2020
DOI: 10.1029/2019wr026807
|View full text |Cite
|
Sign up to set email alerts
|

Scaling Point‐Scale (Pedo)transfer Functions to Seamless Large‐Domain Parameter Estimates for High‐Resolution Distributed Hydrologic Modeling: An Example for the Rhine River

Abstract: Moving toward high-resolution gridded hydrologic models asks for novel parametrization approaches. A high-resolution conceptual hydrologic model (wflow_sbm) was parameterized for the Rhine basin in Europe based on point-scale (pedo)transfer functions, without further calibration of effective model parameters on discharge. Parameters were estimated on the data resolution, followed by upscaling of parameter fields to the model resolution. The method was tested using a 6-hourly time step at four model resolutions… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
39
0

Year Published

2020
2020
2023
2023

Publication Types

Select...
7
1
1

Relationship

1
8

Authors

Journals

citations
Cited by 45 publications
(48 citation statements)
references
References 93 publications
0
39
0
Order By: Relevance
“…Fundamental changes are expected in snow-dominated regions (Hock et al, 2019); alpine climatic changes go along with declining seasonal snowpacks (Steger et al, 2013;Beniston et al, 2018;Hanzer et al, 2018), thawing permafrost (Serreze et al, 2000;Schuur et al, 2015;Elberling et al, 2013;Beniston et al, 2018) and retreating glaciers (Zemp et al, 2006;Radić and Hock, 2014;Hanzer et al, 2018). These cryospheric changes, in turn, impact water availability in and outside mountain areas (Barnett et al, 2005;Stewart, 2009;Junghans et al, 2011; Future Rhine floods Viviroli et al, 2011). The European Alps, for example, are the source region of numerous large rivers that form the basis of the economic and cultural development in various cities and communities (Beniston, 2012).…”
Section: Introductionmentioning
confidence: 99%
“…Fundamental changes are expected in snow-dominated regions (Hock et al, 2019); alpine climatic changes go along with declining seasonal snowpacks (Steger et al, 2013;Beniston et al, 2018;Hanzer et al, 2018), thawing permafrost (Serreze et al, 2000;Schuur et al, 2015;Elberling et al, 2013;Beniston et al, 2018) and retreating glaciers (Zemp et al, 2006;Radić and Hock, 2014;Hanzer et al, 2018). These cryospheric changes, in turn, impact water availability in and outside mountain areas (Barnett et al, 2005;Stewart, 2009;Junghans et al, 2011; Future Rhine floods Viviroli et al, 2011). The European Alps, for example, are the source region of numerous large rivers that form the basis of the economic and cultural development in various cities and communities (Beniston, 2012).…”
Section: Introductionmentioning
confidence: 99%
“…The use of radar/satellite derived rainfall fields (e.g., Huza et al, 2014 ; Cecinati et al, 2017 ) may also have a significant impact on the uncertainty of the rainfall fields and therefore on the joint posterior uncertainty of the discharge. This might be particularly useful if combined with spatially-distributed hydrological models, such as the WaSIM-ETH ( Schulla & Jasper, 2007 ) and wflow_sbm ( Imhoff et al, 2020 ) models. Our methodology also applies to spatially distributed modelling approaches, although computing time will likely increase.…”
Section: Discussionmentioning
confidence: 99%
“…To improve the estimation of the initial conditions of the hydrological model, and thus improve the hydrological forecasts, data assimilation methods (most commonly based on the Ensemble Kalman Filter approach) are applied. Another way to improve hydrological forecasting skill is to improve hydrological modelling (e.g., using better historical forcing datasets), which Imhoff et a.l [25] investigated for the Rhine River as part of IMPREX. Finally, statistical post-processing methods, which mainly aim to increase reliability of probabilistic predictions (e.g., Bayesian Model Averaging, BMA; Ensemble Model Output Statistics, EMOS), have also been applied to hydrological ensemble forecasts to reduce biases in the output [3,26] prior to IMPREX.…”
Section: Hydrological Model Biases and Post-processingmentioning
confidence: 99%