PRMS-IV, the precipitation-runoff modeling system, version 4

Markstrom, Steven L.; Regan, R. Steven; Hay, Lauren; Viger, Roland J.; Webb, R. M.; Payn, R. A.; LaFontaine, Jacob H.

doi:10.3133/tm6b7

Cited by 195 publications

(203 citation statements)

References 2 publications

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“…Two possible ways to evaluate this are: (1) an analysis of PRMS's internal structure, and (2) the history of PRMS applications. A detailed analysis of PRMS's structure is beyond the scope of this article (see Markstrom et al, 2015); however, PRMS is implemented in a very linear fashion. Each parameter is clearly identified with an equation that is related to simulation of a specific process.…”

Section: Distributed-parameter Hydrology Modelmentioning

confidence: 99%

“…Each hydrologic process simulated by PRMS is encoded in a modular piece of source code (i.e., a "module") and is represented by an algorithm that is based on a physical law (e.g., balance of energy required to melt the ice in a snowpack) or empirical relation with measured or estimated characteristics (e.g., a tank model used to simulate interflow). The reader is referred to Markstrom et al (2015) for a complete description of PRMS.…”

Section: Distributed-parameter Hydrology Modelmentioning

confidence: 99%

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Towards simplification of hydrologic modeling: identification of dominant processes

Markstrom

Hay

Clark

2016

Hydrol. Earth Syst. Sci.

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Abstract. The Precipitation-Runoff Modeling System (PRMS), a distributed-parameter hydrologic model, has been applied to the conterminous US (CONUS). Parameter sensitivity analysis was used to identify: (1) the sensitive input parameters and (2) particular model output variables that could be associated with the dominant hydrologic process(es). Sensitivity values of 35 PRMS calibration parameters were computed using the Fourier amplitude sensitivity test procedure on 110 000 independent hydrologically based spatial modeling units covering the CONUS and then summarized to process (snowmelt, surface runoff, infiltration, soil moisture, evapotranspiration, interflow, baseflow, and runoff) and model performance statistic (mean, coefficient of variation, and autoregressive lag 1). Identified parameters and processes provide insight into model performance at the location of each unit and allow the modeler to identify the most dominant process on the basis of which processes are associated with the most sensitive parameters.The results of this study indicate that: (1) the choice of performance statistic and output variables has a strong influence on parameter sensitivity, (2) the apparent model complexity to the modeler can be reduced by focusing on those processes that are associated with sensitive parameters and disregarding those that are not, (3) different processes require different numbers of parameters for simulation, and (4) some sensitive parameters influence only one hydrologic process, while others may influence many.

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Section: Distributed-parameter Hydrology Modelmentioning

confidence: 99%

Section: Distributed-parameter Hydrology Modelmentioning

confidence: 99%

Towards simplification of hydrologic modeling: identification of dominant processes

Markstrom

Hay

Clark

2016

Hydrol. Earth Syst. Sci.

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“…3.0.5) is a deterministic, distributed-parameter, physically-based process hydrologic model that estimates water-balance relations, stream flow regimes and soil-water relations [43]. Precipitation, minimum and maximum temperature and solar radiation are the primary inputs into PRMS to compute a water balance and energy balance for individual hydrologic response units (HRUs) with distributed parameters [43].…”

Section: Hydrologic Model: Precipitation Runoff Modeling Systemmentioning

confidence: 99%

“…Solar radiation data were not obtained, in which case PRMS internally estimates daily shortwave solar radiation [43]. High resolution gridded METDATA were derived from observations and regional reanalysis using a hybrid method, combining spatially-rich data from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) and temporally rich data from the North America Land Data Assimilation System Phase 2 (NLDAS-2) [44][45][46].…”

Section: Climate Datamentioning

confidence: 99%

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Watershed Response to Climate Change and Fire-Burns in the Upper Umatilla River Basin, USA

Yazzie

Chang

2017

Climate

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This study analyzed watershed response to climate change and forest fire impacts in the upper Umatilla River Basin (URB), Oregon, using the precipitation runoff modeling system. Ten global climate models using Coupled Intercomparison Project Phase 5 experiments with Representative Concentration Pathways (RCP) 4.5 and 8.5 were used to simulate the effects of climate and fire-burns on runoff behavior throughout the 21st century. We observed the center timing (CT) of flow, seasonal flows, snow water equivalent (SWE) and basin recharge. In the upper URB, hydrologic regime shifts from a snow-rain-dominated to rain-dominated basin. Ensemble mean CT occurs 27 days earlier in RCP 4.5 and 33 days earlier in RCP 8.5, in comparison to historic conditions (1980s) by the end of the 21st century. After forest cover reduction in the 2080s, CT occurs 35 days earlier in RCP 4.5 and 29 days earlier in RCP 8.5. The difference in mean CT after fire-burns may be due to projected changes in the individual climate model. Winter flow is projected to decline after forest cover reduction in the 2080s by 85% and 72% in RCP 4.5 and RCP 8.5, in comparison to 98% change in ensemble mean winter flows in the 2080s before forest cover reduction. The ratio of ensemble mean snow water equivalent to precipitation substantially decreases by 81% and 91% in the 2050s and 2080s before forest cover reduction and a decrease of 90% in RCP 4.5 and 99% in RCP 8.5 in the 2080s after fire-burns. Mean basin recharge is 10% and 14% lower in the 2080s before fire-burns and after fire-burns, and it decreases by 13% in RCP 4.5 and decreases 22% in RCP 8.5 in the 2080s in comparison to historical conditions. Mixed results for recharge after forest cover reduction suggest that an increase may be due to the size of burned areas, decreased canopy interception and less evaporation occurring at the watershed surface, increasing the potential for infiltration. The effects of fire on the watershed system are strongly indicated by a significant increase in winter seasonal flows and a slight reduction in summer flows. Findings from this study may improve adaptive management of water resources, flood control and the effects of fire on a watershed system.

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A glacier runoff extension to the Precipitation Runoff Modeling System

Beusekom

Viger

2016

JGR Earth Surface

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A module to simulate glacier runoff, PRMSglacier, was added to PRMS (Precipitation Runoff Modeling System), a distributed‐parameter, physical‐process hydrological simulation code. The extension does not require extensive on‐glacier measurements or computational expense but still relies on physical principles over empirical relations as much as is feasible while maintaining model usability. PRMSglacier is validated on two basins in Alaska, Wolverine, and Gulkana Glacier basin, which have been studied since 1966 and have a substantial amount of data with which to test model performance over a long period of time covering a wide range of climatic and hydrologic conditions. When error in field measurements is considered, the Nash‐Sutcliffe efficiencies of streamflow are 0.87 and 0.86, the absolute bias fractions of the winter mass balance simulations are 0.10 and 0.08, and the absolute bias fractions of the summer mass balances are 0.01 and 0.03, all computed over 42 years for the Wolverine and Gulkana Glacier basins, respectively. Without taking into account measurement error, the values are still within the range achieved by the more computationally expensive codes tested over shorter time periods.

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PRMS-IV, the precipitation-runoff modeling system, version 4

Cited by 195 publications

References 2 publications

Towards simplification of hydrologic modeling: identification of dominant processes

Towards simplification of hydrologic modeling: identification of dominant processes

Watershed Response to Climate Change and Fire-Burns in the Upper Umatilla River Basin, USA

A glacier runoff extension to the Precipitation Runoff Modeling System

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