DEVELOPMENT OF A FLOOD-INUNDATION MODEL NESTING A DISTRIBUTED RAINFALL-RUNOFF MODEL

Abstract. The political pressure on the scientific community to provide medium to long term flood forecasts has increased in the light of recent flooding events in Europe. Such demands can be met by a system consisting of three different model components (weather forecast, rainfall-runoff forecast and flood inundation forecast) which are all liable to considerable uncertainty in the input, output and model parameters. Thus, an understanding of cascaded uncertainties is a necessary requirement to provide robust predictions. In this paper, 10-day ahead rainfall forecasts, consisting of one deterministic, one control and 50 ensemble forecasts, are fed into a rainfall-runoff model (LisFlood) for which parameter uncertainty is represented by six different parameter sets identified through a Generalised Likelihood Uncertainty Estimation (GLUE) analysis and functional hydrograph classification. The runoff of these 52 * 6 realisations form the input to a flood inundation model (LisFlood-FP) which acknowledges uncertainty by utilising ten different sets of roughness coefficients identified using the same GLUE methodology. Likelihood measures for each parameter set computed on historical data are used to give uncertain predictions of flow hydrographs as well as spatial inundation extent. This analysis demonstrates that a full uncertainty analysis of such an integrated system is limited mainly by computer power as well as by how well the rainfall predictions represent potential future conditions. However, these restrictions may be overcome or lessened in the future and this paper establishes a computationally feasible methodological approach to the uncertainty cascade problem.

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Assessment of Flood Risk Exposure for the Foshan-Zhongshan Region in Guangdong Province, China

Water ◽

10.3390/w12041159 ◽

2020 ◽

Vol 12 (4) ◽

pp. 1159

Author(s):

Qi Zhang ◽

Wei Jian ◽

Edmond Yat Man Lo

Keyword(s):

Flood Risk ◽

Pearl River Delta ◽

Risk Exposure ◽

Economic Losses ◽

Flood Inundation ◽

Rainfall Runoff ◽

Proposed Model ◽

Rainfall Runoff Model ◽

The Pearl River ◽

Runoff Model

Floods have caused 20% of the worldwide economic losses resulting from catastrophe events over 2008 to 2018. In China, the annual flood economic losses have exceeded CNY 100 billion from 1990 to 2010, which is equivalent to 1% to 3% of China’s Gross Domestic Product (GDP). This paper presents a rainfall-runoff model coupled with an inundation estimation to assess the flood risk for a basin within the Foshan-Zhongshan area of the Pearl River Delta (PRD) region in China. A Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) model was constructed for the crisscrossing river network in the study basin where the West and North Rivers meet, using publicly accessible meteorological, hydrological and topographical datasets. The developed model was used to analyze two recent flood events, that in July 2017 with large upstream river inflows, and in June 2018 with high local rainfall. Results were further used to develop the needed river rating curves within the basin. Two synthetic events that consider more severe meteorological and hydrological conditions were also analyzed. These results demonstrate the capability of the proposed model for quick assessment of potential flood inundation and the GDP exposure at risk within the economically important PRD region.

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Sensitivity Analysis For Control Parameters Of Hybrid And Standard PSO Algorithms: Application Via A Rainfall-runoff Model Calibration

4th International Symposium on Innovative Approaches in Engineering and Natural Sciences Proceedings ◽

10.36287/setsci.4.6.085 ◽

2019 ◽

Author(s):

Umut Okkan ◽

Umut Kırdemir

Keyword(s):

Sensitivity Analysis ◽

Model Calibration ◽

Control Parameters ◽

Rainfall Runoff ◽

Rainfall Runoff Model ◽

Runoff Model

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Effects of streamflow isotope sampling strategies on the calibration of a tracer‐aided rainfall‐runoff model

Hydrological Processes ◽

10.1002/hyp.14223 ◽

2021 ◽

Author(s):

Jamie Lee Stevenson ◽

Christian Birkel ◽

Aaron J. Neill ◽

Doerthe Tetzlaff ◽

Chris Soulsby

Keyword(s):

Sampling Strategies ◽

Rainfall Runoff ◽

Rainfall Runoff Model ◽

Runoff Model

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Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand

Water ◽

10.3390/w13091226 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1226

Author(s):

Pakorn Ditthakit ◽

Sirimon Pinthong ◽

Nureehan Salaeh ◽

Fadilah Binnui ◽

Laksanara Khwanchum ◽

...

Keyword(s):

Exchange Rate ◽

Rainfall Runoff ◽

Management Planning ◽

Runoff Variation ◽

Groundwater Exchange ◽

Monthly Runoff ◽

Rainfall Runoff Model ◽

Two Parameters ◽

Runoff Model

Accurate monthly runoff estimation is crucial in water resources management, planning, and development, preventing and reducing water-related problems, such as flooding and droughts. This article evaluates the monthly hydrological rainfall-runoff model’s performance, the GR2M model, in Thailand’s southern basins. The GR2M model requires only two parameters: production store (X1) and groundwater exchange rate (X2). Moreover, no prior research has been reported on its application in this region. The 37 runoff stations, which are located in three sub-watersheds of Thailand’s southern region, namely; Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. The Thornthwaite method was utilized for the determination of evapotranspiration. The model’s performance was conducted using three statistical indices: Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The model’s calibration results for 37 runoff stations gave the average NSE, r, and OI of 0.657, 0.825, and 0.757, respectively. Moreover, the NSE, r, and OI values for the model’s verification were 0.472, 0.750, and 0.639, respectively. Hence, the GR2M model was qualified and reliable to apply for determining monthly runoff variation in this region. The spatial distribution of production store (X1) and groundwater exchange rate (X2) values was conducted using the IDW method. It was susceptible to the X1, and X2 values of approximately more than 0.90, gave the higher model’s performance.

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