Reservoir Area Research Articles

Fire danger in the supply areas of hydroelectric reservoirs under the restoration process in the south of Minas Gerais, Brazil

The partial or complete loss of vegetation cover triggers an increase in surface runoff, erosion, and sedimentation of water bodies, including reservoirs for hydroelectric power generation, reducing their life expectancy. To control or mitigate this issue, ecological restoration interventions should prioritize the recovery of areas most vulnerable to these processes, such as springs. Conversely, wildfires cause damage to vegetation cover and hinder ecological restoration and/or natural regeneration processes. Therefore, this study aimed to identify - with the aid of Geographic Information Systems - the temporal and spatial likelihood of fire occurrences in spring recharge areas undergoing ecological restoration and contributing to hydroelectric reservoirs in southern Minas Gerais, Brazil. The findings indicated that the months with the highest probability of wildfire occurrences were August and September (accounting for 66% of cases), requiring increased attention to prevention efforts. Furthermore, locations most susceptible to fires (steeper slopes, more flammable vegetation, and higher anthropogenic use) should be treated as priorities for both prevention and ecological restoration actions.

Study on Early Identification of Rainfall-Induced Accumulation Landslide Hazards in the Three Gorges Reservoir Area

The early identification of potential hazards is crucial for landslide early warning and prevention and is a key focus and challenging issue in landslide disaster research. The challenges of traditional investigation and identification methods include identifying potential hazards of landslides triggered by heavy rainfall and mapping areas susceptible to landslides based on rainfall conditions. This article focuses on the problem of early identification of rainfall-induced accumulation landslide hazards and an early identification method is proposed, which is “first identifying the accumulation that is prone to landslides and then determining the associated rainfall conditions”. This method is based on identifying the distribution and thickness of accumulation, analyzing the rainfall conditions that trigger landslides with varying characteristics, and establishing rainfall thresholds for landslides with different accumulation characteristics, ultimately aiming to achieve early identification of accumulation landslide hazards. In this study, we focus on the Zigui section of the Three Gorges Reservoir as study the area, and eight main factors that influence the distribution and thickness of accumulation are extracted from multi-source data, then the relative thickness information extraction model of accumulation is established by using the BP neural network method. The accumulation distribution and relative thickness map of the study area are generated, and the study area is divided into rocky area (less than 1 m), thin (1 to 5 m), medium (5 to 10 m), and thick area (thicker than 10 m) according to accumulation thickness. Rainfall is a significant trigger for landslide hazards. It increases the weight of the sliding mass and decreases the shear strength of soil and rock layers, thus contributing to landslide events. Data on 101 rainfall-induced accumulation landslides in the Three Gorges Reservoir area and rainfall data for the 10 days prior to each landslide event were collected. The critical rainfall thresholds corresponding to a 90% probability of landslide occurrence with different characteristics were determined using the I-D threshold curve method. Prediction maps of accumulation landslide hazards under various rainfall conditions were generated by analyzing the rainfall threshold for landslides in the Three Gorges Reservoir area, serving as a basis for early identification of rainfall-induced accumulation landslides in the region. The research provides a method for the early identification of landslides caused by heavy rainfall, delineating landslide hazards under different rainfall conditions, and providing a basis for scientific responses, work arrangements, and disaster prevention and mitigation of landslides caused by heavy rainfall.

Multifunctional-Team Approach Achieves Extended-Reach Record Offshore Abu Dhabi

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 216326, “Longest Extended-Reach-Drilling Well Worldwide Drilled in Middle East, Offshore Abu Dhabi, UAE,” by Marah Mohamad Alabed, Naser Salah Alsuwaidi, and Jamie Scott Duguid, SPE, ADNOC, et al. The paper has not been peer reviewed. _ The complete paper describes the engineering design and operational practices that supported the setting of a new extended-reach world record in a mature carbonate field offshore Abu Dhabi. This accomplishment reduces the carbon footprint of the development, allows acquisition of reservoir data earlier in the development plan, and accelerates production while reducing costs. This extended-reach development program is a strong example of what can be accomplished when a multifunctional team cultivates a strategic plan to expand technical and operational capabilities in a drilling campaign. Reservoir and Development-Plan Background The 1-md oil reservoir is a carbonate dominated by packstone and grainstone rock types in terms of storage capacity. Average porosity is 18%, and thickness is 130 ft. Permeability reduces in an southeast/northwest direction, dropping below 1 md. Calcite cementation development has occurred in the westernmost areas of the reservoir because of late oil in these low structural areas. Initially, the target reservoir was drilled at 2-km well spacing with a five-spot waterflooding scheme from wellhead platform towers. Later, development was optimized by a line waterflooding scheme at 250-m well spacing by development from an environmental island. The target reservoir area was partitioned into four development areas (West A, West B, West C, and West D). The partition is based on drilling reachability from environmental islands and underlying geology. The current optimized development began in the West A reservoir area through an artificially constructed island. The remaining reservoir areas required investment of two new islands. High risk is associated with these investment decisions because these reservoir areas degrade in terms of reservoir rock properties. Additionally, an increasing trend of water saturation exists with progress in the northwest direction within the transition zone. The development plan in the West A area is based on drilling segments called AB and BC, first with over-20,000-ft laterals to achieve production buildup by target date. To test the West B area, Segment BC was extended, thus covering both West A and B areas. This option reduced drilling complexity and maximized the reservoir-production rate. Additionally, by adopting the island drilling option instead of appraising a limited area of West B, an extensive area of approximately 10 km could be appraised. To implement this option, a stepout drilling plan was executed by drilling eight extended-reach maximum-reservoir-contact wells in increments to meet the goal of appraising the West B reservoir area. These first wells were extended in increments of 1,000–2,000 ft to test the capability to drill and run completion to target a measured depth (MD) of 45,000 ft. Upon applying learnings from these pilot wells, a stepout to extending wells to 50,000 ft MD and greater was studied. Fig. 1 shows the map of the subject drilled well with an extended MD of 50,000 ft with an underlying oil-saturation map. This optimized field-development strategy has allowed for effective West B reservoir appraisal.

Hydrogeochemistry and Quality Assessment using Water Quality Indices of Surface and Groundwater of Konar Reservoir Region, Hazaribag District, Jharkhand, India

ABSTRACT In the present paper, the water quality of the Konar reservoir area in the Hazaribag district of Jharkhand, India, has been investigated and with this view, the different physico-chemical variables, including pH, Dissolved Oxygen, Electrical Conductivity, TDS, Temperature, cations (Ca2+, Mg2+, Na+, K+), anions (CO32−, HCO3−, Cl−, F−, SO42−, NO3−) have been analyzed. According to Schoeller diagram, the order of major cations and anions for groundwater are Ca2+ > Na+ > Mg2+ > K+ and HCO3− > SO42− > Cl− > NO3− and for surface water are Na+ > Ca2+ > Mg2+ > K+ and Cl− > HCO3− > NO3− > SO42− respectively. The calculated water quality index shows that 100% of the samples of surface water are of excellent water quality, while 17% of the samples of groundwater are of excellent and 83% fall under the good category. The Gibbs Plot indicates that the rock-water interaction mechanism is dominant, Giggenbach triangle indicates that the waters have not yet reached equilibrium and are still in an immature state, although the Piper plot demonstrates that most of the surface water samples (78%) are mixed facies of Ca2+-Mg2+-Cl− and 22% are of Na+–Cl− type. All groundwater samples are mixed facies of Ca2+– HCO3− type. According to the Durov plot, 100% of surface water samples exhibit simple dissolution or mixing and majority of the ions in all groundwater samples are calcium and bicarbonate, showing the influence of limestone deposits. Almost all of the water samples from the Konar reservoir area were found to be appropriate for irrigation, according to the Residual Sodium Carbonate, Residual Sodium Bicarbonate, Sodium Percentage, Permeability Index, Kelley’s Ratio, Sodium Adsorption Ratio, United States Salinity Laboratory (USSL) and Wilcox diagrams.

Risk assessment of exposure to 12 kinds of mycotoxins through consumption of Pericarpium Citri Reticulatae collected from Three Gorges Reservoir area of China

A method for simultaneous determination of 12 mycotoxins in Pericarpium Citri Reticulataeby HPLC-MS/MS was established to analyze the residues of mycotoxins, inwhich from Three Gorges Reservoir area of China, including AFB1, AFB2, AFG1, AFG2, T-2, FB1, FB2, FB3, ZEN, OTA, OTB and DON.In addition, a probabilistic assessment model based on Monte Carlo simulation method was established in combination with pollution data, and the health risk assessment was carried out by the exposure limit method (MOE).The results showed that the method with strong specificity, good linearity and accurate recovery was established and could be used for the determination of 12 mycotoxins in Pericarpium Citri Reticulatae.In general, the total pollution rate of different degrees of pollution in the 36 batches of Pericarpium Citri Reticulatae sampleswas 75 %. It should be noted thatthe proportion of positive samplescontaminated by one toxin was the highest (59.26 %), and the detection rate of FB3 in Pericarpium Citri Reticulataewas the highest (66.67%), followed by AFG1 (44.44 %), indicating that the medicinal material polluted by AFG1 and AFB3 alone or simultaneously was more serious. Specifically, the detection rate of mycotoxins in Chongqing was the highest (92.31%) on account of the high temperature and humidity in Chongqing, followed by Southeast of Sichuan (83.33%) and West of Hubei (45.45%).On the other hand, the MOE of AFB1 and AFB2 calculated were both greater than 10000, indicating that the health risk of AFB1 and AFB2 exposure caused by taking Pericarpium Citri Reticulatae was low, but the risk of high intake population was higher than that of conventional intake population, which needed to be paid attention to. This study can provide a reference for the safety assessment of clinical medication of Pericarpium Citri Reticulatae inThree Gorges Reservoir area.

Water Turbidity Mapping Using Sentinel-2A Imagery and Cloud Based Google Earth Engine in Saguling Reservoir

Turbidity represents the level of suspended sediments in water, that will contribute to a greater effect on the sedimentation process such as siltation in river and reservoir. Knowing the distribution of turbidity is expected to provide an overview of which parts of the reservoir area need special attention to reduce the sedimentation rate of the reservoir. The study location is the Saguling Reservoir. All stages in this study are processed in the Google Earth Engine, a cloud-based computing that only produce one output of water with turbidity index values only. The visualization shows that the normalized difference turbidity index (NDTI) varies with a minimum value index of -0.187228 and a maximum value index of 0.09871. The results of this study are sufficient to describe where the source of turbidity has the potential to become sediment in the reservoir which continues to settle and can gradually accelerate the lifetime of the dam. The map produced can provide an overview for stakeholders who have a task in managing water resources. The higher of turbidity show the worse condition of the catchment area or watershed area upstream. The conditions can be a consideration in planning engineering that might be done so as to reduce sedimentation that occurs in the reservoir.

Analysis of deformation mechanism of rainfall-induced landslide in the Three Gorges Reservoir Area: Piansongshu landslide

The Three Gorges Reservoir Area (TGRA) is characterized by unique geological features that increase its susceptibility to landslides. These slopes are especially prone to destabilization when influenced by external triggers like rainfall. This research focuses on the Piansongshu landslide within the TGRA, aiming at unraveling the complex internal deformation mechanisms of landslides triggered by rainfall and providing critical insights for their prevention and mitigation. The study begins with on-site geological surveys to meticulously examine the macroscopic signs and mechanisms of deformation. It then utilizes the GeoStudio numerical simulation software to assess the landslide's stability, focusing on the changes in internal seepage fields and stability under various rainfall scenarios. Results indicate that continuous rainfall leads to the formation of a temporary saturation zone on the slope, which gradually deepens. In regions with more pronounced deformation, the infiltration line at the leading edge of accumulation notably protrudes towards the surface. Notably, the stability coefficient of the secondary shear surface of the landslide fluctuates more significantly than that of the primary sliding surface. Higher rainfall intensity and longer duration are positively correlated with a more pronounced decrease in stability coefficients. The impact on stability also varies across different rainfall patterns. As rainfall infiltrates over time, the slope's safety factor gradually decreases. This reduction continues even post-rainfall, indicating a delayed restoration period before stability returns to a safe level. These results yield valuable data for forecasting and mitigating landslides.

Potential Landslide Identification in Baihetan Reservoir Area Based on C-/L-Band Synthetic Aperture Radar Data and Applicability Analysis

The Baihetan reservoir region is characterized by complex geomorphology, significant altitude differences, and rugged terrain. Geological hazards in such areas are often characterized by high concealment, wide distribution, and difficulty in field investigation. Traditional identification techniques are unable to detect and monitor geological hazards on a large scale with high efficiency and accuracy. In recent decades, interferometric synthetic aperture radar (InSAR) techniques, such as small baseline subset InSAR (SBAS-InSAR), have been widely applied to landslide identification. However, due to factors such as vegetation and the degree of landslide deformation, single-band synthetic aperture radar (SAR) still has certain limitations in detecting landslides. In this study, SBAS-InSAR was conducted based on ALOS-2 and Sentinel-1 ascending-descending images covering the Baihetan reservoir region. Deformation identification results were utilized to conduct a statistical analysis of the SAR detection performance and landslide characteristics, and the effect of vegetation on the detection effectiveness of different SAR bands was discussed. The study revealed that when surface vegetation coverage reaches a high degree, the percentage of areas with coverage greater than 0.6 is greater than 95%, the SAR coherence is mainly affected by vegetation thickness; the comparison of the difference change in the average coherence of the C/L bands among the four vegetation types shows that the ratio of the average coherence of the L-bands to the C-bands increases by a factor of three with the increase in thickness and the transition from crops to shrubs and trees. The results showed that the L-band has better detectability than the C-band in alpine-canyon terrain with vegetation coverage and complex vegetation composition. However, considering the high temporal resolution and accessibility of Sentinel-1 SAR data, it is still the main data choice for wide-area identification of landslides in the reservoir area, while other satellite-borne SAR data with different wavelengths and resolutions, such as ALOS, can be used to assist in the identification and monitoring of landslide hazards with significant magnitude of deformations and dense vegetation coverage. Therefore, the combined utilization of multi-band SAR data has the potential to enhance the dependability of landslide identification and monitoring, resulting in more accurate detection results.

Disentangling determinants of nearshore fish and crayfish assemblages in a canyon-shaped Mediterranean reservoir

The distinct origin and hydrological characteristics of reservoirs, shaped by fluctuating water levels and seasonal variations, play a crucial role in determining aquatic species assemblages across diverse limnological zones. This study aimed to analyze fish and crayfish assemblages in the nearshore area of a canyon-shaped Mediterranean reservoir, seeking to identify seasonal and spatial convergent patterns and infer the factors influencing them. Samplings were conducted at five stations along the longitudinal profile of the reservoir at depths around 7 m using gillnets and hoops nets. A seasonal pattern emerged regarding species abundance and biomass, with higher values recorded during the warmer period. Additionally, a spatial trend was observed, indicating higher species abundance in the middle part of the reservoir and lower abundance near the dam, particularly during the colder period. Water transparency, temperature, and the distance from the dam were identified as the most significant factors affecting species assemblages in terms of abundance and biomass. This research provides valuable insights into the intricate interplay between environmental factors, seasonal changes, and the assemblages of aquatic species in a Mediterranean reservoir, contributing to our understanding of its ecological dynamics.

Evaluating the long-term effects of best management practices on pollution reduction and soil quality improvement in sloping farmland of the Three Gorges Reservoir area

Reducing nonpoint-source pollution and improving soil quality by applying best management practices (BMPs) on sloping farmland are essential for ensuring water environmental safety and sustainable agricultural development. However, little is known about the accumulative effect of the long-term application of BMPs on reducing pollution and improving soil quality in major land use types of sloping farmland in watersheds. We conducted a nine-year field experiment for citrus orchard and cropland of sloping farmland in the Three Gorges Reservoir area of China. The treatment settings for the citrus experimental plots were as follows: (1) citrus intercropped with white clover (WC), (2) citrus orchard soil mulched with straw (SM), (3) citrus intercropped with Hemerocallis flava contour hedgerows (HF) and (4) conventional citrus planting pattern (C-CK). The treatment settings for the crop experimental plots were as follows: (1) wheat-peanut rotation intercropped with Toona sinensis contour hedgerows (TS), (2) wheat-peanut rotation intercropped with alfalfa contour hedgerows (AF), (3) ryegrass-sesame rotation (RS) and (4) conventional wheat-peanut rotation (W-CK). We found that compared with C-CK and W-CK, (1) runoff, sediment, total nitrogen and total phosphorus losses were reduced by 18.0 %, 66.1 %, 35.6 % and 40.2 %, respectively, in WC; by 22.4 %, 61.2 %, 43.9 % and 52.4 %, respectively, in SM; by 25.4%, 69.5 %, 52.0 % and 58.1 %, respectively, in HF; by 45.1 %, 73.7 %, 64.6 % and 66.0 %, respectively, in TS; by 41.8 %, 67.4 %, 59.3 % and 60.8 %, respectively, in AF; and by 24.1 %, 42.9 %, 39.3 % and 40.3 %, respectively, in RS; (2) the soil quality index values of the BMP treatments increased; and (3) the application of these BMPs did not significantly affect yield or product value. We suggest the application of these BMPs on sloping farmland to (1) efficiently reduce total runoff, total sediment, and soil total nitrogen and total phosphorus losses; (2) improve soil quality; and (3) have no significant effect on output, of which the BMP HF applied to citrus orchard and the BMP TS applied to cropland are the most effective.

Landslide Hazard Assessment for Wanzhou Considering the Correlation of Rainfall and Surface Deformation

The landslide hazard assessment plays a crucial role in landslide risk mitigation and land use planning. The result of landslide hazard assessment corrected by surface deformation, obtained through time-series InSAR, has usually proven to have good application capabilities. However, the issue lies in the uncertainty of InSAR results, where some deformations cannot be calculated, and some are not true deformations. This uncertainty of InSAR results will lead to errors in landslide hazard assessment. Here, we attempt to evaluate landslide hazards by considering combined rainfall and surface deformation. The main objective of this research was to mitigate the impact of bias and explore the accurate landslide hazard assessment method. A total of 201 landslides and 11 geo-environment factors were utilized for landslide susceptibility assessment by support vector machine (SVM) model in Wanzhou District, Three Gorges Reservoir Area (TGRA). The preliminary hazard is obtained by analyzing the statistical data of landslides and rainfall. Based on the SAR image data of Sentinel-1A satellites from September 2019 to October 2021, the SBAS-InSAR method was used to analyze surface deformation. The correlation between surface deformation and rainfall was analyzed, and the deformation factor variables were applied to landslide hazard assessment. The research results demonstrate that the error caused by the uncertainty of InSAR results can be effectively avoided by analyzing the relationship between rainfall and surface deformation. Our results can effectively adjust and correct the hazard results and eliminate the errors in the general hazard assessment. Our proposed method can be used to assess the landslide hazard in more detail and provide a reference for fine risk management and control.

Determination of emission factors from a landfill through an inverse methodology: Experimental determination of ambient air concentrations and use of numerical modelling

The application of numeric modelling for determining the impact of landfills needs for reliable emission source data. In this study, a methodology for the characterization of the emission profiles of the different sources present in landfills for emission factors determination, applying an indirect methodology, is presented. Ambient air concentrations of volatile organic compounds (VOCs), hydrogen sulphide (H2S) and ammonia (NH3) were determined in three potentially emission sources in Can Mata landfill (Hostalets de Pierola, Catalonia, Spain): dumping areas, pre-closed zone and leachate reservoir as well as in biogas, for the determination of emission factors. Multi-sorbent bed and Tenax TA tubes were used for a wide range of VOCs sampling, and analysis was conducted through TD-GC/MS. H2S and NH3 were sampled and analysed using Radiello passive samplers. The highest total VOC (TVOC) concentrations were found in dumping areas (0.7–3.5 mg m−3), followed by leachate reservoir (0.3–0.6 mg m−3) and pre-closed area (77–165 μg m−3). On the other hand, the highest H2S and NH3 concentrations were found in leachate reservoir, presenting values of 0.8–1.1 mg m−3 and 1.7–1.8 mg m−3, respectively. With the application of odour thresholds to the concentrations obtained, the most critical compounds regarding odour annoyances were determined. The highest odour units (O.U.) were found in leachate reservoir due to H2S concentrations, whereas VOCs contributed mainly to O.U. in the dumping areas. The obtained ambient air concentrations were used for the indirect determination of the emission factors through numerical modelling using a Eulerian dispersion model. The emission factors obtained for the landfill for TVOC, H2S and NH3 were in the range of 0.44–10.9 g s-1, 0.16–1.02 g s−1 and 0.23–1.82 g s−1, respectively, depending on the emission source. Reliable emission factors are crucial to obtain landfill impact maps, which are essential for the correct management of these facilities.

MAINTAINING GAS RECOVERY IN LOW-PERMEABILITY FORMATIONS

The purpose of this work was to ensure efficient gas production in low-permeability and heterogeneous formations by numerically modeling the distribution of reservoir pressure drop around production and injection wells. To investigate this goal, we used a simulation based on the combined finite-element-difference method for nonstationary Leibenson piezoelectric conductivity problems. The method is based on the combination of elements of the already known finite difference and finite element methods for the nonstationary piezoelectric conductivity problem. The study found that there are many factors that affect the process of maintaining gas production in poorly permeable areas of a gas-bearing formation. Among them, first of all, after increasing the permeability of the corresponding section of the reservoir where production is created. Another important factor for maintaining gas recovery is the ability to naturally or artificially maintain gas phase infiltration within the affected reservoir area. This aspect is especially important for ensuring stable gas recovery over a long period of operation. During the intensive and long-term operation of the working area of the gas-bearing formation, additional injection wells were installed in the complex of production wells to avoid its depletion. It is important to note that provided a high level of gas phase penetration at the beginning of field operation, other factors of the production process, such as additional production wells and infiltration within the working area, have a lesser impact on the overall pressure distribution in this area. Thus, the study shows that with the passage of time and prolonged operation of the working section of the reservoir, the influence of its penetration decreases, but the importance of injected substances in the injection wells and gas phase infiltration within this section of the reservoir increases.

Effect of freeze‒thaw cycles on root-Soil composite mechanical properties and slope stability.

Natural disasters such as landslides often occur on soil slopes in seasonally frozen areas that undergo freeze‒thaw cycling. Ecological slope protection is an effective way to prevent such disasters. To explore the change in the mechanical properties of soil under the influence of both root reinforcement and freeze‒thaw cycles and its influence on slope stability, the Baijiabao landslide in the Three Gorges Reservoir area was taken as an example. The mechanical properties of soil under different confining pressures, vegetation coverages (VCs) and numbers of freeze‒thaw cycles were studied via mechanical tests, such as triaxial compression tests, wave velocity tests and FLAC3D simulations. The results show that the shear strength of a root-soil composite increases with increasing confining pressure and VC and decreases with increasing number of freeze‒thaw cycles. Bermuda grass roots and confining pressure jointly improve the durability of soil under freeze‒thaw conditions. However, with an increase in the number of freeze‒thaw cycles, the resistance of root reinforcement to freeze‒thaw action gradually decreases. The observed effect of freeze‒thaw cycles on soil degradation was divided into three stages: a significant decrease in strength, a slight decrease in strength and strength stability. Freeze‒thaw cycles and VC mainly affect the cohesion of the soil and have little effect on the internal friction angle. Compared with that of a bare soil slope, the safety factor of a slope covered with plants is larger, the maximum displacement of a landslide is smaller, and it is less affected by freezing and thawing. These findings can provide a reference for research on ecological slope protection technology.

Water-Level Prediction Analysis for the Three Gorges Reservoir Area Based on a Hybrid Model of LSTM and Its Variants

The Three Gorges Hydropower Station, the largest in the world, plays a pivotal role in hydroelectric power generation, flood control, navigation, and ecological conservation. The water level of the Three Gorges Reservoir has a direct impact on these aspects. Accurate prediction of the reservoir’s water level, especially in the dam area, is of utmost importance for downstream regions’ safety and economic development. This study investigates the application and performance of four distinct deep-learning models in predicting water levels. The models evaluated include the Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM), and Convolutional Neural Network–Attention–Long Short-Term Memory (CNN–Attention–LSTM). The performance of these models was assessed using several metrics, namely the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings indicate that the CNN–Attention–LSTM model outperforms the others in all metrics, achieving an R2 value of 0.9940, MAE of 0.5296, RMSE of 0.6748, and MAPE of 0.0032. Moreover, the CNN–LSTM model exhibited exceptional predictive accuracy for lower water levels. These results underscore the potential of deep-learning models in water-level forecasting, particularly highlighting the efficacy of attention mechanisms in enhancing predictive accuracy. Precise water-level predictions are instrumental in optimizing hydropower generation and providing a scientific basis for effective flood control and water resource management.

Optimum Cropping Pattern in the Command Area of Nyari-2 Reservoir Using Teaching Learning-Based Optimization Algorithm

Optimum Cropping Pattern in the Command Area of Nyari-2 Reservoir Using Teaching Learning-Based Optimization Algorithm

Dynamic monitoring of surface area and water volume of reservoirs using satellite imagery, computer vision and deep learning

The development of surface area and estimated volume measurements for water reservoirs using advanced computational techniques has become an increasingly important task for reservoir operation. Thus, the objective of this study is to determine the surface area and estimate the volume of water reservoirs through the segmentation of multispectral satellite images using a Fully Convolutional Neural Network (FCN). This paper introduces a universally applicable framework that utilizes deep convolutional neural networks for segmenting water bodies, particularly focusing on areas in Paraíba State, Northeast Brazil, susceptible to severe droughts. The study utilized Sentinel-2 satellite data obtained from Google Earth Engine for the period between January 2019 and September 2023. The extraction of water surface area from satellite images was achieved using the U-Net model, a deep learning framework based on FCN. The accuracy of water surface area extraction was analyzed using the Intersection-Over-Union (IoU) metric, also known as Jaccard Index. Subsequently, reservoir volumes were calculated using elevation-area-volume curves. The evaluation metrics for the results included root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and Pearson correlation coefficient (r). The network demonstrated high accuracy, achieving a 95% IoU on the test set. This methodology was applied to four reservoirs, yielding promising results that closely aligned the monitored reservoir curves derived from satellite imagery with observed field data. The findings of this study are significant for research on semiarid region changes, extending their relevance to a broader understanding of global environmental dynamics. This work not only contributes to the field of remote sensing in hydrology but also offers practical insights for effective water resource management in drought-prone regions.

Assessment major NOx sources to nitrate of TSP around the Danjiangkou reservoir using isotopes and a Bayesian isotope mixing model

Nitrate (NO3−) is a key component of atmospheric particulate matter. However, studies quantifying NO3− formation and sources in water source areas remain scarce. This study employs NO3− concentration, dual isotopic analyses (δ15N, δ18O), and SIAR and PSCF models to elucidate NO3− formation mechanisms and apportion sources in the Danjiangkou Reservoir area. The result showed that the concentration of NO3− (0.52 μg m−3-2.39 μg m−3), δ15N–NO3- (−2.3‰-4.2‰) and δ18O–NO3- (64.2‰–96.6‰) values varied seasonally in total suspended particulate matter (TSP). The contribution of the NO2 + ·OH pathway was the highest in summer (84%), while the main contributions came from the N2O5 + H2O and NO3 + VOCs pathways in winter (75%). The SIAR model revealed that the contributions of coal combustion and traffic emissions were 34% and 23%, respectively, indicating that fossil fuel release contributed most. Moreover, the contribution of biomass combustion sources was 31%, and soil emission sources had the least (12%). Considering liquefied petroleum gas, gasoline, and diesel vehicles as traffic emission sources, their contribution to NO3− in TSP increased to 50%. The PSCF analysis indicated that external input areas were predominantly from central Henan Province, northern Hubei Province, and central Shaanxi Province. This study quantitatively analyzed the sources of NO3− in atmospheric particulate matter in water sources for the first time, indicating that controlling the emissions of nitrogen oxides from gasoline cars, heavy-duty diesel trucks, and ships could help alleviate haze pollution and enhance air quality in the Danjiangkou reservoir area.

Year-round CO2 emissions from the drawdown area of a tropical reservoir: Strong seasonal and spatial variation

A growing body of literature points to drawdown areas as important sources of atmospheric CO2 within reservoirs. Yet seasonal and temporal patterns of CO2 flux from periodically exposed sediments in drawdown areas remain poorly understood. Here we evaluate the annual and diel (24-hour cycle) variations in CO2 emissions from sediments periodically exposed to the atmosphere. We sampled sediments in the drawdown area of a tropical reservoir, which encompassed two primary adjacent land covers—grassland and forestland—in the watershed of a reservoir located in southeastern Brazil. We also experimentally assessed the effect of rewetting on CO2 emissions from exposed sediments. We found large variations in emissions during all hydrological periods (from 10 to 10116 mg C/m−2(−|-) d-1), except for the late rainy period. Land use and how distant dry sediments were from the open water significantly affected drawdown CO2 emissions, with higher emissions occurring in areas surrounded by forest than those adjacent to grassland. Our diel-cycle analysis did not show significant variation of emissions over daily cycles. Furthermore, a rewetting experiment indicated a significant increase in emissions 30 min after the onset of the rewetting event. Although drawdown areas only cover 20 % of the reservoir’s area, they account for 80 % of the reservoir’s total CO2 emissions. Ultimately, single-time measurements can lead to considerable underestimation (up to 52 %) or overestimation (up to 190 %) of whole-reservoir CO2 emissions.

Geochemical characteristics and sources of heavy metal elements in the southern and western surface soils of Fengdu County, Chongqing

In order to explore the current situation and sources of heavy metal element pollution in the surface soil of the southern and western parts of Fengdu County, Chongqing. In this paper, the test and analysis results of 2955 surface soil samples collected from the project of "Chongqing Jiaoshi Dam Width, Liziwan Width, etc. a total of five 1:50,000 Land Quality Geological Survey" project were carried out. The single pollution index method and the land accumulation index method were used to evaluate the current status of heavy metal pollution in crops and soil. At the same time, correlation analysis and principal component analysis were performed. The results show that: (1) Compared with the soil heavy metal background in the Three Gorges Reservoir area, the average values of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn in the study area are enriched to different degrees, except that the average content of Cr is lower than the background value. Among them, As, Cd and Hg elements were obviously enriched. (2) The calculation results of the single index of heavy metals in the surface soil showed that Cd pollution was the main in the area. Cu was only slightly to slightly polluted, and the number of lightly polluted samples accounted for only 0.07%. (3) The average Igeo trend of heavy metals in surface soil in the region was Cd>Hg>As>Pb>Zn>Cu>Ni>Cr. As, Pb, Zn, Cu, Ni and Cr were mainly non-slightly polluted. Cd and Hg elements were heavily polluted, but the sample accounted for a small proportion. The overall display area is dominated by Cd element pollution. (4) The source analysis results show that As, Cu, Pb, Ni, Zn and Cr in the study area are mainly derived from the earth's crust. Its content is related to the geological setting. The pollution of Hg and Cd is likely to be affected by human activities, mainly from atmospheric deposition.