A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow

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 nonstationary conditions and if extremes are correlated in space. The goal of this study is to implement a space–time model for the projection of seasonal streamflow extremes that considers the nonstationarity (interannual variability) and spatiotemporal 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 modeling (BHM) framework. This model is based on the assumption that streamflow extremes (3 d 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 describing the previous season selected using the deviance information criterion (DIC). Finally, the copula is used to generate streamflow ensembles, which capture spatiotemporal variability and uncertainty. We apply this modeling framework to predict 3 d maximum streamflow in spring (May–June) at seven gauges in the Upper Colorado River basin (UCRB) with 0- to 2-month 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 – El Niño–Southern Oscillation, Atlantic Multidecadal Oscillation, and Pacific Decadal Oscillation – as potential covariates for 3 d spring maximum streamflow. 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 in extreme streamflow 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 spatiotemporal modeling framework helps in the planning of seasonal adaptation and preparedness measures as predictions of extreme spring streamflows become available 2 months before actual flood occurrence.

Analysis of high streamflow extremes in climate change studies: How do we calibrate hydrological models?

Climate change impact studies on hydrological extremes often rely on the use of hydrological models with parameters inferred by using observational data of daily streamflow. In this work we show that this is an error prone procedure when the interest is to develop reliable Empirical Cumulative Distribution Function curves of annual streamflow maximum. As an alternative approach we introduce a methodology, coined Hydrological Calibration of eXtremes (HyCoX), in which the calibration of the hydrological model is carried out by directly targeting the probability distribution of high flow extremes. In particular, hydrological simulations conducted during a reference period, as driven by climate models’ outputs, are constrained to maximize the probability that the modeled and observed high flow extremes belong to the same population. The application to the Adige river catchment (southeastern Alps, Italy) by means of HYPERstreamHS, a distributed hydrological model, showed that this procedure preserves statistical coherence and produce reliable quantiles of the annual maximum streamflow to be used in assessment studies.

CO2, nitrogen deposition and a discontinuous climate response drive water use efficiency in global forests

Reduced stomatal conductance is a common plant response to rising atmospheric CO2 and increases water use efficiency (W). At the leaf-scale, W depends on water and nitrogen availability in addition to atmospheric CO2. In hydroclimate models W is a key driver of rainfall, droughts, and streamflow extremes. We used global climate data to derive Aridity Indices (AI) for forests over the period 1965–2015 and synthesised those with data for nitrogen deposition and W derived from stable isotopes in tree rings. AI and atmospheric CO2 account for most of the variance in W of trees across the globe, while cumulative nitrogen deposition has a significant effect only in regions without strong legacies of atmospheric pollution. The relation of aridity and W displays a clear discontinuity. W and AI are strongly related below a threshold value of AI ≈ 1 but are not related where AI > 1. Tree ring data emphasise that effective demarcation of water-limited from non-water-limited behaviour of stomata is critical to improving hydrological models that operate at regional to global scales.

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

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.

Network-based approach to unravel sea-surface temperature and streamflow connectivity at different timescales

<p>Understanding the interactions between oceanic conditions and streamflow can deepen our knowledge on hydrological aspects. Most studies exploring this relationship only focus on seasonal or annual scales. However, various atmospheric and oceanic phenomena occur at different timescales and need to be accounted to attribute connectivity between sea-surface temperature and streamflow to specific oceanic and climate processes. In this study, we have investigated the influence of sea-surface temperature (SST) on German streamflow at timescales ranging from sub-seasonal to decadal. We apply wavelets' concepts to decompose the time series into multiple frequency signals and fed into complex networks to identify spatial connections. We employ degree centrality metric and average link distance concepts to interpret the outcomes of coupled SST-Streamflow networks. Our results indicate that the SST anomaly at North Atlantic Ocean region has a stable connection with German streamflow at shorter timescales up to annual scale. We also noticed scale-specific connections in the Pacific, Indian and Southern ocean regions at different timescales ranging from seasonal to decadal scale. Scale-specific connections exhibited by the streamflow stations at all timescales makes it difficult to cluster based on degree centrality. We observed that streamflow stations are influenced by short-range local connections at lower timescales and long-range teleconnections at higher time scale. Our preliminary analysis highlight that the low frequent streamflow extremes have long-range connections, usually not captured at the original scale, and geographical proximity plays a role in high-frequency streamflow signals, according to Tobler’s first law of geography. The results obtained from this study reconfirms reported existing streamflow influences and helped gain insights over other possible large-scale climatic influences.</p>