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Climate changes significantly impact environmental and hydrological processes. Precipitation is one of the most significant climatic parameters and its variability and trends have great influences on environmental and socioeconomic development. We investigate the spatio-temporal variability of precipitation occurrence frequency, mean precipitation depth, PVI and total precipitation in China based on long-term precipitation series from 1961 to 2015. As China’s topography is diverse and precipitation is affected by topography strongly, ANUSPLIN can model the effect of topography on precipitation effectively is adopted to generate the precipitation interpolation surface. Mann–Kendall trend analysis and simple linear regression was adopted to examine long-term trend for these indicators. The results indicate ANUSPLIN precipitation surface is reliable and the precipitation variation show different regional and seasonal trend. For example, there is a sporadic with decreasing frequency precipitation trend in spring and a uniform with increasing frequency trend in summer in Yangtze Plain, which may affect spring ploughing and alteration of flood risk for this main rice-production areas of China. In north-western China, there is a uniform with increasing precipitation frequency and intensity trend, which is beneficial for this arid region. Our study could be helpful for other counties with similar climate types.

Background The receptor-like kinase (RLK) gene families in plants contains a large number of members. They are membrane proteins with an extracellular receptor domain and participate in biotic and abiotic stress responses. Results In this study, we identified RLKs in 15 representative plant genomes, including wheat, and classified them into 64 subfamilies by using four types of phylogenetic trees and HMM models. Conserved exon‒intron structures with conserved exon phases in the kinase domain were found in many RLK subfamilies from Physcomitrella patens to Triticum aestivum . Domain distributions of RLKs were also diagrammed. Collinearity events and tandem gene clusters suggested that polyploidization and tandem duplication events contributed to the member expansions of T. aestivum RLKs. Global expression pattern analysis was performed by using public transcriptome data. These analyses were involved in T. aestivum, Aegilops tauschii and Brachypodium distachyon RLKs under biotic and abiotic stresses. We also selected 9 RLKs to validate the transcriptome prediction by using qRT‒PCR under drought treatment and with Fusarium graminearum infection. The expression trends of these 9 wheat RLKs from public transcriptome data were consistent with the results of qRT‒PCR, indicating that they might be stress response genes under drought or F. graminearum treatments. Conclusion In this study, we identified, classified, evolved, and expressed RLKs in wheat and related plants. Thus, our results will provide insights into the evolutionary history and molecular mechanisms of wheat RLKs.

As a critical ecological-economic nexus along China’s Belt and Road Initiative, the Kashgar region exemplifies tensions between rapid socioeconomic development and ecological fragility in arid Central Asia. Landscape degradation monitoring in arid regions is severely constrained by data scarcity; most studies rely on 15–20 year windows insufficient for detecting decadal-scale threshold behaviors. This study fills a critical research gap by integrating multi-method landscape analysis with a relatively long 43-year land use record (1980–2023). Our integrated analytical framework combines land use dynamic degree, intensity indices, transfer matrices, landscape fragmentation metrics, and spatial autocorrelation analysis, with all results validated through sensitivity analyses (parameterization robustness Spearman rs > 0.96; scale robustness rs > 0.93). We quantified unidirectional anthropogenic landscape reorganization: cultivated and construction land expanded 3,671.78 km 2 and 973.41 km 2 respectively, while natural landscapes (forest, grassland, water bodies) collectively declined 2,115.82 km 2 . Construction land displayed the highest transformation intensity (8.41% annual dynamic degree), with peak land use intensity coinciding with China’s Western Development Strategy (2000–2010: Δ La  = 3.31, R  = 2.14%). Quantitative landscape fragmentation escalated markedly: patch density increased 68% (0.056 → 0.095/km 2 ), shape complexity increased 33% (LSI: 56.097 → 74.590), and spatial connectivity declined 3.25% (CONTAG: 66.870% → 64.698%). Transfer analysis demonstrated that construction land exhibited a persistent unidirectional inflow imbalance, while unused land showed a consistent unidirectional outflow imbalance; other land types underwent bidirectional transitions. Landscape ecological security exhibited distinctive three-phase dynamics (improvement → degradation → recovery) with strong persistent spatial autocorrelation ( Moran’s I : 0.78–0.81, p < 0.001), generating stable \"high-high\" clusters in oases and vulnerable \"low-low\" clusters in desert margins. Quantified attribution analysis (Grey Relational Analysis, Spearman correlations) revealed that GDP growth was the strongest driver of construction land expansion (GRG = 0.87), while population growth was the primary driver of cultivated land expansion (GRG = 0.85). Our preliminary observations suggest a potential monitoring reference zone (Δ La ≈ 2.5–3.5 per decade) for early-warning systems; rigorous cross-regional validation is essential before establishing universal thresholds. Persistence of ecological degradation into 2010–2020 despite reduced land use change ( LC : 1.20% → 0.15%) indicates hysteresis dynamics in arid systems. This 43-year dataset and validated analytical framework provide critical baseline data and evidence-based quantitative thresholds for early-warning systems and territorial spatial planning in ecologically fragile arid zones.

BackgroundSoil organic carbon (SOC) plays a crucial role in the global carbon cycle and terrestrial ecosystem functions. It is widely known that climate change and soil water content (SWC) could influence the SOC dynamics; however, there are still debates about how climate change, especially climate warming, and SWC impact SOC. We investigated the spatiotemporal changes in SOC and its responses to climate warming and root-zone SWC change using the coupled hydro-biogeochemical model (SWAT-DayCent) and climate scenarios data derived under the three Representative Concentration Pathways (RCPs2.6, 4.5, and 8.5) from five downscaled Global Climate Models (GCMs) in a typical loess watershed––the Jinghe River Basin (JRB) on the Chinese Loess Plateau.ResultsThe air temperature would increase significantly during the future period (2017–2099), while the annual precipitation would increase by 2.0–13.1% relative to the baseline period (1976–2016), indicating a warmer and wetter future in the JRB. Driven by the precipitation variation, the root-zone SWC would also increase (by up to 27.9% relative to the baseline under RCP4.5); however, the SOC was projected to decrease significantly under the future warming climate. The combined effects of climate warming and SWC change could more reasonably explain the SOC loss, and this formed hump-shaped response surfaces between SOC loss and warming-SWC interactions under both RCP2.6 and 8.5, which can help explain diverse warming effects on SOC with changing SWC.ConclusionsThe study showed a significant potential carbon source under the future warmer and wetter climate in the JRB, and the SOC loss was largely controlled by future climate warming and the root-zone SWC as well. The hump-shaped responses of the SOC loss to climate warming and SWC change demonstrated that the SWC could mediate the warming effects on SOC loss, but this mediation largely depended on the SWC changing magnitude (drier or wetter soil conditions). This mediation mechanism about the effect of SWC on SOC would be valuable for enhancing soil carbon sequestration in a warming climate on the Loess Plateau.

The high degree of spatial variability in climate conditions, and a lack of meteorological data for East Asia, present challenges to conducting surface water research in the context of the hydrological cycle. In addition, East Asia is facing pressure from both water resource scarcity and water pollution. The consequences of water pollution have attracted public concern in recent years. The low frequency and difficulty of monitoring water quality present challenges to understanding the continuous spatial distributions of non-point source pollution mechanisms in East Asia. The China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) was developed to provide high-resolution, high-quality meteorological data for use by the scientific community. Applying CMADS can significantly reduce the meteorological input uncertainty and improve the performance of non-point source pollution models, since water resources and non-point source pollution can be more accurately localised. In addition, researchers can make use of high-resolution time series data from CMADS to conduct spatial- and temporal-scale analyses of meteorological data. This Special Issue, “Application of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) in East Asia”, provides a platform to introduce recent advances in the modelling of water quality and quantity in watersheds using CMADS and hydrological models, and underscores its application to a wide range of topics.

Hydrological models are widely used as simplified, conceptual, mathematical representatives for water resource management. The performance of hydrological modeling is usually challenged by model calibration and uncertainty analysis during modeling exercises. In this study, a multicriteria sequential calibration and uncertainty analysis (MS-CUA) method was proposed to improve the efficiency and performance of hydrological modeling with high reliability. To evaluate the performance and feasibility of the proposed method, two case studies were conducted in comparison with two other methods, sequential uncertainty fitting algorithm (SUFI-2) and generalized likelihood uncertainty estimation (GLUE). The results indicated that the MS-CUA method could quickly locate the highest posterior density regions to improve computational efficiency. The developed method also provided better-calibrated results (e.g., the higher NSE value of 0.91, 0.97, and 0.74) and more balanced uncertainty analysis results (e.g., the largest P/R ratio values of 1.23, 2.15, and 1.00) comparing with other traditional methods for both case studies.

A well-planned layout for origin-based cold storage is crucial for minimizing post-harvest losses, reducing costs, improving logistics efficiency, and mitigating environmental impacts. To address gaps in existing research on multi-technology coordination and trade-offs among siting objectives, this paper proposes an integrated multi-method framework that combines Geographic Information Systems (GIS), multi-criteria decision making (MCDM), and an improved Harris Hawks Optimization (IHHO) algorithm for facility siting optimization. We develop an evaluation system that incorporates logistics infrastructure, natural conditions, and agricultural development and use a GIS-MCDM model with spatial constraints to delineate highly suitable areas. K-medoids spatial clustering and an economies-of-scale cost model are then used, with IHHO determining the optimal number, locations, and capacities of facilities. A case study in Helan County, China, indicated that highly suitable zones are concentrated in the south, accounting for approximately 1.25% of the study area; nine candidate regions were identified, and six optimal sites were selected. Scenario analysis revealed that higher fixed construction costs favor larger facilities, while growing demand supports centralized, high-capacity cold stores rather than dispersed, smaller ones. Overall, the proposed framework provides a systematic tool for scientific planning and suitability assessment of on-farm cold-chain infrastructure, with the potential to enhance logistics efficiency, reduce postharvest losses, and promote sustainable agricultural development.

Agricultural price prediction is a hot research topic in the field of agriculture, and accurate prediction of agricultural prices is crucial to realize the sustainable and healthy development of agriculture. It explores traditional forecasting methods, intelligent forecasting methods, and combination model forecasting methods, and discusses the challenges faced in the current research landscape of agricultural commodity price prediction. The results of the study show that: (1) The use of combined models for agricultural product price forecasting is a future development trend, and exploring the combination principle of the models is a key to realize accurate forecasting; (2) the integration of the combination of structured data and unstructured variable data into the models for price forecasting is a future development trend; and (3) in the prediction of agricultural product prices, both the accuracy of the values and the precision of the trends should be ensured. This paper reviews and analyzes the methods of agricultural product price prediction and expects to provide some help for the development of research in this field.

Hydrological model accuracy is often constrained by limited in-situ data. This study develops a coupled framework integrating machine-learning-based remote sensing retrievals with the Environmental Fluid Dynamics Code (EFDC) to improve reservoir water quality simulations. Landsat imagery (2013–2023) and multiple algorithms (Random Forest, Gradient Boosting, AdaBoost, etc.) were used to derive spatiotemporal distributions of total nitrogen (TN), total phosphorus (TP), and chlorophyll-a (Chl-a), which were incorporated as dynamic boundary conditions in EFDC. The coupled model reduced simulation errors by 0.13–5.28% and increased mean R² from 0.70 to 0.81. Compared with standalone EFDC, retrieval-based estimates showed lower mean relative errors for TN (20.61%), TP (28.95%), and Chl-a (26.08%). Seasonal analysis revealed Chl-a peaks in June (14.6 µg/L) and TN/TP accumulation in summer. Scenario simulations indicated that external load reduction (5–30%) effectively decreased TN and TP but had limited influence on Chl-a due to threshold effects. The optimal integrated strategy (30% external load reduction + 1.0% outflow reduction, scenario f-1) achieved concurrent reductions of 27.4% (TN), 23.7% (TP), and 13.2% (Chl-a), averaging 21.4% across all indicators. Critically, scenario analysis revealed that reducing external inflow loads alone produced limited suppression of algal biomass due to threshold effects and internal nutrient buffering, whereas combined reductions in inflow loading and moderate adjustments to hydrodynamic outflow regulation were necessary to achieve meaningful Chl-a control. These findings demonstrate that alterations to both nutrient inflow and reservoir hydrodynamics are essential levers for eutrophication management, with implications for operational decision-making in data-scarce reservoir systems worldwide.

Agricultural drought poses a severe threat to food security in the North China Plain, necessitating accurate and timely monitoring approaches. This study presents a novel drought assessment framework that innovatively integrates multiple remote sensing indices through an optimized random forest algorithm, achieving unprecedented accuracy in regional drought monitoring. The framework introduces three key innovations: (1) a systematic integration of six drought-related factors including vegetation condition index (VCI), temperature condition index (TCI), precipitation condition index (PCI), land cover type (LC), aspect (ASPECT), and available water capacity (AWC); (2) an optimized random forest algorithm configuration with 100 decision trees and enhanced feature extraction capability; and (3) a robust triple-validation strategy combining standardized precipitation evapotranspiration index (SPEI), comprehensive meteorological drought index (CI), and soil moisture verification. The framework demonstrates exceptional performance with R2 values consistently above 0.80 for monthly assessments, reaching 0.86 during autumn and 0.73 during summer seasons. Particularly, it achieves 87% accuracy in mild drought (−1.0 < SPEI ≤ −0.5) and 85% in moderate drought (−1.5 < SPEI ≤ −1.0) detection. The 20-year (2000–2019) spatiotemporal analysis reveals that moderate drought events dominated the region (23.7% of total occurrences), with significant intensification during the 2010–2012 and 2014–2016 periods. Summer drought frequency peaked at 12–15 months in south-central Shandong (37°N, 117°E) and eastern Henan (34°N, 114°E). The framework’s high spatial resolution (1 km) and comprehensive validation protocol establish a reliable foundation for agricultural drought monitoring and water resource management, offering a transferable methodology for regional drought assessment worldwide.