Reservoir trophic state confounds flow-ecology relationships in regulated streams

Reservoir-derived subsidies provide a potential management opportunity for novel river ecosystems

Aquatic ecosystems world-wide are being irreversibly altered, suggesting that new and innovative management strategies are necessary to improve ecosystem function and sustainability. In river ecosystems degraded by dams environmental flows and selective withdrawal (SWD) infrastructure have been used to improve habitat for native species. Yet, few studies have quantified nutrient and food web export subsidies from upstream reservoirs, despite their potential to subsidize downstream riverine food webs. We sampled nutrient, phytoplankton, and zooplankton concentrations in outflows from the Shasta-Keswick reservoir complex in Northern California over a 12-month period to understand how SWD operation and internal reservoir conditions interact to influence subsidies to the Sacramento River. We found that nutrients, phytoplankton, and zooplankton were continuously exported from Shasta Reservoir to the Sacramento River and that gate operations at Shasta Dam were important in controlling exports. Further, our results indicate that gate operations and water-export depth strongly correlated with zooplankton community exports, whereas internal reservoir conditions (mixing and residence time) controlled concentrations of exported zooplankton biomass and chlorophyll a. These results demonstrate that reservoirs can be an important source of nutrient and food web subsidies and that selective withdrawal infrastructure may provide a valuable management tool to control ecosystem-level productivity downstream of dams.

Integrating the flow regime and water quality effects into a niche-based metacommunity dynamics model for river ecosystems

Aquatic community dynamics are closely dominated by flow regime and water quality conditions, which are increasingly threatened by dam regulation, water diversion, and nutrition pollution. However, further understanding of the ecological impacts of flow regime and water quality conditions on aquatic multi-population dynamics has rarely been integrated into existing ecological models. To address this issue, a new niche-based metacommunity dynamics model (MDM) is proposed. The MDM aims to simulate the coevolution processes of multiple populations under changing abiotic environments, pioneeringly applied to the mid-lower Han River, China. The quantile regression method was used for the first time to derive ecological niches and competition coefficients of the MDM, which are demonstrated to be reasonable by comparing them with the empirical evidence. Simulation results show that the Nash efficiency coefficients for fish, zooplankton, zoobenthos, and macrophytes are more than 0.64, while the Pearson correlation coefficients for them are no less than 0.71. Overall, the MDM performs effectively in simulating metacommunity dynamics. For all river stations, the average contributions of biological interaction, flow regime effects, and water quality effects to multi-population dynamics are 64%, 21%, and 15%, respectively, suggesting that the population dynamics are dominated by biological interaction. For upstream stations, the fish population is 8%-22% more responsive to flow regime alteration than other populations, while other populations are 9%-26% more responsive to changes in water quality conditions than fish. For downstream stations, flow regime effects on each population account for less than 1% due to more stable hydrological conditions. The innovative contribution of this study lies in proposing a multi-population model to quantify the effects of flow regime and water quality on aquatic community dynamics by incorporating multiple indicators of water quantity, water quality, and biomass. This work has potential for the ecological restoration of rivers at the ecosystem level. This study also highlights the importance of considering threshold and tipping point issues when analyzing the "water quantity-water quality-aquatic ecology" nexus in future works.

River ecological flow early warning forecasting using baseflow separation and machine learning in the Jiaojiang River Basin, Southeast China

Ecological flow early warning is crucial for the rational management of watershed water resources. However, determining of accurate ecological flow threshold and choosing the appropriate forecasting model are challenging tasks. In this study, we initially developed a baseflow separation and Tennant method-based technique for calculating ecological river flow. Then an ecological flow early warning model was created using the machine learning technique based on distributed gradient enhancement framework (LightGBM). Finally, we utilized the framework of Shapley Additive Planning (SHAP) to explain how various hydrometeorological factors affect the variations in ecological flow conditions. The Jiaojiang River basin in southeast China is selected as the study area, and the hydrological stations in upstream of Baizhiao (BZA) and Shaduan (SD) are chosen for key analysis. The results of these applications show that the monthly baseflow frequency of the river ecological flow conditions of the two stations in the dry season is 20 % (7.49 m³/s) and 30 % (4.79 m³/s), respectively. The ecological flow level early warning forecasting accuracy is close to 90 % in the BZA and SD stations during dry and wet seasons. The variations of ecological flow are most affected by evaporation and base flow index. The results of this study can serve as a strong basis for the effective allocation and utilization of locally available water resources.

New integrated hydrologic approach for the assessment of rivers environmental flows into the Urmia Lake

Recent research has greatly focused on the environmental water supplement of rivers individually and independently. However, a comprehensive and integrated view of all rivers in the basin is simultaneously required in closed basins leading to lakes and wetlands. This has affected Lake Urmia, which is the second largest saltwater lake in the world. It has been in danger of drying up in recent years as a result of not allocating the required environmental flow (e-flow) due to the increase in water resource consumption in the agricultural sector and climate changes. In this study, a method derived from the flow duration curve shifting (FDCS) method is presented in addition to explaining the possibility of providing the e-flow of rivers leading to the lake. The method can make the least amount of change in the hydrological characteristics of rivers while providing the volume of required water by the ecosystem of lakes or downstream wetlands. Unlike the conventional method which presents the results on a monthly basis, the above-mentioned method is based on daily data of hydrometric stations and can calculate the amount of the environmental requirement of rivers in real-time according to the upstream inlet of the river. This method has been used in the Urmia Lake basin. According to the results, it can provide the environmental requirement of the lake by allocating 70.5% of the annual flow of rivers and thus can save the lake and the ecosystem of the region from the current critical conditions.