Climate Change Management Research Articles

Improving high uncertainty of evapotranspiration products under extreme climatic conditions based on deep learning and ERA5 reanalysis data

Accurate quantification of Terrestrial Evapotranspiration (ET) is crucial for understanding global water resource management amid climate change. However, against the backdrop of frequent global extreme climate events, existing ET products don’t accurately reflect ground data under extreme climate conditions (high temperature and high vapor pressure deficit (VPD)) and no studies have addressed this uncertainty. This study, based on data from 150 flux towers (FLUXNET ET), utilizes four daily-scale ET product datasets (REA, GLEAMv3.6b, REA, ERA5) and ERA5 reanalysis meteorological data as inputs for Convolutional Neural Network (CNN), to explore ways to improve the accuracy of ET products under extreme climatic conditions, while three Machine Learning (ML) models were selected as comparisons. An additional 35 nearby sites provide independent validation for model applicability. The results show that the estimation accuracy of ET products under extreme climatic conditions has an average decrease of 37.5 % in the correlation coefficient (R2) and an average increase of 40 % in the root-mean-square-error (RMSE), whereas the CNN significantly improves this high uncertainty and outperforms the other ML models. The accuracy was significantly improved with the addition of ET products (R2 = 0.924, RMSE=0.323 mm/d) compared to having only meteorological data as input (R2 = 0.888, RMSE=0.369 mm/d). Under extreme climatic conditions, the R2 and RMSE of CNN are 0.786 (171.6 % average improvement) and 0.448 (65.4 % average reduction) compared to ET products, with PBias ranging from −9% to 9 % and the most significant improvement in the America (region), EBF (land cover type), and Cwa (climate type). Furthermore, using ET products as inputs significantly enhances the CNN’s estimation accuracy, yielding R2 and RMSE improvements between 10 % and 30 %. At the 35 independent validation sites, the CNN still performs well, with R2 and RMSE around 0.720 and 0.510 mm/d under extreme climatic conditions, and the accuracy within 500 km is even higher. This study enhances model precision, providing a valuable tool for optimizing water management and tackling climate change. These advancements help formulate precise irrigation strategies and water policies, particularly in extreme weather-prone regions, supporting ecological stability and mitigating water scarcity.

Internet of Things (IoT) for Environmental Monitoring

Purpose: The general objective of this study was to explore the Internet of Things for environmental monitoring. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the Internet of Things for environmental monitoring. Preliminary empirical review revealed that IoT technologies have significantly enhanced environmental management practices by revolutionizing data collection and analysis across various ecosystems. By integrating IoT sensors with existing monitoring frameworks, real-time data on air and water quality, agriculture, wildlife habitats, and urban green spaces was efficiently gathered. This data facilitated proactive decision-making, early detection of environmental risks, and evidence-based policy formulation to address climate change, biodiversity conservation, and sustainable resource management challenges. Despite challenges like data security and interoperability, collaborative efforts among stakeholders paved the way for more effective environmental monitoring and sustainable development initiatives globally. Unique Contribution to Theory, Practice and Policy: The Complex Adaptive Systems Theory, Diffusion of Innovations Theory and Resource Dependence Theory may be used to anchor future studies on the Internet of Things technology. The study provided several recommendations that contributed significantly to theory, practice, and policy in environmental management. The study emphasized interdisciplinary approaches to enhance theoretical frameworks, advocating for advanced models and algorithms integrating IoT with environmental science and data analytics. In practice, it recommended widespread adoption of IoT-enabled sensor networks with enhanced capabilities for precise and reliable data collection. Policy-wise, the study called for regulatory frameworks supporting IoT integration, data standards, and international cooperation to address global environmental challenges collaboratively. Capacity building and continuous research and development were also highlighted to optimize IoT technologies for sustainable environmental monitoring and management globally.

Groundwater: Sinking Cities, Urbanisation, Global Drying, Population Growth

An examination of a few examples of aquifer-use shows the importance and fragility of groundwater, with poor management leading to over-extraction by individuals and authorities producing subsidence – sinking cities. Freshwater is one of our most precious resources and it is rapidly disappearing, leading to global drying. At the same time, the global and urban populations are increasing, with civil unrest increasing due, in part, to freshwater shortages. The increasing global population and global urbanisation are driving an increase in water use, restriction of aquifer recharge and increased aquifer pollution. It is argued that urban population growth with attendant increased water use, combined with climate change and poor management, is significant in water stress. Particular attention must be paid to the effect of rising populations on local water resources, especially groundwater, and the knock-on effect on urban sustainability.

Changes in precipitation phases based on the multi-discrimination method in the Tibetan Plateau

Global warming is rapidly changing the precipitation structure, and the shift in precipitation phases is even more pronounced in the Tibetan Plateau (TP), a region sensitive to global climate change. The accuracy of precipitation phase discrimination is essential for studying precipitation variability. Based on the observed data from 112 meteorological stations in the study area obtained from the China Meteorological Administration (CMA) and the National Climatic Data Center (NCDC) before 1980, we compared and assessed six methods for distinguishing precipitation phases and the optimal method was determined to complete the precipitation phase data after 1980 in the study area. Subsequently, the changes in precipitation amount, intensity, and frequency were analyzed by classifying them into light, moderate, heavy, and extreme precipitation using the percentile threshold discrimination method. It found that the dynamic threshold temperature method (presented by Ding et al., 2014) was the most effective to distinguish precipitation phases in the TP, and its performance was further improved from 80.9% to 86.1% after using frequency intersection and probability guarantee methods. Precipitation was mainly concentrated in July. Meanwhile, the proportion of snowfall to total precipitation (S/P) was approximately 9%. From 1961 to 2020, the amount of total precipitation, rainfall and snowfall in the TP exhibited increasing trends, while the sleet demonstrated a persistent decline. Spatial and temporal heterogeneity existed in the changes of precipitation of different phases. The average single precipitation amount increased. Moreover, the changes in the amount and frequency of extreme precipitation were more pronounced. Increased frequency and amount of both rainfall and snowfall were distributed in the central and eastern parts of the study area with a more significant proportion. In contrast, the peripheral areas witnessed a decrease. The spatial distribution of frequency and amount of different precipitation intensities was comparable to that of the total precipitation. This study offers novel procedure into changes of precipitation phases at high altitudes, which will inform future research on climate change and water resource management.

Harnessing Technology for Agricultural Sustainability: Case Studies and Future Directions

Agricultural sustainability has become a critical focus in addressing the challenges of food security, climate change, and resource management. This article explores the role of technology in promoting sustainable agricultural practices through detailed case studies and an analysis of future directions. By examining various technological innovations such as precision agriculture, automated irrigation systems, and biotechnology, the study highlights their impact on increasing crop yields, reducing resource use, and minimizing environmental footprints. The case studies from different regions demonstrate successful implementations and the benefits of integrating technology into farming practices. The research also identifies key barriers to adoption, including high initial costs, lack of technical expertise, and the need for supportive policy frameworks. The findings suggest that while technology has the potential to significantly enhance agricultural sustainability, its success largely depends on the collaboration between stakeholders, including farmers, governments, and tech companies. The study concludes with recommendations for future research and policy development to support the widespread adoption of sustainable technologies in agriculture. Emphasizing the importance of continued innovation and education, this article aims to contribute to the ongoing discourse on sustainable agriculture and provide a roadmap for future efforts.

Application of African indigenous knowledge systems and practices for climate change and disaster risk management for policy formulation

Indigenous knowledge of and practices for climate change and disaster risk management have been used to respond to climate and disaster risk since antiquity. These indigenous practices have continuously been evolving with the change in human societies and their environment. In Africa, dominance of contemporary science, sometimes refer to as the ‘Western version of science’ or global science, has greatly led to the abandoning of the African Indigenous Knowledge (AIK) systems in formal decision-making process. However, recent years have witnessed increasing references to indigenous knowledge systems in policy documents.This paper assessed the application of the African indigenous knowledge of and practices for climate change and disaster risk management by African governments in policy formulation and implementation. Whereas references to the need to apply the indigenous knowledge of and practices for climate change and disaster risk management are abound in academic documents and policy instruments, this research has found that the application of the African indigenous knowledge of and practices for climate change and disaster risk management remains elusive. The research identified key barriers that hinder the application of the AIK for climate change and disaster risk management in decision making, and suggested some policy recommendations that would contribute to the utilisation of indigenous knowledge systems in policy formulation and implementation.The study was undertaken in seven African countries. These include Ethiopia, Kenya, Nigeria, Cameroon, Burkina Faso, Tunisia, and Mozambique. The seven countries represents the six climate zones on the African continent.

On the History of Water as a Human Right and Its Recognition in the Cuban Constitution

Abstract To say that water is one of the most important natural elements for life would be unnecessary. In fact, because of its incalculable value, it is recognised as blue gold. Unfortunately, it is sometimes forgotten that its protection is one of the main challenges facing society today. The scarcity of the vital liquid is becoming increasingly noticeable due to climate change, population growth and careless management by humans. On closer examination it can be appreciated as a natural element, a chemical element, an economic good, a cultural good, among other denominations because it is so versatile, but above all, it is a human right. Although the latter is not something that has always been established in this way, it took many years for it to be officially recognised. In this research, a historical, doctrinal and comparative study is carried out on the regulation of water as a human right, with special reference to its treatment in Cuban constitutionalism. In order to carry out this study, an analysis is made based on the role played by water in the development of humanity, its recognition as a human right and its inclusion in Cuba’s constitutional history. Theoretical-legal, historical-legal, analytical-legal and document analysis research methods were used for this purpose.

Evaluating impacts of climate and management on reservoir water quality using environmental fluid dynamics code

Climate change and human interference, notably nutrient input, affect the water quality. Nitrogen (N) and phosphorus (P) are pivotal in managing eutrophication. This study investigated the effects of water dynamics and chemical constituents on water quality in Hongfeng Lake, a typical weakly stratified reservoir suffering from algae blooms in Southwest China, using the Environmental Fluid Dynamics Code. Leveraging climate, hydrological, and water quality data, we constructed, calibrated, and validated the temperature-hydrodynamics-water quality-sediment model. Various scenarios were analyzed, including wind speed, air temperature, solar radiation, rainfall, water discharge, N and P external input, and internal release. The findings revealed that no rain and warming increased trophic state index (TSI) and chlorophyll-a (Chl-a) concentration, and no solar radiation initially elevated nitrate concentration, followed by an increase in ammonium concentration. Besides, no solar radiation and changes in rainfall significantly increased total phosphate concentration. The management scenarios of N and P reduction, halving tributary, and mainstream flow scenarios improved water quality and reduced eutrophication. The wind speed under the N and P reduced scenarios showed that a doubling in wind led to increased concentrations of the particulate organic matter, Chl-a, and dissolved oxygen, alongside decreased ammonium and nitrate, while TSI exhibited minimal change. However, 5- and 10-times wind speed scenarios amplified TSI in shallow water, potentially due to a substantial rise in internal nutrient release. The degradation trend observed in drinking water quality amid climate change (warming and flooding) raises concerns regarding health-related risks. These simulations provided the quantified influence of climate change and environmental management strategies on water quality in the weakly stratified reservoir, notably highlighting the looming threat of exacerbated eutrophication due to warming, necessitating more stringent N and P reduction measures compared to current practices.

Contributions of Soil Moisture and Vegetation on Surface-Air Temperature Difference during the Rapid Warming Period

The difference (DIF) between land surface temperature (Ts) and near surface air temperature (Ta) is the key indicator of the energy budget of the land surface, which has a more complex process than the individual Ts or Ta. However, the spatiotemporal variations and influencing factors of DIF remain incomplete. The contribution of vegetation and soil moisture (SM) as key driving factors to DIF is not yet clear. Here, we analyzed the spatiotemporal variation patterns of DIF in China from 2011 to 2023 using in situ Ts and Ta data. A convergent cross-mapping method was employed to explore the causal relationship between SM, NDVI and DIF, and subsequently calculated the contribution of NDVI and SM variations to DIF under different climatic backgrounds. The results indicate that during the study period, DIF values were all above 0 °C and showed a significant increasing trend with a national mean slope of 0.02 °C/a. In general, vegetation and SM have a driving effect on DIF, with vegetation contributing more to DIF (0.11) than SM (0.08) under different surface properties. The background values of SM and temperature have a significant effect on the spatial and temporal distribution of DIF, as well as the correlation of vegetation and soil moisture to DIF. The study outcomes contribute to a better understanding of the coupling relationship between the land surface and atmosphere, which are also crucial for addressing climate change and ecological environmental management.

The impact of emission trading schemes and government pressure on corporate climate change disclosure in China

With the various impacts of global warming and climate change on businesses, corporate climate change disclosure has become a highly prominent topic in recent years, drawing significant attention from various sectors of society. In emerging economies experiencing rapid economic growth, such as China, climate change disclosure holds significant relevance for businesses in presenting their sustainability efforts to their stakeholders and society. Therefore, it is necessary to investigate the key factors propelling climate change disclosure. This study focuses on listed companies in the pilot regions of China's Emissions Trading Scheme (CN-ETS) to explore the influence of external factors on disclosure performance. The difference-in-differences approach is conducted to examine the impact of ETS policy implementation on corporate climate change disclosure. The results indicate that ETS does have a certain level of effect on disclosure performance. Additionally, the influence is more significant for companies facing higher government pressure. Our findings provide several policy implications for the future development of climate management and corporate climate change disclosure practices in China and other emerging economies.

Incidence, identification and antibiotic resistance of Salmonella spp. in the well waters of Tadla Plain, Morocco

Concerns about challenges with water availability in the Tadla Plain region of Morocco have grown as a result of groundwater contamination brought on by human activity, climate change, and insufficient groundwater management. The objective of the study is to measure the number of resistant bacteria in the groundwater of Beni Moussa and Beni Aamir, as well as to evaluate the level of water pollution in this area. 200 samples were therefore gathered from 43 wells over the course of four seasonal campaigns in 2017 and 2018. Additionally, the samples were examined to determine whether Salmonella species were present and if they were resistant to the 16 antibiotics that were tested. Salmonella spp. have been identified in 31 isolated strains in total, accounting for 18.02% of all isolated strains. Data on antibiotic resistance show that 58.1% of Salmonella spp. strains are multidrug-resistant (MDR); 38.7% of Salmonella strains are tolerant to at least six antibiotics, 19.4% to at least nine antibiotics, 9.7% to four to seven antibiotics, 6.5% to at least eleven antibiotics, and the remaining 3.2% to up to twelve antibiotics. A considerable level of resistance to cefepime (61.29%), imipenem (54.84%), ceftazidime (45.16%), ofloxacin (70.97%), and ertapenem (74.19%) was found in the data. Consequently, it is important to monitor and regulate the growth of MDR in order to prevent the groundwater's quality from declining.

Interactive effects of soil salinity and nitrogen fertilizer types on nitrous oxide and ammonia fluxes

Soil salinization, impaired by climate change and poor management practices, poses a global threat, particularly in arid and semi-arid regions, leading to significant land degradation. This study aims to investigate the effects of different nitrogen (N) fertilizer sources (urea, ammonium-sulfate, and biogas waste) on CO2, N2O, and NH3 emissions and soil enzyme activities in two soil types varying in salinity level (non-saline: EC = 1.15 dS m−1, and saline: EC = 35.80 dS m−1) in a robotized continuous-flow soil incubation system. Our results showed a sharp increase in N2O and CO2 emissions (up to 0.51 ± 0.02 g N2O-N ha−1 day−1, 28.1 ± 3.9 kg CO2-C ha−1 day−1) in non-saline soils following soil rewetting, attributed to bacterial denitrification. However, this pattern was not observed in saline soils, suggesting that salinity causes partial inhibition to the regeneration of soil organic matter mineralization and denitrification processes after rewetting. Although salinity did not alter the overall cumulative N2O losses in any fertilizer treatment, it significantly delayed the evolution of N2O peak during the incubation period. On the other hand, NH3 volatilization was significantly higher in N-fertilized saline soils compared to non-saline soils (241% and 157% in ammonium-sulfate and biogas waste treatments, respectively), except for urea treatment, likely due to the decrease in nitrification rates. Furthermore, the study clearly showed lower soil enzyme activity levels for both nitrate reductase and urease activity. Interestingly, the lowest NH3 emissions were measured in urea treatment in both soils. Overall, our findings highlight the complex interplay between soil salinity, nitrogen fertilizer sources, and microbial processes, significantly influencing gaseous nitrogen emissions and N cycling in agricultural soils. Identifying the specific fertilizer treatments that minimize or maximize gaseous nitrogen losses in varying soil salinity, may guide the selection of appropriate fertilization strategies for farmers and policymakers to mitigate environmental impacts of fertilizer use during agricultural production.

Assessing small hydropower viability in water-scarce regions: environmental flow and climate change impacts using a SWAT+ based tool

Water-scarce regions, like the Mediterranean, face worsening conditions due to climate change, intensifying pressure on key economic sectors such as hydropower. Additionally, environmental conservation policies, particularly the implementation of environmental flows, present challenges for hydropower systems. Certainty regarding the impact of these factors on future hydropower production is crucial for informed decision-making in the transition to sustainable energy. This study introduces S + HydPower, a tool coupled with SWAT+ to assess climate change and watershed management effects on small hydropower plant (SHP) systems. In this study, we used this tool to investigate the consequences of implementing environmental flows and climate change on run-of-river SHPs in the Catalan River Basin District (CRBD), in Catalonia. The results show that applying environmental flows would lead to a significant 27% reduction in SHP production. However, this reduction would represent only 0.25% of the region’s current energy demand. Furthermore, the study reveals a potential 38% to 73% reduction in SHP production by the end of the twenty-first century due to the combined effects of environmental flows and climate change. This suggests a substantial decline in run-of-river SHP’s contribution to the CRBD’s electricity supply. These findings emphasize the need to explore alternative and sustainable energy sources to ensure the long-term reliability and resilience of the region’s energy supply.

The development of a sustainable climate resilience building framework for Southern Zimbabwe

Resilience building has increasingly been embraced as a framework for managing climate change and variability induced disaster. The study sought to develop a Sustainable Climate Resilience Building framework for managing climate change adversities in Mwenezi District of Southern Zimbabwe. A mixed method approach which utilized both qualitative and quantitative techniques was used. The choice of two approaches was based on a phenomenological constructivist belief that problems are best solved using multiple sources. The qualitative approach depended on questionnaires, interviews, focus group discussions and direct field observations. The quantitative approach depended on closed ended questions and use of statistical package for social scientists (SPSS version 25.0) for data analysis. Qualitative data was subjected to content analysis. The findings obtained revealed that Non-Governmental Organizations and Government Departments have collaborated and initiated resilience interventions to manage climate change impacts. Some of the interventions implemented can be categorized as crop production and livestock production interventions. However there are some challenges obtained hindering communities’ capacity to build resilience these are namely, ignorance, lack of technical know-how and lack of financial resources to buy resilient inputs on the side of communities. Based on the findings, a Sustainable Climate Resilience Building framework was developed to manage climate change adversities. The researchers recommend communities to adopt a Sustainable Climate Resilience Building framework. This is because the framework provides a pathway for building community resilience to effectively manage climate change adversities.

Conventional management has a greater negative impact on Phaseolus vulgaris L. rhizobia diversity and abundance than water scarcity.

Drought is one of the biggest problems for crop production and also affects the survival and persistence of soil rhizobia, which limits the establishment of efficient symbiosis and endangers the productivity of legumes, the main source of plant protein worldwide. Since the biodiversity can be altered by several factors including abiotic stresses or cultural practices, the objective of this research was to evaluate the effect of water availability, plant genotype and agricultural management on the presence, nodulation capacity and genotypic diversity of rhizobia. A field experiment was conducted with twelve common bean genotypes under irrigation and rain-fed conditions, both in conventional and organic management. Estimation of the number of viable rhizobia present in soils was performed before the crop establishment, whereas the crop yield, nodule number and the strain diversity of bacteria present in nodules were determined at postharvest. Rainfed conditions reduced the number of nodules and of isolated bacteria and their genetic diversity, although to a lesser extent than the agrochemical inputs related to conventional management. In addition, the effect of water scarcity on the conventional management soil was greater than observed under organic conditions. The preservation of diversity will be a key factor to maintain crop production in the future, as problems caused by drought will be exacerbated by climate change and organic management can help to maintain the biodiversity of soil microbiota, a fundamental aspect for soil health and quality.

Innovations in Solar-Powered Desalination: A Comprehensive Review of Sustainable Solutions for Water Scarcity in the Middle East and North Africa (MENA) Region

Water scarcity poses significant challenges in arid regions like the Middle East and North Africa (MENA) due to constant population growth, considering the effects of climate change and water management aspects. The desalination technologies face problems like high energy consumption, high investment costs, and significant environmental impacts by brine discharge. This paper researches the relationships among water scarcity, energy-intensive desalination, and the development of renewable energy in MENA, with a particular focus on the Gulf Cooperation Council (GCC) countries. It examines innovations in solar-powered desalination, considering both solar photovoltaic (PV) and solar thermal technologies, in combination with traditional thermal desalination methods such as multi-effect distillation (MED) and multi-stage flash (MSF). The environmental impacts associated with desalination by brine discharge are also discussed, analyzing innovative technological solutions and avoidance strategies. Utilizing bibliometrics, this report provides a comprehensive analysis of scientific literature for the assessment of the research landscape in order to recognize trends in desalination technologies in the MENA region, providing valuable insights into emerging technologies and research priorities. Despite challenges such as high initial investment costs, technical complexities, and limited funding for research and development, the convergence of water scarcity and renewable energy presents significant opportunities for integrated desalination systems in GCC countries. Summarizing, this paper emphasizes the importance of interdisciplinary approaches and international collaboration by addressing the complex challenges of water scarcity and energy sustainability in the MENA region. By leveraging renewable energy sources and advancing desalination technologies, the region can achieve water security while mitigating environmental impacts and promoting economic development.

Study on the Development of Property Insurance Industry Based on ARIMA and CGDAM-WRIR Models

This study aims to address the climate change challenges faced by the property-casualty insurance industry by using the ARIMA model to assess the potential impacts of extreme weather events on the insurance industry and the CGDAM-WRIR model to analyze the regional impacts of tornadoes and the impacts of climate change on communities. With these models, we provide accurate risk management and underwriting strategy adjustment recommendations. Future research will further explore complex models, overcome limitations, integrate multi-source data, and develop real-time predictive models to help the insurance industry better manage climate change uncertainty, safeguard property owner interests, and ensure market sustainability and resilience.

Predicting trajectories of temperate forest understorey vegetation responses to global change

Predicting forest understorey community responses to global change and forest management is vital given the importance of the understorey for biodiversity conservation and forest functioning. Though substantial effort has gone into disentangling the impact of global change on understorey communities, scarcity of information on site-specific environmental drivers across large temporal-spatial scales has limited our ability to predict global change effects at specific forest sites. In this study, using vegetation resurvey and soil data from 1363 plots across temperate Europe, we applied a machine learning approach (gradient boosting regression, GBR) to model and predict site-specific responses of four understorey properties to global change. We applied our final GBR models at 8 forest sites in Austria to validate the model performance, predict understorey trajectories, and evaluate the effect of alternative scenarios for future nitrogen(N) deposition, climate change and forest management on the projected trajectories. Our results showed that the R² value of the four final GBR models on the independent testing dataset ranged between 0.611 and 0.723 and the most important environmental drivers in predicting the trajectory of understorey properties at specific forest sites were soil pH, soil total carbon-to-nitrogen ratio, overstorey shade-casting ability and regional-scale mean annual precipitation. The out-of-sample R2 value of the four final GBR models on the Austrian data ranged between 0.224 and 0.561. The forecasted trajectories for the Austrian forest sites showed that site-specific understorey responses to near-future climate warming were expected to be weak. Under N deposition decreases, the proportion of woody species was predicted to increase, while species richness and total vegetation cover were predicted to decrease. Furthermore, under a closed canopy, the understorey community was predicted to shift towards more woody species and more forest specialists, albeit with reduced species richness and vegetation cover. Given expected warming and declining N pollution pressures, our presented GBR models allow the prediction of trajectories of understorey vegetation responses to global change and management interventions at specific forest sites. Such projections could aid forest management in addressing challenges posed by global change.

Prototype Biodiversity Digital Twin: Forest Biodiversity Dynamics

Forests are crucial in supporting biodiversity and providing ecosystem services. Understanding forest biodiversity dynamics under different management strategies and climate change scenarios is essential for effective conservation and management. This paper introduces the Forest Biodiversity Dynamics Prototype Digital Twin (pDT), integrating forest and biodiversity models to predict the effects of management options on forest ecosystems. The primary objective is to identify optimal management strategies that promote biodiversity, focusing on conservation and adaptation to different climate conditions. We start with the case of Finnish forests and bird species and plan to expand to include more European countries and a variety of species as the pDT is further developed.

Remote sensing estimation of dissolved organic carbon concentrations in Chinese lakes based on Landsat images

Long-term estimation of dissolved organic carbon (DOC) dynamics based on satellite data provides important information to comprehensively understand the lake carbon cycle, climate change, and sustainable water resource management. Here, we proposed a novel DOC concentration estimation model based on water classification using a large field dataset (n = 1929) collected from 123 Chinese lakes. We divided water into terrestrial-DOM-dominated and endogenous-DOM-dominated water bodies using the optimal spectral slope ratio (SR) threshold determined by setting the classification scenario. Then, we calibrated the optimal DOC estimation model using stepwise quadratic polynomial regression for the different types of water and determined the final DOC estimation model. The final DOC estimation model performed well in both the calibration and validation datasets, with R2 ≥ 0.78, RMSD ≤ 0.78 mg/L, and MAPD ≤ 16.51 %. Subsequently, we mapped the spatiotemporal dynamics of DOC concentrations in Chinese lakes with surface area greater than 1 km2 (n = 2621) between 1986 and 2021 using the Landsat data. Spatially, we observed obvious spatial heterogeneity in the DOC concentrations in the Chinese lakes, with the highest DOC concentration occurring in the Northeast Region (NER) (4.39 ± 0.21 mg/L) and the lowest DOC concentration occurring in the Qinghai-Tibet Plateau Region (QTR) (3.76 ± 1.12 mg/L). Temporally, the average DOC concentration in Chinese lakes generally decreased at a rate of − 0.09 mg/L decade−1 from 1986 to 2021, during which 56.5 % of the lakes experienced a decrease, 29.6 % of the lakes experienced no obvious change, and 13.9 % of the lakes experienced an increase. We also revealed that human activities are the main drivers of DOC increases in lakes, while vegetation and climate change are the main drivers of DOC decreases in lakes. Our results contribute to improving the accuracy of organic carbon estimations in lakes worldwide and provides meaningful insights into the lake carbon cycle..