Quantifying the impacts of human water use and climate variations on recent drying of Lake Urmia basin: the value of different sets of spaceborne and in situ data for calibrating a global hydrological model

Abstract

Abstract. During the last decades, the endorheic Lake Urmia basin in northwestern Iran has suffered from declining groundwater tables and a very strong recent reduction in the volume of Lake Urmia. For the case of Lake Urmia basin, this study explores the value of different locally and globally available observation data for adjusting a global hydrological model such that it can be used for distinguishing the impacts of human water use and climate variations. The WaterGAP Global Hydrology Model (WGHM) was for the first time calibrated against multiple in situ and spaceborne data to analyze the decreasing lake water volume, lake river inflow, loss of groundwater, and total water storage in the entire basin during 2003–2013. The calibration process was done using an automated approach including a genetic algorithm (GA) and non-dominated sorting genetic algorithm II (NSGA-II). Then the best-performing calibrated models were run with and without considering water use to quantify the impact of human water use. Observations encompass remote-sensing-based time series of annual irrigated areas in the basin from MODIS, monthly total water storage anomaly (TWSA) from GRACE satellites, and monthly lake volume anomalies. In situ observations include time series of annual inflow into the lake and basin averages of groundwater level variations based on 284 wells. In addition, local estimates of sectoral water withdrawals in 2009 and return flow fractions were utilized. Calibration against MODIS and GRACE data alone improved simulated inflow into Lake Urmia but inflow and lake volume loss were still overestimated, while groundwater loss was underestimated and seasonality of groundwater storage was shifted as compared to observations. Lake and groundwater dynamics could only be simulated well if calibration against groundwater levels led to an adjustment of the fractions of human water use from groundwater and surface water. Thus, in some basins, globally available satellite-derived observations may not suffice for improving the simulation of human water use. According to WGHM simulations with 18 optimal parameter sets, human water use was the reason for 52 %–57 % of the total basin water loss of about 10 km3 during 2003–2013, for 39 %–43 % of the Lake Urmia water loss of about 8 km3, and for up to 87 %–90 % of the groundwater loss. Lake inflow was 39 %–45 % less than it would have been without human water use. The study shows that even without human water use Lake Urmia would not have recovered from the significant loss of lake water volume caused by the drought year 2008. These findings can support water management in the basin and more specifically Lake Urmia restoration plans.

A time series analysis of permanent GNSS stations in the northwest network of Iran

“…As a matter of fact, because of the drought in 2007 [Trigo et al, 2010] and the increase of groundwater extraction in this region, the mean groundwater level has been decreased considerably. Forootan et al [2014] have indicated that the groundwater storage in the Lake Urmia region changes by -11.2 mm/yr based on GRACE product from 2005 to 2011; see also Hosseini-Moghari et al [2020]. According to the findings of Tourian et al [2015], the lake's water level and its area has been decreased at an average rate of 34 ± 1 cm/yr and 220 ± 6 km 2 /yr between 2002 to 2014, respectively.…”

Saline lakes as barriers against pollution: a multidisciplinary overview

“…When saline lakes are connected to the underlying aquifer, groundwater abstractions for urban, industrial and/or agricultural uses may alter the hydroperiod of the waterbody by decreasing subsurface and surface runoff inputs. As a result, a disconnection of the lake from the aquifer and progressive desiccation can occur (Hosseini-Moghari et al, 2020).…”

Soil moisture and streamflow deficit anomaly index: An approach to quantify drought hazards by combining deficit and anomaly

“…S max is one of the key components for computing SMDAI. WaterGAP calibration and validation studies have 390 indicated that Smax may be underestimated in WaterGAP by a factor of two or more (Hosseini-Moghari et al, 2020). In order to understand the sensitivity of SMDAI to changes in S max , we ran a version of WaterGAP in which S max was doubled https://doi.org/10.5194/nhess-2020-265 Preprint.…”