Uncertainty in hydrological signatures

Westerberg, Ida; McMillan, Hilary

doi:10.5194/hess-19-3951-2015

Cited by 153 publications

(151 citation statements)

References 77 publications

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“…Since the hydrological signatures introduced so far do not explicitly consider low-and high-flow events, we defined high-and low-flow days using thresholds based on the median and mean daily flow, respectively (Clausen and Biggs, 2000;Olden and Poff, 2003;Westerberg and McMillan, 2015). We computed the average duration and average frequency of high-and low-flow events.…”

Section: Methodsmentioning

confidence: 99%

The CAMELS data set: catchment attributes and meteorology for large-sample studies

Addor

Newman

Mizukami

et al. 2017

Hydrol. Earth Syst. Sci.

495

566

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Abstract. We present a new data set of attributes for 671 catchments in the contiguous United States (CONUS) minimally impacted by human activities. This complements the daily time series of meteorological forcing and streamflow provided by Newman et al. (2015b). To produce this extension, we synthesized diverse and complementary data sets to describe six main classes of attributes at the catchment scale: topography, climate, streamflow, land cover, soil, and geology. The spatial variations among basins over the CONUS are discussed and compared using a series of maps. The large number of catchments, combined with the diversity of the attributes we extracted, makes this new data set well suited for large-sample studies and comparative hydrology. In comparison to the similar Model Parameter Estimation Experiment (MOPEX) data set, this data set relies on more recent data, it covers a wider range of attributes, and its catchments are more evenly distributed across the CONUS. This study also involves assessments of the limitations of the source data sets used to compute catchment attributes, as well as detailed descriptions of how the attributes were computed. The hydrometeorological time series provided by Newman et al. (2015b, https://doi.org/10.5065/D6MW2F4D) together with the catchment attributes introduced in this paper (https://doi.org/10.5065/D6G73C3Q) constitute the freely available CAMELS data set, which stands for Catchment Attributes and MEteorology for Large-sample Studies.

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Section: Methodsmentioning

confidence: 99%

The CAMELS data set: catchment attributes and meteorology for large-sample studies

Addor

Newman

Mizukami

et al. 2017

Hydrol. Earth Syst. Sci.

495

566

View full text Add to dashboard Cite

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“…The runoff signatures are viewed in such a way that streamflow data can be broken up into several samples, each of them a manifestation of catchment functioning (Euser et al, 2013;Hrachowitz et al, 2014;Westerberg and McMillan, 2015). Five different signatures are used in this study and described in the following:…”

Section: Evaluation Strategy 321 Hydrological Signaturesmentioning

confidence: 99%

Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach

Garavaglia

Lay

Gottardi

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Model intercomparison experiments are widely used to investigate and improve hydrological model performance. However, a study based only on runoff simulation is not sufficient to discriminate between different model structures. Hence, there is a need to improve hydrological models for specific streamflow signatures (e.g., low and high flow) and multi-variable predictions (e.g., soil moisture, snow and groundwater). This study assesses the impact of model structure on flow simulation and hydrological realism using three versions of a hydrological model called MOR-DOR: the historical lumped structure and a revisited formulation available in both lumped and semi-distributed structures. In particular, the main goal of this paper is to investigate the relative impact of model equations and spatial discretization on flow simulation, snowpack representation and evapotranspiration estimation. Comparison of the models is based on an extensive dataset composed of 50 catchments located in French mountainous regions. The evaluation framework is founded on a multi-criterion split-sample strategy. All models were calibrated using an automatic optimization method based on an efficient genetic algorithm. The evaluation framework is enriched by the assessment of snow and evapotranspiration modeling against in situ and satellite data. The results showed that the new model formulations perform significantly better than the initial one in terms of the various streamflow signatures, snow and evapotranspiration predictions. The semi-distributed approach provides better calibration-validation performance for the snow cover area, snow water equivalent and runoff simulation, especially for nival catchments.

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“…It is critical to systematically assess model performance across spatial and temporal scales to understand how model structure, parameterization, and hydroclimatic setting affect model performance. Furthermore, evaluation of model performance points out the need to understand the uncertainty of the observations used for model evaluation [Hamilton and Moore, 2012;McMillan et al, 2012;Westerberg and McMillan, 2015] as well as uncertainties in other water balance terms.…”

Section: Model Performance Uncertainty and Communication Of Resultsmentioning

confidence: 99%

Accelerating advances in continental domain hydrologic modeling

Archfield

Clark

Arheimer

et al. 2015

Water Resources Research

138

121

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In the past, hydrologic modeling of surface water resources has mainly focused on simulating the hydrologic cycle at local to regional catchment modeling domains. There now exists a level of maturity among the catchment, global water security, and land surface modeling communities such that these communities are converging toward continental domain hydrologic models. This commentary, written from a catchment hydrology community perspective, provides a review of progress in each community toward this achievement, identifies common challenges the communities face, and details immediate and specific areas in which these communities can mutually benefit one another from the convergence of their research perspectives. Those include: (1) creating new incentives and infrastructure to report and share model inputs, outputs, and parameters in data services and open access, machine-independent formats for model replication or reanalysis; (2) ensuring that hydrologic models have: sufficient complexity to represent the dominant physical processes and adequate representation of anthropogenic impacts on the terrestrial water cycle, a process-based approach to model parameter estimation, and appropriate parameterizations to represent large-scale fluxes and scaling behavior; (3) maintaining a balance between model complexity and data availability as well as uncertainties; and (4) quantifying and communicating significant advancements toward these modeling goals.

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Uncertainty in hydrological signatures

Cited by 153 publications

References 77 publications

The CAMELS data set: catchment attributes and meteorology for large-sample studies

The CAMELS data set: catchment attributes and meteorology for large-sample studies

Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach

Accelerating advances in continental domain hydrologic modeling

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