A Space-Time Modeling Framework for Projection of Seasonal Streamflow Extremes
Journal Article
Overview
abstract
We developed a space-time model to project seasonal streamflow extremes; on a river network for at several lead times. In this, the extremes –; 3-day maximum streamflow - at each gauge location on the network are; assumed to be realized from a Generalized Extreme Value (GEV); distribution with temporal non-stationary parameters. The parameters are; modeled as a linear function of suitable covariates. In addition, the; spatial dependence of the extremes across the network is modeled via a; Gaussian copula. The parameters of the non-stationary GEV at each; location are estimated via maximum likelihood, whereas those of the; Copula are estimated via maximum pseudo-likelihood. Best subset of; covariates are selected using AIC. Ensembles of streamflow in time,; which are based on the varying temporal covariates and from the Copula,; are generated, consequently, capturing the spatial and temporal; variability and the attendant uncertainty. We applied this framework to; project spring (May-Jun) season 3-day maximum flow at seven gauges in; the Upper Colorado River Basin (UCRB) network, at 0 ~ 3; months lead time. In this basin, almost all of the annual flow and; extremes that cause severe flooding, arrives during the spring season as; a result of melting of snow accumulated during the preceding winter; season. As potential covariates, we used indices of large scale climate; teleconnection – ENSO, AMO, and PDO, regional mean snow water; equivalent and temperature from the preceding winter season. The skill; of the probabilistic projections of flow extremes is assessed by rank; histograms and skill scores such as CRPSS and ES for marginal and; spatial performance. We also evaluate the utility of Gaussian Copula by; computing spatial threshold exceedance probabilities compared to a model; without the Copula – i.e. independent model at each gauge. The; validation indicates that the model is able to capture the space-time; variability of flow extremes very well, and the skills increase with; decreasing lead time. Also the use of climate variables enhances skill; relative to using just the snow information. The median projections and; their uncertainties are highly consistent with the observations with a; Gaussian copula than without it, indicating the role of spatial; dependence. This framework will be of use in long leading planning of; flood risk mitigation strategies.
