River basins of the Crimean Peninsula: Spatial differentiation in agroecological state and risks of soil degradation

Recent improvements in earth observation technologies and Geographical Information System (GIS) based spatial analysis methods require us to examine the efficiency of the different data-driven methods and decision rules for soil salinity monitoring and degradation mapping. The main objective of this study was to analyze the environmental impacts of the Lake Urmia drought on soil salinity and degradation risk in the plains surrounding the hyper-saline lake. We monitored the impacts of the lake drought on soil salinity by applying spatiotemporal indices to time-series satellite images (1990–2020) in Google Earth Engine environment. We also computed the soil salinity ratio to validate the results and determine the most efficient soil salinity monitoring techniques. We then mapped the soil degradation risk based on GIS spatial decision-making methods. Our results indicated that the Urmia Lake drought is leading to the formation of extensive salt lands, which impact the fertility of the farmlands. The land affected by soil salinity has increased from 2.86% in 1990 to 16.68% in 2020. The combined spectral response index, with a performance of 0.95, was the most efficient image processing method to assess soil salinity. The soil degradation risk map showed that 38.45% of the study area has a high or very high risk of degradation, which is a significant threat to food production. This study presents an integrated geoinformation approach for time-series soil salinity monitoring and degradation risk mapping that supports future studies by comparing the efficiency of different methods as state of the art. From a practical perspective, the results also provide key information for decision-makers, authorities, and local stakeholders in their efforts to mitigate the environmental impacts of lake drought and sustain the food production to sustain the 7.3 million residents.

Soil, a significant natural resource, plays a crucial role in supporting various ecosystems and serves as the foundation of Pakistan’s economy due to its primary use in agriculture. Hence, timely monitoring of soil type and salinity is essential. However, traditional methods for identifying soil types and detecting salinity are time-consuming, requiring expert intervention and extensive laboratory experiments. The objective of this study is to propose a model that leverages MODIS Terra data to identify soil types and detect soil salinity. To achieve this, 195 soil samples were collected from Lahore, Kot Addu, and Kohat, dating from October 2022 to November 2022. Simultaneously, spectral data of the same regions were obtained to spatially map soil types and salinity of bare land. The spectral reflectance of band values, salinity indices, and vegetation indices were utilized to classify the soil types and predict soil salinity. To perform the classification and regression tasks, the study employed three popular techniques in the research community: Random Forest (RF), Ada Boost (AB), and Gradient Boosting (GB), along with Decision Tree (DT), K-Nearest Neighbor (KNN), and Extra Tree (ET). A 70–30 test train validation split was used for the implementation of these techniques. The efficacy of the multi-class classification models for soil types was evaluated using accuracy, precision, recall, and f1-score. On the other hand, the regression models’ performances were evaluated and compared using R-squared (R), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results demonstrated that Random Forest outperformed other methods for both predicting soil types (accuracy = 65.38, precision = 0.60, recall = 0.57, and f1-score = 0.57) and predicting salinity (R = 0.90, MAE = 0.56, MSE = 0.98, RMSE = 0.97). Finally, the study designed a web portal to enable real-time prediction of soil types and salinity using these models. This web portal can be utilized by farmers and decision-makers to make informed decisions regarding soil, crop cultivation, and agricultural planning.

Soil Quality Assessment of Salinized Farmland Neighboring a Chinese Oil Exploitation Area

Induced soil degradation risks and plant responses by salinity and sodicity in intensive irrigated agro-ecosystems of seasonally-frozen arid regions

سابقه و هدف: ایران از کشورهای مهم تولیدکننده خرما می‌باشد و نه‌تنها دارای سابقه طولانی از این نظر است بلکه در حال حاضر نیز از لحاظ تولید خرما، رتبه دوم را در جهان به خود اختصاص داده است. بنابراین، کشت و تولید خرما در ایران هم از نظر ملّی و هم برای ساکنان استان‌های تولیدکننده، دارای اهمیت ویژه‌ای است. لذا برنامه‌ریزی برای استفاده بهینه از منابع تولید مانند خاک و اراضی برای توسعه نخیلات در کشور نیز جایگاه ویژه‌‌ای می‌یابد. در این راستا ارزیابی تناسب اراضی و به-کارگرفتن اراضی به تناسب پتانسیل و ظرفیتشان برای کاربری خاص، چاره این مهم به‌نظر می‌رسد. لیکن یکی از ضروریات ارزیابی تناسب اراضی، تعیین نیازهای رویشی گیاهان از جمله وضعیت خاک به‌عنوان بستر تولید است. هدف از انجام این مطالعه، بررسی تأثیر خصوصیات خاک بر عملکرد خرما و درجه‌بندی آن‌ها برای انجام مطالعات تناسب اراضی بود. مواد و روش‌ها: نخست 91 نخلستان با تنوع خاک و عملکرد در استان‌های کرمان، فارس، خوزستان، هرمزگان و بوشهر انتخاب و در هر باغ، یک پروفیل خاک مطالعه شده و پرسش‌نامه‌ کاربری اراضی تکمیل گردید. در نمونه‌های خاک جمع-آوری شده، آزمایش‌های فیزیکو-شیمیایی و حاصلخیزی مورد نیاز انجام شد. رگرسیون چندمتغیره بین عملکرد به عنوان متغیر وابسته و متغیرهای مستقل شامل شوری، درصد سدیم تبادلی، pH، گچ، آهک، رس، شن، سیلت، سنگریزه، پتاسیم و فسفر قابل جذب به روش گام به گام، بررسی گردید. سپس با بررسی روابط رگرسیون ساده بین ویژگی‌های اراضی مهم و موثر با عملکرد، معادلات و نمودارهای مربوطه ترسیم و درجه‌بندی خصوصیات اراضی انجام شد. جدول نیاز رویشی پیشنهادی با داده‌های خاک و عملکرد 20 باغ ارزیابی و صحت‌سنجی شد. یافته‌ها: نتایج نشان داد که پتاسیم، درصد شن، شوری خاک، درصد سدیم تبادلی و آهک، بیشترین، و pH و کربن آلی خاک، کمترین دامنه تغییرات را دارند. حداکثر مقدار آهک و گچ به ترتیب 74 و 17 درصد و بافت خاک از شنی تا رسی متغیر بود. نتایج رگرسیونی نشان داد به ترتیب متغیرهای مستقل شوری خاک، درصد سدیم تبادلی، آهک، گچ، سنگریزه، پتاسیم و فسفر قابل جذب، بر عملکرد موثر هستند. ضریب تبیین رگرسیون چند متغیره نشان داد که متغیرهای وارد شده به مدل توانسته‌اند 79 درصد از واریانس مربوط به متغیر وابسته را تعیین نمایند. در معادلات رگرسیون ساده، شوری خاک، درصد سدیم تبادلی، گچ، آهک و سنگریزه، اثر کاهشی و کربن آلی، فسفر و پتاسیم قابل جذب، اثر افزایشی بر عملکرد داشتند. بیشترین سهم در کاهش عملکرد خرما مربوط به شوری خاک، سنگریزه، درصد سدیم تبادلی و آهک بود. نتیجه‌گیری: ضریب تبیین بین عملکرد و شاخص خاک بدست آمده از جدول نیاز خاک و اراضی پیشنهادی برای خرما، 0.79 گردید که نشان‌دهنده دقت قابل‌قبول جدول ارائه شده است. حد مجاز شوری خاک، درصد سدیم تبادلی، گچ و آهک برای نخل خرما به‌ترتیب 8 دسی زیمنس بر متر، 12، 8 و 38 درصد به‌دست آمد.

In species that provide parental care, parents will sometimes cannibalize their own young (i.e., filial cannibalism). Here, we quantified the frequency of whole-clutch filial cannibalism in a species of giant salamander (eastern hellbender; Cryptobranchus alleganiensis) that has experienced precipitous population declines with unknown causes. We used underwater artificial nesting shelters deployed across a gradient of upstream forest cover to assess the fates of 182 nests at 10 sites over 8 years. We found strong evidence that nest failure rates increased at sites with low riparian forest cover in the upstream catchment. At several sites, reproductive failure was 100%, mainly due to cannibalism by the caring male. The high incidence of filial cannibalism at degraded sites was not explained by evolutionary hypotheses for filial cannibalism based on poor adult body condition or low reproductive value of small clutches. Instead, larger clutches at degraded sites were most vulnerable to cannibalism. We hypothesize that high frequencies of filial cannibalism of large clutches in areas with low forest cover could be related to changes in water chemistry or siltation that influence parental physiology or that reduce the viability of eggs. Importantly, our results identify chronic nest failure as a possible mechanism contributing to population declines and observed geriatric age structure in this imperiled species.

The aim of the study was to evaluate the effects of coniferous forest cover in the catchment basin and relative catchment area (catchment area to lake volume ratio) on phytoplankton composition in humic lakes. The study was carried out in 11 small and shallow lakes situated in the West Polesie region (Eastern Poland). The lakes were divided with respect to forest cover in their catchment basins into two groups: high forest cover — HFC (more than 60%) and low forest cover — LFC (less than 60%). The study showed that both, land use in the catchments (proportion of forests) and the relative catchment area determined physicochemical and biological parameters in the lakes. The high relative catchment area affects their high productivity expressed by high chlorophyll a concentration and low water visibility. The lakes of the LFC group had low water colour as well as high concentration of total phosphorus (Ptot), reaction (pH), and conductivity of water and a large number of cyanophytes and chlorophytes. The dominant species, e.g., Planktolyngbya limnetica, Limnothrix planctonica, Planktothrix agardhii, Coenococcus planctonicus, were characteristic of high trophic status. In the lakes of the HFC group, Ptot, pH, conductivity of water and the contribution of cyanophytes and chlorophytes was considerably lower, whereas the water colour and the number of raphidophytes represented by Gonyostomum semen was high. The large number of raphidophytes and the small amount of chlorophytes and cyanophytes in the lakes of the HFC group indicated the lake naturalness.

The Opequon Creek watershed is located in northern VA and the eastern panhandle of WV. Currently, the main creeks in the watershed do not meet VA or WV state water quality standards for recreational uses and aquatic life. In both states, the creeks are listed as impaired due to high levels of nutrients, bacteria, benthic and biologic impairment. The Opequon Creek is part of the upper Potomac River watershed, and ultimately impacts water quality in the Chesapeake Bay watershed. The main aim of this study was to develop a methodology that can be used to reduce nutrient loadings entering the bay area and improve water quality in Opequon watershed by implementing four innovative agricultural BMPs. The study develops an integrated approach to nutrient reduction incorporating three models involving water quality modeling, nutrient fate and transportation and an optimization model to recommend a least cost strategy for nutrient reduction.;Four optimization scenarios were evaluated, involving a uniform, holistic, prioritization, and targeted reduction approaches. A uniform reduction approach evaluated each subwatershed to meet a reduction goal. Using specific land use contributions, an annual cost of {dollar}5.9 million would be required to meet N and P reduction goals on 14 of the 17 subwatersheds. The holistic approach is a scenario whereby the entire watershed's nutrient reduction strategy is evaluated to meet the nutrient reduction goal at the Opequon watershed mouth. However, no optimal solution was found for this approach using agricultural BMPs. When BMPs were implemented on all acres of crop and pasture land, a total cost of {dollar}19.3 million was computed with only 43% of the reduction goal is achieved for P and 42% for N. In the third scenario, a prioritization approach targets priority subwatersheds. High priority subwatersheds were identified using the WCMS nutrient levels and public participation prioritization exercise in watershed management. The same three subwatersheds were identified as high priority by both methods: Mill, Tuscarora and Middle Creeks. Using P as the only constraint, the total cost of BMP implementation for these three subwatersheds under the Chesapeake Bay values was approximately {dollar}1.1 million compared to {dollar}282,000 using specific land use specific values. This result showed that nutrient reduction costs are much lower under specific land use contributions than using the Chesapeake Bay wide averages. The final scenario involved a targeted approach where reduction goals are to be met for both the Virginia and West Virginia parts of the Opequon watershed.

Wind speed data from reanalysis models is used for a wide range of industrial and research applications in the wind energy sector. These range from resource estimation in the development phase to planning of the future (wind) energy system and the integration of wind energy into electrical grids. Validation of reanalysis models for offshore conditions far away from the coast is relatively straightforward, for which very good agreement between reanalysis models and wind speed measurements has been demonstrated. In contrast, validation in heterogeneous and complex terrain is much more difficult.This study uses an extensive measurement dataset with high-quality wind speed measurements at heights relevant for modern wind energy applications (100 – 200 m) to evaluate and compare a set of reanalysis models, i.e. ERA5, COSMO-REA6, CERRA, and NEWA. The evaluation dataset in this study comprises approximately 100 lidar and mast measurements, mainly carried out for wind park developments, distributed over Germany and covering a wide range of topographic conditions.The analysis focuses on identifying local topographic effects and indicators that influence the quality of the agreement between the reanalysis models and the measurements (i.e. correlation) as well as systematic deviations (biases). The investigated topographic effects include orographic exposure, land /forest cover, and sub-grid scale orography. In a preliminary analysis based on 44 measurement stations, a clear correlation between the bias in the reanalysis models (compared to the measurements) and the orographic exposure of the measurement location could be demonstrated. At measurement locations with ground heights exceeding the height of the grid cell of the reanalysis model, the model data underestimated the observed wind speeds. The opposite could be observed at measurement location below grid height.These findings are especially important for wind energy applications, as wind farm developments tend to concentrate on areas with high exposure and specific land use types. Moreover, the observed relationships with local geography potentially provide the possibility to empirically correct and downscale wind speeds from reanalysis models. This allows to better represent the wind resource available for wind parks and to estimate the uncertainties based on local geographical conditions.

AimIn this study, we assessed the importance of local‐ to landscape‐scale effects of land cover and land use on flying insect biomass.LocationDenmark and parts of Germany.MethodsWe used rooftop‐mounted car nets in a citizen science project (“InsectMobile”) to allow for large‐scale geographic sampling of flying insects. Volunteers sampled insects along 278 five‐km routes in urban, farmland, grassland, wetland and forest landscapes in the summer of 2018. The bulk insect samples were dried overnight to obtain the sample biomass. We extracted proportional land use variables in buffers between 50 and 1,000 m along the routes and compiled them into land cover categories to examine the effect of each land cover, and specific land use types, on insect biomass.ResultsWe found a negative association between urban cover and flying insect biomass (1% increase in urban cover = 1% [95% CI: −3.0 to 0.0] decrease in biomass in Denmark, and a 3% [95% CI: −3.0 to 0.0] decrease in Germany) at a landscape scale (1,000‐m buffer). In Denmark, we also found positive effects of semi‐natural land cover types, that is protected grassland (largest at the landscape scale, 1000 m) and forests (largest at intermediate scales, 250 m). Protected grassland cover had a stronger positive effect on insect biomass than forest cover did. For farmland cover, the positive association with insect biomass was not clearly modified by any variable associated with farmland use intensity. The negative association between insect biomass and urban land cover appeared to be reduced by increased urban green space.Main conclusionsOur results show that land cover has an impact on flying insect biomass with the magnitude of this effect varying across spatial scales. However, the vast expanse of grey space in urbanized areas has a direct negative impact on flying insect biomass across all spatial scales examined.

Abstract. The interaction between forest and climate exhibits regional differences due to a variety of biophysical mechanisms. Observational and modeling studies have investigated the impacts of forested and non-forested areas on a single climate variable, but the influences of forest cover change on a combination of temperature and precipitation (e.g., drought) have not been explored, owing to the complex relationship between drought conditions and forests. In this study, we use historical forest and climate datasets to explore the relationship between forest cover fraction and drought from 1992–2018. A set of linear models and an analysis of variance approach are utilized to investigate the effect of forest cover change, precipitation and temperature on droughts across different timescales and climate zones. Our findings reveal that precipitation is the dominant factor (among the three factors) leading to drought in the equatorial, temperate and snow regions, while temperature controls drought in the arid region. The impact of forest cover changes on droughts varies under different precipitation and temperature quantiles. Precipitation modulates forest cover's impact on long-term drought in the arid region, while temperature modulates the impact of forest cover changes on both short- and long-term drought in the arid region as well as only on long-term drought in the temperate region. Forest cover can also modulate the impacts of precipitation and temperature on drought. High forest cover leads to a combined effect of precipitation and temperature on long-term drought in arid and snow regions, while precipitation is the only dominant factor in low forest cover conditions. In contrast, low forest cover triggers a strong combined effect of precipitation and temperature on drought in the temperate region. Our findings improve the understanding of the interaction between land cover change and the climate system and further assist decision-makers to modulate land management strategies in different regions in light of climate change mitigation and adaptation.

<strong class="journal-contentHeaderColor">Abstract.</strong> The interaction between forest and climate exhibits regional differences due to a variety of biophysical mechanisms. Observational and modelling studies have investigated the impacts of forested and non-forested areas on a single climate variable, but the influences of forest cover change on a combination of temperature and precipitation (e.g., drought) have not been explored owing to the complex relationship between drought conditions and forests. In this study, we use the historical forest and climate datasets to explore the changes in forest fraction and drought from 1992&ndash;2018. A set of linear models and an analysis of variance approach are utilized to investigate the effect of various factors on droughts across different time scales and climate zones. Our findings reveal that precipitation is the dominant factor leading to drought in the equatorial, temperate, and snow regions, while temperature controls drought in the arid region. The impact of forest cover on droughts varies under different precipitation and temperature quantiles. Precipitation modulates forest cover's impact on long-term drought in arid and snow regions, while temperature modulates forest cover's impact on both short- and long-term drought in the arid region as well as only on long-term drought in temperate and snow regions. Forest cover can also modulate the impacts of precipitation and temperature on drought. High forest cover leads to a combined effect of precipitation and temperature on long-term drought in equatorial, arid and snow regions, while precipitation is the only dominant factor in low forest cover conditions. In contrast, low forest cover triggers a strong combined effect of precipitation and temperature on drought in the temperate region. Our findings improve the understanding of the interaction between land cover change and the climate system and further assist decision-makers to modulate land management strategies in different regions in light of climate change mitigation and adaptation.

<strong class="journal-contentHeaderColor">Abstract.</strong> The interaction between forest and climate exhibits regional differences due to a variety of biophysical mechanisms. Observational and modelling studies have investigated the impacts of forested and non-forested areas on a single climate variable, but the influences of forest cover change on a combination of temperature and precipitation (e.g., drought) have not been explored owing to the complex relationship between drought conditions and forests. In this study, we use the historical forest and climate datasets to explore the changes in forest fraction and drought from 1992&ndash;2018. A set of linear models and an analysis of variance approach are utilized to investigate the effect of various factors on droughts across different time scales and climate zones. Our findings reveal that precipitation is the dominant factor leading to drought in the equatorial, temperate, and snow regions, while temperature controls drought in the arid region. The impact of forest cover on droughts varies under different precipitation and temperature quantiles. Precipitation modulates forest cover's impact on long-term drought in arid and snow regions, while temperature modulates forest cover's impact on both short- and long-term drought in the arid region as well as only on long-term drought in temperate and snow regions. Forest cover can also modulate the impacts of precipitation and temperature on drought. High forest cover leads to a combined effect of precipitation and temperature on long-term drought in equatorial, arid and snow regions, while precipitation is the only dominant factor in low forest cover conditions. In contrast, low forest cover triggers a strong combined effect of precipitation and temperature on drought in the temperate region. Our findings improve the understanding of the interaction between land cover change and the climate system and further assist decision-makers to modulate land management strategies in different regions in light of climate change mitigation and adaptation.

A comprehensive insight into the geography of forest cover in Italy: Exploring the importance of socioeconomic local contexts

Dynamics of a coupled socio-environmental model: An application to global CO2 emissions