scholarly journals Sensitivity of the performance of a conceptual rainfall–runoff model to the temporal sampling of calibration data

2012 ◽  
Vol 44 (3) ◽  
pp. 484-494 ◽  
Author(s):  
Satish Bastola ◽  
Conor Murphy
Keyword(s):  
Temporal Resolution ◽  
Model Calibration ◽  
Flood Frequency Analysis ◽  
Calibration Data ◽  
Rainfall Runoff ◽  
Time Step ◽  
Scaling Relationship ◽  
Simple Scaling ◽  
Rainfall Runoff Model ◽  
Runoff Model

The effect of the time step of calibration data on the performance of a hydrological model is examined through a numerical experiment where HYMOD, a rainfall–runoff model, is calibrated with data of varying temporal resolution. A simple scaling relationship between the parameters of the model and modelling time step is derived which enables information from daily hydrological records to be used in modelling at time steps much shorter than daily. Model parameters were found to respond differently depending upon the degree of aggregation of calibration data. A loss in performance, especially in terms of the Nash–Sutcliffe measure, is evident when behavioural simulators derived with one modelling time step are used for simulation at another time step. The loss in performance is greater when parameters derived from a longer time step were used for simulating flow with a shorter time step. The application of a simple scaling relationship derived from a multi-time step model calibration significantly decreased the loss in model performance. Such an approach may offer the prospect of conducting higher temporal resolution flood frequency analysis when finer scale data for model calibration are not available or limited.

scholarly journals From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution

2014 ◽  
Vol 11 (12) ◽  
pp. 13259-13309 ◽  
Author(s):  
M. Herrnegger ◽  
H. P. Nachtnebel ◽  
K. Schulz
Keyword(s):  
Temporal Resolution ◽  
Numerical Experiments ◽  
Inverse Model ◽  
High Temporal Resolution ◽  
Rainfall Runoff ◽  
Time Step ◽  
Areal Rainfall ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Rainfall Estimates

Abstract. This paper presents a novel technique to calculate mean areal rainfall in a high temporal resolution of 60 min on the basis of an inverse conceptual rainfall–runoff model and runoff observations. Rainfall exhibits a large spatio-temporal variability, especially in complex alpine terrain. Additionally, the density of the monitoring network in mountainous regions is low and measurements are subjected to major errors, which lead to significant uncertainties in areal rainfall estimates. The most reliable hydrological information available refers to runoff, which in the presented work is used as input for a rainfall–runoff model. Thereby a conceptual, HBV-type model is embedded in an iteration algorithm. For every time step a rainfall value is determined, which results in a simulated runoff value that corresponds to the observation. To verify the existence, uniqueness and stability of the inverse rainfall, numerical experiments with synthetic hydrographs as inputs into the inverse model are carried out successfully. The application of the inverse model with runoff observations as driving input is performed for the Krems catchment (38.4 km2), situated in the northern Austrian Alpine foothills. Compared to station observations in the proximity of the catchment, the inverse rainfall sums and time series have a similar goodness of fit, as the independent INCA rainfall analysis of Austrian Central Institute for Meteorology and Geodynamics (ZAMG). Compared to observations, the inverse rainfall estimates show larger rainfall intensities. Numerical experiments show, that cold state conditions in the inverse model do not influence the inverse rainfall estimates, when considering an adequate spin-up time. The application of the inverse model is a feasible approach to obtain improved estimates of mean areal rainfall. These can be used to enhance interpolated rainfall fields, e.g. for the estimation of rainfall correction factors, the parameterisation of elevation dependency or the application in real-time flood forecasting systems.

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 Preliminary Study of Computational Time Steps in a Physically Based Distributed Rainfall–Runoff Model

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1269 ◽  
Author(s):  
Yun Choi ◽  
Mun-Ju Shin ◽  
Kyung Kim
Keyword(s):  
Optimal Parameter ◽  
Computational Time ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Stream Network ◽  
Time Step ◽  
Simulation Results ◽  
Rainfall Runoff Model ◽  
Parameter Values ◽  
Runoff Model

The choice of the computational time step (dt) value and the method for setting dt can have a bearing on the accuracy and performance of a simulation, and this effect has not been comprehensively researched across different simulation conditions. In this study, the effects of the fixed time step (FTS) method and the automatic time step (ATS) method on the simulated runoff of a distributed rainfall–runoff model were compared. The results revealed that the ATS method had less peak flow variability than the FTS method for the virtual catchment. In the FTS method, the difference in time step had more impact on the runoff simulation results than the other factors such as differences in the amount of rainfall, the density of the stream network, or the spatial resolution of the input data. Different optimal parameter values according to the computational time step were found when FTS and ATS were used in a real catchment, and the changes in the optimal parameter values were smaller in ATS than in FTS. The results of our analyses can help to yield reliable runoff simulation results.

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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