A case study for ANN-based rainfall–runoff model considering antecedent soil moisture conditions in Imha Dam watershed, Korea

2015 ◽  
Vol 74 (2) ◽  
pp. 1261-1272 ◽  
Author(s):  
Boosik Kang ◽  
Young Hun Ku ◽  
Young Do Kim
Keyword(s):  
Soil Moisture ◽  
Rainfall Runoff ◽  
Antecedent Soil Moisture ◽  
Rainfall Runoff Model ◽  
Moisture Conditions ◽  
Runoff Model

scholarly journals Flood and landslide warning based on rainfall thresholds and soil moisture indexes: the HEWS (Hydrohazards Early Warning System) for Sicily

2017 ◽  
Vol 44 ◽  
pp. 79-88 ◽  
Author(s):  
Giuseppina Brigandì ◽  
Giuseppe Tito Aronica ◽  
Brunella Bonaccorso ◽  
Roberto Gueli ◽  
Giuseppe Basile
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Rainfall Runoff ◽  
Rainfall Thresholds ◽  
Rainfall Runoff Model ◽  
Moisture Conditions ◽  
Runoff Model ◽  
Scs Cn

Abstract. The main focus of the paper is to present a flood and landslide early warning system, named HEWS (Hydrohazards Early Warning System), specifically developed for the Civil Protection Department of Sicily, based on the combined use of rainfall thresholds, soil moisture modelling and quantitative precipitation forecast (QPF). The warning system is referred to 9 different Alert Zones in which Sicily has been divided into and based on a threshold system of three different increasing critical levels: ordinary, moderate and high. In this system, for early flood warning, a Soil Moisture Accounting (SMA) model provides daily soil moisture conditions, which allow to select a specific set of three rainfall thresholds, one for each critical level considered, to be used for issue the alert bulletin. Wetness indexes, representative of the soil moisture conditions of a catchment, are calculated using a simple, spatially-lumped rainfall–streamflow model, based on the SCS-CN method, and on the unit hydrograph approach, that require daily observed and/or predicted rainfall, and temperature data as input. For the calibration of this model daily continuous time series of rainfall, streamflow and air temperature data are used. An event based lumped rainfall–runoff model has been, instead, used for the derivation of the rainfall thresholds for each catchment in Sicily characterised by an area larger than 50 km2. In particular, a Kinematic Instantaneous Unit Hydrograph based lumped rainfall–runoff model with the SCS-CN routine for net rainfall was developed for this purpose. For rainfall-induced shallow landslide warning, empirical rainfall thresholds provided by Gariano et al. (2015) have been included in the system. They were derived on an empirical basis starting from a catalogue of 265 shallow landslides in Sicily in the period 2002–2012. Finally, Delft-FEWS operational forecasting platform has been applied to link input data, SMA model and rainfall threshold models to produce warning on a daily basis for the entire region.

scholarly journals Impact of downscaled rainfall biases on projected runoff changes

2020 ◽  
Vol 24 (6) ◽  
pp. 2981-2997
Author(s):  
Stephen P. Charles ◽  
Francis H. S. Chiew ◽  
Nicholas J. Potter ◽  
Hongxing Zheng ◽  
Guobin Fu ◽  
...  
Keyword(s):  
Water Supply ◽  
Grid Point ◽  
Daily Rainfall ◽  
Rainfall Runoff ◽  
Rainfall Frequency ◽  
Rainfall Runoff Model ◽  
Projected Changes ◽  
Runoff Model ◽  
Regional Water

Abstract. Realistic projections of changes to daily rainfall frequency and magnitude, at catchment scales, are required to assess the potential impacts of climate change on regional water supply. We show that quantile–quantile mapping (QQM) bias-corrected daily rainfall from dynamically downscaled WRF simulations of current climate produce biased hydrological simulations, in a case study for the state of Victoria, Australia (237 629 km2). While the QQM bias correction can remove bias in daily rainfall distributions at each 10 km × 10 km grid point across Victoria, the GR4J rainfall–runoff model underestimates runoff when driven with QQM bias-corrected daily rainfall. We compare simulated runoff differences using bias-corrected and empirically scaled rainfall for several key water supply catchments across Victoria and discuss the implications for confidence in the magnitude of projected changes for mid-century. Our results highlight the imperative for methods that can correct for temporal and spatial biases in dynamically downscaled daily rainfall if they are to be suitable for hydrological projection.

scholarly journals Rainfall-Runoff Model Based on ANN with LM, BR and PSO as Learning Algorithms

2019 ◽  
Vol 8 (3) ◽  
pp. 971-979
Keyword(s):  
Prediction Model ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Natural Phenomenon ◽  
Rainfall Runoff ◽  
Flood Prediction ◽  
Prone Area ◽  
Rainfall Runoff Model ◽  
Runoff Model

Rainfall-runoff model requires comprehensive computation as its relation is a complex natural phenomenon. Various inter-related processes are involved with factors such as rainfall intensity, geomorphology, climatic and landscape are all affecting runoff response. In general there is no single rainfall-runoff model that can cater to all flood prediction system with varying topological area. Hence, there is a vital need to have custom-tailored prediction model with specific range of data, type of perimeter and antecedent hour of prediction to meet the necessity of the locality. In an attempt to model a reliable rainfall-runoff system for a flood-prone area in Malaysia, 3 different approach of Artificial Neural Networks (ANN) are modelled based on the data acquired from Sungai Pahang, Pekan. In this paper, the ANN rainfall-runoff models are trained by the Levenberg Marquardt (LM), Bayesian Regularization (BR) and Particle Swarm Optimization (PSO). The performances of the learning algorithms are compared and evaluated based on a 12-hour prediction model. The results demonstrate that LM produces the best model. It outperforms BR and PSO in terms of convergence rate, lowest mean square error (MSE) and optimum coefficeint of correlation. Furthermore, the LM approach are free from overfitting, which is a crucial concern in conventional ANN learning algorithm. Our case study takes the data of rainfall and runoff from the year 2012 to 2014. This is a case study in Pahang river basin, Pekan, Malaysia.

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