Wflow_sbm, a spatially distributed hydrologic model: from global data to local applications

2020 ◽  
Author(s):  
Willem van Verseveld ◽  
Hélène Boisgontier ◽  
Laurène Bouaziz ◽  
Dirk Eilander ◽  
Arjen Haag ◽  
...  
Keyword(s):  
High Resolution ◽  
Flow Direction ◽  
Hydrologic Model ◽  
Rooting Depth ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Point Scale ◽  
Climate Zones ◽  
Area Of Interest ◽  
Global Data

<p>In this contribution we present the wflow_sbm hydrologic model concept, which is a conceptual bucket-style hydrologic model based on simplified physical relationships including kinematic wave routing for surface and subsurface lateral flow. The model maximizes the use of global data for local applications and allows us to automatically setup a high resolution (~1km<sup>2</sup>) wflow_sbm model for any basin in the world. For most discharge gauging stations in selected basins from different climate zones, wflow_sbm showed promising results without further calibration. Depending on the geographical area of interest two model parameters, besides anthropogenic interference like reservoir and lake management, show most sensitivity: rooting depth and horizontal saturated hydraulic conductivity.</p><p>We extended the parameter estimation of the wflow_sbm hydrological model for the Rhine basin (Imhoff et al, 2019) with point-scale (pedo)transfer-functions (PTFs) in conjunction with scaling operators as applied in Multiscale Parameter Regionalization (MPR) to the global scale at high resolution (~1km<sup>2</sup>). The state-of-the-art hydro-MERIT dataset at 3 arcsec resolution (Yamazaki et al. (2019)) is scaled to model resolution whilst conserving the drainage network using a newly developed extended Effective Area Method (EAM) for flow direction scaling which builds on the original EAM (Yamazaki et al. 2009). Compared to EAM and the double maximum method, the extended EAM method shows improved skill. The automated model setup derives subgrid information about land slope, river slope and length. River widths are derived from power law relationships between hydro-MERIT river widths and global discharge estimates through multiple linear regression based on GRDC data, precipitation and upstream area with clustering on climate zones. Soil hydraulic parameters are derived from the 250m ISRIC SoilGrids product using PTFs. Furthermore, parameters for interception and rooting depth are derived and upscaled using global or regional land cover maps. Monthly LAI profiles are derived from MODIS (500m) and upscaled. Lake and reservoir parameters are derived from HydroLAKES and GRanD, respectively. The models are run using forcing from globally available data sets like ERA5 and CHIRPS.</p><p> </p><p>Imhoff, R., van Verseveld, W., Osnabrugge, B., A. Weerts, Scaling point-scale pedotransfer functions to seamless large-domain parameter estimates for high-resolution distributed hydrological modelling: An example for the Rhine river, submitted to WRR, 2019.</p><p>Yamazaki D., D. Ikeshima, J. Sosa, P.D. Bates, G.H. Allen, T.M. Pavelsky, MERIT Hydro: A high-resolution global hydrography map based on latest topography datasets, Water Resources Research, 2019, doi: 10.1029/2019WR024873.</p><p>Yamazaki, D., T. Oki., and S. Kanae, Deriving a global river network map and its sub‐grid topographic characteristics from a fine‐resolution flow direction map, Hydrol. Earth Syst. Sci., 13, 2241– 2251, 2009.</p>

scholarly journals Extreme runoff response to short-duration convective rainfall in South-West Germany

2011 ◽  
Vol 8 (6) ◽  
pp. 10739-10780
Author(s):  
V. Ruiz-Villanueva ◽  
M. Borga ◽  
D. Zoccatelli ◽  
L. Marchi ◽  
E. Gaume ◽  
...  
Keyword(s):  
High Resolution ◽  
Flash Flood ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
West Germany ◽  
Structure Evolution ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Precipitation Frequency ◽  
South West

Abstract. The 2 June 2008 flood-producing storm on the Starzel river basin in South-West Germany is examined as a prototype for organized convective systems that dominate the upper tail of the precipitation frequency distribution and are likely responsible for the flash flood peaks in this region. The availability of high-resolution rainfall estimates from radar observations and a rain gauge network, together with indirect peak discharge estimates from a detailed post-event survey, provides the opportunity to study the hydrometeorological and hydrological mechanisms associated with this extreme storm and the ensuing flood. Radar-derived rainfall, streamgauge data and indirect estimates of peak discharges are used along with a distributed hydrologic model to reconstruct hydrographs at multiple locations. The influence of storm structure, evolution and motion on the modeled flood hydrograph is examined by using the "spatial moments of catchment rainfall" (Zoccatelli et al., 2011). It is shown that downbasin storm motion had a noticeable impact on flood peak magnitude. Small runoff ratios (less than 20%) characterized the runoff response. The flood response can be reasonably well reproduced with the distributed hydrological model, using high resolution rainfall observations and model parameters calibrated at a river section which includes most of the area impacted by the storm.

High-resolution pan-European multi-model simulations of hydrologic states and fluxes

2020 ◽  
Author(s):  
Bibi S Naz ◽  
Wendy Sharples ◽  
Klaus Goergen ◽  
Stefan Kollet
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Groundwater Flow ◽  
Hydrologic Model ◽  
Hydrological Processes ◽  
Model Parameters ◽  
Time Step ◽  
European Continent ◽  
Model Simulations ◽  
Community Land Model

<p> <span>High-resolution large-scale predictions of hydrologic states and fluxes are important for many regional-scale applications and water resource management. However, because of uncertainties related to forcing data, model structural errors arising from simplified representations of hydrological processes or uncertain model parameters, model simulations remain uncertain. To quantify this uncertainty, multi-model simulations were performed at 3km resolution over the European continent using the Community Land Model (CLM3.5) and the ParFlow hydrologic model. While Parflow uses a similar approach as CLM in simulating the snow, vegetation and land-atmosphere exchange processes, it simulates three-dimensional variably saturated groundwater flow solving Richards equation and overland flow with a two-dimensional kinematic wave approximation. </span><span>The </span><span>CLM</span><span>3.5</span><span> uses a simple groundwater model to account for groundwater recharge and discharge processes. Both models were driven with the COSMO-REA6 reanalysis dataset at 6km resolution for the time period from 2000 to 2006 at an hourly time step, and both used the same datasets for the static input variables (such as topography, vegetation and soil properties). The performance of both models was analyzed through comparisons with independent observations including satellite-derived and in-situ soil moisture, evapotranspiration, river discharge, water table depth and total water storage datasets. Overall, both models capture the interannual variability in the hydrologic states and fluxes well, however differences in performance between models showed the uncertainty associated with the representation of hydrological processes, such as groundwater flow and soil moisture and its control on latent and sensible heat fluxes at the surface.</span></p>

Improvement of the simulation of the water and energy cycle using Multiscale Parameter Regionalization (MPR)

2020 ◽  
Author(s):  
Stephan Thober ◽  
Matthias Kelbling ◽  
Florian Pappenberger ◽  
Christel Prudhomme ◽  
Gianpaolo Balsamo ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
High Resolution ◽  
Parameter Space ◽  
Land Surface ◽  
Hydrologic Model ◽  
Estimation Methods ◽  
Surface Model ◽  
Model Parameters ◽  
Environmental Models ◽  
Energy Cycle

<p>The representation of the water and energy cycle in environmental models is closely linked to the parameter values used in the process parametrizations. The dimension of the parameter space in spatially distributed environmental models corresponds to the number of grid cells multiplied by the number of parameters per grid cell. For large-scale simulations on national and continental scales, the dimensionality of the parameter space is too high for efficient parameter estimation using inverse estimation methods. A regularization of the parameter space is necessary to reduce its dimensionality. The Multiscale Parameter Regionalization (MPR) is one approach to achieve this.</p><p>MPR translates local geophysical properties into model parameters. It consists of two steps: 1) local high-resolution geophysical data sets (e.g. soil maps) are translated into model parameters using a transfer function. 2) the high-resolution model parameters are scaled to the model resolution using suitable upscaling operators (e.g., harmonic mean). The MPR technique was introduced into the mesoscale hydrologic model (mHM, Samaniego et al. 2010, Kumar et al. 2013) and it is key factor for its success on transferring parameters across scales and locations.  </p><p>In this study, we apply MPR to vegetation and soil parameters in the land surface model HTESSEL. This model is the land-surface component of the European Centre for Medium-Range Weather Forecasting seasonal forecasting system. About 100 hard-coded parameters have been extracted to allow for a comprehensive sensitivity analysis and parameter estimation.</p><p>We analyze simulated evaporation and runoff fluxes by HTESSEL using parameters estimated by MPR in comparison to a default HTESSEL setup over Europe. The magnitude of simulated long-term fluxes deviates the most (up to 10% and 20% for evapotranspiration and runoff, respectively) in regions with a large subgrid variability in geophysical attributes (e.g., soil texture). The choice of transfer functions and upscaling operators influences the magnitude of these differences and governs model performance assessed after calibration against observations (e.g. streamflow).</p><p><strong>References:</strong></p><p>Samaniego L., et al.  <strong>https://doi.org/10.1029/2008WR007327</strong></p><p>Kumar, R., et al.  <strong>https://doi.org/10.1029/2012WR012195</strong></p>

scholarly journals A large-scale, high-resolution hydrological model parameter data set for climate change impact assessment for the conterminous US

2014 ◽  
Vol 18 (1) ◽  
pp. 67-84 ◽  
Author(s):  
A. A. Oubeidillah ◽  
S.-C. Kao ◽  
M. Ashfaq ◽  
B. S. Naz ◽  
G. Tootle
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Impact Assessment ◽  
Spatial Resolution ◽  
Climate Impact ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Data Set ◽  
Model Parameter ◽  
Climate Impact Assessment

Abstract. To extend geographical coverage, refine spatial resolution, and improve modeling efficiency, a computation- and data-intensive effort was conducted to organize a comprehensive hydrologic data set with post-calibrated model parameters for hydro-climate impact assessment. Several key inputs for hydrologic simulation – including meteorologic forcings, soil, land class, vegetation, and elevation – were collected from multiple best-available data sources and organized for 2107 hydrologic subbasins (8-digit hydrologic units, HUC8s) in the conterminous US at refined 1/24° (~4 km) spatial resolution. Using high-performance computing for intensive model calibration, a high-resolution parameter data set was prepared for the macro-scale variable infiltration capacity (VIC) hydrologic model. The VIC simulation was driven by Daymet daily meteorological forcing and was calibrated against US Geological Survey (USGS) WaterWatch monthly runoff observations for each HUC8. The results showed that this new parameter data set may help reasonably simulate runoff at most US HUC8 subbasins. Based on this exhaustive calibration effort, it is now possible to accurately estimate the resources required for further model improvement across the entire conterminous US. We anticipate that through this hydrologic parameter data set, the repeated effort of fundamental data processing can be lessened, so that research efforts can emphasize the more challenging task of assessing climate change impacts. The pre-organized model parameter data set will be provided to interested parties to support further hydro-climate impact assessment.

scholarly journals A trading-space-for-time approach to probabilistic continuous streamflow predictions in a changing climate – accounting for changing watershed behavior

2011 ◽  
Vol 15 (11) ◽  
pp. 3591-3603 ◽  
Author(s):  
R. Singh ◽  
T. Wagener ◽  
K. van Werkhoven ◽  
M. E. Mann ◽  
R. Crane
Keyword(s):  
Spatial Relationship ◽  
Model Performance ◽  
Climate Scenario ◽  
Hydrologic Model ◽  
Climatic Conditions ◽  
Low Flow ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Watershed Model ◽  
The Impact

Abstract. Projecting how future climatic change might impact streamflow is an important challenge for hydrologic science. The common approach to solve this problem is by forcing a hydrologic model, calibrated on historical data or using a priori parameter estimates, with future scenarios of precipitation and temperature. However, several recent studies suggest that the climatic regime of the calibration period is reflected in the resulting parameter estimates and model performance can be negatively impacted if the climate for which projections are made is significantly different from that during calibration. So how can we calibrate a hydrologic model for historically unobserved climatic conditions? To address this issue, we propose a new trading-space-for-time framework that utilizes the similarity between the predictions under change (PUC) and predictions in ungauged basins (PUB) problems. In this new framework we first regionalize climate dependent streamflow characteristics using 394 US watersheds. We then assume that this spatial relationship between climate and streamflow characteristics is similar to the one we would observe between climate and streamflow over long time periods at a single location. This assumption is what we refer to as trading-space-for-time. Therefore, we change the limits for extrapolation to future climatic situations from the restricted locally observed historical variability to the variability observed across all watersheds used to derive the regression relationships. A typical watershed model is subsequently calibrated (conditioned) on the predicted signatures for any future climate scenario to account for the impact of climate on model parameters within a Bayesian framework. As a result, we can obtain ensemble predictions of continuous streamflow at both gauged and ungauged locations. The new method is tested in five US watersheds located in historically different climates using synthetic climate scenarios generated by increasing mean temperature by up to 8 °C and changing mean precipitation by −30% to +40% from their historical values. Depending on the aridity of the watershed, streamflow projections using adjusted parameters became significantly different from those using historically calibrated parameters if precipitation change exceeded −10% or +20%. In general, the trading-space-for-time approach resulted in a stronger watershed response to climate change for both high and low flow conditions.

scholarly journals Using High-Resolution Satellite Rainfall Products to Simulate a Major Flash Flood Event in Northern Italy

2013 ◽  
Vol 14 (1) ◽  
pp. 171-185 ◽  
Author(s):  
Efthymios I. Nikolopoulos ◽  
Emmanouil N. Anagnostou ◽  
Marco Borga
Keyword(s):  
High Resolution ◽  
Complex Terrain ◽  
Flash Flood ◽  
Flash Floods ◽  
Hydrologic Model ◽  
Flood Event ◽  
Medium Size ◽  
Soil Conditions ◽  
Model Parameters ◽  
Satellite Rainfall

Abstract Effective flash flood warning procedures are usually hampered by observational limitations of precipitation over mountainous basins where flash floods occur. Satellite rainfall estimates are available over complex terrain regions, offering a potentially viable solution to the observational coverage problem. However, satellite estimates of heavy rainfall rates are associated with significant biases and random errors that nonlinearly propagate in hydrologic modeling, imposing severe limitations on the use of these products in flood forecasting. In this study, the use of three quasi-global and near-real-time high-resolution satellite rainfall products for simulating flash floods over complex terrain basins are investigated. The study uses a major flash flood event that occurred during 29 August 2003 on a medium size mountainous basin (623 km2) in the eastern Italian Alps. Comparison of satellite rainfall with rainfall derived from gauge-calibrated weather radar estimates showed that although satellite products suffer from large biases they could represent the temporal variability of basin-averaged precipitation. Propagation of satellite rainfall through a distributed hydrologic model revealed that systematic error in rainfall was severely magnified when transformed to error in runoff under dry initial soil conditions. Simulation hydrographs became meaningful only after recalibrating the model for each satellite rainfall input separately. However, the unrealistic values of model parameters after recalibration show that this approach is erroneous and that model recalibration using satellite rainfall data should be treated with care. Overall, this study highlights the need for improvement of satellite rainfall retrieval algorithms in order to allow a more appropriate use of satellite rainfall products for flash flood applications.

scholarly journals From maps to movies: high-resolution time-varying sensitivity analysis for spatially distributed watershed models

2013 ◽  
Vol 17 (12) ◽  
pp. 5109-5125 ◽  
Author(s):  
J. D. Herman ◽  
J. B. Kollat ◽  
P. M. Reed ◽  
T. Wagener
Keyword(s):  
Sensitivity Analysis ◽  
High Resolution ◽  
The United States ◽  
Hydrologic Model ◽  
Laboratory Research ◽  
Model Parameters ◽  
Time Varying ◽  
Resolution Time ◽  
Watershed Models ◽  
Spatially Distributed

Abstract. Distributed watershed models are now widely used in practice to simulate runoff responses at high spatial and temporal resolutions. Counter to this purpose, diagnostic analyses of distributed models currently aggregate performance measures in space and/or time and are thus disconnected from the models' operational and scientific goals. To address this disconnect, this study contributes a novel approach for computing and visualizing time-varying global sensitivity indices for spatially distributed model parameters. The high-resolution model diagnostics employ the method of Morris to identify evolving patterns in dominant model processes at sub-daily timescales over a six-month period. The method is demonstrated on the United States National Weather Service's Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) in the Blue River watershed, Oklahoma, USA. Three hydrologic events are selected from within the six-month period to investigate the patterns in spatiotemporal sensitivities that emerge as a function of forcing patterns as well as wet-to-dry transitions. Events with similar magnitudes and durations exhibit significantly different performance controls in space and time, indicating that the diagnostic inferences drawn from representative events will be heavily biased by the a priori selection of those events. By contrast, this study demonstrates high-resolution time-varying sensitivity analysis, requiring no assumptions regarding representative events and allowing modelers to identify transitions between sets of dominant parameters or processes a posteriori. The proposed approach details the dynamics of parameter sensitivity in nearly continuous time, providing critical diagnostic insights into the underlying model processes driving predictions. Furthermore, the approach offers the potential to identify transition points between dominant parameters and processes in the absence of observations, such as under nonstationarity.

scholarly journals A trading-space-for-time approach to probabilistic continuous streamflow predictions in a changing climate

2011 ◽  
Vol 8 (4) ◽  
pp. 6385-6417 ◽  
Author(s):  
R. Singh ◽  
T. Wagener ◽  
K. van Werkhoven ◽  
M. Mann ◽  
R. Crane
Keyword(s):  
Historical Data ◽  
A Priori ◽  
Model Performance ◽  
Hydrologic Model ◽  
Climatic Conditions ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Changing Climate ◽  
Predictions In Ungauged Basins ◽  
The Impact

Abstract. Understanding the implications of potential future climatic conditions for hydrologic services and hazards is a crucial and current science question. The common approach to this problem is to force a hydrologic model, calibrated on historical data or using a priori parameter estimates, with future scenarios of precipitation and temperature. Recent studies suggest that the climatic regime of the calibration period is reflected in the resulting parameter estimates and that the model performance can be negatively impacted if the climate for which projections are made is significantly different from that during calibration. We address this issue by introducing a framework for probabilistic streamflow predictions in a changing climate wherein we quantify the impact of climate on model parameters. The strategy extends a regionalization approach (used for predictions in ungauged basins) by trading space-for-time to account for potential parameter variability in a future climate that is beyond the historically observed one. The developed methodology was tested in five US watersheds located in dry to wet climates using synthetic climate scenarios generated by increasing the historical mean temperature from 0 to 8 °C and by changing historical mean precipitation from −30 % to +40 % of the historical values. Validation on historical data shows that changed parameters perform better if future streamflow differs from historical by more than 25 %. We found that the thresholds of climate change after which the streamflow projections using adjusted parameters were significantly different from those using fixed parameters were 0 to 2 °C for temperature change and −10 % to 20 % for precipitation change depending upon the aridity of the watershed. Adjusted parameter sets simulate a more extreme watershed response for both high and low flows.

The role of parameter estimation strategies on ensemble streamflow prediction results across extratropical Andean catchments

2020 ◽  
Author(s):  
Eduardo Muñoz-Castro ◽  
Pablo A. Mendoza ◽  
Ximena Vargas
Keyword(s):  
Hydrologic Model ◽  
Snow Accumulation ◽  
Weighting Coefficient ◽  
Parameter Estimates ◽  
Model Parameters ◽  
High Mountain ◽  
Streamflow Prediction ◽  
Streamflow Forecasts ◽  
Ensemble Streamflow Prediction ◽  
Seasonal Streamflow

<p>In catchments with a highly variable flow regime, an accurate and reliable hydrological forecasting framework is critical to support water resources management. However, due to model structural deficiencies and changing climatic conditions, the parameter estimates during the calibration period are expected to vary with hydrological conditions. This work aims to test the added value of incorporating potential non-stationarities in hydrologic model parameters on seasonal streamflow forecasts in high-mountain environments, using the ensemble streamflow prediction (ESP) methodology. To this end, we apply the GR4J rainfall-runoff model coupled with the snow accumulation and ablation CemaNeige module in six basins located in Central Chile (30-36° S). We explore the effects of four parameter selection strategies on the quality of seasonal streamflow forecasts produced with the ESP method: (i) a single set of parameters for the entire hindcast period (our benchmark), (ii) using parameters calibrated with a ‘leave-one-year-out’ approach, (iii) using parameter sets based on expected hydroclimatic conditions, and (iv) dual data assimilation to improve the initial condition and parameters before the forecast initialization. Results show that parameters related to production store capacity in GR4J model, and degree-day melt coefficient and weighting coefficient for snow pack thermal state in the CemaNeige module have a high inter-annual variability, with variations of 50% with respect to the benchmark scenario.</p>

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