EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

The impact of a multi-criteria calibration on the performances of the DREAM model

Silvano Fortunato Dal Sasso1, Alonso Pizarro2, Ruodan Zhuang1, Yijian Zeng3, Paolo Nasta4, Nunzio Romano4,5, José Gomis Cebolla6, Felix Frances6, Brigitta Toth7,8, Zhongbo Su3,9, and Salvatore Manfreda10
Silvano Fortunato Dal Sasso et al.
  • 1Department of European and Mediterranean Cultures: Architecture, Environment and Cultural Heritage (DICEM), University of Basilicata, 75100 Matera, Italy.
  • 2Escuela de Ingeniería en Obras Civiles, Universidad Diego Portales, 8370109 Santiago, Chile
  • 3Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands
  • 4Department of Agricultural Sciences, AFBE Division, University of Naples Federico II, Portici, Italy
  • 5Interdepartmental Center for Environmental Research (C.I.R.AM.), University of Naples Federico II, Napoli, Italy
  • 6Research Group of Hydrological and Environmental Modelling (GIHMA), Research Institute of Water and Environmental Engineering, Universidad Politecnica de Valencia, 46022 València, Spain
  • 7Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, H-1022 Budapest, Hungary
  • 8Department of Crop Production and Soil Science, University of Pannonia, 8360 Keszthely, Hungary
  • 9Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of Ministry of Education, School of Water and Environment, Chang’an University, Xi’an, China
  • 10Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy

Water resources observation and modelling are essential to better understand hydrological processes and improve water resource management. However, the reliability of hydrological simulation is strongly controlled by the quality and type of field observations used for the calibration and validation processes. Therefore, it is critical to develop proper strategies for model calibration and validation in order to reduce prediction uncertainties. Standard hydrological calibration relies mainly on the time series of total streamflow at the catchment outlet; nevertheless, this leads to a limited insight into the spatial behaviour of a river basin. In this work, we use simulations from the physically-based distributed DREAM model to discuss the importance of multi-criteria calibration to obtain consistent parameter sets. The calibration methodology exploits a physical based filter to decompose the streamflow times series in two time series referring to the surface component and the baseflow. Therefore, we adopted a multi-criteria calibration procedures which optimizes: (a) the total streamflow measured at the basin outlet (used as a reference study case); b) both the surface runoff and baseflow measured at the basin outlet; and (c) the combination the time series of the two components along with the annual water balance components. In addition, we also explored the use of a lumped parametrization against a spatial parametrization derived from the soil type characteristics of the river basin. In all cases, parameter optimization was carried out using an automatic calibration performed by a genetic algorithm (GA) tool. The study was carried out for two experimental catchments located in Basilicata and Campania regions (Southern Italy). The performed experiments showed that the inclusion of physical information during the calibration process results in a general improvement of model reliability.

This research is a part of iAqueduct project funded under the Water JPI 2018 Joint Call, Closing the Water Cycle Gap – on Sustainable Management of Water Resources - Water Works 2017.

How to cite: Dal Sasso, S. F., Pizarro, A., Zhuang, R., Zeng, Y., Nasta, P., Romano, N., Cebolla, J. G., Frances, F., Toth, B., Su, Z., and Manfreda, S.: The impact of a multi-criteria calibration on the performances of the DREAM model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6588,, 2022.


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