On the Operational Flood Forecasting Practices Using Low-Quality Data Input of a Distributed Hydrological Model

Binquan Li, Zhongmin Liang, Qingrui Chang, Wei Zhou, Huan Wang, Jun Wang and Yiming Hu
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Binquan Li: Institute of Water Science and Technology, Hohai University, Nanjing 210098, China
Zhongmin Liang: College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Qingrui Chang: School of Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China
Wei Zhou: School of Public Administration, Hohai University, Nanjing 211100, China
Huan Wang: State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
Jun Wang: College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Yiming Hu: College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China

Sustainability, 2020, vol. 12, issue 19, 1-16

Abstract: Low-quality input data (such as sparse rainfall gauges, low spatial resolution soil type and land use maps) have limited the application of physically-based distributed hydrological models in operational practices in many data-sparse regions. It is necessary to quantify the uncertainty in the deterministic forecast results of distributed models. In this paper, the TOPographic Kinematic Approximation and Integration (TOPKAPI) distributed model was used for deterministic forecasts with low-quality input data, and then the Hydrologic Uncertainty Processor (HUP) was used to provide the probabilistic forecast results for operational practices. Results showed that the deterministic forecasts by TOPKAPI performed poorly in some flood seasons, such as the years 1997, 2001 and 2008, despite which the overall accuracy of the whole study period 1996–2008 could be acceptable and generally reproduced the hydrological behaviors of the catchment (Lushi basin, China). The HUP model can not only provide probabilistic forecasts (e.g., 90% predictive uncertainty bounds), but also provides deterministic forecasts in terms of 50% percentiles. The 50% percentiles obviously improved the forecast accuracy of selected flood events at the leading time of one hour. Besides, the HUP performance decayed with the leading time increasing (6, 12 h). This work revealed that deterministic model outputs had large uncertainties in flood forecasts, and the HUP model may provide an alternative for operational flood forecasting practices in those areas with low-quality data.

Keywords: deterministic flood forecasting; probabilistic flood forecasting; distributed hydrological models; hydrologic uncertainty processor; low-quality data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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