Effects of urban catchment characteristics on combined sewer overflows

Pollution from Combined Sewer Overflows (CSOs) cause diffuse environmental problems, which are still not satisfactorily addressed by current management practices. In this study, a sensitivity analysis was conducted on several CSO environmental impact indicators, with respect to parameters that characterise climate, urban catchment and the CSO structure activation threshold. The sensitivity analysis was conducted by running 10000 simulations with the Storm Water Management Model, using a simplified modelling approach. The indicators were calculated at yearly scale to evaluate overall potential effects on water bodies. The results could be used to estimate pollution load ranges, known the values of the input parameters, and to investigate suitable strategies to reduce pollution of the receiving water bodies. The percentage of impervious surface of the catchment was found the most influent parameter on all the indicators, and its reduction can contain the discharged pollutant mass. The activation threshold, instead, resulted the second least influent parameter on all the indicators, suggesting that its regulation alone would not be a suitable strategy to reduce CSO pollution. However, along with the reduction of the imperviousness, its increase could effectively decrease the concentration of pollutant in the overflow. The results also indicate that neither adopting sustainable urban drainage practices, nor interventions on the CSO device, significantly affect the frequency of the overflows. Therefore, restricting this latter was found to be ineffective for the reduction of both the discharged pollutant mass and the concentration of pollutant in the overflow.

A snapshot on vertical variability of dissolved organic matter in the epilagic zone of the eastern Indian Ocean

Marine dissolved organic matter (DOM) plays an important role in aquatic ecosystems and is an essential reservoir of organic carbon in the marine carbon cycle. In this study, seawater DOM samples from 33 stations were collected in spring 2022 (April to May, 20 stations) and autumn 2020 (October to November, 13 stations) to better characterize and compare DOM variability within 200 m water layer in the eastern Indian Ocean (EIO). Hydrological parameters, nutrients and spectroscopic indices were calculated and evaluated for two cruises. In addition, excitation emission matrix spectroscopy combined with parallel factor analysis (EEM-PARAFAC) was used to directly analyse seawater DOM samples. The sources and processes of DOM in the EIO were assessed by fluorescence index (FI), freshness index (β/α), Biological index (BIX), and humification index (HIX). Three fluorescent components were identified in DOM samples from two cruises, including: C1 (tryptophan- and tyrosine-like), C2 (marine and/or terrestrial humic-like), and C3 (terrestrial humic-like). The components of C1 accounted for 39.45 % ± 8.79 %, C2 for 33.05 % ± 6.42 %, and C3 for 27.20 % ± 4.47 % within 200 m water layer. The intensity of the DOM fluorescence seems to varied due to seasonal differences. In particular, the source of the DOM fraction varied at <100 m layer, which may also be related to the structure of the microbial community. Further, there is a strong correlation between the depth of seawater and hydrographic parameters, fluorescence indices and fluorescence components. Nutrients (nitrate, dissolved inorganic phosphate, and dissolved silicate) and humic-like fractions are more likely to accumulate in the deeper layers of the ocean. Thus, these results provide some data support for the variability of DOM fractions on a vertical scale in the EIO.

Novel spatial models for analysis the long-term impact of LULC changes on hydrological components at sub-basin level

The main objective of this research is to assess the impacts land use and land cover changes (LULC) on hydrological components using novel spatial models at sub-basin scales. The Soil and Water Assessment Tool (SWAT) was employed to analyze the long-term effect of LULC on hydrological components. The results of the calibrated and validated SWAT model demonstrated that run-off and actual evapotranspiration (ET) are expected to experience the largest increase, more than 130% and 90% in autumn, whereas the largest decrease is anticipated to occur in the summer and winter for potential evapotranspiration (PET) (-59%) and ET (-80%) by the projected time. The impacts of hydrological components, elevation, LULC, and an indicator of urbanization and land-use intensity (La) on water yield (WYLD) at sub-basin levels were then considered by four novel spatial models due to the problem of multicollinearity which is prevalent in traditional models. In particular, the Moran eigenvector spatially varying coefficients (MESVC) showed that the soil class out of LULC categories and lateral flow among hydrological properties are expected to have a statistically significant effect on spatial fluctuation of WYLD at the sub-basin scale. The results of spatially filtered unconditional quantile regression (SF-UQR) confirm the findings of the MESVC model and further implied that the lateral flow remains as a statistically significant contributor to WYLD only in lower quantiles (e.g., for quantiles lower than 0.25). The impacts of LULCs on WYLD were statistically lower than the effects caused by the hydrological components.

Image-driven hydrological parameter coupled identification of flood plain wetland conservation and restoration sites

A good many works focus on wetland vulnerability; some works also explore restoration sites at a very limited spatial extent. But the satellite image-driven hydrological data-based approach adopted in this work is absolutely new. Moreover, existing work only focused on identifying restoration sites in the present context, but for devising long-term sustainable planning, predicted hydrological parameters based on possible restoration sites may be an effective tool. Considering this, the present work focused on exploring hydrological data (water presence frequency (WPF), hydro-period (HP) and water depth (WD)) from time-series satellite images. This exploration may resolve the hydrological data scarcity of wetland over the wider geographical areas. Using these parameters, we developed wetland restoration and conservation sites for different historical years (2008, 2018) and predicted years (2028) using ensemble machine learning (EML) models. From the analysis, it was found that water depth, hydro-period and WPF became poorer over the period, and the trend may seem to continue in predicted years. Among the applied EML models, Random Subspace (RS) predicted wetland restoration and conservation sites precisely over others. The predicted area under high-priority restoration sites is 34% in 2018, which was 14% in 2008. In 2028, 12% more areas may fall in this priority level. Wetland away from main streams (mainly ortho-fluvial wetland) and fringe wetland parts should be given more priority for restoration. These present and predicted information will effectively help to frame sustainable wetland restoration planning.

Intra-monsoonal variation of zooplankton population in the Sundarbans Estuarine System, India

The present study was conducted during July 2013 (early phase of monsoon or EM) and September 2013 (later phase of monsoon or LM) to ascertain the intra-monsoonal variation on zooplankton, by selecting 15 study stations in the river Saptamukhi, one of the main estuaries in the Sundarbans Estuarine System (SES). In 2013, SES experienced an unusually high monsoonal rainfall also exacerbated by cloud burst event at Himalayan region (upper stretches of SES) which tremendously increased the river runoff. The present work was aimed to decipher the effect of this unusual precipitation during the monsoon season on zooplankton assemblages along with different hydrological parameters. The abundance of zooplankton was recorded as lower during EM compared to LM. Altogether, 56 zooplankton taxa were identified with copepods forming the predominant population. Thirty-three copepod species were reported with 25 calanoid species forming the bulk of the biomass followed by 5 and 3 species of cyclopoids and harpacticoid, respectively. A combination of multivariate cluster analysis, biotic indices, and canonical correspondence analysis revealed noticeable alterations in the zooplankton community structure across the spatio-temporal scale. Furthermore, significant intra-monsoonal changes in zooplankton population correlated with several hydrological parameters were clearly noticed. Paracalanus parvus, Bestiolina similis and Oithona similis were observed to be the most dominant copepod species in both sampling periods. The result of the present study provides new insight on estuarine zooplankton community after unusual rainfall during monsoon season, and provides further evidence to support the conservation and management of the SES ecosystem.

Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models

Efficacious operation for dam and reservoir system could guarantee not only a defenselessness policy against natural hazard but also identify rule to meet the water demand. Successful operation of dam and reservoir systems to ensure optimal use of water resources could be unattainable without accurate and reliable simulation models. According to the highly stochastic nature of hydrologic parameters, developing accurate predictive model that efficiently mimic such a complex pattern is an increasing domain of research. During the last two decades, artificial intelligence (AI) techniques have been significantly utilized for attaining a robust modeling to handle different stochastic hydrological parameters. AI techniques have also shown considerable progress in finding optimal rules for reservoir operation. This review research explores the history of developing AI in reservoir inflow forecasting and prediction of evaporation from a reservoir as the major components of the reservoir simulation. In addition, critical assessment of the advantages and disadvantages of integrated AI simulation methods with optimization methods has been reported. Future research on the potential of utilizing new innovative methods based AI techniques for reservoir simulation and optimization models have also been discussed. Finally, proposal for the new mathematical procedure to accomplish the realistic evaluation of the whole optimization model performance (reliability, resilience, and vulnerability indices) has been recommended.