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1. Hydrometeorological Observations and Modeling of an Extreme Rainfall Event Using WRF and WRF-Hydro during the RELAMPAGO Field Campaign in Argentina

   https://doi.org/10.1175/JHM-D-20-0133.1  

   Pal, Sujan; Dominguez, Francina; Dillon, María Eugenia; Alvarez, Javier; Garcia, Carlos Marcelo; Nesbitt, Stephen W.; Gochis, David (February 2021, Journal of Hydrometeorology)

   null (Ed.)

   Abstract Some of the most intense convective storms on Earth initiate near the Sierras de Córdoba mountain range in Argentina. The goal of the RELAMPAGO field campaign was to observe these intense convective storms and their associated impacts. The intense observation period (IOP) occurred during November–December 2018. The two goals of the hydrometeorological component of RELAMPAGO IOP were 1) to perform hydrological streamflow and meteorological observations in previously ungauged basins and 2) to build a hydrometeorological modeling system for hindcast and forecast applications. During the IOP, our team was able to construct the stage–discharge curves in three basins, as hydrological instrumentation and personnel were successfully deployed based on RELAMPAGO weather forecasts. We found that the flood response time in these river locations is typically between 5 and 6 h from the peak of the rain event. The satellite-observed rainfall product IMERG-Final showed a better representation of rain gauge–estimated precipitation, while IMERG-Early and IMERG-Late had significant positive bias. The modeling component focuses on the 48-h simulation of an extreme hydrometeorological event that occurred on 27 November 2018. Using the Weather Research and Forecasting (WRF) atmospheric model and its hydrologic component WRF-Hydro as an uncoupled hydrologic model, we developed a system for hindcast, deterministic forecast, and a 60-member ensemble forecast initialized with regional-scale atmospheric data assimilation. Critically, our results highlight that streamflow simulations using the ensemble forecasting with data assimilation provide realistic flash flood forecast in terms of timing and magnitude of the peak. Our findings from this work are being used by the water managers in the region. 

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2. Spatial bias in medium-range forecasts of heavy precipitation in the Sacramento River basin: Implications for water management

   https://doi.org/https://doi.org/10.1175/JHM-D-19-0226.1  

   Brodeur, Zachary; Steinschneider, Scott (June 2020, Journal of hydrometeorology)

   Forecasts of heavy precipitation delivered by atmospheric rivers (ARs) are becoming increasingly important for both flood control and water supply management in reservoirs across California. This study examines the hypothesis that medium-range forecasts of heavy precipitation at the basin scale exhibit recurrent spatial biases that are driven by mesoscale and synoptic-scale features of associated AR events. This hypothesis is tested for heavy precipitation events in the Sacramento River basin using 36 years of NCEP medium-range reforecasts from 1984 to 2019. For each event we cluster precipitation forecast error across western North America for lead times ranging from 1 to 15 days. Integrated vapor transport (IVT), 500-hPa geopotential heights, and landfall characteristics of ARs are composited across clusters and lead times to diagnose the causes of precipitation forecast biases. We investigate the temporal evolution of forecast error to characterize its persistence across lead times, and explore the accuracy of forecasted IVT anomalies across different domains of the North American west coast during heavy precipitation events in the Sacramento basin. Our results identify recurrent spatial patterns of precipitation forecast error consistent with errors of forecasted synoptic-scale features, especially at long (5–15 days) leads. Moreover, we find evidence that forecasts of AR landfalls well outside of the latitudinal bounds of the Sacramento basin precede heavy precipitation events within the basin. These results suggest the potential for using medium-range forecasts of large-scale climate features across the Pacific–North American sector, rather than just local forecasts of basin-scale precipitation, when designing forecast-informed reservoir operations. 

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3. Snow Phenology and Hydrologic Timing in the Yukon River Basin, AK, USA

   https://doi.org/10.3390/rs13122284  

   Pan, Caleb G.; Kirchner, Peter B.; Kimball, John S.; Du, Jinyang; Rawlins, Michael A. (June 2021, Remote Sensing)

   The Yukon River basin encompasses over 832,000 km2 of boreal Arctic Alaska and northwest Canada, providing a major transportation corridor and multiple natural resources to regional communities. The river seasonal hydrology is defined by a long winter frozen season and a snowmelt-driven spring flood pulse. Capabilities for accurate monitoring and forecasting of the annual spring freshet and river ice breakup (RIB) in the Yukon and other northern rivers is limited, but critical for understanding hydrologic processes related to snow, and for assessing flood-related risks to regional communities. We developed a regional snow phenology record using satellite passive microwave remote sensing to elucidate interactions between the timing of upland snowmelt and the downstream spring flood pulse and RIB in the Yukon. The seasonal snow metrics included annual Main Melt Onset Date (MMOD), Snowoff (SO) and Snowmelt Duration (SMD) derived from multifrequency (18.7 and 36.5 GHz) daily brightness temperatures and a physically-based Gradient Ratio Polarization (GRP) retrieval algorithm. The resulting snow phenology record extends over a 29-year period (1988–2016) with 6.25 km grid resolution. The MMOD retrievals showed good agreement with similar snow metrics derived from in situ weather station measurements of snowpack water equivalence (r = 0.48, bias = −3.63 days) and surface air temperatures (r = 0.69, bias = 1 day). The MMOD and SO impact on the spring freshet was investigated by comparing areal quantiles of the remotely sensed snow metrics with measured streamflow quantiles over selected sub-basins. The SO 50% quantile showed the strongest (p < 0.1) correspondence with the measured spring flood pulse at Stevens Village (r = 0.71) and Pilot (r = 0.63) river gaging stations, representing two major Yukon sub-basins. MMOD quantiles indicating 20% and 50% of a catchment under active snowmelt corresponded favorably with downstream RIB (r = 0.61) from 19 river observation stations spanning a range of Yukon sub-basins; these results also revealed a 14–27 day lag between MMOD and subsequent RIB. Together, the satellite based MMOD and SO metrics show potential value for regional monitoring and forecasting of the spring flood pulse and RIB timing in the Yukon and other boreal Arctic basins. 

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4. Cool-Season Precipitation Forecast Evaluation over the Headwaters of Central Valley and the Colorado River Basin

   Tien, YC; Gebremichael, M (April 2026, Climate dynamics)

   Winter precipitation forecasting at sufficient lead times has several benefits, including aiding water allocation decisions and supporting individual water users’ decision-making. This study evaluates the performance of ensemble-mean cool-season (December through March) precipitation forecasts from individual models within the North American Multi-Model Ensemble (NMME) over the Colorado River Basin and California’s Sacramento-San Joaquin-Tulare basins (hereafter referred to as the SST). These ensemble-mean forecasts are compared to a newly developed statistical forecasting model, using the rain-gauge-based Parameter-elevation Regressions on Independent Slopes Model (PRISM) as the reference product. While NMME models effectively capture the spatial pattern of mean precipitation, they struggle to predict year-to-year variability and extremes. Forecast skill is higher in the Colorado Basin than in the SST Basin. Anomaly correlations between ensemble-mean NMME forecasts and observations vary by model and basin, with GEM5-NEMO and GFDL-SPEAR showing relatively higher skill. Performance in forecasting droughts and wet/dry years remains inconsistent across models, with most models missing key events such as the 2023 and 2017 wet years and the 2022 drought. Simple statistical models using key atmospheric–oceanic predictors outperformed the more complex ensemble-mean NMME dynamical models in both basins. The most effective predictors were the Oceanic Niño Index (ONI), Tropical South Atlantic (TSA) sea surface temperatures, and the Quasi-Biennial Oscillation (QBO) for the SST Basin, and the North Atlantic Oscillation (NAO) and Tropical North Atlantic (TNA) sea surface temperatures for the Colorado Basin. 

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5. Knowledge‐Guided Machine Learning for Operational Flood Forecasting

   https://doi.org/10.1029/2024WR039064  

   McEachran, Zac; Ghosh, Rahul; Renganathan, Arvind; Sharma, Somya; Lindsay, Kelly; Steinbach, Michael; Nieber, John; Duffy, Christopher; Kumar, Vipin (November 2025, Water Resources Research)

   We present a knowledge‐guided machine learning framework for operational hydrologic forecasting at the catchment scale. Our approach, a Factorized Hierarchical Neural Network (FHNN), has two main components: inverse and forward models. The inverse model uses observed precipitation, temperature, and streamflow data to generate a representation of the current underlying catchment state. The forward model predicts streamflow using the learned catchment state. The FHNN architecture is designed to model multi‐scale processes and capture their interactions, a critical ability for flood modeling. FHNN also improves forecasts based on real‐time data through an inference‐based data integration approach using inverse modeling. FHNN's data integration approach improves forecasts in response to observed data more efficiently than data assimilation methods that require computationally intensive optimization. We compare the FHNN to a leading deep learning alternative (autoregressive LSTM) on the large‐sample CAMELS‐US data set, and operational flood forecast data from the US National Weather Service (NWS). Official NWS flood forecasts are generated by expert human forecasters using a physics‐based model, in a human‐in‐the‐loop process. Thus, we assess the flood forecast ability of FHNN by directly comparing its performance against these NWS expert‐derived forecasts. The human forecaster creates a more accurate forecast within the first 12–18 hr of a forecast's issuance, but FHNN has significantly better predictions thereafter. This research lays the groundwork for leveraging the predictive performance of AI‐based models with the expertise in forecasting agencies to produce better river forecasts. 

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