Regional analysis of parameter sensitivity for simulation of streamflow and hydrological fingerprints

Höllering, Simon; Wienhöfer, Jan; Ihringer, J.; Samaniego, Luis; Zehe, Erwin

doi:10.5194/hess-2017-442

Cited by 2 publications

(2 citation statements)

References 38 publications

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“…In this study we apply FSO to estimate new TFs for the mHM parameters K S (saturated hydraulic conductivity [cm/day]) and FieldCap (field capacity [−]). These parameters affect the storage and conductivity of soil water and have a high sensitivity for streamflow estimation (Cuntz et al., 2015; Höllering et al., 2018). We want to minimize the effect of parameter dependency because this is the first large‐scale application of FSO with real‐world data.…”

Section: Function Space Optimization (Fso)mentioning

confidence: 99%

Automatic Regionalization of Model Parameters for Hydrological Models

Feigl

Thober

Schweppe

et al. 2022

Water Resources Research

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Large-domain, spatially contiguous hydrological and land surface models are important tools for managing our water supplies. Hydrological information on the continental or global scale is needed to handle new emerging international and global water management challenges, which include topics like water allocation in international, national, and large river basins, operational flood forecasting services, global water security or the influence of climate extremes on water resources (Archfield et al., 2015). These applications are particularly challenging in areas without hydrologic measurements, which includes a majority of basins worldwide that are effectively ungauged (Hrachowitz et al., 2013). This results in the need for further development in large-domain hydrological modeling to simulate water fluxes and states in both gauged and ungauged basins in different climates in a spatially consistent manner (Rakovec et al., 2019).In 1982, Jim Dooge stated that "the parameterization of hydrologic processes to the grid-scale of general circulation models is a problem that has not been tackled, let alone solved" (Dooge, 1982) and shortly after that Leavesley et al. (1983) concluded that optimization of distributed parameters of hydrological models is an "ill-posed" problem due to the large number of degrees of freedom. Since then, model parameterization is still one of the major unsolved problems in hydrology (Blöschl et al., 2019). One way to potentially solve this problem is to relate hydrological model parameters/structures to landscape properties (e.g., K.

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Section: Function Space Optimization (Fso)mentioning

confidence: 99%

Automatic Regionalization of Model Parameters for Hydrological Models

Feigl

Thober

Schweppe

et al. 2022

Water Resources Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this study we apply FSO to estimate new TFs for the mHM parameters K S (saturated hydraulic conductivity, [cm/day]) and F ieldCap (field capacity, [-]). These parameters affect the storage and conductivity of soil water and have a high sensitivity for streamflow estimation (Cuntz et al, 2015;Höllering et al, 2018). We want to minimize the effect of parameter dependency because this is the first large-scale application of FSO with real-world data.…”

Section: Linking Fso With the Mesoscale Hydrologic Model (Mhm)mentioning

confidence: 99%

Automatic regionalization of model parameters for hydrological models

Feigl

Thober

Schweppe

et al. 2022

Preprint

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Parameter estimation is one of the most challenging tasks in large-scale distributed modeling, because of the high dimensionality of the parameter space. Relating model parameters to catchment/landscape characteristics reduces the number of parameters, enhances physical realism, and allows the transfer of hydrological model parameters in time and space. This study presents the first large-scale application of automatic parameter transfer function (TF) estimation for a complex hydrological model. The Function Space Optimization (FSO) method can automatically estimate TF structures and coefficients for distributed models. We apply FSO to the mesoscale Hydrologic Model (mHM, mhm-ufz.org), which is the only available distributed model that includes a priori defined TFs for all its parameters. FSO is used to estimate new TFs for the parameters "saturated hydraulic conductivity" and "field capacity", which both influence a range of hydrological processes. The setup of mHM from a previous study serves as a benchmark. The estimated TFs resulted in predictions in 222 validation basins with a median NSE of 0.68, showing that even with 5 years of calibration data, high performance in ungauged basins can be achieved. The performance is similar to the benchmark results, showing that the automatic TFs can achieve comparable results to TFs that were developed over years using expert knowledge. In summary, the findings present a step towards automatic TF estimation of model parameters for distributed models.

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Regional analysis of parameter sensitivity for simulation of streamflow and hydrological fingerprints

Cited by 2 publications

References 38 publications

Automatic Regionalization of Model Parameters for Hydrological Models

Automatic Regionalization of Model Parameters for Hydrological Models

Automatic regionalization of model parameters for hydrological models

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