Integrated water quality modeling in a river-reservoir system to support watershed management

Assimilation of satellite-derived profile observations for enhanced estimation of water quality variables in a deep reservoir

Accurate and reliable estimation of the temperature and Dissolved Oxygen (DO) profiles in deep reservoirs is crucial for effective water quality management. Integrating incoming observations through a data assimilation scheme can ensure the precision and certainty of the predictions. As surface water quality variables cannot reflect the overall conditions of deep reservoirs, assimilating vertical profile observations is essential. This study pioneers the use of the ensemble Kalman filter to incorporate satellite-derived profile observations into CE-QUAL-W2, a two-dimensional hydrodynamic and water quality simulation model for deep reservoirs. This method was used to update the initial conditions of the temperature and DO vertical profile in the Wadi Dayqah Reservoir in Oman. Five different scenarios were evaluated to identify the optimal choice of state vector and assimilated observations and to evaluate the effect of cross-covariance between temperature and DO in multivariate data assimilation. Additionally, the approach was further refined by examining the performance of assimilating surface observations, observations from two stations, fewer data points in the vertical profile, and the effect of the ensemble size. The results indicate a significant improvement in model accuracy after data assimilation. The optimal scenario-updating and assimilating both the temperature and DO vertical profiles-reduced the root mean square error (RMSE) of the DO profile estimation by 44 %, from 1.37 to 0.77 in the segment near the dam. The RMSE decreased by 27 % for temperature, dropping from 0.66 to 0.48. Overall, across the entire reservoir, improvements were 11 % for temperature and 27 % for DO. Assimilating surface information was adequate only on dates when thermal stratification was absent. Additionally, as the correlation between consecutive vertical layers can be accurately estimated by ensembles, proper performance is achievable even with fewer observations in the vertical profile.

Local environmental factors are the main drivers of phytoplankton biovolume in subtropical streams of Brazil

The ecological attributes of phytoplankton in freshwater environments are strongly influenced by limnological factors and temporal variability. In this study, we investigated the importance of local environmental and regional (spatial and landscape) predictors in structuring stream phytoplankton from the perspective of metacommunity theory. We seasonally sampled phytoplankton and abiotic variables from nine streams in three subtropical basins. Variation partitioning was used to investigate the influence of environmental, landscape, and spatial predictors on phytoplankton biovolume. Independent of the hydrological period (dry and rainy), the phytoplankton communities were predominantly structured by local environmental factors. In addition, the different land uses considered (landscape) showed weak significance during the dry season, with emphasis on the rural category. Biovolume values remained low, and diatoms and green algae were the most representative groups. Our findings are consistent with recognized ecological patterns for potamoplankton and emphasize local environmental filters as a fundamental regulator of phytoplankton biodiversity in lotic environments.

Physiological strategies of acute thermal conditions of Rhamdia voulezi collected in the Iguaçu river watershed, Paraná, Brazil: biochemical markers of metabolic and oxidative stress

Thermal pollution creates substantial challenges that alter energy demand and produce reactive oxygen species that damage fish DNA, proteins, and lipids. Rhamdia voulezi is a species of fish native to the Iguaçu river, Paraná, Brazil, that does not have scientific records of minimum (CTmin) and maximum (CTmax) temperatures required for survival. As it is a top predator species in the food chain and lives at temperatures below 22 °C, the loss of the species can cause functional problems in controlling the ecosystem and energy flow. The study evaluated the tissue metabolism of the brain, heart, and muscle of R. voulezi (n = 72) subjected to acute thermal stress of 31 °C for 2, 6, 12, 24, and 96 h after acclimatization to 21 °C. The biochemical markers SOD, GPx, MDH, HK, and CK of the brain, PCO of the heart and CAT, glycogen, G6PDH, and ALT of muscle were significant. PCA, IBR, thermal sensitive, and condition factor suggested that R. voulezi has different physiological strategies for acclimatization to 31 °C to mobilize and sustain the metabolic needs of oxygenation and energy allocation/utilization for tissue ATP production.

A probabilistic decision support tool for prediction and management of rainfall-related poor water quality events for a drinking water treatment plant

A data-driven Bayesian Network (BN) model was developed for a large Australian drinking water treatment plant, whose raw water comes from a river into which a number of upstream dams outflow water and smaller tributaries flow. During wet weather events, the spatial distribution of rainfall has a crucial role on the incoming raw water quality, as runoff from specific sub-catchments usually causes significant turbidity and conductivity issues, as opposed to larger dam outflows which have typically better water quality. The BN relies on a conceptual model developed following expert consultation, as well as a combination of different types (e.g. water quality, flow, rainfall) and amount (e.g. high-frequency, daily, scarce depending on variable) of historical data. The validated model proved to have acceptable accuracy in predicting the probability of different incoming raw water quality ranges, and can be used to assess different scenarios (e.g. timing, flow) of dam water releases, for the purpose of achieving dilution of the tributary's poor-quality water and mitigate related drinking water treatment challenges.