Search Results (274)

Accurate river discharge forecasting remains a critical challenge in hydrology, particularly in data-scarce mountainous regions where in situ observations are limited. This study investigated the potential of long short-term memory (LSTM) networks to improve discharge prediction by leveraging spatially distributed reanalysis data. Using the ERA5-Land dataset, we developed an LSTM model that integrates grid-based meteorological inputs and assesses their relative importance. We conducted experiments on two snow-dominated basins with contrasting physiographic characteristics, the Uba River basin in Kazakhstan and the Flathead River basin in the USA, to answer three research questions: (1) whether full-grid input outperforms reduced configurations and models trained on Caravan, (2) the impact of spatial resolution on accuracy and efficiency, and (3) the effect of partial spatial coverage on prediction reliability. Specifically, we compared the full-grid LSTM with a single-cell LSTM, a basin-average LSTM, a Caravan-trained LSTM, and coarser cell aggregations. The results demonstrate that the full-grid LSTM consistently yields the highest forecasting performance, achieving a median Nash–Sutcliffe efficiency of 0.905 for Uba and 0.93 for Middle Fork Flathead, while using coarser grids and random subsets reduces performance. Our findings highlight the critical importance of spatial input richness and provide a reproducible framework for grid selection in flood-prone basins lacking dense observation networks. Full article

Various data such as power grid sensors and manual observed icing, CMA (China Meteorological Administration) Land Surface Data Assimilation System (CLDAS) products, and the Fifth Generation Atmospheric Reanalysis of the Global Climate from Europe Center of Middle Range Weather Forecast (ERA5) are adopted to diagnose an icing process under a cold surge during 16–23 December 2023 in northeastern Yunnan Province. The results show that: (1) in the early stage of the process, mainly the freezing types, such as GG (temperature > 0 °C, relative humidity ≥ 75%) and DG (temperature < 0 °C, relative humidity ≥ 75%), occur. At the end of the process, an increase in icing type as GD (temperature > 0 °C, relative humidity < 75%) appears. (2) Significant differences exist in the elements during different stages of icing, and the atmospheric thermal, dynamic, and water vapor conditions are conducive to the occurrence of freezing rain during ice accretion. The main impact weather systems of this process include a strong high ridge in the mid to high latitudes of East Asia, transverse troughs in front of the high ridge south to Lake Baikal, low altitude troughs, and ground fronts. The transverse trough in front of the high ridge can cause cold air to accumulate and then move eastward and southward. The southerly flows, surface fronts, and other low-pressure systems can provide powerful thermodynamic and moisture conditions for ice accumulation. Full article

This study evaluates the performance of nine satellite and model-based daily surface soil moisture products, encompassing sixteen algorithm versions across mainland China to support sustainable land and water management. The assessment utilizes 2018 in situ measurements from over 2400 stations in China’s Automatic Soil Moisture Monitoring Network. All products were standardized to a 0.25° × 0.25° grid in the WGS-84 coordinate system through reprojection and resampling for consistent comparison. Daily averaged station observations were matched to product pixels using a 10 km radius buffer, with the mean station value as the reference for each time series after rigorous quality control. Results reveal distinct performance rankings, with SMAP-based products, particularly the SMAP_IB descending orbit variant, achieving the lowest unbiased root mean square deviation (ubRMSD) and highest correlation with in situ data. Blended products like ESA CCI and NOAA SMOPS, alongside reanalysis datasets such as ERA5 and MERRA2, outperformed SMOS and China’s FY3 products. The SoMo.ml product showed the broadest spatial coverage and strong temporal consistency, while FY3-based products showed limitations in spatial reliability and seasonal dynamics capture. These findings provide critical insights for selecting appropriate soil moisture datasets to enhance sustainable agricultural practices, optimize water resource allocation, monitor ecosystem resilience, and support climate adaptation strategies, therefore advancing sustainable development across diverse geographical regions in China. Full article

The lower Yarlung Zangbo River Basin’s Great Bend region, characterized by extreme topography and intense orographic precipitation processes, presents significant challenges for accurate precipitation estimation using reanalysis products. Therefore, this study evaluates four widely used products (ERA5-Land, MSWEP, CMA, and TPMFD) against station observations (2014–2022) in this critical area. Performance was rigorously assessed using correlation analysis, error metrics (RMSE, MAE, RBIAS), and spatial regression. The region exhibits strong seasonality, with 62.1% of annual rainfall occurring during the monsoon (June-October). Results indicate TPMFD performed best overall, capturing spatiotemporal patterns effectively (correlation coefficients 0.6–0.8, low RBIAS). Conversely, ERA5-Land significantly overestimated precipitation, particularly in rugged northeast areas, suggesting poor representation of orographic effects. MSWEP and CMA underestimated rainfall with variable temporal consistency. Topographic analysis confirmed slope, aspect, and longitude strongly control precipitation distribution, aligning with classical orographic mechanisms (e.g., windward enhancement, lee-side rain shadows) and monsoonal moisture transport. Spatial regression revealed terrain features explain 15.4% of flood-season variation. TPMFD most accurately captured these terrain-precipitation relationships. Consequently, findings underscore the necessity for terrain-sensitive calibration and data fusion strategies in mountainous regions to improve precipitation products and hydrological modeling under orographic influence. Full article

In a warming climate, rising temperature are expected to influence atmospheric humidity. This study examined the spatio-temporal dynamics of temperature (TEMP) and relative humidity (RH) across Equatorial Africa from 1980 to 2020. The analysis used RH data from European Centre of Medium-range Weather Forecasts Reanalysis v.5 (ERA5) reanalysis, TEMP and precipitation (PRE) from Climate Research Unit (CRU), and soil moisture (SM) and evapotranspiration (ET) from the Global Land Evaporation Amsterdam Model (GLEAM). In addition, four teleconnection indices were considered: El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO). This study used the Mann–Kendall test and Sen’s slope estimator to analyze trends, alongside multiple linear regression to investigate the relationships between TEMP, RH, and key climatic variables—namely evapotranspiration (ET), soil moisture (SM), and precipitation (PRE)—as well as large-scale teleconnection indices (e.g., IOD, ENSO, PDO, and NAO) on annual and seasonal scales. The key findings are as follows: (1) mean annual TEMP exceeding 30 °C and RH less than 30% were concentrated in arid regions of the Sahelian–Sudano belt in West Africa (WAF), Central Africa (CAF) and North East Africa (NEAF). Semi-arid regions in the Sahelian–Guinean belt recorded moderate TEMP (25–30 °C) and RH (30–60%), while the Guinean coastal belt and Congo Basin experienced cooler, more humid conditions (TEMP < 20 °C, RH (60–90%). (2) Trend analysis using Mann–Kendal and Sen slope estimator analysis revealed spatial heterogeneity, with increasing TEMP and deceasing RH trends varying by region and season. (3) The warming rate was higher in arid and semi-arid areas, with seasonal rates exceeding annual averages (0.18 °C decade⁻¹). Winter (0.27 °C decade⁻¹) and spring (0.20 °C decade⁻¹) exhibited the strongest warming, followed by autumn (0.18 °C decade⁻¹) and summer (0.10 °C decade⁻¹). (4) RH trends showed stronger seasonal decline compared to annual changes, with reduction ranging from 5 to 10% per decade in certain seasons, and about 2% per decade annually. (5) Pearson correlation analysis demonstrated a strong negative relationship between TEMP and RH with a correlation coefficient of r = − 0.60. (6) Significant associations were also observed between TEMP/RH and both climatic variables (ET, SM, PRE) and large scale-teleconnection indices (ENSO, IOD, PDO, NAO), indicating that surface conditions may reflect a combination of local response and remote climate influences. However, further analysis is needed to distinguish the extent to which local variability is independently driven versus being a response to large-scale forcing. Overall, this research highlights the physical mechanism linking TEMP and RH trends and their climatic drivers, offering insights into how these changes may impact different ecological and socio-economic sectors. Full article

Precipitation serves as a crucial indicator of climate change and a vital part of the water cycle in mountainous regions. ERA5-Land, a cutting-edge global reanalysis dataset designed for land applications, has been extensively utilized in climate-related studies. In this research, we assessed the reliability of ERA5-Land monthly averaged reanalysis precipitation data in the Qilian Mountains (QLM). We did this by comparing it with the observations from 17 meteorological stations spanning from 1979 to 2017. The findings indicated that, overall, the ERA5-Land reanalysis precipitation data tended to overestimate the observed precipitation in the Qilian Mountains. The determination coefficient (R²) of the linear regression between ERA5-Land reanalysis precipitation and the observations was 0.97. This value implies that ERA5-Land reanalysis precipitation generally has good applicability in the Qilian Mountains. However, the annual-scale root mean square error (RMSE) was 3.97. This suggests that ERA5-Land reanalysis precipitation data cannot be directly applied to studies at a single meteorological station. The deviation between the ERA5-Land reanalysis precipitation data and the observed precipitation data can be ascribed to the altitude difference between meteorological stations and ERA5-Land grid points. Generally, as the altitude difference between meteorological stations and ERA5-Land grid points increases, the precipitation deviation also rises. This research can furnish a reference for the application of ERA5-Land reanalysis precipitation data in the Qilian Mountains. Full article

Accurate estimation of soil organic carbon (SOC) content is crucial for assessing terrestrial ecosystem carbon stocks. Although traditional methods offer relatively high estimation accuracy, they are limited by poor timeliness and high costs. Combining measured data, remote sensing technology, and machine learning (ML) algorithms enables rapid, efficient, and accurate large-scale prediction. However, single ML models often face issues like high feature variable redundancy and weak generalization ability. Integrated models can effectively overcome these problems. This study focuses on the Weigan–Kuqa River oasis (Wei-Ku Oasis), a typical arid oasis in northwest China. It integrates Sentinel-2A multispectral imagery, a digital elevation model, ERA5 meteorological reanalysis data, soil attribute, and land use (LU) data to estimate SOC. The Boruta algorithm, Lasso regression, and its combination methods were used to screen feature variables, constructing a multidimensional feature space. Ensemble models like Random Forest (RF), Gradient Boosting Machine (GBM), and the Stacking model are built. Results show that the Stacking model, constructed by combining the screened variable sets, exhibited optimal prediction accuracy (test set R² = 0.61, RMSE = 2.17 g∙kg⁻¹, RPD = 1.61), which reduced the prediction error by 9% compared to single model prediction. Difference Vegetation Index (DVI), Bare Soil Evapotranspiration (BSE), and type of land use (TLU) have a substantial multidimensional synergistic influence on the spatial differentiation pattern of the SOC. The implementation of TLU has been demonstrated to exert a substantial influence on the model’s estimation performance, as evidenced by an augmentation of 24% in the R² of the test set. The integration of Boruta–Lasso combination screening and Stacking has been shown to facilitate the construction of a high-precision SOC content estimation model. This model has the capacity to provide technical support for precision fertilization in oasis regions in arid zones and the management of regional carbon sinks. Full article

Aerosols play a critical role in modulating the land–atmosphere energy balance, influencing regional climate dynamics, and affecting air quality. Xinjiang, a typical arid and semi-arid region in China, frequently experiences dust events and complex aerosol transport processes. This study provides a comprehensive analysis of the spatiotemporal evolution and potential source regions of aerosols in Xinjiang from 2005 to 2023, based on Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products (MCD19A2), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical profiles, ground-based PM_2.5 and PM₁₀ concentrations, MERRA-2 and ERA5 reanalysis datasets, and HYSPLIT backward trajectory simulations. The results reveal pronounced spatial and temporal heterogeneity in aerosol optical depth (AOD). In Northern Xinjiang (NXJ), AOD exhibits relatively small seasonal variation with a wintertime peak, while Southern Xinjiang (SXJ) shows significant seasonal and interannual variability, characterized by high AOD in spring and a minimum in winter, without a clear long-term trend. Dust is the dominant aerosol type, accounting for 96.74% of total aerosol content, and AOD levels are consistently higher in SXJ than in NXJ. During winter, aerosols are primarily deposited in the near-surface layer as a result of local and short-range transport processes, whereas in spring, long-range transport at higher altitudes becomes more prominent. In NXJ, air masses are primarily sourced from local regions and Central Asia, with stronger pollution levels observed in winter. In contrast, springtime pollution in Kashgar is mainly influenced by dust emissions from the Taklamakan Desert, exceeding winter levels. These findings provide important scientific insights for atmospheric environment management and the development of targeted dust mitigation strategies in arid regions. Full article

The Marine Atmospheric Boundary Layer (MABL), as a critical component of Earth’s climate system, governs the exchange of matter and energy between the ocean surface and the lower atmosphere. This study presents shipborne Doppler lidar observations conducted during 12 January to 3 February 2024, along the southeastern Chinese coast. Employing a Coherent Doppler Wind Lidar (CDWL) system onboard the R/V “Yuezhanyu” research vessel, we investigated the spatiotemporal variability of MABL characteristics through integration with ERA5 reanalysis data. The key findings reveal a significant positive correlation between MABL height and surface sensible heat flux in winter, underscoring the dominant role of sensible heat flux in boundary layer development. Through the Empirical Orthogonal Function (EOF) analysis of the ERA5 regional boundary layer height, sensible heat flux, and sea level pressure, we demonstrate MABL height over the coastal seas typically exceeds the corresponding terrestrial atmospheric boundary layer height and exhibits weak diurnal variation. The CDWL observations highlight complex wind field dynamics influenced by synoptic conditions and maritime zones. Compared to onshore regions, the MABL over offshore areas further away from land has lower wind shear changes and a more uniform wind field. Notably, the terrain of Taiwan, China, induces significant low-level jet formations within the MABL. Low-level jets and low boundary layer height promote the pollution episode observed by CDWL. This research provides new insights into MABL dynamics over East Asian marginal seas, with implications for improving boundary layer parameterization in regional climate models and advancing our understanding of coastal meteorological processes. Full article

Reanalysis air temperature data, characterized by temporal continuity but limited spatial resolution, are commonly downscaled to achieve higher spatial resolution to meet the demands of regional climatological studies and related research fields. However, when large spatial scale differences are involved, the adaptability of statistical downscaling models across different scales warrants further investigation. In this study, a stepwise downscaling method is proposed, employing multiple linear regression (MLR), Cubist regression tree, random forest (RF), and extreme gradient boosting (XGBoost) models to downscale the 3-hourly ERA5-Land reanalysis air temperature data at the resolution of 0.1° to that of 30 m. A comparative analysis was performed to evaluate the accuracy of downscaled ERA5-Land air temperature results obtained from the stepwise and the direct downscaling methods, based on observed air temperatures at meteorological stations and the spatial distribution of air temperature estimated by a remote sensing method. In addition, variations in the importance of driving factors across different spatial scales were examined. The results indicate that the stepwise downscaling method exhibits higher accuracy than the direct downscaling method, with a more pronounced performance improvement in winter. Compared with the direct downscaling method, the RMSE value of the MLR, Cubist, RF, and XGBoost models under the stepwise downscaling method were reduced by 0.48 K, 0.38 K, 0.48 K, and 0.50 K, respectively, at meteorological station locations. In terms of spatial distribution, the stepwise downscaling results demonstrate greater consistency with the estimated spatial distribution of air temperature, and it can capture air temperature variations across different land surface types more accurately. Furthermore, the stepwise downscaling method is capable of effectively capturing changes in the importance of driving factors across different spatial scales. These results generally suggest that the stepwise downscaling method can significantly improve the accuracy of air temperature downscaled from reanalysis data by adopting multiple resolutions as the intermediate downscaling process. Full article

Integrated foliar spraying has been proposed as an effective measure to mitigate the increasingly severe impacts of hot–dry–windy (HDW) events on winter wheat yield under ongoing climate change, and its physiological effectiveness has been mechanistically validated. However, there are still few quantitative assessments of the application of this technology at the regional scale. First, hourly meteorological data from the ERA5-Land reanalysis (1981–2020) were matched to the centroids of 599 counties within China’s major winter wheat-producing regions, allowing precise alignment with county-level yield data. Subsequently, spatial and temporal trends of sub-daily HDW events were analyzed. These HDW events were classified according to daily duration into three categories: short-duration (HDW_sd1, 1 h d⁻¹), moderate-duration (HDW_sd2, 2–3 h d⁻¹), and prolonged-duration (HDW_sd3, 4–8 h d⁻¹). Finally, a difference-in-differences (DiD) approach combined with panel matching methods was employed to quantitatively assess the effectiveness of integrated foliar spraying technology—comprising plant growth regulators, essential nutrients, fungicides, and insecticides—on wheat yield improvements under varying irrigation conditions. The results indicate that HDW is a major compound event threatening high-yield and stable-yield regions within the main winter wheat production areas of China, and in the study area, the annual average number of HDW days ranges from 3 to 13 days, increasing by 1–4 days dec⁻¹. While HDW events continue to intensify, the integrated foliar spraying technology effectively mitigates yield losses due to HDW stress. Specifically, yield increases of up to 18–20% were observed in counties with sufficient irrigation infrastructure since the large-scale implementation began in 2012, particularly in regions exposed to more than 2 days of HDW stresses annually. However, the effectiveness of integrated foliar spraying was notably compromised in areas lacking adequate irrigation infrastructure, highlighting the necessity of reliable irrigation conditions. In these poorly irrigated areas, yield improvements remained limited and inconsistent, typically fluctuating around negligible levels. These findings underscore that robust irrigation infrastructure is pivotal to unlock the yield benefits of integrated foliar spraying technology, while also highlighting its transformative potential in advancing climate-smart agriculture globally—particularly in regions grappling with intensifying compound stress events driven by climate change, where this innovation could foster resilient and adaptive food systems to counter escalating environmental extremes. Full article

Evapotranspiration (ET) is a critical component of the hydrological cycle. The eddy covariance data at 40 flux stations in different climatic regions in China were used to evaluate the accuracy of five reanalysis actual ET datasets (ERA5, ERA5-LAND, GLDAS-2.1, MERRA-2, TerraClimate) and four remote sensing estimation ET datasets (ETMonitor, GLEAM4.2a, PML_V2, SiTHv2), which are widely used by the hydrometeorological and climatological communities, in terms of the root mean square error, Pearson correlation coefficient, mean absolute deviation, and Taylor skill score. The results show that remote sensing products outperform reanalysis datasets. Among them, ETMonitor has the highest accuracy, followed by PML_V2 and SiTHv2. TerraClimate and MERRA-2 have the least agreement with the observations at flux sites across nearly all evaluation metrics. All products can capture the seasonality of ET in China, but underestimate ET in northwest China and overestimate ET in southern China throughout the year. We tried to merge three optimal data products (ETMonitor, PML_V2, and SiTHv2) using the triple collocation analysis method to improve the ET estimation, but the results showed that the improvement by the data fusion approach is marginal. The estimation of the multi-year average evapotranspiration during the period from 2001 to 2020 ranges from 397.8 mm/year (GLEAM4.2a) to 504.8 mm/year (ERA5-Land) in China. From 2001 to 2020, annual evapotranspiration in China generally increased, but with varying rates across different products. MERRA-2 showed the largest annual increase rate (3.71 mm/year), while SiTHv2 had the smallest (0.17 mm/year). There are no significant changes in the seasonality of ET by most ET products from 2001 to 2020, except for PML_V2 and SiTHv2, which indicate an increase in seasonality in terms of the evapotranspiration concentration index. This ET intercomparison addresses a key knowledge gap in terrestrial water flux quantification, aiding climate and hydrological research. Full article

The Total Precipitable Water (TPW) is a key variable of atmospheres, and its spatiotemporal distribution is of great importance in global climate change. This paper addresses the TPW retrieval over both sea and land surfaces from the data acquired by the Microwave Humidity Sounder II (MWHS-II) on Fengyun 3D (FY-3D) satellite. First, the Back Propagation Neural Network (BPNN) algorithms are developed with the spatiotemporal matching samples of the MWHS-II data with the fifth-generation European Centre for Medium-Range Weather Forecast (ECMWF) atmospheric reanalysis (ERA5) data. Then, the TPWs at spatial resolutions of 0.25° in longitude and latitude between 65°S and 65°N over both sea and land surfaces are retrieved from the pixel-aggregated FY-3D MWHS-II data in 2022. Finally, the TPWs retrieved in this work are validated with the radiosonde TPWs over both sea and land surfaces, and they are also compared to the F18 Special Sensor Microwave Imager Sounder (SSMIS) TPWs over sea surfaces. The results indicate that the BPNN algorithms developed in this work are valid and superior to the D-matrix method, the Ridge method, the Lasso method, the physical method, the random forest (RF) method, the support vector machine (SVM) method, and the eXtreme Gradient Boosting (XGBoost) method. Against the radiosonde TPWs, the mean error (ME), the root mean square error (RMSE), and mean absolute error (MAE) of the TPWs retrieved in this work are −1.17 mm, 3.46 mm, and 2.63 mm over sea surfaces, respectively, and they are −0.80 mm, 4.04 mm, and 3.13 mm over land surfaces, respectively. The TPWs retrieved in this work are much more accurate than the F18 SSMIS TPWs. Full article

The study of the hydrological regimes of rivers in different regions of the globe has revealed the need to include the soil moisture content in flood prediction models. This paper investigates the nature of the dependence of soil moisture content on soil texture in the East Kazakhstan region. Data from ERA-5-land reanalysis, soil maps, hydrogeological maps, and the meteorological data of Kazhydromet were used. The years for analysis were selected due to their different moisture conditions. This study analyzed soil moisture within the root zone (0–28 cm depth). A JavaScript-based algorithm was developed in Google Earth Engine to analyze soil moisture and total precipitation across five Soil Texture Index categories during the growing seasons (April–September) of 2013, 2022, and 2023. Final cartographic processing and spatial distribution analysis were conducted using ESRI ArcGIS Pro 3.3. The study of soil moisture’s relationship with different soil textures in the East Kazakhstan region has revealed several key trends. The maximum values of soil moisture for each texture class change very slightly from year to year. The minimum soil moisture values fluctuate more strongly from year to year. The regression analysis demonstrates a statistically significant relationship between precipitation and soil moisture. The best performance is achieved when using a 1-day lag for 2013 and varying optimal lags for 2022 and 2023 (ranging from 1 to 3 days) during the high-precipitation period (months 6–9), with filtering applied to remove days with negligible rainfall. Full article

Soil moisture (SM) estimates are essential for drought monitoring, hydrological modeling, and climate resilience planning applications. While satellite and model-derived SM products effectively capture SM dynamics, their coarse spatial resolutions (~10–36 km) hinder their ability to represent SM variability in heterogeneous landscapes influenced by local factors. This study proposes a novel downscaling framework that employs an Artificial Neural Network (ANN) on a cloud-computing platform to improve the spatial resolution and representation of multi-source SM datasets. A data analysis was conducted by integrating Google Earth Engine (GEE) with the computing capabilities of the python language through Google Colab. The framework downscaled Soil Moisture Active Passive (SMAP), European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation (ERA5-Land), and Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) at 500 m for Kenya, East Africa. This was achieved by leveraging ten input variables comprising elevation, slope, surface albedo, vegetation, soil texture, land surface temperatures (day and night), evapotranspiration, and geolocations. The coarse SM datasets exhibited spatiotemporal consistency, with a standard deviation below 0.15 m³/m³, capturing over 95% of the variability in the original data. Validation against in situ SM data at the station confirmed the framework’s reliability, achieving an average UbRMSE of less than 0.04 m³/m³ and a correlation coefficient (r) over 0.52 for each downscaled dataset. Overall, the framework improved significantly in r values from 0.48 to 0.64 for SMAP, 0.47 to 0.63 for ERA5-Land, and 0.60 to 0.69 for FLDAS. Moreover, the performance of FLDAS and its downscaled version across all climate zone is consistent. Despite the uncertainties among the datasets, the framework effectively improved the representation of SM variability spatiotemporally. These results demonstrate the framework’s potential as a reliable tool for enhancing SM applications, particularly in regions with complex environmental conditions. Full article