Catchment classification: hydrological analysis of catchment behavior through process-based modeling along a climate gradient

Carrillo, G.; Troch, P. A.; Sivapalan, Murugesu; Wagener, Thorsten; Harman, Ciaran J.; Sawicz, K. A.

doi:10.5194/hess-15-3411-2011

Cited by 120 publications

(80 citation statements)

References 52 publications

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“…We use the 12 model parameterizations to decouple climate and landscape properties to gain insight into the role of climate-vegetation-soil interactions in long-term hydrologic partitioning. Carrillo et al (2011) present the results of applying our hydrologic model to these 12 catchments, and demonstrate that the resulting model parameterizations are capable of capturing the hydrologic response across the climate gradient at different temporal scales, from decades to daily. These 12 behavioral catchment models are subjected to the 12 different climate forcings, resulting in 144 10 yr model simulations.…”

Section: P a Troch Et Al: Signatures Of Catchment Co-evolutionmentioning

confidence: 99%

Climate-vegetation-soil interactions and long-term hydrologic partitioning: signatures of catchment co-evolution

Troch

Carrillo

Sivapalan

et al. 2013

Hydrol. Earth Syst. Sci.

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165

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Abstract. Budyko (1974) postulated that long-term catchment water balance is controlled to first order by the available water and energy. This leads to the interesting question of how do landscape characteristics (soils, geology, vegetation) and climate properties (precipitation, potential evaporation, number of wet and dry days) interact at the catchment scale to produce such a simple and predictable outcome of hydrological partitioning? Here we use a physically-based hydrologic model separately parameterized in 12 US catchments across a climate gradient to decouple the impact of climate and landscape properties to gain insight into the role of climate-vegetation-soil interactions in long-term hydrologic partitioning. The 12 catchment models (with different paramterizations) are subjected to the 12 different climate forcings, resulting in 144 10 yr model simulations. The results are analyzed per catchment (one catchment model subjected to 12 climates) and per climate (one climate filtered by 12 different model parameterization), and compared to water balance predictions based on Budyko's hypothesis (E/P = ϕ(E p /P ); E: evaporation, P : precipitation, E p : potential evaporation). We find significant anti-correlation between average deviations of the evaporation index (E/P ) computed per catchment vs. per climate, compared to that predicted by Budyko. Catchments that on average produce more E/P have developed in climates that on average produce less E/P , when compared to Budyko's prediction. Water and energy seasonality could not explain these observations, confirming previous results reported by Potter et al. (2005). Next, we analyze which model (i.e., landscape filter) characteristics explain the catchment's tendency to produce more or less E/P . We find that the time scale that controls subsurface storage release explains the observed trend. This time scale combines several geomorphologic and hydraulic soil properties. Catchments with relatively longer subsurface storage release time scales produce significantly more E/P . Vegetation in these catchments have longer access to this additional groundwater source and thus are less prone to water stress. Further analysis reveals that climates that give rise to more (less) E/P are associated with catchments that have vegetation with less (more) efficient water use parameters. In particular, the climates with tendency to produce more E/P have catchments that have lower % root fraction and less light use efficiency. Our results suggest that their exists strong interactions between climate, vegetation and soil properties that lead to specific hydrologic partitioning at the catchment scale. This co-evolution of catchment vegetation and soils with climate needs to be further explored to improve our capabilities to predict hydrologic partitioning in ungauged basins.

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Section: P a Troch Et Al: Signatures Of Catchment Co-evolutionmentioning

confidence: 99%

Climate-vegetation-soil interactions and long-term hydrologic partitioning: signatures of catchment co-evolution

Troch

Carrillo

Sivapalan

et al. 2013

Hydrol. Earth Syst. Sci.

Self Cite

165

View full text Add to dashboard Cite

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“…2) The hillslope width function introduced by Fan and Bras [1998] is one example of a subgrid landscape configuration which imparts characteristic hydrologic responses in hillslope runoff formulations Hilberts et al, 2004;Bogaart and Troch, 2006;Carrillo et al, 2011]. Additionally, variable resolution models, such as tRIBS Vivoni et al, 2004;Vivoni et al, 2005] provide another means of representing different mechanisms of hydrologic complexity based on landform criteria, and such variable resolution approaches are currently being developed for broader earth system applications as well [Skamarock et al, 2012].…”

Section: 1002/2015wr017096mentioning

confidence: 99%

Improving the representation of hydrologic processes in Earth System Models

Clark

Fan

Lawrence

et al. 2015

Water Resources Research

468

404

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Many of the scientific and societal challenges in understanding and preparing for global environmental change rest upon our ability to understand and predict the water cycle change at large river basin, continent, and global scales. However, current large-scale land models (as a component of Earth System Models, or ESMs) do not yet reflect the best hydrologic process understanding or utilize the large amount of hydrologic observations for model testing. This paper discusses the opportunities and key challenges to improve hydrologic process representations and benchmarking in ESM land models, suggesting that (1) land model development can benefit from recent advances in hydrology, both through incorporating key processes (e.g., groundwater-surface water interactions) and new approaches to describe multiscale spatial variability and hydrologic connectivity; (2) accelerating model advances requires comprehensive hydrologic benchmarking in order to systematically evaluate competing alternatives, understand model weaknesses, and prioritize model development needs, and (3) stronger collaboration is needed between the hydrology and ESM modeling communities, both through greater engagement of hydrologists in ESM land model development, and through rigorous evaluation of ESM hydrology performance in research watersheds or Critical Zone Observatories. Such coordinated efforts in advancing hydrology in ESMs have the potential to substantially impact energy, carbon, and nutrient cycle prediction capabilities through the fundamental role hydrologic processes play in regulating these cycles.

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“…Recession characteristics have also been used for catchment classification (Wagener et al, 2007;Carrillo et al, 2011;, especially with a view to a heuristic understanding of the interaction between climate (in terms of available precipitation and changing evapotranspiration rates), transferable catchment characteristics and the corresponding streamflow dynamics. While absolute values of the recession characteristics may not be so important for such applications, the analysis of Spearman's rank correlation coefficient and the regression coefficient between characteristics obtained with different RAMs has elucidated differences in the relative values.…”

Section: The Effect Of Different Rams To Distinguish Catchments' Recementioning

confidence: 99%

Are streamflow recession characteristics really characteristic?

Stoelzle

Stahl

Weiler

2013

Hydrol. Earth Syst. Sci.

109

134

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Abstract. Streamflow recession has been investigated by a variety of methods, often involving the fit of a model to empirical recession plots to parameterize a non-linear storageoutflow relationship based on the dQ/dt-Q method. Such recession analysis methods (RAMs) are used to estimate hydraulic conductivity, storage capacity, or aquifer thickness and to model streamflow recession curves for regionalization and prediction at the catchment scale. Numerous RAMs have been published, but little is known about how comparably the resulting recession models distinguish characteristic catchment behavior. In this study we combined three established recession extraction methods with three different parameter-fitting methods to the power-law storage-outflow model to compare the range of recession characteristics that result from the application of these different RAMs. Resulting recession characteristics including recession time and corresponding storage depletion were evaluated for 20 mesoscale catchments in Germany. We found plausible ranges for model parameterization; however, calculated recession characteristics varied over two orders of magnitude. While recession characteristics of the 20 catchments derived with the different methods correlate strongly, particularly for the RAMs that use the same extraction method, not all rank the catchments consistently, and the differences among some of the methods are larger than among the catchments. To elucidate this variability we discuss the ambiguous roles of recession extraction procedures and the parameterization of the storage-outflow model and the limitations of the presented recession plots. The results suggest strong limitations to the comparability of recession characteristics derived with different methods, not only in the model parameters but also in the relative characterization of different catchments. A multiple-methods approach to investigating streamflow recession characteristics should be considered for applications whenever possible.

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Catchment classification: hydrological analysis of catchment behavior through process-based modeling along a climate gradient

Cited by 120 publications

References 52 publications

Climate-vegetation-soil interactions and long-term hydrologic partitioning: signatures of catchment co-evolution

Climate-vegetation-soil interactions and long-term hydrologic partitioning: signatures of catchment co-evolution

Improving the representation of hydrologic processes in Earth System Models

Are streamflow recession characteristics really characteristic?

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