scholarly journals A coupled stochastic rainfall-evapotranspiration model for hydrological impact analysis

2017 ◽  
Author(s):  
Minh Tu Pham ◽  
Hilde Vernieuwe ◽  
Bernard De Baets ◽  
Niko E. C. Verhoest
Keyword(s):  
Time Series ◽  
Impact Analysis ◽  
Hydrological Model ◽  
Hydrological Cycle ◽  
Rainfall Runoff ◽  
Hydrological Impact ◽  
Long Time ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

Abstract. A hydrological impact analysis concerns the study of the consequences of certain scenarios on one or more variables or fluxes in the hydrological cycle. In such exercise, discharge is often considered, as especially extreme high discharges often cause damage due to the coinciding floods. Investigating extreme discharges generally requires long time series of precipitation and evapotranspiration that are used to force a rainfall-runoff model. However, such kind of data may not be available and one should resort to stochastically-generated time series, even though the impact of using such data on the overall discharge, and especially on the extreme discharge events is not well studied. In this paper, stochastically-generated rainfall and coinciding evapotranspiration time series are used to force a simple conceptual hydrological model. The results obtained are comparable to the modelled discharge using observed forcing data. Yet, uncertainties in the modelled discharge increase with an increasing number of stochastically-generated time series used. Notwithstanding this finding, it can be concluded that using a coupled stochastic rainfall-evapotranspiration model has a large potential for hydrological impact analysis.

scholarly journals A coupled stochastic rainfall–evapotranspiration model for hydrological impact analysis

2018 ◽  
Vol 22 (2) ◽  
pp. 1263-1283 ◽  
Author(s):  
Minh Tu Pham ◽  
Hilde Vernieuwe ◽  
Bernard De Baets ◽  
Niko E. C. Verhoest
Keyword(s):  
Time Series ◽  
Impact Analysis ◽  
Hydrological Cycle ◽  
Rainfall Runoff ◽  
Hydrological Impact ◽  
Long Time ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Vine Copulas ◽  
Runoff Model

Abstract. A hydrological impact analysis concerns the study of the consequences of certain scenarios on one or more variables or fluxes in the hydrological cycle. In such an exercise, discharge is often considered, as floods originating from extremely high discharges often cause damage. Investigating the impact of extreme discharges generally requires long time series of precipitation and evapotranspiration to be used to force a rainfall-runoff model. However, such kinds of data may not be available and one should resort to stochastically generated time series, even though the impact of using such data on the overall discharge, and especially on the extreme discharge events, is not well studied. In this paper, stochastically generated rainfall and corresponding evapotranspiration time series, generated by means of vine copulas, are used to force a simple conceptual hydrological model. The results obtained are comparable to the modelled discharge using observed forcing data. Yet, uncertainties in the modelled discharge increase with an increasing number of stochastically generated time series used. Notwithstanding this finding, it can be concluded that using a coupled stochastic rainfall–evapotranspiration model has great potential for hydrological impact analysis.

scholarly journals The Impact of the Variability of Precipitation and Temperatures on the Efficiency of a Conceptual Rainfall-Runoff Model

2016 ◽  
Vol 24 (4) ◽  
pp. 1-7 ◽  
Author(s):  
P. Sleziak ◽  
J. Szolgay ◽  
K. Hlavčová ◽  
J. Parajka
Keyword(s):  
Hydrological Model ◽  
Differential Evolution Algorithm ◽  
Rainfall Runoff ◽  
University Of Technology ◽  
Wide Range ◽  
Evolution Algorithm ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
The Relationship ◽  
Runoff Model

AbstractThe main objective of the paper is to understand how the model’s efficiency and the selected climatic indicators are related. The hydrological model applied in this study is a conceptual rainfall-runoff model (the TUW model), which was developed at the Vienna University of Technology. This model was calibrated over three different periods between 1981-2010 in three groups of Austrian catchments (snow, runoff, and soil catchments), which represent a wide range of the hydroclimatic conditions of Austria. The model’s calibration was performed using a differential evolution algorithm (Deoptim). As an objective function, we used a combination of the Nash-Sutcliffe coefficient (NSE) and the logarithmic Nash-Sutcliffe coefficient (logNSE). The model’s efficiency was evaluated by Volume error (VE). Subsequently, we evaluated the relationship between the model’s efficiency (VE) and changes in the climatic indicators (precipitation ΔP, air temperature ΔT). The implications of findings are discussed in the conclusion.

scholarly journals Assessment of the Impact of Changes in Deforestation under the Effect of Severe Windstorms on Runoff Conditions in Small River Basins

2019 ◽  
Vol 27 (3) ◽  
pp. 37-43
Author(s):  
Zuzana Štefunková ◽  
Kamila Hlavčová ◽  
Marija Mihaela Labat
Keyword(s):  
River Basin ◽  
Forest Cover ◽  
Hydrological Cycle ◽  
Rainfall Runoff ◽  
Current Topic ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
The Given ◽  
Runoff Model ◽  
Natural Means

Abstract Forests represent the most natural means of retaining water in a basin. Assessing the impact of a forest on a hydrological cycle is a very current topic that hydrologists and water managers deal with. The differences between site conditions and a forest itself, together with various methods of exploring this issue, lead to inconsistent opinions on the extent of the ability of a forest cover to prevent or minimize surface runoff. This article is therefore focused on an assessment of the impact of changes in the composition of a forest under the effect of severe windstorms on runoff conditions in the selected river basin. The most severe windstorms in the last 25 years and their impact on changes in forest cover in the selected area of the Ipoltica River basin have been assessed in this article. The most significant severe windstorm in terms of its impact on changes in the forest communities was Filip, which occurred in the year 2007. Therefore, the impact of changes in forest cover on the given territory was examined for the period after the year 2007. The WetSpa rainfall-runoff model was used to assess the changes.

Exploring signatures-based calibration of hydrological models for prediction in ungauged basins.

2020 ◽  
Author(s):  
Marco Dal Molin ◽  
Dmitri Kavetski ◽  
Mario Schirmer ◽  
Fabrizio Fenicia
Keyword(s):  
Time Series ◽  
Accurate Estimation ◽  
Ungauged Basins ◽  
Rainfall Runoff ◽  
Multiple Sources ◽  
Important Challenge ◽  
Rainfall Runoff Model ◽  
Approximate Bayesian ◽  
Correct Estimation ◽  
Runoff Model

<p>One of the open challenges in catchment hydrology is prediction in ungauged basins (PUB), i.e. being able to predict catchment responses (typically streamflow) when measurements are not available. One of the possible approaches to this problem consists in calibrating a model using catchment response statistics (called signatures) that can be estimated at the ungauged site.<br>An important challenge of any approach to PUB is to produce reliable and precise predictions of catchment response, with an accurate estimation of the uncertainty. In the context of PUB through calibration on regionalized streamflow signatures, there are multiple sources of uncertainty that affect streamflow predictions, which relate to:</p><ul><li>The use streamflow signatures, which, by synthetizing the underlying time series, reduce the information available for model calibration;</li> <li>The regionalization of streamflow signatures, which are not observed, but estimated through some signature regionalization model;</li> <li>The use of a rainfall-runoff model, which carries uncertainties related to input data, parameter values, and model structure.</li> </ul><p>This study proposes an approach that separately accounts for the uncertainty related to the regionalization of the signatures from the other types; the implementation uses Approximate Bayesian Computation (ABC) to infer the parameters of the rainfall-runoff model using stochastic streamflow signatures. <br>The methodology is tested in six sub-catchments of the Thur catchment in Switzerland; results show that the regionalized model produces streamflow time series that are similar to the ones obtained by the classical time-domain calibration, with slightly higher uncertainty but similar fit to the observed data. These results support the proposed approach as a viable method for PUB, with a focus on the correct estimation of the uncertainty.</p>

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 Calculating the hydrological response of a mountain catchment using conventional and unconventional (CML) rainfall observations: the case study of Mallero catchment

2020 ◽  
Author(s):  
Greta Cazzaniga ◽  
Carlo De Michele ◽  
Cristina Deidda ◽  
Michele D'Amico ◽  
Antonio Ghezzi ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Hydrological Modelling ◽  
Rainfall Runoff ◽  
Signal Attenuation ◽  
Large Variability ◽  
Field Reconstruction ◽  
Rain Gauges ◽  
Weather Radars ◽  
Rainfall Runoff Model ◽  
Runoff Model

<p>Rainfall plays a critical role in the hydrological cycle, being the main downward forcing. It is well known that rainfall exhibits large variability in space and time due to the storm dynamics and its interaction with the topography. It is a difficult task to reconstruct the rainfall over an area accurately. Rainfall is usually collected through rain gauges, disdrometers, and weather radars. Rain gauges and disdrometers provide quite accurate measurements of rainfall on the ground, but at a single site, while weather radars provide an indication of rainfall field variability in space, even if their use is restricted to plain areas.</p><p>Recently, unconventional observations have been considered for the monitoring of rainfall. These consist in signal attenuation measurements induced by rain on a mesh of point-to-point commercial microwave links (CML). These data, integrated with the ones collected by a network of conventional rain gauges, can provide further information about rainfall dynamics leading to improvements in hydrological modelling, which requires accurate description of the rainfall field.</p><p>The work we are going to describe is part of MOPRAM (MOnitoring Precipitation through a Network of RAdio links at Microwaves), a scientific project funded by Fondazione Cariplo (see also the EGU abstract of Nebuloni et al., 2020). Here we use rainfall data, obtained both from a rain gauge network and from signal attenuation measurements, into a hydrological model in order to evaluate the improvement in the hydrological modelling due to a better description of the rainfall field. We consider a semi-distributed rainfall-runoff model and we apply it to the Mallero catchment (Western Rhaetian Alps, Northern Italy), with the outlet located in Sondrio. This catchment is equipped with 13 microwave links and a network of 13 rain gauges.</p><p>Firstly, we implement and test the Rain field Reconstruction Algorithm (RRA), which retrieves the 2D rainfall field from CML data through a tomographic inversion technique, developed by D’Amico et al., 2016. By RRA we generate synthetic rainfall maps from attenuation data measured by 13 links located in the Mallero basin, for a few historical events in the period 2016-2019. To improve the accuracy of rainfall field reconstruction, we also integrate the reconstructed maps with on ground data from 13 rain gauges. These maps are used as input to the hydrological rainfall-runoff model. Finally, we compare the observed discharge with the calculated one using the hydrological model and different rainfall inputs.</p>

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.

scholarly journals Comparative assessment of predictions in ungauged basins – Part 3: Runoff signatures in Austria

2013 ◽  
Vol 17 (6) ◽  
pp. 2263-2279 ◽  
Author(s):  
A. Viglione ◽  
J. Parajka ◽  
M. Rogger ◽  
J. L. Salinas ◽  
G. Laaha ◽  
...  
Keyword(s):  
Time Series ◽  
Predictive Performance ◽  
Comparative Assessment ◽  
Ungauged Basins ◽  
Rainfall Runoff ◽  
High Flows ◽  
Seasonal Runoff ◽  
Rainfall Runoff Model ◽  
Predictions In Ungauged Basins ◽  
Runoff Model

Abstract. This is the third of a three-part paper series through which we assess the performance of runoff predictions in ungauged basins in a comparative way. Whereas the two previous papers by Parajka et al. (2013) and Salinas et al. (2013) assess the regionalisation performance of hydrographs and hydrological extremes on the basis of a comprehensive literature review of thousands of case studies around the world, in this paper we jointly assess prediction performance of a range of runoff signatures for a consistent and rich dataset. Daily runoff time series are predicted for 213 catchments in Austria by a regionalised rainfall–runoff model and by Top-kriging, a geostatistical estimation method that accounts for the river network hierarchy. From the runoff time-series, six runoff signatures are extracted: annual runoff, seasonal runoff, flow duration curves, low flows, high flows and runoff hydrographs. The predictive performance is assessed in terms of the bias, error spread and proportion of unexplained spatial variance of statistical measures of these signatures in cross-validation (blind testing) mode. Results of the comparative assessment show that, in Austria, the predictive performance increases with catchment area for both methods and for most signatures, it tends to increase with elevation for the regionalised rainfall–runoff model, while the dependence on climate characteristics is weaker. Annual and seasonal runoff can be predicted more accurately than all other signatures. The spatial variability of high flows in ungauged basins is the most difficult to estimate followed by the low flows. It also turns out that in this data-rich study in Austria, the geostatistical approach (Top-kriging) generally outperforms the regionalised rainfall–runoff model.

scholarly journals Climatic and basin factors affecting the flood frequency curve: PART I – A simple sensitivity analysis based on the continuous simulation approach

2000 ◽  
Vol 4 (3) ◽  
pp. 463-482 ◽  
Author(s):  
A. M. Hashemi ◽  
M. Franchini ◽  
P. E. O’Connell
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Flood Frequency ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Frequency Curve ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Insight Into ◽  
Frequency Curves

Abstract. Regionalized and at-site flood frequency curves exhibit considerable variability in their shapes, but the factors controlling the variability (other than sampling effects) are not well understood. An application of the Monte Carlo simulation-based derived distribution approach is presented in this two-part paper to explore the influence of climate, described by simulated rainfall and evapotranspiration time series, and basin factors on the flood frequency curve (ffc). The sensitivity analysis conducted in the paper should not be interpreted as reflecting possible climate changes, but the results can provide an indication of the changes to which the flood frequency curve might be sensitive. A single site Neyman Scott point process model of rainfall, with convective and stratiform cells (Cowpertwait, 1994; 1995), has been employed to generate synthetic rainfall inputs to a rainfall runoff model. The time series of the potential evapotranspiration (ETp) demand has been represented through an AR(n) model with seasonal component, while a simplified version of the ARNO rainfall-runoff model (Todini, 1996) has been employed to simulate the continuous discharge time series. All these models have been parameterised in a realistic manner using observed data and results from previous applications, to obtain ‘reference’ parameter sets for a synthetic case study. Subsequently, perturbations to the model parameters have been made one-at-a-time and the sensitivities of the generated annual maximum rainfall and flood frequency curves (unstandardised, and standardised by the mean) have been assessed. Overall, the sensitivity analysis described in this paper suggests that the soil moisture regime, and, in particular, the probability distribution of soil moisture content at the storm arrival time, can be considered as a unifying link between the perturbations to the several parameters and their effects on the standardised and unstandardised ffcs, thus revealing the physical mechanism through which their influence is exercised. However, perturbations to the parameters of the linear routing component affect only the unstandardised ffc. In Franchini et al. (2000), the sensitivity analysis of the model parameters has been assessed through an analysis of variance (ANOVA) of the results obtained from a formal experimental design, where all the parameters are allowed to vary simultaneously, thus providing deeper insight into the interactions between the different factors. This approach allows a wider range of climatic and basin conditions to be analysed and reinforces the results presented in this paper, which provide valuable new insight into the climatic and basin factors controlling the ffc. Keywords: stochastic rainfall model; rainfall runoff model; simulation; derived distribution; flood frequency; sensitivity analysis

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