Search Results (65)

The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly across regions and environmental conditions, due to in sensor characteristics, retrieval algorithms, and the lack of localized calibration. This study proposes a multi-source long short-term memory (MLSTM) for improving ASCAT, ESA–CCI, and SMAP SM products by combining in-situ SM measurements and four key auxiliary variables: precipitation (PRE), land surface temperature (LST), fractional vegetation cover (FVC), and evapotranspiration (ET). First, the in-situ measured data from four in-situ observation networks were corrected using the LSTM method to match the grid sizes of ASCAT (0.1°), ESA–CCI (0.25°), and SMAP (0.1°) SM products. The RPE, LST, FVC, and ET were used as inputs to the LSTM to obtain loss data against in-situ SM measurements. Second, the ASCAT, ESA–CCI, and SMAP SM datasets were used as inputs to the LSTM to generate loss data, which were subsequently corrected using LSTM-derived loss data based on in-situ SM measurements. When the mean squared error (MSE) loss values were minimized, the improvement for ASCAT, ESA–CCI, and SMAP products was considered the best. Finally, the improved ASCAT, ESA–CCI, and SMAP were produced and evaluated by the correlation coefficient (R), root mean square error (RMSE), and standard deviation (SD). The results showed that the RMSE values of the improved ASCAT, ESA–CCI, and SMAP products against the corrected in-situ SM data in the OZNET network were lower, i.e., 0.014 cm³/cm³, 0.019 cm³/cm³, and 0.034 cm³/cm³, respectively. Compared with the ESA–CCI and SMAP products, the ASCAT product was greatly improved, e.g., in the SNOTEL network, the Root Mean-Square Deviation (RMSD) values of 0.1049 cm³/cm³ (ASCAT) and 0.0662 cm³/cm³ (improved ASCAT). Overall, the MLSTM-based algorithm has the potential to improve the global satellite SM product. 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

Background/Objective: Salinity is a growing problem affecting a large portion of global agricultural land, particularly in areas where water resources are scarce. The objective of this study was to provide physiological and molecular information on salt-tolerant citrus rootstocks to mitigate the detrimental effects of salinity on citriculture. Methods: Ten accessions belonging to eight Citrus species and four to Poncirus trifoliata Raf. were tested for salinity tolerance (0 and 15 mM NaCl for 1 year) in terms of vegetative and Cl⁻ tissue distribution traits. In addition, most accessions were evaluated for leaf Na⁺ and other cations. Results: All salt tolerant accessions tended to restrict the leaf Cl⁻ content, although in a lower degree than the Cleopatra mandarin. However, differences in their ability to restrict leaf [Na⁺] were evident, contributing to a classification of trifoliate and sour orange accessions that matched their genotypic grouping based on allele sharing at a marker targeting candidate gene coding for the NPF5.9 transporter within LCL-6 quantitative trait locus. Conclusions: Our markers targeting LCl-6 candidate genes coding for NPF5.9, PIP2.1, and CHX20 (citrus GmSALT3 ortholog) could be efficient tools for managing the detected salt tolerance diversity in terms of both Cl⁻ and Na⁺ homeostasis in rootstock breeding programs derived from these species, in addition to Citrus reshni. Full article

Xinjiang is located in an inland arid area, and it faces significant challenges in water resource supply and demand, with a fragile ecological environment. Exploring the internal relationship between the time–space distribution of agricultural water–soil matching and the evolution of the ecosystem service value (ESV) in the Tailan River Irrigation District of Xinjiang from 2000 to 2020, this study provides theoretical guidance for the balance of agricultural water–soil resources and the healthy and sustainable development of the ecological environment in the irrigation district. By integrating the water–soil matching coefficient and the equivalent factor method, the spatiotemporal distribution of agricultural water–soil matching and the spatiotemporal evolution of the ESV under the change of land use (LU) in the irrigation district are analyzed. Based on the Pearson correlation, the trade-off synergy between the two is explored. The results show that the following occurred in the past 20 years: (1) Grassland and dryland are the two categories of land with the biggest transfer-out and transfer-in areas in the Tailan River Irrigation District, and the conversion areas are mostly in Jiamu Town and Guleawati Township. (2) The area and reclamation rate of the irrigation district increased gradually, among which the highest reclamation rate was 85.93% in Kezile Town and the lowest was 76.37% in Guleawati Township. The average Gini coefficient of agricultural water–soil in the irrigation district is 0.118, which is absolutely fair. (3) Kezile Town has the highest agricultural water consumption, but the matching of agricultural water–soil always fluctuates between the best and the worst. The agricultural water consumption in Communist Youth League Town is the lowest, but the matching of agricultural water–soil has remained the best for many years. (4) The ESV of the irrigation district showed an overall increasing trend, from CNY 243 million in 2000 to CNY 678 million in 2020; in addition, soil conservation, hydrological regulation, grassland, and dryland contributed the most to ESV in each period. (5) There was a significant trade-off relationship between agricultural water–soil matching and ecosystem services in the Tailan River Irrigation District, while there was a significant synergistic relationship between ecosystem services. Full article

Accurate data collection and time series creation are crucial for understanding these changes. However, many areas lack reliable data due to geopolitical issues and government permissions. Urgent action is needed for sustainable water management. This study uses Gravity Recovery and Climate Experiment (GRACE) data to analyze monthly fluctuations in groundwater storage in the Missan region of Iraq from January 2022 to December 2023, using Goddard Space Flight Center (GSFC) mascon, Jet Propulsion Laboratory Downscaled (JPL_D), and Catchment Land Surface Model (CLSM). This study revealed the variability in GWS over the area using RS data and in integration with available monitoring wells. To investigate GWS variability, GSFC, JPL_D, and CLSM observed a downward trend in GWS in 2022; GSFC exhibits the highest negative groundwater trend, while CLSM has the lowest negative trend. Then, from January to June 2023, GSFC had the highest positive trend, while CLSM had the lowest positive trend. Most of the study period has a negative trend for remote sensing that matches the monitoring well data in situ, in which wells 1, 2, and 4 are negative trends of the study period. In conclusion, these results improve the role of remote sensing in groundwater monitoring in small-scale region unconfined aquifers, which supports decision-making in water resource management. The findings illustrated a match between the results derived from the GRACE data and monitoring well data. Full article

A two-dimensional longitudinal profile model was used to evaluate groundwater flow along a 48 km flowline in the Southeastern Hueco Aquifer, extending from the Diablo Plateau in Texas to the Sierra de San Ignacio in Chihuahua, Mexico. The model, incorporating geologically distributed permeability values, closely matched the predevelopment potentiometric surface. Predicted recharge rates and travel times aligned with published estimates and environmental isotopes, suggesting potential transboundary groundwater movement. The model estimated recharge rates needed to reach flow capacity, or the maximum volume a system can transmit, typically saturating the water table. Current moisture levels are insufficient, but flow capacity may have been reached during late Pleistocene pluvial periods. Required recharge rates were 297% higher than initial calibration in the U.S. and 1080% higher in Mexico, with only U.S. estimates appearing plausible for the Pleistocene–Holocene transition. These findings are relevant to regional waste disposal considerations because water tables near land surface present a risk to groundwater resources. A transient simulation modeled hydraulic head decay due to recharge abatement linked to climate change over 14,000 years. It simulated a decrease from a “flow capacity” recharge rate of 10.4 mm/year to 3.5 mm/year today. The modeling simulations ended with the hydraulic head remaining only 20 m above current levels, suggesting a minimal-to-negligible fossil hydraulic gradient in the low-permeability flow system. Full article

Surface soil moisture (SSM) plays a pivotal role various fields, including agriculture, hydrology, water environment, and meteorology. To investigate the impact of land use types and fractional vegetation cover (FVC) on the accuracy of SSM estimation, this study conducted a comprehensive analysis of SSM estimation performance across diverse land use scenarios (e.g., multiple land use combinations and cropland) and varying FVC conditions. Sentinel-2 NDVI and MOD09A1 NDVI were fused by the Enhanced Spatial and Temporal Adaptive Reflection Fusion Model (ESTARFM) to obtain NDVI with a temporal resolution better than 8 d and a spatial resolution of 20 m, which improved the matching degree between NDVI and the Sentinel-1 backscattering coefficient (σ⁰). Based on the σ⁰, NDVI, and in situ SSM, combined with the water cloud model (WCM), the SSM estimation model is established, and the model of each land use and FVC is validated. The model has been applied in Handan. The results are as follows: (1) Compared with vertical–horizontal (VH) polarization, vertical–vertical (VV) polarization is more sensitive to soil backscattering (${\mathsf{\sigma }}_{\mathrm{soil}}^0$). In the model for multiple land use combinations (Multiple-Model) and the model for the cropland (Cropland-Model), the R² increases by 0.084 and 0.041, respectively. (2) The estimation accuracy of SSM for the Multiple-Model and Cropland-Model is satisfactory (Multiple-Model, RMSE = 0.024 cm³/cm³, MAE = 0.019 cm³/cm³, R² = 0.891; Cropland-Model, RMSE = 0.023 cm³/cm³, MAE = 0.018 cm³/cm³, R² = 0.886). (3) When the FVC > 0.75, the accuracy of SSM by the WCM is low. It is suggested the model should be applied to the cropland where the FVC ≤ 0.75. This study clarified the applicability of SSM estimation by microwave remote sensing (RS) in different land uses and FVCs, which can provide scientific reference for regional agricultural irrigation and agricultural water resources management. The findings highlight that the VV polarization-based model significantly improves SSM estimation accuracy, particularly in croplands with FVC ≤ 0.75, offering a reliable tool for optimizing irrigation schedules and enhancing water use efficiency in agriculture. These results can aid in better water resource management, especially in regions with limited water availability, by providing precise soil moisture data for informed decision-making. Full article

Previous studies on carrying capacity have primarily focused on measuring agricultural production conditions while neglecting the coupling effects among production conditions, production materials, and the external environment (the coupling effects of agricultural water, soil, energy, and the external environment). Therefore, this paper introduces the concept of the carrying capacity of a regional agricultural water–land–energy coupling system (WLECS); develops an evaluation framework comprising 27 indicators from the perspectives of stability, collaboration, and resilience and constructs an improved random forest model based on the red-billed blue magpie optimizer (RBMO). Finally, it is applied to the evaluation of WLECS carrying capacity in China’s main grain producing area (Heilongjiang Province). The results demonstrate that the constructed RBMO-RF model exhibits stability and reasonableness with high fitting accuracy. The collaboration weight accounts for the highest proportion (0.438), indicating that the collaboration within the subsystem has the greatest impact on the carrying capacity. In terms of time scale, the WLECS carrying capacity in Heilongjiang Province shows an upward trend, characterized by three stages: a “low-level fluctuation period”, a “growth period”, and a “rapid growth period”. In terms of spatial scale, the overall spatial pattern is low in the West and high in the East, and stable in the North and South. The key driving factors are the effective irrigation index, indirect water footprint, and agricultural water-land matching degree. The research results demonstrate the carrying capacity of the WLE coupling system holds significant implications for formulating regional agricultural resource optimization allocation plans and promoting agricultural sustainable development. Full article

Water and land resources (WLR) are the most important basic resources for social and economic development. The effective alignment of WLR is crucial for maximizing resource utilization and promoting sustainable regional development. This study focuses on Western Jilin Province (WJP), China, employing the degree of coupling coordination model, spatial autocorrelation, and the center of gravity transfer model to assess and characterize the spatio-temporal differentiation patterns of water and land resource matching from 2006 to 2020. Five indicators—annual average temperature (AAT), urbanization rate (UR), population density (PD), reclamation rate (RR), and water resource utilization rate (WRUR)—were selected as influencing factors. A Tobit model was constructed to elucidate the driving mechanisms behind the evolution of the WLR coupling coordination degree (CCD) in WJP. The results indicate the following: (1) From a temporal perspective, the coupling coordination degree of WLR in WJP has shown a year-on-year increase from 2006 to 2020, transitioning from a moderate imbalance to intermediate coordination, reflecting a trend of continuous improvement. (2) Regarding spatial distribution, the overall center of gravity of water and land resource coupling coordination remained relatively stable between 2006 and 2020; however, the direction of distribution gradually shifted from the northeast to the southwest and then from the northwest to the southeast. (3) The AAT, PD, and RR from 2006 to 2020 were all statistically significant at p < 0.01. Notably, the RR positively influences the CCD of WLR, whereas the AAT and PD exert a negative impact. In contrast, the UR and WRUR do not significantly affect the CCD of WLR. Full article

As a vital part of the geo-environment and water cycle, ecosystem health and human development are dependent on water resources. Water supply and demand are influenced significantly by land use and cover change (LUCC) which shapes the surface ecosystems by altering their structure and function. Under future climate change scenarios, LUCC may greatly impact regional water balance, yet the impact is still not well understood. Therefore, examining the spatial relationship between LUCC and water yield services is crucial for optimizing land resources and informing sustainable development policies. In this study, we focused on the Hanjiang River Basin and used the patch-generating land use simulation (PLUS) model, coupled with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, to assess water yield services under three Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios. For the first time, we considered the impact of future changes in socio-economic and water use indicators on water demand using correction factors and ARIMA projections. The relationship between water supply and demand was explored using this approach, and LUCC’s effects on this balance are also discussed. Results indicate that: (1) The patterns of LUCC are similar for the three scenarios from 2030 to 2050, with varying levels of decrease for cropland and significant growth of built-up areas, with increases of 6.77% to 19.65% (SSP119), 7.66% to 22.65% (SSP245), and 15.88% to 46.69% (SSP585), respectively, in the three scenarios relative to 2020; (2) The future supply and demand trends for the three scenarios of produced water services are similar, and the overall supply and demand risks are all on a downward trend. Water demand continues to decline, and by 2050, the water demand of the 3 scenarios will decrease by $96.275\times {10}^8\mathrm{t}$, $81.210\times {10}^8\mathrm{t}$, and $84.13\times {10}^8\mathrm{t}$ relative to 2020, respectively; while supply decreases from 2030 to 2040 and rises from 2040 to 2050; (3) Both water supply and demand distributions exhibit spatial correlation, and the distribution of hotspots is similar. The water supply and demand are well-matched, with an overall supply-demand ratio greater than 1.5; (4) LUCC can either increase or decrease water yield. Built-up land provides more water supply compared to other land types, while forest land has the lowest average water supply. Limiting land use type conversions can enhance the water supply. Full article

The increasing water demand and the disparities in the spatiotemporal distribution of water resources will lead to increasingly severe water shortages in arid areas. Accurate evapotranspiration estimation is the basis for evaluating water stress and informing sustainable water resource management. In this study, we constructed a surface energy balance algorithm for land (SEBAL) model based on the Google Earth Engine platform to invert the actual evapotranspiration (ETa) in typical irrigation areas in Xinjiang, northwest China, during the growing season from 2005 to 2021. The inversion results were evaluated using the observed evaporation data and crop evapotranspiration estimated by the FAO Penman–Monteith method. The water stress index (WSI) was then calculated based on the simulated ETa. The impacts of climatic factors, hydrological conditions, land-use change, and irrigation patterns on ETa and WSI were analyzed. The results indicated the following: (1) The ETa simulated by the SEBAL model matched well with the observed data and the evapotranspiration estimated using the FAO Penman–Monteith approach, with correlation coefficients greater than 0.7. (2) The average ETa was 704 mm during the growing season, showing an increasing trend in the irrigation area of the Yanqi Basin (IAY), whereas for the irrigation area of Burqin (IAB) the average ETa was 677 mm during the growing season, showing an increasing trend. The land cover type mainly influenced the spatial distribution of ETa in the two study areas. (3) The WSI in both irrigation areas exhibited a decreasing trend, with the WSI in the IAY lower than that in the IAB. (4) Climate warming, increases in irrigation areas, and changes in cropping patterns led to increased ETa in the IAY and IAB; the overall decreasing trend in the WSI derived from the popularization of agricultural water-saving irrigation patterns in both regions, which reduces ineffective evapotranspiration and contributes positively to solving the water shortage problem in the basins. This study provides insight into water resource management in the Xinjiang irrigation areas. Full article

More and more offshore heavy oil resources are discovered and exploited as the focus of the oil and gas industry shifts from land to sea. However, unlike onshore heavy oil reservoirs, offshore heavy oil reservoirs not only have active edge and bottom water but also have different exploitation methods. In this paper, a typical special heavy oil reservoir in China was analyzed in detail, based on geology–reservoir–engineering integration technology. Firstly, it is identified as a self-sealing bottom water heavy oil reservoir by analyzing its geological characteristics and hydrocarbon accumulation mechanism. Secondly, the water cut is initially controlled by oil viscosity, but subsequently, by reservoir thickness through the analysis of oil and water production data. Thirdly, the bottom oil–water contact of the reservoir was re-corrected to build an accurate 3D geological model, based on the production history matching of a single well and the whole reservoir. Lastly, a scheme of thermal production coupled with cold production was proposed to exploit this special reservoir, and the parameters of steam, N₂, and CO₂ injection and production were optimized to predict oil production. This work can provide a valuable development model for the efficient exploitation of similar offshore special heavy oil reservoirs. Full article

The matching relationship between water and land resources in the karst mountains is closely related to regional ecological and environmental security, human well-being, and high-quality socio-economic development. Based on a massive literature review, this review systematically summarizes the research overview, development process, and matching mechanism of karst water and land resources. The results show the following: (1) Since 1990, the number of publications on karst water and land resources has shown a steady upward trend, with the journals covering multiple fields, characterized by multidisciplinary and interdisciplinary features. (2) The matching relationship between water and land resources in karst mountainous areas has experienced three stages: “single element–binary matching–multiple coupling”. It reveals the evolutionary process from focusing on the single internal system of water and land resources to focusing on the mutual matching relationship between water and land resources, and then to the study of multiple coupling between water and land resources system and other external systems. (3) The internal coordinated development of the water and land resources system in karst mountainous areas depends on the joint interactions of natural, economic, and social factors, while the external matching mainly focuses on the mechanism around the three aspects of water and land resources and agricultural production, ecological environment, and economic and social development. Furthermore, the review proposes that future research should explore the matching of water and land resources in karst mountainous areas through theoretical framework construction, model innovation, scale refinement, and mechanism analysis. The expected results will provide a scientific reference for advancing theoretical research on karst water and land resources and optimizing their management. Full article

The current global scenario of unequal access to water and electricity motivates the search for solutions based on available resources, such as renewable energies and desalination. Additionally, adequate sizing of renewables requires extensive and reliable time series, which are usually unavailable. Reanalysis models are an option to consider, but only after evaluating their local accuracy, generally through performance metrics. This study evaluated the performance of the solar radiation, temperature, and wind speed products from MERRA2 and ERA5-Land in comparison to ground data, as well as their influence on the optimal initial configuration of a renewable energy system for desalination in La Guajira, Colombia. HOMER Pro was the software tool employed to establish the best arrangements for the resulting renewable power systems, and the study included a sensitivity analysis considering different annual capacity shortages, operating hours, and energy needs for desalting. ERA5-Land performed better than MERRA2 in matching the time series from the local station. The relative error of the cost of electricity of systems dimensioned from reanalysis was less than 3% compared to systems from ground measurements, with a renewable fraction above 98%. For the study area, ERA5-Land reanalysis represents a reliable alternative to address the scarcity of solar resource records, but both reanalyses failed to reproduce the wind speed regime. Full article

Water, soil, and heat are strategic supporting elements for human survival and social development. The degree of matching between human-land-water-heat elements directly influences the sustainable development of a region. However, the current evaluation of the matching of human-land-water-heat elements overlooks the influence of elevation factors on the matching results, especially evident in mountainous areas. Taking the Yunnan Plateau with distinctive mountainous features as the research subject, divided into 11 elevation ranges, the Lorenz Gini coefficient, asymmetry coefficient, matching distance, and imbalance index are used to assess the spatial matching and balance of human-land-water-heat elements. A projection tracing model is employed to analyze its water resource carrying capacity. Analyses revealed that the Gini coefficient of monthly precipitation from the 1950s to 2022 on the Yunnan Plateau increases with increasing latitude, whereas the correlation with elevation is notably lower. The asymmetry coefficient increases gradually from west to east with change in longitude. The mismatch of the human–land–water–heat system in regions at different elevations is in the order 1800–2000 m > 2000–2200 m > 1400–1600 m > 800 m > other areas. The matching of the human–land–water–heat system in different wet–dry years and seasons also fluctuates with elevation, resulting in serious seasonal drought and water shortage problems in mountainous areas with elevations of 1200–1600, 1800–2000 m, and >2600 m. The spatial equilibrium of temperature and precipitation in regions of different elevations is best, followed by that of cultivated land, while that of the population is the worst. The Gini coefficients for different water cycle processes of precipitation, surface runoff, and regulating storage capacity for water supply continue to increase. Specifically, the Gini coefficient of industrial water supply is the highest, reaching 0.576, and that of agricultural irrigation is the lowest (0.424). Through artificial regulation of lake and reservoir water, seasonal changes in the demand for agricultural irrigation water are offset to achieve a demand–supply balance and matching of land and water resources. The water resource capacity of different elevation ranges is evenly underloaded. However, the potential of the water resource capacity varies obviously with elevation in the order 2000–2200 m < 1800–2000 m < 1600–8000 m < 1400–1600 m < other areas. It appears that the greater the human–land–water–heat system mismatch, the smaller the regional potential of the water resource capacity. Full article