Water plays a vital role in the proper functioning of the Earth's ecosystems, and practically all human activities, such as agriculture, manufacturing, transportation, and energy production. The proliferation of industrial and agricultural activities in modern society, however, poses threats to water resources in the form of chemical, biological, and thermal pollution. On the other hand, tremendous advancements in science and technology offer valuable tools to address water sustainability challenges. Key technologies, including sensing technology, wireless communications and networking, hydrodynamic modeling, data analysis, and control, enable intelligently wireless networked water cyber-physical systems (CPS) with embedded sensors, processors, and actuators that can sense and interact with the water environment. This article provides an overview of water CPS for sustainability from four critical aspects: sensing and instrumentation; communications and networking; computing; and control. The article also explores opportunities and design challenges of relevant techniques.

Accurate representations of lake–ice–atmosphere interactions in regional climate modeling remain one of the most critical and unresolved issues for understanding large-lake ecosystems and their watersheds. To date, the representation of the Great Lakes two-way interactions in regional climate models is achieved with one-dimensional (1D) lake models applied at the atmospheric model lake grid points distributed spatially across a 2D domain. While some progress has been made in refining 1D lake model processes, such models are fundamentally incapable of realistically resolving a number of physical processes in the Great Lakes. In this study, a two-way coupled 3D lake-ice–climate modeling system [Great Lakes–Atmosphere Regional Model (GLARM)] is developed to improve the simulation of large lakes in regional climate models and accurately resolve the hydroclimatic interactions. Model results are compared to a wide variety of observational data and demonstrate the unique skill of the coupled 3D modeling system in reproducing trends and variability in the Great Lakes regional climate, as well as in capturing the physical characteristics of the Great Lakes by fully resolving the lake hydrodynamics. Simulations of the climatology and spatiotemporal variability of lake thermal structure and ice are significantly improved over previous coupled, 1D simulations. At seasonal and annual time scales, differences in model results are primarily observed for variables that are directly affected by lake surface temperature (e.g., evaporation, precipitation, sensible heat flux) while no significant differences are found in other atmospheric variables (e.g., solar radiation, cloud cover). Underlying physical mechanisms for the simulation improvements using GLARM are also discussed.

The Maritime Continent (MC) plays a vitally important role in the Earth's climate system from both oceanic and atmospheric perspectives. While the critical role of ocean‐atmosphere coupled dynamics over the MC has long been recognized, development of two‐way coupled regional climate models for this region is still in its early stages. In this work, the authors review recent progress in two‐way coupled ocean‐atmosphere regional climate modeling. Development of coupled models and their applications in the MC are summarized. Model performances are discussed with a focus on regional oceanic and atmospheric characteristics. Through a critical review of modeling advances and limitations in simulating sea surface temperature, precipitation, and oceanic throughflows, the authors identify deficiencies of current models and discuss possible reasons. The review shows that model biases mainly stem from unresolved physical processes, inadequate model representations of the coupled system, and uncertainties in model configurations. The study reveals large‐scale coupled modes of variability, local air‐sea interactions, atmospheric dynamics, and oceanic processes play various roles in the observed modeling biases. Lastly, the authors offer suggestions on emerging opportunities for improving regional coupled modeling over the MC.

Lake Superior, the largest lake in the world by surface area and third largest by volume, features strong spatiotemporal thermal variability due to its immense size and complex bathymetry. The objectives of this study are to document our recent modeling experiences on the simulation of the lake thermal structure and to explore underlying dynamic explanations of the observed modeling success. In this study, we use a three-dimensional hydrodynamic model (FVCOM—Finite Volume Community Ocean Model) and an assimilative weather forecasting model (WRF—Weather Research and Forecasting Model) to study the annual heating and cooling cycle of Lake Superior. Model experiments are carried out with meteorological forcing based on interpolation of surface weather observations, on WRF and on Climate Forecast System Reanalysis (CFSR) reanalysis data, respectively. Model performance is assessed through comparison with satellite products and in situ measurements. Accurate simulations of the lake thermal structure are achieved through (1) adapting the COARE algorithm in the hydrodynamic model to derive instantaneous estimates of latent/sensible heat fluxes and upward longwave radiation based on prognostic surface water temperature simulated within the model as opposed to precomputing them with an assumed surface water temperature; (2) estimating incoming solar radiation and downward longwave radiation based on meteorological measurements as opposed to meteorological model-based estimates; (3) using the weather forecasting model to provide high-resolution dynamically constrained wind fields as opposed to wind fields interpolated from station observations. Analysis reveals that the key to the modeling success is to resolve the lake-atmosphere interactions and apply appropriate representations of different meteorological forcing fields, based on the nature of their spatiotemporal variability. The close agreement between model simulation and observations also suggests that the 3-D hydrodynamic model can provide reliable spatiotemporal estimates of heat budgets over Lake Superior and similar systems. Although there have been previous studies which analyzed the impact of the spatiotemporal variability of overwater wind fields on lake circulation, we believe this is the first detailed analysis of the importance of spatiotemporal variability of heat flux components on hydrodynamic simulation of 3-D thermal structure in a lake.