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Thermal stratification has become more extensive and prolonged because of global warming, and this change has had a significant impact on the distribution patterns of the phytoplankton communities. However, the response of phytoplankton community structures and assembly processes to thermal stratification is not fully understood. We predicted that the structure and assembly processes of phytoplankton communities would be affected by thermal stratification among water layers associated with environmental condition changes, reflecting certain patterns in temporal and spatial scales. Phytoplankton from Danjiangkou Reservoir were collected from October 2021 to July 2022 to verify this prediction. During the sampling period, Danjiangkou Reservoir remained thermally stratified with stability. The composition of the phytoplankton community in the surface layer significantly differed from that in both the thermocline and bottom layer. The phenomenon of thermal stratification affected the pattern of nitrogen and phosphorus distribution and, thus, the processes of the phytoplankton community structures. Deterministic processes had a greater influence on the assembly of the phytoplankton communities in the surface and bottom layers. In contrast, stochastic processes were more prevalent in the assembly of the thermocline phytoplankton community. The phytoplankton community within the thermocline layer exhibited a broader niche range than that in the surface and bottom layers, showing notable dissimilarity from that of the bottom layer. Canonical correspondence analysis (CCA) revealed that the vertical distributions of the phytoplankton communities were significantly correlated with NH4+-N, pH, and water temperature (WT). In summary, this study explained the distribution patterns of phytoplankton community structures and assembly processes in deep-water reservoirs during the stratification period. Additionally, the study explored the potential of using the distribution patterns of phytoplankton in stratified-state deep-water reservoirs under a subtropical–warm temperate climate as climate indicators in the context of global warming. [ABSTRACT FROM AUTHOR]

There is an emerging challenge within water resources on how, and to what extent, borrowing concepts from landscape ecology might help re-define traditional concepts in hydrology in a more tangible manner.

Climate and land cover changes are key factors in river basins' management. This study investigates on the one hand 60-year (1960 to 2019) rainfall and temperature variability using station data combined with gridded data, and on the other hand land cover changes for the years 1990, 2005, and 2020 in the Sota catchment (13,410 km2, North Benin, West Africa). The climate period is different from the chosen land use change period due to the unavailability of satellite images. Standardized anomaly index, break points, trend analysis, and Thiessen's polygon were applied. Satellite images were processed and ground truthing was carried out to assess land cover changes. The analyses revealed a wet period from 1960 to 1972, a dry period from 1973 to 1987, and another wet period from 1988 to 2019. The annual rainfall decreases from the south to the north of the catchment. In addition, rainfall showed a non-significant trend over the study period, and no significant changes were identified between the two normals (1960–1989 and 1990–2019) at catchment scale, although some individual stations exhibited significant trends. Temperatures, in contrast, showed a significant increasing trend over the study period at catchment scale, with significant break points in 1978, 1990, and 2004 for Tmax, and 1989 for Tmin. An increase of 0.4 °C and 1.2 °C is noted, respectively, for Tmax and Tmin between the two normals. The study also revealed increases in agricultural areas (212.1%), settlements (76.6%), waterbodies (2.9%), and baresoil (52%) against decreases in woodland (49.6%), dense forest (42.2%), gallery forest (21.2%), and savanna (31.9%) from 1990 to 2020. These changes in climate and land cover will have implications for the region. Appropriate adaptation measures, including Integrated Water Resources Management and afforestation, are required. [ABSTRACT FROM AUTHOR]

Water tanks as traditional rainwater harvesting systems for agriculture are widely distributed in South India. They have a strong impact on hydrological processes, affecting streamflow in rivers as well as evapotranspiration. This study aims at an accurate representation of water harvesting systems in a hydrologic model to improve model performance and assessment of the catchment water balance. To this end, spatio‐temporal variations of water bodies between the years 2016 and 2018 and the months of January and May 2017 were derived from Sentinel‐2 satellite data to parameterize the water tanks (reservoir) parameters in the Soil and Water Assessment Tool (SWAT+) model of the Adyar basin, Chennai, India. Approximately 16% of the basin is covered by water tanks. The initial model performance was evaluated for two model setups, with and without water tanks. The best model run was selected with a multi‐metric approach comparing observed and modelled monthly streamflow for 5000 model runs. The final model evaluation was carried out by comparing estimated water body areas by the model and remote sensing observations for January to May 2017. The results showed that representing water tanks in the hydrologic model led to an improvement in the representation of the seasonal variations of streamflow for the whole simulation period (2004–2018). The model performance was classified as good and very good for the calibration (2004–2011) and validation (2012–2018) periods as NSE varies between 0.67 and 0.85, KGE varies between 0.65 and 0.72, PBIAS varies between −24.1 and −23.6, and RSR varies between 0.57 and 0.39. The best fit was shown for the high and middle flow segments of the hydrograph where the coefficient of determination (R2) ranges from 0.81 to 0.97 and 0.75 to 0.81, respectively. The monthly variation of water body areas in 2017 estimated by the hydrologic model was consistent with changes observed in remote sensing surveys. In summary, the water tank parametrization using remote sensing techniques enhanced the hydrologic model's efficiency and applicability for future studies. [ABSTRACT FROM AUTHOR]