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

2019 ◽  
Author(s):  
Umut Okkan ◽  
Umut Kırdemir
Keyword(s):  
Sensitivity Analysis ◽  
Model Calibration ◽  
Control Parameters ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

Multicore Parallel Genetic Algorithm with Tabu Strategy for Rainfall-Runoff Model Calibration

2017 ◽  
Vol 22 (8) ◽  
pp. 04017024 ◽  
Author(s):  
Shengli Liao ◽  
Qianying Sun ◽  
Chuntian Cheng ◽  
Ruhong Zhong ◽  
Huaying Su
Keyword(s):  
Genetic Algorithm ◽  
Model Calibration ◽  
Rainfall Runoff ◽  
Parallel Genetic Algorithm ◽  
Rainfall Runoff Model ◽  
Runoff Model

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 Uncertainty and sensitivity analysis of GIS based continuous hydrological modelling

2021 ◽  
Author(s):  
Harry R. Manson
Keyword(s):  
Sensitivity Analysis ◽  
Curve Number ◽  
Monte Carlo Analysis ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Lumped Model ◽  
Daily Average ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

The impact of uncertainty in spatial and a-spatial lumped model parameters for a continuous rainfall-runoff model is evaluated with respect to model prediction. The model uses a modified SCS-Curve Number approach that is loosely coupled with a geographic information system (GIS). The rainfall-runoff model uses daily average inputs and is calibrated using a daily average streamflow record for the study site. A Monte Carlo analysis is used to identify total model uncertainty while sensitivity analysis is applied using both a one-at-a-time (OAT) approach as well as through application of the extended Fourier Amplitude Sensitivity Technique (FAST). Conclusions suggest that the model is highly followed by model inputs and finally the Curve Number. While the model does not indicate a high degree of sensitivity to the Curve Number at present conditions, uncertainties in Curve Number estimation can potentially be the cause of high predictive errors when future development scenarios are evaluated.

Impacts of uncertain river flow data on rainfall-runoff model calibration and discharge predictions

2010 ◽  
pp. n/a-n/a ◽  
Author(s):  
Hilary McMillan ◽  
Jim Freer ◽  
Florian Pappenberger ◽  
Tobias Krueger ◽  
Martyn Clark
Keyword(s):  
Model Calibration ◽  
River Flow ◽  
Rainfall Runoff ◽  
Flow Data ◽  
Rainfall Runoff Model ◽  
Runoff Model
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