Using uncertainty and sensitivity analysis for finding the best rainfall-runoff model in mountainous watersheds (Case study: the Navrood watershed in Iran)

2019 ◽  
Vol 16 (3) ◽  
pp. 529-541 ◽  
Author(s):  
Arash Adib ◽  
Morteza Lotfirad ◽  
Ali Haghighi
Keyword(s):  
Sensitivity Analysis ◽  
Rainfall Runoff ◽  
Uncertainty And Sensitivity Analysis ◽  
Mountainous Watersheds ◽  
Rainfall Runoff Model ◽  
Runoff Model

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 Spatial sensitivity analysis of snow cover data in a distributed rainfall–runoff model

2014 ◽  
Vol 11 (10) ◽  
pp. 11987-12025 ◽  
Author(s):  
T. Berezowski ◽  
J. Nossent ◽  
J. Chormański ◽  
O. Batelaan
Keyword(s):  
Sensitivity Analysis ◽  
Distributed Data ◽  
Rainfall Runoff ◽  
Spatial Sensitivity ◽  
Wetspa Model ◽  
Spatial Parameters ◽  
Spatial Sensitivity Analysis ◽  
Rainfall Runoff Model ◽  
Different Response ◽  
Runoff Model

Abstract. As the availability of spatially distributed data sets for distributed rainfall–runoff modelling is strongly growing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hydrological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis (SA) method for snow cover fraction input data (SCF) for a distributed rainfall–runoff model to investigate if the model is differently subjected to SCF uncertainty in different zones of the model. The analysis was focused on the relation between the SCF sensitivity and the physical, spatial parameters and processes of a distributed rainfall–runoff model. The methodology is tested for the Biebrza River catchment, Poland for which a distributed WetSpa model is setup to simulate two years of daily runoff. The SA uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm, which uses different response functions for each 4 km × 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by co-occurrence of different environmental factors such as: geomorphology, soil texture, land-use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for the spatial sensitivity analysis approach and that the SCF is spatially sensitive in the WetSpa model.

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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