Battling Water Limits to Growth: Lessons from Water Trends in the Central Plateau of Iran.

The Zayandeh-Rud River Basin in the central plateau of Iran continues to grapple with water shortages due to a water-intensive development path made possible by a primarily supply-oriented water management approach to battle the water limits to growth. Despite inter-basin water transfers and increasing groundwater supply, recurring water shortages and associated tensions among stakeholders underscore key weaknesses in the current water management paradigm. There was an alarming trend of groundwater depletion in the basin's four main aquifers in the 1993-2016 period as indicated by the results of the Mann-Kendall3 (MK3) test and Innovative Trend Analysis (ITA) of groundwater volume. The basin's water resources declined by more than 6 BCM in 2016 compared to 2005 based on the equivalent water height (EWH) derived from monthly data (2002-2016) from the GRACE. The extensive groundwater depletion is an unequivocal evidence of reduced water availability in the face of growing basin-wide demand, necessitating water saving in all water use sectors. Implementing an integrated water resources management plan that accounts for evolving water supply priorities, growing demand and scarcity, and institutional changes is an urgent step to alleviate the growing tensions and preempt future water insecurity problems that are bound to occur if demand management approaches are delayed.

Trend and abrupt change analysis in water quality of Urmia Lake in comparison with changes in lake water level.

Urmia Lake, as the largest lake in Iran borders, has a special role in the ecosystem of the region. The water level in this lake declines in recent year remarkably, so monitoring the lake water quality is important from an environmental view. In this research, the changes in the qualitative variables of the lake water (including electrical conductivity (EC), pH, total dissolved solids (TDS), and sodium adsorption ratio (SAR)) are compared with the changes in the lake's water level based on the Mann-Kendall nonparametric test. Further, abrupt change points in the time series of quality variables were detected by the Pettitt test. Studies were carried out on samples collected from five different stations during 2005-2015. The results showed that the water level of Urmia Lake had a significant decreasing trend and also, except for TDS, the other investigated quality variables had negative trends during the studied period. It was observed that in general, the values of the Z statistic in the stations located in the eastern part of the lake were more than the stations located in the western part, and also the stations located in the northern parts had a higher trend than those in the south of the bridge.

Daily runoff prediction using the linear and non-linear models.

Runoff prediction, as a nonlinear and complex process, is essential for designing canals, water management and planning, flood control and predicting soil erosion. There are a number of techniques for runoff prediction based on the hydro-meteorological and geomorphological variables. In recent years, several soft computing techniques have been developed to predict runoff. There are some challenging issues in runoff modeling including the selection of appropriate inputs and determination of the optimum length of training and testing data sets. In this study, the gamma test (GT), forward selection and factor analysis were used to determine the best input combination. In addition, GT was applied to determine the optimum length of training and testing data sets. Results showed the input combination based on the GT method with five variables has better performance than other combinations. For modeling, among four techniques: artificial neural networks, local linear regression, an adaptive neural-based fuzzy inference system and support vector machine (SVM), results indicated the performance of the SVM model is better than other techniques for runoff prediction in the Amameh watershed.

Investigating meteorological/groundwater droughts by copula to study anthropogenic impacts.

A critical understanding of the water crisis of Lake Urmia is the driver in this paper for a basin-wide investigation of its Meteorological (Met) droughts and Groundwater (GW) droughts. The challenge is to formulate a data-driven modelling strategy capable of discerning anthropogenic impacts and resilience patterns through using 21-years of monthly data records. The strategy includes: (i) transforming recorded timeseries into Met/GW indices; (ii) extracting their drought duration and severity; and (iii) deriving return periods of the maximum drought event through the copula method. The novelty of our strategy emerges from deriving return periods for Met and GW droughts and discerning anthropogenic impacts on GW droughts. The results comprise return periods for Met/GW droughts and their basin-wide spatial distributions, which are delineated into four zones. The information content of the results is statistically significant; and our interpretations hint at the basin resilience is already undermined, as evidenced by (i) subsidence problems and (ii) altering aquifers' interconnectivity with watercourses. These underpin the need for a planning system yet to emerge for mitigating impacts and rectifying their undue damages. The results discern that aquifer depletions stem from mismanagement but not from Met droughts. Already, migration from the basin area is detectable.

Prediction of Groundwater Level in Ardebil Plain Using Support Vector Regression and M5 Tree Model.

The Ardebil plain, which is located in northwest Iran, has been faced with a recent and severe decline in groundwater level caused by a decrease of precipitation, successive long-term droughts, and overexploitation of groundwater for irrigating the farmlands. Predictions of groundwater levels can help planners to deal with persistent water deficiencies. In this study, the support vector regression (SVR) and M5 decision tree models were used to predict the groundwater level in Ardebil plain. The monthly groundwater level data from 24 piezometers for a 17-year period (1997 to 2013) were used for training and test of models. The model inputs included the groundwater levels of previous months, the volume of entering precipitation into every cell, and the discharge of wells. The model output was the groundwater level in the current month. In order to evaluate the performance of models, the correlation coefficient (R) and the root-mean-square error criteria were used. The results indicated that both SVR and M5 decision tree models performed well for the prediction of groundwater level in the Ardebil plain. However, the results obtained from the M5 decision tree model are more straightforward, more easily applied, and simpler to interpret than those from the SVR. The highest accuracy was obtained using the SVR model to predict the groundwater level from the Ghareh Hasanloo and Khalifeloo piezometers with R = 0.996 and R = 0.983, respectively.