Annual Precipitation Research Articles

Mixed effects of climate and species richness on aboveground carbon stock in subtropical Atlantic forests

Tropical forests are global biodiversity hotspots and are crucial in the global carbon (C) cycle. Understanding the drivers of aboveground carbon stock (AGC) in a heterogeneous and biodiverse system can shed light on the processes underlying the relationship between biodiversity and carbon accumulation. Here, we investigate how biodiversity, environment, and landscape structure affect AGC. We examined such associations in 349 plots comprising over 95,346 km2 the Atlantic Forest of southern Brazil, encompassing three forest types: Dense Ombrophylous Forest (DF), Mixed Ombrophylous Forest (MF), and Seasonal Deciduous Forest (SF). Each plot was described by environmental variables, landscape metrics, and biodiversity (species richness and functional diversity). We used diversity, environmental, and landscape variables to build generalized linear mixed models and understand which can affect the forest AGC. We found that species richness is associated positively with AGC in all forest types, combined and separately. Seasonal temperature and isothermality affect AGC in all forest types; additionally, stocks are positively influenced by annual precipitation in SF and isothermality in MF. Among landscape metrics, total fragment edge negatively affects carbon stocks in MF. Our results show the importance of species diversity for carbon stocks in subtropical forests. The climate effect was also relevant, showing the importance of these factors, especially in a world where climate change tends to affect forest stock capacity negatively.

Predicting the Potential Distribution of Hypericum perforatum under Climate Change Scenarios Using a Maximum Entropy Model.

H. perforatum, as one of the Traditional Chinese Medicinal materials, possesses a variety of pharmacological activities and high medicinal value. However, in recent years, the wild resources of H. perforatum have been severely depleted due to global climate change and human activities, and artificial cultivation faces problems such as unstable yield and active ingredient content. This poses a serious obstacle to the development and utilization of its resources. Therefore, this experiment took H. perforatum as the research object and used 894 distribution records of H. perforatum and 36 climatic environmental factors, using the MaxEnt model and GIS technology to explore the main climatic factors affecting the distribution of H. perforatum. Additionally, by utilizing the principles of ecological niche theory, the potential suitable distribution regions of H. perforatum across past, present, and future timelines were predicted, which can ascertain the dynamics of its spatial distribution patterns and the trend of centroid migration. The results indicate that the main environmental factors affecting the geographical distribution of H. perforatum are solar radiation in April (Srad4), solar radiation in September (Srad9), mean temperature of driest quarter (Bio9), solar radiation in November (Srad11), annual mean temperature (Bio1), and annual precipitation (Bio12). Under future climate scenarios, there is a remarkable trend of expansion in the suitable distribution areas of H. perforatum. The centroid migration indicates a trend of migration towards the northwest direction and high-altitude areas. These results can provide a scientific basis for formulating conservation and sustainable use management strategies for H. perforatum resources.

Biomass allocation and carbon storage in the major cereal crops: A meta‐analysis

AbstractCrop biomass is the reservoir of carbon (C), a valuable input to the soil, thus supporting the soil fauna and enhancing soil health. There are limited studies that compared the major cereal crops for C storage for regenerative agriculture and to optimize C sequestration strategies. The objective of this study was to quantify the extent of variation in biomass allocation and C storage between maize (Zea mays L.), sorghum (Sorghum bicolor [L.] Moench), and wheat (Triticum aestivum L.) for crop production, and C sequestration potential. The study used metadata from 40 global studies that reported on the allocation of plant biomass and C between roots and shoots of the major cereal crops. Key statistics were computed to determine the variability between genotypes for total plant biomass (Pb), shoot biomass (Sb), root biomass (Rb), root‐to‐shoot biomass ratio (Rb/Sb), total plant carbon content, shoot carbon content, root carbon content, total plant carbon stock (PCs), shoot carbon stock, root carbon stock, and root‐to‐shoot carbon stock ratio (RCs/SCs). Maize exhibited the highest variability for Pb (with a coefficient of variation [CV] of 31.2% and a mean of 4.2 ± 1.3 Mg ha−1 year−1), followed by wheat (CV of 24.2% and a mean of 1.5 ± 0.4 Mg ha−1 year−1) and sorghum (CV of 16.8% and a mean of 2.0 ± 0.8 Mg ha−1 year−1), respectively. A similar trend was observed for PCs, with maize (CV of 40.1% and mean of 1.6 ± 0.7 Mg ha−1 year−1) showing the highest total plant C stock variability, followed by wheat (24.4% and 0.2 ± 0.1 Mg ha−1 year−1) and sorghum (16.3% and 0.9 ± 0.3 Mg ha−1 year−1), respectively. Maize (with a CV of 24.4% and mean of 0.1 ± 0.03 Mg ha−1 year−1) exhibited the highest variability for Rb/Sb, while wheat (30.92% and 0.2 ± 0.05 Mg ha−1 year−1) exhibited the highest variability for RCs/SCs. Correlation analysis revealed the following significant associations: Pb and mean annual temperature (MAT) (r = −0.47), and Sb and MAT (r = −0.43), and Pb and mean annual precipitation (MAP) (r = −0.34), and Sb and MAP (r = −0.30). Rb had a strong, significant positive correlation with MAT (r = 0.72) and MAP (r = 0.85). The meta‐analysis revealed that maize and sorghum have the highest variability for Pb and plant carbon stocks, while wheat exhibited the highest variability for the below‐ground biomass and carbon stocks. The data aided in crop selection and suggested that the best cultivars could be developed and identified for production and C sequestration potential for cultivation by farmers, land rehabilitation, and climate change mitigation.

Whole genome resequencing reveals the adaptability of native chickens to drought, tropical and frigid environments in Xinjiang

Chickens exhibit extensive genetic diversity and are distributed worldwide. Different chicken breeds have evolved to thrive in diverse environmental conditions. However, research on the genetic mechanisms underlying chicken adaptation to extreme environments, such as tropical, frigid and drought-prone regions, remains limited. In this study, we conducted whole-genome sequencing of 240 individuals from six native chicken breeds in Xinjiang, China, as well as 4 publicly available chicken breeds inhabiting regions with varying annual precipitations, temperatures, and altitudes. Our analysis revealed several genetic variants among the examined breeds. Furthermore, we investigated the genetic diversity and population structure of breeds residing in extreme drought and temperature environments by comparing them. Notably, native chicken breeds exhibited different genetic diversity and population structures. Moreover, we identified candidate genes associated with chicken adaptability to the environment, such as CORO2A, CTNNA3, AGMO, GRID2, BBOX1, COL3A1, INSR, SOX5, MAP2 and PLPPR1. Additionally, pathways such as lysosome, cysteine and methionine metabolism, glycosaminoglycan degradation, and Wnt signaling may be play crucial roles in regulating chicken adaptation to drought environments. Overall, these findings contribute to our understanding of the genetic mechanisms governing chicken adaptation to extreme environments, and also offer insights for enhancing the resilience of chicken breeds to different climatic conditions.

Cliff dynamics in western Crimea

The paper considers the long-term dynamics of clayey cliffs of the Western Crimea in view of the problem of further recreational development of the Crimean Peninsula. Open-source satellite imagery and long-term (over 40 years) cliff section measurements were analyzed. The paper provides data on the geomorphology of individual coastal sites. It is shown that landslides are the main mechanism causing the cliff retreat on the larger part of the coastline. Slumps are typical for the southern part of the region and their movement episodes are relatively rare. Landslides become more active during the winter — spring period, when the moisture content of clayey cliff rocks increases substantially and the abrasion intensifies. Landslides can also be triggered by short-term heavy precipitation, which is usually observed in the summer. There is no definite relationship between the amount of annual precipitation, storm activity and landslide activity, either synchronous or with a time lag. No regularity in landslide dynamics was identified. It was found that the average long-term rates of cliff edge retreat are 0.1–1.2 m/year. In the northern part of the coast, the rates are the highest, decreasing towards the southern part of the region. The obtained average annual rates of cliff edge retreat are significantly less than those previously reported in the literature. There is a decrease in the beach-forming sediment supply due to a reduction in the stretch of the cliffs. Out of 50 km coastline with cliffs, only 39 km are left in the study area due to being covered by various structures and terracing. The paper also discusses anthropogenic activity, which leads to the formation and movement of man-made landslides and an increase in natural landslide activity. It is shown that the construction of transverse beach-retaining structures leads to blocking of littoral sediment transport, and to increase in the cliff retreat rate outside the protected reach.

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.

Shifts of the pond area ratio for lowland polders: Implication for nutrient control

Shifts for natural ecosystems were increasingly concerned due to its profound impacts on ecosystem services. Ponds within lowland artificial watersheds (polders) play a critical role in nitrogen (N) and phosphorus (P) cycling. From the perspective of N & P control in management practices, it is needed to determine an optimal pond area ratio for polders. For this purpose, our study proposed a process-based modelling framework to investigate the response of polder N & P loss to pond area, and thus to determine the threshold value of pond area ratio to achieve maximum N & P reduction for polders. The proposed framework included two process-based models (NDP and PDP) specially developed to describe N & P dynamics in lowland polders. To evaluate the proposed performance of the framework, it was applied to 171 polders in Zhong River Watershed in Lake Taihu Basin, eastern China. Our investigation results revealed that the correlation between polder N & P reduction rate and pond area ratio had an abrupt shift of 13.6 %, 14.7 % for N & P, respectively. Therefore, polders with a pond area ratio of 13.6–14.7 % had the largest N & P reduction (5.27 and 0.19 kg/ha). Polder size affected P reduction rate, with smaller polders (<200 ha) showing a higher P reduction rate, while it did not affect N reduction rate. Compared with annual precipitation, rainy-season precipitation more significantly (P<0.01) determined polder N & P reduction. This study demonstrated the use of our process-based framework in characterizing the shifts for the pond area ratio for polders, and thus provided technical support for N & P control of lowland areas in water management practices.

Land-Use Composition, Distribution Patterns, and Influencing Factors of Villages in the Hehuang Valley, Qinghai, China, Based on UAV Photogrammetry

Rapid changes in land use have rendered existing data for land-use classification insufficient to meet the current data requirements for rural revitalization and improvements in the living environment. Therefore, we used unmanned aerial vehicle (UAV) remote sensing imagery and an object-based human-assisted approach to obtain ultra-high-resolution land-use data for 55 villages and accurately analyzed village land-use composition and distribution patterns. The highest proportion of land use in the villages is built-up land (33.01% ± 8.89%), and the proportion of road land is 17.76% ± 6.92%. The proportions for forest land and grassland are 16.41% ± 7.80% and 6.51% ± 4.93%, respectively. The average size of the villages is 25.85 ± 17.93 hm2, which is below the national average. The villages have a relatively scattered distribution, mostly concentrated on both sides of the main roads. The correlation analysis indicates that mean annual temperature (MAT) and annual precipitation (AP) are the primary factors influencing the land-use composition of villages, with contribution rates of 50.56% and 12.51%, respectively. The use of UAV remote sensing imagery to acquire ultra-high-resolution land-use data will provide a scientific basis for the planning of the living environment in the villages of the Hehuang Valley.

Landslide Susceptibility Assessment by Machine Learning and Frequency Ratio Methods Using XRAIN Radar-Acquired Rainfall Data

This study is an efficiency comparison between four methods for the production of landslide susceptibility maps (LSMs), which include random forest (RF), artificial neural network (ANN), and logistic regression (LR) as the machine learning (ML) techniques and frequency ratio (FR) as a statistical method. The study area is located in the Southern Hiroshima Prefecture in western Japan, a locality known to suffer from rainfall-induced landslide disasters, the most recent one in July 2018. The landslide conditioning factors (LCFs) considered in this study are lithology, land use, altitude, slope angle, slope aspect, distance to drainage, distance to lineament, soil class, and mean annual precipitation. The rainfall LCF data comprise XRAIN (eXtended RAdar Information Network) radar records, which are novel in the task of LSM production. The accuracy of the produced LSMs was calculated with the area under the receiver operating characteristic curve (AUROC), and an automatic hyperparameter tuning and result comparison system based on AUROC scores was utilized. The calculated AUROC scores of the resulting LSMs were 0.952 for the RF method, 0.9247 for the ANN method, 0.9016 for the LR method, and 0.8424 for the FR. It is also noteworthy that the ML methods are substantially swifter and more practical than the FR method and allow for multiple and automatic experimentations with different hyperparameter settings, providing fine and accurate outcomes with the given data. The results evidence that ML techniques are more efficient when dealing with hazard assessment problems such as the one exemplified in this study. Although the conclusion that the RF method is the most accurate for LSM production as found by other authors in the literature, ML method efficiency may vary depending on the specific study area, and thus the use of an automatic multi-method LSM production system with hyperparameter tuning such as the one utilized in this study is advised. It was also found that XRAIN radar-acquired mean annual precipitation data are effective when used as an LCF in LSM production.

Identifying clusters of precipitation for the Brazilian Legal Amazon based on magnitude of trends and its correlation with sea surface temperature

Prioritizing watershed management interventions relies on delineating homogeneous precipitation regions. In this study, we identify these regions in the Brazilian Legal Amazon based on the magnitude of Sen’s Slope trends using annual precipitation data from September to August, employing the Google Earth Engine platform. Utilizing the silhouette method, we determine four distinct clusters representing zones of homogeneous precipitation patterns. Cluster 0 exhibits a significant median increase in precipitation of 3.20 mm year−1 over the period from 1981 to 2020. Cluster 1 shows a notable increase of 8.13 mm year−1, while Clusters 2 and 3 demonstrate reductions in precipitation of − 1.61 mm year−1 and − 3.87 mm year−1, respectively, all statistically significant. Notably, the region known as the arc of deforestation falls within Cluster 2, indicating a concerning trend of reduced precipitation. Additionally, our analysis reveals significant correlations between Sea Surface Temperature (SST) in various oceanic regions and precipitation patterns over the Brazilian Legal Amazon. Particularly noteworthy is the strong positive correlation with SST in the South Atlantic, while negative correlations are observed with SST in the South Pacific and North Atlantic. These findings provide valuable insights for enhancing climate adaptation strategies in the Brazilian Legal Amazon region.

Orinoco revisited: Comprehensive analysis of the Orinoco River basin present and future hydroclimate

The Orinoco River basin, ranked as South America’s third-largest catchment, is pivotal in contributing to the Atlantic Ocean’s water volume. This study provides a comprehensive update on the basin’s surface water balance, examining trends using gridded precipitation and total evaporation datasets. We also explore projected changes in precipitation until the end of the 21st century, focusing on the RCP8.5 climate change scenario. To achieve this, we utilize data from regional climate models designed by the CORDEX-CORE experiment, selecting an ensemble that excels in performance across South America and Central America. We estimate the accuracy of reference datasets in capturing water balance dynamics. We identify increasing trends in precipitation and total evaporation across most of the basin, enhancing our understanding of its long-term hydrological balance. Notably, the Andean and Guianese sectors of the basin contribute equally to half of the mean surface runoff, although the latter encompasses only 30% of the total area. This underscores the key role of the Guianese shield sub-basins. In regional climate modeling, despite some underestimation, the model runs for the CORDEX South America domain simulate effectively the precipitation across the basin. Regarding climate scenarios, our analysis using the RCP8.5 scenario projects an average annual precipitation reduction of around 45% for the entire basin. These findings emphasize the urgency of adopting measures to mitigate potential adverse effects on the Orinoco River basin’s hydrological sustainability in response to evolving climate patterns.

Predicating risk habitats of Oncomelania hupensis, the intermediate host of schistosoma japonicum under multiple environmental drivers

BackgroundOncomelania hupensis (O. hupensis), the unique intermediate host for Schistosoma japonicum, exerts a substantial influence on the risk of schistosomiasis. Being amphibious freshwater snails, the growth, development, and reproductive distribution of O. hupensis are intricately tied to climatic environmental variables. This study aims to predict O. hupensis habitat risks along the Yangtze River in China, considering multiple environmental factors. MethodsData pertaining to the distribution of O. hupensis, including both presence and absence records, with the Jiangsu section of the Yangtze River basin for the period 2017–2021, were retrieved from the Jiangsu Schistosomiasis Control Information Platform. Ten machine learning algorithms and an ensemble model were used to explore environmental drivers. Three datasets (Snail_CLIM, Snail_TOPO, and Snail_ALL) incorporating climatic and topographic variables were examined for their impact on model accuracy. We conducted validation using the AUC and TSS metrics. Moreover, we utilized the data from the 2022 snail field survey for model external validation. ResultsThe findings demonstrate that snail_ALL, which incorporates both climatic and topographic variables, exhibits superior performance (ensemble model: sensitivity = 98.000, specificity = 95.960, AUC = 0.994). Among the ten model algorithms, Random Forest (RF) exhibited the highest degree of accuracy and stability (Snail_ALL: AUC = 1.000 ± 0.000, TSS = 0.985 ± 0.005). The key environmental factors affecting snail distribution included the distance to the nearest river, elevation, annual precipitation, and annual average pressure. High-risk areas manifested as two distinct concentrations: downstream of the Luhe District in Nanjing and at the confluence of Zhenjiang and Yangzhou. The results of 2022 field validation showed that over 90 % of the data points for snail breeding sites are concentrated in medium to high-risk areas. ConclusionBy selecting pertinent environmental variables and employing ensemble modeling techniques, we can accurately predict O. hupensis habitats. The resulting risk distribution map for snail habitats not only provides valuable insights but also serves as a guiding tool for targeted monitoring and control measures. The holds particular significance within the contest of the Yangtze River protection and restoration projects.

Assessment of CMIP6 GCMs for selecting a suitable climate model for precipitation projections in Southern Thailand

The selection of General Circulation Models (GCMs) is critical due to computational limitations and underlying uncertainties. This study provides a comprehensive assessment of three bias correction (BC) methods, namely the delta change method (DT), quantile mapping (QM), and empirical quantile mapping (EQM). Utilizing precipitation data from 30 observation stations across Southern Thailand, the evaluation encompasses five CMIP6 GCM models (CAMS-CSM1-0, CanESM5, CNRM-CM6-1, CNRM-ESM2-1, IPSL-CM6A-LR). Evaluation metrics, root mean square error (RMSE), mean absolute error (MAE), Pearson's correlation (r), index of agreement (d), and mean bias error (MBE) are employed to assess BC methods. Evaluation measures suggest that the DT method outperforms EQM and QM, with higher accuracy and lower errors (DT: RMSE = 3.61, MAE = 2.32; EQM: RMSE = 3.82, MAE = 3.70). Taylor diagrams show that CNRM-ESM2-1 has the highest correlation across sites (r = 0.36), albeit with a wider dispersion, while CanESM5 has a more balanced performance. Significant annual precipitation increases are projected for different spans 2021-30, 2061-70, and 2091–2100, particularly under SSP585, which will influence flood risk, water management, and climate adaptation. Future projections with SSP585 continuously projecting a larger probability of increased precipitation. The DT method is recommended for the downscaling of CMIP6 GCMs for precipitation projections in Southern Thailand, recognizing its superior performance. The study's findings provide a foundation for informed decision-making and adaptation planning in southern Thailand, urging policymakers to prioritize climate resilience and adaptation strategies while using a multi-model ensemble approach for robust climate forecasts.

Optimizing tillage practice based on water supply during the growing season in wheat and maize production in northern China

Optimizing tillage practices to improve crop yield and crop water productivity (CWP) is essential for promoting sustainable dryland agriculture. This meta-analysis used the water supply during the growing season (WS, total soil water storage at sowing time, and growing-season precipitation) instead of mean annual precipitation (MAP), to assess the effects of different tillage practices on the yield and CWP of winter wheat and spring maize in northern China. The results showed that WS had a stronger correlation with crop yield than MAP, especially for winter wheat. Compared with conventional tillage, no-tillage combined with straw mulching (SM) increased winter yield by 36.56 % and CWP by 17.93 % when WS ≤ 650 mm, and subsoiling tillage increased winter wheat yield by 21.17 % and CWP by 9.29 % in winter wheat yield when WS > 650 mm. SM and subsoiling tillage performed better in regions with higher mean annual temperature (MAT) than that with lower MAT. For spring maize, no tillage, no-tillage combined with plastic film mulching (PM), subsoiling tillage and rotary tillage significantly increased yield and CWP under different WS conditions and the highest effect was showed in PM under higher WS condition; meanwhile, PM and subsoiling tillage performed good under various temperature conditions, while rotary tillage performed better in areas with lower temperature. Thus, the recommended tillage practice for spring maize is PM in northeast China, and subsoiling tillage in north-central and northwest China; for winter wheat, subsoiling tillage is recommended for north-central China, and SM is recommended for northwest China. Taken together, our results indicate that it is more accurate to use WS rather than the MAP to evaluate appropriate tillage practices in drylands. Furthermore, WS can also replace MAP for yield estimation and prediction.

Topographic and climatic effects on Pinus halepensis s.l. growth at its drought tolerance margins under climatic change

Under global warming, drought will reduce productivity of Pinus halepensis s.l. (subspecies halepensis and brutia) and cause a retreat from its rear edge distribution (latitudinal/elevational) in the Mediterranean. To test whether topography can influence this scenario, we studied for approximately 40 years the growth of six natural pine stands in water-limited habitats on the islands of Zakinthos and Samos (eastern Mediterranean Greece), and determined the critical moisture sources that drove pine growth. Dominant pines were selected with no permanent water sources under contrasting moisture conditions created by topography (“wet”-gulley/valley vs. “dry”-upslope habitats). The responses of P. halepensis s.l. to drought under a moderate and a worst case scenario were tested, projected under global warming (approx. − 25% and 40% in annual precipitation compared to 1961–1990 average). Our results show that “wet” habitat pines had higher productivity under normal to wet climate. However, the more precipitation declined, “wet” habitat tree growth was reduced at a significantly faster rate, but also showed a faster recovery, once rainfall returned. Thus, Pinus halepensis s.l. populations in gullies/valleys, may be more drought resilient and less likely to retreat towards higher elevation/latitudes under global warming, compared to pines on dry upslope sites. Under moderate drought, both ecosystems relied on deeper moisture pools supplied by rainfall of the previous 3–6 years (including the year of growth). However, valley/gully habitat pines on significantly deeper soils (and probably on deeper heavily weathered bedrock), appeared to utilize surface moisture from winter/spring rainfall more efficiently for survival and recovery. Thus, deep soils may provide the key “buffer” for pine survival in such ecosystems that could act as potential refugia for P. halepensis s.l. under climate change.

Precipitation, temperature, and landcovers drive spatiotemporal variability of groundwater nitrate concentration across the Continental United States

Groundwater nitrate contamination, especially in agriculturally active regions, is a well-recognized environmental concern. Understanding how this contamination evolves across the continental USA (CONUS) and through time is important to designing effective mitigation strategies. Despite extensive research on nitrate contamination, no existing studies can accurately predict changes in groundwater nitrate concentrations over time across the CONUS. To bridge this gap, we compiled a comprehensive dataset for a systematic evaluation of the potential influence of climate dynamics, landcover changes, and crucial soil and geological properties on groundwater contamination. We employed an interpretable machine learning approach, using 293,775 groundwater nitrate observations and 12 independent variables, to estimate annual groundwater nitrate concentrations at the county level from 2001 to 2020. Our model is the first one capable of accurately forecasting temporal changes in groundwater nitrate concentration across the entire CONUS. Our analysis reveals county level groundwater nitrate concentration changes occurred over the past two decades, particularly in regions initially with high concentrations in 2001, ranging from −16.2 mg/L-N to +6.5 mg/L-N between 2001 and 2020. 27 counties in the country appeared to have new concentrations greater than or equal to the maximum concentration level (MCL) at least once during this period. We revealed direct relationships between groundwater nitrate concentrations and climate factors, including that temperature and precipitation dominate the interannual variability in groundwater nitrate concentration in 75.2 % of counties. Notably, we have established a clear correlation between groundwater nitrate concentration and precipitation. Specifically, when annual precipitation falls below a threshold of about 748 mm, an increase of precipitation can directly result in elevated nitrate concentrations in groundwater, indicating heightened vulnerability to contamination due to climate change. This study forms a pivotal foundation for forecasting groundwater nitrate concentration changes across the continent and assessing the potential impact of climate change on future groundwater nitrate concentrations.

Predicting the Potential Distribution of Quercus oxyphylla in China under Climate Change Scenarios

Global climate changes are expected to profoundly shape species distribution. Quercus oxyphylla, a valuable evergreen broad-leaved tree species, is rigorously conserved and managed in China owing to its substantial scientific, economic, and ecological value. However, the impact of projected climate change on its future distribution and potential climatic drivers remains unclear. Here, a maximum entropy model (MaxEnt) was used to explore the distribution of Q. oxyphylla in China under current conditions and three future scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the 2050s and 2070s. We optimized the model using the ‘ENMeval’ package to obtain the best parameter combination (RM = 1, FC = LQHPT), and multiple evaluation metrics (AUC ≥ 0.9; TSS ≥ 0.6; Kappa ≥ 0.75) verified the high accuracy of the model and the reliability of the prediction results. We found the following: (1) The potential distribution of Q. oxyphylla spans across 28 provinces in China under current climatic conditions, predominantly in southern regions, with Sichuan exhibiting the largest suitable area for survival. The total suitable habitat covers 244.98 × 104 km2, comprising highly, moderately, and poorly suitable habitats of 51.66 × 104 km2, 65.98 × 104 km2, and 127.34 × 104 km2, respectively. (2) Under future climate conditions, the overall geographical boundaries of Q. oxyphylla are predicted to remain similar to the present one, with an increase of 10.29% in the 2050s and 11.31% in the 2070s. In the 2050s, the total suitable habitats for Q. oxyphylla under the three scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) might increase by 8.83%, 9.62%, and 12.42%, while in the 2070s they might increase by 10.39%, 17.21%, and 6.33%, respectively. (3) Moreover, the centroid of the suitable area is expected to migrate southwestward under the three scenarios in the future. (4) Annual precipitation, isothermality, and temperature annual range emerged as the main factors influencing the distribution of Q. oxyphylla, with contributions of 55.9%, 25.7%, and 13.5%, respectively. Our findings refined the spatial arrangement of Q. oxyphylla growth and revealed its climate resilience. This suggested that under climate change, Sichuan and Shaanxi are the optimal regions for cultivation and management, while appropriate conservation strategies should be formulated in Tibet and Hubei.

Climate Change Impacts on Precipitation Dynamics in the Southern Marmara Region of Turkey

Understanding the dynamics of precipitation patterns is crucial for effective water management strategies, especially in regions vulnerable to the impacts of climate change. This study investigates the projected changes in annual and seasonal precipitation across the Southern Marmara Region of Turkey by comparing the averages of the reference period (1971-2000) with those of the future period (2061-2090). Employing multiple climate models (GFDL, HADGEM, and MPI) and Representative Concentration Pathways (RCP4.5 and RCP8.5), the analysis includes Mann-Kendall trend tests and Sen's slope method to determine trends in precipitation patterns. Key findings reveal significant variability in precipitation projections among different models and scenarios, with implications for water resource management, agriculture, and ecosystem resilience in provinces such as Çanakkale, Balıkesir, Bursa, Bilecik, and Yalova. According to the annual rainfall change rates relative to the reference period, Balıkesir province stands out as the most resilient province against climate change with average rates of 8.81% and 7.09% under the HADGEM and MPI model simulations, respectively. Regarding seasonal variations, Bilecik province is expected to experience a significant decrease in rainfall, reaching up to -53.78% under the MPI RCP8.5 scenario. In terms of within-period changes in annual rainfall values, the strongest declining trend was identified with Z=-2.03 in Bilecik province under the MPI RCP8.5 scenario conditions by the Mann-Kendall test. On the other hand, for seasonal variations, Bursa province demonstrates the most robust decreasing trend under the GFDL RCP4.5 conditions (Z=-2.89). The study emphasizes the importance of considering spatially varying precipitation patterns and potential shifts in atmospheric circulation for sustainable water resource management amidst climate variability and change in the Southern Marmara region. These findings provide critical insights for policymakers and stakeholders involved in developing adaptive strategies to address the challenges posed by future climate scenarios.

A novel quantity assessment of landscape ecological risk using human-nature driving mechanism for sustainable society

The rapid advancement of global economic integration and urbanization has severely damaged the stability of the ecological environment and hindered the ecological carbon sink capacity. In this study, we evaluated the spatiotemporal evolution pattern of landscape ecological risk (LER) in the Loess Plateau from 2010 to 2020. This was examined under the driving mechanism of human and natural dual factors. We combined the random forest algorithm with the Markov chain to jointly simulate and predict the development trend of LER in 2030. From 2010 to 2020, LER on the Loess Plateau showed a distribution pattern with higher values in the southeast and lower values in the northwest. Under the interaction of human and natural factors, annual precipitation exerted the strongest constraint on LER. The driving of land use and natural factors significantly influenced the spatial differentiation of the LER, with a q-value >0.30. In all three projected scenarios for 2030, there was an increase in construction land area and a significant reduction in cultivated land area. The urban development scenario showed the greatest expansion of high-risk areas, with a 5.29 % increase. Conversely, the ecological protection scenario showed a 1.53 % increase in high-risk areas. The findings have provided a reference for ecological risk prevention and control, and sustainable development of the ecological environment in arid regions.

Is it necessary to increase the maize planting density in China?

Increasing the maize planting density is considered a potential approach for increasing the grain yield in China, but there is no consensus regarding its yield-increasing effect and the influence of specific factors. Thus, we established a database (2721 pairs of data from 187 publications) to quantify the effects of increasing the maize planting density on the phenotypic traits and yield, to determine an appropriate maize planting density for each planting area, and to quantify the effects of environmental factors and agricultural imputs on the outcomes of increasing the planting density. We found that increasing the planting density increased the individual competition among maize plants, with negative effects on their growth, but the grain yield increased by 11.18–13.43 % due to the increased population biomass. Using the database, we found large differences in the optimal density and peak grain yield among maize planting areas, and the factors that caused these differences were analyzed based on subgroup analysis. Field management practices significantly influenced the outcomes of increasing the planting density. In particular, higher agricultural inputs (irrigation amount, and nitrogen and phosphorus application rates) enhanced the positive effect of increasing plant density on the optimal plant density and peak grain yield. In addition, the effects of increasing the planting density were significantly influenced by environmental (climate and soil) factors. When the mean annual temperature was 7–14°C and the mean annual precipitation was 400–800 mm, the yield and maximum peak yield were highest in relatively fertile (high total nitrogen, available nitrogen, and soil organic matter contents) and neutral (pH 6–8) soils. Our results highlight the need to increase the current maize planting density and provide a scientific basis for determining reasonable planting densities for different maize growing areas in China.