A Brief Analysis of Conceptual Model Structure Uncertainty Using 36 Models and 559 Catchments

Knoben, Wouter; Freer, Jim; Peel, Murray C.; Fowler, Keirnan; Woods, Ross

doi:10.1029/2019wr025975

Cited by 90 publications

(89 citation statements)

References 113 publications

(206 reference statements)

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“…Hydrological response units (HRUs) are perhaps the most well-known technique to group geospatial attributes in hydrological models. HRUs can be built based on various geospatial characteristics; for example, Kirkby and Weyman (1974), Knudsen et al (1986), Flügel (1995), Winter (2001), and Savenije (2010) all have proposed to use geospatial indices to discretize a catchment into hydrological units with distinct hydrological behavior. HRUs can be built based on soil type such as proposed by Park and Van De Giesen (2004).…”

Section: Introductionmentioning

confidence: 99%

Flexible vector-based spatial configurations in land models

Gharari

Clark

Mizukami

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Land models are increasingly used in terrestrial hydrology due to their process-oriented representation of water and energy fluxes. A priori specification of the grid size of the land models is typically defined based on the spatial resolution of forcing data, the modeling objectives, the available geospatial information, and computational resources. The variability of the inputs, soil types, vegetation covers, and forcing is masked or aggregated based on the a priori grid size. In this study, we propose an alternative vector-based implementation to directly configure a land model using unique combinations of land cover types, soil types, and other desired geographical features that have hydrological significance, such as elevation zone, slope, and aspect. The main contributions of this paper are to (1) implement the vector-based spatial configuration using the Variable Infiltration Capacity (VIC) model; (2) illustrate how the spatial configuration of the model affects simulations of basin-average quantities (i.e., streamflow) as well as the spatial variability of internal processes (snow water equivalent, SWE, and evapotranspiration, ET); and (3) describe the work and challenges ahead to improve the spatial structure of land models. Our results show that a model configuration with a lower number of computational units, once calibrated, may have similar accuracy to model configurations with more computational units. However, the different calibrated parameter sets produce a range of, sometimes contradicting, internal states and fluxes. To better address the shortcomings of the current generation of land models, we encourage the land model community to adopt flexible spatial configurations to improve model representations of fluxes and states at the scale of interest.

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Section: Introductionmentioning

confidence: 99%

Flexible vector-based spatial configurations in land models

Gharari

Clark

Mizukami

et al. 2020

Hydrol. Earth Syst. Sci.

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“…By showing that CAMELS catchment attributes do not contain all hydrologically relevant information, we also show that we need better attributes if we want to identify model structures or parameter values based on catchment attributes. This is reinforced by a recent model intercomparison study using the same data set which did not find a relation between model structures and static catchment attributes (Knoben et al, 2020).…”

Section: Discussionmentioning

confidence: 90%

Including Regional Knowledge Improves Baseflow Signature Predictions in Large Sample Hydrology

Gnann

McMillan

Woods

et al. 2021

Water Resources Research

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A catchment's hydrological response is controlled by climatic forcing and by the landscape through which water moves. Yet when we compare large samples of catchments, we often find climate to be the only good predictor of the hydrological response and a lot of variability is left unexplained. This contradicts extensive evidence from field and regional studies which shows the importance of catchment form (e.g., geology) on catchment hydrological processes, particularly on baseflow processes. We hypothesize that this is due to limitations in (a) the catchment attributes we use to inform our analyses and (b) the hydrological signatures we use to describe the hydrological response. To test these hypotheses, we use a large sample of catchment data across the contiguous United States. By reviewing literature from several U.S. regions, we show that region‐specific knowledge is underutilized in large sample studies. To organize the findings from these regions, we propose and apply a framework based on standardized perceptual models. Informed by these perceptual models, we use both available and newly calculated catchment attributes to show that baseflow signature predictions can be improved regionally. Multiple baseflow signatures are needed to better distinguish between different baseflow sources, such as the subsurface, surface water bodies, and snow. We conclude with pointing at potential future directions and argue that we should aim at a more systematic and hydrologically motivated selection of catchment attributes and hydrological signatures.

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“…A second way to improve model performance is to focus on the spatial representation of extremes, which may be improved by considering spatially distributed features of model response or spatial correlation within a spatial calibration framework. Such a framework could build upon existing spatial verification metrics such as the spatial prediction comparison test used, e.g., to validate precipitation fore-casts (SPCT; Gilleland, 2013), empirical orthogonal functions (EOFs), or Kappa statistics (Koch et al, 2015). For the calibration and evaluation of spatially distributed hydrological models, Koch et al (2018) recently proposed the SPAtial EFficiency (SPAEF) metric, which reflects three equally weighted components: correlation, coefficient of variation, and histogram overlap.…”

Section: Potential Ways To Improve Model Performancementioning

confidence: 99%

Flood spatial coherence, triggers, and performance in hydrological simulations: large-sample evaluation of four streamflow-calibrated models

Brunner

Melsen

Wood

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. Floods cause extensive damage, especially if they affect large regions. Assessments of current, local, and regional flood hazards and their future changes often involve the use of hydrologic models. A reliable hydrologic model ideally reproduces both local flood characteristics and spatial aspects of flooding under current and future climate conditions. However, uncertainties in simulated floods can be considerable and yield unreliable hazard and climate change impact assessments. This study evaluates the extent to which models calibrated according to standard model calibration metrics such as the widely used Kling–Gupta efficiency are able to capture flood spatial coherence and triggering mechanisms. To highlight challenges related to flood simulations, we investigate how flood timing, magnitude, and spatial variability are represented by an ensemble of hydrological models when calibrated on streamflow using the Kling–Gupta efficiency metric, an increasingly common metric of hydrologic model performance also in flood-related studies. Specifically, we compare how four well-known models (the Sacramento Soil Moisture Accounting model, SAC; the Hydrologiska Byråns Vattenbalansavdelning model, HBV; the variable infiltration capacity model, VIC; and the mesoscale hydrologic model, mHM) represent (1) flood characteristics and their spatial patterns and (2) how they translate changes in meteorologic variables that trigger floods into changes in flood magnitudes. Our results show that both the modeling of local and spatial flood characteristics are challenging as models underestimate flood magnitude, and flood timing is not necessarily well captured. They further show that changes in precipitation and temperature are not always well translated to changes in flood flow, which makes local and regional flood hazard assessments even more difficult for future conditions. From a large sample of catchments and with multiple models, we conclude that calibration on the integrated Kling–Gupta metric alone is likely to yield models that have limited reliability in flood hazard assessments, undermining their utility for regional and future change assessments. We underscore that such assessments can be improved by developing flood-focused, multi-objective, and spatial calibration metrics, by improving flood generating process representation through model structure comparisons and by considering uncertainty in precipitation input.

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A Brief Analysis of Conceptual Model Structure Uncertainty Using 36 Models and 559 Catchments

Cited by 90 publications

References 113 publications

Flexible vector-based spatial configurations in land models

Flexible vector-based spatial configurations in land models

Including Regional Knowledge Improves Baseflow Signature Predictions in Large Sample Hydrology

Flood spatial coherence, triggers, and performance in hydrological simulations: large-sample evaluation of four streamflow-calibrated models

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