Application of a land surface model for simulating rainfall streamflow hydrograph: 2. Comparison with hydrological models

2011 ◽  
Vol 38 (3) ◽  
pp. 274-283 ◽  
Author(s):  
O. N. Nasonova
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Hydrological Models

scholarly journals Evaluating Regional and Global Hydrological Models against Streamflow and Evapotranspiration Measurements

2016 ◽  
Vol 17 (3) ◽  
pp. 995-1010 ◽  
Author(s):  
Yongqiang Zhang ◽  
Hongxing Zheng ◽  
Francis H. S. Chiew ◽  
Jorge Peña Arancibia ◽  
Xinyao Zhou
Keyword(s):  
Time Series ◽  
Time Scale ◽  
Land Surface ◽  
Land Surface Model ◽  
Daily Rainfall ◽  
Surface Model ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Runoff Variability ◽  
Global Water

Abstract Land surface and global hydrological models are often used to characterize global water and energy fluxes and stores and to model their future trajectories. This study evaluates estimates of streamflow and evapotranspiration (ET) obtained with a priori parameterization from a land surface model [CSIRO Atmosphere Biosphere Land Exchange (CABLE)] and a global hydrological model (H08) against a global dataset of streamflow from 644 largely unregulated catchments and ET from 98 flux towers and benchmarks their performance against two lumped conceptual daily rainfall–runoff models [modèle du Génie Rural à 4 paramètres Journalier (GR4J) and a simplified version of the HYDROLOG model (SIMHYD)]. The results show that all four models perform poorly in simulating the monthly and annual runoff values, with the rainfall–runoff models outperforming both CABLE and H08. The model biases in runoff are generally reflected as a complementary opposite bias in ET. All models can generally reproduce the observed seasonal and interannual runoff variability. The correlations between the modeled and observed runoff time series are reasonable, with the rainfall–runoff models performing slightly better than CABLE and H08 at the monthly time scale and all four models performing similarly at the annual time scale. The results suggest that while the land surface and global hydrological models cannot adequately simulate the actual runoff time series and long-term average volumes, they can reasonably simulate the monthly and interannual runoff variability and trends and can therefore be reliably used for broadscale or comparative regional and global water and energy balance assessments and simulations of future trajectories. They can be improved through validating the models or calibrating some of the more sensitive and less physically based parameters.

scholarly journals Comparing model performance of two rainfall-runoff models in the Rhine basin using different atmospheric forcing data sets

2008 ◽  
Vol 12 (3) ◽  
pp. 943-957 ◽  
Author(s):  
A. H. te Linde ◽  
J. C. J. H. Aerts ◽  
R. T. W. L. Hurkmans ◽  
M. Eberle
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Model Performance ◽  
Land Surface Model ◽  
Change Scenario ◽  
Surface Model ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Lumped Model ◽  
Rhine Basin

Abstract. Due to the growing wish and necessity to simulate the possible effects of climate change on the discharge regime on large rivers such as the Rhine in Europe, there is a need for well performing hydrological models that can be applied in climate change scenario studies. There exists large variety in available models and there is an ongoing debate in research on rainfall-runoff modelling on whether or not physically based distributed models better represent observed discharges than conceptual lumped model approaches do. In addition, it is argued that Land Surface Models (LSMs) carry the potential to accurately estimate hydrological partitioning, because they solve the coupled water and energy balance. In this paper, the hydrological models HBV and VIC were compared for the Rhine basin by testing their performance in simulating discharge. Overall, the semi-distributed conceptual HBV model performed much better than the distributed land surface model VIC (E=0.62, r2=0.65 vs. E=0.31, r2=0.54 at Lobith). It is argued here that even for a well-documented river basin such as the Rhine, more complex modelling does not automatically lead to better results. Moreover, it is concluded that meteorological forcing data has a considerable influence on model performance, irrespectively to the type of model structure and the need for ground-based meteorological measurements is emphasized.

scholarly journals Inundation prediction in tropical wetlands from JULES-CaMa-Flood global land surface simulations

2021 ◽  
Author(s):  
Toby Richard Marthews ◽  
Simon J. Dadson ◽  
Douglas B. Clark ◽  
Eleanor M. Blyth ◽  
Garry Hayman ◽  
...  
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Seasonal Cycles ◽  
Hydrological Models ◽  
Tropical Wetlands ◽  
Surface Models ◽  
Global Land ◽  
The Tropics ◽  
Global Data

Abstract. Wetlands play a key role in hydrological and biogeochemical cycles and provide multiple ecosystem services to society. However, reliable data on the extent of global inundated areas and the magnitude of their contribution to local hydrological dynamics remain surprisingly uncertain. Global hydrological models and Land Surface Models (LSMs) include only the most major inundation sources and mechanisms, therefore quantifying the uncertainties in available data sources remains a challenge. We address these problems by taking a leading global data product on inundation extents (GIEMS) and matching against predictions from a sophisticated global hydrodynamic model (CaMa-Flood) that uses runoff data generated from the JULES land surface model. The ability of the model to reproduce patterns and dynamics showed by the observational product is assessed in a number of case studies across the tropics (including the Sudd, Pantanal, Congo and Amazon), which show that it performs well in large wetland regions, with a good match between corresponding seasonal cycles. However, at finer spatial scale, water inputs (e.g. groundwater inflow to wetland) may become underestimated in comparison to water outputs (e.g. infiltration and evaporation from wetland); or the opposite may occur, depending on the wetland concerned. Additionally, some wetlands display a clear spatial displacement between observed and simulated inundation as a result of over- or under-estimation of overbank flooding upstream. This study provides timely data that can contribute to our current ability to make critical predictions of inundation events at both regional and global levels.

scholarly journals Investigating the Ability of a Land Surface Model to Simulate Streamflow with the Accuracy of Hydrological Models: A Case Study Using MOPEX Materials

2009 ◽  
Vol 10 (5) ◽  
pp. 1128-1150 ◽  
Author(s):  
Olga N. Nasonova ◽  
Yeugeniy M. Gusev ◽  
Yeugeniy E. Kovalev
Keyword(s):  
Land Surface ◽  
Random Search ◽  
Model Performance ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Parameters ◽  
Hydrological Models ◽  
Physical Constraints ◽  
Daily Streamflow ◽  
Automated Optimization

Abstract In the Model Parameter Estimation Experiment (MOPEX) project, after calibration of model parameters, complex rainfall–runoff hydrological models (HMs) simulated streamflow better than land surface models (LSMs), including the Soil–Water–Atmosphere–Plant (SWAP) model. A possible explanation for this is that the LSMs may not have been well calibrated. To test this statement, different strategies to calibrate SWAP using daily streamflow from 12 MOPEX basins were investigated. Optimization of parameter values was performed using a combination of an automated optimization algorithm and manual efforts. For automated optimization, two different global optimization algorithms were used: 1) a random search technique and 2) a shuffled complex evolution method developed by the University of Arizona (SCE-UA). Two objective functions, based on the Nash–Sutcliffe coefficient of efficiency and the mean systematic error, were applied. The number of calibrated parameters ranged from 10 to 15. All adjusted parameters were kept within a reasonable range so as not to violate physical constraints while providing a close match between simulated and measured daily streamflow. The results of streamflow simulations with different sets of optimal parameters were compared with each other, with observations, and with simulation results obtained by the HMs that participated in the MOPEX project. The new SWAP calibration strategies resulted in significant improvement of SWAP streamflow simulations, which came close to the best HM results. It was confirmed that model performance depends greatly on the calibration strategy and that the land surface model SWAP, with appropriate calibration, can simulate runoff with the accuracy that is comparable to the accuracy of hydrological models.

L'apport de la télédétection spatiale à la modélisation des surfaces continentales

2020 ◽  
pp. 052
Author(s):  
Jean-Christophe Calvet ◽  
Jean-Louis Champeaux
Keyword(s):  
Satellite Data ◽  
Land Surface ◽  
Land Surface Model ◽  
Static Model ◽  
Geographic Information ◽  
Surface Model ◽  
Model Parameters

Cet article présente les différentes étapes des développements réalisés au CNRM des années 1990 à nos jours pour spatialiser à diverses échelles les simulations du modèle Isba des surfaces terrestres. Une attention particulière est portée sur l'intégration, dans le modèle, de données satellitaires permettant de caractériser la végétation. Deux façons complémentaires d'introduire de l'information géographique dans Isba sont présentées : cartographie de paramètres statiques et intégration au fil de l'eau dans le modèle de variables observables depuis l'espace. This paper presents successive steps in developments made at CNRM from the 1990s to the present-day in order to spatialize the simulations of the Isba land surface model at various scales. The focus is on the integration in the model of satellite data informative about vegetation. Two complementary ways to integrate geographic information in Isba are presented: mapping of static model parameters and sequential assimilation of variables observable from space.

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