Identifying the Influence of Systematic Errors in Potential Evapotranspiration on Rainfall–Runoff Models

2022 ◽  
Vol 27 (2) ◽  
Author(s):  
Dilhani I. Jayathilake ◽  
Tyler Smith
Keyword(s):  
Potential Evapotranspiration ◽  
Systematic Errors ◽  
Rainfall Runoff

Assessing systematic errors in rainfall-runoff models

1973 ◽  
Vol 20 (2) ◽  
pp. 121-136 ◽  
Author(s):  
A.P. Aitken
Keyword(s):  
Systematic Errors ◽  
Rainfall Runoff

Estimating potential evapotranspiration: the effect of random and systematic errors

1989 ◽  
Vol 46 (4) ◽  
pp. 285-296 ◽  
Author(s):  
Steven J. Meyer ◽  
Kenneth G. Hubbard ◽  
Donald A. Wilhite
Keyword(s):  
Potential Evapotranspiration ◽  
Systematic Errors

The effect of rainfall measurement uncertainties on rainfall–runoff processes modelling

2007 ◽  
Vol 55 (4) ◽  
pp. 103-111 ◽  
Author(s):  
D. Stransky ◽  
V. Bares ◽  
P. Fatka
Keyword(s):  
Measurement Errors ◽  
Dynamic Test ◽  
Systematic Errors ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Test Duration ◽  
Rainfall Runoff ◽  
Rainfall Volume ◽  
Rainfall Runoff Model ◽  
Runoff Model

Rainfall data are a crucial input for various tasks concerning the wet weather period. Nevertheless, their measurement is affected by random and systematic errors that cause an underestimation of the rainfall volume. Therefore, the general objective of the presented work was to assess the credibility of measured rainfall data and to evaluate the effect of measurement errors on urban drainage modelling tasks. Within the project, the methodology of the tipping bucket rain gauge (TBR) was defined and assessed in terms of uncertainty analysis. A set of 18 TBRs was calibrated and the results were compared to the previous calibration. This enables us to evaluate the ageing of TBRs. A propagation of calibration and other systematic errors through the rainfall–runoff model was performed on experimental catchment. It was found that the TBR calibration is important mainly for tasks connected with the assessment of peak values and high flow durations. The omission of calibration leads to up to 30% underestimation and the effect of other systematic errors can add a further 15%. The TBR calibration should be done every two years in order to catch up the ageing of TBR mechanics. Further, the authors recommend to adjust the dynamic test duration proportionally to generated rainfall intensity.

scholarly journals The HOOPLA toolbox: a HydrOlOgical Prediction LAboratory to explore ensemble rainfall-runoff modeling

2020 ◽  
Author(s):  
Antoine Thiboult ◽  
Gregory Seiller ◽  
Carine Poncelet ◽  
François Anctil
Keyword(s):  
Data Assimilation ◽  
Potential Evapotranspiration ◽  
Hydrological Modelling ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Technical Report ◽  
Flexible Framework ◽  
Hydrological Prediction ◽  
Easy Integration

Abstract. This technical report introduces the HydrOlOgical Prediction LAboratory (HOOPLA) developed at Université Lavalfor ensemble lumped hydrological modelling. HOOPLA includes functionalities to perform calibration, simulation, and forecast for multiple hydrological models and various time steps. It includes a range of hydrometeorological tools such as calibration algorithms, data assimilation techniques, potential evapotranspiration formulas and a snow accounting routine. HOOPLA is a flexible framework coded in MATLAB that allows easy integration of user-defined hydrometeorological tools. This report also illustrates HOOPLA's functionalities using a set of 31 Canadian catchments.

scholarly journals The utilization of a GR4J model and wavelet-based artificial neural network for rainfall–runoff modelling

2018 ◽  
Vol 19 (5) ◽  
pp. 1295-1304
Author(s):  
C. Sezen ◽  
T. Partal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Potential Evapotranspiration ◽  
Data Driven ◽  
Rainfall Runoff ◽  
Ann Model ◽  
The Usa ◽  
Streamflow Data ◽  
Artificial Neural ◽  
Better Than

Abstract Data-driven models and conceptual models have been utilized in an attempt to perform rainfall–runoff modelling. The aim of this study is comparing the performance of an artificial neural network (ANN) model, wavelet-based artificial neural network (WANN) model and GR4J lumped daily conceptual model for rainfall–runoff modelling of two rivers in the USA. It was obtained that the performance of the data-driven models (ANN, WANN) is better than the GR4J model especially when streamflow data the preceding day (Qt-1) and streamflow data the preceding two days (Qt-2) are used as input data in the ANN and WANN models for the simulation of low and high flows, in particular. On the other hand, when only precipitation and potential evapotranspiration data are used as input variables, the GR4J model performs better than the data-driven models.

Which potential evapotranspiration input for a lumped rainfall–runoff model?

2005 ◽  
Vol 303 (1-4) ◽  
pp. 290-306 ◽  
Author(s):  
Ludovic Oudin ◽  
Frédéric Hervieu ◽  
Claude Michel ◽  
Charles Perrin ◽  
Vazken Andréassian ◽  
...  
Keyword(s):  
Potential Evapotranspiration ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

scholarly journals NUMERICAL SIMULATION OF THE RAINFALL-RUNOFF PROCESS ON A DAILY BASIS

1973 ◽  
Vol 4 (3) ◽  
pp. 171-190 ◽  
Author(s):  
STEEN ASGER NIELSEN ◽  
EGGERT HANSEN
Keyword(s):  
Moisture Content ◽  
Potential Evapotranspiration ◽  
Daily Basis ◽  
Major Influence ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Frozen Ground ◽  
Statistical Measures ◽  
Simulation Results ◽  
Physical Elements

A digital model has been developed for the simulation of the rainfall-runoff process of rural watersheds. Input data are daily values of precipitation and temperature together with mean monthly potential evapotranspiration. The model produces daily values of streamflow as well as information on the time variation of the soil moisture content. In all, ten model parameters have to be identified, seven of which have a major influence on the performance of the model. The model operates by accounting continuously for the moisture content in four different and mutually interrelated storages representing physical elements in the watershed. It has been applied to three different Danish watersheds. Several statistical measures of accuracy have been utilized for a quantitative evaluation of the simulation results. The simulations demonstrate that the main shortcomings of the model are due to the lack of a procedure accounting for frozen ground during extended periods of frost, which could improve some of the simulation results during winter and spring.

scholarly journals Opportunities and challenges for the use of scintillometer-based catchment-averaged evapotranspiration estimates as model forcing

2013 ◽  
Vol 10 (4) ◽  
pp. 3973-4013
Author(s):  
B. Samain ◽  
V. R. N. Pauwels
Keyword(s):  
Potential Evapotranspiration ◽  
Seasonal Pattern ◽  
Rainfall Runoff ◽  
Data Set ◽  
Meteorological Forcing ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Stable Conditions ◽  
Low Pass ◽  
Rainfall Runoff Model

Abstract. To date, lumped rainfall-runoff models rely on rough estimates of catchment-averaged potential evapotranspiration (ETp) rates as meteorological forcing. A model parameter converts this ETp input into actual evapotranspiration (ETact) estimates. This paper examines the potential use of scintillometer-based ETact rates for rainfall-runoff modeling. It has been found that the reservoir-structure of the rainfall-runoff model functions as a low-pass filter for the ETp input. If the long-term volume of the ETp used in the model simulations is consistent with the data set used for calibration, a good match of the seasonal pattern, using temporally constant ETp data, is sufficient to obtain adequate discharge simulations. However, these results are then obtained with strongly erroneous evapotranspiration estimates. A better match of the diurnal cycle does not lead to better model results. Replacing the ETp inputs by scintillometer-based ETact estimates does not lead to better model predictions. Small underestimations of ETact under stable conditions, which occur at night and during the Winter, and which accumulate to significant amounts, are the cause of this problem. Consistent with other studies, the scintillometer-based ETact estimates can be considered reliable and realistic under unstable conditions. These values can thus be used as forcing for rainfall-runoff models.

Which potential evapotranspiration input for a lumped rainfall-runoff model?

2005 ◽  
Vol 303 (1-4) ◽  
pp. 275-289 ◽  
Author(s):  
Ludovic Oudin ◽  
Claude Michel ◽  
François Anctil
Keyword(s):  
Potential Evapotranspiration ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

Effect of Spatial Rainfall Distribution on Sewer Flows

1984 ◽  
Vol 16 (8-9) ◽  
pp. 177-188 ◽  
Author(s):  
Wolfgang Schilling
Keyword(s):  
Catchment Area ◽  
Input Data ◽  
Systematic Errors ◽  
Rainfall Event ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Sewer System ◽  
Rainfall Distribution ◽  
Spatially Homogeneous

Errors in computing flows are caused by uncertainties about input data as well as uncertainties about appropriate models and model parameters. It is investigated how errors of the input “rainfall”, especially errors in spatial resolution, propagate by computation of sewer flows. Results of a case study about a sewer system with a catchment area of 2 km2 and ca. 1000 reaches between manholes show−that mean errors in rainfall event depths of about 20 % occur even with five raingages within or close to the catchment−that the errors by disregarding the spatial distribution are amplified rather than damped by the rainfall-runoff transformation and−that using spatially homogeneous rainfall input for flow computations cause systematic errors due to the wrong assumption of spatially homogeneous initial losses and due to dominating directions of storm movement.

Sign in / Sign up

Export Citation Format

Share Document