WATERSHED FUNCTIONS<sup>1</sup>

Black, Peter E.

doi:10.1111/j.1752-1688.1997.tb04077.x

Cited by 113 publications

(91 citation statements)

References 6 publications

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“…[29] Following in these footsteps, the core of our strategy is a hierarchical focus on the primary functions of any watershed and their representation in the water balance Black [1997] suggested three hydrological functions (collection, storage and discharge) and two ecological functions (chemical and habitat) of a watershed. Our interest, however, is in the extraction of diagnostic information from watershed input-state-output observations-i.e., we seek representations for which the functionality can be detected and somehow quantified from the data.…”

Section: The Basis For a Diagnostic Approachmentioning

confidence: 99%

A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model

Yılmaz

Gupta

Wagener

2008

Water Resources Research

499

461

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[1] Distributed hydrological models have the potential to provide improved streamflow forecasts along the entire channel network, while also simulating the spatial dynamics of evapotranspiration, soil moisture content, water quality, soil erosion, and land use change impacts. However, they are perceived as being difficult to parameterize and evaluate, thus translating into significant predictive uncertainty in the model results. Although a priori parameter estimates derived from observable watershed characteristics can help to minimize obstacles to model implementation, there exists a need for powerful automated parameter estimation strategies that incorporate diagnostic information regarding the causes of poor model performance. This paper investigates a diagnostic approach to model evaluation that exploits hydrological context and theory to aid in the detection and resolution of watershed model inadequacies, through consideration of three of the four major behavioral functions of any watershed system; overall water balance, vertical redistribution, and temporal redistribution (spatial redistribution was not addressed). Instead of using classical statistical measures (such as mean squared error), we use multiple hydrologically relevant ''signature measures'' to quantify the performance of the model at the watershed outlet in ways that correspond to the functions mentioned above and therefore help to guide model improvements in a meaningful way. We apply the approach to the Hydrology Laboratory Distributed Hydrologic Model (HL-DHM) of the National Weather Service and show that diagnostic evaluation has the potential to provide a powerful and intuitive basis for deriving consistent estimates of the parameters of watershed models.Citation: Yilmaz, K. K., H. V. Gupta, and T. Wagener (2008), A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model, Water Resour. Res., 44, W09417,

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Section: The Basis For a Diagnostic Approachmentioning

confidence: 99%

A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model

Yılmaz

Gupta

Wagener

2008

Water Resources Research

499

461

View full text Add to dashboard Cite

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“…[9] Abiotic attributes of water quantity and quality, such as amounts of runoff, the presence of wetlands, concentration of dissolved oxygen, and nutrient enrichment of water bodies are important indicators of stress to the physical system that will impact biological and human systems [Richardson and Gatti, 1999;Heathcote, 1998;Black, 1997]. Within each subbasin the abiotic component was assessed by evaluating two subcategories, water quantity and water quality.…”

Section: Methodsmentioning

confidence: 99%

Integrating stakeholder values with multiple attributes to quantify watershed performance

Shriver

Randhir

2006

Water Resources Research

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[1] Integrating stakeholder values into the process of quantifying impairment of ecosystem functions is an important aspect of watershed assessment and planning. This study develops a classification and prioritization model to assess potential impairment in watersheds. A systematic evaluation of a broad set of abiotic, biotic, and human indicators of watershed structure and function was used to identify the level of degradation at a subbasin scale. Agencies and communities can use the method to effectively target and allocate resources to areas of greatest restoration need. The watershed performance measure (WPM) developed in this study is composed of three major components: (1) hydrologic processes (water quantity and quality), (2) biodiversity at a species scale (core and priority habitat for rare and endangered species and species richness) and landscape scale (impacts of fragmentation), and (3) urban impacts as assessed in the built environment (effective impervious area) and population effects (densities and density of toxic waste sites). Simulation modeling using the Soil and Water Assessment Tool (SWAT), monitoring information, and spatial analysis with GIS were used to assess each criterion in developing this model. Weights for attributes of potential impairment were determined through the use of the attribute prioritization procedure with a panel of expert stakeholders. This procedure uses preselected attributes and corresponding stakeholder values and is data intensive. The model was applied to all subbasins of the Chicopee River Watershed of western Massachusetts, an area with a mixture of rural, heavily forested lands, suburban, and urbanized areas. Highly impaired subbasins in one community were identified using this methodology and evaluated for principal forms of degradation and potential restoration policies and BMPs. This attribute-based prioritization method could be used in identifying baselines, prioritization policies, and adaptive community planning.

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“…At their ideal values (REμ, REσ = 0 and MC = 1), the simulated time series is identical to the measured one. Moreover they account for errors in modelling the first three fundamental hydrological functions of a watershed as identified by Black (1997), Wagener et al (2007) and Yilmaz et al (2008): the overall water balance resulting from the partition of precipitation between evapotranspiration and infiltration plus direct runoff (REμ); the soil water storage dynamics 5 distributing the excess precipitation among faster and slower runoff components (REσ); and the water release and routing processes determining timing and shape of the hydrograph (MC).…”

Section: River Discharge Scoresmentioning

confidence: 99%

Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model

Gelati¹,

Decharme²,

Calvet³

et al. 2017

Preprint

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Abstract. The understanding of land surface hydrology is critical for planning human activities involving freshwater 10 resources. We assess how atmospheric forcing data uncertainties affect land surface model (LSM) simulations by means of an extensive evaluation exercise using a number of state-of-the-art remote sensing and station-based datasets. discharge. The atmospheric forcing data are also compared to reference datasets. Precipitation is the most uncertain forcing variable across datasets, while the most consistent are air temperature, and SW and LW radiation. At the monthly time scale, SSM and LAI simulations are relatively insensitive to forcing uncertainties. Some discrepancies with ESA-CCI appear to be forcing-independent and may be due to different assumptions underlying the LSM and the remote sensing retrieval algorithm. All simulations overestimate average summer and early autumn LAI. Forcing uncertainty impacts on simulated 25 river discharge are larger on mean values and standard deviations than on correlations with GRDC data. Anomaly correlation coefficients are not inferior to those computed from raw monthly discharge time series, indicating that the model reproduces inter-annual variability fairly well. However, simulated river discharge time series generally feature larger variability compared to measurements. They also tend to overestimate winter-spring high flows and underestimate summer-autumn low flows. Considering that several differences emerge between simulations and reference data, which may not be completely 30 explained by forcing uncertainty, we suggest several research directions. These range from further investigating the Hydrol. Earth Syst. Sci. Discuss., https://doi

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WATERSHED FUNCTIONS¹

Cited by 113 publications

References 6 publications

A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model

A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model

Integrating stakeholder values with multiple attributes to quantify watershed performance

Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model

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WATERSHED FUNCTIONS1

Cited by 113 publications

References 6 publications

A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model

A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model

Integrating stakeholder values with multiple attributes to quantify watershed performance

Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model

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WATERSHED FUNCTIONS¹