Dynamics of streamflow permanence in a headwater network: Insights from catchment-scale model simulations

Drainage network dynamics in an agricultural headwater sub-basin

Headwaters provide many ecosystems services. Currently, these vulnerable systems are subject to threats related to human activities. This work aims to analyse the spatial pattern changes (expansion/contraction) in the drainage network (DN) of a headwater sub-basin under agriculture between 1966 and 2019 in the Argentine Pampas Region. We study and discuss the hydrometeorological and land use context to understand the spatial and temporal dynamics of the DN, and propose a conceptual model that synthesizes the complex interactions between the factors involved in that dynamics. A broad (1950-2019, at the Del Azul Creek basin) and a short (1996-2019, at the sub-basin of the Videla Creek -SVC-) temporal and spatial scale analysis of data were carried out. We studied rainfall, evapotranspiration, water table depth, streamflow and land use. Temporal and spatial changes in the DN of the SVC were analysed by aerial photos and historical satellite images. Four wet and three dry periods were identified, and close surface-subsurface water interactions typical of plains, were found. The area under agriculture showed a first gradual increase (1975-2012), which turned sharp from 2012 (30,908 ha year-1), with a leading role of soybeans' sown area. The area of the DN increased 1.4699*105 m2 between 1966 and 2010, both under dry conditions, which evidenced its expansion. The study of the flatlands' particular hydrology within the current land use and management trends provided key elements to understand DN area's changes. Complex interactions between processes associated with climatic forcing and the system's sensitivity (its state to receive and process the inputs), are involved in the spatial and temporal dynamics of the DN. Our work improves the understanding of the functioning of these vulnerable systems within agricultural areas, nowadays under productive pressures associated with increasing global food demand, and threats to changes in the hydrological dynamics by global change.

Predicting wastewater treatment plant influent in mixed, separate, and combined sewers using nearby surface water discharge for better wastewater-based epidemiology sampling design

For wastewater sample collection approaches supporting public health applications, few high hydrologic activity normalizing guidelines currently consider readily available environmental flow data that may earlier capture information regarding periods of influent mixing and dilution of wastewater with groundwater and runoff. This study aimed to identify wastewater sampling rules for high hydrological activity events, allowing for an earlier decision point in the control of dilution before sample collection. We defined the sampling rules via data-driven models (Random Forest and linear regression) using environmental data (i.e., wastewater treatment facility influent rates, nearby stream discharge flow, and precipitation). These models were applied to five treatment plants in Jefferson County, Kentucky (USA) in mixed, separate, and combined sewers with different population sizes. We proposed cutoffs of 10 %, 25 %, and 50 % flow conditions for orientation towards public health samples. The results showed a strong nonlinear relationship between nearby stream discharge and treatment facility flow rates, which was used to infer the hydrological conditions that produce high volumes of diluted wastewater in the sewer system. Accumulated Local Effects and SHapley Additive exPlanations aided in deciphering the relationship between the predictors and response variables of the Random Forest models. The influent rate to the treatment plant from the previous day and two USGS stream gages were needed to adequately predict the degree of infiltration and inflow mixing on a given day. Surface water discharge data can be used to provide an earlier workflow decision point during wet weather periods to improve understanding of flow conditions for wastewater-based epidemiological studies to inform laboratory analysis and data interpretation. Not only total flow, but also the specific proportions of infiltration and inflow to wastewater volume in influent should be considered when analyzing data for normalization purposes, and our method provides a starting point for doing so rapidly and at low cost.

Non-perennial segments in river networks

Non-perennial river segments - those that recurrently cease to flow or frequently dry - occur in all river networks and are globally more abundant than perennial (always flowing) segments. However, research and management have historically focused on perennial river segments. In this Review, we outline how non-perennial segments are integral parts of river networks. Repeated cycles of flowing, non-flowing and dry phases in non-perennial segments influence biodiversity and ecosystem dynamics at different spatial scales, from individual segments to entire river networks. Varying configurations of perennial and non-perennial segments govern physical, chemical and ecological responses to changes in the flow regimes of each river network, especially in response to human activities. The extent of non-perennial segments in river networks has increased owing to warming, changing hydrological patterns and human activities, and this increase is predicted to continue. Moreover, the dry phases of flow regimes are expected to be longer, drier and more frequent, albeit with high regional variability. These changes will likely impact biodiversity, potentially tipping some ecosystems to compromised stable states. Effective river-network management must recognize ecosystem services (such as flood risk management and groundwater recharge) provided by non-perennial segments and ensure their legislative and regulatory protection, which is often lacking.