scholarly journals Automatic Estimation of Parameter Transfer Functions for Distributed Hydrological Models - Function Space Optimization Applied on the mHM Model

2021 ◽  
Author(s):  
Moritz Feigl ◽  
Robert Schweppe ◽  
Stephan Thober ◽  
Mathew Herrnegger ◽  
Luis Samaniego ◽  
...  
Keyword(s):  
Function Space ◽  
Transfer Functions ◽  
Hydrological Models ◽  
Automatic Estimation ◽  
Distributed Hydrological Models

Automatic estimation of parameter transfer functions for distributed hydrological models - a case study with the mHM model

2020 ◽  
Author(s):  
Moritz Feigl ◽  
Stephan Thober ◽  
Mathew Herrnegger ◽  
Luis Samaniego ◽  
Karsten Schulz
Keyword(s):  
Transfer Function ◽  
Function Space ◽  
Hydrological Model ◽  
Transfer Functions ◽  
Dimensional Space ◽  
Model Parameters ◽  
Hydrological Models ◽  
Estimation Of Parameters ◽  
General Applicability ◽  
Distributed Hydrological Models

<p>The estimation of parameters for spatially distributed rainfall runoff models is a long-studied, complex and ill-posed problem. Relating parameters of distributed hydrological models to geophysical properties of catchments could potentially solve some of the major difficulties connected to it.</p><p>One way to define this relationship is by the use of explicit equations called parameter transfer functions, which relate geophysical catchment properties to the model parameters. Computing parameter fields using transfer functions would result in spatially consistent parameter fields and the potential to extrapolate to other catchments. A further advantage is that the dimensionality of the parameter space is reduced because the transfer function parameters are applied to all computational units (i.e., grid cells). However, the structure and parameterization of transfer functions is often only implicitly assumed or needs to be derived by a laborious literature guided trial and error process.</p><p>For this reason we use Function Space Optimization (FSO), a symbolic regression approach which automatically estimates the structure and parameterization of transfer functions from catchment data. FSO transfers the search of the optimal function to a searchable continuous vector space. To create this space, a text generating neural network with a variational autoencoder (VAE) architecture is used. It is trained to map possible transfer functions and their distributions to a 6-dimensional space. After training, a continuous optimization is applied to search for the optimal transfer function in this function space. FSO was already tested in a virtual experiment using a parsimonious hydrological model, where its ability to solve the problem of transfer function estimation was shown.</p><p>Here, we further test FSO by applying it in a real world setting to the mesoscale hydrological model (mHM). mHM is a widely applied distributed hydological model, which uses transfer functions for all its parameters. For this study, we estimate transfer functions for the parameters porosity and field capacity, which both influence a range of hydrologic processes, e.g. infiltration and evapotranspiration. We compare the FSO estimated transfer functions with the already existing mHM transfer functions and examine their influence on the model performance.</p><p>In summary, we show the general applicability of FSO for distributed hydrological models and the advantages and capabilities of automatically defining parameter transfer functions.</p>

Catchment to model space mapping – learning transfer functions from data by symbolic regression

2021 ◽  
Author(s):  
Moritz Feigl ◽  
Robert Schweppe ◽  
Stephan Thober ◽  
Mathew Herrnegger ◽  
Luis Samaniego ◽  
...  
Keyword(s):  
Transfer Function ◽  
Function Space ◽  
Transfer Functions ◽  
Continuous Optimization ◽  
Model Space ◽  
Symbolic Regression ◽  
Regression Method ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Distributed Hydrological Models

<p>The Function Space Optimization (FSO) method, recently developed by Feigl et al. (2020), automatically estimates the transfer function structure and coefficients to parameterize spatially distributed hydrological models. FSO is a symbolic regression method, searching for an optimal transfer function in a continuous optimization space, using a text generating neural network (variational autoencoder).</p><p>We apply our method to the distributed hydrological model mHM (www.ufz.de/mhm), which is based on a priori defined transfer functions. We estimate mHM transfer functions for the parameters “saturated hydraulic conductivity” and “field capacity”, which both influence a range of hydrologic processes, e.g. infiltration and evapotranspiration.</p><p>The FSO and standard mHM approach are compared using data from 229 basins, including 7 large training basins and 222 smaller validation basins, distributed across Germany. For training, 5 years of data of 7 gauging stations is used, while up to 35 years, with a median of 32 years, are used for validation. This setup is adopted from a previous study by Zink et al. (2017), testing mHM in the same basins and which is used as a benchmark. Maps of soil properties (sand/clay percentage, bulk density) and topographic properties (aspect, slope, elevation) are used as possible inputs for transfer functions.</p><p>FSO estimated transfer functions improved the mHM model performance in the validation catchments significantly when compared to the benchmark results, and only show a small decrease in performance compared to the training results. Results demonstrate that an automatic estimation of parameter transfer functions by FSO is beneficial for the parameterization of distributed hydrological models and allows for a robust parameter transfer to other locations.</p><p> </p><p>Feigl, M., Herrnegger, M., Klotz, D., & Schulz, K. (2020). Function Space Optimization: A symbolic regression method for estimating parameter transfer functions for hydrological models. Water resources research, 56(10), e2020WR027385.</p><p>Zink, M., Kumar, R., Cuntz, M., & Samaniego, L. (2017). A high-resolution dataset of water fluxes and states for Germany accounting for parametric uncertainty. Hydrol. Earth Syst. Sci, 21, 1769-1790.</p>

Split-parameter structure for the automatic calibration of distributed hydrological models

2007 ◽  
Vol 332 (1-2) ◽  
pp. 226-240 ◽  
Author(s):  
Félix Francés ◽  
Jaime Ignacio Vélez ◽  
Jorge Julián Vélez
Keyword(s):  
Automatic Calibration ◽  
Hydrological Models ◽  
Split Parameter ◽  
Distributed Hydrological Models

scholarly journals Model Uncertainty Analysis Methods for Semi-Arid Watersheds with Different Characteristics: A Comparative SWAT Case Study

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1177 ◽  
Author(s):  
Lufang Zhang ◽  
Baolin Xue ◽  
Yuhui Yan ◽  
Guoqiang Wang ◽  
Wenchao Sun ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
River Basin ◽  
Model Uncertainty ◽  
Assessment Tool ◽  
Rapid Development ◽  
Swat Model ◽  
Observation Data ◽  
Hydrological Models ◽  
Distributed Hydrological Models ◽  
Different Characteristics

Distributed hydrological models play a vital role in water resources management. With the rapid development of distributed hydrological models, research into model uncertainty has become a very important field. When studying traditional hydrological model uncertainty, it is very common to use multisite observation data to evaluate the performance of the model in the same watershed, but there are few studies on uncertainty in watersheds with different characteristics. This study is based on the Soil and Water Assessment Tool (SWAT) model, and uses two common methods: Sequential Uncertainty Fitting Version 2 (SUFI-2) and Generalized Likelihood Uncertainty Estimation (GLUE) for uncertainty analysis. We compared these methods in terms of parameter uncertainty, model prediction uncertainty, and simulation effects. The Xiaoqing River basin and the Xinxue River basin, which have different characteristics, including watershed geography and scale, were used for the study areas. The results show that the GLUE method had better applicability in the Xiaoqing River basin, and that the SUFI-2 method provided more reasonable and accurate analysis results in the Xinxue River basin; thus, the applicability was higher. The uncertainty analysis method is affected to some extent by the characteristics of the watershed.

Integration of earth observation data in distributed hydrological models: the Senegal River basin

2003 ◽  
Vol 29 (6) ◽  
pp. 701-710 ◽  
Author(s):  
Inge Sandholt ◽  
Jens Andersen ◽  
Gorm Dybkjær ◽  
Lotte Nyborg ◽  
Medou Lô ◽  
...  
Keyword(s):  
River Basin ◽  
Earth Observation ◽  
Observation Data ◽  
Hydrological Models ◽  
Senegal River ◽  
Earth Observation Data ◽  
Distributed Hydrological Models ◽  
Senegal River Basin
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