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. This study found that levels of biodiversity (e.g., number of species) can influence the degrees of its alterations by SHPs occurring along elevational gradients. The results of the study reveal that the construction of SHPs has various effects on biodiversity. For example, low-altitude areas with low biodiversity (species richness less than 12) showed a small increase in biodiversity compared to high-altitude areas (species richness more than 12) under SHP disturbances. The increases in the effective habitat area of the river segment could be a driver of the enhanced biodiversity in response to SHP effects. Changes in the numerically dominant species contributed to the overall level of community variation from disturbances. Location-specific strategies may mitigate the effects of SHPs and perhaps other disturbances.

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. Using the spatial weighted centroid method, we elucidate the emerging spatial movement of the DPI within 168 major global river basins and analyze its influential factors. Improved drought detection and performance disparity of DPI as compared with multi-scale (i.e., 1, 3, 6, 9, 12-month) Standardized Precipitation Index, ensemble soil moisture anomaly, and Total Storage Deficit Index corroborate the robustness and improved insights of DPI. Higher increasing trends in DPI are detected over dryland basins (0.39 ± 0.43 %/a) than in the humid zones (0.15 ± 0.34 %/a). Six hotspot basins, namely, Don, Yellow, Haihe, Rio Grande, Sao Francisco, and Ganges river basins, are identified with increasing (2.1-3.5%/a) DPI during 2003-2021. The interannual occurrence of the highest DPI, spatial shifts, and relative contribution of DPI's constituent variables correspond well to the climatic and anthropogenic changes in humid and dry land basins. The absolute latitudinal/longitudinal shifts of ∼2° (as high as ∼3.2/4.9°) in DPI in 30% (47 out of 168 basins) of the global basins highlight the need for analyzing the water scarcity problems from both the perspectives of long-term trends and spatial shifts. Our findings provide a global assessment of the spatiotemporal shifts of drought potential and will be beneficial to understanding the anthropogenic and climatic influences on water resource management under a changing environment.

Instability in a carbon pool driven by multiple dissolved organic matter sources in a eutrophic lake basin: Potential factors for increased greenhouse gas emissions.

Lakes serve as vital reservoirs of dissolved organic matter (DOM) and play pivotal roles in biogeochemical carbon cycles. However, the sources and compositions of DOM in freshwater lakes and their potential effects on lake sediment carbon pools remain unclear. In this study, seven inflowing rivers in the Lake Taihu basin were selected to explore the potential effects of multi-source DOM inputs on the stability of the lake sediment carbon pool. The results showed the high concentrations of dissolved organic carbon in the Lake Taihu basin, accompanied by a high complexity level. Lignins constituted the majority of DOM compounds, surpassing 40% of the total, while the organic carbon content was predominantly composed of humic acids (1.02-3.01 g kg-1). The high amounts of lignin oxidative cleavage led to CHO being the main molecular structure in the DOM of the seven rivers. The carbon constituents within the sediment carbon reservoir exhibited a positive correlation with dissolved CH4 and CO2, with a notable emphasis on humic acid and dissolved CH4 (R2 = 0.86). The elevated concentration of DOM, coupled with its intricate composition, contributed to the increases in dissolved greenhouse gases (GHGs). Experiments showed that the mixing of multi-source DOM can accelerate the organic carbon mineralization processes. The unit carbon emission efficiency was highest in the mixed group, reaching reached 160.9 µmol∙Cg-1, which also exhibited a significantly different carbon pool. The mixed decomposition of DOM from different sources influenced the roles of the lake carbon pool as source and sink, indicating that the multi-source DOM of this lake basin was a potential driving factor for increased carbon emissions. These findings have improved our understanding of the sources and compositions of DOM in lake basins and revealed their impacts on carbon emissions, thereby providing a theoretical basis for improving assessments of lake carbon emissions.

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. To address this, a framework integrating desktop and in situ UV-Vis spectrometers was developed to establish reliable conversion models. The absorption spectra obtained from a desktop spectrometer were utilized to create models for estimating nitrate-nitrogen (NO3-N), total nitrogen (TN), chemical oxygen demand (COD), total phosphorus (TP), and suspended solids (SS). We validated these models using the absorption spectra obtained from an in situ spectrometer. Partial least squares regression (PLSR) employing selected wavelengths and principal component regression (PCR) employing all wavelengths demonstrated high accuracy in estimating NO3-N and COD, respectively. The artificial neural network (ANN) was proved suitable for predicting TN in stream water with low NH4-N concentration using all wavelengths. Due to the dominance of photo-responsive phosphorus species adsorbed onto suspended solids, PLSR and PCR methods utilizing all wavelengths effectively estimated TP and SS, respectively. The determination coefficients (R2) of all the calibrated models exceeded 0.6, and most of the normalized root mean square errors (NRMSEs) were within 0.4. Our approach shows excellent efficiency and potential in establishing reliable models monitoring nitrogen, phosphorus, COD, and SS simultaneously. This approach eliminates the need for time-consuming and uncertain in situ absorption spectrum measurements during model setup, which may be affected by fluctuating natural and anthropogenic environmental conditions.

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

Lakes are hot spots for methane (CH4) 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 CH4 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 CH4 production, particularly to reveal the underlying mechanisms of the methane paradox. The carbon isotopic analysis showed that the δ13Cpoc ranged from -30.28 ‰ to -21.14 ‰, indicating that cyanobacteria-derived carbon is an important source of POC. The overlying water was aerobic but contained high concentrations of dissolved CH4. Particularly, in hyper-eutrophic lakes, such as Lakes Taihu, Chaohu, and Dianshan, the dissolved CH4 concentrations were 2.11, 1.01, and 2.44 µmol/L, while the dissolved oxygen concentrations were 3.11, 2.92, and 3.17 mg/L, respectively. The intensified eutrophication increased the POC concentration, concomitantly promoting the dissolved CH4 concentration and the CH4 flux. These correlations revealed the role of POC in CH4 production and emission fluxes, particularly as a possible cause of the methane paradox, which is crucial for accurately evaluating the carbon budget and balance in shallow freshwater lakes.

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. A novel conditional artificial neural network (CANN) model is employed for temporal disaggregation to obtain the monthly maximum of 1 hourly rainfall in the future periods and subsequently, a zero-inflated generalized extreme value function (ZIGEV) is applied for extreme value analysis (EVA) to obtain rainfall intensity with different return periods. Secondly, the province-level flood risk change maps are developed based on a novel flood risk change index. The combination of CANN and ZIGEV performs better in EVA than traditional approaches by reducing the uncertainty from the stationarity assumption of temporal disaggregation and bias in the disaggregated rainfall. Rainfall intensity is projected to increase more in higher return periods and shorter durations towards the late-century, predominantly over Southern and Central Cambodia. Projected rainfall intensity-duration-frequency (IDF) curves in the capital city, Phnom Penh, reveal that the occurrence frequency of heavy rainfall in a given duration (e.g., 48 h) is likely to become ~10-fold in the mid-century. Results of province-level flood risk change maps indicate that Southeastern and Northwestern regions should be prioritized for employing adaption strategies. Our results will assist the policymakers in further mapping the flood susceptibility and vulnerability in different spatiotemporal scales across various communities and localities in the country and beyond.

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 (169.96 ± 19.67 km3) equivalent to the water storage capacity of the world's biggest dam (Kariba Dam, Zimbabwe). Its likely contribution to sea-level rise (0.47 ± 0.06 mm) is about 64% of that from annual global glacier melt. Our results typically contrast the GW recovery paradox in South India (e.g., a declining trend of -84.48 ± 38.81 mm/a (p < 0.05) in Andhra Pradesh during 2000-2020), reveal high seasonal variability (e.g., up to ~6 m in Maharashtra), and illustrate the skewed effect of survivor bias in the traditional assessments. We infer the significant impact of underlying hydrogeology and the implementation of water-related policies and projects on the GWL dynamic and variability in the region. Projected GWL reveals a likely water scarcity situation for about 2.8 million km2 area and one billion residents of the country up to 2050. Our observation-based analysis offers insights into the state-level monthly GW dynamics, which is critical for efficient interstate resource allocation, development plans, and policy interventions with broad methodological implications for the water-scarce countries.

Modeling watershed-scale 137Cs transport in a forested catchment affected by the Fukushima Dai-ichi Nuclear Power Plant accident.

The Fukushima nuclear accident in 2011 resulted in 137Cs contamination of large areas in northeast Japan. A watershed-scale 137Cs transport model was developed and applied to a forested catchment in Fukushima area. This model considers 137Cs wash-off from vegetation, movement through soils, and transport of dissolved and particulate 137Cs adsorbed to clay, silt and sand. Comparisons between measurements and simulations demonstrated that the model well reproduced 137Cs concentrations in the stream fed from the catchment. Simulations estimated that 0.57 TBq of 137Cs was exported from the catchment between June, 2011 and December, 2014. Transport largely occurred with eroded sediment particles at a ratio of 17:70:13 of clay, silt, and sand. The overall 137Cs reduction ratio by rainfall-runoff wash-off was about 1.6%. Appreciable 137Cs remained in the catchment at the end of 2014. The largest rate of 137Cs reduction by wash-off was simulated to occur in subwatersheds of the upper catchment. However, despite relatively low initial deposition, middle portions of the watershed exported proportionately more 137Cs by rainfall-runoff processes. Simulations indicated that much of the transported 137Cs originates from erosion over hillsides and river banks. These results suggested that areas where 137Cs accumulates with redeposited sediments can be targeted for decontamination and also provided insight into 137Cs transport at the watershed scale to assess risk management and decontamination planning efforts.

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. It is therefore important to understand the transport of contaminated sediments in the hydrological system and to predict changes in the spatial distribution of radiocesium concentrations by taking the land-surface processes related to sediment migration into consideration. In this study, we developed a distributed model to simulate the transport of water and contaminated sediment in a watershed hydrological system, and applied this model to a partially forested mountain catchment located in an area highly contaminated by the radioactive fallout. Observed discharge, sediment concentration, and cesium concentration measured from June 2011 until December 2012 were used for calibration of model parameters. The simulated discharge and sediment concentration both agreed well with observed values, while the cesium concentration was underestimated in the initial period following the accident. This result suggests that the leaching of radiocesium from the forest canopy, which was not considered in the model, played a significant role in its transport from the catchment. Based on the simulation results, we quantified the long-term fate of radiocesium over the study area and estimated that the effective half-life of (137)Cs deposited in the study area will be approximately 22 y due to the export of contaminated sediment by land-surface processes, and the amount of (137)Cs remaining in the catchment will be reduced to 39% of the initial total within 30 y after contamination. This study provides a perspective on the transport of suspended sediments and radiocesium in catchments with similar land use and radiocesium contamination.