Riley Chad Hales

Water balance calculations at the watershed scale are fundamental to water resource planning and the sustainable management of limited water supplies. These calculations rely on stream and canal gaging networks operated by local, state and federal entities, whose availability has varied over time due to cost, staffing constraints, and limitations on suitable gaging locations. The Green River Basin (GRB) above Fontenelle Dam in Wyoming illustrates this trend, where the number of operational stream gaging sites has varied over time and the majority of locations have less than 15 years of streamflow records. Recent advancements in the ability to perform streamflow reconstruction and estimate agricultural water use offer a new avenue for estimating the water balance for watersheds with discontinuous gage observations. But the use of these datasets and approaches has not been tested. Therefore, this paper proposes and tests a novel framework that combines discontinuous streamflow observations with new datasets (OpenET, ET-Demands, and GEOGLOWS) to calculate monthly water balances in the GRB from water year 1991 to 2023. Focusing on two main test basins, the Green River and the New Fork River, the integration of modern datasets enables the successful calculation of the water balance in the GRB with good agreement with downstream gaging records, achieving a Nash–Sutcliffe efficiency (NSE) of 0.88 for the New Fork River and 0.80 for the Green River. By improving the ability to quantify water balance components in data-limited basins, this framework supports more transparent water accounting and informed decision-making for sustainable water management, including irrigation planning, drought response, and long-term resource allocation in semi-arid river systems.

Accurate streamflow information is critical for early flood and drought warning. However, global hydrological forecasting systems are affected by residual errors in meteorological forcing, model structure, and routing, which propagate into simulated streamflow. Within the GEOGLOWS River Forecast System (RFS), ERA5 runoff biases are routed into streamflow simulations. The most effective operational bias-correction method, MFDC-QM, requires local discharge observations and cannot be applied consistently in ungauged basins. This study evaluates a pre-routing, grid-scale runoff bias-correction framework that adjusts ERA5 runoff before routing by combining Flow Duration Curve (FDC) mapping and Sparse Cumulative Distribution Function (CDF) matching, using GSCD as a spatially distributed reference runoff data. Baseline GEOGLOWS RFS, pre-routing correction, and MFDC-QM were compared for 1980–2025 using 16,517 gauging stations, Kling–Gupta Efficiency (KGE), and paired significance tests. Globally, the median KGE increased modestly from 0.16 to 0.22, compared with 0.48 for MFDC-QM. Results demonstrate a clear regional dependence: pre-routing correction produced statistically significant gains in South America and Africa (p < 0.05), where ERA5 runoff exhibits stronger residual biases, but had limited effects in Europe and North America, where dense hydrometeorological networks likely impose stronger observational constraints on the underlying reanalysis. These patterns show that pre-routing correction is most valuable where residual forcing bias is large and observational constraints are limited, complementing observation-based post-processing in ungauged, data-limited regions.

In response to the growing need for effective hydrological hazard forecasting under increasing climate variability, this chapter presents the Global Water Level Forecast System (GWLFS), an open-source web platform that integrates Earth Observation data with ensemble hydrological forecasts to support real-time flood risk monitoring worldwide. The system combines satellite-derived water level observations from the Hydroweb project (over 20 000 virtual stations) with GEOGLOWS ECMWF streamflow simulations, applying a discharge-to-water level transformation (DWLT) method to generate a retrospective water level simulation and a 15 day 52-ensemble water level forecast. Using monthly duration curves, DWLT statistically links simulated discharge to satellite altimetry, enabling the estimation of synthetic water level time series and high-flow thresholds based on Gumbel-distributed return periods. Transformed outputs are visualized through an interactive web application built on the Tethys Platform, offering dynamic colour-coded warnings, forecast animations, historical comparisons and performance metrics. The system architecture utilizes high-performance tools such as Polars and a microservices backend to enable scalability and rapid processing across more than 25 000 monitoring stations. Validation using the Kling–Gupta efficiency (KGE) metric demonstrates the model's reliability across diverse hydroclimatic regions.

This study assesses the accuracy of flood extent predictions in five U.S. watersheds. We generated flood maps for four return periods using various digital elevation models (DEMs)—FABDEM, SRTM, ALOS, and USGS 3DEP—and two versions of the GEOGLOWS River Forecast System (RFS) hydrography. These comparisons are notable because they build on operational global hydrology models so subsequent work can develop global modeled flood products. Models were made using the Automated Rating Curve (ARC) and Curve2Flood tools. Accuracy was measured against USGS reference maps using the F-statistic. Our results show that flood map accuracy generally increased with higher return periods. The most consistent and reliable improvements in accuracy occurred when both the DEM and hydrography datasets were upgraded to higher-resolution sources. While DEM improvements generally had a greater impact, hydrography refinements were more important for lower return periods when flood extents were the smallest. Generally, DEM resolution improved accuracy metrics more as the return period increased and hydrography and bare earth DEMs mattered more as the return period decreased. There was a 38.9% increase in the mean F-statistic between the two principal pairings of interest (FABDEM-RFS2 and SRTM 30 m DEM-RFS1). FABDEM’s bare-earth representation combined with RFS2 sometimes outperformed higher-resolution non-bare-earth DEMs, suggesting that there remains a need for site-specific investigation. Using ARC and Curve2Flood with FABDEM and RFS2 is a suitable baseline combination for general flood extent application.

Flooding is a global problem that impacts people, communities, and governments every year. A better understanding of flooding in an area can enable an improved emergency response before a flood hits. Flood maps are a crucial tool to translate what, for most, is an abstract streamflow into a more understandable and actionable representation of who and what is at risk. Satellite-based flood maps are a useful tool that has potential global applications. We developed methods to determine areas that are suitable for generating satellite-based synthetic flood maps. For our processes, we used Forecasting Inundation Extents using REOF analysis (FIER), a data-driven method of synthesizing flood maps by correlating extracted spatial and temporal patterns from satellite imagery with historical hydrological variables. To overcome the limitation of only using places where gauges are installed, we used large-scale hydrological models, namely the National Water Model (NWM) and the GEOGLOWS Streamflow Model, to provide simulated retrospective streamflow data to train our model. We evaluated locations where both optical and radar imagery would be suitable for creating these models. The procedures we developed and the results that we obtained are potentially transferable to many satellite data sources and methods of model generation.

Effective decision-making in water resource management requires timely and reliable streamflow information. This study demonstrates how the GEOGLOWS Hydrologic Model, River Forecast System (RFS), can generate actionable hydrologic status products, focusing on a case study in the Nile River Basin. Through collaboration with stakeholders at the Nile Basin Initiative (NBI), we identify key information needs and apply standardized low flow calculations, including the Standardized Streamflow Index (SSI) and the 95th percentile (Q95) threshold, to assess stream conditions. Additionally, we apply the World Meteorological Organization’s (WMO) Hydrologic Status and Outlook System (HydroSOS) method for streams and generate the associated HydroSOS-styled graphs and maps. We present the hydrologic status products in a customized web application for stakeholders in the Nile Basin. We discuss how RFS can be applied globally to provide hydrologic information.

Flood early warning systems are crucial for disaster risk reduction strategies, enabling communities to take timely action against threats. However, the effectiveness of these systems depends on accurate and timely hydrological data, particularly river discharge and water level measurements. Unfortunately, many regions face significant challenges in obtaining hydrological data, especially discharge data, due to outdated rating curves, high equipment costs, and logistical constraints. In contrast, water-level measurements offer reduced uncertainty and are often more accessible, providing an alternative to hydrological modeling in data-scarce regions. To address these limitations, we developed and validated the Discharge-to-Water Level Transformation (DWLT) method, which uses the monthly duration curves to transform discharge simulations from the GEOGLOWS ECMWF Global Hydrological Model into water level predictions using data from over 19,000 ground- and satellite-based river gauge stations. The results indicate that the water levels generated by DWLT are closely aligned with the observed water levels, especially when using satellite measurements, which offer a valuable alternative when ground-based data are scarce. Despite quality issues such as spikes and zero-level inconsistencies in ground-based data and temporal limitations such as short monitoring periods and infrequent measurements in satellite-based data, the methodology shows promising potential for large-scale and local hydrological applications. This work supports future flood forecasting and water resource management efforts, highlighting water level as an effective variable in hydrological modeling.

Temperature and precipitation data are crucial for hydrology and meteorology. In 2014, Ecuador started an automatic gauge network which monitors these variables. The measurements are not publicly available. Global gridded datasets from numerical models and remote sensors were previously the only way to obtain measurements for temperature and precipitation. Now that in situ measurements are beginning to be available in significant quantities, we assessed the performance of IMERG, CHIRPS, GLDAS and ERA5 for both temperature and precipitation using the in situ data. We used the Pearson R correlation coefficient, ME (Mean Error), MAE (Mean Absolute Error), and RMSE (Root Mean Square Error). We found that global gridded data were more suited for determining averages over time rather than for giving exact values at specific times for in situ gauges. The Pearson R values increased for all datasets when we used monthly aggregations in place of daily aggregations, suggesting that the monthly values are more correlated than the daily. The Pearson R value for temperature increased from 0.158 to 0.719 for the ERA5 dataset. Additionally, we show the statistical values for each of the three regions in Ecuador. We found that the IMERG and CHIRPS datasets, which contain station data, performed significantly better for both RMSE and MAE. Both IMERG and CHIRPS have a RMSE value a little over 260, whereas ERA5 and GLDAS had values over 300. We discuss the short comings of these datasets as being related to their relatively coarse resolution, lack of in situ data in Ecuador to calibrate against, and the rapidly varying terrain of Ecuador. We recommend using higher temporal and spatial resolution datasets for immediate applications. We recommend repeating this analysis in the future when more automatic gauges and longer time periods are available to facilitate a more detailed analysis which is presently not possible.

Atmospheric deposition (AD) is a less understood and quantified source of nutrient loading to waterbodies. AD occurs via settling (large particulates), contact (smaller particulates and gaseous matter), and precipitation (rain, snow) transport pathways. Utah Lake is a shallow eutrophic freshwater lake located in central Utah, USA, with geophysical characteristics that make it particularly susceptible to AD-related nutrient loading. Studies have shown AD to be a significant contributor to the lake’s nutrient budget. This study analyzes nutrient samples from nine locations around the lake and four precipitation gauges over a 6-year study period using three different methods to estimate AD from the precipitation transport pathway. The methods used are simple averaging, Thiessen polygons, and inverse distance weighting, which we use to spatially interpolate point sample data to estimate nutrient lake loads. We hold that the inverse distance weighting method produces the most accurate results. We quantify, present, and compare nutrient loads and nutrient loading rates for total phosphorus (TP), total inorganic nitrogen (TIN), and ortho phosphate (OP) from precipitation events. We compute loading rates for the calendar year (Mg/yr) from each of the three analysis methods along with monthly loading rates where Mg is 10⁶ g. Our estimated annual precipitation AD loads for TP, OP, and TIN are 120.96 Mg/yr (132.97 tons/yr), 60.87 Mg/yr (67.1 tons/yr), and 435 Mg/yr (479.5 tons/yr), respectively. We compare these results with published data on total AD nutrient loads and show that AD from precipitation is a significant nutrient source for Utah Lake, contributing between 25% and 40% of the total AD nutrient load to the lake.