Radar analyses of extreme rainfall and flooding in urban drainage basins

Villarini, Gabriele; Smith, James A.; Baeck, Mary Lynn; Sturdevant-Rees, Paula; Krajewski, Witold F.

doi:10.1016/j.jhydrol.2009.11.048

Cited by 71 publications

(43 citation statements)

References 59 publications

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“…Rainfall intensity temporal patterns affect shallow landslide triggering and hazard evolution. Geophysical Research Letters, 47. e2019GL085994 https://doi.org/ 10.1029/2019GL085994 flash floods, urban hydrology, and rainfall extremes; Gabriele et al, 2017;Villarini et al, 2010;Smith et al, 2007;Yang et al, 2004;Ntelekos et al, 2008). Motivated by studies on the role of the temporal patterns of rainfall intensity (hyetograph) on the hydrology of landscapes (e.g., Parsons & Stone, 2006;Robinson & Sivapalan, 1997;Singh, 1997;Woolhiser & Goodrich, 1988), we seek to examine the role of different temporal patterns of rainfall intensities on landslide initiation for the same rainfall amount.…”

Section: Citationmentioning

confidence: 99%

Rainfall Intensity Temporal Patterns Affect Shallow Landslide Triggering and Hazard Evolution

Fan

Lehmann

Chen

et al. 2020

Geophysical Research Letters

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A characteristic of rainfall‐induced landslides is the gradual loading by infiltrating rainwater that weakens the soil mantle and could progress to abrupt soil mass release. The temporal patterns of similar rainfall amounts affect the hydrological response of a catchment and thus influence landslide dynamics. We use a novel catchment scale landslide hydromechanical triggering model to systematically study how different asymmetric distributions of rainfall intensities affect landslide dynamics and hazard evolution. Evaluating rainfall events with similar durations and total amounts shows that early rainfall peak intensity exerts stronger destabilizing effects, relative to delayed rainfall peak intensity. Intense rainfall events reduce landslide triggering relative to uniform‐intensity rainfall events due to reduced water infiltration. With advances in highly resolved and real‐time precipitation observations, the study offers a means for improving prediction of landslide timing and hazard evolution and identifying hydrological scenarios that enhance landslide activity.

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Section: Citationmentioning

confidence: 99%

Rainfall Intensity Temporal Patterns Affect Shallow Landslide Triggering and Hazard Evolution

Fan

Lehmann

Chen

et al. 2020

Geophysical Research Letters

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“…The strength of radar-based quantitative precipitation estimates (QPEs) is the high spatio-temporal resolution together with a large spatial coverage (Sauvageot 1994;Sun et al 2000;Terblanche et al 2001;Borga 2002;Villarini et al 2010). These features potentially improve the simulation and forecasting of streamflows (Borga 2002;Yang et al 2004;Atencia et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the potential of radar-based rainfall estimates for streamflow and flood simulations in the Philippines

Abon

Kneis

Crisologo

et al. 2015

Geomatics, Natural Hazards and Risk

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This case study evaluates the suitability of radar-based quantitative precipitation estimates (QPEs) for the simulation of streamflow in the Marikina River Basin (MRB), the Philippines. Hourly radar-based QPEs were produced from reflectivity that had been observed by an S-band radar located about 90 km from the MRB. Radar data processing and precipitation estimation were carried out using the open source library wradlib. To assess the added value of the radarbased QPE, we used spatially interpolated rain gauge observations (gauge-only (GO) product) as a benchmark. Rain gauge observations were also used to quantify rainfall estimation errors at the point scale. At the point scale, the radarbased QPE outperformed the GO product in 2012, while for 2013, the performance was similar. For both periods, estimation errors substantially increased from daily to the hourly accumulation intervals. Despite this fact, both rainfall estimation methods allowed for a good representation of observed streamflow when used to force a hydrological simulation model of the MRB. Furthermore, the results of the hydrological simulation were consistent with rainfall verification at the point scale: the radar-based QPE performed better than the GO product in 2012, and equivalently in 2013. Altogether, we could demonstrate that, in terms of streamflow simulation, the radar-based QPE can perform as good as or even better than the GO product À even for a basin such as the MRB which has a comparatively dense rain gauge network. This suggests good prospects for using radar-based QPE to simulate and forecast streamflow in other parts of the Philippines where rain gauge networks are not as dense.

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“…Ground‐based weather radar data represent a viable alternative to satellite‐based rainfall estimates. They are characterized by higher spatial and temporal resolutions (5 minutes and about 1‐km horizontal resolution) and have already been used to study rainfall and flooding in landfalling tropical cyclones [e.g., Glitto and Choy , 1997; Blackwell , 2000; Sturdevant‐Rees et al , 2001; Medlin et al , 2007; Curtis et al , 2007; Matyas , 2009; Javier et al , 2010; Villarini et al , 2010]. Radar data, however, are affected by several sources of uncertainties (consult Villarini and Krajewski [2010] for a recent review) and a radar can cover only a limited region (on the order of 150 km) in open water before the storm makes landfall.…”

Section: Introductionmentioning

confidence: 99%

Characterization of rainfall distribution and flooding associated with U.S. landfalling tropical cyclones: Analyses of Hurricanes Frances, Ivan, and Jeanne (2004)

et al. 2011

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[1] Rainfall and flooding associated with landfalling tropical cyclones are examined through empirical analyses of three hurricanes (Frances, Ivan, and Jeanne) that affected large portions of the eastern U.S. during September 2004. Three rainfall products are considered for the analyses: NLDAS, Stage IV, and TMPA. Each of these products has strengths and weaknesses related to their spatio-temporal resolution and accuracy in estimating rainfall. Based on our analyses, we recommend using the Stage IV product when studying rainfall distribution in landfalling tropical cyclones due to its fine spatial and temporal resolutions (about 4-km and hourly) and accuracy, and the capability of estimating rainfall up to 150 km from the coast. Lagrangian analyses of rainfall distribution relative to the track of the storm are developed to represent evolution of the temporal and spatial structure of rainfall. Analyses highlight the profound changes in rainfall distribution near landfall, the changing contributions to the rainfall field from eyewall convection, inner rain bands and outer rain bands, and the key role of orographic amplification of rainfall. We also present new methods for examining spatial extreme of flooding from tropical cyclones and illustrate the links between evolving rainfall structure and spatial extent of flooding.

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Radar analyses of extreme rainfall and flooding in urban drainage basins

Cited by 71 publications

References 59 publications

Rainfall Intensity Temporal Patterns Affect Shallow Landslide Triggering and Hazard Evolution

Rainfall Intensity Temporal Patterns Affect Shallow Landslide Triggering and Hazard Evolution

Evaluating the potential of radar-based rainfall estimates for streamflow and flood simulations in the Philippines

Characterization of rainfall distribution and flooding associated with U.S. landfalling tropical cyclones: Analyses of Hurricanes Frances, Ivan, and Jeanne (2004)

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