Abstract

Urban stormwater management involves strategies to mitigate the adverse effects of urban runoff. These strategies aim to reduce flood risk and enhance the environmental values and sustainability of urban water systems. But the effective urban stormwater management is highly dependent on the factors such as spatial variability of urban watershed characteristics as well as the scale of the area under consideration. This insight has prompted increasing utilization of physically based urban watershed models such as the Environmental Protection Agency (EPA) Stormwater Management Model (SWMM). Thus, in this study we utilized SWMM for modeling the dynamic runoff response in a stormwater zone of Hyderabad, India. The study results are validated for a particular observed 48-hour storm event occurred on 3rd and 4th of October 2015. Five years (2010-2014) of hourly rainfall data have been used to estimate the design storms with 2, 5, 10, and 25 years return periods using Gumbel distribution. The results indicate that the existing drainage system is insufficient to withstand the generated runoff by the design storm of 2-year return period and causes notable flooding. The water elevation profile generated from the initial node, representing the beginning of the drainage system, to the outfall indicates that the flooding occurred at the outfall during the peak precipitation event. Also, the hydrographs which were developed through SWMM at different links and outfalls are crucial in designing the sumps or ponds and determining pump capacities, aiding in effective stormwater management practices.

Abstract

Multivariate drought indices including various hydrological processes into account can be more valuable under climatic and anthropogenic changes. Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized precipitation Actual Evapotranspiration Index (SPAEI) are the drought indices used to estimate drought index, considering precipitation, and evapotranspiration (ET) into account. Many studies used empirical, machine learning, and process-based model estimates of ET for the calculation of drought indices of SPEI and SPAEI. However, the sensitivity of ET estimates on drought characteristics at the catchment scale is highly complex. The present study aimed to include and analyze the sensitivity of various approaches of empirical (Budyko, Penman–Monteith, Hargreaves, and Turc), modeled (SWAT), and remote sensing (MODIS) in the drought characterization using SPEI and SPAEI. The present methodology was tested on a dry-sub-humid river catchment of India, the Tunga-Bhadra River catchment for the period of 2000–2012. The performance of statistical indicators (Nash–Sutcliffe Efficiency and R2) for SPEI values by various empirical methods of Potential Evapotranspiration (PET) (i) Penman–Monteith (ii) Hargreaves against remote sensing PET were 0.93 and 0.95, respectively, which are high in comparison with SWAT simulated PET-based SPEI values, which shows NSE values of 0.85 against remote sensing PET-based SPEI.

Abstract

Event-based models focus on modelling of peak runoff from rainfall data. Conceptual models indicate simplified models that provide reasonably accurate answers despite their crude nature. Rainfall-runoff models are used to transform a rainfall event into a runoff event. This paper focuses on reviewing computational simulation of rainfall-runoff processes over a catchment. Lumped conceptual, event-based rainfall-runoff models have remained the dominant practice for design flood estimation in Australia for many years due to their simplicity, flexibility, and accuracy under certain conditions. Attempts to establish regionalization methods for prediction of design flood hydrographs in ungauged catchments have seen little success. Therefore, as well as reviewing key rainfall-runoff model components for design flood estimation with a special focus on event-based conceptual models, this paper covers the aspects of regionalization to promote their applications to ungauged catchments.

Abstract

This study proposed a nonparametric copula hazard framework in the joint risk of river water temperature (RWT) and low flow (LF) events for aquatic ecosystems, specifically ectotherm fish. This nonparametric copula density can adapt to any mutual dependence structure, providing greater flexibility. This can reduce the risk of misspecification if the underlying assumption is violated compared to conventional parametric or semiparametric copula settings. The analysis uses nonparametric copulas densities like Beta kernel copula estimator (BKCE), Bernstein copula estimator (BCE), and Transformation kernel estimator (TKE), conjoined with Gaussian Kernel density estimations (GKDEs) and parametric marginals for joint annual maximum RWT (AMRWT) and LF. The study compares different models for analyzing five Swiss river basins: parametric copulas with best-fitted GKDE and parametric margins, versus nonparametric copula. Six bandwidth selectors are estimated to fit GKDE. BKCE with GKDE margins outperformed most stations, while TKE and BCE density with GKDE margins work best for only one station. However, at one station, BKCE with best-fitted parametric margins is outperformed. All stations except Station 2473 are characterized by temperature and low flows that may be conducive to stress of a number of aquatic species, causing high AMRWT (exceeding 19 °C) and minimum LF or specific discharge, SD) quantiles at low AND-joint return periods (RP). AND (i.e. low flow AND high temperature) hazard events are less likely to occur together than OR hazard events, while univariate RPs happen more often than OR-joint RPs. In addition, the joint RP of AMRWT, given LF at different percentiles, significantly affected AMRWT for various LF conditions. Higher AMRWT with low flow conditions results in lower joint RP (except station 2473), and this stress level would be reduced when conditioned with high LF events at the same AMRWT. Station 2473 has high LF even at low percentiles, and with low AMRWT makes it very less stressful than other station under different LF conditions. Summer river flow maintenance can improve aquatic environments with high RWT. Analyzing joint statistics is crucial to understanding mutual risk in freshwater ecosystems.

Abstract

Microplastic pollution in freshwater ecosystems has emerged as a significant environmental concern, warranting comprehensive investigation, and understanding. This study employs bibliometric analysis to systematically review and synthesize the existing literature on microplastic pollution in freshwater environments from 2013 to 2023. The exponential growth in research output was uncovered by analyzing 885 documents sourced from the Web of Science database, with an average annual growth rate of 73.13% and an average document citation of 30.17. Our findings highlight the dominance of primary and secondary microplastics as pollutants, their ecological consequences, and the resultant socio-economic implications. Notably, the Science of the Total Environment and Environmental Pollution journals emerge as leading publication venues, while China, Germany, and the USA lead in research contributions, underlining the global nature of microplastic pollution research. The analysis further outlines the most commonly cited works, identifying pivotal studies that have shaped current understanding and future research directions. This bibliometric analysis provides a comprehensive overview of the research landscape on microplastic pollution in freshwater ecosystems, helping researchers to identify knowledge gaps and emerging trends. These insights can guide future research directions and inform policymakers and stakeholders on where scientific efforts should be concentrated to better understand and address the impacts of microplastic pollution.

Abstract

Risk assessment is vital for comprehending and managing river water quality control. By ensuring a systematic approach for evaluating potential risks, it encourages the adoption of efficient management strategies, the use of informed decision-making, and maintaining of river ecosystems. An effective framework is developed by integrating the empirical based approach with Gumbel-Copula function to estimate the river water quality risk under high water temperatures and low flow rates. Based on Akaike Information Criterion (AIC) among Copula based Gumbel, Gaussian, t, and Clay-ton functions, the Gumbel-Copula function was utilized in the study. The percentiles ranging from 5 to 95 with 5 percentile interval of Stream Flows (SF) and reverse order percentiles of Water Temperatures (WT) are employed in the estimation of risk of river water quality. The current risk quantification approach was implemented using observed datasets on Yamuna (2000 to 2015) and Bhadra (2006 to 2017) river systems. The results reveal that at 50 percentile (SF=380 m3/s and WT=27.38 ℃) the join risk is about 75.29% for Yamuna River and similarly for Bhadra river the risk is about 77.62% (SF=7.20 m3/s and WT=25 ℃).