Lake Urmia restoration success story: A natural trend or a planned remedy?

Lake Urmia is the second-largest hypersaline lake in the world. There has been a drastic water level drop of 7.2 m from 1995 to 2016. Beginning in October 2013, the Lake Urmia Restoration Plan (LURP) launched a 10-year program. An increase in water level and a relative improvement in Lake Urmia condition has been observed since 2017. It is an undecided and controversial issue whether the recent positive trend of Lake Urmia has been due to the LURP activities or it is a natural contribution of climate factors variations. To shed some light on this issue, we examine three other lakes, adjacent to the Lake Urmia basin, with similar rainfall variability to investigate their status during the same period. Van (Turkey), Mosul, and Tharthar (both in Iraq), are evaluated as well as Lake Urmia. Three decades of remotely sensed data including precipitation ($P$), water level ($\mathit{WL}$), and lake extent ($A$) were considered for the mentioned lakes. A significant correlation was observed between standardized $\mathit{WL}-P$, and $A-P$ over the long-term period, especially for the recent three years ($R$² = 0.63–0.87). We show that the cumulative precipitation in the antecedent months played a major role in the improvement of these lakes' situation but with different time lags (up to 6 months for Van and Mosul lakes and up to 36 months for Lake Urmia and Tharthar lake). These findings could inform the planners of LURP to adopt strategies for achieving a sustainable state of Lake Urmia based on a more realistic outlook.     

Flood Frequency Analysis of Interconnected Rivers by Copulas

Flood frequency analysis (FFA) considering the confluence of interconnected rivers is important for hydraulic structures (such as dams or diversions) design, but it has received little attention. This study develops a copula-based method for FFA and quantile estimation considering the confluence of two interconnected rivers, along with the uncertainty estimation by a nonparametric bootstrapping algorithm. Flood probability distribution and return periods are estimated for the two rivers by mapping from bivariate to univariate peak flow quantile estimation. The methodology is applied to the case study of Qezel Ozan and Shahrud Rivers which merge to one of the largest reservoir dams in Iran: Sefidrud (Manjil) dam. According to the results from Peak flow records from Gilvan station (GPF) at Qezel Ozan River and from Loshan station (LCF) at Shahrud River, Gaussian copula with Weibull and gamma margins fits best. Also, it shows that some peak flow quantiles with the same magnitudes have a different probability of occurrences at the confluence of the rivers, and the bivariate estimation uncertainty usually plays an important role in FFA. These findings suggest the use of bivariate instead of univariate distributions to the peak flows at the confluence of interconnected rivers, in which the sampling uncertainty should be considered.

     

Trend Detection of Drought in Arid and Semi-Arid Regions of Iran Based on Implementation of Reconnaissance Drought Index (RDI) and Application of Non-Parametrical Statistical Method

Drought is known as one of the main natural hazards especially in arid and semi-arid regions where there are considerable issues in regard to water resources management. Also, climate changes has been introduced as a global concern and therefore, under conditions of climate change and global warming, the investigation of drought severity trend in regions such as Iran which is mainly covered by arid and semi-arid climate conditions is in the primary of importance. Therefore, in this study, based on the application of Reconnaissance Drought Index (RDI) for assessment drought severities, and also the implementation of non-parametric Mann- Kendall statistics and Sen’s slope estimator, the trends in different time series of RDI (3, 6, 9, 12, 18 and 24 monthly time series) were investigated. Results indicated the frequent decreasing trends in RDI time series particularly for long term time series (12, 18 and 24 monthly time series) than short term ones. Decreasing trend in RDI time series means the increasing trend in drought severities. Since the water resources especially ground water in most cases are affected by long term droughts, therefore, increasing trend in drought intensities in long term ones can be a threat for water resources management in surveyed areas.