scholarly journals Assessment of the indirect calibration of a rainfall-runoff model for ungauged catchments in Flanders

2013 ◽  
Vol 10 (1) ◽  
pp. 103-144
Author(s):  
N. De Vleeschouwer ◽  
V. R. N. Pauwels
Keyword(s):  
Time Series ◽  
Model Performance ◽  
Distributed Model ◽  
Calibration Data ◽  
Rainfall Runoff ◽  
Discharge Time ◽  
Ungauged Catchments ◽  
Ungauged Catchment ◽  
First Case ◽  
Spectral Calibration

Abstract. In this paper the potential of discharge-based indirect calibration of the Probability Distributed Model (PDM), a lumped rainfall-runoff (RR) model, is examined for six selected catchments in Flanders. The concept of indirect calibration indicates that one has to estimate the calibration data because the catchment is ungauged. A first case in which indirect calibration is applied is that of spatial gauging divergence: Because no observed discharge records are available at the outlet of the ungauged catchment, the calibration is carried out based on a rescaled discharge time series of a very similar donor catchment. Both a calibration in the time domain and the frequency domain (a.k.a. spectral domain) are carried out. Furterhermore, the case of temporal gauging divergence is considered: Limited (e.g. historical or very recent) discharge records are available at the outlet of the ungauged catchment. Additionally, no time overlap exists between the forcing and discharge records. Therefore, only an indirect spectral calibration can be performed in this case. To conclude also the combination case of spatio-temporal gauging divergence is considered. In this last case only limited discharge records are available at the outlet of a donor catchment. Again the forcing and discharge records are not contemporaneous which only makes feasible an indirect spectral calibration. The modelled discharge time series are found to be acceptable in all three considered cases. In the case of spatial gauging divergence, indirect temporal calibration results in a slightly better model performance than indirect spectral calibration. Furthermore, indirect spectral calibration in the case of temporal gauging divergence leads to a better model performance than indirect spectral calibration in the case of spatial gauging divergence. Finally, the combination of spatial and temporal gauging divergence does not necessarily lead to a worse model performance compared to the separate cases of spatial and temporal gauging divergence.

scholarly journals Assessment of the indirect calibration of a rainfall-runoff model for ungauged catchments in Flanders

2013 ◽  
Vol 17 (5) ◽  
pp. 2001-2016 ◽  
Author(s):  
N. De Vleeschouwer ◽  
V. R. N. Pauwels
Keyword(s):  
Time Series ◽  
Model Performance ◽  
Distributed Model ◽  
Calibration Data ◽  
Rainfall Runoff ◽  
Discharge Time ◽  
Ungauged Catchments ◽  
First Case ◽  
Spectral Calibration ◽  
The Time Domain

Abstract. In this paper the potential of discharge-based indirect calibration of the probability-distributed model (PDM), a lumped rainfall-runoff (RR) model, is examined for six selected catchments in Flanders. The concept of indirect calibration indicates that one has to estimate the calibration data because the catchment is ungauged or scarcely gauged. A first case in which indirect calibration is applied is that of spatial gauging divergence: because no observed discharge records are available at the outlet of the ungauged catchment, the calibration is carried out based on a rescaled discharge time series of a very similar donor catchment. Both a calibration in the time domain and the frequency domain (also known as spectral domain) are carried out. Furthermore, the case of temporal gauging divergence is considered: limited (e.g. historical or very recent) discharge records are available at the outlet of the scarcely gauged catchment. Additionally, no time overlap exists between the forcing and discharge records. Therefore, only an indirect spectral calibration can be performed in this case. To conclude also the combination case of spatio-temporal gauging divergence is considered. In this last case only limited discharge records are available at the outlet of a donor catchment. Again the forcing and discharge records are not concomitant, which only makes feasible an indirect spectral calibration. For most catchments the modelled discharge time series is found to be acceptable in the considered cases. In the case of spatial gauging divergence, indirect temporal calibration results in a better model performance than indirect spectral calibration. Furthermore, indirect spectral calibration in the case of temporal gauging divergence leads to a better model performance than indirect spectral calibration in the case of spatial gauging divergence. Finally, the combination of spatial and temporal gauging divergence does not lead to a notably worse model performance compared to the case of spatial gauging divergence.

scholarly journals Calibration of hydrological model parameters for ungauged catchments

2007 ◽  
Vol 11 (2) ◽  
pp. 703-710 ◽  
Author(s):  
A. Bárdossy
Keyword(s):  
Hydrological Model ◽  
Model Performance ◽  
Model Parameters ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Rainfall Runoff Model ◽  
Climate Statistics ◽  
Runoff Model ◽  
German Part

Abstract. The parameters of hydrological models for catchments with few or no discharge records can be estimated using regional information. One can assume that catchments with similar characteristics show a similar hydrological behaviour and thus can be modeled using similar model parameters. Therefore a regionalisation of the hydrological model parameters on the basis of catchment characteristics is plausible. However, due to the non-uniqueness of the rainfall-runoff model parameters (equifinality), a workflow of regional parameter estimation by model calibration and a subsequent fit of a regional function is not appropriate. In this paper a different approach for the transfer of entire parameter sets from one catchment to another is discussed. Parameter sets are considered as tranferable if the corresponding model performance (defined as the Nash-Sutclife efficiency) on the donor catchment is good and the regional statistics: means and variances of annual discharges estimated from catchment properties and annual climate statistics for the recipient catchment are well reproduced by the model. The methodology is applied to a set of 16 catchments in the German part of the Rhine catchments. Results show that the parameters transfered according to the above criteria perform well on the target catchments.

scholarly journals Fully distributed rainfall-runoff modeling using spatial-temporal graph neural network

2022 ◽  
Author(s):  
Zhongrun Xiang ◽  
Ibrahim Demir
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Spatial Information ◽  
Model Performance ◽  
Grid Cell ◽  
Great Promise ◽  
Rainfall Runoff ◽  
Streamflow Forecasting ◽  
Runoff Modeling ◽  
Runoff Model

Recent studies using latest deep learning algorithms such as LSTM (Long Short-Term Memory) have shown great promise in time-series modeling. There are many studies focusing on the watershed-scale rainfall-runoff modeling or streamflow forecasting, often considering a single watershed with limited generalization capabilities. To improve the model performance, several studies explored an integrated approach by decomposing a large watershed into multiple sub-watersheds with semi-distributed structure. In this study, we propose an innovative physics-informed fully-distributed rainfall-runoff model, NRM-Graph (Neural Runoff Model-Graph), using Graph Neural Networks (GNN) to make full use of spatial information including the flow direction and geographic data. Specifically, we applied a time-series model on each grid cell for its runoff production. The output of each grid cell is then aggregated by a GNN as the final runoff at the watershed outlet. The case study shows that our GNN based model successfully represents the spatial information in predictions. NRM-Graph network has shown less over-fitting and a significant improvement on the model performance compared to the baselines with spatial information. Our research further confirms the importance of spatially distributed hydrological information in rainfall-runoff modeling using deep learning, and we encourage researchers to incorporate more domain knowledge in modeling.

scholarly journals Value of uncertain streamflow observations for hydrological modelling

2018 ◽  
Vol 22 (10) ◽  
pp. 5243-5257 ◽  
Author(s):  
Simon Etter ◽  
Barbara Strobl ◽  
Jan Seibert ◽  
H. J. Ilja van Meerveld
Keyword(s):  
Time Series ◽  
Model Calibration ◽  
Model Performance ◽  
Error Distribution ◽  
Random Errors ◽  
Ungauged Catchments ◽  
Error Distributions ◽  
Data Points ◽  
Log Normal ◽  
Synthetic Time Series

Abstract. Previous studies have shown that hydrological models can be parameterised using a limited number of streamflow measurements. Citizen science projects can collect such data for otherwise ungauged catchments but an important question is whether these observations are informative given that these streamflow estimates will be uncertain. We assess the value of inaccurate streamflow estimates for calibration of a simple bucket-type runoff model for six Swiss catchments. We pretended that only a few observations were available and that these were affected by different levels of inaccuracy. The level of inaccuracy was based on a log-normal error distribution that was fitted to streamflow estimates of 136 citizens for medium-sized streams. Two additional levels of inaccuracy, for which the standard deviation of the error distribution was divided by 2 and 4, were used as well. Based on these error distributions, random errors were added to the measured hourly streamflow data. New time series with different temporal resolutions were created from these synthetic streamflow time series. These included scenarios with one observation each week or month, as well as scenarios that are more realistic for crowdsourced data that generally have an irregular distribution of data points throughout the year, or focus on a particular season. The model was then calibrated for the six catchments using the synthetic time series for a dry, an average and a wet year. The performance of the calibrated models was evaluated based on the measured hourly streamflow time series. The results indicate that streamflow estimates from untrained citizens are not informative for model calibration. However, if the errors can be reduced, the estimates are informative and useful for model calibration. As expected, the model performance increased when the number of observations used for calibration increased. The model performance was also better when the observations were more evenly distributed throughout the year. This study indicates that uncertain streamflow estimates can be useful for model calibration but that the estimates by citizen scientists need to be improved by training or more advanced data filtering before they are useful for model calibration.

scholarly journals Rainfall-Runoff Modelling Using Hydrological Connectivity Index and Artificial Neural Network Approach

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 212 ◽  
Author(s):  
Haniyeh Asadi ◽  
Kaka Shahedi ◽  
Ben Jarihani ◽  
Roy Sidle
Keyword(s):  
Time Series ◽  
Model Performance ◽  
Connectivity Index ◽  
Data Driven ◽  
Hydrological Connectivity ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Climatic Parameters ◽  
River Catchment ◽  
Monthly Runoff

The input selection process for data-driven rainfall-runoff models is critical because input vectors determine the structure of the model and, hence, can influence model results. Here, hydro-geomorphic and biophysical time series inputs, including Normalized Difference Vegetation Index (NDVI) and Index of Connectivity (IC; a type of hydrological connectivity index), in addition to climatic and hydrologic inputs were assessed. Selected inputs were used to develop Artificial Neural Networks (ANNs) in the Haughton River catchment and the Calliope River catchment, Queensland, Australia. Results show that incorporating IC as a hydro-geomorphic parameter and remote sensing NDVI as a biophysical parameter, together with rainfall and runoff as hydro-climatic parameters, can improve ANN model performance compared to ANN models using only hydro-climatic parameters. Comparisons amongst different input patterns showed that IC inputs can contribute to further improvement in model performance, than NDVI inputs. Overall, ANN model simulations showed that using IC along with hydro-climatic inputs noticeably improved model performance in both catchments, especially in the Calliope catchment. This improvement is indicated by a slight increase (9.77% and 11.25%) in the Nash–Sutcliffe efficiency and noticeable decrease (24.43% and 37.89%) in the root mean squared error of monthly runoff from Haughton River and Calliope River, respectively. Here, we demonstrate the significant effect of hydro-geomorphic and biophysical time series inputs for estimating monthly runoff using ANN data-driven models, which are valuable for water resources planning and management.

scholarly journals Effectiveness of a regional model calibrated to different parts of a flow regime in regionalisation

2015 ◽  
Vol 12 (7) ◽  
pp. 7057-7098
Author(s):  
H. S. Kim
Keyword(s):  
Flow Regime ◽  
Model Performance ◽  
Balance Model ◽  
Rainfall Runoff ◽  
Regional Models ◽  
Response Dynamics ◽  
Dry Period ◽  
Ungauged Catchments ◽  
Different Parts ◽  
Calibration Approach

Abstract. The objective of this study was to reduce the parameter uncertainty which has an effect on the identification of the relationship between the catchment characteristics and the catchment response dynamics in ungauged catchments. A water balance model calibrated to represent the rainfall runoff characteristics over long time scales had a potential limitation in the modelling capacity to accurately predict the hydrological effects of non-stationary catchment response dynamics under different climate conditions (distinct wet and dry periods). The accuracy and precision of hydrological modelling predictions was assessed to yield a better understanding for the potential improvement of the model's predictability. In the assessment of model structure suitability to represent the non-stationary catchment response characteristics, there was a flow-dependent bias in the runoff simulations. In particular, over-prediction of the streamflow was dominant for the dry period. The poor model performance during the dry period was associated with the largely different impulse response estimates for the entire period and the dry period. The refined calibration approach was established based on assessment of model deficiencies. The rainfall–runoff models were separately calibrated to different parts of the flow regime, and the calibrated models for the separated time series were used to establish the regional models of relevant parts of the flow regime (i.e. wet and dry periods). The effectiveness of the parameter values for the refined approach in regionalisation was evaluated through investigating the accuracy of predictions of the regional models. The predictability was demonstrated using only the dry period to highlight the improvement in model performance easily veiled by the performance of the model for the whole period. The regional models from the refined calibration approach clearly enhanced the hydrological behaviour by improving the identification of the relationships between the catchment attributes and the catchment response dynamics representing the time constants in fitting recession parts of hydrograph (i.e. improving the parameter identifiability representing the different behaviour of the catchment) in regionalisation.

scholarly journals A Review on Rainfall Runoff Simulation at Ungauged Catchment

2018 ◽  
Vol 7 (4.35) ◽  
pp. 162
Author(s):  
W.N.C.W. Zanial ◽  
M.A. Malek ◽  
M.N.M. Reba
Keyword(s):  
Alternative Methods ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Ungauged Catchments ◽  
Ungauged Catchment ◽  
Rain Gauges ◽  
Other Information ◽  
Physically Based ◽  
Set Up

Ungauged catchment occurs when no runoff data are available or when very few ground rain gauges are located in a huge catchment.  For these catchments, the parameters to be used in rainfall‐runoff models cannot be attained just by adjusting runoff information and thus should be procured by different techniques. Show parameters that require orientation are normally transposed from comparable measured catchments. The rainfall runoff simulation is very important to estimate and predict the flow in ungauged catchment. This investigation reviews ideas to differentiate hydrological comparability for transposing parameters from a gauged to an ungauged catchment. Model parameters that are physically based are generally derived from other information close to the ungauged catchment of intrigue. The primary challenge with rainfall‐runoff demonstrating in ungauged catchments is the absence of neighborhood ground precipitation and streamflow information to be utilized in aligning the proposed show parameters. Parameter alignment is useful since adjustment can represent the impacts of hydrological set up in a specific catchment. Since hydrological models are especially reliant on their limit conditions, the alignment practice directed can modify the predispositions of info information utilized. Parameters' adjustment can fundamentally improve the execution of rainfall‐runoff models since it included media properties of soil and vegetation which are exceptionally heterogeneous and basically are in every case inadequately known. Alternative methods for ungauged catchments are required which are the subject of this study. This study summarizes the important methods used in an ungauged catchments, discusses the issues of using satellite data as a substitute input to rainfall‐runoff models and its comparison with point scale ground data.

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