The notion of convergent and transdisciplinary integration, which is about braiding together different knowledge systems, is becoming the mantra of numerous initiatives aimed at tackling pressing water challenges. Yet, the transition from rhetoric to actual implementation is impeded by incongruence in semantics, methodologies, and discourse among disciplinary scientists and societal actors. This paper confronts these disciplinary barriers by advocating a synthesis of existing and missing links across the frontiers distinguishing hydrology from engineering, the social sciences and economics, Indigenous and place-based knowledge, and studies of other interconnected natural systems such as the atmosphere, cryosphere, and ecosphere. Specifically, we embrace ‘integrated modeling’, in both quantitative and qualitative senses, as a vital exploratory instrument to advance such integration, providing a means to navigate complexity and manage the uncertainty associated with understanding, diagnosing, predicting, and governing human-water systems. While there are, arguably, no bounds to the pursuit of inclusivity in representing the spectrum of natural and human processes around water resources, we advocate that integrated modeling can provide a focused approach to delineating the scope of integration, through the lens of three fundamental questions: a) What is the modeling ‘purpose’? b) What constitutes a sound ‘boundary judgment’? and c) What are the ‘critical uncertainties’ and how do they propagate through interconnected subsystems? More broadly, we call for investigating what constitutes warranted ‘systems complexity’, as opposed to unjustified ‘computational complexity’ when representing complex natural and human-natural systems, with particular attention to interdependencies and feedbacks, nonlinear dynamics and thresholds, hysteresis, time lags, and legacy effects.

Flood risk assessment is primarily performed by a single flood driver at a specific location. A significant flaw in this approach is the oversight of the nonlinear interactions between various flood drivers (e.g., river flooding, tides, storm surges, and fluvial regimes), potentially resulting in compound flooding. This oversight can lead to underestimating the socioeconomic consequences of compound floods, which often surpass the risks posed by individual drivers acting alone. This study employs a deep learning model, mainly Long Short-Term Memory to predict water levels in tidal rivers under the influence of various flood drivers. The model is used to predict water levels at specific locations, using upstream river discharge, downstream water levels, and initial water levels as input variables. To account for coincidence/concurrence of drivers, we use Copula functions as a probabilistic approach to model the correlation between peak river discharge and coastal water levels as input features for the DL Model. The application of the proposed method is illustrated by applying it to a case study in the Buffalo Bayou area near Houston, TX. The results show that, for a 50-year flood, considering prior water level conditions represented by the 10th and 90th percentile baseflow scenarios, the projected flood inundation area can vary significantly, ranging from 30% to 70% for the same return period. The proposed methodology advances flood hazard assessment in coastal regions by capturing the complex interplay of different flood drivers and offering a robust yet practical flood inundation mapping approach.

Drought has severe impacts on the structure and functionality of terrestrial and riverine ecosystems. The mechanism and duration of drought recovery are critical subjects that can have crucial ramifications for ecology, crop yield, carbon uptake, and ecosystem services, and it has not been thoroughly investigated. This study assesses drought recovery of terrestrial and riverine ecosystems for agricultural and hydrological droughts, respectively. Soil moisture simulations from Phase 2 of the North American Land Data Assimilation System (NLDAS-2) are employed to characterize agricultural drought, and streamflow data from the United States Geological Survey (USGS) are utilized for assessing hydrological droughts. Drought recovery for riverine ecosystems is studied considering both quantity and quality of streamflow. Water temperature, dissolved oxygen, and turbidity are the water quality variables considered in this study. Riverine drought recovery is assessed using a multi-stage framework that is applied to 400 streamflow stations across the CONUS for the study period of 1950-2016. On the other hand, terrestrial drought recovery is investigated utilizing ecosystem Gross Primary Productivity (GPP), a metric of photosynthetic activity, for the regions impacted by agricultural drought. GPP data is acquired from the Moderate resolution Imaging Spectroradiometer (MODIS) sensor onboard Terra satellite at 1km spatial resolution and 8-day temporal resolution across the CONUS during 2000 to 2015. The drought affected regions are assumed to be recovered when the post-drought GPP reverts to its regional average value. Results show that in general, riverine drought recovery takes about two months when considering water quality variables, whereas terrestrial drought recovery duration varies between 1 to 4 months depending on drought severity. Additionally, results indicate that drought recovery duration is positively correlated with drought severity.