scholarly journals Snow Model Verification Using Ensemble Prediction and Operational Benchmarks

2008 ◽  
Vol 9 (6) ◽  
pp. 1402-1415 ◽  
Author(s):  
Kristie J. Franz ◽  
Terri S. Hogue ◽  
Soroosh Sorooshian
Keyword(s):  
Snow Water Equivalent ◽  
Initial Conditions ◽  
Model Performance ◽  
Hydrologic Model ◽  
Snow Accumulation ◽  
Forecast Skill ◽  
Model Verification ◽  
Ensemble Prediction ◽  
Snow Model ◽  
Rainfall Runoff Model

Abstract Hydrologic model evaluations have traditionally focused on measuring how closely the model can simulate various characteristics of historical observations. Although advancing hydrologic forecasting is an often-stated goal of numerous modeling studies, testing in a forecasting mode is seldom undertaken, limiting information derived from these analyses. One can overcome this limitation through generation, and subsequent analysis, of ensemble hindcasts. In this study, long-range ensemble hindcasts are generated for the available period of record for a basin in southwestern Idaho for the purpose of evaluating the Snow–Atmosphere–Soil Transfer (SAST) model against the current operational benchmark, the National Weather Service’s (NWS) snow accumulation and ablation model SNOW17. Both snow models were coupled with the NWS operational rainfall runoff model and ensembles of seasonal discharge and weekly snow water equivalent (SWE) were evaluated. Ensemble predictions from both the SAST and SNOW17 models were better than climatology forecasts, for the period studied. In most cases, the accuracy of the SAST-generated predictions was similar to the SNOW17-generated predictions, except during periods of significant melting. Differences in model performance are partially attributed to initial condition errors. After updating the SWE state in the snow models with the observed SWE, the forecasts were improved during the first 2–4 weeks of the forecast window and the skills were essentially equal in both forecasting systems for the study watershed. Climate dominated the forecast uncertainty in the latter part of the forecast window while initial conditions controlled the forecast skill in the first 3–4 weeks of the forecast. The use of hindcasting in the snow model analysis revealed that, given the dominance of the initial conditions on forecast skill, streamflow predictions will be most improved through the use of state updating.

scholarly journals Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall–runoff model

2017 ◽  
Vol 21 (2) ◽  
pp. 879-896 ◽  
Author(s):  
Tirthankar Roy ◽  
Hoshin V. Gupta ◽  
Aleix Serrat-Capdevila ◽  
Juan B. Valdes
Keyword(s):  
Model Performance ◽  
Hydrologic Model ◽  
Actual Evapotranspiration ◽  
Hydrologic Models ◽  
Catchment Scale ◽  
Model Driven ◽  
Alternative Approach ◽  
Precipitation Estimates ◽  
Daily Water ◽  
Rainfall Runoff Model

Abstract. Daily, quasi-global (50° N–S and 180° W–E), satellite-based estimates of actual evapotranspiration at 0.25° spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate the use of these data to improve the performance of a simple lumped catchment-scale hydrologic model driven by satellite-based precipitation estimates to generate streamflow simulations for a poorly gauged basin in Africa. In one approach, we use GLEAM to constrain the evapotranspiration estimates generated by the model, thereby modifying daily water balance and improving model performance. In an alternative approach, we instead change the structure of the model to improve its ability to simulate actual evapotranspiration (as estimated by GLEAM). Finally, we test whether the GLEAM product is able to further improve the performance of the structurally modified model. Results indicate that while both approaches can provide improved simulations of streamflow, the second approach also improves the simulation of actual evapotranspiration significantly, which substantiates the importance of making diagnostic structural improvements to hydrologic models whenever possible.

scholarly journals Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model

2012 ◽  
Vol 6 (6) ◽  
pp. 1323-1337 ◽  
Author(s):  
T. M. Saloranta
Keyword(s):  
Snow Water Equivalent ◽  
Statistical Evaluation ◽  
Model Performance ◽  
Spatiotemporal Variability ◽  
Spatiotemporal Resolution ◽  
Model Code ◽  
Temperature And Precipitation ◽  
Rugged Topography ◽  
Snow Model ◽  
Snow Conditions

Abstract. Daily maps of snow conditions have been produced in Norway with the seNorge snow model since 2004. The seNorge snow model operates with 1 × 1 km resolution, uses gridded observations of daily temperature and precipitation as its input forcing, and simulates, among others, snow water equivalent (SWE), snow depth (SD), and the snow bulk density (ρ). In this paper the set of equations contained in the seNorge model code is described and a thorough spatiotemporal statistical evaluation of the model performance from 1957–2011 is made using the two major sets of extensive in situ snow measurements that exist for Norway. The evaluation results show that the seNorge model generally overestimates both SWE and ρ, and that the overestimation of SWE increases with elevation throughout the snow season. However, the R2-values for model fit are 0.60 for (log-transformed) SWE and 0.45 for ρ, indicating that after removal of the detected systematic model biases (e.g. by recalibrating the model or expressing snow conditions in relative units) the model performs rather well. The seNorge model provides a relatively simple, not very data-demanding, yet nonetheless process-based method to construct snow maps of high spatiotemporal resolution. It is an especially well suited alternative for operational snow mapping in regions with rugged topography and large spatiotemporal variability in snow conditions, as is the case in the mountainous Norway.

scholarly journals Probabilistic Forecasts of Snow Water Equivalent and Runoff in Mountainous Areas*

2015 ◽  
Vol 16 (5) ◽  
pp. 2169-2186 ◽  
Author(s):  
Stefanie Jörg-Hess ◽  
Nena Griessinger ◽  
Massimiliano Zappa
Keyword(s):  
Snow Water Equivalent ◽  
Similarity Score ◽  
Snow Accumulation ◽  
Ensemble Prediction ◽  
Regional Patterns ◽  
Ensemble Prediction System ◽  
Mountainous Regions ◽  
Runoff Volume ◽  
Probabilistic Forecasts ◽  
Snow Water

Abstract Good initial states can improve the skill of hydrological ensemble predictions. In mountainous regions such as Switzerland, snow is an important component of the hydrological system. Including estimates of snow cover in hydrological models is of great significance for the prediction of both flood and streamflow drought events. In this study, gridded snow water equivalent (SWE) maps, derived from daily snow depth measurements, are used within the gridded version of the conceptual hydrological model Precipitation Runoff Evapotranspiration Hydrotope (PREVAH) to replace the model SWE at initialization. The ECMWF Ensemble Prediction System (ENS) reforecast is used as meteorological input for 32-day forecasts of streamflow and SWE. Experiments were performed in several parts of the Alpine Rhine and the Thur River. Predictions where modeled SWE estimates were replaced with SWE maps could successfully enhance the predictability of SWE up to a lead time of 25 days, especially at the beginning and the end of the snow season. Additionally, the prediction of the runoff volume was improved, particularly in catchments where the snow accumulation, and thus the runoff volume, had been greatly overestimated. These improvements in predictions have been made without affecting the ability of the forecast system to discriminate between the different runoff volumes observed. A spatial similarity score was first used in the context of SWE forecast verification. This confirmed the findings of the time series analysis and yielded additional insight on regional patterns of extended range SWE predictability.

scholarly journals How crucial is it to account for the Antecedent Moisture Conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments

2009 ◽  
Vol 6 (2) ◽  
pp. 1707-1736 ◽  
Author(s):  
L. Berthet ◽  
V. Andréassian ◽  
C. Perrin ◽  
P. Javelle
Keyword(s):  
Soil Moisture ◽  
Initial Conditions ◽  
Flood Forecasting ◽  
Hydrologic Model ◽  
Data Availability ◽  
Data Set ◽  
Soil Moisture Accounting ◽  
Antecedent Moisture ◽  
Rainfall Runoff Model ◽  
Event Based

Abstract. This paper compares event-based and continuous hydrological modelling approaches for real-time forecasting of river flows. Both approaches are compared using a lumped hydrologic model (whose structure includes a soil moisture accounting (SMA) store and a routing store) on a data set of 178 French catchments. The main focus of this study was to investigate the actual impact of soil moisture initial conditions on the performance of flood forecasting models and the possible compensations with updating techniques. The rainfall runoff model assimilation technique we used does not impact the SMA component of the model but only its routing part. Tests were made by running the SMA store continuously or on event basis, everything else being equal. The results show that the continuous approach remains the reference to ensure good forecasting performances. We show, however, that the possibility to assimilate the last observed flow considerably reduces the differences in performance. Last, we present a robust alternative to initialize the SMA store where continuous approaches are impossible because of data availability problems.

scholarly journals Hydrologic Prediction over the Conterminous United States Using the National Multi-Model Ensemble

2014 ◽  
Vol 15 (4) ◽  
pp. 1457-1472 ◽  
Author(s):  
Kingtse C. Mo ◽  
Dennis P. Lettenmaier
Keyword(s):  
United States ◽  
Initial Conditions ◽  
Dominant Role ◽  
The United States ◽  
Hydrologic Model ◽  
Forecast Skill ◽  
Model Ensemble ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Ensemble Mean

Abstract The authors analyzed the skill of monthly and seasonal soil moisture (SM) and runoff (RO) forecasts over the United States performed by driving the Variable Infiltration Capacity (VIC) hydrologic model with forcings derived from the National Multi-Model Ensemble hindcasts (NMME_VIC). The grand ensemble mean NMME_VIC forecasts were compared to ensemble streamflow prediction (ESP) forecasts derived from the VIC model forced by resampling of historical observations during the forecast period (ESP_VIC), using the same initial conditions as NMME_VIC. The forecast period is from 1982 to 2010, with the forecast initialized on 1 January, 1 April, 5 July, and 3 October. Overall, forecast skill is seasonally and regionally dependent. The authors found that 1) the skill of the grand ensemble mean NMME_VIC forecasts is comparable with that of the individual model that has the highest skill; 2) for all forecast initiation dates, the initial conditions play a dominant role in forecast skill at 1-month lead, and at longer lead times, forcings derived from NMME forecasts start to contribute to forecast skill; and 3) the initial conditions dominate contributions to skill for a dry climate regime that covers the western interior states for all seasons and the north-central part of the country for January. In this regime, the forecast skill for both methods is high even at 3-month lead. This regime has low mean precipitation and precipitation variations, and the influence of precipitation on SM and RO is weak. In contrast, a wet regime covers the region from the Gulf states to the Tennessee and Ohio Valleys for forecasts initialized in January and April, the Southwest monsoon region, the Southeast, and the East Coast in summer. In these dynamically active regions, where rainfall depends on the path of the moisture transport and atmospheric forcing, forecast skill is low. For this regime, the climate forecasts contribute to skill. Skillful precipitation forecasts after lead 1 have the potential to improve SM and RO forecast skill, but it was found that this mostly was not the case for the NMME models.

scholarly journals Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model

2012 ◽  
Vol 6 (2) ◽  
pp. 1337-1366 ◽  
Author(s):  
T. M. Saloranta
Keyword(s):  
Snow Water Equivalent ◽  
Statistical Evaluation ◽  
Model Performance ◽  
Model Fit ◽  
Spatiotemporal Variability ◽  
Spatiotemporal Resolution ◽  
Model Code ◽  
Temperature And Precipitation ◽  
Snow Model ◽  
Snow Conditions

Abstract. Daily maps of snow conditions have been produced in Norway with the seNorge snow model since 2004. The seNorge snow model operates with 1×1 km resolution, uses gridded observations of daily temperature and precipitation as its input forcing, and simulates among others snow water equivalent (SWE), snow depth (SD), and the snow bulk density (ρ). In this paper the set of equations contained in the seNorge model code is described and a~thorough spatiotemporal statistical evaluation of the model performance in 1957–2011 is made using the two major sets of extensive in-situ snow measurements that exist for Norway. The evaluation results show that the seNorge model generally overestimates both SWE and ρ, and that the distribution of model fit for SWE has a clear dependency on elevation throughout the snow season. However, the R2-values for model fit are 0.60 for (log-transformed) SWE and 0.45 for ρ, indicating that after removal of the detected systematic model biases (e.g. by recalibrating the model or expressing snow conditions in relative units) the model performs rather well. The seNorge model provides a relatively simple, not very data-demanding, yet still process-based method to construct snow maps of high spatiotemporal resolution. It is especially well-suited alternative for operational snow mapping in regions with rugged topography and large spatiotemporal variability in snow conditions, as is the case in the mountainous Norway.

scholarly journals Using Satellite-Based Evapotranspiration Estimates to Improve the Structure of a Simple Conceptual Rainfall-Runoff Model

2016 ◽  
Author(s):  
Tirthankar Roy ◽  
Hoshin V. Gupta ◽  
Aleix Serrat-Capdevila ◽  
Juan B. Valdes
Keyword(s):  
Model Performance ◽  
Hydrologic Model ◽  
Actual Evapotranspiration ◽  
Catchment Scale ◽  
Modified Model ◽  
Model Driven ◽  
Alternative Approach ◽  
Precipitation Estimates ◽  
Daily Water ◽  
Rainfall Runoff Model

Abstract. Daily, quasi-global (50° N-S and 180° W-E), satellite-based estimates of actual evapotranspiration at 0.25° spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate use of these data to improve the performance of a simple lumped catchment scale hydrologic model driven by satellite-based precipitation estimates to generate streamflow simulations for a poorly gauged basin in Africa. In one approach, we use GLEAM to constrain the evapotranspiration estimates generated by the model, thereby modifying the daily water balance and improving model performance. In an alternative approach, we instead change the structure of the model to improve its ability to simulate actual evapotranspiration (as estimated by GLEAM). Finally, we test whether the GLEAM product is able to further improve the performance of the structurally modified model. The results suggest that the modified model can provide improved simulations of both streamflow and evapotranspiration, even if GLEAM-satellite-based evapotranspiration data are not available.

ULYSSES: a system for global multi-model hydrological seasonal predictions

2021 ◽  
Author(s):  
Luis Samaniego ◽  
Stephan Thober ◽  
Matthias Kelbling ◽  
Robert Schweppe ◽  
Oldrich Rakovec ◽  
...  
Keyword(s):  
Land Surface ◽  
Snow Water Equivalent ◽  
Initial Conditions ◽  
Model Performance ◽  
Seasonal Prediction ◽  
Climate Data ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Production Chain ◽  
Data Store

<p>The Copernicus Climate Change Service aims at facilitating the emergence of a downstream market of climate services with the ultimate goal of supporting the development of a climate-smart society. Central to this vision is the free and unrestricted distribution of high-quality climate data through the Climate Data Store [1], with seasonal meteorological predictions among them. Within this unique and challenging framework, ULYSSES [2] will provide the first "seamless'' multi-model hydrological seasonal prediction system, with a global coverage at a spatial resolution of 0.1° The ULYSSES modeling chain is based on the successfully tested EDgE proof of concept [3] using four state-of-the-art hydrological models (Jules, HTESSEL, mHM, and PCR-GLOBWB). A unique feature of this production chain consists of using the same land surface datasets (e.g. DEM, soil properties) with identical spatio-temporal resolutions and forecast inputs for all HMs, and the same river routing scheme (i.e., the multi-scale routing model mRM).</p><p>The initial conditions of the production chain will be based on ERA5-Land dataset and the seasonal forecasts will be driven by a 25-member ensemble generated by the ECMWF-SEAS5 model. ULYSSES aims at generating six essential hydrological variables: snow-water equivalent, snowmelt, evapotranspiration, soil moisture, total runoff, and streamflow with a lead-time of up to six months.  The seasonal forecast was verified at 250+ gauges distributred in all continents during the hind-casting period from 1993 to 2019. The operational forecasting period —in testing phase— started in January 2021 and be extended through until July 2021.  The first operational ULYSSES forecast will be made available by the 10th of each month starting in January 2021.</p><p>All input data sets (ERA5-Land), seasonal forecasts (SEAS5) and ULYSSES outputs will be made available in the Copernicus Climate Data Store [1] and will be open access. We aim to engage institutions and researchers around the world that are willing to evaluate the forecasts model performance, with the aim of improving the system in the future. In this talk, the modelling chain concept, model setup and verification of initial results will be presented.</p><ul><li>[1] https://cds.climate.copernicus.eu</li> <li>[2] https://www.ufz.de/ulysses</li> <li>[3] https://doi.org/10.1175/BAMS-D-17-0274.1</li> </ul>

regionalization of the potential to increase rainfall-runoff model performance by multi-objective calibration using modis data over Austria

2020 ◽  
Author(s):  
Martin Kubáň ◽  
Patrik Sleziak ◽  
Adam Brziak ◽  
Kamila Hlavčová ◽  
Ján Szolgay
Keyword(s):  
Soil Moisture ◽  
Hydrological Model ◽  
Snow Water Equivalent ◽  
Model Performance ◽  
State Variables ◽  
Rainfall Runoff ◽  
Multi Objective ◽  
Snow Water ◽  
Rainfall Runoff Model ◽  
Runoff Model

<p>A multi-objective calibration of the parameters of conceptual hydrologic models has the potential to improve the consistency of the simulated model states, their representativeness with respect to catchment states and thereby to reduce the uncertainty in the estimation of hydrological model outputs. Observed in-situ or remotely sensed state variables, such as the snow cover distribution, snow depth, snow water equivalent and soil moisture were often considered as additional information in such calibration strategies and subsequently utilized in data assimilation for operational streamflow forecasting. The objective of this paper is to assess the effects of the inclusion of MODIS products characterizing soil moisture and the snow water equivalent in a multi-objective calibration strategy of an HBV type conceptual hydrological model under the highly variable physiographic conditions over the whole territory of Austria.</p><p>The methodology was tested using the Technical University of Vienna semi-distributed rainfall-runoff model (the TUW model), which was calibrated and validated in 213 Austrian catchments. For calibration we use measured data from the period 2005 to 2014. Subsequently, we simulated discharges, soil moisture and snow water equivalents based on parameters from the multi-objective calibration and compared these with the respective MODIS values. In general, the multi-objective calibration improved model performance when compared to results of model parametrisation calibrated only on discharge time series. Sensitivity analyses indicate that the magnitude of the model efficiency is regionally sensitive to the choice of the additional calibration variables. In the analysis of the results we indicate ranges how and where the runoff, soil moisture and snow water equivalent simulation efficiencies were sensitive to different setups of the multi-objective calibration strategy over the whole territory of Austria. It was attempted to regionalize the potential to increase of the overall model performance and the improvement in the consistency of the simulation of the two-state variables. Such regionalization may serve model users in the selection which remotely sensed variable or their combination is to be preferred in local modelling studies.</p>

scholarly journals Coupling the atmospheric model RAMS 6.0 /ECHAM 4.1 to hydrologic model SMA/HMS for operating a reservoir in Brazil’s semiarid

RBRH ◽  
2017 ◽  
Vol 22 (0) ◽  
Author(s):  
Samuellson Lopes Cabral ◽  
José Nilson Bezerra Campos ◽  
Cleiton da Silva Silveira
Keyword(s):  
General Circulation ◽  
Hydrological Model ◽  
Initial Conditions ◽  
Circulation Model ◽  
Atmospheric Model ◽  
Skill Score ◽  
Hydrologic Model ◽  
Water Release ◽  
Simulated Precipitation ◽  
Rainfall Runoff Model

ABSTRACT The planning and the efficiency of water resources are subject to the uncertainties of the input data of climate and hydrological models. Prediction of water inflow to reservoirs that would help decision making for the various water uses, contain uncertainties fundamentally the initial conditions assumed in the modeled processes. This paper evaluates the coupling of a regional atmospheric model with a hydrological model to make streamflow forecast for seasonal operation of Orós reservoir, Ceará State, Brazil. RAMS model, version 6.0, was forced by the ECHAM 4.5 atmospheric general circulation model over Alto Jaguaribe basin to obtain the rainfall data. To remove biases in the simulated precipitation fields was applied the probability density function (PDF) correction on them. Then the corrected precipitation data were inserted in the hydrologic Soil Moisture Account (SMA) model from the Hydrologic Modeling System (HEC-HMS). For SMA calibration, it was used the Nash-Sutcliffe objective function. Finally, decisions to water release from the Orós were evaluated using the Heidke Skill Score (HSS). The SMA model showed a satisfactory performance with Nash-Sutcliffe values of 0.92 (0.87) in the calibration (validation) phase, indicating that it is a rainfall runoff model alternative. For decisions in releasing water from the Orós reservoir, using climate predictions, obtained HSS = 0.43. The results show that the simulated rainfall coupled with a hydrological model is able to represent the hydrological operation of Brazilian semiarid reservoir.

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