scholarly journals A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset

2016 ◽  
Author(s):  
Jaap Schellekens ◽  
Emanuel Dutra ◽  
Alberto Martínez-de la Torre ◽  
Gianpaolo Balsamo ◽  
Albert van Dijk ◽  
...  
Keyword(s):  
Land Surface ◽  
Snow Water Equivalent ◽  
Water Cycle ◽  
Signal To Noise Ratio ◽  
Model Performance ◽  
Meteorological Forcing ◽  
Unconstrained Model ◽  
Total Runoff ◽  
Ensemble Mean ◽  
Benchmark Datasets

Abstract. The dataset presented here consists of an ensemble of ten global hydrological and land surface models for the period 1979–2012 using a reanalysis-based meteorological forcing dataset (0.5° resolution). The current dataset serves as a state-of-the-art in current global hydrological modelling and as a benchmark for further improvements in the coming years. A signal-to-noise ratio analysis revealed low inter-model agreement over (i) snow-dominate regions and (ii) tropical rainforest and monsoon areas. The large uncertainty of precipitation in the tropics is not being reflected in the ensemble runoff. Verification of the results against benchmark datasets for evapotranspiration, snow cover, snow water equivalent, soil moisture anomaly and total water storage anomaly using the tools from The International Land Model Benchmarking Project (ILAMB) showed overall useful model performance, while the ensemble mean generally outperformed the single model estimates. The results also show that there is currently no single best model for all variables and that model performance is spatially variable. In our unconstrained model runs the ensemble mean of total runoff into the ocean was 46 268 km3/yr (334 kg/m2/yr) while the ensemble mean of total evaporation was 537 kg/m2/yr. All data are made available openly through a Water Cycle Integrator portal (WCI, wci.earth2observe.eu), and via a direct http and ftp download. The portal follows the protocols of the open geospatial consortium such as OPeNDAP, WCS and WMS. The doi for the data is: doi:10.5281/zenodo.167070

scholarly journals A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset

2017 ◽  
Vol 9 (2) ◽  
pp. 389-413 ◽  
Author(s):  
Jaap Schellekens ◽  
Emanuel Dutra ◽  
Alberto Martínez-de la Torre ◽  
Gianpaolo Balsamo ◽  
Albert van Dijk ◽  
...  
Keyword(s):  
Land Surface ◽  
Snow Water Equivalent ◽  
Water Cycle ◽  
Signal To Noise Ratio ◽  
Model Performance ◽  
Meteorological Forcing ◽  
Unconstrained Model ◽  
Total Runoff ◽  
Ensemble Mean ◽  
Benchmark Datasets

Abstract. The dataset presented here consists of an ensemble of 10 global hydrological and land surface models for the period 1979–2012 using a reanalysis-based meteorological forcing dataset (0.5° resolution). The current dataset serves as a state of the art in current global hydrological modelling and as a benchmark for further improvements in the coming years. A signal-to-noise ratio analysis revealed low inter-model agreement over (i) snow-dominated regions and (ii) tropical rainforest and monsoon areas. The large uncertainty of precipitation in the tropics is not reflected in the ensemble runoff. Verification of the results against benchmark datasets for evapotranspiration, snow cover, snow water equivalent, soil moisture anomaly and total water storage anomaly using the tools from The International Land Model Benchmarking Project (ILAMB) showed overall useful model performance, while the ensemble mean generally outperformed the single model estimates. The results also show that there is currently no single best model for all variables and that model performance is spatially variable. In our unconstrained model runs the ensemble mean of total runoff into the ocean was 46 268 km3 yr−1 (334 kg m−2 yr−1), while the ensemble mean of total evaporation was 537 kg m−2 yr−1. All data are made available openly through a Water Cycle Integrator portal (WCI, wci.earth2observe.eu), and via a direct http and ftp download. The portal follows the protocols of the open geospatial consortium such as OPeNDAP, WCS and WMS. The DOI for the data is https://doi.org/10.1016/10.5281/zenodo.167070.

scholarly journals Presentation and discussion of the high resolution atmosphere-land surface subsurface simulation dataset of the virtual Neckar catchment for the period 2007–2015

2020 ◽  
Author(s):  
Bernd Schalge ◽  
Gabriele Baroni ◽  
Barbara Haese ◽  
Daniel Erdal ◽  
Gernot Geppert ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Overland Flow ◽  
Water Cycle ◽  
Numerical Models ◽  
Model Performance ◽  
Surface Model ◽  
Meteorological Model ◽  
The Real ◽  
Terrestrial System

Abstract. Coupled numerical models, which simulate water and energy fluxes in the subsurface-land surface-atmosphere system in a physically consistent way are a prerequisite for the analysis and a better understanding of heat and matter exchange fluxes at compartmental boundaries and interdependencies of states across these boundaries. Complete state evolutions generated by such models may be regarded as a proxy of the real world, provided they are run at sufficiently high resolution and incorporate the most important processes. Such a virtual reality can be used to test hypotheses on the functioning of the coupled terrestrial system. Coupled simulation systems, however, face severe problems caused by the vastly different scales of the processes acting in and between the compartments of the terrestrial system, which also hinders comprehensive tests of their realism. We used the Terrestrial Systems Modeling Platform TerrSysMP, which couples the meteorological model COSMO, the land-surface model CLM, and the subsurface model ParFlow, to generate a virtual catchment for a regional terrestrial system mimicking the Neckar catchment in southwest Germany. Simulations for this catchment are made for the period 2007–2015, and at a spatial resolution of 400 m for the land surface and subsurface and 1.1 km for the atmosphere. Among a discussion of modelling challenges, the model performance is evaluated based on real observations covering several variables of the water cycle. We find that the simulated (virtual) catchment behaves in many aspects quite close to observations of the real Neckar catchment, e.g. concerning atmospheric boundary-layer height, precipitation, and runoff. But also discrepancies become apparent, both in the ability of the model to correctly simulate some processes which still need improvement such as overland flow, and in the realism of some observation operators like the satellite based soil moisture sensors. The whole raw dataset is available for interested users. The dataset described here is available via the CERA database (Schalge et al., 2020): https://doi.org/10.26050/WDCC/Neckar_VCS_v1.

scholarly journals Performance evaluation of global hydrological models in six large Pan-Arctic watersheds

2020 ◽  
Vol 163 (3) ◽  
pp. 1329-1351 ◽  
Author(s):  
Anne Gädeke ◽  
Valentina Krysanova ◽  
Aashutosh Aryal ◽  
Jinfeng Chang ◽  
Manolis Grillakis ◽  
...  
Keyword(s):  
Climate Change ◽  
Performance Evaluation ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Explanatory Power ◽  
Model Performance ◽  
Climate Change Impacts ◽  
Hydrological Processes ◽  
Climate Data ◽  
Seasonal Discharge

AbstractGlobal Water Models (GWMs), which include Global Hydrological, Land Surface, and Dynamic Global Vegetation Models, present valuable tools for quantifying climate change impacts on hydrological processes in the data scarce high latitudes. Here we performed a systematic model performance evaluation in six major Pan-Arctic watersheds for different hydrological indicators (monthly and seasonal discharge, extremes, trends (or lack of), and snow water equivalent (SWE)) via a novel Aggregated Performance Index (API) that is based on commonly used statistical evaluation metrics. The machine learning Boruta feature selection algorithm was used to evaluate the explanatory power of the API attributes. Our results show that the majority of the nine GWMs included in the study exhibit considerable difficulties in realistically representing Pan-Arctic hydrological processes. Average APIdischarge (monthly and seasonal discharge) over nine GWMs is > 50% only in the Kolyma basin (55%), as low as 30% in the Yukon basin and averaged over all watersheds APIdischarge is 43%. WATERGAP2 and MATSIRO present the highest (APIdischarge > 55%) while ORCHIDEE and JULES-W1 the lowest (APIdischarge ≤ 25%) performing GWMs over all watersheds. For the high and low flows, average APIextreme is 35% and 26%, respectively, and over six GWMs APISWE is 57%. The Boruta algorithm suggests that using different observation-based climate data sets does not influence the total score of the APIs in all watersheds. Ultimately, only satisfactory to good performing GWMs that effectively represent cold-region hydrological processes (including snow-related processes, permafrost) should be included in multi-model climate change impact assessments in Pan-Arctic watersheds.

Characterization of Northern Hemisphere Snow Water Equivalent Datasets, 1981–2010

2015 ◽  
Vol 28 (20) ◽  
pp. 8037-8051 ◽  
Author(s):  
L. R. Mudryk ◽  
C. Derksen ◽  
P. J. Kushner ◽  
R. Brown
Keyword(s):  
Northern Hemisphere ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
European Space Agency ◽  
Land Surface Model ◽  
Surface Model ◽  
Temporal Consistency ◽  
Meteorological Forcing ◽  
Temporal Correlations ◽  
Snow Water

Abstract Five, daily, gridded, Northern Hemisphere snow water equivalent (SWE) datasets are analyzed over the 1981–2010 period in order to quantify the spatial and temporal consistency of satellite retrievals, land surface assimilation systems, physical snow models, and reanalyses. While the climatologies of total Northern Hemisphere snow water mass (SWM) vary among the datasets by as much as 50%, their interannual variability and daily anomalies are comparable, showing moderate to good temporal correlations (between 0.60 and 0.85) on both interannual and intraseasonal time scales. Wintertime trends of total Northern Hemisphere SWM are consistently negative over the 1981–2010 period among the five datasets but vary in strength by a factor of 2–3. Examining spatial patterns of SWE indicates that the datasets are most consistent with one another over boreal forest regions compared to Arctic and alpine regions. Additionally, the datasets derived using relatively recent reanalyses are strongly correlated with one another and show better correlations with the satellite product [the European Space Agency (ESA)’s Global Snow Monitoring for Climate Research (GlobSnow)] than do those using older reanalyses. Finally, a comparison of eight reanalysis datasets over the 2001–10 period shows that land surface model differences control the majority of spread in the climatological value of SWM, while meteorological forcing differences control the majority of the spread in temporal correlations of SWM anomalies.

scholarly journals Creation of the WATCH Forcing Data and Its Use to Assess Global and Regional Reference Crop Evaporation over Land during the Twentieth Century

2011 ◽  
Vol 12 (5) ◽  
pp. 823-848 ◽  
Author(s):  
G. P. Weedon ◽  
S. Gomes ◽  
P. Viterbo ◽  
W. J. Shuttleworth ◽  
E. Blyth ◽  
...  
Keyword(s):  
Twentieth Century ◽  
Land Surface ◽  
Water Cycle ◽  
Potential Evapotranspiration ◽  
Reanalysis Data ◽  
Meteorological Forcing ◽  
Global Average ◽  
Murray Darling Basin ◽  
Terrestrial Water ◽  
Reference Crop

Abstract The Water and Global Change (WATCH) project evaluation of the terrestrial water cycle involves using land surface models and general hydrological models to assess hydrologically important variables including evaporation, soil moisture, and runoff. Such models require meteorological forcing data, and this paper describes the creation of the WATCH Forcing Data for 1958–2001 based on the 40-yr ECMWF Re-Analysis (ERA-40) and for 1901–57 based on reordered reanalysis data. It also discusses and analyses model-independent estimates of reference crop evaporation. Global average annual cumulative reference crop evaporation was selected as a widely adopted measure of potential evapotranspiration. It exhibits no significant trend from 1979 to 2001 although there are significant long-term increases in global average vapor pressure deficit and concurrent significant decreases in global average net radiation and wind speed. The near-constant global average of annual reference crop evaporation in the late twentieth century masks significant decreases in some regions (e.g., the Murray–Darling basin) with significant increases in others.

scholarly journals Presentation and discussion of the high-resolution atmosphere–land-surface–subsurface simulation dataset of the simulated Neckar catchment for the period 2007–2015

2021 ◽  
Vol 13 (9) ◽  
pp. 4437-4464
Author(s):  
Bernd Schalge ◽  
Gabriele Baroni ◽  
Barbara Haese ◽  
Daniel Erdal ◽  
Gernot Geppert ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Overland Flow ◽  
Water Cycle ◽  
Numerical Models ◽  
Model Performance ◽  
Small Scale ◽  
The Real ◽  
Community Land Model ◽  
Terrestrial System

Abstract. Coupled numerical models, which simulate water and energy fluxes in the subsurface–land-surface–atmosphere system in a physically consistent way, are a prerequisite for the analysis and a better understanding of heat and matter exchange fluxes at compartmental boundaries and interdependencies of states across these boundaries. Complete state evolutions generated by such models may be regarded as a proxy of the real world, provided they are run at sufficiently high resolution and incorporate the most important processes. Such a simulated reality can be used to test hypotheses on the functioning of the coupled terrestrial system. Coupled simulation systems, however, face severe problems caused by the vastly different scales of the processes acting in and between the compartments of the terrestrial system, which also hinders comprehensive tests of their realism. We used the Terrestrial Systems Modeling Platform (TerrSysMP), which couples the meteorological Consortium for Small-scale Modeling (COSMO) model, the land-surface Community Land Model (CLM), and the subsurface ParFlow model, to generate a simulated catchment for a regional terrestrial system mimicking the Neckar catchment in southwest Germany, the virtual Neckar catchment. Simulations for this catchment are made for the period 2007–2015 and at a spatial resolution of 400 m for the land surface and subsurface and 1.1 km for the atmosphere. Among a discussion of modeling challenges, the model performance is evaluated based on observations covering several variables of the water cycle. We find that the simulated catchment behaves in many aspects quite close to observations of the real Neckar catchment, e.g., concerning atmospheric boundary-layer height, precipitation, and runoff. But also discrepancies become apparent, both in the ability of the model to correctly simulate some processes which still need improvement, such as overland flow, and in the realism of some observation operators like the satellite-based soil moisture sensors. The whole raw dataset is available for interested users. The dataset described here is available via the CERA database (Schalge et al., 2020): https://doi.org/10.26050/WDCC/Neckar_VCS_v1.

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>

scholarly journals The Effects of Spatial and Temporal Resolution of Gridded Meteorological Forcing on Watershed Hydrological Responses

2021 ◽  
Author(s):  
Pin Shuai ◽  
Xingyuan Chen ◽  
Utkarsh Mital ◽  
Ethan T. Coon ◽  
Dipankar Dwivedi
Keyword(s):  
Spatial Resolution ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Hydrological Cycle ◽  
Critical Role ◽  
Diurnal Fluctuation ◽  
Spatiotemporal Resolution ◽  
Meteorological Forcing ◽  
Watershed Models ◽  
Creek Watershed

Abstract. Meteorological forcing plays a critical role in accurately simulating the watershed hydrological cycle. With the advancement of high-performance computing and the development of integrated watershed models, simulating the watershed hydrological cycle at high temporal (hourly to daily) and spatial resolution (10s of meters) has become efficient and computationally affordable. These hyperresolution watershed models require high resolution of meteorological forcing as model input to ensure the fidelity and accuracy of simulated responses. In this study, we utilized the Advanced Terrestrial Simulator (ATS), an integrated watershed model, to simulate surface and subsurface flow and land surface processes using unstructured meshes at the Coal Creek Watershed near Crested Butte (Colorado). We compared simulated watershed hydrologic responses including streamflow, and distributed variables such as evapotranspiration, snow water equivalent (SWE), and groundwater table drivenby three publicly available, gridded meteorological forcing (GMF) – Daily Surface Weather and Climatological Summaries (Daymet), Parameter-elevation Regressions on Independent Slopes Model (PRISM), and North American Land Data Assimilation System (NLDAS). By comparing various spatial resolutions (ranging from 400 m to 4 km) of PRISM, the simulated streamflow only becomes marginally worse when spatial resolution of meteorological forcing is coarsened to 4 km (or 30 % of the watershed area). However, the 4 km resolution has much worse performance than finer resolution in spatially distributedvariables such as SWE. By comparing models forced by different temporal resolutions of NLDAS (hourly to daily), GMF in sub-daily resolution preserves the dynamic watershed responses (e.g., diurnal fluctuation of streamflow) that are absent in results forced by daily resolution. Conversely, the simulated streamflow shows better performance using daily resolution compared to that using sub-daily resolution. Our findings suggest that the choice of GMF and its spatiotemporal resolution depends on the quantity of interest and its spatial and temporal scale, which may have important implications on model calibration and watershed management decisions.

scholarly journals Ensemble Evaluation of Hydrologically Enhanced Noah-LSM: Partitioning of the Water Balance in High-Resolution Simulations over the Little Washita River Experimental Watershed

2011 ◽  
Vol 12 (1) ◽  
pp. 45-64 ◽  
Author(s):  
Enrique Rosero ◽  
Lindsey E. Gulden ◽  
Zong-Liang Yang ◽  
Luis G. De Goncalves ◽  
Guo-Yue Niu ◽  
...  
Keyword(s):  
Water Table ◽  
Land Surface ◽  
Water Cycle ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Parameters ◽  
Shallow Water Table ◽  
Total Runoff ◽  
Physically Based ◽  
Subsurface Runoff

Abstract The ability of two versions of the Noah land surface model (LSM) to simulate the water cycle of the Little Washita River experimental watershed is evaluated. One version that uses the standard hydrological parameterizations of Noah 2.7 (STD) is compared another version that replaces STD’s subsurface hydrology with a simple aquifer model and topography-related surface and subsurface runoff parameterizations (GW). Simulations on a distributed grid at fine resolution are compared to the long-term distribution of observed daily-mean runoff, the spatial statistics of observed soil moisture, and locally observed latent heat flux. The evaluation targets the typical behavior of ensembles of models that use realistic, near-optimal sets of parameters important to runoff. STD and GW overestimate the ratio of runoff to evapotranspiration. In the subset of STD and GW runs that best reproduce the timing and the volume of streamflow, the surface-to-subsurface runoff ratio is overestimated and simulated streamflow is much flashier than observations. Both models’ soil columns wet and dry too quickly, implying that there are structural shortcomings in the formulation of STD that cannot be overcome by adding GW’s increased complexity to the model. In its current formulation, GW extremely underestimates baseflow’s contribution to total runoff and requires a shallow water table to function realistically. In the catchment (depth to water table >10 m), GW functions as a simple bucket model. Because model parameters are likely scale and site dependent, the need for even “physically based” models to be extensively calibrated for all domains on which they are applied is underscored.

Development of a “nature run” for observing system simulation experiments (OSSEs) for snow mission development

2022 ◽  
Keyword(s):  
Land Surface ◽  
Snow Water Equivalent ◽  
System Simulation ◽  
Meteorological Forcing ◽  
Simulation Performance ◽  
Spatially Distributed ◽  
Snow Water ◽  
Snow Conditions ◽  
The University ◽  
Spatially Varying

Abstract Snow is a fundamental component of global and regional water budgets, particularly in mountainous areas and regions downstream that rely on snowmelt for water resources. Land surface models (LSMs) are commonly used to develop spatially distributed estimates of snow water equivalent (SWE) and runoff. However, LSMs are limited by uncertainties in model physics and parameters, among other factors. In this study, we describe the use of model calibration tools to improve snow simulations within the Noah-MP LSM as the first step in an Observing System Simulation Experiment (OSSE). Noah-MP is calibrated against the University of Arizona (UA) SWE product over a Western Colorado domain. With spatially varying calibrated parameters, we run calibrated and default Noah-MP simulations for water years 2010-2020. By evaluating both simulations against the UA dataset, we show that calibration decreases domain averaged temporal RMSE and bias for snow depth from 0.15 to 0.13 m and from -0.036 to -0.0023 m, respectively, and improves the timing of snow ablation. Increased snow simulation performance also improves estimates of model-simulated runoff in four of six study basins, though only one has statistically significant improvement. Spatially distributed Noah-MP snow parameters perform better than default uniform values. We demonstrate that calibrating variables related to snow albedo calculations and rain-snow partitioning, among other processes, is a necessary step for creating a nature run that reasonably approximates true snow conditions for the OSSEs. Additionally, the inclusion of a snowfall scaling term can address biases in precipitation from meteorological forcing datasets, further improving the utility of LSMs for generating reliable spatiotemporal estimates of snow.

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