Preprints
https://doi.org/10.5194/hess-2021-270
https://doi.org/10.5194/hess-2021-270

  16 Jun 2021

16 Jun 2021

Review status: this preprint is currently under review for the journal HESS.

A space-time Bayesian hierarchical modeling framework for projection of seasonal streamflow extremes

Álvaro Ossandón1,2, Manuela I Brunner3,4, Balaji Rajagopalan1,5, and William Kleiber6 Álvaro Ossandón et al.
  • 1Department of Civil, Environmental and Architectural Engineering, University of Colorado, Boulder CO, USA
  • 2Department of Civil Engineering, Santa Maria University, Valparaiso, Chile
  • 3Research Applications Laboratory, National Center for Atmospheric Research, Boulder CO, USA
  • 4Institute of Earth and Environmental Sciences, University of Freiburg, Freiburg, Germany
  • 5Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder CO, USA
  • 6Department of Applied Mathematics, University of Colorado, Boulder CO, USA

Abstract. Timely projections of seasonal streamflow extremes can be useful for the early implementation of annual flood risk adaptation strategies. However, predicting seasonal extremes is challenging particularly under non-stationary conditions and if extremes are connected in space. The goal of this study is to implement a space-time model for projection of seasonal streamflow extremes that considers the nonstationarity and spatio-temporal dependence of high flows. We develop a space-time model to project seasonal streamflow extremes for several lead times up to 2 months using a Bayesian Hierarchical Modelling (BHM) framework. This model is based on the assumption that streamflow extremes (3-day maxima) at a set of gauge locations are realizations of a Gaussian elliptical copula and generalized extreme value (GEV) margins with nonstationary parameters. These parameters are modeled as a linear function of suitable covariates from the previous season selected using the deviance information criterion (DIC). Finally, the copula is used to generate streamflow ensembles, which capture spatio-temporal variability and uncertainty. We apply this modelling framework to predict 3-day maximum flow in spring (May-June) at seven gauges in the Upper Colorado River Basin (UCRB) with 0 to 2 months lead time. In this basin, almost all extremes that cause severe flooding occur in spring as a result of snowmelt and precipitation. Therefore, we use regional mean snow water equivalent and temperature from the preceding winter season as well as indices of large-scale climate teleconnections – ENSO, AMO, and PDO – as potential covariates for 3-day maximum flow. Our model evaluation, which is based on the comparison of different model versions and the energy skill score, indicates that the model can capture the space-time variability of extreme flow well and that model skill increases with decreasing lead time. We also find that the use of climate variables slightly enhances skill relative to using only snow information. Median projections and their uncertainties are consistent with observations thanks to the representation of spatial dependencies through covariates in the margins and a Gaussian copula. This spatio-temporal modeling framework helps to plan seasonal adaptation and preparedness measures as predictions of extreme spring flows become available 2 months before actual flood occurrence.

Álvaro Ossandón et al.

Status: open (until 26 Aug 2021)

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Álvaro Ossandón et al.

Data sets

Projection of Seasonal Streamflow Extremes for UCRB Dataset Alvaro Ossandon https://doi.org/10.4211/hs.d8c1b413951843cf9be968e9d2a4aa79

Álvaro Ossandón et al.

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Short summary
Timely projections of seasonal streamflow extremes on a river network can be useful for flood risk mitigation. However, this is challenging particularly under space-time non-stationarity. We develop a space-time Bayesian Hierarchical Model (BHM) using temporal climate covariates and Copulas to project seasonal streamflow extremes and the attendant uncertainties. We demonstrate this on the Upper Colorado River Basin to project spring flow extremes using preceding winter’s climate teleconnections.