The current Tropical Cyclones (TCs) scaling system, Saffir‐Simpson Hurricane Wind Scale (SSHWS), characterizes the hazardousness of these events solely based on wind speed. This is despite the fact that TCs are classic examples of compound hazards during which multiple hazard drivers that are wind, storm surge, and intense rainfall interact and yield in impacts greater than the sum of individuals. Studies have shown that people's decision to evacuate is highly related to the estimated SSHWS category. Thus, the current SSHWS ‐based classification of TCs yields an underestimation of the hazardousness of TCs and so may misguide the threatened communities. Here, we propose a new scaling system that uses Copulas for categorizing TCs based on the likelihood of a given set of severity for rainfall, surge, and wind speed. We use a variety of data sources to obtain the timing and intensity of wind speed, rainfall along the track, and the associated maximum surge for 102 TCs that have made landfall in the United States' Atlantic and Gulf coasts between 1979 and 2020. Comparing the outputs of our scaling system with official damage reporting for the costliest TCs in the history of the United States, we show that the proposed approach significantly improves TC hazard communication and can be useful for informing decision makers and emergency responders.

This study developed a multi-criteria decision analysis (MCDA) framework to prioritize sites and types of low impact development (LID) practices. This framework was systemized as a web-based system coupled with the Storm Water Management Model (SWMM). Using TOPSIS method, which is a type of MCDA method, multiple types and sites of designated LID practices are prioritized. This system is named the Water Management Prioritization Module (WMPM). WMPM can simultaneously determine the priority of multiple LID types and sites. In this study, an infiltration trench and permeable pavement were considered for multiple sub-catchments in South Korea to demonstrate the WMPM procedures. The TOPSIS method was manually incorporated to select the vulnerable target sub-catchments and to prioritize the LID planning scenarios for multiple types and sites considering social, hydrologic and physical-geometric factors. In this application, the Delphi method and entropy theory were used to determine the subjective and objective weights, respectively.

Developers are increasingly looking for the best management practices to reduce the risk of floods in rapidly growing urban areas. Low impact development (LID) is regarded as one of the most suitable solutions for urban stormwater management and thus the U.S. EPA’s (Environmental Protection Agency) SWMM5.1 (Storm Water Management Model) added the hydrological simulation function for LID structures in 2009. However, SWMM5.1 cannot consider the optimal or best physical specifications of LID design and planning fitted to a study area, nor can it instantly derive the best combination for multiple LID designs and plans. Therefore, in this study, a web-based decision support system (DSS) for the EPA’s SWMM 5.1, referred to as the Water Management Analysis Module (WMAM) is used to decide the most effective specifications of design and planning parameters for LID structure. This study was carried out over an urban catchment of University Technology Malaysia campus located in Johor, Malaysia. The hydrologic cycles with and without LID were simulated using EPA SWMM5.1. The sensitivity analysis and multiple scenario analysis were performed using WMAM. As a result, the effective specification of LID design parameters indicates that peak flow is reduced to 20.95% and 17.5% for two sub-catchments, S1 (highest by area) and S6 (lowest by area) by installing an LID structure. Thus, this study provides a tool for the best solution for what values for physical parameters will be the best for a specified LID type and what capacities can achieve the particular objectives using WMAM.

Tropical cyclones are among the most devastating natural disasters that pose risk to people and assets all around the globe. The Saffir-Simpson scale is commonly used to inform threatened communities about the severity of hazard, but lacks consideration of other potential drivers of a hazardous situation (e.g. terrestrial and coastal flooding). Here, we propose an alternative approach that accounts for multiple components and their likelihood of coincidence for appropriate characterization of hurricane hazard. We assess the marginal and joint probability of wind-speed and rainfall from landfalling Atlantic tropical cyclones in the United States between 1979 ∼ 2017 to characterize the hazard associated with these events. We then integrate the vulnerability of affected communities to have a better depiction of risk that is comparable to the actual cost of these hurricanes. Our results show that the multihazard indexing approach significantly better characterizes the hurricane hazard, and is more appropriate for risk-informed decision-making.

Drought and tropical storm (TS) are associated with water deficit and surplus, respectively. Soil moisture is a key component in the hydrological cycle that plays an important role in monitoring drought and reflects the infiltrated or stored water due to TS rainfall. Therefore, soil moisture information can be used for the assessment of whether TS can ameliorate severe drought conditions. Here, we use downscaled 1 km Soil Moisture Active Passive (SMAP) dataset generated at the Center for Complex Hydrosystems Research (CCHR) at the University of Alabama, and Hurricane Database 2^nd generation (HURDAT2) to examine the coincidence of extreme events between agricultural droughts and Atlantic TS in the Contiguous United States from 2015 to 2019. The CDF (Cumulative Distribution Function) matching approach is employed to correct the bias using the root zone soil moisture data provided by the North American Land Data Assimilation System Phase 2 (NLDAS-2), and then weekly Standardized Soil Moisture Index (SSI) is calculated for characterization of agricultural drought. As a result, we estimate the frequency of TS impacted regions in the US, the ratio of droughts ameliorated and exacerbated by TS, and the regions where TS highly affect the offset of drought. Our findings indicate detailed spatial information of the offset of drought conditions based on a high-resolution dataset and provide potential information in terms of mitigating drought and TS for the future.