestimation compared to the SWAT model with ground-based rainfall input. Additionally, rainfall estimates of two satellite products of the Tropical Rainfall Measuring Mission (TRMM), 3B42 and 3B42RT, are used in the calibrated SWAT- model after bias correction. The monthly runoff predictions obtained with 3B42- rainfall have 0.42 < R2 < 0.72 and−0.06 < NSE < 0.74 which are slightly better than those obtained with 3B42RT- rainfall, but not as good as the SWAT-SM2R-AMSRE. Therefore, despite the aforementioned limitations, using SM2R-AMSRE rainfall data in a hydrological model like SWAT appears to be a viable approach in basins with limited ground-based rainfall data.

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Section: Hydrological Model Swat and Its Implementation To Krb Basinmentioning

confidence: 99%

Section: Swat-calibration Validation and Sensitivity Analysis Of Stmentioning

confidence: 99%

Predicting Rainfall and Runoff Through Satellite Soil Moisture Data and SWAT Modelling for a Poorly Gauged Basin in Iran

Fereidoon

Koch

Brocca

2019

Water

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“…the objective or cost function, is computed by the hydro-economic model, simulating the hydrological constraints by classical hydrological models, and (2) the minimization of that objective function is done by some kind of an optimization routine (e.g. [19]).…”

Section: Basic Concepts Of An Optimal Hydro-economic Modelmentioning

confidence: 99%

“…The MODSIM simulation model is a generalized river basin network model for developing basin-wide strategies of short-term water management, long-term operational planning, drought contingency planning, water rights analysis and conflict resolution between different water users [18,44]. This model has enjoyed widespread application across the world to simulate operations as mentioned [45][46][47]19].…”

Section: Modsim Water Resources Management Modelmentioning

confidence: 99%

“…However, the authors did not consider the CWP index, impacts of climate change and management of the conjunctive water uses. In fact, few of these issues have been addressed by Fereidoon and Koch [19] who employed a MODSIM-LINGO-PSO algorithm to maximize the economic benefits of the Karkheh Dam in Iran, in terms of water allocation for agriculture, under the impact of future climate change. The authors separated the optimization into a three-stage procedure, wherefore in the first step the MODSIM allocates the available water of Karkheh Dam, with its inflow simulated by the SWAT-hydrological model.…”

Section: Introductionmentioning

confidence: 99%

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Agro-Economic Water Productivity-Based Hydro-Economic Modeling for Optimal Irrigation and Crop Pattern Planning in the Zarrine River Basin, Iran, in the Wake of Climate Change.

Emami¹,

Koch²

2018

Preprint

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For water-stressed regions like Iran improving the effectiveness and productivity of agricultural water-use is of utmost importance due to climate change and unsustainable demands. Therefore, a hydro-economic model has been developed here for the Zarrine River Basin with the central concept of that demands are value-sensitive functions, where quantities of water-uses at different locations and times have a changeable economic benefits. To do this, the potential crop yields and the surface and groundwater resources, especially Boukan Dam inflow are simulated using the hydrologic model, SWAT, based on predicted climatic scenarios i.e. quantile mapping-downscaled projections. Then, to allocate the agricultural water based on the agro- economic crop water productivity (AEWP) of crops, a basin-wide water management tool, MODSIM, is customized. Next, a simulation- optimization model has been developed using a coupled CSPSO-MODSIM, to optimize the total AEWP, considering climatic impact and crop pattern scenarios, for 2020-2038, 2050-2068 and 2080-2098 periods. Finally, the optimum crop pattern and crop water irrigation depths are presented for different RCPs and periods. The results indicated that this approach will improve considerably the AEWPs and decrease the agricultural water-use up to 40%. Thus, this integrated model is able to support water authorities and other stakeholder in a water-scarce basin, as is the study area.

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Using SWAT‐MEA to determine optimal placement of crop management systems under no‐till

2022

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No‐till is one of the common conservation practices implemented in the Fort Cobb Reservoir Watershed (FCREW) located in central Oklahoma to improve soil and water resources while ensuring sustainable crop production. In this study, we used the recently developed Multi‐Objective Evolutionary Algorithm for the Soil and Water Assessment Tool model (SWAT‐MEA) to determine the optimal spatial placement of crop management systems when converting from conventional tillage to no‐till in the FCREW. A previously calibrated and validated Soil Water and Assessment Tool (SWAT) model for the FCREW for conventionally tilled land under winter wheat (Triticum aestivum L.), sorghum (Sorghum bicolor L.), and peanuts (Arachis hypogaea L.) cropping system was used. The objective functions for this study were maximizing crop yield ranks while minimizing sediment, total P (TP), and total N (TN) yield loads. Four modifications made in the SWAT‐MEA in order to achieve the defined goal of this study include an automated process of conversion from conventional to no‐till, ability to use crop yield ranks in the optimization process and to sum up species of N and P to TN and TP, and creation of a public domain standalone executable. Results indicated that converting from a conventionally tilled to no‐till system under optimal placement of crop management systems reduced TN, TP, and sediment yield losses by 45, 32, and 65%, respectively, while crop yields were not significantly affected. Overall, this case study shows that the SWAT‐MEA was able to successfully identify the optimal placement of crop management systems under no‐till condition and can be used for decision making.

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SWAT-MODSIM-PSO optimization of multi-crop planning in the Karkheh River Basin, Iran, under the impacts of climate change

Cited by 64 publications

References 49 publications

Predicting Rainfall and Runoff Through Satellite Soil Moisture Data and SWAT Modelling for a Poorly Gauged Basin in Iran

Predicting Rainfall and Runoff Through Satellite Soil Moisture Data and SWAT Modelling for a Poorly Gauged Basin in Iran

Agro-Economic Water Productivity-Based Hydro-Economic Modeling for Optimal Irrigation and Crop Pattern Planning in the Zarrine River Basin, Iran, in the Wake of Climate Change.

Using SWAT‐MEA to determine optimal placement of crop management systems under no‐till

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