Methods of Tail Dependence Estimation

Characterization and quantification of climate extremes and their dependencies are fundamental to the studying of natural hazards. This chapter reviews various parametric and nonparametric tail dependence coefficient estimators. The tail dependence coefficient describes the dependence (degree of association) between concurrent extremes at different locations. Accurate and reliable knowledge of the spatial characteristics of extremes can help improve the existing methods of modeling the occurrence probabilities of extreme events. This chapter will review these methods and use two case studies to demonstrate the application of tail dependence analysis

Changes in the Exposure of California’s Levee-Protected Critical Infrastructure to Flooding Hazard in a Warming Climate

Levee systems are an important part of California\u27s water infrastructure, engineered to provide resilience against flooding and reduce flood losses. The growth in California is partly associated with costly infrastructure developments that led to population expansion in the levee protected areas. Therefore, potential changes in the flood hazard could have significant socioeconomic consequences over levee protected areas, especially in the face of a changing climate. In this study, we examine the possible impacts of a warming climate on flood hazard over levee protected land in California. We use gridded maximum daily runoff from global circulation models (GCMs) that represent a wide range of variability among the climate projections, and are recommended by the California\u27s Fourth Climate Change Assessment Report, to investigate possible climate-induced changes. We also quantify the exposure of several critical infrastructure protected by the levee systems (e.g. roads, electric power transmission lines, natural gas pipelines, petroleum pipelines, and railroads) to flooding. Our results provide a detailed picture of change in flood risk for different levees and the potential societal consequences (e.g. exposure of people and critical infrastructure). Levee systems in the northern part of the Central Valley and coastal counties of Southern California are likely to observe the highest increase in flood hazard relative to the past. The most evident change is projected for the northern region of the Central Valley, including Butte, Glenn, Yuba, Sutter, Sacramento, and San Joaquin counties. In the leveed regions of these counties, based on the model simulations of the future, the historical 100-year runoff can potentially increase up to threefold under RCP8.5. We argue that levee operation and maintenance along with emergency preparation plans should take into account the changes in frequencies and intensities of flood hazard in a changing climate to ensure safety of levee systems and their protected infrastructure

A New Normal for Streamflow in California in a Warming Climate: Wetter Wet Seasons and Drier Dry Seasons

In this study, we investigate changes in future streamflows in California using bias-corrected and routed streamflows derived from global climate model (GCM) simulations under representative concentration pathways (RCPs): RCP4.5 and RCP8.5. Unlike previous studies that have focused mainly on the mean streamflow, annual maxima or seasonality, we focus on projected changes across the distribution of streamflow and the underlying causes. We report opposing trends in the two tails of the future streamflow simulations: lower low flows and higher high flows with no change in the overall mean of future flows relative to the historical baseline (statistically significant at 0.05 level). Furthermore, results show that streamflow is projected to increase during most of the rainy season (December to March) while it is expected to decrease in the rest of the year (i.e., wetter rainy seasons, and drier dry seasons). We argue that the projected changes to streamflow in California are driven by the expected changes to snow patterns and precipitation extremes in a warming climate. Changes to future low flows and extreme high flows can have significant implications for water resource planning, drought management, and infrastructure design and risk assessment

Climate‐Induced Changes in the Risk of Hydrological Failure of Major Dams in California

Existing major reservoirs in California, with average age above 50 years, were built in the previous century with limited data records and flood hazard assessment. Changes in climate and land use are anticipated to alter statistical properties of inflow to these infrastructure systems and potentially increase their hydrological failure probability. Because of large socioeconomic repercussions of infrastructure incidents, revisiting dam failure risks associated with possible shifts in the streamflow regime is fundamental for societal resilience. Here we compute historical and projected flood return periods as a proxy for potential changes in the risk of hydrological failure of dams in a warming climate. Our results show that hydrological failure probability is likely to increase for most dams in California by 2100. Noticeably, the New Don Pedro, Shasta, Lewiston, and Trinity Dams are associated with highest potential changes in flood hazard

Article Accounting for Uncertainties of the TRMM Satellite Estimates

Abstract: Recent advances in the field of remote sensing have led to an increase in available rainfall data on a regional and global scale. Several NASA sponsored satellite missions provide valuable precipitation data. However, the advantages of the data are limited by complications related to the indirect nature of satellite estimates. This study intends to develop a stochastic model for uncertainty analysis of satellite rainfall fields through simulating error fields and imposing them over satellite estimates. In order to examine reliability and performance of the presented model, ensembles of satellite estimates are simulated for a large area across the North and South Carolina. The generated ensembles are then compared with original satellite estimates with respect to statistical properties and spatial dependencies. The results show that the model can be used to describe the uncertainties associated to TRMM multi-satellite precipitation estimates. The presented model is validated using random sub-samples of the observations based on the bootstrap technique. The results indicate that the model performs reasonably well with different numbers of available rain gauges

Heat Wave Intensity Duration Frequency Curve: A Multivariate Approach for Hazard and Attribution Analysis

Atmospheric warming is projected to intensify heat wave events, as quantified by multiple descriptors, including intensity, duration, and frequency. While most studies investigate one feature at a time, heat wave characteristics are often interdependent and ignoring the relationships between them can lead to substantial biases in frequency (hazard) analyses. We propose a multivariate approach to construct heat wave intensity, duration, frequency (HIDF) curves, which enables the concurrent analysis of all heat wave properties. Here we show how HIDF curves can be used in various locations to quantitatively describe the likelihood of heat waves with different intensities and durations. We then employ HIDF curves to attribute changes in heat waves to anthropogenic warming by comparing GCM simulations with and without anthropogenic emissions. For example, in Los Angeles, CA, HIDF analysis shows that we can attribute the 21% increase in the likelihood of a four-day heat wave (temperature \u3e 31 °C) to anthropogenic emissions

Avoiding Water Bankruptcy in the Drought-Troubled Southwest: What the US and Iran Can Learn from Each Other

The 2021 water year ends on Sept. 30, and it was another hot, dry year in the western U.S., with almost the entire region in drought. Reservoirs vital for farms, communities and hydropower have fallen to dangerous lows. The biggest blow came in August, when the U.S. government issued its first ever water shortage declaration for the Colorado River, triggering water use restrictions. In response, farmers and cities across the Southwest are now finding new, often unsustainable ways to meet their future water needs. Las Vegas opened a lower-elevation tunnel to Lake Mead, a Colorado River reservoir where water levels reached unprecedented lows at 35% of capacity. Farmers are ratcheting up groundwater pumping. Officials in Arizona, which will lose nearly one-fifth of its river water allotment under the new restrictions, even floated the idea of piping water hundreds of miles from the Mississippi River

On the key role of droughts in the dynamics of summer fires in Mediterranean Europe

Summer fires frequently rage across Mediterranean Europe, often intensified by high temperatures and droughts. According to the state-of-the-art regional fire risk projections, in forthcoming decades climate effects are expected to become stronger and possibly overcome fire prevention efforts. However, significant uncertainties exist and the direct effect of climate change in regulating fuel moisture (e.g. warmer conditions increasing fuel dryness) could be counterbalanced by the indirect effects on fuel structure (e.g. warmer conditions limiting fuel amount), affecting the transition between climate-driven and fuel-limited fire regimes as temperatures increase. Here we analyse and model the impact of coincident drought and antecedent wet conditions (proxy for the climatic factor influencing total fuel and fine fuel structure) on the summer Burned Area (BA) across all eco-regions in Mediterranean Europe. This approach allows BA to be linked to the key drivers of fire in the region. We show a statistically significant relationship between fire and same-summer droughts in most regions, while antecedent climate conditions play a relatively minor role, except in few specific eco-regions. The presented models for individual eco-regions provide insights on the impacts of climate variability on BA, and appear to be promising for developing a seasonal forecast system supporting fire management strategies.We thank the European Forest Fire Information System-EFFIS (http://effis.jrc.ec.europa.eu) of the European Commission Joint Research Centre for the fire data. We acknowledge the SPEI data providers (http://sac.csic. es/spei/database.html). Special thanks to Joaquín Bedia, Esteve Canyameras, Xavier Castro and Andrej Ceglar for helpful discussions on the study. This work was partially funded by the Project of Interest “NextData” of the Italian Ministry for Education, University and Research and by the EU H2020 Project 641762 “ECOPOTENTIAL: Improving Future Ecosystem Benefits through Earth Observations”. Ricardo Trigo was supported by IMDROFLOOD funded by Portuguese FCT (WaterJPI/0004/2014).Peer ReviewedPostprint (published version

A large-scale methane model by incorporating the surface water transport

Author Posting. © American Geophysical Union, 2016. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Biogeosciences 121 (2016): 1657–1674, doi:10.1002/2016JG003321.The effect of surface water movement on methane emissions is not explicitly considered in most of the current methane models. In this study, a surface water routing was coupled into our previously developed large-scale methane model. The revised methane model was then used to simulate global methane emissions during 2006–2010. From our simulations, the global mean annual maximum inundation extent is 10.6 ± 1.9 km2 and the methane emission is 297 ± 11 Tg C/yr in the study period. In comparison to the currently used TOPMODEL-based approach, we found that the incorporation of surface water routing leads to 24.7% increase in the annual maximum inundation extent and 30.8% increase in the methane emissions at the global scale for the study period, respectively. The effect of surface water transport on methane emissions varies in different regions: (1) the largest difference occurs in flat and moist regions, such as Eastern China; (2) high-latitude regions, hot spots in methane emissions, show a small increase in both inundation extent and methane emissions with the consideration of surface water movement; and (3) in arid regions, the new model yields significantly larger maximum flooded areas and a relatively small increase in the methane emissions. Although surface water is a small component in the terrestrial water balance, it plays an important role in determining inundation extent and methane emissions, especially in flat regions. This study indicates that future quantification of methane emissions shall consider the effects of surface water transport.The finacial support for this work is from the Open Fund of State Key Laboratory of Remote Sensing Science of China (OFSLRSS201501); 2 Supported by the Fundamental Research Funds for the Central Universities (20720160109).2016-12-2

Probabilistic modeling of crop-yield loss risk under drought: A spatial showcase for sub-Saharan Africa

Assessing the risk of yield loss in African drought-affected regions is key to identify feasible solutions for stable crop production. Recent studies have demonstrated that Copula-based probabilistic methods are well suited for such assessment owing to reasonably inferring important properties in terms of exceedance probability and joint dependence of different characterization. However, insufficient attention has been given to quantifying the probability of yield loss and determining the contribution of climatic factors. This study applies the Copula theory to describe the dependence between drought and crop yield anomalies for rainfed maize, millet, and sorghum crops in sub-Saharan Africa (SSA). The environmental policy integrated climate model, calibrated with Food and Agriculture Organization country-level yield data, was used to simulate yields across SSA (1980-2012). The results showed that the severity of yield loss due to drought had a higher magnitude than the severity of drought itself. Sensitivity analysis to identify factors contributing to drought and high-temperature stresses for all crops showed that the amount of precipitation during vegetation and grain filling was the main driver of crop yield loss, and the effect of temperature was stronger for sorghum than for maize and millet. The results demonstrate the added value of probabilistic methods for drought-impact assessment. For future studies, we recommend looking into factors influencing drought and high-temperature stresses as individual/concurrent climatic extremes. © 2022 The Author(s). Published by IOP Publishing Ltd