Assessing mountain block water storage changes in river basins using water balance and GRACE: A case study on Lake Urmia Basin of Iran

Study region: Lake Urmia Basin (LUB), northwestern Iran, encloses the largest salt lake in Iran, where severe drawdown of the lake water level is causing salt storms that threaten the health of the basin's inhabitants. Study focus: Different in-situ and RS-based datasets are used to estimate the water storage change (WSC) in LUB and partition it into the basin floor WSC and mountain block WSC components. Water balance components for LUB were estimated for the 2001–2011 period, using RS-based datasets for precipitation, actual evapotranspiration, soil moisture and snow storage. Ground-based data were used for validation of RS data as well as water balance closure purpose. GRACE terrestrial water storage data were also used to validate the water balance method results. New hydrological insights for the region: GRACE results confirmed an average WSC of − 921 million cubic meters per year (MCM/yr) (−17.6 mm over the basin area). WSC time series in the basin floor (average: −913 MCM/yr, or −53 mm over the basin floor area) was estimated by summing the related water storage components, and then subtracted from the basin WSC to estimate the mountain block WSC time series (average: −8 MCM/yr, or −0.2 mm over the mountain block area). To evaluate its authenticity, the estimated mountain block WSC time series was then compared to mountain block rivers’ base flow and springs' discharge, showing a considerable correlation between the time series. Considering the faster water resources depletion in the basin floor (where anthropological factors are significant) than in the mountain block (where anthropological factors are rare), one can conclude that anthropological factors may have the primary role in deteriorating water resources conditions in the basin

Assimilation of Satellite-Based Data for Hydrological Mapping of Precipitation and Direct Runoff Coefficient for the Lake Urmia Basin in Iran

Water management in arid basins often lacks sufficient hydro-climatological data because, e.g., rain gauges are typically absent at high elevations and inflow to ungauged areas around large closed lakes is difficult to estimate. We sought to improve precipitation and runoff estimation in an arid basin (Lake Urmia, Iran) using methods involving assimilation of satellite-based data. We estimated precipitation using interpolation of rain gauge data by kriging, downscaling the Tropical Rainfall Measuring Mission (TRMM), and cokriging interpolation of in-situ records with Remote Sensing (RS)-based data. Using RS-based data application in estimations gave more precise results, by compensating for lack of data at high elevations. Cokriging interpolation of rain gauges by TRMM and Digitized Elevation Model (DEM) gave 4-9 mm lower Root Mean Square Error (RMSE) in different years compared with kriging. Downscaling TRMM improved its accuracy by 14 mm. Using the most accurate precipitation result, we modeled annual direct runoff with Kennessey and Soil Conservation Service Curve Number (SCS-CN) models. These models use land use, permeability, and slope data. In runoff modeling, Kennessey gave higher accuracy. Calibrating Kennessey reduced the Normalized RMSE (NRMSE) from 1 in the standard model to 0.44. Direct runoff coefficient map by 1 km spatial resolution was generated by calibrated Kennessey. Validation by the closest gauges to the lake gave a NRMSE of 0.41 which approved the accuracy of modeling.Urmia Lake Restoration Committee; University of OuluOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Assimilation of satellite-based data for hydrological mapping of precipitation and direct runoff coefficient for the Lake Urmia basin in Iran

Abstract Water management in arid basins often lacks sufficient hydro-climatological data because, e.g., rain gauges are typically absent at high elevations and inflow to ungauged areas around large closed lakes is difficult to estimate. We sought to improve precipitation and runoff estimation in an arid basin (Lake Urmia, Iran) using methods involving assimilation of satellite-based data. We estimated precipitation using interpolation of rain gauge data by kriging, downscaling the Tropical Rainfall Measuring Mission (TRMM), and cokriging interpolation of in-situ records with Remote Sensing (RS)-based data. Using RS-based data application in estimations gave more precise results, by compensating for lack of data at high elevations. Cokriging interpolation of rain gauges by TRMM and Digitized Elevation Model (DEM) gave 4–9 mm lower Root Mean Square Error (RMSE) in different years compared with kriging. Downscaling TRMM improved its accuracy by 14 mm. Using the most accurate precipitation result, we modeled annual direct runoff with Kennessey and Soil Conservation Service Curve Number (SCS-CN) models. These models use land use, permeability, and slope data. In runoff modeling, Kennessey gave higher accuracy. Calibrating Kennessey reduced the Normalized RMSE (NRMSE) from 1 in the standard model to 0.44. Direct runoff coefficient map by 1 km spatial resolution was generated by calibrated Kennessey. Validation by the closest gauges to the lake gave a NRMSE of 0.41 which approved the accuracy of modeling