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In this book, readers discover how Earths water fills lakes and oceans, and how it reenters the atmosphere to form clouds. They also discover how raindrops form and why groundwater collects.

The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released its most advanced reanalysis product, the ERA5 dataset. It was designed and generated with methods giving it multiple advantages over the previous release, the ERA-Interim reanalysis product. Notably, it has a finer spatial resolution, is archived at the hourly time step, uses a more advanced assimilation system and includes more sources of data. This paper aims to evaluate the ERA5 reanalysis as a potential reference dataset for hydrological modelling by considering the ERA5 precipitation and temperatures as proxies for observations in the hydrological modelling process, using two lumped hydrological models over 3138 North American catchments. This study shows that ERA5-based hydrological modelling performance is equivalent to using observations over most of North America, with the exception of the eastern half of the US, where observations lead to consistently better performance. ERA5 temperature and precipitation biases are consistently reduced compared to ERA-Interim and systematically more accurate for hydrological modelling. Differences between ERA5, ERA-Interim and observation datasets are mostly linked to precipitation, as temperature only marginally influences the hydrological simulation outcomes.

Introduces basic scientific principles about water and the water cycle, providing instructions for simple experiments that examine such topics as solubility, density, the pH scale, and capillarity.

Drought intensity is commonly characterized using meteorologically‐based metrics that do not provide insight into water deficits within deeper hydrologic systems. In contrast, global positioning system (GPS) displacements are sensitive to both local and regional hydrologic‐storage fluctuations. While a few studies have leveraged this sensitivity to produce geodetic drought indices, hydrologic drought characterization using GPS is not commonly accounted for in drought assessment and management. To motivate this application, we produce a new geodetic drought index (GDI) and quantify its ability to characterize hydrologic drought conditions in key surface and sub‐surface hydrologic reservoirs/pools across California. In northern California, the GDI exhibits a strong regional association with surface‐reservoir storage at the 1‐month time scale (correlation coefficient: 0.83) and groundwater levels at the 3‐month time scale (correlation coefficient: 0.87), along with moderate associations with stream discharge at the daily (instantaneous) time scale (correlation coefficient: 0.50). Groundwater in southern California is best characterized with a 12‐month GDI (correlation coefficient: 0.77), and surface‐reservoir storage is optimized with the 3‐month GDI (correlation coefficient: 0.72). Two sigma uncertainties are ±0.03. Differences between northern and southern California reveal that the GDI is sensitive to unique aquifer and drainage basin characteristics. In addition to capturing long‐term hydrologic trends, rapid changes in the GDI initiate during clusters of large atmospheric river events that closely mirror fluctuations in traditional hydrologic and meteorological observations. We show that GPS‐based hydrologic drought indices provide a significant opportunity to improve drought assessment, in California and beyond, by improving our understanding of the hydrologic cycle. Plain Language Summary Although quantifying the total volume of water loss is of critical importance during periods of drought, drought intensity is often characterized using meteorologic observations, such as precipitation, rather than using more holistic hydrologic observations, such as reservoir levels and groundwater. While precipitation is a good measure of the amount of water entering a region, precipitation models do not determine the amount of water retained in a watershed or the amount lost due to runoff and evapotranspiration, which are important factors for drought management. We address this need by producing a hydrologically based drought index that captures changes in both surface and subsurface hydrologic reservoirs/pools using surface‐loading geodesy, which quantifies changes in water volume based on how the shape of the Earth changes under the weight of the water. In this study, we use three‐dimensional global positioning system data to develop a geodetic drought index (GDI). Comparison with independent hydrologic observations indicates strong regional and temporal correlations with reservoir storage, groundwater fluctuations, and stream discharge observations, suggesting the GDI can effectively characterize variations in total hydrologic storage. The GDI provides an opportunity to improve hydrologic models for drought management and to advance our understanding of the water cycle. Key Points Current drought assessment methods rely primarily on meteorologic drought indices that do not characterize total water storage The geodetic drought index quantifies hydrologic drought and is especially sensitive to groundwater and surface‐reservoir storage Drought metrics based on geodetic data improve characterization of total water storage, providing unique insight for drought management

Learn about the different forms that water takes, and the ways we interact with the various forms.

On the Tibetan Plateau, the limited ground-based rainfall information owing to a harsh environment has brought great challenges to hydrological studies. Satellite-based rainfall products, which allow for a better coverage than both radar network and rain gauges on the Tibetan Plateau, can be suitable alternatives for studies on investigating the hydrological processes and climate change. In this study, a newly developed daily satellite-based precipitation product, termed Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks – Climate Data Record (PERSIANN-CDR), is used as input for a hydrologic model to simulate streamflow in the upper Yellow and Yangtze River basins on the Tibetan Plateau. The results show that the simulated streamflows using PERSIANN-CDR precipitation and the Global Land Data Assimilation System (GLDAS) precipitation are closer to observation than that using limited gauge-based precipitation interpolation in the upper Yangtze River basin. The simulated streamflow using gauge-based precipitation are higher than the streamflow observation during the wet season. In the upper Yellow River basin, gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation have similar good performance in simulating streamflow. The evaluation of streamflow simulation capability in this study partly indicates that the PERSIANN-CDR rainfall product has good potential to be a reliable dataset and an alternative information source of a limited gauge network for conducting long-term hydrological and climate studies on the Tibetan Plateau.

\"The water cycle becomes a down-to-earth reality when children follow Pitter on his overland journey from cloud to ocean, and Patter on her journey from cloud to ocean by way of an underground route. In the ocean they meet and join in a cloud once again. 'Explore More' endnotes provide additional explanations of water cycle principles\"-- Provided by the publisher.

Hydrologic modeling faces challenges due to various sources of uncertainty, the inherent nonlinearity, and high dimensionality of Earth systems. Data assimilation (DA) methods are known to improve the accuracy and account for uncertainties in modeling; however, they may be limited by restrictive assumptions about error distributions and challenges associated with updating model prognostic variables, hence, representing the posterior distributions. To address these challenges, we present a new hydrologic DA method inspired by the similarities in theoretical backgrounds of DA and generative deep learning. The proposed Hydrologic Generative Ensemble Data Assimilation (HydroGEnDA) leverages deep learning‐based autoencoders, and deep generative modeling to perform DA in a unified latent space and finally a resampling method in physical space. The HydroGEnDA benefits from an autoencoder that transforms data to a latent space and a generative model that learns the underlying distribution of model states, then conditioning the sampling from this distribution to the observed data. Finally, resampling in physical space further improves the performance of the DA method. The HydroGEnDA involves an offline training stage without relying on observations, utilizing hydrologic model outputs instead to train the deep learning models. Following the training stage, the inference stage assimilates observed data to update the states. The method is tested through several synthetic experiments with varying observation noise levels with the Lorenz‐63 model, as well as real hydrologic case studies using the coupled SNOW‐17 and SAC‐SMA models across diverse watersheds. The results demonstrate that the HydroGEnDA outperforms previous DA methods in both experiments.

\"[Readers] will learn about the water cycle, discovering how rain and snow flow into our lakes, rivers, and oceans, and later evaporate into the sky again.\"--Amazon.com.

Satellite remote sensing enhances model predictions by providing insights into terrestrial and hydrological processes. While data assimilation techniques have proven promising, there is a lack of standardized and effective approaches for integrating multiple observations simultaneously. This study presents a novel assimilation framework, the multi‐observation local ensemble‐Kalman‐filter (MoLEnKF), designed to effectively integrate multiple variables, even at scales different than the model. Evaluation of MoLEnKF in the Amazon River basin includes assimilation experiments with remote sensing data only, including water surface elevation (WSE), terrestrial water storage (TWS), flood extent (FE), and soil moisture (SM). MoLEnKF demonstrates improvements in a scenario where regions lack in‐situ hydroclimatic records and when assuming uncertainties of large‐scale hydrologic‐hydrodynamic models. Assimilating WSE outperforms daily discharge and water‐level estimations, achieving 38% and 36% error reduction, respectively. However, the monthly evapotranspiration estimate achieves the greatest error reduction by assimilating SM with 11%. MoLEnKF always remains in second position in a ranking of error and uncertainty reduction, providing an intermediate condition, being able to holistically outperform univariate experiments. MoLEnKF also outperform state‐of‐the‐art models in many cases. This study suggests potential improvements, urging exploration of correlations between assimilated variables and adaptive localization methods based on seasonality. The flexibility and the elegant way of expressing the LEnKF equations by MoLEnKF facilitates their application with different types of variables, compatible with large‐scale hydrologic‐hydrodynamic models and missions such as SWOT. Its robustness ensures easy replicability worldwide, facilitating hydrological reanalysis and improved forecasting, establishing MoLEnKF as a valuable tool for the scientific community in hydrological research.