6 May 2024

Monitoring Geomorphology to Inform Ecological Outcomes Downstream of Reservoirs Affected by Sediment Release

Abstract: Increasingly, reservoir managers are seeking techniques that improve sediment management while considering long-term sedimentation and reduced operational flexibility. These techniques, often termed sustainable sediment management, involve passing sediment through reservoirs and into downstream rivers. Conceptually, restoring sediment continuity can benefit ecosystem function by increasing floodplain connectivity, contributing to the heterogeneity of channel geomorphology, and supporting the continuity of nutrient cycling. However, when a change is made to operations, geomorphic changes may need to be monitored to document benefits and mitigate any unexpected effects of the change. This investigation develops a geomorphic monitoring plan for downstream reaches affected by sediment-release operations at reservoirs. The monitoring objectives are aligned with potential geomorphic change caused by changes to sediment supply and the associated effects on river function. A tiered approach is presented to explain the quality of information that can be assessed from increasing levels of data collection. A general conceptual model is described in which geomorphic data may be linked to physical habitat conditions and, therefore, ecological processes. The geomorphic monitoring plan for the Tuttle Creek Reservoir water injection dredging (WID) pilot project is presented as a case study. This technical note establishes a general framework for monitoring the design for sustainable sediment management in different ecological and geomorphic contexts.

2 May 2024

Data-Driven Modeling of Groundwater Level Using Machine Learning

Purpose: This US Army Engineer Research and Development Center (ERDC), Coastal and Hydraulics Laboratory engineering technical note (CHETN) documents a preliminary study on the use of specialized machine learning (ML) methods to model the variations in groundwater level (GWL) with time. This approach uses historical groundwater observation data at seven gage locations in Wyoming, USA, available from the USGS database and historical data on several relevant meteorological variables obtained from the ERA5 reanalysis dataset produced by the Copernicus Climate Change Service (usually referred to as C3S) at the European Center for Medium-Range Weather Forecasts to predict future GWL values for a desired period of time. The results presented in this report indicate that the ML method has the potential to predict both short-term (4-hourly) as well as daily variations in GWL several days into the future for the chosen study region, thus alleviating the need for employing sophisticated process-based numerical models with complicated model structure configurations.