Improving the realism of hydrologic model functioning through multivariate parameter estimation

2016 ◽  
Vol 52 (10) ◽  
pp. 7779-7792 ◽  
Author(s):  
O. Rakovec ◽  
R. Kumar ◽  
S. Attinger ◽  
L. Samaniego
Keyword(s):  
Parameter Estimation ◽  
Hydrologic Model ◽  
Multivariate Parameter

scholarly journals A comparison of the discrete cosine and wavelet transforms for hydrologic model input data reduction

2017 ◽  
Vol 21 (7) ◽  
pp. 3827-3838 ◽  
Author(s):  
Ashley Wright ◽  
Jeffrey P. Walker ◽  
David E. Robertson ◽  
Valentijn R. N. Pauwels
Keyword(s):  
Parameter Estimation ◽  
Data Reduction ◽  
Wavelet Transforms ◽  
Input Data ◽  
Hydrologic Model ◽  
Data Uncertainty ◽  
Discrete Wavelet ◽  
Rainfall Time Series ◽  
Data Set ◽  
Model Input

Abstract. The treatment of input data uncertainty in hydrologic models is of crucial importance in the analysis, diagnosis and detection of model structural errors. Data reduction techniques decrease the dimensionality of input data, thus allowing modern parameter estimation algorithms to more efficiently estimate errors associated with input uncertainty and model structure. The discrete cosine transform (DCT) and discrete wavelet transform (DWT) are used to reduce the dimensionality of observed rainfall time series for the 438 catchments in the Model Parameter Estimation Experiment (MOPEX) data set. The rainfall time signals are then reconstructed and compared to the observed hyetographs using standard simulation performance summary metrics and descriptive statistics. The results convincingly demonstrate that the DWT is superior to the DCT in preserving and characterizing the observed rainfall data records. It is recommended that the DWT be used for model input data reduction in hydrology in preference over the DCT.

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 An integrated multi-fingerprint sensitivity-nested approach for regional model parameter estimation and catchment similarity assessment

2016 ◽  
Author(s):  
Simon Höllering ◽  
Jürgen Ihringer ◽  
Luis Samaniego ◽  
Erwin Zehe
Keyword(s):  
Parameter Estimation ◽  
Hydrologic Model ◽  
Hydrologic Processes ◽  
Response Characteristics ◽  
Typical Sample ◽  
Novel Approach ◽  
Spatially Distributed ◽  
Estimation Scheme ◽  
Streamflow Response ◽  
Nested Approach

Abstract. The present study provides a novel approach to the challenge of identifying behavioural parameters in the context of parameter sensitivity and related hydrologic similarity classification. A methodical framework is presented wherein global sensitivity analysis of a spatially distributed conceptual hydrologic model within 14 different mesoscale headwater catchments is combined with a parameter estimation scheme based upon both classification by (1) physiographic and climate and (2) related dynamic response characteristics represented by hydrologic signatures (fingerprints) creating an interface between hydrologic variables of observed and simulated origin. Changing ranks in (3) partial parameter sensitivities within the catchments indicate that hydrologic dynamics might be governed by different hydrologic processes. Model simulated and the respective observed response fingerprints are found to cluster within typical sample regions. These findings show a general model adequacy to represent mesoscale streamflow response processes that relate temporally dominant parameters and allow a reasonable constraint on the parameter space. The senstivity-nested approach may be useful to calibrate hydrologic models sequentially on streamflow sections as well as on constraining (observable) single or combined hydrologic fingerprints and also to transfer results to similar sites, ungauged or anthropogenically altered.

scholarly journals MPR 1.0: A stand-alone Multiscale Parameter Regionalization Tool for Improved Parameter Estimation of Land Surface Models

2021 ◽  
Author(s):  
Robert Schweppe ◽  
Stephan Thober ◽  
Matthias Kelbling ◽  
Rohini Kumar ◽  
Sabine Attinger ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Parameter Space ◽  
Land Surface ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Land Surface Models ◽  
Continental Scale ◽  
Environmental Models ◽  
Surface Models

Abstract. Distributed environmental models such as land surface models (LSM) require model parameters in each spatial modelling unit (e.g. grid cell), thereby leading to a high-dimensional parameter space. One approach to decrease the dimen- sionality of parameter space in these models is to use regularization techniques. One such highly efficient technique is the Multiscale Parameter Regionalization (MPR) framework that translates high-resolution predictor variables (e.g., soil textural properties) into model parameters (e.g., porosity) via transfer functions (TFs) and upscaling operators that are suitable for every modeled process. This framework yields seamless model parameters at multiple scales and locations in an effective manner. However, integration of MPR into existing modeling workflows has been hindered thus far by hard-coded configurations and non-modular software designs. For these reasons, we redesigned MPR as a model-agnostic, stand-alone tool. It is a useful software for creating graphs of netCDF variables, wherein each node is a variable and the links consist of TFs and/or upscaling operators. In this study, we present and verify our tool against a previous version, which was implemented in the mesoscale hydrologic model mHM (www.ufz.de/mhm). By using this tool for the generation of continental-scale soil hydraulic param- eters applicable to different models (Noah-MP and HTESSEL), we showcase its general functionality and flexibility. Further, using model parameters estimated by the MPR tool leads to significant changes in long-term estimates of evapotranspiration, as compared to their default parameterizations. For example, a change of up to 25 % in long-term evapotranspiration flux is observed in Noah-MP and HTESSEL in the Mississippi River basin. We postulate that use of the stand-alone MPR tool will considerably increase the transparency and reproducibility of the parameter estimation process in distributed (environmental) models. It will also allow a rigorous uncertainty estimation related to the errors of the predictors (e.g., soil texture fields), transfer function and its parameters, and remapping (or upscaling) algorithms.

scholarly journals Multiscale and Multivariate Evaluation of Water Fluxes and States over European River Basins

2015 ◽  
Vol 17 (1) ◽  
pp. 287-307 ◽  
Author(s):  
Oldrich Rakovec ◽  
Rohini Kumar ◽  
Juliane Mai ◽  
Matthias Cuntz ◽  
Stephan Thober ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Soil Moisture ◽  
Regional Scale ◽  
Water Storage ◽  
River Basins ◽  
Hydrologic Model ◽  
Grand Challenge ◽  
Water Fluxes ◽  
Total Water ◽  
Wide Range

Abstract Accurately predicting regional-scale water fluxes and states remains a challenging task in contemporary hydrology. Coping with this grand challenge requires, among other things, a model that makes reliable predictions across scales, locations, and variables other than those used for parameter estimation. In this study, the mesoscale hydrologic model (mHM) parameterized with the multiscale regionalization technique is comprehensively tested across 400 European river basins. The model fluxes and states, constrained using the observed streamflow, are evaluated against gridded evapotranspiration, soil moisture, and total water storage anomalies, as well as local-scale eddy covariance observations. This multiscale verification is carried out in a seamless manner at the native resolutions of available datasets, varying from 0.5 to 100 km. Results of cross-validation tests show that mHM is able to capture the streamflow dynamics adequately well across a wide range of climate and physiographical characteristics. The model yields generally better results (with lower spread of model statistics) in basins with higher rain gauge density. Model performance for other fluxes and states is strongly driven by the degree of seasonality that each variable exhibits, with the best match being observed for evapotranspiration, followed by total water storage anomaly, and the least for soil moisture. Results show that constraining the model against streamflow only may be necessary but not sufficient to warrant the model fidelity for other complementary variables. The study emphasizes the need to account for other complementary datasets besides streamflow during parameter estimation to improve model skill with respect to “hidden” variables.

scholarly journals A Comparison of the Discrete Cosine and Wavelet Transforms for Hydrologic Model Input Data Reduction

2017 ◽  
Author(s):  
Ashley Wright ◽  
Jeffrey P. Walker ◽  
David E. Robertson ◽  
Valentijn R. N. Pauwels
Keyword(s):  
Parameter Estimation ◽  
Data Reduction ◽  
Wavelet Transforms ◽  
Input Data ◽  
Hydrologic Model ◽  
Data Uncertainty ◽  
Discrete Wavelet ◽  
Rainfall Time Series ◽  
Data Set ◽  
Model Input

Abstract. The treatment of input data uncertainty in hydrologic models is of crucial importance in the analysis, diagnosis and detection of model structural errors. Model input data reduction techniques decrease the dimensionality of input data, thus allowing modern parameter estimation algorithms to more efficiently estimate errors associated with input uncertainty and model structure. The Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are used to reduce the dimensionality of rainfall time series observations from the 438 catchments in the MOdel Parameter Estimation eXperiment (MOPEX) data set. The rainfall time signals are then reconstructed and compared to the measured hyetographs using standard simulation performance summary metrics and descriptive statistics as well as peak discharge errors. The results convincingly demonstrate that the DWT is superior to the DCT and best preserves and characterizes the observed rainfall data records. It is recommended that the DWT be used for model input data reduction in hydrology in preference over the DCT.

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