Assessing the economic effects of drought using Positive Mathematical Planning model under climate change scenarios.

In recent decades, regions all around the world have experienced severe droughts adversely affecting their agricultural production. Climate change, along with limited access to water will alter future production and agricultural development. The purpose of this study is to provide a perspective for the future cultivation regime in the Divandarre region in the Sepidrood catchment in Iran, using historical climatic, agricultural, and economic information. Future precipitation values are determined for three climate scenarios, then downscaled and converted to pixel-based precipitation maps using the Moving Least Squares method. Future droughts are identified using the Standardized Precipitation Index at 3, 6, and 9-month intervals based on precipitation values and the relationship between different types of droughts (meteorological, agricultural and hydrological). We introduce a new coefficient, the water cost coefficient, derived from drought characteristics that captures the added irrigation cost in drought years because of increased water price. Using the Positive Mathematical Planning method and considering limited land and water, predicted future prices and costs based on a linear regression of supply-demand, and the annual water cost coefficient values, an agroeconomic model is built. After prediction of future price and cost based on historical data from 2005 to 2018, we run future scenarios based on various price and cost values to determine the optimal annual cultivation area for each crop from 2020 to 2040. All scenarios indicate a decline in cultivation area for all crops making agriculture less beneficial in the future. The cultivation regime moves away from more water-consuming products with less economic value (e.g. watermelon) toward less water-consuming, more expensive products (e.g. lentils). The findings of this model along with expert economic judgments help determine the economic effects of climate change on irrigation, farmers' decisions, and water policies, including water markets, and improving irrigation efficiency. Authorities and farmers could adapt to drought shocks and changes in the market while experiencing less revenue loss.

Hybrid global gridded snow products and conceptual simulations of distributed snow budget: evaluation of different scenarios in a mountainous watershed.

Considering snowmelt in mountainous areas as the important source of streamflow, the snow accumulation/melting processes are vital for accurate simulation of the hydrological regimes. The lack of snow-related data and its uncertainties/conceptual ambiguity in snowpack modeling are the different challenges of developing hydroclimatological models. To tackle these challenges, Global Gridded Snow Products (GGSPs) are introduced, which effectively simplify the identification of the spatial characteristics of snow hydrological variables. This research aims to investigate the performance of multi-source GGSPs using multi-stage calibration strategies in hydrological modeling. The used GGSPs were Snow-Covered Area (SCA) and Snow Water Equivalent (SWE), implemented individually or jointly to calibrate an appropriate water balance model. The study area was a mountainous watershed located in Western Iran with a considerable contribution of snowmelt to the generated streamflow. The results showed that using GGSPs as complementary information in the calibration process, besides streamflow time series, could improve the modeling accuracy compared to the conventional calibration, which is only based on streamflow data. The SCA with NSE, KGE, and RMSE values varying within the ranges of 0.47-0.57, 0.54-0.65, and 4-6.88, respectively, outperformed the SWE with the corresponding metrics of 0.36-0.59, 0.47-0.60, and 5.22-7.46, respectively, in simulating the total streamflow of the watershed. In addition to the superiority of the SCA over SWE, the two-stage calibration strategy reduced the number of optimized parameters in each stage and the dependency of internal processes on the streamflow and improved the accuracy of the results compared with the conventional calibration strategy. On the other hand, the consistent contribution of snowmelt to the total generated streamflow (ranging from 0.9 to 1.47) and the ratio of snow melting to snowfall (ranging from 0.925 to 1.041) in different calibration strategies and models resulted in a reliable simulation of the model.

Water quality assessment of the most important dam (Latyan dam) in Tehran, Iran.

A pattern of water quality affected by the input regime and site specification is one of the most important issues in water supply resources and demand management. The present study aims to assess the water quality of Latyan dam, the important reservoir that supplies drinking water of Tehran, the capital city of Iran. Monthly water sampling was performed at four depths of the dam. The physical and chemical properties of water including temperature, pH, turbidity, total dissolved solids (TDS), electrical conductivity (EC), hardness, as well as Ca2+, Mg2+, Na+, K+, Cl-, SO42-, silica, ammonium, nitrate, nitrite, and total phosphorus (TP) concentration, were monitored from May 2014 to January 2017. The results of statistical analysis indicated that the quality of water in the dam was affected by the depth in view of five variables of EC, pH, turbidity, SO42-, and TP (p Ë‚ 0.05). Moreover, it was determined that ten out of 17 variables including EC, turbidity, TDS, Ca2+, K, Cl-, SO42-, silica, nitrate, and TP were statistically significant (p Ë‚ 0.05) based on seasonal variation analyses. Comparing the mean values, it could be concluded that EC, TDS, Ca2+, K+, Cl-, and SO42- were slightly higher during the winter probably owing to the diluting effects of seasonal precipitation. Nutrient-like distribution, nitrate, and TP, as well as silica, were much higher during the spring representing a high-diatoms activity and eutrophication. Turbidity was higher in the fall compared to the other seasons, which also demonstrated rainfall effects. Evaluation of the aggressiveness index (AI) suggests that water is moderately corrosive, and noncorrosive in the winter compared to other seasons. Moreover, according to the findings of this research and their comparison with the national and international water quality guidelines/standards, it could be said that the water quality of Latyan dam during the study period was acceptable and thus it was usable as a drinking water supply. However, due to the changes occurred in nitrate and phosphorus and extensive development of eutrophication problem, accurate and continuous evaluation of water quality in this reservoir is deemed necessary.