Search Results (116)

High water quality in reservoirs used for drinking water supply and located within protected areas is of crucial importance for sustainable water-resource management. This study aims to evaluate the multi-depth water quality dynamics of the Ribnica Reservoir in western Serbia, combining two standardized assessment tools: the Serbian Water Quality Index (SWQI) and the Canadian Water Quality Index (CWQI). Data collected at various depths during 2021 and 2022 were analyzed to assess physico-chemical parameters and their impact on water quality, while the absence of microbiological data was noted as a limitation affecting the comprehensiveness of the assessment. The SWQI results indicated a general improvement in water quality over time, with values ranging from medium (82) to excellent (95) in 2021 and increasing from good (89) to excellent (98) in 2022. In contrast, the CWQI revealed specific risks, notably elevated concentrations of aluminum, mercury, and chromium, and reduced dissolved oxygen levels, with overall CWQI values ranging from poor (40) to good (88) depending on depth and parameter variability. The study highlights the necessity for continuous, comprehensive monitoring, including microbiological analyses and seasonal assessments, both within the reservoir and in the Crni Rzav River and its tributaries, to better understand pollutant sources and catchment influences. Strengthening microbiological and heavy metal monitoring, along with implementing proactive management strategies, is essential for preserving the Ribnica Reservoir’s ecological integrity and securing its long-term role in drinking water provision. Full article

The High Andean region of Peru, characterized by a complex orography, has unique and highly biodiverse ecosystems. This region has several headwater basins that play a critical role in the hydrological cycle, providing diverse ecosystem services essential to sustain biodiversity and supply water to human communities. Despite the importance of this region, it faces significant human intervention, particularly mining activities, which affect basin headwaters and jeopardize water security. This study aimed to calibrate the Biological Monitoring Working Party (BMWP) index to evaluate water quality in High Andean rivers in Peru affected by mining activities, using aquatic macroinvertebrates as bioindicators. We used a 15-year dataset (2008 to 2023) from three headwater basins in the High Andean region; this dataset included physicochemical water quality parameters, trace metals, and aquatic macroinvertebrates. The BMWP was calibrated for the High Andean region of Peru with this dataset (BMWP/PeIAZIM); afterward, it was validated to assess water quality in an area influenced by mining activities in this region. The results allowed us to differentiate between aquatic macroinvertebrate families tolerant to mining pollution and highly sensitive families. The sites heavily affected by mining activity returned very low BMWP/PeIAZIM scores; sites with no mining impact had the highest scores. These findings indicate that the calibrated index can be used for water resource management in the High Andean region, contributing to the conservation of its ecosystems. Full article

Nepal is known for its complex terrain, climate, and vegetation dynamics, resulting in tremendous hydrologic variability and complexity. Accurately quantifying the water balances at the national level in Nepal is extremely challenging and is currently not available. This study constructed long-term (2000–2022) water balances for 358 watersheds across Nepal by integrating watershed hydrometeorological monitoring data, remote sensing products including Leaf Area Index and land use and land cover data, with an existing ecohydrological model, Water Supply Stress Index (WaSSI). The WaSSI model’s performance is assessed at both watershed and national levels using observed water yield (Q) and evapotranspiration (ET) products derived from remote sensing (ETMonitor, PEW, SSEBop) and eddy flux network (i.e., FLUXCOM). We show that the WaSSI model captured the seasonal dynamics of ET and Q, providing new insights about climatic controls on ET and Q across Nepal. At the national scale, the simulated long-term (2000–2020) mean annual Q and ET was about half of the precipitation (1567 mm), but both Q and ET varied tremendously in space and time as influenced by a monsoon climate and mountainous terrain. We found that watersheds in the central Gandaki River basin had the highest Q (up to 1600 mm yr⁻¹) and ET (up to 1000 mm yr⁻¹). This study offers a validated ecohydrological modeling tool for the Himalaya region and a national benchmark dataset of the water balances for Nepal. These products are useful for quantitative assessment of ecosystem services and science-based watershed management at the national scale. Future studies are needed to improve the WaSSI model and remote sensing ET products by conducting ecohydrological research on key hydrologic processes (i.e., forest ET, streamflow generations of small watersheds) across physiographic gradients to better answer emerging questions about the impacts of environmental change in Nepal. Full article

Dams are crucial for ensuring water and electricity supply, while also providing significant flood protection. Regular monitoring of dam deformations is of vital socio-economic and ecological significance. In Germany, dams must be constructed and operated according to generally accepted rules of engineering. The safety concept for dams based on these rules relies on structural safety, professional operation and maintenance, safety monitoring, and precautionary measures. Rather time-consuming in situ techniques have been employed for these measurements, which permit monitoring deformations with either high spatial or temporal resolution, but not both. As a means of measuring large-scale deformations in the millimeter range, the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique of Persistent Scatterer Interferometry (PSI) is already being applied in various fields. However, when considering the operational monitoring of dams using PSI, specific characteristics need to be considered. For example, the geographical location of the dam in space, as well as its shape, size, and land cover. All these factors can affect the visibility of the structure for the use with PSI and, in certain cases, limit the applicability of SAR data. The visibility of dams for PSI monitoring is often limited, particularly in cases where observation is typically not feasible due to factors such as geographical and structural characteristics. While corner reflectors can improve visibility, their large size often makes them unsuitable for dam infrastructure and may raise concerns with heritage protection for listed dams. Addressing these challenges, electronic corner reflectors (ECRs) offer an effective alternative due to their small and compact size. In this study, we analyzed the strategic placement of ECRs on dam structures. We developed a new CR Index, which identifies areas where PSI alone is insufficient due to unfavorable geometric or land use conditions. This index categorizes visibility potential into three classes, presented in a ‘traffic light’ map, and is instrumental in selecting optimal installation sites. We furthermore investigated the signal stability of ECRs over an extended observation period, considering the Amplitude Dispersion Index (ADI). It showed values between 0.1 and 0.4 for many dam structures, which is comparable to normal corner reflectors (CRs), confirming the reliability of these signals for PSI analysis. This work underscores the feasibility of using ECRs to enhance monitoring capabilities at dam infrastructure. Full article

Monitoring agricultural drought is crucial for mitigating yield losses in winter wheat, especially in the Huang-Huai-Hai (HHH) region of China. Current drought indices often fall short in accurately representing the water supply–demand dynamics for crops, neglect irrigation practices, and overemphasize drought intensity rather than its evolution and overall impact. To address these concerns, we developed a novel transpiration drought index utilizing the Water Balance for Winter Wheat (WBWW) model. This index integrated variations in atmospheric conditions, soil moisture conditions, crop resistance, and irrigation practices to enhance the evaluation of water supply and demand dynamics. The WBWW model was initially validated against field transpiration measurements, achieving an R² of 0.7573, thereby confirming its reliability for subsequent analyses. To create a mechanistic understanding of crop water supply and demand, we adopted the reduction rate of actual and potential transpiration to identify drought events and constructed joint probability distributions of drought duration and severity using copulas. This led to the development of the Winter Wheat Drought Assessment Index (WDAI). The grade threshold for the WDAI was established based on historical drought data from the HHH region through a series of statistical threshold determination methods. Our findings showed that the WDAI successfully identified 87.36% of drought samples according to their recorded grades, with 97.13% within one grade of historical records. Comparative analyses with retained regional data and existing indices—the Crop Water Deficit Index (CWDI) and the Relative Soil Moisture Index (RSMI)—further demonstrated its effectiveness. Our study represents a robust tool for dynamic drought monitoring in the HHH region and offers critical insights into agricultural irrigation practices. Full article

Establishing a sound agricultural investment risk measurement and dynamic monitoring mechanism is a key path to optimize the efficiency of agricultural capital allocation and ensure the stability of the global food supply chain. Based on the five dimensions of politics, economy, society, agricultural management, and bilateral diplomatic and economic relations with China, this paper constructs an index system to assess the risks of China’s agricultural investment in 49 countries along “the Belt and Road” and uses nuclear density analysis, a Markov chain, and other methods to analyze the spatio-temporal evolution characteristics of different risks during 1995–2022. A deep neural network model is constructed to monitor the investment risk dynamically. The research shows that China’s agricultural investment risk to most of the countries along the route (61.22%) is at a normal level, and risk in bilateral diplomatic and economic relations with China is the most critical influencing factor. The agricultural investment risk among countries along the route has a significant positive spatial correlation and dynamic infectivity and shows a trend of gradually transferring from high risk to low risk in the long run. Endowment of agricultural water resources, natural disasters, and other indicators have the greatest impact on the high risk. Unemployment status and communication level have the greatest influence on the low risk. Investment relationship and endowment of agricultural land resources have the least influence on different investment risk levels. On this basis, the paper puts forward some policy suggestions for expanding the investment scale and strengthening dynamic monitoring. This paper enriches the index system of China’s agricultural investment risk and provides a reference for other countries’ agricultural investment and regional economic belt construction. Full article

Droughts are a major driver of global environmental degradation, threatening lives and causing significant economic losses, with approximately 80% of these losses linked to agricultural drought, characterized by soil moisture deficits. Remote sensing technology offers high spatiotemporal resolution data for continuous monitoring of soil moisture and drought severity. However, the effectiveness of remote sensing drought indices across different soil depths remains unclear. This study assessed the performance of eight widely used drought indices—Perpendicular Drought Index (PDI), Modified Perpendicular Drought Index (MPDI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Temperature–Vegetation Dryness Index (TVDI), and Standardized Precipitation–Evapotranspiration Index (SPEI) at multiple timescales—in monitoring soil moisture at five depths (0–50 cm, at 10 cm intervals) across nine agricultural regions of China from 2001 to 2020. Results reveal that the monitoring performance of drought indices varies significantly across regions and soil depths, with a general decline in performance as soil depth increases. For soil depths between 10–40 cm, VCI and NVSWI exhibited the highest accuracy, while PDI, MPDI, and VHI performed optimally in the Northeast China Plain. At 50 cm depth, however, optical remote sensing indices struggled to accurately capture soil moisture conditions. Additionally, TCI and TVDI showed notable lag effects, with 4-month and 5-month delays, respectively, while SPEI exhibited cumulative effects over 3–6 months. These findings provide critical insights to guide the selection of appropriate drought indices for soil moisture monitoring, aiding agricultural drought management and decision-making. Full article

Vegetable production in intensive protected agriculture systems has evolved due to its intensity and economic importance. Sensors are increasingly common for decision-making in crop management and control of environmental variables, obtaining optimal yields, such as estimating vegetation indices. Innovation and technological advances in unmanned vehicle platforms have improved spatial, spectral, and temporal resolution. However, in protected agriculture systems, the use is limited due to the assumption of having controlled environmental conditions for indeterminate vegetable production. Therefore, sequential monitoring of NDVI is proposed during the 2022 and 2023 agricultural cycles using the Green Seeker^® sensor and agronomic variables. This has created a database to generate predictive models of development and yield as a function of nutrient status. The results obtained indicate high significance levels for the development and NDVI curves in all phenological stages; in contrast to the yield predictive models, this is due to the maximum values (close to one) recorded for NDVI inside the greenhouse in comparison to the yield prediction obtained from the 18th week of harvest. Evaluating the models between NDVI and agronomic variables is not an index that offers certainty in predicting yield in indeterminate crops in protected agriculture production systems. This is due to the constant optimal development in response to controlled environmental conditions, nutrient status, and water supply inside the greenhouse, without the sustainability of yield, which decreases in the final stages of production until production becomes economically unprofitable. Full article

Controlled Environment Agriculture (CEA) offers a viable solution for sustainable crop production, yet the optimization of the latter requires precise modeling and resource management. This study introduces a novel hybrid plant growth model integrating stochastic, empirical, and optimization approaches, using Internet of Things sensors for real-time data collection. Unlike traditional methods, the hybrid model systematically captures environmental variability, simulates plant growth dynamics, and optimizes resource inputs. The prototype growth chamber, equipped with IoT sensors for monitoring environmental parameters such as light intensity, temperature, CO₂, humidity, and water intake, was primarily used to provide accurate input data for the model and specifically light intensity, water intake and nutrient intake. While experimental tests on lettuce were conducted to validate initial environmental conditions, this study was focused on simulation-based analysis. Specific tests simulated plant responses to varying levels of light, water, and nutrients, enabling the validation of the proposed hybrid model. We varied light durations between 6 and 14 h/day, watering levels between 5 and 10 L/day, and nutrient concentrations between 3 and 11 g/day. Additional simulations modeled different sowing intervals to capture internal plant variability. The results demonstrated that the optimal growth conditions were 14 h/day of light, 9 L/day of water, and 5 g/day of nutrients; maximized plant biomass (200 g), leaf area (800 cm²), and height (90 cm). Key novel metrics developed in this study, the Growth Efficiency Ratio (GER) and Plant Growth Index (PGI), provided solid tools for evaluating plant performance and resource efficiency. Simulations showed that GER peaked at 0.6 for approximately 200 units of combined inputs, beyond which diminishing returns were observed. PGI increased to 0.8 to day 20 and saturated to 1 by day 30. The role of IoT sensors was critical in enhancing model accuracy and replicability by supplying real-time data on environmental variability. The hybrid model’s adaptability in the future may offer scalability to diverse crop types and environmental settings, establishing a foundation for its integration into decision-support systems for large-scale indoor farming. Full article

This study examined the ecological status of the Streževo Reservoir in North Macedonia, focusing on zooplankton as an indicator of water quality. Built in 1982, the Streževo Reservoir serves several purposes, including irrigation, water supply, and hydropower generation. The research project investigated the seasonal and vertical variation in zooplankton abundance and biomass as well as the influence of environmental factors. Sampling was conducted seasonally (spring, summer, and autumn) in 2010 and 2011 across the longitudinal profile (epilimnion, metalimnion, and hypolimnion) of the reservoir at three sampling stations: the inflow of the Šemnica River, a central station in open water, and a site near the dam. The Streževo Reservoir is characterized by significantly pronounced seasonal and vertical temperature stratifications. The species diversity of the zooplankton was low, with only 21 taxa identified. Seasonal oscillations in abundance were statistically significant, with maximum values in the summer period and minimum values in spring. The Shannon diversity index displayed the lowest diversity values in the autumn, in the hypolimnion, and the highest values in the summer, in the metalimnion. The RDA analysis showed that temperature was the most important predictor of zooplankton abundance distribution, followed by Chl a concentration and TN. According to the Zooplankton Index of Quality Assessment (Zoo-IQ), during the investigated period the reservoir had good water quality in all three studied seasons, as well as through the whole profile. Overall, the study highlights the importance of zooplankton as an indicator of water quality and provides valuable insights into the ecological status of the Streževo Reservoir. The novelty of this study lies in its comprehensive examination of the interconnected dynamics affecting reservoir ecology, particularly as the present study is the first to perform such an analysis for the Streževo Reservoir. It highlights the impacts of thermal stratification on biochemical processes, the seasonal variations in dissolved oxygen and phosphorus levels due to phytoplankton activity, and the influences of temperature on zooplankton diversity and abundance. Furthermore, it introduces the Zoo-IQ index as an innovative tool for assessing water quality through zooplankton analysis, emphasizing its relevance as an early indicator of ecological changes in freshwater systems. Moreover, this multi-faceted approach underscores the complexity of reservoir ecosystems and the importance of proactive management strategies to the mitigation of water quality fluctuations. This study underlines the need for continuous monitoring and proactive management strategies to address the aging of reservoirs. Full article

Chyulu Hills, Kenya, serves as one of the region’s water towers by supplying groundwater to surrounding streams and springs in southern Kenya. In a semiarid region, this water is crucial to the survival of local people, farms, and wildlife. The Chyulu Hills is also very prone to fires, and large areas of the range burn each year during the dry season. Currently, there are no detailed fire records or burn scar maps to track the burn history. Mapping burn scars using remote sensing is a cost-effective approach to monitor fire activity over time. However, it is not clear whether spectral burn indices developed elsewhere can be directly applied here when Chyulu Hills contains mostly grassland and bushland vegetation. Additionally, burn scars are usually no longer detectable after an intervening rainy season. In this study, we calculated the Differenced Normalized Burn Ratio (dNBR) and two versions of the Relative Differenced Normalized Burn Ratio (RdNBR) using Landsat Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) data to determine which index, threshold values, instrument, and Sentinel near-infrared (NIR) band work best to map burn scars in Chyulu Hills, Kenya. The results indicate that the Relative Differenced Normalized Burn Ratio from Landsat OLI had the highest accuracy for mapping burn scars while also minimizing false positives (commission error). While mapping burn scars, it became clear that adjusting the threshold value for an index resulted in tradeoffs between false positives and false negatives. While none were perfect, this is an important consideration going forward. Given the length of the Landsat archive, there is potential to expand this work to additional years. Full article

Agricultural water resources in Xinjiang, China, face significant supply and demand contradictions. Agricultural water demand risk is a key factor impacting water resource management. This study employs the copula function (CF) and Monte Carlo (MC) methods to evaluate agricultural water demand risk at 66 stations in Xinjiang. The evaluation is based on the marginal distributions of precipitation (PR) and reference evapotranspiration (RET). The findings classify Xinjiang’s precipitation–evapotranspiration relationship into three types: evapotranspiration, precipitation, and transition. Regions south of the Tianshan Mountains (TMs) primarily exhibit evapotranspiration characteristics. The Ili River Valley and areas north of the TMs display precipitation characteristics. Other areas north of the TMs have transitional characteristics. Both annual precipitation and RET in Xinjiang follow the Generalized Extreme Value (GEV) distribution. The Frank CF effectively describes the coupling relationship between precipitation and RET, revealing a negative correlation. This negative correlation is stronger north of the TMs and weaker to the south. The agricultural water demand risk in Xinjiang varies significantly across regions, with the precipitation–RET relationship being a crucial influencing factor. The demand index (DI) for agricultural water decreases as the risk probability (RP) increases. The stability of the DI is greatest in evapotranspiration-type regions, followed by transition-type, and weakest in precipitation-type regions. When the RP is constant, the DI decreases in the order of evapotranspiration, transition, and precipitation types. This study quantifies the spatial pattern of agricultural water demand risk in Xinjiang. The advantage of the CF–MC method lies in its ability to assess this risk without needing crop planting structures and its ability to evaluate spatial variations. However, it is less effective in areas with few meteorological stations or short monitoring periods. Future efforts should focus on accurately assessing water demand risk in data-deficient areas. The findings are crucial for guiding the regulation and efficient use of agricultural water resources in Xinjiang. Full article

Drought, a significant calamity in the natural domain, has extensive worldwide repercussions. Drought, primarily characterized by reduced soil moisture (SM), presents a significant risk to both the world environment and human existence. Various drought indicators have been suggested to accurately represent the changing pattern of SM. The study examines various indices related to the Drought Severity Index (DSI), Evaporation Stress Index(ESI), Vegetation Supply Water Index(VSWI), Temperature-Vegetation Dryness Index(TVDI), Temperature Vegetation Precipitation Dryness Index(TVPDI), Vegetation Health Index(VHI), and Temperature Condition Index (TCI). An evaluation was conducted to assess the effectiveness of seven drought indicators, such as DSI, ESI, TVPDI, VSWI, etc., in capturing the changes in SM in Mu Us Sandy Land. The research results indicated that DSI and ESI had the highest accuracy, while TVDI and VSWI showed relatively lower accuracy. However, their smaller fluctuations in the time series demonstrated stronger adaptability to different regions. Additionally, the delayed impact of aridity indices on soil moisture, variable attributes, temperature, and vegetation coverage in sandy land and grassland areas with low, medium, and high coverage all contributed to the effectiveness of the four aridity indices (DSI, ESI, VSWI, and TVPDI) in capturing the dynamics of soil moisture. The primary element that affects the effectiveness of TVDI is the divergence of the relationship curve between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), which is a kind of deterioration. This paper presents a very efficient approach for monitoring soil moisture dynamics in dry and semi-arid regions. It also analyzes the patterns of soil moisture changes, offering valuable scientific insights for environmental monitoring and ecological enhancement. Full article

Drought, as a natural disaster with wide-ranging impacts and long duration, has an adverse effect on the global economy and ecosystems. In this paper, four remote sensing drought indices, namely the Crop Water Stress Index (CWSI), Vegetation Supply Water Index (VSWI), Temperature Vegetation Dryness Index (TVDI), and Normalized Difference Water Index (NDWI), are selected for drought analysis. The correlation analysis is carried out with the self-calibrated Palmer Drought Severity Index (sc-PDSI), and based on the optimal index (CWSI), the spatiotemporal characteristics of drought in Shaanxi Province from 2001 to 2021 were studied by SEN trend analysis, Mann–Kendall test, and a center of gravity migration model. The results show that (1) the CWSI performs best in drought monitoring in Shaanxi Province and is suitable for drought studies in this region. (2) Drought in Shaanxi Province shows a decreasing trend from 2001 to 2021; the main manifestation of this phenomenon is the decrease in the occurrence of severe drought, with severe drought covering less than 10% of the area in 2010 and subsequent years. The most severely affected regions in the province are the northern Loess Plateau region and Guanzhong Plain region. In terms of the overall trend, only 0.21% of the area shows an increase in drought, primarily concentrated in the Guanzhong Plain region and the outskirts of the Qinling–Bashan mountainous region. (3) Drought conditions are generally improving, with the droughts’ center of gravity moving northeastward at a rate of 3.31 km per year. The results of this paper can provide a theoretical basis and a practical reference for drought control and decision-making in Shaanxi Province. Full article

The aim of this work was to determine the concentrations, distribution, and fate of potentially toxic elements [lead (Pb), zinc (Zn), nickel (Ni), copper (Cu), mercury (Hg), arsenic (As), and cadmium (Cd)] in sediments of a hydropower plant water reservoir located in the Brazilian Cerrado biome (used as system model). The purpose of this study was achieved with an analysis of the level of contamination based on the geoaccumulation index (Igeo) and factor contamination (FC) and comparisons with values established by environmental legislation. The physical–chemical–biological properties of sediment samples, the distribution, and the fate of potentially toxic elements (PTEs) in the basin of the stream studied were also investigated using Pearson’s correlation coefficient (r) and principal component analysis (PCA). Cu, Hg, and Cd concentrations in the sediment samples from most of the points analyzed were above level II of the categorization stipulated in environmental legislation, characterizing sediments of poor quality. Moreover, Igeo and FC values indicated potential pollution of the water reservoir sediment by Cd. Concentrations of Cd exceeding 0.34 mg kg⁻¹ surpassed the reference values for water quality established by Conama Resolution No. 454/2012, highlighting the urgent need for ongoing sediment quality monitoring strategies. Hence, the study water reservoir was classified as being moderately to extremely polluted due to the fate of potentially toxic metals in the sediment samples. Frequent monitoring of the sediment quality in watersheds with hydropower plants is indispensable for the assessment of water resources, considering the importance of the water supply and power generation for the population. Moreover, water contaminated by PTEs poses potential risks to river basins, as well as to human and animal health. The results of this work can assist in the investigation of other water reservoirs around the world. Full article