Soil Moisture Characteristics Research Articles

Preferential Hydrologic States and Tipping Characteristics of Global Surface Soil Moisture

AbstractA dynamic transition in soil hydrologic states through meteorological variability and terrestrial feedback governs soil‐vegetation‐climate (SVC) interactions, constrained by critical soil moisture (SM) thresholds. However, observational and scaling constraints limit critical SM threshold estimation at the remote‐sensing (RS) footprint scale. Using global surface SM (θRS) from NASA’s Soil Moisture Active Passive (SMAP) satellite, we characterize the seasonal preferential hydrologic states of θRS and derive three tipping characteristics to estimate the intensity (Mean Tipping Depth, ), frequency (Tipping Count, η), and duration (Mean Tipped Time, ) of the excursion of θRS from wet‐ to dry‐average conditions. The preferential state provides the seasonally dominant hydrological states of θRS, while tipping characteristics capture the ecosystem linkages of the dynamic transition in θRS hydrologic states. Globally, θRS predominantly exhibits a (unimodal) dry‐preferential state, especially over arid/semi‐arid drylands and a unimodal wet‐preferential θRS state in high‐latitude boreal forests and tundra biomes. Prevalence of (bimodal) bistable θRS state overlaps with regions of strong positive SM‐precipitation coupling and monsoonal climate in semi‐arid/subhumid climates. Seasonal preferential hydrologic states co‐vary with the regional variability in plant water stress threshold and land‐atmospheric coupling strength. Tipping characteristics of θRS show sensitivity to intra‐biome variability in SVC coexistence patterns and display high skill in partitioning global ecoregions. While and η are climate‐controlled, is moderated by soil and vegetation through their influence over θRS drydown during water‐limited evapotranspiration. Preferential states and tipping characteristics find applications in quantifying SVC coexistence patterns, climate model diagnosis, and assessing ecosystem sensitivity to climate change.

Spatial distribution characteristics and influencing factors of shallow and deep soil moisture under ecological restoration in the loess plateau, China

AbstractDeep soil moisture is one of the primary factors restricting vegetation growth on the Loess Plateau (LP) of China. Investigating the spatial variations and influencing factors of shallow and deep soil moisture is of great significance for ecological restoration on the LP. In this study, we sampled disturbed soil up to a depth of 10 m with a soil drill. A 200 × 200 m grid was superimposed on a digital topographic map, and 78 sampling sites were selected across the Zhifanggou (ZFG) watershed. According to the results, the vertical profile of soil could be subdivided into four layers: the shallow active layer (SAL; 0–0.5 m), the shallow rainfall recharge layer (SRL; 0.5–2.2 m), the deep excessive layer (DEL; 2.2–4 m), and the deep stable layer (DSL; 4–10 m). The horizontal distribution characteristics of soil moisture in various soil layers were similar, with a generally high concentration in the south and a low concentration in the north. In addition, a relatively low soil moisture content (SMC) occurred in the center of the SRL. Soil moisture was negatively correlated with slope and slope aspect between 0 and 2.2 m (p < 0.05), whereas slope and slope aspect had no significant effects on deep soil moisture (>2.2 m). The SMCs of the four soil layers, from lowest to highest, were as follows: forestland, shrubland, and grassland. The SMC of forestland was negatively correlated with restoration age, whereas the SMC of grassland was positively correlated with restoration age. This study deepens our understanding of shallow and deep soil moisture distribution characteristics and the corresponding ecological restoration on the LP and provides a reference for the rational use of deep soil moisture.

Characteristics of Soil Moisture and Heat Change during Freeze–Thaw Process in the Alpine Grassland of Duogerong Basin in the Source of the Yellow River

To deeply understand the characteristics of soil freeze–thaw water–heat change in the alpine grassland in the Duogerong Basin of the Yellow River source, the soil water–heat profile change monitoring was carried out based on the field monitoring station in the Duogerong Basin of the Yellow River source. By analyzing the comprehensive monitoring data from September 2022 to September 2023, the characteristics of the soil temperature and water content changes in the freeze–thaw cycle of the alpine grassland in the Duogerong Basin at the source of the Yellow River were explored. The results showed that the temperature and water content of each layer of the soil profile changed periodically, and the range of change was negatively correlated with the depth. The annual freeze–thaw process at the observation site is divided into five stages: 31 October to 3 November is the short initial freezing period, 4 November to 18 April is the stable freezing period, 19 April to 26 April is the early ablation period, 27 April to 30 April is the late ablation period, and 1 May to 30 October is the complete ablation period. The maximum soil freezing depth during the observation period was about 250 cm. Soil temperature and moisture content change affect each other; soil water is essential in heat transfer, and the two correlate well. The research results provide theoretical support for further understanding the characteristics of soil hydrothermal changes during the freeze–thaw process in the alpine grassland permafrost area at the source of the Yellow River.

Coupling Deep Learning and Physically Based Hydrological Models for Monthly Streamflow Predictions

AbstractThis study proposes a new hybrid model for monthly streamflow predictions by coupling a physically based distributed hydrological model with a deep learning (DL) model. Specifically, a simplified hydrological model is first developed by optimally selecting grid cells from a distributed hydrological model according to their soil moisture characteristics. It is then driven by bias corrected general circulation model (GCM) predictions to generate soil moistures for the forecasting months. Finally, model‐simulated soil moisture along with other predictors from multiple sources are used as inputs of the DL model to predict future monthly streamflows. The proposed hybrid model, using the simplified Variable Infiltration Capacity (VIC) as the hydrological model and the combination of Convolutional Neural Network and Gated Recurrent Unit (CNN‐GRU) as the DL model, is applied to predict 1‐, 3‐, and 6‐month ahead reservoir inflows for the Danjiangkou Reservoir in China. The results show that the hybrid model consistently performs better than VIC and CNN‐GRU models with great improvement in Kling‐Gupta efficiency (KGE) values for lead times up to 6 months. Additional tests indicate that hybrid models based on CNN‐GRU outperform those based on LASSO, XGBoost, CNN, and GRU models. Moreover, compared with the distributed hydrological model, the hybrid model greatly reduces the computation burden of rolling prediction. It also saves decision‐makers the time and effort of trying different combinations of predictors, which is indispensable when building DL models. Overall, the new hybrid model demonstrates great potential for monthly streamflow prediction where training data are limited.

Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN.

This research utilized in situ soil moisture observations in a coupled grid Soil and Water Assessment Tool (SWAT) and Parallel Data Assimilation Framework (PDAF) data assimilation system, resulting in significant enhancements in soil moisture estimation. By incorporating Wireless Sensor Network (WSN) data (WATERNET), the method captured and integrated local soil moisture characteristics, thereby improving regional model state estimations. The use of varying observation search radii with the Local Error-subspace Transform Kalman Filter (LESTKF) resulted in improved spatial and temporal assimilation performance, while also considering the impact of observation data uncertainties. The best performance (improvement of 0.006 m3/m3) of LESTKF was achieved with a 20 km observation search radii and 0.01 m3/m3 observation standard error. This study assimilated wireless sensor network data into a distributed model, presenting a departure from traditional methods. The high accuracy and resolution capabilities of WATERNET's regional soil moisture observations were crucial, and its provision of multi-layered soil temperature and moisture observations presented new opportunities for integration into the data assimilation framework, further enhancing hydrological state estimations. This study's implications are broad and relevant to regional-scale water resource research and management, particularly for freshwater resource scheduling at small basin scales.

Soil-Matric-Potential-Based Irrigation Scheduling to Increase Yield and Water Productivity of Okra

A field experiment was conducted on okra (Abelmoschus esculentus L.) for assessing the sustainability of yield with optimum irrigation schedule based on soil moisture depletion. Four irrigation treatments: Irrigation at I1:20%, I2:30%, I3:40% and I4:50% of soil moisture depletion rate in main plots and three fertilizer treatments: Fertigation at F1:100%, F2:80% and F3:60% of recommended NPK (100:25:40 kg/ha) in subplots were tested. Soil matric potential was recorded continuously using electronic tensiometers. The soil moisture characteristics curve was derived for various soil matric potential value sand the soil water content. The irrigation controller triggered solenoid valves for irrigation when soil moisture depletion reached a prespecified level in each treatment. Soil moisture depletion values were significantly predicted based on a regression model calibrated for each treatment over the crop growing period. The model gave minimum prediction error (PE) for I1, followed by I2, I3 and I4, respectively. Plant growth and yield parameters were significantly influenced by the soil moisture availability under each treatment. It is recommended that irrigation be scheduled at 20% soil moisture depletion rate together with 100% NPK fertilizer application for attaining sustainable yield of okra (12.3 t/ha), apart from maximum WUE (3.5 kg/m3) and plant growth parameters under semiarid inceptisols.

Vermicompost-based amendment compensated for the reducing effect of water stress on growth and yield of quinoa by improving soil moisture characteristic

Vermicompost-based amendment compensated for the reducing effect of water stress on growth and yield of quinoa by improving soil moisture characteristic

Assessing the effects of agricultural waste returning on soil moisture characteristics

AbstractAgricultural waste is an emerging and promising resource due to its abundant organic compounds and high water‐holding capacity. It plays a crucial role in improving soil water use efficiency and contributing to sustainable agricultural development. This study aimed to investigate the impact of returning agricultural waste on soil moisture characteristics. Different volume ratios (4:5:1:0, 3:5:1:1, 2:5:1:2, 1:5:1:3, and 0:5:1:4) of agricultural waste blend (AWB) consisting of cattle manure, yellow loam soil, wheat straw, and distiller's grains were examined, and the water‐holding performance of the five AWB ratios under various suction forces was compared. The analysis confirmed the suitability of the Van Genuchten‐Mualem model for fitting soil moisture characteristic curves at different ratios. The study also examined the influences of AWB ratios on soil‐specific water capacity, hydraulic parameters, and equivalent pore size. The results showed that agricultural waste effectively increased soil‐specific water capacity, available water content, soil WHC(Water‐holding capacity), and soil pore count. Furthermore, reducing cattle manure and increasing distiller's grains enhanced these effects. For instance, when the AWB ratio was 0:5:1:4, soil‐specific water capacity increased by 47.7%, while field WHC and available water content increased by 9.9% and 3.8%, respectively. Additionally, the number of extremely small and small pores increased by 1.4% and 0.77%, respectively. These findings provide valuable theoretical guidance for agricultural waste treatment and improving soil WHC. Based on the results, it is recommended to increase the proportion of distiller's grains and reduce organic fertilizer when using agricultural waste for improving soil WHC.

Soil Moisture Distribution and Time Stability of Aerially Sown Shrubland in the Northeastern Margin of Tengger Desert (China)

Considering the importance of soil moisture in hydrological processes, it is crucial to understand the water distribution and time stability of different aerial shrub soils. There are few studies on the soil moisture of aerial vegetation in the northeastern margin of the Tengger Desert. Based on long-term monitoring data from the aerial seeding area in the northeastern margin of the Tengger Desert, the distribution characteristics of soil moisture and the temporal stability of soil moisture were studied. From June to October 2022, the soil moisture monitoring instrument WatchDog was used to monitor the long-term soil moisture changes (0–200 cm) in the four aerial afforestation plots of Hedysarum scoparium, mixed forest land (Hedysarum scoparium dominant species), mixed forest land (Calligonum mongolicum dominant species), and Calligonum mongolicum. The Spearman rank correlation coefficient was used to study the temporal stability of soil moisture in the four plots. Rainfall data were collected through small weather stations. The results show that the average soil water storage of four kinds of aerial shrub land in the study area was the highest in August, and the average soil water storage of different forest lands was different. The soil water content of the surface layer (0–30 cm) fluctuated the most in different months. The variation in soil water content in the shallow layer (30–100 cm) was smaller than that in the surface layer. The fluctuation of soil water content in the middle layer (100–150 cm) and deep layer (150–200 cm) was relatively stable. There was no strong variability in soil moisture content, and the temporal variation coefficient of surface soil moisture was the highest (31.44–39.8%), which showed moderate variability. The temporal variation coefficient of soil moisture in the shallow, middle and deep layers of all kinds of plots was significantly reduced, and the soil moisture stability of different aerial shrub land was the same. Spearman rank correlation analysis showed that the spatial pattern of soil water content in the surface layer (0–30 cm) and deep layer (150–200 cm) was more stable over time, that is, the temporal stability of soil water content was higher, and the temporal stability of soil water content in the middle and shallow layers of different types of shrub land was different. The research results help us to understand the soil hydrological process in the aerial seeding afforestation area in the northeastern margin of Tengger Desert, rationally arrange soil moisture monitoring points, efficiently manage and utilize water resources in the aerial seeding area, and provide a theoretical basis for local vegetation restoration and the optimization of the ecological environment.

Study on Spatiotemporal Transport Characteristics of Soil Moisture in Layered Heterogeneous Vadose Zone Based on HYDRAS-3D

The heterogeneity of layered soils affects the transport processes of water in the vadose zone. However, the mechanism of soil moisture transport in the vadose zone under heterogeneous media conditions, especially the three-dimensional transport mechanism, is a frontier problem to be studied. In order to reveal the law of soil water transport in the vadose zone under heterogeneous media, this paper constructs a three-dimensional model of water transport using HYDRAS-3D (V 2.x) software through in situ tests of water transport in 3 × 3 × 4 m sample and verifies and analyzes the model. The vertical distribution and variability of soil water content and the temporal and spatial rules of soil water transversal transport at the soil–layer interface were analyzed. The results indicate that after adjusting the parameters in the HYDRAS-3D model using measured values, the simulation results are reasonable, and the model has high reliability. To represent the water content variability characteristics of the test tube profile, water movement in the vadose zone was classified as follows: (I) steady period (April–September, less affected by rainfall), (II) slow change period (January–March), and (III) rainfall rapid change period (June, when the impact of rainfall on the test cylinder was greater). The two largest values of the soil water potential variation gradient, 19.9 and 17.8 cm/d, were observed in the silty clay layer of the test cylinder, and the influence of evaporation and infiltration in the silty clay layer was most notable at the interface. The lateral transport of soil water at the interface was influenced to a certain degree by the layered heterogeneity, wherein the fine sand layer exhibited the most pronounced impact. This was followed by silt and silty clay layers, with the silt layer showing the lowest degree of influence. The research results can provide scientific reference for the rational planning of water resources in the Yinchuan Plain.

Long-Term Characteristics of Surface Soil Moisture over the Tibetan Plateau and Its Response to Climate Change

Soil moisture over the Tibetan Plateau (TP) can affect hydrological cycles on local and remote scales through land–atmosphere interactions. However, TP long-term surface soil moisture characteristics and their response to climate change are still unclear. In this study, we firstly evaluate two satellite-based products—SSM/I (the Special Sensor Microwave Imagers) and ECV COMBINED (the Essential Climate Variable combined)—and three reanalysis products—ERA5-Land (the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis), MERRA2 (the second version of Modern-Era Retrospective Analysis for Research and Applications), and GLDAS Noah (the Noah land surface model driven by Global Land Data Assimilation System)—against two in situ observation networks. SSM/I and GLDAS Noah outperform the other soil moisture products, followed by MERRA2 and ECV COMBINED, and ERA5-Land has a certain degree of uncertainty in evaluating TP surface soil moisture. Analysis of long-term soil moisture characteristics during 1988–2008 shows that annual and seasonal mean soil moisture have similar spatial distributions of soil moisture decreasing from southeast to northwest. Additionally, a significant increasing trend of soil moisture is found in most of the TP region. With a non-linear machine learning method, we quantify the contribution of each climatic variable to warm-season soil moisture. It indicates that precipitation dominates soil moisture changes rather than air temperature. Pixel-wise partial correlation coefficients further show that there are significant positive correlations between precipitation and soil moisture over most of the TP region. The results of this study will help to understand the role of TP soil moisture in land–atmosphere coupling and hydrological cycles under climate change.

The Characterization of the Vertical Distribution of Surface Soil Moisture Using ISMN Multilayer In Situ Data and Their Comparison with SMOS and SMAP Soil Moisture Products

In this paper, we investigated the vertical distribution characteristics of surface soil moisture based on ISMN (International Soil Moisture Network) multilayer in situ data (5, 10, and 20 cm; 2, 4, and 8 in) and performed comparisons between the in situ data and four microwave satellite remote sensing products (SMOS L2, SMOS-IC, SMAP L2, and SMAP L4). The results showed that the mean soil moisture difference between layers can be −0.042~−0.024 (for the centimeter group)/−0.067~−0.044 (for the inch group) m3/m3 in negative terms and 0.020~0.028 (for the centimeter group)/0.036~0.040 (for the inch group) m3/m3 in positive terms. The surface soil moisture was found to have very significant stratification characteristics, and the interlayer difference was close to or beyond the SMOS and SMAP 0.04 m3/m3 nominal retrieval accuracy. Comparisons revealed that the satellite retrievals had a higher correlation with the field measurements of 5 cm/2 in, and SMAP L4 had the smallest difference with the in situ data. The mean difference caused by using 10 cm/4 in and 20 cm/8 in in situ data instead of the 5 cm/2 in data could be about −0.019~−0.018/−0.18~−0.015 m3/m3 and −0.026~−0.023/−0.043~−0.039 m3/m3, respectively, meaning that there would be a potential depth mismatch in the data validation.

Estimation of the soil hydraulic properties and parameter characteristics of Benggang erosion in granite in southern China

AbstractBenggang is an erosional landform that forms in granite hills due to coupled hydraulic and gravity erosion, and its unique development makes it difficult to obtain moisture characteristics through direct means. In this study, the soil moisture characteristics were investigated on the collapsing walls with different weathering degrees of two different Benggang types defined as scoop‐shaped Benggang and strip‐shaped Benggang in the typical Benggang erosion areas of southeastern Guangxi. The soil water characteristic curves (SWCCs) of the collapsing wall were measured by the centrifuge method to explore the moisture transport mechanism of Benggang development. The response of moisture characteristics to the Benggang morphology was not significant, but the soil layer changes in the collapsing wall had a significant impact. The Atterberg limits of the surface layer and laterite layer were significantly higher than those of the transition layer and sandy layer, with a liquid limit (LL) value exceeding 55%, indicating high LL clay. The Van Genuchten (VG), Gardner and Fredlund–Xing (FX) models were used to fit the SWCCs, and the results indicated that the VG model had a high fitting accuracy (R2 > 0.984), followed by the FX model (R2 > 0.943); however, the Gardner model had a low fitting accuracy (R2 > 0.891). With the change in matrix suction, the sandy layer had the maximum water release capacity, followed by the transition layer, and finally the surface layer and laterite layer. The variation in equivalent pore size distributions with soil layers showed that the micropores in the laterite layer of the two Benggangs accounted for 44.91% and 44.38% of the total porosity. The macropores of the sandy layer accounted for 35.40% and 36.84% of the total porosity. Both the unsaturated hydraulic conductivity K(θ) and water diffusivity D(θ) exhibited two abrupt changes with increasing soil volumetric water content, and K(θ) and D(θ) of the lower layers were larger than those of the upper layers. The heterogeneity of soil texture and pore conditions among different collapsing wall soil layers can significantly affect the Benggang water conditions. This study provides a database for further investigation of the Benggang development mechanism.

Response of soil moisture and water use efficiency to straw mulching amount and mulching period in black soil zone of northeast China

To reveal the mechanisms of straw mulching amounts and mulching periods on soil moisture in the black soil zone of Northeast China. Three types of straw mulching (0.4, 0.8, and 1.2 kg/m2) and two mulching periods (sowing stage and three-leaf stage) were set up as interactive experiments, and no straw mulching was used as the control (CK) to analyze the response mechanisms of soil moisture content, water consumption, and water use efficiency to straw mulching periods and mulching amounts at different depths. The results showed that straw mulching improved the moisture storage capacity compared with no straw mulching, and the straw mulching rate of 0.8 kg/m2 at the three-leaf stage improved the moisture storage capacity and reduced the water consumption capacity compared with the straw mulching at the sowing stage, CK and other straw mulching treatments at the three-leaf stage; the crop yield and water use efficiency increased and then decreased with the increase of straw mulching. The increase of crop yield and water use efficiency under the straw mulching treatments at the three-leaf stage was higher than that under the same straw mulching treatments at the sowing stage. The analysis of the dual effects of straw mulching amounts and mulching periods on moisture gain and loss showed that the straw mulching amounts of 0.8 kg/m2 at the three-leaf stage had the best effect on soil moisture characteristics and water use efficiency.

Discrimination and mapping ground surface freeze and thaw states over Northeastern China based on the improved dual-index algorithm

Accurate delineation of spatiotemporal variations in ground surface soil freeze and thaw (F/T) states is essential to appraise many geoscience issues, such as the hydrological circulation and land surface-atmosphere feedbacks. Recently, an Improved Dual-index algorithm (DIA) method was proposed by accounting for the influence of soil moisture variations on the discrimination accuracy with passive microwave remote sensing (RS) data products. Compared with the original DIA, the Improved DIA method has proven to be a more practical approach on surface soil F/T states discrimination. However, the method has only been applied and verified in cold regions of high-altitude (e.g., Tibetan Plateau), it's applicability and effectiveness in the cold areas in mid-high latitudes, where the geographic and climatic conditions are quite different, yet remained to be further explored. The present study investigated the feasibility of using AMSR-E (the Advanced Microwave Scanning Radiometer-EOS) and AMSR2 (the second Advance Microwave Scanning Radiometer) passive microwave RS data products to discriminate the F/T states of the ground surface for a long period from 2002 to 2019 by means of the Improved DIA method over a typical mid-high latitude cold region of Northeastern China. Seasonal variation characteristics of soil moisture in mid-high latitude areas were similar with those in high-altitude areas, even though the spatial heterogeneity of soil water content was significant in different regions. Discriminating surface soil F/T states with the Improved DIA method derived overall discriminating accuracy of about 91.6% in the study area, which demonstrated excellent feasibility of the Improved DIA method in mid-high latitude cold regions. The mapping results shown surface soil F/T cycle in Northeastern China responding to climate change was examined from the perspective of regional average, both the proportion of frozen soil area and frozen days showed significant decreasing trends continuously with differed quite spatially. The discriminating accuracy of the Improved DIA method was found to be lower in plain areas with dense populations and large farmland areas compared to mountainous areas when human activities were not taken into consideration, as quantifying human activities can be challenging. The Improved DIA method has been well verified in both high-altitude and high-latitude regions; it has great potential in global scale research.

Preferential flow influences the temporal stability of soil moisture in a headwater catchment

The dynamics and controls of soil moisture spatial organization are crucial to modeling water flux and land–atmosphere interactions across landscapes. Despite this importance, the control of subsurface preferential flow on the temporal stability of soil moisture spatial organization (TSSS) has not been thoroughly examined. This study monitored daily soil moisture at multiple depths (5–162 cm) for three consecutive years at 33 sites across a forested headwater catchment spanning 7.9 ha. The standard deviation of the relative difference in soil moisture between each monitoring location and the catchment mean (i.e., SDRD, a measure of TSSS) was calculated to quantify the influence of controlling factors on TSSS. Factors considered included soil moisture monitoring frequency, soil and terrain attributes, soil wetness condition, and preferential flow. The analysis was conducted separately for wet and dry seasons. The results indicated that (1) soil moisture monitoring frequency had a considerable impact on TSSS, particularly in valley and swales during dry seasons. As the monitoring frequency increased from 1 day to 28 days, the SDRD values of areas with varied soil–terrain attributes all decreased, suggesting a more stable TSSS. However, this also implied that coarser monitoring frequencies might not capture short-term (e.g., daily) soil moisture processes and associated TSSS dynamics. (2) The relationship between soil–terrain attributes and TSSS was stronger during dry seasons than wet seasons (R2 of 0.597 vs. 0.301, P < 0.05). (3) TSSS was significantly correlated with preferential flow frequency (r = 0.611, P < 0.05) at the study catchment. Including preferential flow frequency as a regression variable improved the explanation and prediction of TSSS. This study confirms the vital role of preferential flow in regulating TSSS in complex terrain. These findings underscore the need for high-frequency monitoring to accurately characterize soil moisture distributions over time and space in similar environments.

Retracted: Spatial-Temporal Variation Characteristics of Soil Moisture and Nutrients Based on Nanomaterials in the Root Zone of Haloxylon ammodendron Seedlings

Retracted: Spatial-Temporal Variation Characteristics of Soil Moisture and Nutrients Based on Nanomaterials in the Root Zone of Haloxylon ammodendron Seedlings

Evaluation of Remote Sensing and Reanalysis Products for Global Soil Moisture Characteristics

Soil moisture (SM) exists at the land-atmosphere interface and serves as a key driving variable that affects global water balance and vegetation growth. Its importance in climate and earth system studies necessitates a comprehensive evaluation and comparison of mainstream global remote sensing/reanalysis SM products. In this study, we conducted a thorough verification of ten global remote sensing/reanalysis SM products: SMAP DCA, SMAP SCA-H, SMAP SCA-V, SMAP-IB, SMOS IC, SMOS L3, LPRM_C1, LPRM_C2, LPRM_X, and ERA5-Land. The verification was based on ground observation data from the International SM Network (ISMN), considering both static factors (such as climate zone, land cover type, and soil type) and dynamic factors (including SM, leaf area index, and land surface temperature). Our goal was to assess the accuracy and applicability of these products. We analyzed the spatial and temporal distribution characteristics of global SM and discussed the vegetation effect on SM products. Additionally, we examined the global high-frequency fluctuations in the SMAP L-VOD product, along with their correlation with the normalized difference vegetation index, leaf area index, and vegetation water content. Our findings revealed that product quality was higher in regions located in tropical and arid zones, closed shrubs, loose rocky soil, and gray soil with low soil moisture, low leaf area index, and high average land surface temperature. Among the evaluated products, SMAP-IB, SMAP DCA, SMAP SCA-H, SMAP SCA-V, and ERA5-Land consistently performed better, demonstrating a good ability to capture the spatial and temporal variations in SM and showing a correlation of approximately 0.60 with ISMN. SMOS IC and SMOS L3 followed in performance, while LPRM_C1, LPRM_C2, and LPRM_X exhibited relatively poor results in SM inversion. These findings serve as a valuable reference for improving satellite/reanalysis SM products and conducting global-scale SM studies.

Strategy for Deriving Sacramento Model Parameters Using Soil Properties to Improve Its Runoff Simulation Performances

Physically-based parameter estimations are essential to improve the simulation performance of a hydrologic model and to produce physically reasonable parameters with spatial consistency. This study proposed a parameter derivation strategy to improve the Sacramento Soil Moisture Accounting (SAC-SMA) model simulation performance based on the publicly accessible Harmonized World Soil Database (HWSD). The HWSD soil properties were used to estimate the soil moisture characteristics, and the HWSD soil texture classifications and International Geosphere-Biosphere Programme (IGBP) land cover types were used to identify the Soil Conservation Service (SCS) runoff curve number (CN). After the soil moisture characteristics and CNs were identified, the major parameters of the SAC-SMA model were derived. The simulation results were evaluated using the Nash efficiency coefficient (NSEC), and Free Search (FS) algorithm was used to further adjust and calibrate the parameters. Compared with the simulation accuracy (NSEC = 0.66~0.88) and parameter transferability (NSEC = 0.22~0.83) obtained for the SAC-SMA model using directly calibrated parameters, the HWSD data-derived parameters allowed the SAC-SMA model to achieve a similar simulation accuracy (NSEC = 0.65~0.86) and a better transferability (NSEC = 0.61~0.85).

Performance analyses of effective rainfall estimation methods for accurate quantification of agricultural water footprint

Water footprint (WF) assessments have become a significant tool for the sustainable management in recent years. Effective rainfall (Peff) is a critical indicator for characterizing soil moisture (green water, WFgreen) and calculating irrigation requirements (blue water, WFblue). However, majority of the water footprint analyses employ empirical or numerical models to predict Peff, and the number of studies for experimental validation of these models are quite insufficient. The main scope of this study is to test the performance of commonly used Peff estimation models in relation to the soil water balance (SWB) of an experimental site. Accordingly, the daily and monthly soil water budget is estimated from a maize field which is characterized as semi-arid land with continental climate (Ankara, Turkey), equipped with moisture sensors. Then, Peff, WFgreen, and WFblue parameters are calculated using FP, US-BR, USDA-SCS, FAO/AGLW, CROPWAT, and SuET methods and compared with SWB method. Employed models were highly variable. CROPWAT and US-BR predictions were the most accurate. In majority of months, the CROPWAT method estimated the Peff with a maximum deviation of 5% from the SWB method. In addition, the CROPWAT method predicted blue WF with an error less than 1%. The widely utilized USDA-SCS approach did not produce expected results. The FAO-AGLW method provided the lowest performance for each parameter. We also find that the errors in estimating Peff in semi-arid conditions cause green and blue WF outputs to be quite less accurate than the dry and humid cases. This study provides one of the most detailed assessments about the impact of effective rainfall on the blue and green WF results with high temporal resolution. The findings of this study are important for the accuracy and performance of the formulae used in Peff estimations and to develop more precise blue and green WF analyses in the future.