The complex relationship between precipitation and soil moisture plays a critical role in land surface hydrology. Traditionally, the analysis of this relationship is restricted by the spatial coverage of both soil moisture and precipitation data that are collected through in‐situ observations at limited locations. In this study, we utilized the National Aeronautics and Space Administration (NASA)’s remote sensing products of soil moisture (SMAP: Soil Moisture Active Passive) and precipitation (TRMM: Tropical Rainfall Measuring Mission), which provide near‐global coverage, to investigate the co‐variation of precipitation and soil moisture regionally, as a function of ecosystem types and climate regimes. We apply information on land cover and climate regimes to provide insight about correlation strength of soil moisture and precipitation. The results indicate that most of the globe has a moderate to strong positive correlation of SMAP soil moisture and TRMM precipitation data during the study period. In relation to land cover, soil moisture and precipitation have the strongest correlations in regions of limited vegetation, whereas forests and densely vegetated regions have weaker correlations. As for climate regimes, they have the strongest correlations in arid or cold regions, and weaker correlations in humid, temperate locations. While remotely sensed soil moisture data are less reliable in dense vegetation, these results confirm that drier, less vegetated climates show a highly linear relationship between soil moisture and rainfall.

This study highlights the features of vine copula for examining compound events involving underlying conditions that amply the compounding effects. To illustrate, we study compound floods in Texas (TX), USA. These compound floods consist of combinations of precipitation and surface runoff with the El Niño‐Southern Oscillation (ENSO) and rising temperatures as underlying conditions. Although the individual variable of precipitation and runoff may not itself be extreme, large exceedances can lead to flooding situations when combined. The presence of underlying conditions (e.g., El Niño and/or rising temperatures) can exacerbate the associated flood impacts. We use observational data during May–August for each climate division of TX. A three‐dimensional vine copula is used first to quantify the ENSO effect on precipitation and runoff through conditioning sets of vine copula. We further examine the interplay of a warming signal and El Niño to reveal their mutual effects on compound floods by placing these two factors as interrelated conditions in a four‐dimensional vine copula. Our results show that El Niño is much stronger than the other ENSO states in conditioning a high likelihood of TX compound floods by amplifying mean and extreme states of rainfall and runoff. Conditioned by both El Niño and global temperatures, a slight reduction occurs in TX compound floods under the warmer condition. This is consistent with the trend of precipitation and runoff composites under given conditions, while no appreciable changes are found to suggest a different joint effect of El Niño and rising temperatures on TX compound floods.

Hydropower release decision making relies on multisource information, such as climate conditions, downstream water quality, inflow and storage, regulation and engineering constraints, and so on. The decision tree (DT) method is one of the commonly used techniques to simulate reservoir operation and release strategies because of its simplicity and effectiveness. However, the performances and simulation accuracy vary among different DT models due to many structures and splitting rules associated with each DT model. In this study, we propose a dynamic merge technique (DMerge), which adopts a concept from particle swarm optimization, to postprocess outputs from different DT models with the purpose of increasing the simulation accuracy and producing a model ensemble with dynamically changing weights throughout the validation phase. A case study of Shasta Lake in northern California is presented, where the daily hydropower releases are predicted and compared using the DMerge, AdaBoost DT, random forest, and extremely randomized trees methods. Results show that the DMerge method has the best statistics compared to other popular DT algorithms. Furthermore, scenario tests were carried out to analyze the sensitivity to model inputs (i.e., hydrological condition, reservoir storage and regulation, climate phenomenon indices, and water quality) with respect to explaining the variability of hydropower releases. According to the results, we found that the hydropower releases are a complex decision-making process and water quality and climate conditions could play an even more significant role than both hydrological forcing and system states in our case study. The proposed DMerge method is a robust and efficient technique in solving water-energy prediction and simulation problems, and it is suitable for joint use with other data-driven approaches.

Understanding the evolution of wildfire regimes throughout the United States (US) is crucial in the preparation, mitigation, and planning for national wildfires. Recent wildfire trajectories demonstrating an increase in both frequency and size across the US have made documenting the changes in wildfire regimes a topic of growing importance. While previous studies have examined wildfire regimes using ecoregions, this study analyzes wildfire regimes through the Geographic Area Coordination Center (GACC) regions across the Contiguous US over 34 years, 1984–2017. GACCs are geopolitical boundaries designed by wildfire agencies to promote an efficient way to distribute resources during emergencies such as wildfires. Wildfire observations originate from the Monitoring Trends in Burn Severity (MTBS) database which records large fire events that are 1000(500) acres or greater in the Western (Eastern) US. Using GACCs and MTBS data, this study examines wildfire regimes across the Contiguous US through the following three parameters: total burned area, frequency, and average burned area. This study characterizes the trend direction of the wildfire parameters and which are statistically significant. Results demonstrate that most GACC regions display statistically significant trends, including wildfire regimes that are beyond the Western US (e.g., Southern GACC). The Northwest and Southwest GACCs demonstrate statistically significant positive trends in every parameter observed. The California and Great Basin GACCs demonstrate statistically significant positive trends in the average burned area. The Eastern GACC is the only region to not display any significant trends. Determining significant wildfire regimes and their trend direction can help wildfire agencies to minimize the negative impacts on the environment, society, and economy.

An object-based verification approach is employed to assess the performance of the commonly used high-resolution satellite precipitation products: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Climate Prediction center MORPHing technique (CMORPH), and Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42RT. The evaluation of the satellite precipitation products focuses on the skill of depicting the geometric features of the localized precipitation areas. Seasonal variability of the performances of these products against the ground observations is investigated through the examples of warm and cold seasons. It is found that PERSIANN is capable of depicting the orientation of the localized precipitation areas in both seasons. CMORPH has the ability to capture the sizes of the localized precipitation areas and performs the best in the overall assessment for both seasons. 3B42RT is capable of depicting the location of the precipitation areas for both seasons. In addition, all of the products perform better on capturing the sizes and centroids of precipitation areas in the warm season than in the cold season, while they perform better on depicting the intersection area and orientation in the cold season than in the warm season. These products are more skillful on correctly detecting the localized precipitation areas against the observations in the warm season than in the cold season.