scholarly journals Evaluation of AMIP II Global Climate Model Simulations of the Land Surface Water Budget and Its Components over the GEWEX-CEOP Regions

2007 ◽  
Vol 8 (3) ◽  
pp. 304-326 ◽  
Author(s):  
P. Irannejad ◽  
A. Henderson-Sellers
Keyword(s):  
Surface Water ◽  
Water Balance ◽  
Land Surface ◽  
Water Budget ◽  
Global Climate Model ◽  
Atmospheric Model ◽  
Water Balance Components ◽  
Land Surface Water ◽  
Simulated Precipitation ◽  
Basin Scale

Abstract The land surface water balance components simulated by 20 atmospheric global circulation models (AGCMs) participating in phase II of the Atmospheric Model Intercomparison Project (AMIP II) are analyzed globally and over seven Global Energy and Water Cycle Experiment Coordinated Enhanced Observing Period basins. In contrast to the conclusions from analysis of AMIP I, the results presented here suggest that the group average of available AGCMs does not outperform all individual AGCMs in simulating the surface water balance components. Analysis shows that the available reanalysis products are not appropriate for evaluation of AGCMs’ simulated land surface water components. The worst simulation of the surface water budget is in the Murray–Darling, the most arid basin, where all the reanalyses and seven of the AGCMs produce a negative surface water budget, with evaporation alone exceeding precipitation and soil moisture decreasing over the whole AMIP II period in this basin. The spatiotemporal correlation coefficients between observed and AGCM-simulated runoff are smaller than those for precipitation. In almost all basins (except for the two most arid basins), the spatiotemporal variations of the AGCMs’ simulated evaporation are more coherent and agree better with observations, compared to those of simulated precipitation. This suggests that differences among the AGCMs’ surface water budget predictions are not solely due to model-generated precipitation differences. Specifically, it is shown that different land surface parameterization schemes partition precipitation between evaporation and runoff differently and that this, in addition to the predicted differences in atmospheric forcings, is responsible for different predictions of basin-scale water budgets. The authors conclude that the selection of a land surface scheme for an atmospheric model has significant impacts on the predicted continental and basin-scale surface hydrology.

scholarly journals HydroPy (v1.0): A new global hydrology model written in Python

2021 ◽  
Author(s):  
Tobias Stacke ◽  
Stefan Hagemann
Keyword(s):  
Surface Water ◽  
Water Balance ◽  
Land Surface ◽  
Max Planck ◽  
Water Balance Components ◽  
Meteorological Model ◽  
Land Surface Water ◽  
Hydrology Model ◽  
Flexible Infrastructure ◽  
The Tropics

Abstract. Global hydrological models (GHMs) are a useful tool in the assessment of the land surface water balance. They are used to further the understanding of interactions between water balance components as well as their past evolution and potential future development under various scenarios. While GHMs are a part of the Hydrologist's toolbox since several decades, the models are continuously developed. In our study, we present the HydroPy model, a revised version of an established GHM, the Max-Planck Institute for Meteorology's Hydrology Model (MPI-HM). Being rewritten in Python, the new model requires much less effort in maintenance and due to its flexible infrastructure, new processes can be easily implemented. Besides providing a thorough documentation of the processes currently implemented in HydroPy, we demonstrate the skill of the model in simulating the land surface water balance. We find that evapotranspiration is reproduced realistically for the majority of the land surface but is underestimated in the tropics. The simulated river discharge correlates well with observations. Biases are evident for the annual accumulated discharge, however they can – at least to some part – be attributed to discrepancies between the meteorological model forcing data and the observations. Finally, we show that HydroPy performs very similar to MPI-HM and, thus, conclude the successful transition from MPI-HM to HydroPy.

scholarly journals HydroPy (v1.0): a new global hydrology model written in Python

2021 ◽  
Vol 14 (12) ◽  
pp. 7795-7816
Author(s):  
Tobias Stacke ◽  
Stefan Hagemann
Keyword(s):  
Surface Water ◽  
Water Balance ◽  
Land Surface ◽  
Max Planck ◽  
Water Balance Components ◽  
Meteorological Model ◽  
Land Surface Water ◽  
Hydrology Model ◽  
Flexible Infrastructure ◽  
The Tropics

Abstract. Global hydrological models (GHMs) are a useful tool in the assessment of the land surface water balance. They are used to further the understanding of interactions between water balance components and their past evolution as well as potential future development under various scenarios. While GHMs have been part of the hydrologist's toolbox for several decades, the models are continuously being developed. In our study, we present the HydroPy model, a revised version of an established GHM, the Max Planck Institute for Meteorology's Hydrology Model (MPI-HM). Being rewritten in Python, the new model requires much less effort in maintenance, and due to its flexible infrastructure, new processes can be easily implemented. Besides providing a thorough documentation of the processes currently implemented in HydroPy, we demonstrate the skill of the model in simulating the land surface water balance. We find that evapotranspiration is reproduced realistically for the majority of the land surface but is underestimated in the tropics. The simulated river discharge correlates well with observations. Biases are evident for the annual accumulated discharge; however, they can – at least to some extent – be attributed to discrepancies between the meteorological model forcing data and the observations. Finally, we show that HydroPy performs very similarly to MPI-HM and thus conclude the successful transition from MPI-HM to HydroPy.

scholarly journals Basin-scale assessment of the land surface water budget in the National Centers for Environmental Prediction operational and research NLDAS-2 systems

2016 ◽  
Vol 121 (6) ◽  
pp. 2750-2779 ◽  
Author(s):  
Youlong Xia ◽  
Brian A. Cosgrove ◽  
Kenneth E. Mitchell ◽  
Christa D. Peters-Lidard ◽  
Michael B. Ek ◽  
...  
Keyword(s):  
Surface Water ◽  
Land Surface ◽  
Water Budget ◽  
Scale Assessment ◽  
Land Surface Water ◽  
Basin Scale ◽  
Environmental Prediction

Remote sensing: hydrology

2009 ◽  
Vol 33 (4) ◽  
pp. 490-509 ◽  
Author(s):  
Qiuhong Tang ◽  
Huilin Gao ◽  
Hui Lu ◽  
Dennis P. Lettenmaier
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Land Surface ◽  
Water Budget ◽  
Current Status ◽  
State Variables ◽  
Surface Hydrology ◽  
Earth Observing System ◽  
Land Surface Water ◽  
Terrestrial Water

Satellite remote sensing is a viable source of observations of land surface hydrologic fluxes and state variables, particularly in regions where in situ networks are sparse. Over the last 10 years, the study of land surface hydrology using remote sensing techniques has advanced greatly with the launch of NASA’s Earth Observing System (EOS) and other research satellite platforms, and with the development of more sophisticated retrieval algorithms. Most of the constituent variables in the land surface water balance (eg, precipitation, evapotranspiration, snow and ice, soil moisture, and terrestrial water storage variations) are now observable at varying spatial and temporal resolutions and accuracy via remote sensing. We evaluate the current status of estimates of each of these variables, as well as river discharge, the direct estimation of which is not yet possible. Although most of the constituent variables are observable by remote sensing, attempts to close the surface water budget from remote sensing alone have generally been unsuccessful, suggesting that current generation sensors and platforms are not yet able to provide hydrologically consistent observations of the land surface water budget at any spatial scale.

scholarly journals Evaluation of the land surface water budget in NCEP/NCAR and NCEP/DOE reanalyses using an off-line hydrologic model

2001 ◽  
Vol 106 (D16) ◽  
pp. 17841-17862 ◽  
Author(s):  
Edwin P. Maurer ◽  
Greg M. O'Donnell ◽  
Dennis P. Lettenmaier ◽  
John O. Roads
Keyword(s):  
Surface Water ◽  
Land Surface ◽  
Water Budget ◽  
Hydrologic Model ◽  
Land Surface Water

scholarly journals Modifying a dynamic global vegetation model for simulating large spatial scale land surface water balance

2012 ◽  
Vol 9 (1) ◽  
pp. 1207-1249 ◽  
Author(s):  
G. Tang ◽  
P. J. Bartlein
Keyword(s):  
Soil Moisture ◽  
Surface Water ◽  
Water Balance ◽  
Spatial Scale ◽  
Surface Runoff ◽  
Land Surface ◽  
Large Spatial Scale ◽  
Land Surface Water ◽  
The Us ◽  
Land Covers

Abstract. Water balance models of simple structure are easier to grasp and more clearly connect cause and effect than models of complex structure. Such models are essential for studying large spatial scale land surface water balance in the context of climate and land cover change, both natural and anthropogenic. This study aims to (i) develop a large spatial scale water balance model by modifying a dynamic global vegetation model (DGVM), and (ii) test the model's performance in simulating actual evapotranspiration (ET), soil moisture and surface runoff for the coterminous United States (US). Toward these ends, we first introduced development of the "LPJ-Hydrology" (LH) model by incorporating satellite-based land covers into the Lund-Potsdam-Jena (LPJ) DGVM instead of dynamically simulating them. We then ran LH using historical (1982–2006) climate data and satellite-based land covers at 2.5 arc-min grid cells. The simulated ET, soil moisture and surface runoff were compared to existing sets of observed or simulated data for the US. The results indicated that LH captures well the variation of monthly actual ET (R2 = 0.61, p < 0.01) in the Everglades of Florida over the years 1996–2001. The modeled monthly soil moisture for Illinois of the US agrees well (R2 = 0.79, p < 0.01) with the observed over the years 1984–2001. The modeled monthly stream flow for most 12 major rivers in the US is consistent R2 > 0.46, p < 0.01; Nash-Sutcliffe Coefficients >0.52) with observed values over the years 1982–2006, respectively. The modeled spatial patterns of annual ET and surface runoff are in accordance with previously published data. Compared to its predecessor, LH simulates better monthly stream flow in winter and early spring by incorporating effects of solar radiation on snowmelt. Overall, this study proves the feasibility of incorporating satellite-based land-covers into a DGVM for simulating large spatial scale land surface water balance. LH developed in this study should be a useful tool for studying effects of climate and land cover change on land surface hydrology at large spatial scales.

scholarly journals Verification and Intercomparison of Multimodel Simulated Land Surface Hydrological Datasets over the United States

2011 ◽  
Vol 12 (4) ◽  
pp. 531-555 ◽  
Author(s):  
Yun Fan ◽  
Huug M. van den Dool ◽  
Wanru Wu
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Surface Water ◽  
Water Balance ◽  
Time Scales ◽  
Land Surface ◽  
The United States ◽  
Surface Model ◽  
Spatial And Temporal Variability ◽  
Water Balance Components

Abstract Several land surface datasets, such as the observed Illinois soil moisture dataset; three retrospective offline run datasets from the Noah land surface model (LSM), Variable Infiltration Capacity (VIC) LSM, and Climate Prediction Center leaky bucket soil model; and three reanalysis datasets (North American Regional Reanalysis, NCEP/Department of Energy Global Reanalysis, and 40-yr ECMWF Re-Analysis), are used to study the spatial and temporal variability of soil moisture and its response to the major components of land surface hydrologic cycles: precipitation, evaporation, and runoff. Detailed analysis was performed on the evolution of the soil moisture vertical profile. Over Illinois, model simulations are compared to observations, but for the United States as a whole some impressions can be gained by comparing the multiple soil moisture–precipitation–evaporation–runoff datasets to one another. The magnitudes and partitioning of major land surface water balance components on seasonal–interannual time scales have been explored. It appears that evaporation has the most prominent annual cycle but its interannual variability is relatively small. For other water balance components, such as precipitation, runoff, and surface water storage change, the amplitudes of their annual cycles and interannual variations are comparable. This study indicates that all models have a certain capability to reproduce observed soil moisture variability on seasonal–interannual time scales, but offline runs are decidedly better than reanalyses (in terms of validation against observations) and more highly correlated to one another (in terms of intercomparison) in general. However, noticeable differences are also observed, such as the degree of simulated drought severity and the locations affected—this is due to the uncertainty in model physics, input forcing, and mode of running (interactive or offline), which continue to be major issues for land surface modeling.

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