scholarly journals Observational Uncertainty in Hydrological Modelling using Data Depth

2017 ◽  
Vol 19 (3) ◽  
pp. 489-497
Keyword(s):  
Hydrological Model ◽  
Meteorological Data ◽  
River Basin Management ◽  
Hydrological Modelling ◽  
Data Depth ◽  
Basin Management ◽  
Flow Generation ◽  
Observational Uncertainty ◽  
Using Data ◽  
Quantifying Uncertainty

For any river basin management, one needs tools to predict runoff at different time and spatial resolutions. Hydrological models are tools which account for the storage, flow of water and water balance in a watershed, which include exchanges of water and energy within the earth, atmosphere and oceans and utilise metrological data to generate flow. There are several sources of error in meteorological data, namely, through measurement at point level, interpolation, etc. When an erroneous input is passed to a model, one cannot expect an error free output from the prediction. Every prediction is associated with uncertainty. Quantification of these uncertainties is of prime importance in real world forecasting. In this study, an attempt has been made to study uncertainty associated with hydrological modelling, using the idea of data depth. To see the effect of uncertainty in rainfall on flow generation through a model, the input to a model was altered by adding an error and a different realisation was made. A Monte Carlo simulation generated a large number of hydrological model parameter sets drawn from the uniform distribution. The model was run using these parameters for each realisation of the rainfall. The parameters which are good for different realisations are more likely to be good parameters sets. For each parameter set, data depth was calculated and a likelihood was assigned to each parameter set based on the depth values. Based on this, the frequency distribution of the likelihood was analysed as well. The results show that uncertainty in hydrological modelling are multiplicative. The proposed methodology to assign prediction uncertainty is demonstrated using the ‘TopNet’ model for the Waipara river catchment located in the central east of the South Island, New Zealand. The results of this study will be helpful in calibration of hydrological model and in quantifying uncertainty in the prediction.

Parameter transferability between multiple gridded input datasets challenges hydrological model performance under changing climate

2021 ◽  
Author(s):  
Moctar Dembélé ◽  
Bettina Schaefli ◽  
Grégoire Mariéthoz
Keyword(s):  
West Africa ◽  
Large Scale ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Model Performance ◽  
Hydrologic Model ◽  
Hydrological Modelling ◽  
Climate Change Scenarios ◽  
Depth Analysis ◽  
Input Dataset

<p>The diversity of remotely sensed or reanalysis-based rainfall data steadily increases, which on one hand opens new perspectives for large scale hydrological modelling in data scarce regions, but on the other hand poses challenging question regarding parameter identification and transferability under multiple input datasets. This study analyzes the variability of hydrological model performance when (1) a set of parameters is transferred from the calibration input dataset to a different meteorological datasets and reversely, when (2) an input dataset is used with a parameter set, originally calibrated for a different input dataset.</p><p>The research objective is to highlight the uncertainties related to input data and the limitations of hydrological model parameter transferability across input datasets. An ensemble of 17 rainfall datasets and 6 temperature datasets from satellite and reanalysis sources (Dembélé et al., 2020), corresponding to 102 combinations of meteorological data, is used to force the fully distributed mesoscale Hydrologic Model (mHM). The mHM model is calibrated for each combination of meteorological datasets, thereby resulting in 102 calibrated parameter sets, which almost all give similar model performance. Each of the 102 parameter sets is used to run the mHM model with each of the 102 input datasets, yielding 10404 scenarios to that serve for the transferability tests. The experiment is carried out for a decade from 2003 to 2012 in the large and data-scarce Volta River basin (415600 km2) in West Africa.</p><p>The results show that there is a high variability in model performance for streamflow (mean CV=105%) when the parameters are transferred from the original input dataset to other input datasets (test 1 above). Moreover, the model performance is in general lower and can drop considerably when parameters obtained under all other input datasets are transferred to a selected input dataset (test 2 above). This underlines the need for model performance evaluation when different input datasets and parameter sets than those used during calibration are used to run a model. Our results represent a first step to tackle the question of parameter transferability to climate change scenarios. An in-depth analysis of the results at a later stage will shed light on which model parameterizations might be the main source of performance variability.</p><p>Dembélé, M., Schaefli, B., van de Giesen, N., & Mariéthoz, G. (2020). Suitability of 17 rainfall and temperature gridded datasets for large-scale hydrological modelling in West Africa. Hydrology and Earth System Sciences (HESS). https://doi.org/10.5194/hess-24-5379-2020</p>

scholarly journals Regionalization of hydrological model parameters using data depth

2011 ◽  
Vol 42 (5) ◽  
pp. 356-371 ◽  
Author(s):  
András Bárdossy ◽  
Shailesh Kumar Singh
Keyword(s):  
Hydrological Model ◽  
Data Depth ◽  
Model Parameters ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Catchment Characteristics ◽  
Rainfall Runoff Model ◽  
Using Data ◽  
Runoff Model

The parameters of hydrological models with no or short discharge records can only be estimated using regional information. We can assume that catchments with similar characteristics show a similar hydrological behaviour. A regionalization of hydrological model parameters on the basis of catchment characteristics is therefore plausible. However, due to the non-uniqueness of the rainfall/runoff model parameters (equifinality), a procedure of a regional parameter estimation by model calibration and a subsequent fit of a regional function is not appropriate. In this paper, a different procedure based on the depth function and convex combinations of model parameters is introduced. Catchment characteristics to be used for regionalization can be identified by the same procedure. Regionalization is then performed using different approaches: multiple linear regression using the deepest parameter sets and convex combinations. The assessment of the quality of the regionalized models is also discussed. An example of 28 British catchments illustrates the methodology.

scholarly journals ALANA RIVER BASIN MANAGEMENT

2021 ◽  
Vol 52 (6) ◽  
pp. 1304-1317
Author(s):  
Arkhawan Jawhar Sharef
Keyword(s):  
River Basin ◽  
Water Demand ◽  
Water Distribution ◽  
River Basin Management ◽  
Planning System ◽  
Basin Management ◽  
Domestic Water ◽  
Weap Model ◽  
The Government ◽  
Using Data

The main aim of this study is to highlight how the Alana River Basin (ARB) will meet future water needs and optimal monthly water distribution policies. The Water Evaluation and Planning System (WEAP) model was applied in order to determine the ARB and the operation plan policy using data for the past 16 years (2000-2015). The model result determined that the current regulations and practices regarding water use and pollution patterns are inadequate. An ideal scheme has been developed and implemented for various scenarios such as domestic water needs, irrigation water, and tourism. Four scenarios have been considered in this study, which are basic scenarios, a higher population growth scenario, a severe flow requirements scenario, and a scenario of adding dams. Comparisons show that the demand for water from April to September is high due to the agriculture season and the high temperature. The maximum demand for water is 3 million cubic meters, which occurs in August, while the proposed dam provides only 2 million cubic meters. In addition, the maximum demand for domestic water is 0.4733 MCM and the minimum is 0.36978 MCM, which is roughly the same level of water demand for every month. Accordingly, the government should start supplementing the Gali Bale dam. However, it is not a sufficient dam to cover the water demand, so the construction of additional dams is strongly suggested.

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