Bubbles entrained by spilled water at hydroelectric projects increase the concentration of total dissolved gas (TDG), which may lead to gas bubble disease in fish. In this paper, the TDG dynamics downstream of Wells Dam are investigated using a two-phase flow model that accounts for the effect of the bubbles on the flow field. The TDG is calculated with a transport equation in which the source is the bubble/liquid mass transfer, a function of the gas volume fraction and bubble size. The model uses anisotropic turbulence modeling and includes attenuation of normal fluctuation at the free surface to capture the flow field and TDG mixing. The model is validated using velocity and TDG field data. Simulations under two plant operational configurations are performed to gain a better understanding of the effect of spill operations on the production, transport, and mixing of TDG. Model results indicate that concentrated spill releases create surface jets that result in the lowest TDG concentration downstream. On ...

A quantitative understanding of the interplay between the different components of the hydrologic cycle at the watershed scale can be gained from analyzing high-frequency hydrologic time series. High-frequency measurements of precipitation, soil water content, shallow groundwater, and streamflow were collected and analyzed in Otter Creek, a 122 km² watershed located in Northeast Iowa, USA. For selected rainfall events occurring in 2014, it was found that there is at least 4 h of delay between soil water content and water table time series response and streamflow peak. This is true even when the water table was approximately 6.5 m below the ground surface before rainfall started. Data reveal a strong linear dependence between the soil water content and the water table, which suggests the existence of a capillary fringe that extends approximately 2.5 m above the water table. The highest streamflow values in Otter Creek occurred when both the water table was close to the ground surface and the near surface soil (top 65 cm) was close to full saturation. Analyses show that, in the study area, data on depth to water table or deep soil water content have the potential to play a key role in the development of a flood warning system. The transformation of rainfall into streamflow is a complex process that we simplified in this study. Additional analyses using physically based coupled surface-subsurface models or non-linear or stochastic models are recommended for more rigorous analysis.

Climate change has intensified the frequency and severity of droughts, significantly impacting water resources, agriculture, and ecosystems. Traditional drought indicators typically focus on recent conditions rather than future projections, and conventional forecasting methods often struggle to capture the complex, non-linear relationships between long-term climate variables and droughts. This project aims to fill this gap by developing a machine-learning model to project drought conditions in Iowa, specifically focusing on the U.S. Drought Monitor categories. The developed model, a Long Short-Term Memory neural network, was validated to assess its reliability and accuracy. With a Root Mean Squared Error of 0.19 and an R² of 91%, the model achieved a high level of accuracy, making it effective in guiding conservation practices and enabling timely interventions. The model was trained on historical data from 2012 to 2019 and thoroughly evaluated using out-of-sample data from 2002 to 2011. It exhibited strong performance in the projection of drought conditions across Iowa’s Hydrologic Unit Code 08 watersheds. Drought conditions for the period 2030–2050 were projected using three general circulation models (GCMs): MPI-ESM1-2-HR, BCC-CSM2-MR, and CNRM-ESM2-1. These projections were conducted under two contrasting Shared Socioeconomic Pathways (SSPs): SSP1-2.6, representing a low-emissions sustainability scenario, and SSP5-8.5, reflecting a high-emissions, fossil–fuel–intensive trajectory. Results indicate that droughts in the coming decades will become more intense, prolonged, and frequent, with projections suggesting intensities up to twice as severe and durations and frequencies in northwestern regions up to nine times higher than historical records. Moreover, this research developed an interactive application for visualizing future drought conditions in Iowa. This tool aids users in making informed water management decisions by providing stakeholders with detailed visualizations and technical information.