Flooding impacts are on the rise globally, and concentrated in urban areas. Currently, there are no operational systems to forecast flooding at spatial resolutions that can facilitate emergency preparedness and response actions mitigating flood impacts. We present a framework for real‐time flood modeling and uncertainty quantification that combines the physics of fluid motion with advances in probabilistic methods. The framework overcomes the prohibitive computational demands of high‐fidelity modeling in real‐time by using a probabilistic learning method relying on surrogate models that are trained prior to a flood event. This shifts the overwhelming burden of computation to the trivial problem of data storage, and enables forecasting of both flood hazard and its uncertainty at scales that are vital for time‐critical decision‐making before and during extreme events. The framework has the potential to improve flood prediction and analysis and can be extended to other hazard assessments requiring intense high‐fidelity computations in real‐time.

The hydrologic cycle is an exquisitely coordinated and balanced interaction between the atmosphere, the ocean, and the land that controls, among other things, the planet's temperature by moving large quantities of matter and energy. The system is incredibly complex with a myriad of positive and negative feedbacks acting at a variety of scales. Much of what we experience in our natural and altered environments results from these complex interactions. Surprisingly (or maybe not) this complexity many times results in beautifully organized expressions of the hydrologic state that are commonly amenable to fairly simple explanations. This paper illustrates hydrologic complexity and organization in the context of the author's and collaborator's work during the past decades, a lot published in Water Resources Research. Topics include the impact of soil moisture on the atmosphere and vice versa, the impact of deforestation on the Amazon cloud climate and precipitation, the estimation of surface energy and mass fluxes, the self‐organization of landscapes and river basins over very long time periods, and the roles of vegetation on landscape evolution.

In order to accurately estimate surface fluxes, groundwater levels need to be specified accurately. However, monitoring Groundwater (GW) is challenging. The main goal of this study is to develop an innovative, yet simple, approach to detect shallow GW based solely on spaceborne soil moisture observations and apply this approach to improve the lower boundary conditions of soil moisture models. We simulated the influence of shallow GW on surface soil moisture under various environmental conditions and the results showed that the mean soil moisture and its Coefficient of Variation (CV) are both sensitive to shallow GW. Using those observations, we developed an algorithm that uses empirical functions calculating thresholds of mean soil moisture and CV to detect occurrence of shallow GW. We tested this algorithm in five different locations representing various climatic regions across the US, Argentina, and Australia. The algorithm performed well by correctly identifying periods of GW influence 92.4% of the time. We confirmed the algorithm by applying it over two regions: The Pampas, Argentina, and Southwestern Australia. The results indicate that our SMAP‐based algorithm successfully detects the shallow groundwater influenced time periods and significantly improves one particular soil moisture model output when the lower boundary conditions are adjusted according to the detection of shallow groundwater. The SMAP‐based method presented here addresses the growing need for observation‐based approaches to detect shallow groundwater at regional to global scales that may be beneficial to improving land surface and Earth system models.

The dynamics of soil organic carbon (SOC) in tropical forests play an important role in the global carbon (C) cycle. Past attempts to quantify the net C exchange with the atmosphere in regional and global budgets do not systematically account for dynamic feedbacks among linked hydrological, geomorphological, and biogeochemical processes, which control the fate of SOC. Here we quantify effects of geomorphic perturbations on SOC oxidation and accumulation in two adjacent wet tropical forest watersheds underlain by contrasting lithology (volcaniclastic rock and quartz diorite) in the Luquillo Critical Zone Observatory. This study uses the spatially explicit and physically based model of SOC dynamics tRIBS‐ECO (Triangulated Irregular Network‐based Real‐time Integrated Basin Simulator‐Erosion and Carbon Oxidation) and measurements of SOC profiles and oxidation rates. Our results suggest that hillslope erosion at the two watersheds may drive C sequestration or CO₂release to the atmosphere, depending on the forest type and land use. The net erosion‐induced C exchange with the atmosphere was controlled by the spatial distribution of forest types. The two watersheds were characterized by significant erosion and dynamic replacement of upland SOC stocks. Results suggest that the landscape underlain by volcaniclastic rock has reached a state close to geomorphic equilibrium, and the landscape underlain by quartz diorite is characterized by greater rates of denudation. These findings highlight the importance of the spatially explicit and physical representation of C erosion driven by local variation in lithological and geomorphological characteristics and in forest cover.

Distributed hydrologic models describe both hillslope and channel processes. The presence of a large number of hillslopes requires that flood routing through a channel network be computationally efficient and able to account for hillslope or lateral inflow. This paper presents an analytical solution to the linearized Saint-Venant equation with lateral inflow uniformly distributed along the channel. The solution is given as the sum of two functions. The first function, obtained by several earlier authors, represents the response to the upstream inflow; the second function, derived in this study, represents the contribution from lateral or hillslope inflow. We test the latter on a simple channel, and the results compare well with those of a detailed numerical model for channels with steep to fairly gentle bed slopes. The errors due to linearization are presented, and the effects of channel-bed slope and the reference velocity are discussed. A run time comparison between the numerical and the analytical solu...