Process-based modelling of erosion, sediment transport and reservoir siltation in mesoscale semi-arid catchments

Bronstert, Axel; Araújo, José Carlos de; Batalla, Ramón J.; Costa, Antônio Celso Spı́nola; Delgado, José Miguel; Francke, Till; Foerster, Saskia; Guentner, A.; López‐Tarazón, José A.; Mamede, George Leite; Mueller, Eva Nora; Vericat, Damià

doi:10.1007/s11368-014-0994-1

Cited by 46 publications

(41 citation statements)

References 55 publications

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“…Reservoirs can be simulated by treating large strategic reservoirs in an explicit manner while representing smaller ones as lumped water bodies of different size classes to efficiently account for water retention of many small reservoirs in a study region . The model was developed for and successfully applied in the semiarid areas of northeastern Brazil (Medeiros et al, 2010;Krol et al, 2011;de Araújo and Medeiros, 2013;Medeiros et al, 2014) and used for other dryland regions, such 10 as in India (Jackisch et al, 2014) and Spain (Mueller et al, 2009(Mueller et al, , 2010Bronstert et al, 2014).…”

Section: Meteorological Hindcastsmentioning

confidence: 99%

Seasonal drought prediction for semiarid northeast Brazil: About the added value of a process-based hydrological model

Pilz¹,

Delgado²,

Voss³

et al. 2018

Preprint

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Abstract. The semiarid northeast of Brazil is one of the most densely populated dryland regions in the world and recurrently affected by severe droughts. Thus, reliable seasonal forecasts of streamflow and reservoir storage are of high value for water managers. Such forecasts can be generated by applying either hydrological models representing underlying processes or statistical relationships exploiting correlations among meteorological and hydrological variables. This work evaluates and compares the performances of seasonal reservoir storage forecasts derived by a process-based hydrological model and a statistical Driven by observations, both models achieve similar simulation accuracies. In a hindcast experiment, however, the accuracy of estimating regional reservoir storages was considerably lower using the process-based hydrological model, whereas the resolution and reliability of drought event predictions were similar by both approaches. Further investigations regarding the deficiencies of the process-based model revealed a significant influence of antecedent wetness conditions and a higher 10 sensitivity of model prediction performance towards rainfall forecast quality.Based on the findings of this study, we recommend using a statistical approach for merely predicting reservoir level and drought events at regionally and monthly aggregated scales. However, for forecasts at finer scales of space and time or for the investigation of underlying processes, the costly initialisation and application of a process-based model is worthwhile. Furthermore, the application of innovative data products, such as remote sensing data, and operational model correction methods, like 15 data assimilation, may allow for an enhanced exploitation of the advanced capabilities of process-based hydrological models.

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Section: Meteorological Hindcastsmentioning

confidence: 99%

Seasonal drought prediction for semiarid northeast Brazil: About the added value of a process-based hydrological model

Pilz¹,

Delgado²,

Voss³

et al. 2018

Preprint

Self Cite

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show abstract

“…As hydrologic connectivity of the landscape can be represented in a realistic manner, the model has been used for a number of studies investigating runoff redistribution and erosion processes (e.g. Güntner and Bronstert, 2004;Mueller et al, 2010;Medeiros et al, 2010;Bronstert et al, 2014).…”

Section: Discretisation Approaches In Semi-distributed Hydrological Mmentioning

confidence: 99%

lumpR 2.0.0: an R package facilitating landscape discretisation for hillslope-based hydrological models

2017

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Abstract. The characteristics of a landscape pose essential factors for hydrological processes. Therefore, an adequate representation of the landscape of a catchment in hydrological models is vital. However, many of such models exist differing, amongst others, in spatial concept and discretisation. The latter constitutes an essential pre-processing step, for which many different algorithms along with numerous software implementations exist. In that context, existing solutions are often model specific, commercial, or depend on commercial back-end software, and allow only a limited or no workflow automation at all.Consequently, a new package for the scientific software and scripting environment R, called lumpR, was developed. lumpR employs an algorithm for hillslope-based landscape discretisation directed to large-scale application via a hierarchical multi-scale approach. The package addresses existing limitations as it is free and open source, easily extendible to other hydrological models, and the workflow can be fully automated. Moreover, it is user-friendly as the direct coupling to a GIS allows for immediate visual inspection and manual adjustment. Sufficient control is furthermore retained via parameter specification and the option to include expert knowledge. Conversely, completely automatic operation also allows for extensive analysis of aspects related to landscape discretisation.In a case study, the application of the package is presented. A sensitivity analysis of the most important discretisation parameters demonstrates its efficient workflow automation. Considering multiple streamflow metrics, the employed model proved reasonably robust to the discretisation parameters. However, parameters determining the sizes of subbasins and hillslopes proved to be more important than the others, including the number of representative hillslopes, the number of attributes employed for the lumping algorithm, and the number of sub-discretisations of the representative hillslopes.

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“…Recent reviews highlighted the development of empirical and physically based distributed models (see Merritt et al, 2003, Aksoy, 2005Francke et al, 2008 for the Non-parametric method; Delgado et al, 2010 for the HYLUC model; Bronstert et al, 2014 for the WASA model; Bussi et al, 2014 for TETIS;Molina-Navarro et al, 2014 for SWAT model). For instance, it was pointed out that simple empirical lumped models cannot easily describe the complexity of the sediment cycle.…”

Section: Introductionmentioning

confidence: 99%

Application of a distributed 2D overland flow model for rainfall/runoff and erosion simulation in a Mediterranean watershed

Juez

Tena²,

Fernández-Pato

et al. 2018

CIG

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Soil erosion has reemerged as an environmental problem associated with climate change that requires the help of simulation tools for forecasting future consequences. This topic becomes even more relevant in Mediterranean catchments due to the highly variable and irregular rainfall regime. Hence, an approach that includes the rainfall/runoff and erosion phenomena is required for quantifying the amount of soil the catchments are transferring to the rivers. As the calibration process of the infiltration and erosion parameters can become cumbersome in terms of iterations to the optimal values to fit experimental data, a Simplified Catchment Model (SCM) is introduced as a first approach. The set of tuning constants that provides the best fit are used as input for re-calibrating the parameters by means of the simulation of the real catchment. The modeling effort here presented opens its application to the analysis of the hydro-sedimentary processes at larger temporal and spatial scales.

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Process-based modelling of erosion, sediment transport and reservoir siltation in mesoscale semi-arid catchments

Cited by 46 publications

References 55 publications

Seasonal drought prediction for semiarid northeast Brazil: About the added value of a process-based hydrological model

Seasonal drought prediction for semiarid northeast Brazil: About the added value of a process-based hydrological model

lumpR 2.0.0: an R package facilitating landscape discretisation for hillslope-based hydrological models

Application of a distributed 2D overland flow model for rainfall/runoff and erosion simulation in a Mediterranean watershed

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