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In this study, decision-making models in uncertain conditions are developed to identify optimal strategies for reducing competition between agricultural and environmental water demand. The decision-making models are applied to the irrigated Miyandoab Plain, located upstream of endorheic Lake Urmia in Northwestern Iran. Decision-making models are conceptualized based on static and dynamic Bayesian Belief Networks (BBN). The static BBN evaluates the effects of management strategies and drought conditions on environmental flow and agricultural profit at the annual scale, while the dynamic BBN accounts for monthly dynamics of water demand and conjunctive use. The reliability and performance of BBNs depend on the quantity and quality of data used to train the BBN and create conditional probability tables (CPTs). In this study, simulated outputs from a multi-period simulation-optimization model (Dehganipour et al., 2020) are used to populate the CPTs in each BBN and reduce the BBN training error. Cross-validation tests and sensitivity analysis are used to evaluate the effectiveness of the resulting BBNs. Sensitivity analysis shows that drought conditions have the most significant impact on environmental flow compared to other variables. Cross-validation tests show that the BBNs are able to reproduce outputs of the complex simulation-optimization model used for training, and therefore provide a computationally fast alternative for decision-making under uncertainty.
Reference: Dehghanipour, A. H., Schoups, G., Zahabiyoun, B., & Babazadeh, H. (2020). Meeting agricultural and environmental water demand in endorheic irrigated river basins: A simulation-optimization approach applied to the Urmia Lake basin in Iran. Agricultural Water Management, 241, 106353.
Amirhossein Dehghanipour; Gerrit Schoups; Hossein Babazadeh; Majid Ehtiat; Bagher Zahabiyoun. Bayesian Belief Networks for the metamodeling of simulation-optimization model to identify optimum water allocation scenario, Application in Miyandoab plain, Urmia Lake basin, Iran. 2021, 1 .
AMA StyleAmirhossein Dehghanipour, Gerrit Schoups, Hossein Babazadeh, Majid Ehtiat, Bagher Zahabiyoun. Bayesian Belief Networks for the metamodeling of simulation-optimization model to identify optimum water allocation scenario, Application in Miyandoab plain, Urmia Lake basin, Iran. . 2021; ():1.
Chicago/Turabian StyleAmirhossein Dehghanipour; Gerrit Schoups; Hossein Babazadeh; Majid Ehtiat; Bagher Zahabiyoun. 2021. "Bayesian Belief Networks for the metamodeling of simulation-optimization model to identify optimum water allocation scenario, Application in Miyandoab plain, Urmia Lake basin, Iran." , no. : 1.
Lake Urmia in northwestern Iran is the largest lake in Iran and the second largest saltwater lake in the world. The water level in Lake Urmia has decreased dramatically in recent years, due to drought, climate change, and the overuse of water resources for irrigation. This shrinking of the lake may affect local climate conditions, assuming that the lake itself affects the local climate. In this study, we quantified the lake’s impact on the local climate by analyzing hourly time series of data on climate variables (temperature, vapor pressure, relative humidity, evaporation, and dewpoint temperature for all seasons, and local lake/land breezes in summer) for the period 1961–2016. For this, we compared high quality, long-term climate data obtained from Urmia and Saqez meteorological stations, located 30 km and 185 km from the lake center, respectively. We then investigated the effect of lake level decrease on the climate variables by dividing the data into periods 1961–1995 (normal lake level) and 1996–2016 (low lake level). The results showed that at Urmia station (close to the lake), climate parameters displayed fewer fluctuations and were evidently affected by Lake Urmia compared with those at Saqez station. The effects of the lake on the local climate increased with increasing temperature, with the most significant impact in summer and the least in winter. The results also indicated that, despite decreasing lake level, local climate conditions are still influenced by Lake Urmia, but to a lesser extent.
Amir Hossein Dehghanipour; Davood Moshir Panahi; Hossein Mousavi; Zahra Kalantari; Massoud Tajrishy. Effects of Water Level Decline in Lake Urmia, Iran, on Local Climate Conditions. Water 2020, 12, 2153 .
AMA StyleAmir Hossein Dehghanipour, Davood Moshir Panahi, Hossein Mousavi, Zahra Kalantari, Massoud Tajrishy. Effects of Water Level Decline in Lake Urmia, Iran, on Local Climate Conditions. Water. 2020; 12 (8):2153.
Chicago/Turabian StyleAmir Hossein Dehghanipour; Davood Moshir Panahi; Hossein Mousavi; Zahra Kalantari; Massoud Tajrishy. 2020. "Effects of Water Level Decline in Lake Urmia, Iran, on Local Climate Conditions." Water 12, no. 8: 2153.
Lake Urmia in northwestern Iran is the largest lake in Iran and the second largest saltwater lake in the world. The water level in Lake Urmia has decreased dramatically in recent years, due to drought, climate change, and overuse of water resources for irrigation. This shrinking of the lake may affect local climate conditions, assuming that the lake itself affects the local climate. In this study, we quantified the lake’s impact on the local climate by analyzing hourly time series of data on climate variables (temperature, vapor pressure, relative humidity, evaporation, and dewpoint temperature for all seasons, and local lake/land breezes in summer) for the period 1961-2016. For this, we compared high quality, long-term climate data obtained from Urmia and Saqez meteorological stations, located 30 km and 185 km from the lake center, respectively. We then investigated the effect of lake level decrease on the climate variables by dividing the data into 1961-1995 (normal lake level) and 1996-2016 (low lake level). The results showed that at Urmia station (close to the lake), climate parameters displayed fewer fluctuations and were evidently affected by Lake Urmia compared with those at Saqez station. The effects of the lake on the local climate increased with increasing temperature, with the most significant impact in summer and the least in winter. The results also indicated that, despite decreasing lake level, local climate conditions are still influenced by Lake Urmia, but to a lesser extent.
Amir Hossein Dehghanipour; Davood Moshir Panahi; Hossein Mousavi; Zahra Kalantari; Massoud Tajrishy. Effects of Water Level Decline in Lake Urmia, Iran, on Local Climate Conditions. 2020, 1 .
AMA StyleAmir Hossein Dehghanipour, Davood Moshir Panahi, Hossein Mousavi, Zahra Kalantari, Massoud Tajrishy. Effects of Water Level Decline in Lake Urmia, Iran, on Local Climate Conditions. . 2020; ():1.
Chicago/Turabian StyleAmir Hossein Dehghanipour; Davood Moshir Panahi; Hossein Mousavi; Zahra Kalantari; Massoud Tajrishy. 2020. "Effects of Water Level Decline in Lake Urmia, Iran, on Local Climate Conditions." , no. : 1.
Competition for water between agriculture and the environment is a growing problem in irrigated regions across the globe, especially in endorheic basins with downstream freshwater lakes impacted by upstream irrigation withdrawals. This study presents and applies a novel simulation-optimization (SO) approach for identifying water management strategies in such settings. Our approach combines three key features for increased exploration of strategies. First, minimum environmental flow requirements are treated as a decision variable in the optimization model, yielding more flexibility than existing approaches that either treat it as a precomputed constraint or as an objective to be maximized. Second, conjunctive use is included as a management option by using dynamically coupled surface water (WEAP) and groundwater (MODFLOW) simulation models. Third, multi-objective optimization is used to yield entire Pareto sets of water management strategies that trade off between meeting environmental and agricultural water demand. The methodology is applied to the irrigated Miyandoab Plain, located upstream of endorheic Lake Urmia in Northwestern Iran. Results identify multiple strategies, i.e., combinations of minimum environmental flow requirements, deficit irrigation, and crop selection, that simultaneously increase environmental flow (up to 16 %) and agricultural profit (up to 24 %) compared to historical conditions. Results further show that significant temporary drops in agricultural profit occur during droughts when long-term profit is maximized, but that this can be avoided by increasing groundwater pumping capacity and temporarily reducing the lake’s minimum environmental flow requirements. Such a strategy is feasible during moderate droughts when resulting declines in groundwater and lake water levels fully recover after each drought. Overall, these results demonstrate the usefulness and flexibility of the methodology in identifying a range of potential water management strategies in complex irrigated endorheic basins like the Lake Urmia basin.
Amir Hossein Dehghanipour; Gerrit Schoups; Bagher Zahabiyoun; Hossein Babazadeh. Meeting agricultural and environmental water demand in endorheic irrigated river basins: A simulation-optimization approach applied to the Urmia Lake basin in Iran. Agricultural Water Management 2020, 241, 106353 .
AMA StyleAmir Hossein Dehghanipour, Gerrit Schoups, Bagher Zahabiyoun, Hossein Babazadeh. Meeting agricultural and environmental water demand in endorheic irrigated river basins: A simulation-optimization approach applied to the Urmia Lake basin in Iran. Agricultural Water Management. 2020; 241 ():106353.
Chicago/Turabian StyleAmir Hossein Dehghanipour; Gerrit Schoups; Bagher Zahabiyoun; Hossein Babazadeh. 2020. "Meeting agricultural and environmental water demand in endorheic irrigated river basins: A simulation-optimization approach applied to the Urmia Lake basin in Iran." Agricultural Water Management 241, no. : 106353.
In this study, we developed a simulation-optimization model for optimum water allocation to meet environmental flow requirements and agricultural demand. The simulation model consists of three modules: a hydrologic module, an agronomic module, and an economic module. The hydrologic module is based on a dynamic coupling of WEAP and MODFLOW, and includes water balances for the crop root zone, the surface water system, and the underlying aquifer. The agronomic module simulates the effect of deficit irrigation on crop yield response in each growth stage, while the economic module calculates the net benefit of crop production. The optimization model contains two objective functions, one related to agricultural production and the other related to environmental flows. These conflicting objective functions are maximized using the Multi-Objective Particle Swarm Optimization algorithm. Decision variables include crop acreages, minimum environmental flow requirements in the river, and the degree of deficit irrigation. We applied the simulation-optimization model to the irrigated Miyandoab plain in the semi-arid northwest of Iran, for the historical period 1984 to 2013. There is competition between irrigation demands in the plain and environmental flow requirements to downstream Lake Urmia, which has been shrinking in recent years due to decreased inflows. Our results quantify what the (Pareto) trade-off looks like between meeting environmental and agricultural water demand in the region. We find that historical water allocations were suboptimal and that both agricultural and environmental benefits can be increased by better management of cropping decisions, deficit irrigation, and environmental flow requirements. We further show that increased groundwater use for irrigation can partly alleviate the trade-off, but that it leads to significant declines in groundwater levels due to the relatively small specific yield of the aquifer.
Amirhossein Dehghanipour; Gerrit Schoups; Bagher Zahabiyoun. Simulation–optimization model for optimum water allocation between environmental and agricultural demand using a coupled WEAP-MODFLOW model: Application in Miyandoab plain, Urmia basin, Iran. 2020, 1 .
AMA StyleAmirhossein Dehghanipour, Gerrit Schoups, Bagher Zahabiyoun. Simulation–optimization model for optimum water allocation between environmental and agricultural demand using a coupled WEAP-MODFLOW model: Application in Miyandoab plain, Urmia basin, Iran. . 2020; ():1.
Chicago/Turabian StyleAmirhossein Dehghanipour; Gerrit Schoups; Bagher Zahabiyoun. 2020. "Simulation–optimization model for optimum water allocation between environmental and agricultural demand using a coupled WEAP-MODFLOW model: Application in Miyandoab plain, Urmia basin, Iran." , no. : 1.
Amir Hossein Dehghanipour; Bagher Zahabiyoun; Gerrit Schoups; Hossein Babazadeh. A WEAP-MODFLOW surface water-groundwater model for the irrigated Miyandoab plain, Urmia lake basin, Iran: Multi-objective calibration and quantification of historical drought impacts. Agricultural Water Management 2019, 223, 1 .
AMA StyleAmir Hossein Dehghanipour, Bagher Zahabiyoun, Gerrit Schoups, Hossein Babazadeh. A WEAP-MODFLOW surface water-groundwater model for the irrigated Miyandoab plain, Urmia lake basin, Iran: Multi-objective calibration and quantification of historical drought impacts. Agricultural Water Management. 2019; 223 ():1.
Chicago/Turabian StyleAmir Hossein Dehghanipour; Bagher Zahabiyoun; Gerrit Schoups; Hossein Babazadeh. 2019. "A WEAP-MODFLOW surface water-groundwater model for the irrigated Miyandoab plain, Urmia lake basin, Iran: Multi-objective calibration and quantification of historical drought impacts." Agricultural Water Management 223, no. : 1.