Journal of Extreme Events

We synthesize the interconnected impacts of Texas’ water and energy resources and infrastructure including the cascading effects due to Winter Storm Uri. The government’s preparedness, communication, policies, and response as well as storm impacts on vulnerable communities are evaluated using available information and data. Where knowledge gaps exist, we propose potential research to elucidate health, environmental, policy, and economic impacts of the extreme weather event. We expect that recommendations made here — while specific to the situation and outcomes of Winter Storm Uri — will increase Texas’ resilience to other extreme weather events not discussed in this paper. We found that out of 14 million residents who were on boil water notices, those who were served by very small water systems went, on average, a minimum of three days longer without potable water. Available county-level data do not indicate vulnerable communities went longer periods of time without power or water during the event. More resolved data are required to understand who was most heavily impacted at the community or neighborhood level. Gaps in government communication, response, and policy are discussed, including issues with identifying — and securing power to — critical infrastructure and the fact that the state’s Emergency Alert System was not used consistently to update Texans during the crisis. Finally, research recommendations are made to bolster weaknesses discovered during and after the storm including (1) reliable communication strategies, (2) reducing disproportionate impacts to vulnerable communities, (3) human health impacts, (4) increasing water infrastructure resilience, and (5) how climate change could impact infrastructure resilience into the future.

Forecasting natural disasters such as inundations can be of great help for emergency bodies and first responders. In coastal communities, this risk is often associated with storm surge. To produce flood forecasts for coastal communities, a system must incorporate models capable of simulating such events based on forecasted weather conditions. In this work, a system for forecasting inundations based predominantly on storm surge is explored. An automation and a coupling strategy were implemented to produce forecasted flood maps automatically. The system leverages an ocean circulation model and a channel water flow model to estimate flood events in South Texas specially alongside the Lower Laguna Madre. The system around the models is implemented using Python and the meteorological forcing input is obtained from weather forecasting models maintained by the National Oceanic and Atmospheric Administration. The forecasted weather data retrieval, data processing and automation of the models are successful, and the complete stack of software can be deployed locally or in cloud solutions to accelerate computations. The resulting system performs as expected and successfully produces flood maps automatically providing vital information for flood emergency management in coastal communities.

Extreme weather and climate events including extreme precipitation have increased in frequency, intensity, and severity due to climate change and hit vulnerable communities disproportionately hard. However, there is a gap in the understanding of the characteristics of extreme precipitation and their effects on socio-economic activities in sub-Saharan Africa societies. The study utilized climate hazards group infrared precipitation with station data (CHIRPS) to analyze the climate characteristics from 1981 to 2019 over Senegal, Burkina Faso, Tanzania, and Malawi. Standardized precipitation evapotranspiration index (SPEI) and standardized precipitation index (SPI) were used to classify the precipitation and water balance anomalies with respect to the long-term observations. It was found that Burkina Faso and Senegal have a similar climate signal with more rainfall in Burkina Faso. Malawi recorded more rainfall than Tanzania. All the four countries recorded a high rainfall variability of extreme events. Standardized hydro climatic indices have shown that these sub-Saharan countries have suffered severe droughts which have negatively affected the socio-economic activities among the rural populations.

With the recurrence of extreme weather events in Central Africa, it becomes imperative to provide high-resolution forecasts for better decision-making by the Early warning systems. This study assesses the performance of the Weather Research and Forecasting (WRF) model to simulate heavy rainfall that affected the city of Douala in Cameroon during 19–21 August 2020. The WRF model is configured with two domains with horizontal resolutions of 15 and 5km, 33 vertical levels using eight cumulus parameterization schemes (CPSs). The WRF model performance is assessed by investigating the agreement between simulations and observations. Categorical and deterministic statistics are used, which include the probability of detection (POD), the success ratio (SR), the equitable threat score (ETS), the pattern correlation coefficient (PCC), the root mean square error (RMSE), the mean absolute error (MAE), and the BIAS. K-index is finally used to assess the capacity of the WRF model to predict the instability of the atmosphere in Douala during the above-mentioned period. It is found that (1) The POD, SR and ETS decrease when the threshold increases, showing the difficulty of the WRF model to predict and locate heavy rainfall events; (2) There are important differences in the rainfall area simulated by the eight CPSs; (3) The BIAS is negative for the eight CPSs, implying that all of the CPSs tested underestimate the rainfall over the study area; (4) Some of the CPSs have good agreement with observations, especially the new modifed Tiedtke and the Betts–Miller–Janjic schemes; (5) The K-index, an atmospheric instability index, is well predicted by the eight CPSs tested in this work. Overall, the WRF model exhibits a strong ability for rainfall simulation in the study area. The results point out that heavy rainfall events in tropical areas are very sensitive to CPSs and study domain. Therefore, sensitivity tests studies should be multiplied in order to identify most suitable CPSs for a given area.