Study region: This study develops the first daily runoff forecast system for Bukan reservoir in Urmia Lake basin (ULB), Iran, a region suffering from water shortages and competing water demands. Study focus: A weather forecast downscaling model is developed for downscaling large-scale raw weather forecasts of ECMWF and NCEP to small-scale spatial resolutions. Various downscaling methods are compared, including deterministic Artificial Intelligence (AI) techniques and a Bayesian Belief Network (BBN). Downscaled precipitation and temperature forecasts are then fed into a rainfall-runoff model that accounts for daily snow and soil moisture dynamics in the sub-basins upstream of Bukan reservoir. The multi-objective Particle Swarm Optimization (MOPSO) method is used to estimate hydrological model parameters by maximizing the simulation accuracy of observed river flow (NSE_Q) and the logarithm of river flow (NSE_LogQ) in each sub-basin. New hydrological insights for the region: Results of the weather forecast downscaling model show that the accuracy of the BBN is greater than the various deterministic AI methods tested. Calibration results of the rainfall-runoff model indicate no significant trade-off between fitting daily high and low flows, with an average NSE_Q and NSE_LogQ of 0.43 and 0.63 for the calibration period, and 0.54 and 0.57 for the validation period. The entire forecasting system was evaluated using inflow observations for years 2020 and 2021, resulting in an NSE of 0.66 for forecasting daily inflow into Bukan reservoir. The inflow forecasts can be used by policymakers and operators of the reservoir to optimize water allocation between agricultural and environmental demands in the ULB.

Evaporation from surface water plays a crucial role in water accounting of basins, water resource management, and irrigation systems management. As such, the simulation of evaporation with high accuracy is very important. In this study, two methods for simulating pan evaporation under different climatic conditions in Iran were developed. In the first method, six experimental relationships (linear, quadratic, and cubic, with two input combinations) were determined for Iran’s six climate types, inspired by a multilayer perceptron neural network (MLP-NN) neuron and optimized with the genetic algorithm. The best relationship of the six was selected for each climate type, and the results were presented in a three-dimensional graph. The best overall relationship obtained in the first method was used as the basic relationship in the second method, and climatic correction coefficients were determined for other climate types using the genetic algorithm optimization model. Finally, the accuracy of the two methods was validated using data from 32 synoptic weather stations throughout Iran. For the first method, error tolerance diagrams and statistical coefficients showed that a quadratic experimental relationship performed best under all climatic conditions. To simplify the method, two graphs were created based on the quadratic relationship for the different climate types, with the axes of the graphs showing relative humidity and temperature, and with pan evaporation, were drawn as contours. For the second method, the quadratic relationship for semi-dry conditions was selected as the basic relationship. The estimated climatic correction coefficients for other climate types lay between 0.8 and 1 for dry, semi-dry, semi-humid, Mediterranean climates, and between 0.4 and 0.6 for humid and very humid climates, indicating that one single relationship cannot be used to simulate pan evaporation for all climatic conditions in Iran. The validation results confirmed the accuracy of the two methods in simulating pan evaporation under different climatic conditions in Iran.

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.

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.