Using satellite data to extract volume–area–elevation relationships for Urmia Lake, Iran

Abstract

Urmia Lake in the northwest of Iran is the second largest hyper-saline lake worldwide. During the past two decades, a significant water level decline has occurred in the lake. The existing estimations for the lake water balance are widely variable because the lake bathymetry is unknown. The main focus of this study is to extract the volume–area–elevation (V–A–L) characteristics of Urmia Lake utilizing remote sensing data and analytical models. V–A–L equations of the lake were determined using radar altimetry data and their concurrent satellite-derived surface data. Next, two approximate models, a power model (PM) and a truncated pyramid model (TPM), were parameterized for Urmia Lake and checked for accuracy. Results revealed that in comparison with the satellite-derived reference volume–elevation equation, the PM slightly over-predicts the volume of Urmia Lake while the TPM under-estimates the lake storage. Variations of the lake area and volume between 1965 and 2011 were examined using the developed V–A–L equations. Results indicated that the lake area and volume have declined from the historical maximum values by 2200km2 and 33km3, respectively. To restore Urmia Lake to a level to maintain ecological benefits, 13.2km3 of water is required. This study demonstrates the use of remote sensing data of different types to derive V–A–L equations of lakes. Substituting satellite-derived V–A–L equations for common empirical formulas leads to more accurate estimations of a lake water balance, which in turn, provides insight to water managers for properly assessing and allocating water resources to downstream ecosystems.