@misc{PilzDelgadoVossetal.2019,
  author    = {Pilz, Tobias and Delgado, Jos{\´e} Miguel Martins and Voss, Sebastian and Vormoor, Klaus Josef and Francke, Till and Cunha Costa, Alexandre and Martins, Eduardo and Bronstert, Axel},
  title     = {Seasonal drought prediction for semiarid northeast Brazil},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {702},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-42795},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-427950},
  pages     = {21},
  year      = {2019},
  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 approach. 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 sensitivity of model prediction performance to rainfall forecast quality. Within the scope of this study, the statistical model proved to be the more straightforward approach for predictions of 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 can be worthwhile. Furthermore, the application of innovative data products, such as remote sensing data, and operational model correction methods, like data assimilation, may allow for an enhanced exploitation of the advanced capabilities of process-based hydrological models.},
  language  = {en}
}
@article{PilzDelgadoVossetal.2019,
  author    = {Pilz, Tobias and Delgado, Jos{\´e} Miguel Martins and Voss, Sebastian and Vormoor, Klaus Josef and Francke, Till and Cunha Costa, Alexandre and Martins, Eduardo and Bronstert, Axel},
  title     = {Seasonal drought prediction for semiarid northeast Brazil},
  series = {Hydrology and Earth System Sciences},
  volume    = {23},
  journal   = {Hydrology and Earth System Sciences},
  publisher = {Copernicus Publications},
  address   = {G{\"o}ttingen},
  issn      = {1027-5606},
  doi       = {10.5194/hess-23-1951-2019},
  pages     = {1951 -- 1971},
  year      = {2019},
  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 approach. 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 sensitivity of model prediction performance to rainfall forecast quality. Within the scope of this study, the statistical model proved to be the more straightforward approach for predictions of 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 can be worthwhile. Furthermore, the application of innovative data products, such as remote sensing data, and operational model correction methods, like data assimilation, may allow for an enhanced exploitation of the advanced capabilities of process-based hydrological models.},
  language  = {en}
}
@phdthesis{CunhaCosta2012,
  author    = {Cunha Costa, Alexandre},
  title     = {Analyzing and modelling of flow transmission processes in river-systems with a focus on semi-arid conditions},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-59694},
  school      = {Universit{\"a}t Potsdam},
  year      = {2012},
  abstract  = {One of the major problems for the implementation of water resources planning and management in arid and semi-arid environments is the scarcity of hydrological data and, consequently, research studies. In this thesis, the hydrology of dryland river systems was analyzed and a semi-distributed hydrological model and a forecasting approach were developed for flow transmission processes in river-systems with a focus on semi-arid conditions. Three different sources of hydrological data (streamflow series, groundwater level series and multi-temporal satellite data) were combined in order to analyze the channel transmission losses of a large reach of the Jaguaribe River in NE Brazil. A perceptual model of this reach was derived suggesting that the application of models, which were developed for sub-humid and temperate regions, may be more suitable for this reach than classical models, which were developed for arid and semi-arid regions. Summarily, it was shown that this river reach is hydraulically connected with groundwater and shifts from being a losing river at the dry and beginning of rainy seasons to become a losing/gaining (mostly losing) river at the middle and end of rainy seasons. A new semi-distributed channel transmission losses model was developed, which was based primarily on the capability of simulation in very different dryland environments and flexible model structures for testing hypotheses on the dominant hydrological processes of rivers. This model was successfully tested in a large reach of the Jaguaribe River in NE Brazil and a small stream in the Walnut Gulch Experimental Watershed in the SW USA. Hypotheses on the dominant processes of the channel transmission losses (different model structures) in the Jaguaribe river were evaluated, showing that both lateral (stream-)aquifer water fluxes and ground-water flow in the underlying alluvium parallel to the river course are necessary to predict streamflow and channel transmission losses, the former process being more relevant than the latter. This procedure not only reduced model structure uncertainties, but also reported modelling failures rejecting model structure hypotheses, namely streamflow without river-aquifer interaction and stream-aquifer flow without groundwater flow parallel to the river course. The application of the model to different dryland environments enabled learning about the model itself from differences in channel reach responses. For example, the parameters related to the unsaturated part of the model, which were active for the small reach in the USA, presented a much greater variation in the sensitivity coefficients than those which drove the saturated part of the model, which were active for the large reach in Brazil. Moreover, a nonparametric approach, which dealt with both deterministic evolution and inherent fluctuations in river discharge data, was developed based on a qualitative dynamical system-based criterion, which involved a learning process about the structure of the time series, instead of a fitting procedure only. This approach, which was based only on the discharge time series itself, was applied to a headwater catchment in Germany, in which runoff are induced by either convective rainfall during the summer or snow melt in the spring. The application showed the following important features: • the differences between runoff measurements were more suitable than the actual runoff measurements when using regression models; • the catchment runoff system shifted from being a possible dynamical system contaminated with noise to a linear random process when the interval time of the discharge time series increased; • and runoff underestimation can be expected for rising limbs and overestimation for falling limbs. This nonparametric approach was compared with a distributed hydrological model designed for real-time flood forecasting, with both presenting similar results on average. Finally, a benchmark for hydrological research using semi-distributed modelling was proposed, based on the aforementioned analysis, modelling and forecasting of flow transmission processes. The aim of this benchmark was not to describe a blue-print for hydrological modelling design, but rather to propose a scientific method to improve hydrological knowledge using semi-distributed hydrological modelling. Following the application of the proposed benchmark to a case study, the actual state of its hydrological knowledge and its predictive uncertainty can be determined, primarily through rejected hypotheses on the dominant hydrological processes and differences in catchment/variables responses.},
  language  = {en}
}