Search Results (455)

The satellite-derived climatic variables offer extensive spatial and temporal coverage for research; however, their inherent biases can subsequently reduce their accuracy for water balance estimate. This study evaluates the effectiveness of bias correction in improving the Tropical Rainfall Measuring Mission (TRMM) rainfall and the Global Land Data Assimilation System (GLDAS) land surface temperature (LST) data and illustrates their long-term (2000–2019) hydrological assessment. The novelty lies in coupling the bias-corrected climate variables with the Thornthwaite–Mather water balance model as well as land use land cover (LULC) for improved predictive hydrological modeling. Bias correction significantly improved the agreement with ground observations, enhancing the R² value from 0.89 to 0.96 for temperature and from 0.73 to 0.80 for rainfall, making targeted inputs ready to predict hydrological dynamics. LULC mapping showed a predominance of agricultural land (64.5%) in the area followed by settlements (20.0%), forest (7.3%), barren land (6.5%), and water bodies (1.7%), with soils being silt loam, clay loam, and clay. With these improved datasets, the model found seasonal rise in potential evapotranspiration (PET), peaking at 120.7 mm in June, with actual evapotranspiration (AET) following a similar trend. The annual water balance showed a surplus of 523.8 mm and deficit of 121.2 mm, which proves that bias correction not only enhances the reliability of satellite data but also reinforces the credibility of hydrological indicators, with a direct, positive impact on evidence-based irrigation planning and flood mitigation and drought management, especially in data-scarce regions. Full article

This study investigates the impact of assimilating FY-3D Microwave Radiation Imager (MWRI) radiance data into the Weather Research and Forecasting (WRF) model, utilizing a 3D-Var data assimilation system, on the forecast accuracy of Typhoon Muifa (2022). The research focuses on the selection of data from different channels, land/ocean coverage, and orbits of the MWRI, along with the synergistic assimilation strategy with MWHS-2 data. Ten assimilation experiments were conducted, starting from 0600 UTC on 14 September 2022, covering a 42 h forecast period. The results show that after assimilating the microwave radiometer data, the brightness temperature deviation in the ocean area was significantly reduced compared to the simulation without data assimilation. This led to an improvement in the accuracy of typhoon track and intensity predictions, particularly for predictions beyond 24 h. Furthermore, the assimilation of land data and single-orbit data (particularly from the western orbit) further enhanced forecast accuracy, while the joint assimilation of MWHS-2 and MWRI data yielded additional error reductions. These findings underscore the potential of satellite data assimilation in improving typhoon forecasting and highlight the need for optimal land observation and channel selection techniques. Full article

Jilin Province, an important commodity grain base in China, relies on groundwater resources for its agricultural development. The implementation of a series of policies, including agricultural subsidies and food security policies, has led to a rapid expansion of the sowing area in recent decades, resulting in an increase in agricultural water demand. This has had a significant impact on the groundwater system. It is therefore imperative to understand the dynamics of the groundwater to ensure the security of water resources, ecological security, and food security. An evaluation of the sustainability of groundwater resources in Jilin Province was conducted through a quantitative analysis of the reliability, resilience, and vulnerability of groundwater. This analysis was informed by the inversion of changes in groundwater reserves over a period of 249 months, commencing from 2002-04 to 2022-12. The inversion process utilized data from the Gravity Recovery and Climate Experiment (GRACE) gravity satellite and Global Land Data Assimilation System (GLDAS), offering a comprehensive view of the temporal dynamics of groundwater reserves in the region. The results indicated the following: (1) Groundwater storage (total amount of water below the surface) in Jilin Province exhibited an overall decreasing trend, with the highest groundwater level recorded in June and the lowest in September on a monthly basis. (2) Prior to September 2010, groundwater reserves were in surplus most of the time. From October 2010 to August 2018, however, they began to fluctuate between surplus and deficit states. Since September 2018, the reserves have been in a long-term deficit, showing an overall downward trend. (3) Prior to 2005, the groundwater system was at a high/extremely high level of sustainability. However, following 2011, it fell to a very low level of sustainability and has continued to deteriorate. (4) The maximum information coefficient and correlation analysis indicate that the sown area is the most significant factor contributing to the decline in the sustainability of the groundwater system. This study reveals the spatial and temporal distribution pattern and evolution trend of groundwater resources sustainability in Jilin Province, and provides theoretical and data support for regional groundwater resources protection and management. Full article

Water masses are continuously redistributing across the Earth, so accurately estimating their availability is essential. Global Navigation Satellite Systems (GNSSs) have demonstrated potential for observing vertical deformations, which is partly driven by terrestrial water storage (TWS) variations. This capability has been used in hydrogeodesy to estimate TWS variations. However, GNSS data inversions are often ill-posed, requiring regularization for stable solutions. This study considers the Least Squares Collocation (LSC) statistical method as an alternative. LSC uses covariance functions to characterize observations, parameters, and their interdependence. By incorporating additional physical information into inverse models, LSC allows ill-posed problems stabilization. To assess LSC effectiveness, we apply it to observed and simulated GNSS vertical displacement on Haiti island. Hydrological signals are modeled using Global Land Data Assimilation (GLDAS) data. In sparse GNSS data regions, findings indicate poor agreement between TWS and hydrological input, with a Root-Mean-Square-Error (RMSE) of 115 kg/m², a correlation of 0.3, and a reduction of 73%. However, in dense simulated GNSS areas, TWS and hydrological input show strong agreement, with an RMSE of 41 kg/m², a correlation of 0.83, and a reduction of 92%. The results confirm LSC potentiality for assessing TWS changes and improving water quantification in dense GNSS station region. Full article

The L-band radiative transfer-forward modeling plays a crucial role in data assimilation for meteorological forecasting. By utilizing information from the underlying surface (typically land surface parameters and variables), such as soil moisture, soil temperature, snow cover, freeze–thaw status, and vegetation, the corresponding brightness temperatures can be simulated through the physical processes described by radiative transfer models. Data assimilation becomes meaningful when the errors introduced by the simulated brightness temperatures are smaller than the simulation accuracy of the land surface variables. However, radiative transfer models at the L-band cannot accurately simulate TB operationally. In this study, four machine learning methods, including random forest (RF), long short-term memory (LSTM), support vector machine (SVM), and deep neural networks (DNN), are employed to reconstruct the forward relationship from land surface parameters to brightness temperatures, serving as an alternative to traditional radiative transfer models. The performance of these methods is evaluated using ground-truthed soil moisture data, soil texture static data, and leaf area index (LAI). The results indicate that DNN and RF exhibit superior performance, with DNN achieving the lowest average unbiased root mean square error (ubRMSE) of 6.238 K for vertical polarization brightness temperature (TBv) and 9.033 K for horizontal polarization brightness temperature (TBh). Regarding correlation coefficients between the retrieved brightness temperatures and satellite measurements, RF leads for H-polarized TB with a value of 0.943, while both RF and SVM perform well for V-polarized TB with values of 0.930 and 0.932, respectively. In conclusion, our study shows that DNN is the optimal method for retrieving brightness temperatures, outperforming other machine learning approaches regarding error metrics and correlation with satellite measurements. These findings highlight the potential of DNN in improving data assimilation processes in meteorological forecasting. Full article

The use of the Soil and Water Assessment Tool (SWAT) model driven by China Meteorological Assimilation Driving Datasets (CMADS) for runoff simulation research is of great significance for regional flood prevention and control. Therefore, from the perspective of production-living-ecological space, this article combined multi-source data such as DEM, soil texture and land use type, in order to construct scenarios of territorial spatial change (TSC) across distinct periods. Based on the CMADS-L40 data and the SWAT model, it simulated the runoff dynamics in the Xitiaoxi River Basin, and analyzed the hydrological response characteristics under different TSCs. The results showed that The SWAT model, driven by CMADS-L40 data, demonstrated robust performance in monthly runoff simulation. The coefficient of determination (R²), Nash–Sutcliffe efficiency coefficient (NSE), and the absolute value of percentage bias (|PBIAS|) during the calibration and validation period all met the accuracy requirements of the model, which validated the applicability of CMADS-L40 data and the SWAT model for runoff simulation at the watershed scale. Changes in territorial spatial patterns are closely correlated with runoff variation. Changes in agricultural production space and forest ecological space show statistically significant negative correlation with runoff change, while industrial production space change exhibits a significant positive correlation with runoff change. The expansion of production space, particularly industrial production space, leads to increased runoff, whereas the enlargement of agricultural production space and forest ecological space can reduce runoff. This article contributes to highlighting the role of land use policy in hydrological regulation, providing a scientific basis for optimizing territorial spatial planning to mitigate flood risks and protect water resources. Full article

The high spatial resolution of radar data enables the detailed resolution of typhoon vortices and their embedded structures; the assimilation of radar data in the initialization of numerical weather prediction exerts an important influence on the forecasting of typhoon track, intensity, and structures up to at least 12 h. For the case of typhoon Morakot (2009), Taiwan radar data was assimilated to adjust the dynamic and thermodynamic structures of the vortex in the model initialization by the three-dimensional variation data assimilation system in the Advanced Region Prediction System (ARPS). The radial wind was directly assimilated to tune the original unbalanced velocity fields through a 3-dimensional variation method, and complex cloud analysis was conducted by using the reflectivity data. The influence of radar data assimilation on typhoon prediction was examined at the stages of Morakot landing on Taiwan Island and subsequently going inland. The results showed that the assimilation made some improvement in the prediction of vortex intensity, track, and structures in the initialization and subsequent forecast. For example, besides deepening the central sea level pressure and enhancing the maximum surface wind speed, the radar data assimilation corrected the typhoon center movement to the best track and adjusted the size and inner-core structure of the vortex to be close to the observations. It was also shown that the specific humidity adjustment in the cloud analysis procedure during the assimilation time window played an important role, producing more hydrometeors and tuning the unbalanced moisture and temperature fields. The neighborhood-based ETS revealed that the assimilation with the specific humidity adjustment was propitious in improving forecast skill, specifically for smaller-scale reflectivity at the stage of Morakot landing, and for larger-scale reflectivity at the stage of Morakot going inland. The calculation of the intensity-scale skill score of the hourly precipitation forecast showed the assimilation with the specific humidity adjustment performed skillful forecasting for the spatial forecast-error scales of 30–160 km. 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

Accurately monitoring terrestrial water storage (TWS) variations is essential due to global climate change and growing water demands. This study investigates TWS changes in Oregon, USA, using Global Navigation Satellite System (GNSS) data from the Nevada Geodetic Laboratory, Gravity Recovery and Climate Experiment (GRACE) level-3 mascon data from the Jet Propulsion Laboratory (JPL), and Noah model data from the Global Land Data Assimilation System (GLDAS) data. The results show that the GNSS inversion offers superior spatial resolution, clearly capturing a water storage gradient from 300 mm in the Cascades to 20 mm in the basin and accurately distinguishing between mountainous and basin areas. However, the GRACE data exhibit blurred spatial variability, with the equivalent water height amplitude ranging from approximately 100 mm to 145 mm across the study area, making it difficult to resolve terrestrial water storage gradients. Moreover, GLDAS exhibits limitations in mountainous regions. The GNSS can provide continuous dynamic monitoring, with results aligning well with seasonal trends seen in GRACE and GLDAS data, although with a 1–2 months phase lag compared to the precipitation data, reflecting hydrological complexity. Future work may incorporate geological constraints, region-specific elastic models, and regularization strategies to improve monitoring accuracy. This study demonstrates the strong potential of GNSS technology for monitoring TWS dynamics and supporting environmental assessment, disaster warning, and water resource management. Full article

A reliable precipitation dataset with high spatial resolution is essential for climate research in the Tarim Basin. This study evaluated the performances of four models, namely a random forest (RF), a long short-term memory network (LSTM), a support vector machine (SVM), and a feedforward neural network (FNN). FNN, which was found to be superior to the other models, was used to integrate eight precipitation datasets spanning from 1990 to 2022 across the Tarim Basin, resulting in a new monthly high-resolution (0.1°) precipitation dataset named MoHiPr-TB. This dataset was subsequently bias-corrected by the China Land Data Assimilation System version 2.0 (CLDAS2.0). Validation results indicate that the corrected MoHiPr-TB not only accurately reflects the spatial distribution of precipitation but also effectively simulates its intensity and interannual and seasonal variations. Moreover, MoHiPr-TB is capable of detecting the precipitation–elevation relationship in the Pamir Plateau, where precipitation initially increases and then decreases with elevation, as well as the synchronous variation of precipitation and elevation in the Tianshan region. Collectively, this study delivers a high-accuracy precipitation dataset for the Tarim Basin, which is anticipated to have extensive applications in meteorological, hydrological, and ecological research. Full article

As a major agricultural center in China, Henan Province is highly dependent on groundwater resources for its socioeconomic development. However, under the triple pressure of intensive agricultural irrigation, surging industrial water demand, and accelerating urbanization, the sustainable use of groundwater resources has become a key issue for regional development. This paper utilizes GRACE satellite data and the Global Land Data Assimilation System (GLDAS) assimilation model from 2003 to 2023 to invert alterations in terrestrial water storage (TWS) and groundwater storage (GWS) in Henan Province. We examine the factors influencing these changes and compare the spherical harmonic coefficient (SH) data with Mascon data, integrating precipitation and soil moisture data. Using the GRACE Mascon data as a reference, GWS in Henan Province exhibited a stable trend from January 2003 to October 2010, with a rate of −0.060 cm/month. From October 2010 to June 2020, GWS demonstrated a declining trend, with a rate of −0.121 cm/month. Conversely, from June 2020 to December 2023, GWS revealed a significant upward trend, with a rate of 0.255 cm/month. The TWS and GWS of the inverse performances of the Centre for Space Research (CSR) SH data and the CRS Mascon data exhibited a similar trend, albeit with differing values. Additionally, the precipitation data, soil moisture, and GLDAS data demonstrated significant seasonal variations, with a lag of approximately two months between changes in precipitation and GWS. Declining GWS could be related to climatic and anthropogenic factors. The changes in groundwater in Henan Province studied in this paper can provide a reference for the sustainable utilization of groundwater resources in the region. Full article

Groundwater storage refers to the water stored in the pore spaces of underground aquifers, which has been increasingly affected by both climate change and anthropogenic activities in recent decades. Therefore, monitoring their changes and the factors that affect it is of great importance. Although the influence of natural factors on groundwater is well-recognized, the impact of human activities, despite being a major contributor to its change, has been less explored due to the challenges in measuring such effects. To address this gap, our study employed an integrated approach using remote sensing and the Google Earth Engine (GEE) cloud-free platform to analyze the effects of various anthropogenic factors such as built-up areas, cropland, and surface water on groundwater storage in the Lake Urmia Basin (LUB), Iran. Key anthropogenic variables and groundwater data were pre-processed and analyzed in GEE for the period from 2000 to 2022. The processes linking these variables to groundwater storage were considered. Built-up area expansion often increases groundwater extraction and reduces recharge due to impervious surfaces. Cropland growth raises irrigation demand, especially in semi-arid areas like the LUB, leading to higher groundwater use. In contrast, surface water bodies can supplement water supply or enhance recharge. The results were then exported to XLSTAT software2019, and statistical analysis was conducted using the Mann–Kendall (MK) non-parametric trend test on the variables to investigate their potential relationships with groundwater storage. In this study, groundwater storage refers to variations in groundwater storage anomalies, estimated using outputs from the Global Land Data Assimilation System (GLDAS) model. Specifically, these anomalies are derived as the residual component of the terrestrial water budget, after accounting for soil moisture, snow water equivalent, and canopy water storage. The results revealed a strong negative correlation between built-up areas and groundwater storage, with a correlation coefficient of −1.00. Similarly, a notable negative correlation was found between the cropland area and groundwater storage (correlation coefficient: −0.85). Conversely, surface water availability showed a strong positive correlation with groundwater storage, with a correlation coefficient of 0.87, highlighting the direct impact of surface water reduction on groundwater storage. Furthermore, our findings demonstrated a reduction of 168.21 mm (millimeters) in groundwater storage from 2003 to 2022. GLDAS represents storage components, including groundwater storage, in units of water depth (mm) over each grid cell, employing a unit-area, mass balance approach. Although storage is conceptually a volumetric quantity, expressing it as depth allows for spatial comparison and enables conversion to volume by multiplying by the corresponding surface area. Full article

Reliable soil moisture projections are critical for optimizing crop productivity and water savings in irrigation in arid and semi-arid regions. However, capturing their spatial and temporal variability is difficult when using individual observations, modeling, or satellite-based methods. Here, we present an integrated framework that combines satellite-derived soil moisture estimates, ground-based observations, the HYDRUS-1D vadose zone model, and the ensemble Kalman filter (EnKF) data assimilation method to improve soil moisture simulations over saline-affected farmland in the Hetao irrigation district. Vegetation effects were first removed using the water cloud model; after correction, a cubic regression using the vertical transmit/vertical receive (VV) signal retrieved surface moisture with an R² value of 0.7964 and a root mean square error (RMSE) of 0.021 cm³·cm⁻³. HYDRUS-1D, calibrated against multi-depth field data (0–80 cm), reproduced soil moisture profiles at 17 sites with RMSEs of 0.017–0.056 cm³·cm⁻³. The EnKF assimilation of satellite and ground observations further reduced the errors to 0.008–0.017 cm³·cm⁻³, with the greatest improvement in the 0–20 cm layer; the accuracy declined slightly with depth but remained superior to either data source alone. Our study improves soil moisture simulation accuracy and closes the knowledge gaps in multi-source data integration. This framework supports sustainable land management and irrigation policy in vulnerable farming regions. Full article

Accurately representing land–atmosphere interactions is essential for numerical weather prediction models, as they have a significant effect on forecasted near-surface meteorological parameters. We used the SURFEX soil model, coupled with the AROME non-hydrostatic numerical weather prediction model at HungaroMet Hungarian Meteorological Service. Land data assimilation techniques are employed to provide the most accurate initial conditions for the AROME-SURFEX system. Initially, the Optimal Interpolation (OI) method was applied to determine the initial conditions for soil temperature and moisture. This study focuses on implementing the more complex and advanced Simplified Extended Kalman Filter (SEKF) for surface data assimilation. The SEKF corrects the soil temperature and soil moisture content using screen-level observations (2-m temperature and relative humidity), offering improvements over OI. We highlight the advantages of the SEKF across different seasons, noting that it is a more physically-based approach with dynamically varying Jacobians. We demonstrate how outlier Jacobians can be filtered using linearity check to handle system nonlinearity. The tuning of appropriate data assimilation parameters, such as observational and background errors, is also crucial for achieving optimal results. We evaluate the impact of the SEKF by conducting forecast verification against in situ atmospheric observations, comparing its performance with that of OI. Our results indicate a significant improvement in winter forecasts. Additionally, a moderate improvement is observed in spring, highlighting the seasonal dependency of the efficiency of the SEKF. 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