@article{FranckeBaroniBrosinskyetal.2018,
  author    = {Francke, Till and Baroni, Gabriele and Brosinsky, Arlena and Foerster, Saskia and Lopez-Tarazon, Jos{\´e} Andr{\´e}s and Sommerer, Erik and Bronstert, Axel},
  title     = {What Did Really Improve Our Mesoscale Hydrological Model?},
  series = {Water resources research},
  volume    = {54},
  journal   = {Water resources research},
  number    = {11},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0043-1397},
  doi       = {10.1029/2018WR022813},
  pages     = {8594 -- 8612},
  year      = {2018},
  abstract  = {Modelers can improve a model by addressing the causes for the model errors (data errors and structural errors). This leads to implementing model enhancements (MEs), for example, meteorological data based on more monitoring stations, improved calibration data, and/or modifications in process formulations. However, deciding on which MEs to implement remains a matter of expert knowledge. After implementing multiple MEs, any improvement in model performance is not easily attributed, especially when considering different objectives or aspects of this improvement (e.g., better dynamics vs. reduced bias). We present an approach for comparing the effect of multiple MEs based on real observations and considering multiple objectives (MMEMO). A stepwise selection approach and structured plots help to address the multidimensionality of the problem. Tailored analyses allow a differentiated view on the effect of MEs and their interactions. MMEMO is applied to a case study employing the mesoscale hydro-sedimentological model WASA-SED for the Mediterranean-mountainous Isabena catchment, northeast Spain. The investigated seven MEs show diverse effects: some MEs (e.g., rainfall data) cause improvements for most objectives, while other MEs (e.g., land use data) only affect a few objectives or even decrease model performance. Interaction of MEs was observed for roughly half of the MEs, confirming the need to address them in the analysis. Calibration and increasing the temporal resolution showed by far stronger impact than any of the other MEs. The proposed framework can be adopted in other studies to analyze the effect of MEs and, thus, facilitate the identification and implementation of the most promising MEs for comparable cases.},
  language  = {en}
}
@article{CarvalhoBrosinskyFoersteretal.2022,
  author    = {Carvalho, Thayslan and Brosinsky, Arlena and Foerster, Saskia and Teixeira, Adunias and Medeiros, Pedro Henrique Augusto},
  title     = {Reservoir sediment characterisation by diffuse reflectance spectroscopy in a semiarid region to support sediment reuse for soil fertilization},
  series = {Journal of soils and sediments : protection, risk assessment and remediation},
  volume    = {22},
  journal   = {Journal of soils and sediments : protection, risk assessment and remediation},
  publisher = {Springer},
  address   = {Heidelberg},
  issn      = {1439-0108},
  doi       = {10.1007/s11368-022-03281-1},
  pages     = {2557 -- 2577},
  year      = {2022},
  abstract  = {Purpose: Soil erosion by water yields sediment to surface reservoirs, reducing their storage capacities, changing their geometry, and degrading water quality. Sediment reuse, i.e., fertilization of agricultural soils with the nutrient-enriched sediment from reservoirs, has been proposed as a recovery strategy. However, the sediment needs to meet certain criteria. In this study, we characterize sediments from the densely dammed semiarid Northeast Brazil by VNIR-SWIR spectroscopy and assess the effect of spectral resolution and spatial scale on the accuracy of N, P, K, C, electrical conductivity, and clay prediction models. Methods Sediment was collected in 10 empty reservoirs, and physical and chemical laboratory analyses as well as spectral measurements were performed. The spectra, initially measured at 1 nm spectral resolution, were resampled to 5 and 10 nm, and samples were analysed for both high and low spectral resolution at three spatial scales, namely (1) reservoir, (2) catchment, and (3) regional scale. Results Partial least square regressions performed from good to very good in the prediction of clay and electrical conductivity from reservoir (<40 km(2)) to regional (82,500 km(2)) scales. Models for C and N performed satisfactorily at the reservoir scale, but degraded to unsatisfactory at the other scales. Models for P and K were more unstable and performed from unsatisfactorily to satisfactorily at all scales. Coarsening spectral resolution by up to 10 nm only slightly degrades the models' performance, indicating the potential of characterizing sediment from spectral data captured at lower resolutions, such as by hyperspectral satellite sensors. Conclusion: By reducing the costly and time-consuming laboratory analyses, the method helps to promote the sediment reuse as a practice of soil and water conservation.},
  language  = {en}
}