Multidimensional risk in a nonstationary climate: changes in joint probability of extreme conditions in space and time

<p>As has been made acutely clear in recent years, many natural and human systems are particularly prone to the co-occurrence of extremes like severe heat, heavy rainfall, storm-surge flooding, severe drought, and extreme wildfire conditions. The co-occurrence of these conditions, both simultaneously (or in rapid succession) in a given location or in different parts of the world, is critical for a broad suite of climate-sensitive concerns, including agricultural markets, food security, poverty vulnerability, supply chains, weather-related insurance and reinsurance, and disaster preparedness and recovery - particularly when those conditions are sufficiently extreme to fall outside of historical experience. This seminar will summarize recent work quantifying changes in the frequency of unprecedented events without consideration for joint probability probability, and then present a framework for quantifying the spatial and temporal co-occurrence of climate stresses in a nonstationary climate. This framework shows that, globally, anthropogenic climate forcing has doubled the joint probability of years that are both warm and dry in the same location (relative to the 1961–1990 baseline). In addition, the joint probability that key crop and pasture regions simultaneously experience severely warm conditions in conjunction with dry years has also increased, including high statistical confidence that human influence has increased the probability of previously unprecedented co-occurring combinations. The potential for this methodology to lend insight for other sectors that are accustomed to deploying resources based on historical probabilities, such as wildfire risk management, will also be discussed.</p>

Next-Generation Intensity-Duration-Frequency Curves for Climate-Resilient Infrastructure Design: Advances and Opportunities

National and international security communities (e.g., U.S. Department of Defense) have shown increasing attention for innovating critical infrastructure and installations due to recurring high-profile flooding events in recent years. The standard infrastructure design approach relies on local precipitation-based intensity-duration-frequency (PREC-IDF) curves that do not account for snow process and assume stationary climate, leading to high failure risk and increased maintenance costs. This paper reviews the recently developed next-generation IDF (NG-IDF) curves that explicitly account for the mechanisms of extreme water available for runoff including rainfall, snowmelt, and rain-on-snow under nonstationary climate. The NG-IDF curve is an enhancement to the PREC-IDF curve and provides a consistent design approach across rain- to snow-dominated regions, which can benefit engineers and planners responsible for designing climate-resilient facilities, federal emergency agencies responsible for the flood insurance program, and local jurisdictions responsible for developing design manuals and approving subsequent infrastructure designs. Further, we discuss the recent advances in climate and hydrologic science communities that have not been translated into actional information in the engineering community. To bridge the gap, we advocate that building climate-resilient infrastructure goes beyond the traditional local design scale where engineers rely on recipe-based methods only; the future hydrologic design is a multi-scale problem and requires closer collaboration between climate scientists, hydrologists, and civil engineers.

Time-Varying Meteorological Drought Index for a Changing Climate

<p>Drought is considered as one of the most complicated natural disasters, whose adverse effects span over different domains such as agriculture, ecosystem, and economy. One of the widely used meteorological drought index is Standardized Precipitation Index (SPI), which is based on the stationary assumption (i.e., the statistical parameters does not change over time). Nevertheless, numerous studies have reported that the precipitation series has undergone remarkable changes, which emphasizes the need for developing a drought index incorporating the dynamic behavior of the precipitation. Hence, in this study, a non-stationary SPI (NSPI) is developed to capture the temporal dynamics of the precipitation and to identify the meteorological drought-prone areas over India. Before modelling, the non-stationarity in the distribution parameters of precipitation series are detected. If non-stationary is observed in any of the parameters, then that particular parameter is modelled as non-stationary, otherwise it is modelled as stationary. The proposed index provides a probability-based description of drought status and its uncertainty bounds, which are computed using Bayesian Inference. Results reveals that the traditional SPI is biased by the lowest magnitude of precipitation leading to overestimation of drought where frequent severe dry events are clustered, which is overcome by NSPI. Additionally, NSPI captured the historical drought, capturing the temporal dynamics of precipitation series in India and is more reliable than SPI. The proposed NSPI is found to be a potential index for drought monitoring in a nonstationary climate.</p>

CHANGES IN THE CHEMICAL DENUDATION INTENSITY IN THE RIVER PECHORA CATCHMENT UNDER THE INFLUENCE OF NON-STATIONARY CLIMATE AND ECONOMIC ACTIVITIES

Introduction. Climate warming, which is currently observed in the Arctic, has the potential to intensify chemical denudation in river basins partially or completely located within permafrost. In order to test this hypothesis, we investigated the longterm dynamics of the rivers’ ion runoff in the Pechora River basin, 42 % of which are located within permafrost. Methods. To study changes in the chemical denudation intensity in nonstationary climate, we analyzed data of systematic observations over the main ion concentrations from 1985 to 2017 in the Pechora River outlet (Naryan-Mar) and its tributaries (Usa, Adzva, Kolva, Sula rivers). The intensity of chemical denudation in the Pechora River basin was assessed in terms of ion runoff. The probable reasons for its changes — water content and concentrations of macrocomponents — were also analyzed. Results. The ion-salt composition of Pechora River water is mainly determined by the dissolution of carbonate minerals in the underlying rocks. Calculations and comparison of ion runoff moduli showed that the studied rivers are comparable in terms of the chemical denudation intensity in catchments. The exceptions were the Sula and Kolva rivers, where, with the river runoff, a relatively high amount of chlorides and hydrocarbonates is carried. Contrary to the initial assumptions about the possible intensification of the chemical denudation process under the conditions of climate warming, we found a decrease in the sulfate runoff moduli in all rivers. In addition to that, individual changes in the runoff moduli for other main ions are observed for the studied rivers. Conclusion. In modern conditions, the chemical denudation intensity remains at the level of the end of the last century, and the long-term dynamics of the Pechora River ion runoff correlates in time with the variability of anthropogenic factors, in particular, wet sulfate deposition with atmospheric waters.

Multidimensional risk in a nonstationary climate: Joint probability of increasingly severe warm and dry conditions

We present a framework for quantifying the spatial and temporal co-occurrence of climate stresses in a nonstationary climate. We find that, globally, anthropogenic climate forcing has doubled the joint probability of years that are both warm and dry in the same location (relative to the 1961–1990 baseline). In addition, the joint probability that key crop and pasture regions simultaneously experience severely warm conditions in conjunction with dry years has also increased, including high statistical confidence that human influence has increased the probability of previously unprecedented co-occurring combinations. Further, we find that ambitious emissions mitigation, such as that in the United Nations Paris Agreement, substantially curbs increases in the probability that extremely hot years co-occur with low precipitation simultaneously in multiple regions. Our methodology can be applied to other climate variables, providing critical insight for a number of sectors that are accustomed to deploying resources based on historical probabilities.