Environmental change is a reason of relevant concern as it is occurring at an unprecedented pace and might increase natural hazards. Moreover, it is deemed to imply a reduced representativity of past experience and data on extreme hydroclimatic events. The latter concern has been epitomized by the statement that “stationarity is dead.” Setting up policies for mitigating natural hazards, including those triggered by floods and droughts, is an urgent priority in many countries, which implies practical activities of management, engineering design, and construction. These latter necessarily need to be properly informed, and therefore, the research question on the value of past data is extremely important. We herein argue that there are mechanisms in hydrological systems that are time invariant, which may need to be interpreted through data inference. In particular, hydrological predictions are based on assumptions which should include stationarity. In fact, any hydrological model, including deterministic and nonstationary approaches, is affected by uncertainty and therefore should include a random component that is stationary. Given that an unnecessary resort to nonstationarity may imply a reduction of predictive capabilities, a pragmatic approach, based on the exploitation of past experience and data is a necessary prerequisite for setting up mitigation policies for environmental risk.

Modeling and mitigating natural hazards: Stationarity is immortal!

MONTANARI, ALBERTO;
2014

Abstract

Environmental change is a reason of relevant concern as it is occurring at an unprecedented pace and might increase natural hazards. Moreover, it is deemed to imply a reduced representativity of past experience and data on extreme hydroclimatic events. The latter concern has been epitomized by the statement that “stationarity is dead.” Setting up policies for mitigating natural hazards, including those triggered by floods and droughts, is an urgent priority in many countries, which implies practical activities of management, engineering design, and construction. These latter necessarily need to be properly informed, and therefore, the research question on the value of past data is extremely important. We herein argue that there are mechanisms in hydrological systems that are time invariant, which may need to be interpreted through data inference. In particular, hydrological predictions are based on assumptions which should include stationarity. In fact, any hydrological model, including deterministic and nonstationary approaches, is affected by uncertainty and therefore should include a random component that is stationary. Given that an unnecessary resort to nonstationarity may imply a reduction of predictive capabilities, a pragmatic approach, based on the exploitation of past experience and data is a necessary prerequisite for setting up mitigation policies for environmental risk.
WATER RESOURCES RESEARCH
Alberto Montanari;Demetris Koutsoyiannis
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/418770
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