Runoff initiation in a preserved semiarid Caatinga small watershed, Northeastern Brazil

Figueiredo, José Vidal de; Araújo, José Carlos de; Costa, Antônio Celso Spı́nola

doi:10.1002/hyp.10801

Cited by 51 publications

(81 citation statements)

References 35 publications

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“…Average annual rainfall is 518 mm, concentrated in the rainy season between January and May. The monthly average temperatures range from 24 • C to 28 • C. The watershed is covered by dense native vegetation characterized by tree heights typically between 5 and 12 m. More detailed information can be found in [10,16,17].…”

Section: Study Areamentioning

confidence: 99%

Hydrology of a Water‐Limited Forest under Climate Change Scenarios: The Case of the Caatinga Biome, Brazil

Pinheiro

Lier

Bezerra

2017

Forests

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Given the strong interactions between climate and vegetation, climate change effects on natural and agricultural ecosystems are common objects of research. Reduced water availability is predicted to take place across large regions of the globe, including Northeastern Brazil. The Caatinga, a complex tropical water-limited ecosystem and the only exclusively Brazilian biome, prevails as the main natural forest of this region. The aim of this study was to examine the soil-water balance for this biome under a climate-warming scenario and with reduced rainfall. Climate change projections were assessed from regional circulation models earlier applied to the Brazilian territory. A statistical climate data generator was used to compose a synthetic weather dataset, which was later integrated into a hydrological model. Compared to simulations with current climate for the same site, under the scenario with climate change, transpiration was enhanced by 36%, and soil-water evaporation and interception were reduced by 16% and 34%, respectively. The greatest change in soil-water components was observed for deep drainage, accounting only for 2% of the annual rainfall. Soil-plant-atmosphere fluxes seem to be controlled by the top layer (0.0-0.2 m), which provides 80% of the total transpiration, suggesting that the Caatinga forest may become completely soil-water pulse dominated under scenarios of reduced water availability.

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Section: Study Areamentioning

confidence: 99%

Hydrology of a Water‐Limited Forest under Climate Change Scenarios: The Case of the Caatinga Biome, Brazil

Pinheiro

Lier

Bezerra

2017

Forests

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“…The catchment is of 25 crucial importance in terms of water supply for the whole state and has been intensively investigated in numerous studies (e.g. Bronstert et al, 2000;Gaiser et al, 2003;de Araújo et al, 2004;Krol et al, 2006;Mamede et al, 2012;van Oel et al, 2012;de Figueiredo et al, 2016). It covers an area of about 70,000 km 2 with a rural population of 2.7 million.…”

Section: Study Sitementioning

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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“…It is part of the upper Jaguaribe River catchment in the northeast of Brazil within the federal state of Ceará. The area has been investigated in a number of studies, in many cases employing the WASA-SED model, and was thus selected to ensure the suitability of the model for the catchment (Medeiros et al, 2010;Krol et al, 2011;de Araújo and Medeiros, 2013;Bronstert et al, 2014;de Figueiredo et al, 2016). The Benguê catchment drains an area of about 926 km 2 .…”

Section: Study Sitementioning

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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Runoff initiation in a preserved semiarid Caatinga small watershed, Northeastern Brazil

Cited by 51 publications

References 35 publications

Hydrology of a Water‐Limited Forest under Climate Change Scenarios: The Case of the Caatinga Biome, Brazil

Hydrology of a Water‐Limited Forest under Climate Change Scenarios: The Case of the Caatinga Biome, Brazil

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

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