Cyanobacteria decay alters CH and CO produced hotspots along vertical sediment profiles in eutrophic lakes.

Cyanobacteria-derived organic carbon has been reported to intensify greenhouse gas emissions from lacustrine sediments. However, the specific processes of CH and CO production and release from sediments into the atmosphere remain unclear, especially in eutrophic lakes. To investigate the influence of severe cyanobacteria accumulation on the production and migration of sedimentary CH and CO, this study examined the different trophic level lakes along the middle and lower reaches of the Yangtze River.

Unexpected increase of sulfate concentrations and potential impact on CH budgets in freshwater lakes.

The continuous increase in sulfate (SO) concentrations discharged by anthropogenic activities lacks insights into their dynamics and potential impact on CH budgets in freshwater lakes. Here we conducted a field investigation in the lakes along the highly developed Yangtze River basin, China, additionally, we analyzed long-term data (1950-2020) from Lake Taihu, a typical eutrophic lake worldwide. We observed a gradual increase in SO concentrations up to 100 mg/L, which showed a positive correlation with the trophic state of the lakes.

Unraveling urban hydro-environmental response to climate change and MCDA-based area prioritization in a data-scarce developing city.

Climate change leads to more frequent and intense heavy rainfall events, posing significant challenges for urban stormwater management, particularly in rapidly urbanizing cities of developing countries with constrained infrastructure. However, the quantitative assessment of urban stormwater, encompassing both its volume and quality, in these regions is impeded due to the scarcity of observational data and resulting limited understanding of drainage system dynamics. This study aims to elucidate the present and projected states of urban flooding, with a specific emphasis on fecal and organic contamination caused by combined sewer overflow (CSO).

Existing levels of biodiversity and river location may determine changes from small hydropower developments.

Global ecosystems are facing anthropogenic threats that affect their ecological functions and biodiversity. However, we still lack an understanding of how biodiversity can mediate the responses of ecosystems or communities to human disturbance across spatial gradients. Here, we examined how existing, spatial patterns of biodiversity influence the ecological effects of small hydropower plants (SHPs) on macroinvertebrates in river ecosystems.

Emerging trends and spatial shifts of drought potential across global river basins.

Droughts have devastating effects on various sectors and are difficult to quantify and track because of the invisible and slow but prevalent propagation. This dilemma is more significant in the case of the complex interactions between land and atmosphere mechanisms, which are inadequately considered in previous drought metrics. Here, we investigate the spatiotemporal variability of the recently devised metric called 'Drought Potential Index (DPI)', which incorporates the antecedent land water storage and current precipitation.

Development of statistical regression and artificial neural network models for estimating nitrogen, phosphorus, COD, and suspended solid concentrations in eutrophic rivers using UV-Vis spectroscopy.

River water quality monitoring is crucial for understanding water dynamics and formulating policies to conserve the water environment. In situ ultraviolet-visible (UV-Vis) spectrometry holds great potential for real-time monitoring of multiple water quality parameters. However, establishing a reliable methodology to link absorption spectra to specific water quality parameters remains challenging, particularly for eutrophic rivers under various flow and water quality conditions.

Particulate organic carbon potentially increases methane emissions from oxic water of eutrophic lakes.

Lakes are hot spots for methane (CH) emissions and particulate organic carbon (POC) production, which describes the methane paradox phenomenon. However, the current understanding of the source of POC and its effect on CH emissions during eutrophication remains unclear. In this study, 18 shallow lakes in different trophic states were selected to investigate the POC source and its contribution to CH production, particularly to reveal the underlying mechanisms of the methane paradox.

Leveraging machine learning methods to quantify 50 years of dwindling groundwater in India.

Global compilations and regional studies, indicative of the unsustainable extraction and subsequent unremittingly depleting groundwater (GW) in India, either provide bulk estimates or are confined to the river basins and therefore conceal inferences from a nationwide policymaking perspective. Here, we provide the state-wise past (2000-2020) and future (2030-2050) assessment of dwindling groundwater in India utilizing in-situ groundwater levels (GWL) from 54,112 wells, remote sensing products, and hydrological simulations. By employing three machine learning methods, we show a decline in GWL of over 80% in North India with a notable shift towards the eastern state of Uttar Pradesh and a cumulative groundwater loss (169.

A framework for quantifying climate-informed heavy rainfall change: Implications for adaptation strategies.

To understand the influence of climate change on heavy rainfalls and reduce the consequential multidimensional risks, we develop a climate-informed and adaptation strategies-related framework by using the information on heavy rainfalls and various socioeconomic factors. For this purpose, we firstly quantify the spatiotemporal characteristics of heavy rainfalls with various durations (1 h to multiple days) and return periods (2-year to 50-year) for the flood-prone country Cambodia, as a case study, during the historical period (1980-2005), mid-century (2040-2065), and late-century (2070-2095), using the latest three hourly climate model datasets under RCP 8.5 and 1 hourly ERA5 reanalysis datasets.

Modeling watershed-scale Cs transport in a forested catchment affected by the Fukushima Dai-ichi Nuclear Power Plant accident.

The Fukushima nuclear accident in 2011 resulted in Cs contamination of large areas in northeast Japan. A watershed-scale Cs transport model was developed and applied to a forested catchment in Fukushima area. This model considers Cs wash-off from vegetation, movement through soils, and transport of dissolved and particulate Cs adsorbed to clay, silt and sand.

Modeling radiocesium transport from a river catchment based on a physically-based distributed hydrological and sediment erosion model.

The accident at the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) in March 2011 resulted in the deposition of large quantities of radionuclides, such as (134)Cs and (137)Cs, over parts of eastern Japan. Since then high levels of radioactive contamination have been detected in large areas, including forests, agricultural land, and residential areas. Due to the strong adsorption capability of radiocesium to soil particles, radiocesium migrates with eroded sediments, follows the surface flow paths, and is delivered to more populated downstream regions and eventually to the Pacific Ocean.