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

2013 ◽  
Vol 10 (8) ◽  
pp. 10775-10808
Author(s):  
J. D. Herman ◽  
J. B. Kollat ◽  
P. M. Reed ◽  
T. Wagener
Keyword(s):  
Sensitivity Analysis ◽  
High Resolution ◽  
The United States ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Time Varying ◽  
Resolution Time ◽  
Watershed Models ◽  
Hydrologic Regimes ◽  
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. Surprisingly, 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 modeled hydrologic regimes 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 hydrologic regimes under nonstationarity.

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 Spatial Pattern Oriented Multi-Criteria Sensitivity Analysis of a Distributed Hydrologic Model

2018 ◽  
Author(s):  
Mehmet Cüneyd Demirel ◽  
Julian Koch ◽  
Gorka Mendiguren ◽  
Simon Stisen
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Variability ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Performance Measures ◽  
Sampling Strategy ◽  
Behavioral Model ◽  
Hydrologic Model ◽  
Actual Evapotranspiration ◽  
Model Parameters

Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represents an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity are typically not reflecting other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definition based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

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.

scholarly journals The Time-Varying Effect of Monetary Policy on Asset Prices

2020 ◽  
Vol 102 (4) ◽  
pp. 690-704 ◽  
Author(s):  
Pascal Paul
Keyword(s):  
Monetary Policy ◽  
Asset Prices ◽  
House Prices ◽  
The United States ◽  
Model Parameters ◽  
Time Varying ◽  
Real Economy ◽  
Monetary Policy Shocks ◽  
Vector Autoregressive ◽  
Policy Shocks

This paper studies how monetary policy jointly affects asset prices and the real economy in the United States. I develop an estimator that uses high-frequency surprises as a proxy for the structural monetary policy shocks. This is achieved by integrating the surprises into a vector autoregressive model as an exogenous variable. I use current short-term rate surprises because these are least affected by an information effect. When allowing for time-varying model parameters, I find that compared to the response of output, the reaction of stock and house prices to monetary policy shocks was particularly low before the 2007–2009 financial crisis.

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>

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>

A Sensitivity Analysis of Two Mesoscale Models: COAMPS and WRF

2020 ◽  
Vol 148 (7) ◽  
pp. 2997-3014
Author(s):  
Caren Marzban ◽  
Robert Tardif ◽  
Scott Sandgathe
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Structure ◽  
Latin Hypercube Sampling ◽  
Forecast Model ◽  
The United States ◽  
Model Parameters ◽  
Analysis Methodology ◽  
Trigger Function ◽  
Multivariate Multiple Regression

Abstract A sensitivity analysis methodology recently developed by the authors is applied to COAMPS and WRF. The method involves varying model parameters according to Latin Hypercube Sampling, and developing multivariate multiple regression models that map the model parameters to forecasts over a spatial domain. The regression coefficients and p values testing whether the coefficients are zero serve as measures of sensitivity of forecasts with respect to model parameters. Nine model parameters are selected from COAMPS and WRF, and their impact is examined on nine forecast quantities (water vapor, convective and gridscale precipitation, and air temperature and wind speed at three altitudes). Although the conclusions depend on the model parameters and specific forecast quantities, it is shown that sensitivity to model parameters is often accompanied by nontrivial spatial structure, which itself depends on the underlying forecast model (i.e., COAMPS vs WRF). One specific difference between these models is in their sensitivity with respect to a parameter that controls temperature increments in the Kain–Fritsch trigger function; whereas this parameter has a distinct spatial structure in COAMPS, that structure is completely absent in WRF. The differences between COAMPS and WRF also extend to the quality of the statistical models used to assess sensitivity; specifically, the differences are largest over the waters off the southeastern coast of the United States. The implication of these findings is twofold: not only is the spatial structure of sensitivities different between COAMPS and WRF, the underlying relationship between the model parameters and the forecasts is also different between the two models.

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