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<p>In mountainous basins, accurately estimating snow water equivalent (<i>SWE</i>) is essential for effective water resource management. While <i>SWE</i> data are typically available from satellite products monthly, this paper aims to develop an algorithm that can estimate <i>SWE</i> using daily precipitation and temperature data sourced from the Imerge and <i>MODIS</i> satellites. Research indicates that snow melting generally follows a linear relationship with temperature. In this study, a new relationship was optimized by calibrating model parameters (<i>a</i> and <i>b</i>) based on surface temperature and precipitation data, using statistical evaluation metrics such as <i>RMSE</i> and <i>NSE</i>, to improve the precision of snow melt estimations in the Lake Urmia catchment, Iran, one of the largest hypersaline lakes in the world. Water supply from snowpack melt on nearby mountains (reaching 3000&#xa0;m above sea level) has a significant impact on the state of this lake, where salinity levels have risen in recent years due to drought and increased agricultural water demands in the catchment. The results revealed that the Nash–Sutcliffe Efficiency (<i>NSE</i>) emerged as a crucial indicator of model performance, demonstrating acceptable data quality. The <i>NSE</i> values ranged between 0.10 and 0.89, indicating that the model had acceptable to good performance in estimating daily <i>SWE</i>. This finding suggests that the optimized model effectively captures the linear dynamics of snowmelt with respect to air temperature, demonstrating its reliability in predictions. The application of data-driven techniques in refining this relationship contributes to enhancing the reliability of <i>SWE</i> estimation models. Furthermore, integrating daily satellite data into <i>SWE</i> estimation models represents a significant advancement in the field, enabling more responsive and adaptive management strategies in response to changing climate conditions. This approach not only enhances our understanding of snow dynamics but also supports better decision making regarding water resource allocation and conservation in mountainous regions.</p>

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Derivation of a new model for estimation of snow water equivalent in mountainous basins

  • 期刊论文

  • 发表日期：

    2025年9月1日

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  • Mozhgan Yarahmadi,
  • Shahram Khalighi Sigaroodi,
  • Mahmood Rahmani Firozjaei,
  • Philip David Hughes

摘要

In mountainous basins, accurately estimating snow water equivalent (SWE) is essential for effective water resource management. While SWE data are typically available from satellite products monthly, this paper aims to develop an algorithm that can estimate SWE using daily precipitation and temperature data sourced from the Imerge and MODIS satellites. Research indicates that snow melting generally follows a linear relationship with temperature. In this study, a new relationship was optimized by calibrating model parameters (a and b) based on surface temperature and precipitation data, using statistical evaluation metrics such as RMSE and NSE, to improve the precision of snow melt estimations in the Lake Urmia catchment, Iran, one of the largest hypersaline lakes in the world. Water supply from snowpack melt on nearby mountains (reaching 3000 m above sea level) has a significant impact on the state of this lake, where salinity levels have risen in recent years due to drought and increased agricultural water demands in the catchment. The results revealed that the Nash–Sutcliffe Efficiency (NSE) emerged as a crucial indicator of model performance, demonstrating acceptable data quality. The NSE values ranged between 0.10 and 0.89, indicating that the model had acceptable to good performance in estimating daily SWE. This finding suggests that the optimized model effectively captures the linear dynamics of snowmelt with respect to air temperature, demonstrating its reliability in predictions. The application of data-driven techniques in refining this relationship contributes to enhancing the reliability of SWE estimation models. Furthermore, integrating daily satellite data into SWE estimation models represents a significant advancement in the field, enabling more responsive and adaptive management strategies in response to changing climate conditions. This approach not only enhances our understanding of snow dynamics but also supports better decision making regarding water resource allocation and conservation in mountainous regions.