@article{GreeneThirumalaiKearneyetal.2019,
  author    = {Greene, Chad A. and Thirumalai, Kaustubh and Kearney, Kelly A. and Delgado, Jose Miguel Martins and Schwanghart, Wolfgang and Wolfenbarger, Natalie S. and Thyng, Kristen M. and Gwyther, David E. and Gardner, Alex S. and Blankenship, Donald D.},
  title     = {The Climate Data Toolbox for MATLAB},
  series = {Geochemistry, geophysics, geosystems},
  volume    = {20},
  journal   = {Geochemistry, geophysics, geosystems},
  number    = {7},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {1525-2027},
  doi       = {10.1029/2019GC008392},
  pages     = {3774 -- 3781},
  year      = {2019},
  abstract  = {Climate science is highly interdisciplinary by nature, so understanding interactions between Earth processes inherently warrants the use of analytical software that can operate across the disciplines of Earth science. Toward this end, we present the Climate Data Toolbox for MATLAB, which contains more than 100 functions that span the major climate-related disciplines of Earth science. The toolbox enables streamlined, entirely scriptable workflows that are intuitive to write and easy to share. Included are functions to evaluate uncertainty, perform matrix operations, calculate climate indices, and generate common data displays. Documentation is presented pedagogically, with thorough explanations of how each function works and tutorials showing how the toolbox can be used to replicate results of published studies. As a well-tested, well-documented platform for interdisciplinary collaborations, the Climate Data Toolbox for MATLAB aims to reduce time spent writing low-level code, let researchers focus on physics rather than coding and encourage more efficacious code sharing. Plain Language Summary This article describes a collection of computer code that has recently been released to help scientists analyze many types of Earth science data. The code in this toolbox makes it easy to investigate things like global warming, El Nino, or other major climate-related processes such as how winds affect ocean circulation. Although the toolbox was designed to be used by expert climate scientists, its instruction manual is well written, and beginners may be able to learn a great deal about coding and Earth science, simply by following along with the provided examples. The toolbox is intended to help scientists save time, help them ensure their analysis is accurate, and make it easy for other scientists to repeat the results of previous studies.},
  language  = {en}
}
@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}
}