Physical habitat modeling for river macroinvertebrate communities.

Habitat models rarely consider macroinvertebrate communities as ecological targets in rivers. Available approaches mainly focus on single macroinvertebrate species, not addressing the ecological needs and functionality of the whole community. This research aimed at providing an approach to model the habitat of the macroinvertebrate communities. The study was carried out in three rivers, located in Italy and characterized by a braiding morphology, gravel riverbeds, and low flows during the summer period. The approach is based on the recently developed Flow-T index, together with a Random Forest (RF) regression, which is employed to apply the Flow-T index at the mesohabitat scale. Using different datasets gathered from field data collection and 2D hydrodynamic simulations, the model was calibrated in the Trebbia River (2019 field campaign) and validated in the Trebbia, Taro, and Enza rivers (2020 field campaign). The RF model selected 12 mesohabitat descriptors as important for the macroinvertebrate community. These descriptors belong to different frequency classes of water depth, flow velocity, substrate grain size, and connectivity to the main river channel. The cross-validation R2 coefficient (R2cv) of the training dataset was 0.71, whereas the R2 coefficient (R2test) for the validation dataset was 0.63. The agreement between the simulated results and the experimental data shows sufficient accuracy and reliability. The outcomes of the study reveal that the model can identify the ecological response of the macroinvertebrate community to possible flow regime alterations and river morphological modifications. Lastly, the proposed approach allowed to extend the MesoHABSIM methodology, widely used for the fish habitat assessment, to a different ecological target community. Further applications of the approach can be related to ecological flows design in both perennial and non-perennial rivers, including river reaches in which fish fauna is absent.

Water Supply Planning in the Face of Drought and Ecosystem Flows: Examining the Impact of the Bay-Delta Plan on Bay Area Water Supply.

In California, recent Bay-Delta Plan legislation attempts to balance water supply and ecosystem protection by requiring 40% of the flow to remain in-stream in the Tuolumne River from February through June. Serious questions remain about what this means for the Bay Area water supply, especially during drought. Our work develops a new approach to analyze how in-stream flow policy coupled with climate change could impact regional water supply over the coming decades. Results show that the new in-stream flow demand would exceed urban water deliveries in a typical year. In wet years, water supply performance is minimally impacted, but in drought, the policy can lead to less water in storage, delayed reservoir recovery, and increased time at critically low storage. Storage impact exceeding 50 000 acre-feet (60 million m3) is anticipated with at least 18% frequency, demonstrating that, climate uncertainty notwithstanding, this impact must be planned for and managed to ensure a reliable future water supply.

Calculation and evaluation of suitable ecological flows for eco-environmental recovery of cascade-developed rivers.

River cascade development affects the hydrological and habitat characteristics of the region and disrupts the dynamic balance of stable river ecosystems. The most profound impact of river cascade development is on the resident fish species. River ecosystem restoration for maximum river habitat improvement is generally based on water security and environment improvement and effectively embodies the nature-based solutions (NbS) concept of naturalized restoration. Yuanjiang (Y.J.) River is an international river in southwest China seriously affected by cascade development. By determining the response of the river ecosystem and using the key performance indicator method, Yuanjiang carp (Cyprinus carpio rubrofuscus) and red giant catfish (Bagarius rutilus) were identified as the key species in the main stream of the Y.J. River., and the ecological effects of river cascade development on them were studied by applying two-dimensional hydrodynamic physical habitat simulation and multi-objective ecological scheduling models. Based on the calculation results for ecological operation optimization of cascade reservoirs, an improved progressive optimality algorithm was used to calculate the ecological flows required to maintain the stability of the river ecosystem. With the increasing extent of cascade development in the river, important indicators, such as the intra-annual, extreme, high, and low flows have changed significantly, and the hydrological characteristics of the main stream have changed rapidly and comprehensively. Habitat suitability curves were used to determine the appropriate water depth and delineate the weighted usable area required for the spawning, nursing, and growing periods of the key fish species. The suitable ecological flows required for the three life-cycle stages of the C. carpio rubrofuscus accounted for 34, 45, and 62 %, respectively, of the multi-year mean natural water inflow at the Qiaotou (Q.T.) cascade, whereas those required for the three respective periods of B. rutilus accounted for 47, 98, and 27 %, respectively, of the multi-year mean natural water inflow at the Madushan (M.D·S.) cascade. Considering the physiological lifecycle demands of the indicator/key fish species and the upper limit of water resources development and utilization in the key river section, the ecological flow precipitation frequency in the Q.T.-Luodie (Q.T.-L.D.) and M.D·S.-Xinjie (M.D·S.-X.J.) sections (currently at 25, 50, and 75 %, respectively) can be increased to 100 % under optimal operating conditions (cascade hydropower station optimal operation). After implementing the multi-objective ecological operation at the Y.J. River main-stream cascade reservoirs, the suitable habitat area for C. carpio rubrofuscus and B. rutilus increased significantly (>10 % and 15 %, respectively). In general, the NbS-based ecological flow calculation method for cascade-developed rivers has a wide range of applications, which can be useful for the eco-environment restoration of rivers and improving the living habitats of waterway organisms.

Linking Altered Flow Regimes to Biological Condition: an Example Using Benthic Macroinvertebrates in Small Streams of the Chesapeake Bay Watershed.

Regionally scaled assessments of hydrologic alteration for small streams and its effects on freshwater taxa are often inhibited by a low number of stream gages. To overcome this limitation, we paired modeled estimates of hydrologic alteration to a benthic macroinvertebrate index of biotic integrity data for 4522 stream reaches across the Chesapeake Bay watershed. Using separate random-forest models, we predicted flow status (inflated, diminished, or indeterminant) for 12 published hydrologic metrics (HMs) that characterize the main components of flow regimes. We used these models to predict each HM status for each stream reach in the watershed, and linked predictions to macroinvertebrate condition samples collected from streams with drainage areas less than 200 km2. Flow alteration was calculated as the number of HMs with inflated or diminished status and ranged from 0 (no HM inflated or diminished) to 12 (all 12 HMs inflated or diminished). When focused solely on the stream condition and flow-alteration relationship, degraded macroinvertebrate condition was, depending on the number of HMs used, 3.8-4.7 times more likely in a flow-altered site; this likelihood was over twofold higher in the urban-focused dataset (8.7-10.8), and was never significant in the agriculture-focused dataset. Logistic regression analysis using the entire dataset showed for every unit increase in flow-alteration intensity, the odds of a degraded condition increased 3.7%. Our results provide an indication of whether altered streamflow is a possible driver of degraded biological conditions, information that could help managers prioritize management actions and lead to more effective restoration efforts.

Predicting hydrologic disturbance of streams using species occurrence data.

Aquatic organisms have adapted over evolutionary time-scales to hydrologic variability represented by the natural flow regime of rivers and streams in their unimpaired state. Rapid landscape change coupled with growing human demand for water have altered natural flow regimes of many rivers and streams on a global scale. Climate non-stationarity is expected to further intensify hydrologic variability, placing increased pressure on aquatic communities. Using a machine learning approach and georeferenced species occurrence data, we modeled and mapped spatial patterns of hydrologic disturbance for streams in Arkansas, Missouri, and eastern Oklahoma. Random forest (RF) models trained on fish community data, hydrologic, and landscape metrics for gaged streams in the National Hydrography (NHDPlusV2) database were used to predict a hydrologic disturbance index (HDI) for ungaged streams. The HDI is part of the USGS Geospatial Attributes of Gages for Evaluating Streamflow (GAGESII) database and is a composite index of watershed-scale disturbance from anthropogenic stressors. Fish presence/absence data had similar overall model prediction accuracy (77%; 95% CI: 0.74, 0.80) as flow variables (76%; CI: 0.73, 0.80). Including topographic variables increased the RF prediction accuracy of both the fish (90%; CI: 0.88, 0.92) and flow models (86%; CI: 0.84, 0.89). Spatial patterns of hydrologic disturbance suggest distinct ecohydrological regions exist where conservation actions may be focused. Streams with low HDI were predominately located in the Ozark Highlands, Boston Mountains, and Ouachita Mountains. Correlation analysis of HDI by flow regime showed groundwater stable streams had the lowest disturbance frequency, with over 50% of stream reaches with low HDI located in forested land cover. HDI was highest for big rivers, intermittent runoff streams and streams in areas of agricultural land use. Our results show long-term georeferenced biological data can provide a valuable resource for predictive modeling of hydrologic disturbance for ungaged rivers and streams.