Predicting discharge from a complex karst system using the ensemble smoother with multiple data assimilation

Can the ensemble smoother with multiple data assimilation be used to predict discharge in an Alpine karst aquifer? The answer is yes, at least, for the Bossea aquifer studied. The ensemble smoother is used to fit a unit hydrograph simultaneously with other parameters in a hydrologic model, such as b...

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Bibliographic Details
Published in:	Stochastic environmental research and risk assessment Vol. 37; no. 1; pp. 185 - 201
Main Authors:	Pansa, Alessandro, Butera, Ilaria, Gómez-Hernández, J. Jaime, Vigna, Bartolomeo
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 2023 Springer Nature B.V
Subjects:	Aquatic Pollution Aquifers Base flow Chemistry and Earth Sciences Computational Intelligence Computer Science Data assimilation Data collection Discharge Earth and Environmental Science Earth Sciences Environment Flow rates Flow velocity Hydrologic models Hydrology Infiltration Infiltration coefficient Karst Math. Appl. in Environmental Science Mathematical models Melting Original Paper Parameterization Parameters Physics Precipitation Probability Theory and Stochastic Processes Rainfall Snow Snowmelt Spring Spring (season) Statistics for Engineering Stochasticity Unit hydrographs Waste Water Technology Water Management Water Pollution Control
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Summary:	Can the ensemble smoother with multiple data assimilation be used to predict discharge in an Alpine karst aquifer? The answer is yes, at least, for the Bossea aquifer studied. The ensemble smoother is used to fit a unit hydrograph simultaneously with other parameters in a hydrologic model, such as base flow, infiltration coefficient, or snow melting contribution. The fitting uses observed discharge flow rates, daily precipitations, and temperatures to define the model parameters. The data assimilation approach gives excellent results for fitting individual events. After the analysis of 27 such events, two average models are defined to be used to predict flow discharge from precipitation and temperature, one model for prediction during spring (when snow melting has an impact) and another one during autumn, yielding acceptable results, particularly for the fall rainfall events. The lesser performance for the spring events may indicate that the snow melting approximation needs to be revised. The results also show that the parameterization of the infiltration coefficient needs further exploration. Overall, the main conclusion is that the ensemble smoother could be used to define a characteristic “signature” of a karst aquifer to be used in forecast analyses. The reasons for using the ensemble smoother instead of other stochastic approaches are that it is easy to use and explain and provides an estimation of the uncertainty about the predictions.
ISSN:	1436-3240 1436-3259
DOI:	10.1007/s00477-022-02287-y