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

Şensoy, Aynur; Uysal, Gökçen

doi:10.1007/s11269-012-0079-0

Cited by 35 publications

(16 citation statements)

References 38 publications

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“…As an example, NSEC increased from 0.60 to 0.97, for test sets. The positive contribution of previous-day discharge in forecasting the following-day discharge is also mentioned in Ṣensoy and Uysal (2012). This success may come from the effect of previous days' hydrometeorological and discharge values' effects on current-day values.…”

Section: Resultsmentioning

confidence: 89%

Use of interactive multisensor snow and ice mapping system snow cover maps (IMS) and artificial neural networks for simulating river discharges in Eastern Turkey

et al. 2016

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Basins located in Eastern Turkey are largely fed by snowmelt runoff during spring and early summer seasons. This study investigates the efficiency of artificial neural networks (ANNs) in snowmelt runoff generation. Although ANNs have been used for streamflow simulating/forecasting in the last two decades, using satellite-based snow-covered area (SCA) maps and meteorological observations as inputs to ANN provides a novel basis for estimating streamflow. The proposed methodology is implemented over Upper Euphrates River Basin in Eastern Turkey. SCA data was acquired from Interactive Multisensor Snow and Ice Mapping System (IMS) for an 8-year period from February 2004 to September 2011. Meteorological observations including daily cumulative precipitation and daily average air temperatures were obtained from Turkish State Meteorological Services. The simulation results are promising with coefficient of correlation varying from 0.67 to 0.98 among proposed models. Past days discharge was found to substantially improve the forecast accuracy. The paper presents the expected basin discharge for 2011 water year based on meteorological observations and SCA input.

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Section: Resultsmentioning

confidence: 89%

Use of interactive multisensor snow and ice mapping system snow cover maps (IMS) and artificial neural networks for simulating river discharges in Eastern Turkey

et al. 2016

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“…Numerous global and regional studies have been conducted on validation of MODIS snow data to identify snow mapping accuracy (Arsenault et al, 2014;Crawford, 2015;Hall and Riggs, 2007;Huang et al, 2011;Maurer et al, 2003;Parajka and Blöschl, 2008;Raleigh et al, 2013;Riggs et al, 2006;Tekeli et al, 2005;. MODIS data have been successfully applied in snowmelt modeling (Day, 2013;Duethmann et al, 2014;Finger et al, 2015;Franz and Karsten, 2013;He et al, 2014;Li and Williams, 2008;Parajka and Blöschl, 2008;Şensoy and Uysal, 2012;Şorman et al, 2009) or in hydro-climatological and topographic research of snow cover variations (Cornwell et al, 2016;Forsythe et al, 2012;Gascoin et. al., 2015;Singh et al, 2003;Tang et.al., 2013;Tong et al, 2009;Wang and Xie, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Richer (2009) derived snow probability maps for a basin in Colorado, using 8-day snow cover products of MODIS for the melting periods of 2000-2006 and investigated spatial and temporal snow distribution trends. Şensoy and Uysal (2012) presented the probability approach in snow depletion forecasting with a limited number of MODIS snow cover data. López-Burgos et al (2013) used the locally-weighted logistic regression (LWLR) method to estimate probabilistic snow occurrences for developing the cloud removal technique.…”

Section: Introductionmentioning

confidence: 99%

“…SCA can be examined both temporally and spatially with daily snow depletion curves (SDCs) which designate the snow coverage on each day of the melt season. One of the well-known conceptual models is Snowmelt Runoff Model (SRM) which enables to use SDCs as the main variable of snowmelt modeling for mountainous basins applied to several catchments around the world (Gómez-Landesa and Rango, 2002;Jain et al, 2010a;Jain et al, 2010b;Lee et al, 2005;Martinec, 1975;Panday et al, 2014;Şensoy and Uysal, 2012;Sensoy et al, 2014a;Sensoy et al, 2014b;Tahir et al, 2011;Tekeli et al, 2005). On the other hand, estimation of SCA is still a challenging issue in operational runoff forecasting.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Snow Cover and Ensemble Streamflow Estimations in the Upper Euphrates Basin

Şorman

Uysal

Şensoy

2018

Journal of Hydrology and Hydromechanics

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Predicting snow cover dynamics and relevant streamflow due to snowmelt is a challenging issue in mountainous basins. Spatio-temporal variations of snow extent can be analyzed using probabilistic snow cover maps derived from satellite images within a relatively long period. In this study, Probabilistic Snow Depletion Curves (P-SDCs) and Probabilistic Snow Lines (P-SLs) are acquired from Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-filtered daily snow cover images. Analyses of P-SDCs show a strong correlation with average daily runoff (R 2 = 0.90) and temperature (R 2 = 0.96). On the other hand, the challenge lies in developing noteworthy methods to use P-SDCs in streamflow estimations. Therefore, the main objective is to explore the feasibility of producing probabilistic runoff forecasts with P-SDC forcing in a snow dominated basin.Upper Euphrates Basin in Turkey has large snow extent and high snowmelt contribution during spring and summer periods. The melting characteristics are defined by P-SDCs using MODIS imagery for 2001-2012. The value of snow probability maps on ensemble runoff predictions is shown with Snowmelt Runoff Model (SRM) during 2013-2015 where the estimated runoff values indicate good consistency (NSE: 0.47-0.93) with forecasts based on the derived P-SDCs. Therefore, the probabilistic approach distinguishes the snow cover characteristics for a region and promotes a useful methodology on the application of probabilistic runoff predictions especially for snow dominated areas.

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“…Numerical weather predictions are usually incorporated into ensemble streamflow prediction (ESP) to provide climate information (e.g., Clark and Hay 2004;Roulin and Vannitsem 2005;Ghile and Schulze 2010;Tang et al 2010;Şensoy and Uysal 2012;Dutta et al 2012). A hydrological forecasting system based on coupling the ensemble weather forecast (EWF) with hydrological models is able to capture the uncertainties associated with the weather forecast and thus to better predict river flows.…”

Section: Introductionmentioning

confidence: 99%

Combining Stochastic Weather Generation and Ensemble Weather Forecasts for Short-Term Streamflow Prediction

Chen

Brissette

2015

Water Resour Manage

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Ensemble streamflow predictions (ESPs) offer great potential benefits for water resource management, as they contain key probabilistic information for analyzing prediction uncertainty. Ensemble weather forecasts (EWFs) are usually incorporated into ESPs to provide climate information. However, there is no simple way to combine both of them, since EWFs are generally biased and under-dispersed. This study presents a new short-term (1 to 7 lead days) probabilistic streamflow prediction system combining stochastic weather generation and EWFs. The bias and under-dispersion of EWFs were first corrected using a weather generatorbased post-processing approach (GPP). The corrected weather forecasts were then coupled with a hydrological model for streamflow forecasts. The proposed GPP forecast was compared against two other forecasts, one using the raw EWF (GFS), and the other using a stochastic weather generator (WG). The comparison was carried out over two Quebec watersheds, using a set of deterministic and probabilistic verification metrics. The deterministic metrics showed that the GPP forecast is consistently the best at predicting the ensemble mean streamflow for both watersheds and for all the leads ranging between 1 and 7 days, followed by the WG forecast. The probabilistic metrics showed negative or near zero skill retained by the GFS forecast for the first 7 lead days. The WG system was much more skillful than the GFS. The GPP forecast consistently displayed the highest skill and reliability in terms of all the metrics applied. With increasing lead days, the skill and reliability of the GPP forecast tend to converge toward that of the WG forecast, indicating that the short-term GPP forecast could easily be linked to a pure WG forecast to extend the forecast horizon.

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

Cited by 35 publications

References 38 publications

Use of interactive multisensor snow and ice mapping system snow cover maps (IMS) and artificial neural networks for simulating river discharges in Eastern Turkey

Use of interactive multisensor snow and ice mapping system snow cover maps (IMS) and artificial neural networks for simulating river discharges in Eastern Turkey

Probabilistic Snow Cover and Ensemble Streamflow Estimations in the Upper Euphrates Basin

Combining Stochastic Weather Generation and Ensemble Weather Forecasts for Short-Term Streamflow Prediction

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