Evaluation of Probabilistic Streamflow Forecasts Based on EPS for a Mountainous Basin in Turkey

AbstractWhen designing water structures or managing a watershed it is a challenging task to determine the response of a basin to storm and/or snowmelt. In this study, the Upper Euphrates Basin (10,275 km2 area and elevation range of 1125-3500 m) located at the headwater of Euphrates River, one of Turkey's most important rivers, is selected as the application area. In this region, snowmelt runoff constitutes approximately 2/3 in volume of the total yearly runoff, therefore, runoff modeling and forecasting during spring and early summer is important in terms of energy and water resources management. The aim of the study is to make a forward-oriented, medium-range flow forecasting using Ensemble Prediction System (EPS) which is a pioneer study for Turkey. Conceptual hydrological model HBV, which has a common usage in the literature, is chosen to predict streamflows. According to the results, Nash-Sutcliffe model efficiencies are 0.85 for calibration (2001-2008) and 0.71 for validation (2009-2014) respectively. After calibrating/validating the hydrologic model, EPS data including 51 different combinations produced by ECMWF is used as probability based weather forecasts. Melting period during March-June of 2011 is chosen as the forecast period. The probabilistic skill of EPS based hydrological model results are analyzed to verify the ensemble forecasts

Using MODIS snow cover maps in modeling snowmelt runoff process in the eastern part of Turkey

Water perhaps is the most valuable natural asset in the Middle East as it was a historical key for settlement and survival in Mesopotamia, "the land between two rivers". At present, the Euphrates and Tigris are the two largest trans-boundary rivers in Western Asia where Turkey, Syria, Iran, Iraq and Saudi Arabia are the riparian countries. The Euphrates and Tigris basins are largely fed from snow precipitation whereby nearly two-thirds occur in winter and may remain in the form of snow for half of the year. The concentration of discharge mainly from snowmelt during spring and early summer months causes not only extensive flooding, inundating large areas, but also the loss of much needed water required for irrigation and power generation purposes during the summer season. Accordingly, modeling of snow-covered area in the mountainous regions of Eastern Turkey, as being one of the major headwaters of Euphrates-Tigris basin, has significant importance in order to forecast snowmelt discharge especially for energy production, flood control, irrigation and reservoir operation optimization

Point-scale energy and mass balance snowpack simulations in the upper Karasu basin, Turkey

Since snowmelt runoff is important in the mountainous parts of the world, substantial efforts have been made to develop snowmelt models with many different levels of complexity to simulate the processes at the ground (soil-vegetation), within the snow, and at the interface with the atmosphere. Snow modifies the exchange of energy between the land surface and atmosphere and significantly affects the distribution of heating in the atmosphere by changing the surface albedo and regulating turbulent heat and momentum fluxes at the surface. Thus, for computing the amount of melt, the only strictly correct way is using an energy budget. A two-layer point model (SNOBAL) was applied to calculate the energy and mass balance of snowmelt in the upper Karasu basin, in eastern Turkey, during the 2002-04 snow seasons. The data on snow and climate were provided from automated snow and meteorological stations installed and upgraded to collect high-quality time series data of snow and meteorological variables, such as snow water equivalent, snow depth, precipitation and radiation, with automated data transfer. A number of analyses of snowpack energy and mass balance were carried out to understand the key processes that have major impacts on the snow simulation. Each form of energy transfer was evaluated during snow accumulation and ablation periods using a 2 h computational time step. The model results are appraised with respect both to temporal distribution (the model application for three consecutive snow seasons at one site) and to areal evaluation (the model application to three different sites for one season). The model performance is evaluated by comparing the results with observed snow water equivalent, snow depth and lysimeter yield. Copyright (c) 2006 John Wiley & Sons, Ltd

Modelling the temporal variation in snow-covered area derived from satellite images for simulating/forecasting of snowmelt runoff in Turkey

Monitoring the change of snow-covered area (SCA) in a basin is vitally important for optimum operation of water resources, where the main contribution comes from snowmelt. A methodology for obtaining the depletion pattern of SCA, which is based on satellite image observations where mean daily air temperature is used, is applied for the 1997 water year and tested for the 1998 water year. The study is performed at the Upper Euphrates River basin in Turkey (10 216 km(2)). The major melting period in this basin starts in early April. The cumulated mean daily air temperature (CMAT) is correlated to the depletion of snow-covered area with the start of melting. The analysis revealed that SCA values obtained from NOAA-AVHRR satellite images are exponentially correlated to CMAT for the whole basin in a lumped manner, where R-2 values of 0.98 and 0.99 were obtained for the water years 1997 and 1998, respectively. The applied methodology enables the interpolation between the SCA observations and extrapolation. Such a procedure reduces the number of satellite images required for analysis and provides solution for the cloud-obscured images. Based on the image availability, the effect of the number of images on the quality of snowmelt runoff simulations is also discussed. In deriving the depletion curve for SCA, if the number of images is reduced, the timing of image analysis within the snowmelt period is found very important. Analysis of the timing of satellite images indicated that images from the early and middle parts of the melt period are more important

Accuracy assessment of MODIS daily snow albedo retrievals with in situ measurements in Karasu basin, Turkey

Over the ablation period of 2004, daily snow albedo values retrieved from the moderate-resolution imaging spectroradiometer (MODIS) Terra were compared with ground-based albedo measurements. Two data sets are used for this study. The first data set is from two automatic weather stations (AWS) located at fixed points in Karasu basin in eastern Turkey. This provided the temporal assessment of MODIS daily snow albedo values. The second data set, consisting of 19 observation points randomly distributed around one of the AWS sites, enables the spatial variability assessment of MODIS albedo retrievals. Both MODIS and in situ albedo observations were aggregated in a geographic information system for further analysis

Simulation of event-based snowmelt runoff hydrographs based on snow depletion curves and the degree-day method

In mountainous regions, runoff from snowmelt is an important hydrologic and economic factor, and therefore early runoff forecasting brings significant advantages to the planning and operation of water resources systems. Similarly, predicting runoff from snowmelt during spring months in the eastern part of Turkey, where most of the water originates from high mountains and contributes to the large dams on the Euphrates River, is of crucial importance. In this study, an event-type, physically based hydrologic model Hydrologic Engineering Center (HEC-1) interfaced within the program Watershed Modeling System is applied to the Upper Karasu Basin to simulate rainfall-snowmelt hydrographs during 3 years. The model relates snowmelt to temperature using the degree-day factor, a common practice in the simulation of snowmelt. The model accounts for growth and depletion of the snowpack in terms of snow water equivalent, which is tracked in each of the elevation zones into which the basin is divided. The ground-truth data for snow are coupled with the snow depletion curves obtained from satellite data (NOAA-AVHRR) to determine snow water equivalent values at the beginning of each event

Snow Data Assimilation and Evaluation Methods for Hydrological, Land Surface, Meteorological and Climate Models – A COST Action HarmoSnow Assessment Questionnaire

This chapter is based on outcomes of the working group 3 Questionnaire of the COST Action ES1404 (www.harmosnow.eu) and provides a discussion of snow data assimilation in research and operational applications, which will be presented in detail in a manuscript (Helmert et al., 2018)