The probability-distributed principle and runoff production at point and basin scales

Moore, Robert J.

doi:10.1080/02626668509490989

Cited by 519 publications

(422 citation statements)

References 11 publications

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“…Input data in HBV are observations of precipitation, air temperature and estimates of potential evapotranspiration. Other examples of conceptual models are the GR4J model (Perrin et al, 2003), NAM-MIKE11 (Havnø et al, 1995;Shamsudin and Hashim, 2002), PCR-GLOBWB model (van Beek et al, 2011), Sacramento model (Burnash, 1995), and the Probability Distributed model (Moore, 1985(Moore, , 2007, among others.…”

Section: Hydrological and Hydrodynamic Modellingmentioning

confidence: 99%

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models

Mazzoleni¹

2017

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Section: Hydrological and Hydrodynamic Modellingmentioning

confidence: 99%

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models

Mazzoleni¹

2017

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“…Moore (1985). It relates the mean areal precipitation and the potential evapotranspiration, for a certain catchment, to the discharge.…”

Section: Model Descriptionmentioning

confidence: 99%

The ensemble particle filter (EnPF) in rainfall-runoff models

Delft

Serafy

Heemink

2009

Stoch Environ Res Risk Assess

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Rainfall-runoff models play a very important role in flood forecasting. However, these models contain large uncertainties caused by errors in both the model itself and the input data. Data assimilation techniques are being used to reduce these uncertainties. The ensemble Kalman filter (EnKF) and the particle filter (PF) both have their own strengths. Research was carried out to a possible combination between both types of filters that will lead to a new type of filters that joins the strengths of both. The so called ensemble particle filter (EnPF) new combination is tested on flood forecasting problems in both the hindcast mode as well as the forecast mode. Several proposed combinations showed considerable improvement when a hindcast comparison on synthetic data was considered. Within the forecast comparison with field data, the suggested EnPF showed remarkable improvements compared to the PF and slight improvements compared to the EnKF.

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“…This rainfall-runoff model is a simple form of the probability distributed model or PDM (Moore, 1985(Moore, , 2007 which has been 'spatially generalized'. That is, previous research developed relationships between the model parameters and catchment properties derived from terrain, land cover and soil/geology information to achieve model transfer to other catchments.…”

Section: The Parameter-generalized Pdmmentioning

confidence: 99%

Use of a grid‐based hydrological model and regional climate model outputs to assess changing flood risk

Bell

Kay

Jones

et al. 2007

Intl Journal of Climatology

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Abstract:A grid-based flow routing and runoff-production model, configured to employ regional climate model (RCM) precipitation estimates as input, is used to assess the effects of climate change on river flows in catchments across the UK. This model, the Grid-to-Grid model or G2G, has previously been calibrated and assessed with respect to observed river flows under current climate conditions. Here, the G2G distributed model together with a lumped catchment model, the parameter-generalized PDM, are applied to simulate river flow and derive flood frequency curves. Two sets of RCM precipitation time-series, on a 25-km grid and at hourly intervals, are used: (1) Current (1961Current ( -1990 and (2) Future (2071-2100). The effect of one extreme rainfall event in the current precipitation series is to raise the estimated peak flows for some catchments for high return periods under present day rainfall conditions. The future flow series does not contain a comparable flow peak. This significantly affects comparison of the flood frequency curves derived from the flow simulations obtained using the current and future precipitation estimates. Such variability in the results and the dependence on one or two extreme rainfall events emphasizes the need to examine more than one set of current/future precipitation scenarios for flood impact studies. In the absence of a formal ensemble of climate predictions, a resampling method is used to investigate the robustness of the modelled changes in flood frequency. Changes in flood frequency at higher return periods are, not surprisingly, found to be generally less robust than at lower return periods. This is particularly the case for catchments in the south and east of England, which were especially affected by the extreme rainfall event in the current precipitation series.

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The probability-distributed principle and runoff production at point and basin scales

Cited by 519 publications

References 11 publications

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models

The ensemble particle filter (EnPF) in rainfall-runoff models

Use of a grid‐based hydrological model and regional climate model outputs to assess changing flood risk

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