Flash flood warning based on rainfall thresholds and soil moisture conditions: An assessment for gauged and ungauged basins

Norbiato, Daniele; Borga, Marco; Esposti, Silvia Degli; Gaumé, Éric; Anquetin, Sandrine

doi:10.1016/j.jhydrol.2008.08.023

Cited by 338 publications

(212 citation statements)

References 27 publications

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“…The Soil Conservation Service Curve Number (SCS CN, Mishra and Singh, 2003) model is adapted to calculate threshold rainfalls taking into account physical properties and soil moistures states at a 4 km Â 4 km gridded scale. Norbiato et al (2008) use a semi-distributed conceptual model (Moore, 1985) to assess FFG method in basins in Northeastern Italy and central France.…”

Section: The Ffg Methodsmentioning

confidence: 99%

“…Global conceptual models are used to calculate the threshold intensity. In particular, Norbiato et al (2008) have demonstrated encouraging results for warning forecasts. They also conclude that the assumption of uniform forcing in space and time has a limited effect on the FFG method.…”

Section: Introductionmentioning

confidence: 96%

“…The main difficulties lie in flash flood timescales (Norbiato et al, 2008). The rapidity of the hydrological responses (from a few hours to one day) reduces the forecast time.…”

Section: Introductionmentioning

confidence: 99%

“…Several research projects have studied the applicability of the FFG method not only in the United States but also over the south-western Mediterranean arc (Carpenter et al, 1999;Ntelekos et al, 2006;Norbiato et al, 2008). Global conceptual models are used to calculate the threshold intensity.…”

Section: Introductionmentioning

confidence: 99%

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Accounting for rainfall systematic spatial variability in flash flood forecasting

Douinot

Roux

Garambois

et al. 2016

Journal of Hydrology

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 17444 Just as with the storms that cause them, flash floods are highly variable and non-linear phenomena in both time and space; hence understanding and anticipating the genesis of flash floods is far from straightforward. There is therefore a huge requirement for tools with the potential to provide advance warning of situations likely to lead to flash floods, and thus provide additional time for the flood forecasting services. The Flash Flood Guidance (FFG) method is used on US catchments to estimate the average number of inches of rainfall for given durations required to produce flash flooding. This rainfall amount is used afterwards as a flood warning threshold. In Europe, flash floods often occur on small catchments (approximately 100 km 2 ) and it has already been shown that the spatial variability of rainfall has a great impact on the catchment response (Le Lay and Saulnier, 2007). Therefore, in this study, an improved FFG method which accounts for rainfall spatial variability is proposed. The objectives of this paper are (i) to assess the FFG method applicability on French Mediterranean catchments with a distributed processoriented hydrological model and (ii) to assess the effect of the rainfall spatial variability on this method. The results confirm the influence of the spatial variability of rainfall events in relation with its interaction with soil properties.

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Section: The Ffg Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

“…The main difficulties lie in flash flood timescales (Norbiato et al, 2008). The rapidity of the hydrological responses (from a few hours to one day) reduces the forecast time.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Accounting for rainfall systematic spatial variability in flash flood forecasting

Douinot

Roux

Garambois

et al. 2016

Journal of Hydrology

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“…Some recent studies suggest the limitations of FFG (Norbiato et al, 2008;Montesarchio et al, 2011;Gourley et al, 2012). The limitations of FFG are in the assumptions of spatially-temporally uniform rainfall and linear response and the use of regional relationships to make inferences about ungauged locations.…”

Section: Introductionmentioning

confidence: 99%

Development of a precipitation–area curve for warning criteria of short-duration flash flood

Bae

Lee

Moon

2018

Nat. Hazards Earth Syst. Sci.

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Abstract. This paper presents quantitative criteria for flash flood warning that can be used to rapidly assess flash flood occurrence based on only rainfall estimates. This study was conducted for 200 small mountainous sub-catchments of the Han River basin in South Korea because South Korea has recently suffered many flash flood events. The quantitative criteria are calculated based on flash flood guidance (FFG), which is defined as the depth of rainfall of a given duration required to cause frequent flooding (1-2-year return period) at the outlet of a small stream basin and is estimated using threshold runoff (TR) and antecedent soil moisture conditions in all sub-basins. The soil moisture conditions were estimated during the flooding season, i.e., July, August and September, over 7 years (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)) using the Sejong University Rainfall Runoff (SURR) model. A ROC (receiver operating characteristic) analysis was used to obtain optimum rainfall values and a generalized precipitation-area (P -A) curve was developed for flash flood warning thresholds. The threshold function was derived as a P -A curve because the precipitation threshold with a short duration is more closely related to basin area than any other variables. For a brief description of the P -A curve, generalized thresholds for flash flood warnings can be suggested for rainfall rates of 42, 32 and 20 mm h −1 in sub-basins with areas of 22-40, 40-100 and > 100 km 2 , respectively. The proposed P -A curve was validated based on observed flash flood events in different sub-basins. Flash flood occurrences were captured for 9 out of 12 events. This result can be used instead of FFG to identify brief flash flood (less than 1 h), and it can provide warning information to decision-makers or citizens that is relatively simple, clear and immediate.

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Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network

Fang

Shen

Kifer

et al. 2017

Geophysical Research Letters

230

135

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The Soil Moisture Active Passive (SMAP) mission has delivered valuable sensing of surface soil moisture since 2015. However, it has a short time span and irregular revisit schedules. Utilizing a state-of-the-art time series deep learning neural network, Long Short-Term Memory (LSTM), we created a system that predicts SMAP level-3 moisture product with atmospheric forcings, model-simulated moisture, and static physiographic attributes as inputs. The system removes most of the bias with model simulations and improves predicted moisture climatology, achieving small test root-mean-square errors (<0.035) and high-correlation coefficients >0.87 for over 75% of Continental United States, including the forested southeast. As the first application of LSTM in hydrology, we show the proposed network avoids overfitting and is robust for both temporal and spatial extrapolation tests. LSTM generalizes well across regions with distinct climates and environmental settings. With high fidelity to SMAP, LSTM shows great potential for hindcasting, data assimilation, and weather forecasting. Plain Language Summary Soil moisture is the water content in soil, and it is a critical component of the water cycle. It controls whether crops or wild vegetation can function properly, the risk of wildfire, and the likelihood of floods. The NASA satellite SMAP, launched in 2015, measures soil moisture near the ground surface over Earth at high accuracy. SMAP data are of great value to global communities to which soil moisture is relevant. However, since it was launched only recently, there is only little overlap with other data sets, which limits its use. To extend SMAP's observations in time, we employ a "deep learning" technology called Long Short-Term Memory (LSTM), which is one of the pillars of artificial intelligence and is bringing revolutionary changes in many scientific fields and our daily lives. We use LSTM to learn patterns of soil moisture dynamics and where physics-based models make mistakes in describing moisture changes. This approach shows great promise for projecting SMAP observations into the long past: because soil moisture has a short memory, 2 years of data seem sufficient to train LSTM to successfully capture its dynamics. Because LSTM can handle large and diverse data, it offers valuable alternatives to older statistical methods.

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Flash flood warning based on rainfall thresholds and soil moisture conditions: An assessment for gauged and ungauged basins

Cited by 338 publications

References 27 publications

Accounting for rainfall systematic spatial variability in flash flood forecasting

Accounting for rainfall systematic spatial variability in flash flood forecasting

Development of a precipitation–area curve for warning criteria of short-duration flash flood

Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network

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