An increase in extreme rainfall frequency across the midwestern United States has been accompanied by an increase in damaging floods. The US has over 90,000 dams, more than 75% of which are small and rarely used for flood mitigation. Recent research focused on operating these ponds for flood reduction using gated outlets, a technique known as activated distributed storage, has confirmed its potential for reducing flood impacts. Here, the authors build upon this work by developing a hydrologic model to simulate the active management of a distributed network of 130 ponds that employs up to 18 h of forecasted rainfall for operational decision making, a process known as forecast-informed reservoir operation (FIRO). Using five observed rainfall events and a single dam operations scheme, the effects of using FIRO for real-time gate operations on both downstream peak flows and basin wide storage utilization are evaluated. Simulation results that use the high-resolution rapid refresh (HRRR) product, were compared to those that (1) use no rainfall forecasts for decision making; and (2) use 18 h of observed rainfall mimicking an ideal forecast. Regardless of forecast accuracy or rainfall accumulation, shorter forecast lead times result in operational decisions that release water early in an event, vacating storage, while longer lead times result in increased storage throughout an event, thus reducing downstream flows. These results indicate that rainfall forecasts may not be solely capable of addressing the complexities governing a distributed storage network's ability to release water. This suggests that a more nuanced approach, utilizing optimal control of the storage network is required to unlock the technique's full potential.

Flash floods induced by high-intensity and short-duration monsoon rainfall can cause severe damage in arid regions. To properly size in-stream infrastructures, such as levees, bridges, and culverts, it is crucial to accurately calculate the peak runoff and sediment load from these flash floods. This case study utilized the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model to simulate flash floods and sediment transport in the Lucky Hills watershed located in the Walnut Gulch Experimental Watershed in southern Arizona. The Lucky Hills watershed has two rain gauges and three flumes to measure runoff and sediment load. The HEC-HMS model was used to simulate the three largest precipitation events observed in 2007, 2009, and 2010 with precipitation volumes of 41.66, 46.36, and 37.85 mm, and durations of 126, 99, and 101 min, respectively. The study discussed various methods for simulating rainfall loss, surface and channel flow routing, and soil erosion. Watershed delineations were adopted to evaluate the accuracy of the simulated runoff and sediment concentration. Results showed that the HEC-HMS model can accurately predict surface runoff and sediment concentration, but the threshold value for subbasin size is critical for the model to converge to accurate results.

Prediction of snowmelt has become a critical issue in much of the western United States given the increasing demand for water supply, changing snow cover patterns, and the subsequent requirement of optimal reservoir operation. The increasing importance of hydrologic predictions necessitates that traditional forecasting systems be re-evaluated periodically to assure continued evolution of the operational systems given scientific advancements in hydrology. The National Weather Service (NWS) SNOW17, a conceptually based model used for operational prediction of snowmelt, has been relatively unchanged for decades. In this study, the Snow-Atmosphere-Soil Transfer (SAST) model, which employs the energy balance method, is evaluated against the SNOW17 for the simulation of seasonal snowpack (both accumulation and melt) and basin discharge. We investigate model performance over a 13-year period using data from two basins within the Reynolds Creek Experimental Watershed located in southwestern Idaho. Both models are coupled to the NWS runoff model [SACramento Soil Moisture Accounting model (SACSMA)] to simulate basin streamflow. Results indicate that while in many years simulated snowpack and streamflow are similar between the two modeling systems, the SAST more often overestimates SWE during the spring due to a lack of mid-winter melt in the model. The SAST also had more rapid spring melt rates than the SNOW17 7, leading to larger errors in the timing and amount of discharge on average. In general, the simpler SNOW17 performed consistently well, and in several years, better than, the SAST model. Input requirements and related uncertainties, and to a lesser extent calibration, are likely to be primary factors affecting the implementation of an energy balance model in operational streamflow prediction. (C) 2008 Elsevier B.V. All rights reserved.