Framework for event-based semidistributed modeling that unifies the SCS-CN method, VIC, PDM, and TOPMODEL

M. S. Bartlett, A. J. Parolari, J. J. McDonnell, A. Porporato*

*Corresponding author for this work

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Hydrologists and engineers may choose from a range of semidistributed rainfall-runoff models such as VIC, PDM, and TOPMODEL, all of which predict runoff from a distribution of watershed properties. However, these models are not easily compared to event-based data and are missing ready-to-use analytical expressions that are analogous to the SCS-CN method. The SCS-CN method is an event-based model that describes the runoff response with a rainfall-runoff curve that is a function of the cumulative storm rainfall and antecedent wetness condition. Here we develop an event-based probabilistic storage framework and distill semidistributed models into analytical, event-based expressions for describing the rainfall-runoff response. The event-based versions called VICx, PDMx, and TOPMODELx also are extended with a spatial description of the runoff concept of prethreshold and threshold-excess runoff, which occur, respectively, before and after infiltration exceeds a storage capacity threshold. For total storm rainfall and antecedent wetness conditions, the resulting ready-to-use analytical expressions define the source areas (fraction of the watershed) that produce runoff by each mechanism. They also define the probability density function (PDF) representing the spatial variability of runoff depths that are cumulative values for the storm duration, and the average unit area runoff, which describes the so-called runoff curve. These new event-based semidistributed models and the traditional SCS-CN method are unified by the same general expression for the runoff curve. Since the general runoff curve may incorporate different model distributions, it may ease the way for relating such distributions to land use, climate, topography, ecology, geology, and other characteristics.

Original languageEnglish
Pages (from-to)7036-7052
Number of pages17
JournalWater Resources Research
Volume52
Issue number9
Early online date29 Aug 2016
DOIs
Publication statusPublished - Sep 2016

Keywords

  • spatially lumped model
  • prethreshold runoff
  • exponential rainfall
  • spatial variability
  • runoff curve
  • runoff threshold
  • runoff parameterization
  • hydrologic similarity
  • climate models
  • storm runoff
  • soil
  • representation
  • variability
  • simulation
  • vegetation
  • scales

Cite this

Framework for event-based semidistributed modeling that unifies the SCS-CN method, VIC, PDM, and TOPMODEL. / Bartlett, M. S.; Parolari, A. J.; McDonnell, J. J.; Porporato, A.

In: Water Resources Research, Vol. 52, No. 9, 09.2016, p. 7036-7052.

Research output: Contribution to journalArticle

Bartlett, M. S. ; Parolari, A. J. ; McDonnell, J. J. ; Porporato, A. / Framework for event-based semidistributed modeling that unifies the SCS-CN method, VIC, PDM, and TOPMODEL. In: Water Resources Research. 2016 ; Vol. 52, No. 9. pp. 7036-7052.
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title = "Framework for event-based semidistributed modeling that unifies the SCS-CN method, VIC, PDM, and TOPMODEL",
abstract = "Hydrologists and engineers may choose from a range of semidistributed rainfall-runoff models such as VIC, PDM, and TOPMODEL, all of which predict runoff from a distribution of watershed properties. However, these models are not easily compared to event-based data and are missing ready-to-use analytical expressions that are analogous to the SCS-CN method. The SCS-CN method is an event-based model that describes the runoff response with a rainfall-runoff curve that is a function of the cumulative storm rainfall and antecedent wetness condition. Here we develop an event-based probabilistic storage framework and distill semidistributed models into analytical, event-based expressions for describing the rainfall-runoff response. The event-based versions called VICx, PDMx, and TOPMODELx also are extended with a spatial description of the runoff concept of prethreshold and threshold-excess runoff, which occur, respectively, before and after infiltration exceeds a storage capacity threshold. For total storm rainfall and antecedent wetness conditions, the resulting ready-to-use analytical expressions define the source areas (fraction of the watershed) that produce runoff by each mechanism. They also define the probability density function (PDF) representing the spatial variability of runoff depths that are cumulative values for the storm duration, and the average unit area runoff, which describes the so-called runoff curve. These new event-based semidistributed models and the traditional SCS-CN method are unified by the same general expression for the runoff curve. Since the general runoff curve may incorporate different model distributions, it may ease the way for relating such distributions to land use, climate, topography, ecology, geology, and other characteristics.",
keywords = "spatially lumped model, prethreshold runoff, exponential rainfall, spatial variability, runoff curve, runoff threshold, runoff parameterization, hydrologic similarity, climate models, storm runoff, soil, representation, variability, simulation, vegetation, scales",
author = "Bartlett, {M. S.} and Parolari, {A. J.} and McDonnell, {J. J.} and A. Porporato",
note = "Funded by the USDA Agricultural Research Service. Grant Number: 58-6408-3-027 the National Science Foundation. Grant Number: CBET-1033467, EAR-1331846, FESD-1338694, EAR-1316258 the Duke WISeNet. Grant Number: DGE-1068871 Acknowledgments This work was partially funded through the USDA Agricultural Research Service through cooperative agreement 58-6408-3-027; and the National Science Foundation through grants CBET-1033467, EAR-1331846, FESD-1338694, EAR-1316258, and the Duke WISeNet grant DGE-1068871. Processed data and code are available by e-mail from the corresponding author.",
year = "2016",
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language = "English",
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pages = "7036--7052",
journal = "Water Resources Research",
issn = "0043-1397",
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T1 - Framework for event-based semidistributed modeling that unifies the SCS-CN method, VIC, PDM, and TOPMODEL

AU - Bartlett, M. S.

AU - Parolari, A. J.

AU - McDonnell, J. J.

AU - Porporato, A.

N1 - Funded by the USDA Agricultural Research Service. Grant Number: 58-6408-3-027 the National Science Foundation. Grant Number: CBET-1033467, EAR-1331846, FESD-1338694, EAR-1316258 the Duke WISeNet. Grant Number: DGE-1068871 Acknowledgments This work was partially funded through the USDA Agricultural Research Service through cooperative agreement 58-6408-3-027; and the National Science Foundation through grants CBET-1033467, EAR-1331846, FESD-1338694, EAR-1316258, and the Duke WISeNet grant DGE-1068871. Processed data and code are available by e-mail from the corresponding author.

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N2 - Hydrologists and engineers may choose from a range of semidistributed rainfall-runoff models such as VIC, PDM, and TOPMODEL, all of which predict runoff from a distribution of watershed properties. However, these models are not easily compared to event-based data and are missing ready-to-use analytical expressions that are analogous to the SCS-CN method. The SCS-CN method is an event-based model that describes the runoff response with a rainfall-runoff curve that is a function of the cumulative storm rainfall and antecedent wetness condition. Here we develop an event-based probabilistic storage framework and distill semidistributed models into analytical, event-based expressions for describing the rainfall-runoff response. The event-based versions called VICx, PDMx, and TOPMODELx also are extended with a spatial description of the runoff concept of prethreshold and threshold-excess runoff, which occur, respectively, before and after infiltration exceeds a storage capacity threshold. For total storm rainfall and antecedent wetness conditions, the resulting ready-to-use analytical expressions define the source areas (fraction of the watershed) that produce runoff by each mechanism. They also define the probability density function (PDF) representing the spatial variability of runoff depths that are cumulative values for the storm duration, and the average unit area runoff, which describes the so-called runoff curve. These new event-based semidistributed models and the traditional SCS-CN method are unified by the same general expression for the runoff curve. Since the general runoff curve may incorporate different model distributions, it may ease the way for relating such distributions to land use, climate, topography, ecology, geology, and other characteristics.

AB - Hydrologists and engineers may choose from a range of semidistributed rainfall-runoff models such as VIC, PDM, and TOPMODEL, all of which predict runoff from a distribution of watershed properties. However, these models are not easily compared to event-based data and are missing ready-to-use analytical expressions that are analogous to the SCS-CN method. The SCS-CN method is an event-based model that describes the runoff response with a rainfall-runoff curve that is a function of the cumulative storm rainfall and antecedent wetness condition. Here we develop an event-based probabilistic storage framework and distill semidistributed models into analytical, event-based expressions for describing the rainfall-runoff response. The event-based versions called VICx, PDMx, and TOPMODELx also are extended with a spatial description of the runoff concept of prethreshold and threshold-excess runoff, which occur, respectively, before and after infiltration exceeds a storage capacity threshold. For total storm rainfall and antecedent wetness conditions, the resulting ready-to-use analytical expressions define the source areas (fraction of the watershed) that produce runoff by each mechanism. They also define the probability density function (PDF) representing the spatial variability of runoff depths that are cumulative values for the storm duration, and the average unit area runoff, which describes the so-called runoff curve. These new event-based semidistributed models and the traditional SCS-CN method are unified by the same general expression for the runoff curve. Since the general runoff curve may incorporate different model distributions, it may ease the way for relating such distributions to land use, climate, topography, ecology, geology, and other characteristics.

KW - spatially lumped model

KW - prethreshold runoff

KW - exponential rainfall

KW - spatial variability

KW - runoff curve

KW - runoff threshold

KW - runoff parameterization

KW - hydrologic similarity

KW - climate models

KW - storm runoff

KW - soil

KW - representation

KW - variability

KW - simulation

KW - vegetation

KW - scales

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JO - Water Resources Research

JF - Water Resources Research

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