The Value of Snow Depletion Forecasting Methods Towards Operational Snowmelt Runoff Estimation Using MODIS and Numerical Weather Prediction Data

Abstract

Forecasting streamflow mainly due to snowmelt in the mountainous eastern part of Turkey is important in terms of effective management of water resources at the headwaters of Euphrates River, where large dam reservoirs are located. Monitoring Snow Covered Area (SCA) and modeling snowmelt forms the backbone of the forecasting studies as the snowmelt dominating runoff constitutes approximately 2/3 of total annual volume of runoff during spring and early summer. Two main motivations of the study are; firstly, to assess the methodologies to forecast SCA using Moderate Resolution Imaging Spectroradiometer (MODIS) data and derive Snow Depletion Curve (SDC) for each elevation zone. Secondly, to forecast 1 day ahead daily discharges using the derived SDCs and Numerical Weather Prediction (NWP) data corrected specifically for the area. The Upper Euphrates Basin (10,275 km(2)) is selected as the pilot basin and MODIS daily snow cover products are analyzed for the snowmelt season. Four different methodologies are proposed and assessed to forecast SDCs; simple averaging, temperature based, stochastic modeling and probabilistic approach. SDCs are derived for the water years 2006-2010, 4 years data are used to derive the equations of the methodologies and 1 year is used to verify their skills. Forecasting discharges 1 day ahead with Snowmelt Runoff Model using NWP data is the second part of the study. Impact of forecasted SDCs with different methodologies is examined with the model. Model applications provide promising results both for the forecasting of SCA and runoff with an overall Model Efficiency higher than 0.60 and 0.85, respectively.