Observational and predictive uncertainties for multiple variables in a spatially distributed hydrological model

Ehlers, L.B.; Sonnenborg, Torben O.; Refsgaard, Jens Christian

doi:10.1002/hyp.13367

Cited by 26 publications

(13 citation statements)

References 59 publications

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“…Different input variables with varying degrees of information content (i.e., different types, quantities, and qualities of inputs) could lead to similar model outcome, for example, equifinality of model predictions from different stochastic realizations of the input data (Zin, 2002) such as rainfall input (Ehlers et al, 2018). Newman et al (2015) developed an ensemble of gridded observation-based daily precipitation and temperature for 1980-2012 for the contiguous United States, which could be used to account for uncertainty of gridded product and model forcing, as well as exploring the equifinality of model inputs.…”

Section: Equifinality Of Model Inputsmentioning

confidence: 99%

Equifinality and Flux Mapping: A New Approach to Model Evaluation and Process Representation Under Uncertainty

Khatami

Peel

Peterson

et al. 2019

Water Resources Research

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Uncertainty analysis is an integral part of any scientific modeling, particularly within the domain of hydrological sciences given the various types and sources of uncertainty. At the center of uncertainty rests the concept of equifinality, that is, reaching a given endpoint (finality) through different pathways. The operational definition of equifinality in hydrological modeling is that various model structures and/or parameter sets (i.e., equal pathways) are equally capable of reproducing a similar (not necessarily identical) hydrological outcome (i.e., finality). Here we argue that there is more to model equifinality than model structures/parameters, that is, other model components can give rise to model equifinality and/or could be used to explore equifinality within model space. We identified six facets of model equifinality, namely, model structure, parameters, performance metrics, initial and boundary conditions, inputs, and internal fluxes. Focusing on model internal fluxes, we developed a methodology called flux mapping that has fundamental implications in understanding and evaluating model process representation within the paradigm of multiple working hypotheses. To illustrate this, we examine the equifinality of runoff fluxes of a conceptual rainfall‐runoff model for a number of different Australian catchments. We demonstrate how flux maps can give new insights into the model behavior that cannot be captured by conventional model evaluation methods. We discuss the advantages of flux space, as a subspace of the model space not usually examined, over parameter space. We further discuss the utility of flux mapping in hypothesis generation and testing, extendable to any field of scientific modeling of open complex systems under uncertainty.

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Section: Equifinality Of Model Inputsmentioning

confidence: 99%

Equifinality and Flux Mapping: A New Approach to Model Evaluation and Process Representation Under Uncertainty

Khatami

Peel

Peterson

et al. 2019

Water Resources Research

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“…Although SRS data are more accessible with higher spatiotemporal resolution compared to in situ observations, they are generally not direct measurements of hydrological processes, which adds a level of uncertainty to any SRS-based parameter estimation study (Ehlers et al, 2018;Knoche et al, 2014;Ma et al, 2018). However, they provide spatial information on hydrological processes, which makes them a unique and relevant information source for spatially distributed representations of the system in models (Stisen et al, 2018;Wambura et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets

Dembélé

Hrachowitz

Savenije

et al. 2020

Water Resources Research

107

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Hydrological model calibration combining Earth observations and in situ measurements is a promising solution to overcome the limitations of the traditional streamflow-only calibration. However, combining multiple data sources in model calibration requires a meaningful integration of the data sets, which should harness their most reliable contents to avoid accumulation of their uncertainties and mislead the parameter estimation procedure. This study analyzes the improvement of model parameter selection by using only the spatial patterns of satellite remote sensing data, thereby ignoring their absolute values. Although satellite products are characterized by uncertainties, their most reliable key feature is the representation of spatial patterns, which is a unique and relevant source of information for distributed hydrological models. We propose a novel multivariate calibration framework exploiting spatial patterns and simultaneously incorporating streamflow and three satellite products (i.e., Global Land Evaporation Amsterdam Model [GLEAM] evaporation, European Space Agency Climate Change Initiative [ESA CCI] soil moisture, and Gravity Recovery and Climate Experiment [GRACE] terrestrial water storage). The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature data set is used for model evaluation. A bias-insensitive and multicomponent spatial pattern matching metric is developed to formulate a multiobjective function. The proposed multivariate calibration framework is tested with the mesoscale Hydrologic Model (mHM) and applied to the poorly gauged Volta River basin located in a predominantly semiarid climate in West Africa. Results of the multivariate calibration show that the decrease in performance for streamflow (−7%) and terrestrial water storage (−6%) is counterbalanced with an increase in performance for soil moisture (+105%) and evaporation (+26%). These results demonstrate that there are benefits in using satellite data sets, when suitably integrated in a robust model parametrization scheme.

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“…Spatial correlations of precipitation are useful for providing information in the paleoclimate reconstruction based on tree ring data (Chen et al, 2016), where one meteorological station having the same climate with the location of tree ring sample is usually needed to calibrate and validate the reconstruction model and the meteorological station used should be within a spatial correlation range from the tree ring sample. In addition, spatial correlations played an important role in the layout of observation sites, choice of sites in the data selection, selection of downscaling strategies, and so forth (Huff and Shipp, 1969;Lorenz et al, 2018;Ehlers et al, 2019;Su et al, 2020). Many researchers have established functions with correlation coefficients decaying with distances to express the spatial correlations of precipitation (Bidin and Chappell, 2003;Krajewski et al, 2003).…”

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confidence: 99%

Spatial correlations of daily precipitation over mainland China

Fan

Yin

Chen

2021

Intl Journal of Climatology

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Knowledge of spatial correlations of precipitation is important for the generation of grid-based surface precipitation data sets, deployment of data collection, selection of downscaling strategies, and interpretation of paleoclimate reconstructions. Spatial correlations of daily precipitation in China were analyzed based on a daily precipitation data set from 1951 through 2014 for 2,208 stations by dividing them into 13 regions. Interstation Pearson correlation coefficient r for the daily precipitation series and the corresponding interstation distance d were calculated for each region. The exponential spatial correlationwas fitted by the r-d pairs, in which c 0 , d 0 and s 0 were the parameter variance, scale and shape, respectively. The results showed that: (a) The determination coefficient R 2 of the correlation model varied from 0.54 to 0.96, with a mean of 0.82 and the regional maximum correlation distance d 0 varied from 102.2 to 201.7 km, with a mean of 155.2 km. Western regions generally had smaller d 0 than eastern regions, which indicates rain events in the western regions were more local; (b) The goodness-of-fit of the model was improved by dividing samples into West-East (W-E) and North-South (N-S) directions. The average of d 0 for all regions(190.2 km) for the W-E direction is larger than that for the N-S direction (142.9 km); (c) The correlation distances in summer and dry years are shorter than those in winter and wet years. However, the difference of correlation distance between dry and wet years was subtle compared with those between summer and winter, and between W-E and N-S directions. Seven regions were divided based on the spatial correlations of daily precipitation and different spatial models were suggested to be used for different regions, seasons and directions when the interpolation of daily precipitation is conducted for the generation of gridded surface precipitation data sets.

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Observational and predictive uncertainties for multiple variables in a spatially distributed hydrological model

Cited by 26 publications

References 59 publications

Equifinality and Flux Mapping: A New Approach to Model Evaluation and Process Representation Under Uncertainty

Equifinality and Flux Mapping: A New Approach to Model Evaluation and Process Representation Under Uncertainty

Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets

Spatial correlations of daily precipitation over mainland China

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