Streamflow forecasting over gauged and ungauged basins play a vital role in water resources planning, especially under the changing climate. Increased availability of large sample hydrology data sets, together with recent advances in deep learning techniques, has presented new opportunities to explore temporal and spatial patterns in hydrological signatures for improving streamflow forecasting. The purpose of this study is to adapt and benchmark several state‐of‐the‐art graph neural network (GNN) architectures, including ChebNet, Graph Convolutional Network (GCN), and GraphWaveNet, for end‐to‐end graph learning. We explicitly represent river basins as nodes in a graph, learn the spatiotemporal nodal dependencies, and then use the learned relations to predict streamflow simultaneously across all nodes in the graph. The efficacy of the developed GNN models is investigated using the Catchment Attributes and MEteorology for Large‐sample Studies (CAMELS) data set under two settings, fixed graph topology (transductive learning), and variable graph topology (inductive learning), with the latter applicable to prediction in ungauged basins (PUB). Results indicate that GNNs are generally robust and computationally efficient, achieving similar or better performance than a baseline model trained using the long short‐term memory (LSTM) network. Further analyses are conducted to interpret the graph learning process at the edge and node levels and to investigate the effect of different model configurations. We conclude that graph learning constitutes a viable machine learning‐based method for aggregating spatiotemporal information from a multitude of sources for streamflow forecasting

Rivers and river habitats around the world are under sustained pressure from human activities and the changing global environment. Our ability to quantify and manage the river states in a timely manner is critical for protecting the public safety and natural resources. In recent years, vector-based river network models have enabled modeling of large river basins at increasingly fine resolutions, but are computationally demanding. This work presents a multistage, physics-guided, graph neural network (GNN) approach for basin-scale river network learning and streamflow forecasting. During training, we train a GNN model to approximate outputs of a high-resolution vector-based river network model; we then fine-tune the pretrained GNN model with streamflow observations. We further apply a graph-based, data-fusion step to correct prediction biases. The GNN-based framework is first demonstrated over a snow-dominated watershed in the western United States. A series of experiments are performed to test different training and imputation strategies. Results show that the trained GNN model can effectively serve as a surrogate of the process-based model with high accuracy, with median Kling–Gupta efficiency (KGE) greater than 0.97. Application of the graph-based data fusion further reduces mismatch between the GNN model and observations, with as much as 50 % KGE improvement over some cross-validation gages. To improve scalability, a graph-coarsening procedure is introduced and is demonstrated over a much larger basin. Results show that graph coarsening achieves comparable prediction skills at only a fraction of training cost, thus providing important insights into the degree of physical realism needed for developing large-scale GNN-based river network models.

Anthropogenic activities, especially dam operations, often induce larger and more frequent stage fluctuations than those occurring in natural rivers. However, long‐term impacts of such flow variations on thermal and biogeochemical dynamics of the associated hyporheic zone (HZ) are poorly understood. In this study, we built a heterogeneous, two‐dimensional, thermo‐hydro‐biogeochemical model for an aerobic respiration dominated river system. Our results revealed important interactions between subdaily to weekly flow variations and mean flow conditions controlled by snowpack at the watershed. High‐frequency stage fluctuations had their strongest thermal and biogeochemical impacts when mean river stage was low during fall and winter. In an abnormal drought year (2015) with low river stages during summer and early fall, high‐frequency stage fluctuations caused the HZ to be warmer than average. Furthermore, high‐frequency stage fluctuation enhanced all biogeochemical reactions by increasing nutrient supply and creating more oxygenated conditions. Overall carbon consumption in the HZ increased due to high‐frequency flow variations. Thermal dynamics altered by high‐frequency stage fluctuation impacted biogeochemical reactions in the HZ less than effects imposed by enhanced nutrient and oxygen supply. In addition to these results, we demonstrated that the HZ's hydrogeologic properties control flow paths that influence residence times and nutrient supply and consequently control spatial distribution of biogeochemical reaction hot spots in the HZ. Here we provide scientific basis for assessing potential ecological consequences of high‐frequency flow variations in a regulated river, as well as guidance for maximizing potential benefits—or minimizing drawbacks—of dam regulation to riverine ecosystems.

There are no direct methods to evaluate calculated soil heat flux (SHF) at the surface (G₀). Instead, validation and cross evaluation of methods for calculating G₀ usually rely on the conventional calorimetric method or the degree of the surface energy balance closure. However, there is uncertainty in the calorimetric method itself, and factors apart from G₀ also contribute to nonclosure of the surface energy balance. Here we used a novel approach to evaluate nine different methods for calculating SHF, including the calorimetric method and methods based on analytical solutions of the heat diffusion equation. The SHF (G_z) measured by a self‐calibrating SHF plate at a depth of z = 5 cm below the surface (hereafter G_{m_5cm}) was deployed as a reference. Each SHF calculation method was assessed by comparing the calculated G_z at the same depth (hereafter G_{c_5cm}) with G_{m_5cm}. The calorimetric method and simple measurement method performed best in determining G_{c_5cm} but still underestimated G_{m_5cm} by 19% during the daytime. Possible causes for this underestimation include errors and uncertainties in SHF measurements and soil thermal properties, as well as the phase lag between G_{c_5cm} and G_{m_5cm}. Our results indicate that the calorimetric method achieves the most accurate SHF estimates if self‐calibrating SHF plates are deployed at two depths (e.g., 5 cm and 10 cm), soil temperature and water content measurements are made in a few depths between the two plates, and soil thermal properties are accurately quantified.

Quantifying the spatiotemporal dynamics in subsurface hydrological flows over a long time window usually employs a network of monitoring wells. However, such observations are often spatially sparse with potential temporal gaps due to poor quality or instrument failure. In this study, we explore the ability of recurrent neural networks to fill gaps in a spatially distributed time-series dataset. We use a well network that monitors the dynamic and heterogeneous hydrologic exchanges between the Columbia River and its adjacent groundwater aquifer at the U.S. Department of Energy's Hanford site. This 10-year-long dataset contains hourly temperature, specific conductance, and groundwater table elevation measurements from 42 wells with gaps of various lengths. We employ a long short-term memory (LSTM) model to capture the temporal variations in the observed system behaviors needed for gap filling. The performance of the LSTM-based gap-filling method was evaluated against a traditional autoregressive integrated moving average (ARIMA) method in terms of error statistics and accuracy in capturing the temporal patterns of river corridor wells with various dynamics signatures. Our study demonstrates that the ARIMA models yield better average error statistics, although they tend to have larger errors during time windows with abrupt changes or high-frequency (daily and subdaily) variations. The LSTM-based models excel in capturing both high-frequency and low-frequency (monthly and seasonal) dynamics. However, the inclusion of high-frequency fluctuations may also lead to overly dynamic predictions in time windows that lack such fluctuations. The LSTM can take advantage of the spatial information from neighboring wells to improve the gap-filling accuracy, especially for long gaps in system states that vary at subdaily scales. While LSTM models require substantial training data and have limited extrapolation power beyond the conditions represented in the training data, they afford great flexibility to account for the spatial correlations, temporal correlations, and nonlinearity in data without a priori assumptions. Thus, LSTMs provide effective alternatives to fill in data gaps in spatially distributed time-series observations characterized by multiple dominant frequencies of variability, which are essential for advancing our understanding of dynamic complex systems.

Meteorological forcing plays a critical role in accurately simulating the watershed hydrological cycle. With the advancement of high-performance computing and the development of integrated watershed models, simulating the watershed hydrological cycle at high temporal (hourly to daily) and spatial resolution (tens of meters) has become efficient and computationally affordable. These hyperresolution watershed models require high resolution of meteorological forcing as model input to ensure the fidelity and accuracy of simulated responses. In this study, we utilized the Advanced Terrestrial Simulator (ATS), an integrated watershed model, to simulate surface and subsurface flow and land surface processes using unstructured meshes at the Coal Creek Watershed near Crested Butte (Colorado). We compared simulated watershed hydrologic responses including streamflow and distributed variables such as evapotranspiration, snow water equivalent (SWE), and groundwater table driven by three publicly available, gridded meteorological forcings (GMFs) – Daily Surface Weather and Climatological Summaries (Daymet), the Parameter-elevation Regressions on Independent Slopes Model (PRISM), and the North American Land Data Assimilation System (NLDAS). By comparing various spatial resolutions (ranging from 400 m to 4 km) of PRISM, the simulated streamflow only becomes marginally worse when spatial resolution of meteorological forcing is coarsened to 4 km (or 30 % of the watershed area). However, the 4 km-resolution has much worse performance than finer resolution in spatially distributed variables such as SWE. Using the temporally disaggregated PRISM, we compared models forced by different temporal resolutions (hourly to daily), and sub-daily resolution preserves the dynamic watershed responses (e.g., diurnal fluctuation of streamflow) that are absent in results forced by daily resolution. Conversely, the simulated streamflow shows better performance using daily resolution compared to that using sub-daily resolution. Our findings suggest that the choice of GMF and its spatiotemporal resolution depends on the quantity of interest and its spatial and temporal scale, which may have important implications for model calibration and watershed management decisions.