Coupling a distributed grid based hydrological model and MM5 meteorological model for flooding alert mapping

Herr, Joel W., Krish Vijayaraghavan, and Eladio Knipping, 2010. Comparison of Measured and MM5 Modeled Meteorology Data for Simulating Flow in a Mountain Watershed. Journal of the American Water Resources Association (JAWRA) 46(6):1255–1263. DOI: 10.1111/j.1752‐1688.2010.00489.x Abstract: Accurate simulation of time‐varying flow in a river system depends on the quality of meteorology inputs. The density of meteorology measurement stations can be insufficient to capture spatial heterogeneity of precipitation, especially in mountainous areas. The Watershed Analysis Risk Management Framework (WARMF) model was applied to the Catawba River watershed of North and South Carolina to simulate flow and water quality in rivers and a series of 11 reservoirs. WARMF was linked with the AMSTERDAM air model to analyze the water quality benefit from reduced atmospheric emissions. The linkage requires accurate simulation of meteorology for all seasons and for all types of precipitation events. WARMF was driven by the mesoscale meteorology model MM5 processed by the Meteorology Chemistry Interface Processor, which provides greater spatial density but less accuracy than meteorology stations. WARMF was also run with measured data from the National Climatic Data Center (NCDC) to compare the performance of the watershed model using measured data vs. modeled meteorology as input. A one year simulation using MM5 modeled meteorology performed better overall than the simulation using NCDC data for the volumetric water balance measure used for calibration, but MM5 represented precipitation from a dissipated hurricane poorly, which propagated into errors of simulated flow.

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“…MM5 has been linked to the Hydrologic Model System for simulation of storms (Yu et al. , 1999; Tomassetti et al. , 2005).…”

Section: Methodsmentioning

confidence: 99%

“…The specific application of MM5 used for this study was developed to study visual impairment of air quality in the southeastern U.S. (Olerud and Sims, 2004). MM5 has been linked to the Hydrologic Model System for simulation of storms (Yu et al, 1999;Tomassetti et al, 2005).…”

Section: Methodsmentioning

confidence: 99%

Comparison of Measured and MM5 Modeled Meteorology Data for Simulating Flow in a Mountain Watershed¹

Herr¹,

Vijayaraghavan²,

Knipping³

2010

J American Water Resour Assoc

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“…A similar approach can be used to calculate the total drained precipitation for each cell. This matrix plays an important role in the definition of the flood alert index discussed in Tomassetti et al (2005).…”

Section: Ca2chym: An Algorithm For Flow Direction Extractionmentioning

confidence: 99%

Cellular automata algorithms for drainage network extraction and rainfall data assimilation

Coppola¹,

Tomassetti²,

Mariotti³

et al. 2007

Hydrological Sciences Journal

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Since 2002, within the framework of the Cetemps Centre of Excellence at the University of L'Aquila, a distributed grid-based hydrological model (CHYM) has been developed to provide a general purpose model for operational flood warning activity. This paper presents two new cellular automata (CA) algorithms used respectively for drainage network extraction and rainfall data assimilation. The first is a cellular automaton-based algorithm for the extraction of a drainage network from an arbitrary digital elevation model. It has been implemented and tested on a large number of different domains. This algorithm is able to define the flow direction at every point on the digital elevation model where singular points are present (pits or flat areas). The second is a CA-based numerical technique for assimilating different data sources of rainfall to rebuild the rainfall field on a grid. This technique has been shown to produce a reasonable rainfield shape without any geometrical artefacts that often produce unrealistic rain gradients in the rainfield.

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“…In this paper a preliminary evaluation of a meteo-hydro ensemble forecast chain, developed at the Center of Excellence in Telesensing of Environment and Model Prediction of Severe events (CETEMPS) is carried out. The meteo-hydrological 15 modeling chain consists on connecting, dynamically downscaled at regional scale, the Weather Research and Forecasting model (WRF-ARW) simulations initialized by 20 members and the control of GFS forecast to the CETEMPS Hydrological Model (CHyM, Tomassetti et al (2005); Coppola et al (2007); Verdecchia et al (2008)). To the purpose of assessing the reliability of an operational regional ensemble, a preliminary study of a heavy precipitation event is used as test case for the above mentioned meteo-hydro ensemble chain.…”

mentioning

confidence: 99%

Regional ensemble forecast for early warning system over small Apennine catchments on Central Italy

Ferretti

Lombardi

Tomassetti

et al. 2019

Preprint

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Abstract. The weather forecasts for precipitation have considerably improved in recent years thanks to the increase of computational power. This allows to use both a higher spatial resolutions and the newly developed parameterization schemes for representing sub-grid scale physical processes. However, precipitation estimation is still affected by errors that can impact on the response of hydrological models. To the aim of considering the uncertainties in the precipitation forecast and how they propagate in the hydrological model, an ensemble approach is investigated. A meteo-hydro ensemble system is built to forecast events in a complex orography terrain where catchments of different size are present. In this context, the meteo-hydrological forecast system is implemented and tested for a severe hydrological event occurred over Central Italy on November 15, 2017. During this period, a flash flood hit the Abruzzo region causing precipitation up to 200 mm/24 hours and producing damages with a high impact on social and economic activities.The newly developed meteo-hydro ensemble system is compared with a high resolution deterministic forecast and with the observations over the same area, showing a very good response. In addition, the ensemble allows for an estimation of the predictability of the event a few days in advance and of the uncertainty of this flood. Although the modelling framework is implemented on the basins of Abruzzo region, it is portable and applicable to other areas.

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Coupling a distributed grid based hydrological model and MM5 meteorological model for flooding alert mapping

Cited by 25 publications

References 8 publications

Comparison of Measured and MM5 Modeled Meteorology Data for Simulating Flow in a Mountain Watershed¹

Comparison of Measured and MM5 Modeled Meteorology Data for Simulating Flow in a Mountain Watershed¹

Cellular automata algorithms for drainage network extraction and rainfall data assimilation

Regional ensemble forecast for early warning system over small Apennine catchments on Central Italy

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Coupling a distributed grid based hydrological model and MM5 meteorological model for flooding alert mapping

Cited by 25 publications

References 8 publications

Comparison of Measured and MM5 Modeled Meteorology Data for Simulating Flow in a Mountain Watershed1

Comparison of Measured and MM5 Modeled Meteorology Data for Simulating Flow in a Mountain Watershed1

Cellular automata algorithms for drainage network extraction and rainfall data assimilation

Regional ensemble forecast for early warning system over small Apennine catchments on Central Italy

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Product

Resources

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Comparison of Measured and MM5 Modeled Meteorology Data for Simulating Flow in a Mountain Watershed¹

Comparison of Measured and MM5 Modeled Meteorology Data for Simulating Flow in a Mountain Watershed¹