Search Results (1,277)

Rice is a staple crop for over half the world’s population, and accurate, timely information on its planted area and production is crucial for food security and agricultural policy, particularly in developing nations like Sri Lanka. However, reliable rice monitoring in regions like Sri Lanka faces significant challenges due to frequent cloud cover and the fragmented nature of smallholder farms. This research introduces a novel, cost-effective method for mapping rice-planted area and yield at field scales in Sri Lanka using optical satellite data. The rice-planted fields were identified and mapped using a phenologically tuned image classification algorithm that highlights rice presence by observing water occurrence during transplanting and vegetation activity during subsequent crop growth. To estimate yields, a random forest regression model was trained at the district level by incorporating a satellite-derived chlorophyll index and environmental variables and subsequently applied at the field level. The approach has enabled the creation of two decades (2000–2022) of reliable, field-scale rice area and yield estimates, achieving map accuracies between 70% and over 90% and yield estimates with less than 20% error. These highly granular results, which are not available through traditional surveys, show a strong correlation with government statistics. They also demonstrate the advantages of a rule-based, phenology-driven classification over purely statistical machine learning models for long-term consistency in dynamic agricultural environments. This work highlights the significant potential of remote sensing to provide accurate and detailed insights into rice cultivation, supporting policy decisions and enhancing food security in Sri Lanka and other cloud-prone regions. Full article

Natural radionuclides may occur in groundwater and pose health risks when present in elevated concentrations. This study evaluates the quality of shallow groundwater in Nysa County (SW Poland) based on the activity concentrations of natural radionuclides radon (²²²Rn) and radium (²²⁶Ra) and estimates the associated radiological risk from water ingestion. Twenty-three groundwater samples were collected from private wells located within two distinct geological units: the Fore-Sudetic Block and the Opole Trough. Activity concentrations of ²²²Rn and ²²⁶Ra were measured using the liquid scintillation counting method. A spatial distribution model for ²²²Rn was developed using inverse distance weighting in QGIS. Local hydrogeochemical background levels were determined using the Q-Dixon test, interquartile range, and Shapiro–Wilk normality test. The background ranged from 2.6 to 3.9 Bq·L⁻¹ in the Opole Trough and from 0 to 10.7 Bq·L⁻¹ in the Fore-Sudetic Block. The lower detection limit (0.05 Bq·L⁻¹) for ²²⁶Ra activity concentration measurements was not exceeded. Effective dose rates were calculated in accordance with the recommendations of the International Commission on Radiological Protection and United Nations Scientific Committee on the Effects of Atomic Radiation. Doses ranged from <1 µSv to over 120 µSv·y⁻¹. Although all samples met national regulatory standards (≤1 mSv·y⁻¹), the World Health Organization reference level (0.1 mSv·y⁻¹) was exceeded in two cases. The results support the need for the radiological monitoring of unregulated private wells and provide a scientific basis for the refinement of legal frameworks and health protection strategies. Full article

Food safety management has evolved with the Hazard Analysis and Critical Control Point (HACCP) system serving as a global benchmark. However, conventional HACCP does not explicitly address environmental sustainability, leading to challenges such as excessive water use, chemical discharge, and energy inefficiency. Green HACCP extends traditional HACCP by integrating Environmental Respect Practices (ERPs) to fill this critical gap between food safety and sustainability. This study is presented as a conceptual paper based on a structured literature review combined with illustrative industry applications. It analyzes the principles, implementation challenges, and economic viability of Green HACCP, contrasting it with conventional systems. Evidence from recent reports and industry examples shows measurable benefits: water consumption reductions of 20–25%, energy savings of 10–15%, and improved compliance readiness through digital monitoring technologies. It explores how digital technologies—IoT for real-time monitoring, AI for predictive optimization, and blockchain for traceability—enhance efficiency and sustainability. By aligning HACCP with sustainability goals and the United Nations Sustainable Development Goals (SDGs), this paper provides academic contributions including a clarified conceptual framework, quantified advantages, and policy recommendations to support the integration of Green HACCP into global food safety systems. Industry applications from dairy, seafood, and bakery sectors illustrate practical benefits, including waste reduction and improved compliance. This study concludes with policy recommendations to integrate Green HACCP into global food safety frameworks, supporting broader sustainability goals. Overall, Green HACCP demonstrates a cost-effective, scalable, and environmentally responsible model for future food production. Full article

Climate change significantly affects hydrological processes in forest ecosystems, particularly in sensitive coastal areas such as the Croatan National Forest (CNF) in North Carolina. Accurate projections of future water yield are essential for managing agriculture, forestry, and natural ecosystems. This study investigates the potential impacts of climate change on water yield using a combination of statistical downscaling and machine learning. Two downscaling methods, a Statistical DownScaling Model (SDSM) and Multivariate Adaptive Constructed Analogs (MACA), were evaluated, with the SDSM providing superior performance for local climate conditions. To improve precipitation input accuracy, twenty ensemble scenarios were generated using the SDSM, and various machine learning algorithms were applied to identify the optimal ensemble. Among these, the Extreme Gradient Boosting (XGBoost) algorithm exhibited the lowest error and was selected for producing high-quality precipitation time series. This methodology is integrated into the MIDAS (Machine Learning-Based Integration of Downscaled Projections for Accurate Simulation) approach, which leverages machine learning to enhance climate input precision and reduce uncertainty in hydrological modeling. Water yield was simulated over the period 1961–2060, combining observed and projected climate data to capture both historical trends and future changes. The results show that combining statistical downscaling with machine learning algorithms can help improve the accuracy of water yield projections under climate change and be useful for water resource planning, forest management, and climate adaptation. Full article

In China, weir-gully-type debris flows pose severe threats to transportation infrastructure, yet existing studies lack systematic analysis of their dynamic processes and early-warning strategies. This study innovatively integrates depth-integral modeling and field monitoring to investigate two unstable weirs upstream of the Zangyinggou Tunnel on the G30 Saiguo Expressway. The main research conclusions are as follows: (1) the influence of terrain and water source conditions on the weir-valley debris flow plays a dominant role; (2) the debris flows triggered by Weir I and II collapses reach the G30 Saiguo Expressway at 3560 s and 4000 s, respectively, with peak destructive capacities (cross-sectional sweep areas of 10.26 m²/s and 11.69 m²/s); (3) a three-level early-warning strategy was proposed, mainly based on water-level gauge monitoring and early warning, supplemented by video surveillance and regular measurement by small unmanned aerial vehicles. This study has established a brand-new idea for the monitoring and early warning of debris flow disasters induced by the collapse of barrier lakes along the G30 km line in Xinjiang. These achievements provide feasible insights for disaster reduction in mountainous transportation corridors, thus having significant practical value for promoting the sustainable development of infrastructure under the United Nations Sustainable Development Goals (SDGs). Full article

A large number of cities have created semantic 3D city models, but these models are rarely used as input data for simulations, such as noise and flooding, in the urban planning process. Reasons for this are that many simulations require detailed land cover (LC) and elevation data that are often not included in the 3D city models, and that there is no linkage between the elevation and land cover data. In this study, we design, implement and evaluate methods to handle LC and elevation data in a 3D city model. The LC data is stored in 2.5D or 3D in the CityGML modules Transportation, Vegetation, WaterBody, CityFurniture and LandUse, and a complete 3D LC partition is created by combining data from these modules. The entire workflow is demonstrated in the paper: creating 2D LC data, extending CityGML, creating 2.5D/3D data from the 2D LC data, dividing the LC data into CityGML modules, storing it in a database (3DCityDB) and finally visualizing the data in Unreal Engine. The study is part of the 3CIM project where a national profile of CityGML for Sweden is created as an Application Domain Extension (ADE), but the result is generally applicable for CityGML implementations. Full article

This study aims to assess the role of headwater systems (HS) in enhancing ecological connectivity and supporting Green Infrastructure in the Centre Region of Portugal. Specifically, it identifies restoration opportunity areas within HS by analysing land-use changes over the past 70 years, modelling land-use scenarios to promote ecological resilience, and evaluating connectivity between HS and Natura 2000 sites. The methodology integrates spatial analysis of historical land-use data with connectivity modelling using least-cost path approaches. Results show substantial transformation in HS areas, notably the expansion of eucalyptus plantations and a decline in agricultural land. Approximately 58% of the HS are identified as requiring restoration, including areas within the Natura 2000 network. The connectivity assessment reveals that HS can function as effective ecological corridors, contributing to improved water regulation, soil conservation, gene flow, and wildfire mitigation. A total of 61 potential ecological linkages between Natura 2000 sites were identified. These findings highlight the strategic importance of integrating HS into regional and national Green Infrastructure planning and supporting the implementation of the EU Biodiversity Strategy for 2030. The study recommends prioritising headwater restoration through multi-scale planning approaches and active involvement of local stakeholders to ensure sustainable land-use management. Full article

Accurate and spatially detailed soil information is essential for supporting sustainable land use planning, particularly in data-scarce regions such as Cyprus, where soil degradation risks are intensified by land fragmentation, water scarcity, and climate change pressure. This study aimed to generate national-scale predictive maps of key soil health descriptors by integrating satellite-based indicators with a recently released geo-referenced soil dataset. A machine learning model was applied to estimate a suite of soil properties, including organic carbon, pH, texture fractions, macronutrients, and electrical conductivity. The resulting maps reflect spatial patterns consistent with previous studies focused on Cyprus and provide high resolution insights into degradation processes, such as organic carbon loss, and salinization risk. These outputs provide added value for identifying priority zones for soil conservation and evidence-based land management planning. While predictive uncertainty is greater in areas lacking ground reference data, particularly in the northeastern part of the island, the modeling framework demonstrates strong potential for a national-scale soil health assessment. The outcomes are directly relevant to ongoing soil policy developments, including the forthcoming Soil Monitoring Law, and provide spatial prediction models and indicator maps that support the assessment and mitigation of soil degradation. Full article

The Qinghai-Tibet Plateau is a sensitive area of global climate change and an “Asian water tower” and lakes in Qinghai-Tibet Plateau changes are of great significance to the regional hydrological cycle and ecological balance. However, the existing research mostly focuses on a single lake or short-term monitoring, and lacks a systematic review of the evolution of knowledge structure and interdisciplinary dynamics. Based on 354 literatures from CNKI (China National Knowledge Infrastructure) and Web of Science, this study used CiteSpace 6.3.R1 software to construct a scientific knowledge map of lake changes in the Qinghai-Tibet Plateau under the background of climate change for the first time. By analyzing the number of publications, research hotspots, institutional cooperation networks and keyword emergence rules, the core triangle structure of ”climate change–Qinghai-Tibet Plateau–lake” was revealed, and the three stages of sedimentary reconstruction (2002–2008), glacier–lake coupling (2005–2014) and human–land system comprehensive research (2015–2025) were divided. The study found that the scientific literature written in Chinese and the scientific literature written in English focused on empirical cases and model simulations, respectively, The research frontiers focused on hot karst lakes (burst intensity 3.71), lake water level (2.97) and carbon cycle (2.13). The research force is centered on the Chinese Academy of Sciences, forming a cluster of institutions in the northwest region, but international cooperation only accounts for 12.3%. Future research needs to deepen multi-source data fusion, strengthen cross-regional comparison, and build an international cooperation network to cope with the complex challenges of plateau lake systems under climate change. This study provides a scientific basis for the paradigm shift and future direction of plateau lake research. Full article

Water quality monitoring is a key requirement for fulfilling various national environmental policies, but with many competing needs and limited resources, data collected can suffer from both spatial and temporal deficiencies. Modelling offers the potential to substitute estimated values into observational gaps, but model validation often requires the very data that are lacking. In this paper we present the results of a pilot study to investigate spatial and temporal issues around the monitoring of faecal indicator bacteria (Escherichia coli) in rivers of the Taw and Torridge catchments in the UK. Statistical analysis of in situ measurements versus simulated data from the catchment models reveals similar seasonal associations between riverine bacterial counts and rainfall patterns. Furthermore, spatial apportionment of livestock to better reflect land use was found to be important in the models, especially in upstream reaches of the catchments. In conclusion, successful monitoring of faecal bacteria levels in UK rivers requires risk-based monitoring (sufficient to identify possible seasonal trends) and informed spatial consideration of sampling sites. Catchment models can be useful aids for directing and augmenting such monitoring programmes, but these models should undergo rigorous validation, particularly in upper catchment areas, to ensure correct model response to changes in land use and/or climate. Full article

Accurate estimation of actual evapotranspiration (ET) is critical for understanding hydrothermal cycles and ecosystem functioning in arid regions, where water scarcity governs ecological resilience. To address persistent gaps in ET quantification, this study integrates multi-source remote sensing data, energy balance modeling, and ground-based validation that significantly enhances spatiotemporal ET accuracy in the vulnerable desert steppe ecosystems. The study utilized meteorological data from several national stations and Landsat-8 imagery to process monthly remote sensing images in 2019. The Surface Energy Balance System (SEBS) model, chosen for its ability to estimate ET over large areas, was applied to derive modeled daily ET values, which were validated by a large-weighted lysimeter. It was shown that ET varied seasonally, peaking in July at 6.40 mm/day, and reaching a minimum value in winter with 1.83 mm/day in December. ET was significantly higher in southern regions compared to central and northern areas. SEBS-derived ET showed strong agreement with lysimeter measurements, with a mean relative error of 4.30%, which also consistently outperformed MOD16A2 ET products in accuracy. This spatial heterogeneity was driven by greater vegetation coverage and enhanced precipitation in the southeast. The steppe ET showed a strong positive correlation with surface temperatures and vegetation density. Moreover, the precipitation gradients and land use were primary controllers of spatial ET patterns. The process-based SEBS frameworks demonstrate dual functionality as resource-optimized computational platforms while enabling multi-scale quantification of ET spatiotemporal heterogeneity; it was therefore a reliable tool for ecohydrological assessments in an arid steppe, providing critical insights for water resource management and drought monitoring. Full article

Monitoring water quality is crucial for achieving clean water and sanitation goals, particularly in remote areas. The project “Morbidity vs. Water Quality for Human Consumption in Tonosí: A Pilot Study” aimed to enhance water quality assessments in Panama using advanced analytical techniques to assess volatile organic compounds, heavy metals, and microbiological pathogens. To support this, the Technical Unit for Water Quality (UTECH) was established, featuring a novel mobile laboratory with cutting-edge technology for accurate testing, minimal chemical reagent use, reduced waste generation, and equipped with a solar-powered battery system. The aim of this paper is to explore the design, deployment, and impact of the UTECH. Furthermore, this study presents results from three sampling points in Tonosí, where several parameters exceeded regulatory limits, demonstrating the capabilities of the UTECH and highlighting the need for ongoing monitoring and intervention. The study also assesses the environmental, social, and economic impacts of the UTECH in alignment with the Sustainable Development Goals and national initiatives. Finally, a SWOT analysis illustrates the UTECH’s potential to improve water quality assessments in Panama while identifying areas for sustainable growth. The study showcases the successful integration of advanced mobile laboratory technologies into water quality monitoring, contributing to sustainable development in Panama and offering a replicable model for similar initiatives in other regions. Full article

Water governance in Europe faces mounting challenges from climate change, demographic pressures, and aging infrastructure—especially in Southern regions increasingly affected by drought and institutional fragmentation. In contrast, Nordic countries such as Denmark and Sweden exhibit coherent, integrated governance systems with strong regulatory oversight. This study introduces the Water Governance Maturity Index (WGMI), a document-based assessment tool designed to evaluate national water governance across five dimensions: institutional capacity, operational effectiveness, environmental ambition, equity, and climate adaptation. Applying the WGMI to eight EU countries—four Nordic and four Southern—reveals a persistent North–South divide in governance maturity. Nordic countries consistently score in the “advanced” or “model” range, while Southern countries face systemic gaps in implementation, climate integration, and territorial inclusion. Based on these findings, the study offers actionable policy recommendations, including the establishment of independent regulators, strengthening of river basin coordination, mainstreaming of climate-water strategies, and expansion of affordability and participation mechanisms. By translating complex governance principles into measurable indicators, the WGMI provides a practical tool for benchmarking reform progress and supporting the EU’s broader agenda for just resilience and climate adaptation. Unlike broader frameworks like SDG 6.5.1, the WGMI’s document-based, dimension-specific approach provides granular, actionable insights for governance reform, enhancing its utility for EU and global policymakers. Full article

The accurate dosing of polyaluminum chloride (PAC) is essential for achieving effective coagulation in drinking water treatment, yet conventional methods such as jar tests are limited in their responsiveness and operational efficiency. This study proposes a hybrid modeling framework that integrates artificial neural networks (ANN) with genetic algorithms (GA) to optimize PAC dosage under variable raw water conditions. Operational data from 400 jar test experiments, collected between 2022 and 2024 at the Yanahurco water treatment plant (Ecuador), were used to train an ANN model capable of predicting six post-treatment water quality indicators, including turbidity, color, and pH. The ANN achieved excellent predictive accuracy (R² > 0.95 for turbidity and color), supporting its use as a surrogate model within a GA-based optimization scheme. The genetic algorithm evaluated dosage strategies by minimizing treatment costs while enforcing compliance with national water quality standards. The results revealed a bimodal dosing pattern, favoring low PAC dosages (~4 ppm) during routine conditions and higher dosages (~12 ppm) when influent quality declined. Optimization yielded a 49% reduction in median chemical costs and improved color compliance from 52% to 63%, while maintaining pH compliance above 97%. Turbidity remained a challenge under some conditions, indicating the potential benefit of complementary coagulants. The proposed ANN–GA approach offers a scalable and adaptive solution for enhancing chemical dosing efficiency in water treatment operations. Full article

Central Asia (CA) faces growing Water–Energy–Food (WEF) Nexus challenges, due to its complex transboundary water management, legacy Soviet-era water infrastructure, and increasing climate and socio-economic pressures. This study presents the development of a System Dynamics Model (SDM) to evaluate WEF interdependencies across the Aral Sea Basin (ASB), including the Amu Darya and Syr Darya river basins and their sub-basins. Different downscaling strategies based on the area, population, or land use have been applied to process open-access databases at the national level in order to match the scope of the study. Climate and socio-economic assumptions were introduced through the integration of already defined Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). The resulting SDM incorporates more than 500 variables interacting through mathematical relationships to generate comprehensive outputs to understand the WEF Nexus concerns. The SDM was successfully calibrated and validated across three key dimensions of the WEF Nexus: final water discharge to the Aral Sea (Mean Absolute Error, MAE, <5%), energy balance (MAE = 4.6%), and agricultural water demand (basin-wide MAE = 1.2%). The results underscore the human-driven variability of inflows to the Aral Sea and highlight the critical importance of transboundary coordination to enhance future resilience. Full article