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Optima Rain Gauge Network Design Based On Multi-Objective Optimization Approach

Bertini, Claudia (författare)
Sapienza Univ Roma, DICEA, Dipartimento Ingn Civile Edile & Ambienlale, Rome, Italy.
Ridolfi, Elena (författare)
Uppsala universitet,Luft-, vatten- och landskapslära
Alfonso, Leonardo (författare)
IHE Delft, Dept Integrated Water Syst & Governance, Inst Water Educ, Hydroinformat Chair, Delft, Netherlands.
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Napolitano, Francesco (författare)
Sapienza Univ Roma, DICEA, Dipartimento Ingn Civile Edile & Ambienlale, Rome, Italy.
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Sapienza Univ Roma, DICEA, Dipartimento Ingn Civile Edile & Ambienlale, Rome, Italy Luft-, vatten- och landskapslära (creator_code:org_t)
AIP Publishing, 2020
2020
Engelska.
Ingår i: International conference on numerical analysis and applied mathematics ICNAAM 2019. - : AIP Publishing. - 9780735440258
  • Konferensbidrag (refereegranskat)
Abstract Ämnesord
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  • Precipitation is a main input to many hydrological applications, such as water management, flood forecasting and hydrological modelling. The goodness of the rainfall field estimation can thus affect their performances. Despite radar-based and satellite-based measurements have nowadays become very common and accurate, rain gauges monitoring stations are still needed. The gauge density and its spatial distribution are two of the key factors influencing the accuracy in precipitation estimation. Even if in the last decades many studies proposed several methodologies for the design of optimal monitoring networks, only few studies use hydrological model performance as a design criterion. The purpose of this study is to define the optimal rain gauge network for the Mignone River catchment (Italy). The optimal network is defined through a multi-objective optimization approach, where the interpolation error of precipitation is minimised and the performance of a hydrological model based on the Width Function Instantaneous Unit Hydrograph theory is maximised. The optimization is run both without and with constraints, which are based on rainfall patterns. A score to choose the best set of points in the Pareto front is presented. The results suggest that there are preferential areas where sensors locations achieve optimal interpolation error and model performance.

Ämnesord

NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Oceanografi, hydrologi och vattenresurser (hsv//swe)
NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Oceanography, Hydrology and Water Resources (hsv//eng)

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