Changes in Climate and Land Cover Affect Seasonal Streamflow Forecasts in the Rio Grande Headwaters

Penn, Colin A.; Clow, David W.; Sexstone, G. A.; Murphy, Sheila F.

doi:10.1111/1752-1688.12863

Cited by 11 publications

(10 citation statements)

References 80 publications

(112 reference statements)

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“…Rather than reactively rescuing stranded fish during intermittency, conservation actions need to examine ways to limit the ultimate causes of streamflow intermittency in order to achieve recovery goals. Such proactive efforts face multiple challenges in the MRG, chiefly declining precipitation [23,94] and surface flows [25,95] coupled with over-appropriation of water [96]. However, proactive actions are likely more effective than reactive actions.…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, the MRG fish assemblage formerly included species like shovelnose sturgeon Scaphirynchus platorhynchus and American eel Anguilla rostrata that would be intolerant of frequent drying [20]. More recently, frequent supra-seasonal drought [21], declines in snowpack [22,23], and human-mediated water abstraction of up to 95% [24,25] in the MRG Basin have resulted in long periods of summer streamflow intermittency, e.g., >100 days and >80 km in extreme years, averaging around 38 days and 35 km annually [18].…”

Section: Introductionmentioning

confidence: 99%

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Fish Rescue during Streamflow Intermittency May Not Be Effective for Conservation of Rio Grande Silvery Minnow

Archdeacon

Diver

Reale

2020

Water

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Streamflow intermittency can reshape fish assemblages and present challenges to recovery of imperiled species. During streamflow intermittency, fish can be subjected to a variety of stressors, including exposure to crowding, high water temperatures, and low dissolved oxygen, resulting in sublethal effects or mortality. Rescue of fishes is often used as a conservation tool to mitigate the negative impacts of streamflow intermittency. The effectiveness of such actions is rarely evaluated. Here, we use multi-year water quality data collected from isolated pools during rescue of Rio Grande silvery minnow Hybognathus amarus, an endangered minnow. We examined seasonal and diel water quality patterns to determine if fishes are exposed to sublethal and critical water temperatures or dissolved oxygen concentrations during streamflow intermittency. Further, we determined survival of rescued Rio Grande silvery minnow for 3–5 weeks post-rescue. We found that isolated pool temperatures were much warmer (>40 °C in some pools) compared to upstream perennial flows, and had larger diel fluctuations, >10 °C compared to ~5 °C, and many pools had critically low dissolved oxygen concentrations. Survival of fish rescued from isolated pools during warmer months was <10%. Reactive conservation actions such as fish rescue are often costly, and in the case of Rio Grande silvery minnow, likely ineffective. Effective conservation of fishes threatened by streamflow intermittency should focus on restoring natural flow regimes that restore the natural processes under which fishes evolved.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fish Rescue during Streamflow Intermittency May Not Be Effective for Conservation of Rio Grande Silvery Minnow

Archdeacon

Diver

Reale

2020

Water

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“…Seasonal and subseasonal streamflow forecasting models rely on the skill of hydroclimatic variables from the previous season, such as snow cover (e.g., Kwon et al, 2009;Pagano et al, 2009;Livneh and Badger, 2020), large-scale climate indices (Ruiz et al, 2007;Lima and Lall, 2010;Robertson and Wang, 2012), or changes in land cover conditions (Penn et al, 2020), among others, to obtain skillful forecasts. Modeling approaches include statistical approaches based on multiple linear regression (Ruiz et al, 2007;Pagano et al, 2009;Penn et al, 2020), physically based models that consider the uncertainty of initial conditions or inputs by perturbing them (Werner and Yeager, 2013;Anghileri et al, 2016;Wood et al, 2016), and Bayesian approaches that account for parameter uncertainty (Kwon et al, 2009;Lima and Lall, 2010;Robertson and Wang, 2012).…”

Section: Introductionmentioning

confidence: 99%

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

Ossandón

Brunner

Rajagopalan

et al. 2022

Hydrol. Earth Syst. Sci.

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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 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.

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“…Seasonal and sub-seasonal streamflow forecasting models rely on the skill of hydro-climatic variables from the previous season, such as snow cover (e.g., Kwon et al, 2009;Livneh and Badger, 2020;Pagano et al, 2009), large scale climate indices (Lima and Lall, 2010;Robertson and Wang, 2012;Ruiz et al, 2007), or changes in land cover conditions (Penn et al, 2020) among others to obtain skillful forecasts. Modelling approaches span statistical approaches based on multiple linear regression (Pagano et al, 2009;Penn et al, 2020;Ruiz et al, 2007); physically-based models that consider the uncertainty of initial conditions or inputs by perturbing them (Anghileri et al, 2016;Wood et al, 2016;Werner and Yeager, 2013); and Bayesian approaches that account for parameter uncertainty (Kwon et al, 2009;Lima and Lall, 2010;Robertson and Wang, 2012).…”

Section: Introductionmentioning

confidence: 99%

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

Ossandón

Brunner

Rajagopalan

et al. 2021

Preprint

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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.

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Changes in Climate and Land Cover Affect Seasonal Streamflow Forecasts in the Rio Grande Headwaters

Abstract: Changing relations between snow, precipitation, and seasonal runoff were evaluated in the Rio Grande Headwaters basin using statistical and physical modeling techniques.

Cited by 11 publications

References 80 publications

Fish Rescue during Streamflow Intermittency May Not Be Effective for Conservation of Rio Grande Silvery Minnow

Fish Rescue during Streamflow Intermittency May Not Be Effective for Conservation of Rio Grande Silvery Minnow

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

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

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