Spatial Patterns Of Soil Moisture Research Articles

Changes in the Spatial Patterns of Near‐Surface Soil Moisture and Environmental Controlling Factors Before and After a Landslide

ABSTRACTRain‐induced landslides are common natural disturbances in forested headwaters that can strongly alter the spatial distributions of hydrological and environmental features. Although many studies have reported the impacts of landslides on the spatial patterns of soil moisture or their environmental controls, studies that have used detailed in situ data sets collected at the same location before and after a landslide are lacking. This study investigated the spatial distribution pattern of the near‐surface soil moisture and environmental controls, including topographic, soil and vegetation features, in a headwater catchment using ground‐based measurements with high spatial resolution after a landslide in 2016. The data set was compared to measurements taken at the same location before the landslide to explore whether the landslide altered the amount, spatial distribution, or potential controlling factors of near‐surface soil moisture. The mean soil moisture decreased across the site after the landslide, even under conditions of greater rainfall input than before the landslide. Spatial variation in soil moisture decreased in the high‐disturbance area but increased in the low‐disturbance area, although autocorrelation distances of soil moisture changed little. The relationships between the spatial mean and standard deviation of soil moisture markedly changed from a convex‐upward shape to a convex‐downward shape in the highly disturbed area. This indicates that the spatial mean of soil moisture exhibited its greatest spatial variation under moderate conditions before the landslide, with the lowest spatial variation occurring after the landslide. Most of the same controlling factors (i.e., slope gradient, topographic wetness index, vegetation density, soil porosity and saturated hydraulic conductivity) were explored after the landslide, but their influence levels greatly decreased or even disappeared. Thus, the landslide weakened the spatial connectedness between soil moisture and environmental features, which has not yet been restored 6 years after the landslide. We suggest that the connectedness between hydrological responses and environmental features is crucial for restoration. Their connectedness can serve as an indicator to identify the stage of ecological succession from the disturbances of a landslide.

Precision Turfgrass Irrigation: Capturing Spatial Soil Moisture Patterns with ECa and Drone Data

Precision Turfgrass Irrigation: Capturing Spatial Soil Moisture Patterns with ECa and Drone Data

Combined effects of rainfall‐runoff events and antecedent soil moisture on runoff generation processes in an upland forested headwater area

AbstractIn this study, we investigate the combined effect of different rainfall‐runoff event types and antecedent soil moisture (ASM) on runoff processes in the headwater elementary discharge area of a small forested upland catchment. The study focuses on (i) the relationship between soil moisture thresholds and runoff generation; (ii) the combined effect of ASM and tree vicinity and (iii) the relationship between different rainfall‐runoff event types and different types of runoff (baseflow and stormflow). The results suggest that ASM has a strong impact on local runoff generation processes. Soil water content (35%–36%) threshold exceedance was related to stormflow runoff generation caused by the activation of quick preferential flow paths in the soil during storm events, especially in the upper and the deepest soil layers. At the same time, unexpected non‐linear increases in baseflow runoff ratios were documented during dry, precipitation‐free, periods and when the 31%–34% soil moisture threshold was exceeded, presumably due to the hydrological connection of farther slope areas during these conditions. Multiple stormflow periods, which exhibited the lowest runoff coefficient, were the most significant events in terms of water retention and soil water recharge due to increased vertical hydrological connectivity enabling more rapid transport to deeper soil layers. However, this rainfall type occurred least often over the study period. The important role of forest stands (individual trees) in creating spatial patterns of soil moisture and preferential infiltration paths to deeper soil layers was also confirmed. These results contribute towards a better conceptualisation of hydrological behaviour in elementary headwater discharge areas and highlight the potential dangers associated with expected increases in extreme weather events.

Spatial downscaling of SMAP soil moisture estimation using multiscale geographically weighted regression during SMAPVEX16

Passive microwave remote sensing missions such as Soil Moisture Active Passive (SMAP) are widely used for global soil moisture (SM) estimation. But the low spatial resolution of passive microwave SM products limits their applications at regional and local scales (normally 1–10 km). Using high-resolution auxiliary data to downscale passive microwave SM products is the primary way to obtain high-resolution SM data. However, the existing SM downscaling methods do not entirely account for the scale differences in the impact of various variables on the distribution of SM. To solve this problem, this paper introduced MGWR (Multiscale Geographically Weighted Regression) as a novel way to analyze the scale differences of normalized difference vegetation index (NDVI) and land surface temperature (LST) on the spatial pattern of SM. Accordingly, a new spatial downscaling algorithm for SMAP SM products is proposed and compared with the geographically weighted regression (GWR) algorithm and moving window algorithm (MW). The algorithm was applied to data taken during the SMAP Validation Experiment 2016 (SMAPVEX16), and the downscaled SM evaluated with in situ SM and aircraft observations. These results show that: 1) the SM downscaling conversion function, based on the MGWR, effectively reveals how different surface parameters relate to SM, with LST influencing SM globally and NDVI affecting it locally, 2) compared with GWR and MW, the 1 km SM obtained by the MGWR-based downscaling method showed better spatial agreement with airborne SM in the semiarid region with the ubRMSE decreasing by 0.023 m3/m3 and 0.026 m3/m3 for GWR and MW respectively, 3) when evaluating the 1 km SM with in situ SM in the semiarid region, the SM obtained through MGWR exhibited lower ubRMSE values as compared to both GWR and MW. Specifically, the median ubRMSE values for MGWR, GWR, and MW were 0.043 m3/m3, 0.053 m3/m3, and 0.094 m3/m3, respectively. In conclusion, this study demonstrates that MGWR can effectively improve the accuracy of downscaled SM in semiarid regions.

Seasonal dynamics and spatial patterns of soil moisture in a loess catchment

Abstract. The spatial and seasonal patterns in soil moisture and the processes controlling them in semi-arid landscapes are not well understood. Loess landscapes minimize any confounding effects of variation in soil characteristics and are thus ideal for studying topographic influences on soil moisture in drylands. In this study, volumetric soil moisture was monitored monthly for 5.5 years at 20 cm intervals between the surface and 5 m depth at 89 sites across a small (0.43 km2) catchment on the Chinese Loess Plateau. The median soil moisture was computed for each month and depth for each monitoring site as a measure of the typical soil moisture conditions. Seasonal changes in soil moisture were mainly concentrated in the shallow (0–100 cm) soil, with a clear seasonal separation between wet conditions in October–March and dry conditions in May–July, even though precipitation is highest in July–August. Soil moisture was higher on the northwest-facing slopes due to increased drying from solar radiation on the southeast-facing slopes. This effect of slope aspect was greater between October and March, when the zenith angle of the sun was lower and the aspect-dependent difference in solar radiation reaching the surface was larger. The wetter, northwest-facing slopes were also characterized by larger annual soil moisture storage changes. Soil texture was nearly uniform across both slopes, and soil moisture was not correlated with the topographic wetness index, suggesting that variations in evapotranspiration dominated the spatial pattern of soil moisture in shallow soils under both wet and dry conditions. Water balance calculations indicate that over 90 % of the annual precipitation was seasonally cycled in the soil between 0 and 300 cm, suggesting that only a minor fraction infiltrates to groundwater and becomes streamflow. Our findings may be broadly applicable to loess regions with monsoonal climates and may have practical implications for catchment-scale hydrologic modeling and the design of soil moisture monitoring networks.

Spatial-temporal coupling pattern between irrigation demand and soil moisture dynamics throughout wheat-maize rotation system in the North China Plain

Irrigation water requirement (Iwr) and soil moisture (SM) are two essential metrics that guide scientific irrigation and agricultural water-saving practices. Although they have been extensively studied in the North China Plain (NCP), an exploration into their spatial-temporal coupling pattern remains absent. Here, the spatial-temporal coupling pattern underlying the variations of Iwr and SM across the winter wheat–summer maize rotation system within the NCP was elucidated from a geographic perspective, by developing a geo-statistic framework that integrated the global spatial autocorrelation, spatial hot-spot technique, geographically weighted regression, and the optimal parameters-based geographic detector. From 2000–2019, the Iwr and SM exerted a distinct geospatial heterogeneity. Winter wheat demonstrated a spatially descending gradient in Iwr from north to south, while the pattern in summer maize Iwr varied greatly across periods. Generally, winter wheat had a higher Iwr, while the geospatial pattern of summer maize Iwr displayed a greater variability. Spatial pattern of SM in 0–100 cm soil horizon exhibited an approximately inverse trajectory to that of Iwr, while SM in 100–200 cm horizon demonstrated a spatial staggered pattern. In most cases, there existed a notable negative coupling between Iwr and SM at 0–100 cm depth, but not universal at 100–200 cm. Nonetheless, a pronounced reciprocal enhancement mechanism between SM of two layers was confirmed. Between 2000 and 2019, a geospatial decoupling trend was detected between Iwr and SM throughout the NCP. Despite remaining universality, the pattern of high irrigation demand induced by soil moisture deficit was being weakened. Given these, climate-adaptive water-saving strategies were updated, which provide a new implication for regional agricultural water-saving practice and sustainable production.

Simulation of Hydrological Processes in the Jing River Basin Based on the WEP Model

Inappropriate vegetation reconstruction in the Loess Plateau region has led to a significant increase in regional evapotranspiration and water consumption, further aggravating the shortage of soil water resources in the Loess Plateau region. The Jing River basin is a typical area for vegetation reconstruction in the Loess Plateau region. A thorough understanding of changes in hydrological processes in the Jing River basin is of significant scientific importance for efficient utilization of soil water resources and sustainable vegetation restoration in the region. In this study, the physically based Water and Energy Transfer Processes (WEP) distributed hydrological model was used to simulate key hydrological processes in the Jing River Basin during different periods before and after the implementation of cropland conversion to forest and grassland from 1980 to 2019. The results showed that after the implementation of cropland conversion to forest and grassland from 2000 to 2019, the average runoff volume in the Jing River Basin decreased by 20.91%. The most significant decrease in average runoff occurred in the central and northern parts of the basin, with a maximum reduction of 48.6%. The decrease in runoff in flood season is more obvious. The peak discharge decreased by 24.91%, and the most significant decrease occurred in the northern and central parts of the basin, ranging from 10.3% to 50.2%. The spatial distribution pattern of average soil moisture in the 0–0.8 m soil layer showed more moisture in the south and less in the north, with the minimum value occurring in certain areas in the eastern part of the basin. Overall, the implementation of cropland conversion to forest and grassland led to a certain degree of decrease in soil moisture in the basin. After the implementation of cropland conversion to forest and grassland, reference evapotranspiration fluctuated only in specific areas of the basin with no significant overall change.

A surrogate modeling method for distributed land surface hydrological models based on deep learning

Surrogate modelling methods have been used to estimate spatially distributed parameters of land surface hydrological models due to their computational efficiency. However, traditional surrogate techniques have trouble in modelling high-dimensional input–output maps. In addition, their applications to a distributed model over large spatial and temporal domains are very unlikely to produce consistent spatial pattern and dynamic relationship of model input and output, leading to unbalanced model performance across different spatiotemporal domains. To address these issues, we employ a deep autoregressive neural network to construct an accurate and reliable surrogate system of distributed dynamic model outputs. The network uses a deep convolutional encoder-decoder architecture to take full advantage of the deep convolutional layers in high-dimensional image processing and spatial feature extraction. Moreover, the autoregressive strategy makes the network good at handling dynamic relationship between the time-varying model input and output. We apply this deep learning (DL) network to building a surrogate model of the Variable Infiltration Capacity (VIC) model simulated soil moisture over the Huaihe river basin of China. The results show that the adopted deep autoregressive neural network can provide an accurate surrogate model of the high-dimensional dynamic relationship between 15 input fields and 1 output field, each covering 408 grids, over multiple years. This surrogate model significantly outperforms the popular long short-term memory (LSTM) model, achieving an average mean squared error (MSE) of 0.26 and an average R2 of 0.76 using the test datasets. The surrogate's MSE and R2 performance represents an average improvement of 43% and 33%, respectively, compared to the LSTM model. In addition, it shows better spatial performance than the LSTM model, demonstrating its superior performance in approximating spatial patterns of VIC simulated soil moisture. The accuracy of this surrogate method and its ability to handle high-dimensional data is promising for advancing parameter uncertainty quantification of distributed land surface hydrological models, which usually have high dimensionality due to the large number of variables involved, including distributed parameters, input forcing variables, and output variables.

Automated analysis of snowmelt from Sentinel-2 imagery to determine variable rate irrigation zones in the American Mountain West

Variable rate irrigation (VRI) is used to save water whilst maintaining crop yields in semiarid regions. A key problem is to be able to inexpensively determine spatial patterns in soil moisture so that VRI zones can be defined. In Southern Idaho, USA, the annual precipitation is low and most fall as winter snow. This research investigates whether snow melt patterns measured using freely available time-series Sentinel 2 imagery from Google Earth Engine can define useful VRI zones for two arable fields (Grace and Rexburg). The normalized difference snow index (NDSI) was computed for each 10 m pixel with snow for all winter images of the fields for 2018–2022. NDSI values were ranked within each image and average ranks were calculated for each month and over several years. The patterns of March NDSI were most similar to patterns in yield and soil moisture observed in previous years. Zones were determined using K-means classification of the mean ranks of March NDSI. Kruskal Wallis H tests showed consistent and significant differences between zones for key soil, plant, and topographic variables. For the Grace site, differences between zones were more consistent in their order of magnitude than VRI zones which were calculated using a labor-intensive method. For the Rexburg site, zones were shown to be better when based on snowmelt data from March 2018 to 2022 rather than just March 2019. It is important to base zones on several years of data because in some years there was no snow observed in the Grace field in March. In locations where the majority of soil moisture comes from snowmelt, basing VRI zones on several years of snowmelt patterns in March is a useful and inexpensive tool for deriving meaningful VRI zones. The code used to automatically extract suitable sentinel images and calculate the NDSI is included so that practitioners can use this approach in other locations.

Variation of soil water stability and dominant influencing factors after sparse forestation in the loess alpine hills of northwest China

Study regionDatong County, located in the loess alpine hills of north-western China. Study focusThis study investigated soil moisture and environmental factors in three catchments (10, 20 and 40 years after afforestation with sparse forests) to examine the influence and dominant factors of afforestation with sparse forests on the stability of soil moisture patterns in the catchment. New hydrological insights for the study regionThe results show that long-term afforestation with sparse forests improved the soil moisture in the catchment. The spatial patterns of soil moisture (SMSP) were highly stable in all three catchments, except for surface (0–20 cm) soil moisture. Afforestation with sparse forests improved the stability of the spatial patterns and the fitting of representative locations (R2:0.818–0.878). In the early and middle afforestation periods, soil moisture was mainly influenced by plant indices such as fine root biomass, shrub grass biomass and leaf area index, whereas in the middle and late periods, the temporal stability of soil moisture gradually became more dependent on soil indices such as the clay content and soil organic carbon. This study provides a scientific basis for improving the understanding of soil hydrological processes in areas afforested with sparse forests and for optimising the selection of sample sites in the loess alpine hilly region.

Spatio-Temporal Heterogeneity of Soil Moisture on Shrub–Grass Hillslope in Karst Region

Influenced by the topography, the spatial variation of soil thickness on karst slopes is very large, and accordingly the spatial variation of soil moisture is also large. Therefore, analyzing the spatial heterogeneity of soil moisture on hillslopes is important for maintaining ecosystem stability. Combining geostatistical methods and GIS technology, the spatial variability and distribution pattern of soil moisture and the influencing factors of spatial variation and surface soil moisture (0–7 cm) on a typical karst shrub–grass hillslope were analyzed. The results showed that the mean soil moisture and coefficient of variation (CV) ranged between 25.7–42.6% and 10.3–20.9%, respectively, showing a moderate variation. The soil moisture presented a moderate or strong spatial autocorrelation in the sampling scale. The occurrence of rainfall events can exert a great influence on reducing the spatial heterogeneity of soil moisture. The spatial distribution pattern of soil moisture showed roughly plaque or stripe distribution. When soil moisture was much lower, the patch space fragmentation of soil moisture was higher. The soil moisture was higher in the low and middle parts of the plot. We can conclude that factors such as topography, vegetation, and weather conditions will exert a significant effect on soil moisture spatial variability. Areas with lower slope and higher vegetation coverage were more conducive to the retention of soil moisture.

High-Resolution Quantitative Retrieval of Soil Moisture Based on Multisource Data Fusion with Random Forests: A Case Study in the Zoige Region of the Tibetan Plateau

Accurate high-resolution soil moisture mapping is critical for surface studies as well as climate change research. Currently, regional soil moisture retrieval primarily focuses on a spatial resolution of 1 km, which is not able to provide effective information for environmental science research and agricultural water resource management. In this study, we developed a quantitative retrieval framework for high-resolution (250 m) regional soil moisture inversion based on machine learning, multisource data fusion, and in situ measurement data. Specifically, we used various data sources, including the normalized vegetation index, surface temperature, surface albedo, soil properties data, precipitation data, topographic data, and soil moisture products from passive microwave data assimilation as input parameters. The soil moisture products simulated based on ground model simulation were used as supplementary data of the in situ measurements, together with the measured data from the Maqu Observation Network as the training target value. The study was conducted in the Zoige region of the Tibetan Plateau during the nonfreezing period (May–October) from 2009 to 2018, using random forests for training. The random forest model had good accuracy, with a correlation coefficient of 0.885, a root mean square error of 0.024 m³/m³, and a bias of −0.004. The ground-measured soil moisture exhibited significant fluctuations, while the random forest prediction was more accurate and closely aligned with the field soil moisture compared to the soil moisture products based on ground model simulation. Our method generated results that were smoother, more stable, and with less noise, providing a more detailed spatial pattern of soil moisture. Based on the permutation importance method, we found that topographic factors such as slope and aspect, and soil properties such as silt and sand have significant impacts on soil moisture in the southeastern Tibetan Plateau. This highlights the importance of fine-scale topographic and soil property information for generating high-precision soil moisture data. From the perspective of inter-annual variation, the soil moisture in this area is generally high, showing a slow upward trend, with small spatial differences, and the annual average value fluctuates between 0.3741 m3/m3 and 0.3943 m3/m3. The intra-annual evolution indicates that the monthly mean average soil moisture has a large geographical variation and a small multi-year linear change rate. These findings can provide valuable insights and references for regional soil moisture research.

Assessing Spatial Patterns of Surface Soil Moisture and Vegetation Cover in Batifa, Kurdistan Region-Iraq: Machine Learning Approach

Assessing Spatial Patterns of Surface Soil Moisture and Vegetation Cover in Batifa, Kurdistan Region-Iraq: Machine Learning Approach

Inter-comparison and integration of different soil moisture downscaling methods over the Qinghai-Tibet Plateau

Soil moisture (SM) is a key state variable in the water, energy cycle between atmosphere and land surface but existing passive microwave soil moisture products typically have spatial resolutions of tens of kilometers, which fails to meet the requirements of regional applications. Even though numerous machine/deep learning methods have been applied to downscale SM, few studies have investigated the performance differences of diverse approaches and attempted to integrate various methods to improve downscaling accuracy. Therefore, this study firstly evaluated and inter-compared the downscaling performances of six machine/deep learning approaches and further proposed a hybrid downscaling method based on Bayesian three-cornered hat merging (MATCH). The daily 1 km seamless soil moisture product during 2015–2019 over the Qinghai-Tibet Plateau was then obtained. Evaluation and inter-comparison results revealed that there was obvious performance discrepancy among different downscaling approaches and the gradient boosting decision tree (GBDT) and random forest (RF) were the best two methods, which performed best in the southern and eastern of the plateau, respectively. While the artificial neural network (ANN) outperformed other approaches in the northwestern areas. Validation against in-situ measurements showed that compared with SMAP SM, the MATCH SM exhibited comparable accuracy and lower error with mean R and ubRMSE values of 0.55 and 0.047 m3/m3. The mean R and ubRMSE values for SMAP SM were 0.67 and 0.056 m3/m3, respectively. In addition, the MATCH SM presented great improvement compared with any single downscaled SM data, having the highest correlation and the lowest ubRMSE scores. While, among different methods, the highest R was 0.50 (GBDT) and the lowest ubRMSE was 0.052 m3/m3 (residual network (ResNet)). Besides, the downscaled SM could accurately reflect the temporal variations of soil moisture and precipitation, and effectively represent the spatial patterns of soil moisture. Satisfactory downscaling results were achieved in arid and semi-arid areas whereas a certain degree of overestimation still existed in the eastern and southeastern regions with dense vegetation and high moisture conditions. Such overestimation was inherent from original SMAP SM but was mitigated after downscaling. In conclusion, performance differences existed among diverse downscaling approaches and the developed MATCH method could maximize the potentials of each method and show encouraging downscaling performances.

The identification of soil moisture as a factor of drought in cilutung watershed during indian ocean dipole year

The Indian Ocean Dipole event in 2019 has caused several drought events in the Cilutung Watershed. The information about the spatial and temporal soil moisture distribution along its changes is needed to identify soil moisture values as a drought factor in the Cilutung Watershed. The Landsat 8 OLI-TIRS data used to get the value of soil moisture based on Soil Moisture Index (SMI) method by applying the triangle method between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI). This study analyses the spatial patterns of soil moisture and identifies physical characteristics that are forming in the areas with dry and very dry soil moisture values in the Cilutung Watershed during the dry period in 2019. The results showed that in the Cilutung Watershed during the dry month period in 2019, the Soil Moisture Index value would be higher on steep slopes, with the land-use consisting of shrubs, forests and mixed dryland agriculture, with the type of soils, red-yellow podzolic while the Soil Moisture Index value would be lower on the flat slopes, with the land-use consisting of residential land and open land, with the type of soils grumusol. Based on the K-Means grouping analysis method, the areas with dry and very dry soil moisture values in the Cilutung Watershed tends to have physical characteristics of flat slopes, the type of land use such as the settlement, mixed dryland agriculture, open land and rice fields, with the soil types grumusol, latosol and andosol.

Evaluating satellite-derived soil moisture data for improving the internal consistency of process-based ecohydrological modelling

Soil moisture is a key variable controlling the energy and water balance at the land surface and satellite-derived soil moisture (SDSM) products can therefore be helpful to constrain parameters of (eco)hydrological models. Due to coarse resolution of many SDSM products (>25 km), previous studies mostly focused on large catchments (>500 km2). However, recent developments in soil moisture remote sensing resulted in SDSM products with improved spatio-temporal resolution, which are potentially better suited for model calibration in small to meso-scale catchments. This study therefore aims at assessing the value of three recent SDSM products (SMAPL3E, SCATSAR and ASCAT DIREX SWI) with high spatio-temporal resolution (spatial sampling 0.5–9 km; temporal resolution 1–3 d) for the calibration of the process-based ecohydrological model EcH2O in a 66-km2 catchment in NE Germany.Satellite-derived soil water index (SWI) data agreed well with in-situ soil moisture observations (Pearson correlation coefficient: 0.67–0.86), with a slightly better performance for SMAPL3E than for the other two products (SCATSAR, ASCAT DIREX SWI). Calibrating the ecohydrological model EcH2O to SWI data based on SMAPL3E improved the dynamics of simulated soil moisture (increase of Pearson correlation coefficient by 0.03), while model performance for streamflow slightly deteriorated (decrease of Nash-Sutcliffe efficiency by 0.02). Including volumetric soil moisture data based on in-situ data or SDSM scaled to in-situ data in the model calibration was necessary to further improve the model with respect to absolute soil moisture levels (increase of Nash-Sutcliffe efficiency for scaled soil moisture from 0.23–0.25 to 0.56–0.59). Comparing spatial patterns of simulated soil moisture and SDSM revealed shortcomings of the simulated and the SDSM data. Simulated soil moisture spatial patterns are influenced by artefacts of the interpolated precipitation patterns, while spatial patterns of SDSM seem too strongly damped. Based on this study, the following recommendations may be derived for practitioners who consider including soil moisture data for model calibration: 1) integrating SDSM in model calibration is beneficial for improving model internal consistency; 2) where available, in-situ soil moisture data should be included in order to better constrain simulated absolute soil moisture levels; 3) at the scale of small to meso-scale catchments (∼100 km2), the temporal dynamics of the SDSM products evaluated in this study are likely more helpful for model calibration than their spatial patterns.

Contrasting effects of native and exotic vegetation on soil infiltrability in the Sonoran Desert

Invasion by exotic grasses is transforming drylands across the planet, but the ecohydrological feedbacks of such invasions are not fully understood. For example, in the Sonoran Desert, previous studies have shown that buffelgrass (Cenchrus ciliaris) alters the spatial patterns of soil moisture, leading researchers to hypothesize that such alterations are related to the plants' effects on soil infiltrability. To evaluate this hypothesis, we compared field-saturated hydraulic conductivity (Kfs) in a native shrubland with that in a neighboring savanna extensively dominated by exotic buffelgrass. We measured Kfs during the dormant and growing seasons in both canopy and intercanopy zones. We found that Kfs was generally lower during the dormant season than during the growing season. There were no significant differences between sites during the dormant season, and at both sites, Kfs was 6–7 times higher under shrubs than in the intercanopies. During the growing season, Kfs for the exotic intercanopy was comparable to that for shrub cluster edges (140 mm h−1) and was more than twice that for the native intercanopy. Both shrubs and buffelgrass improved Kfs by reducing soil bulk density (thus increasing porosity). Additionally, surface roughness in the exotic intercanopy was nearly 3 times higher than in the native intercanopy. The combination of greater surface roughness and higher infiltration rates during the growing season most likely alters hydrological connectivity in savannas invaded by exotic grasses such as buffelgrass. By capturing portions of the runoff generated in the intercanopy, these grasses reduce runon into shrub patches, with potentially substantial impacts on native vegetation dynamics and stability.

Space-time dynamics of soil moisture and groundwater in an agriculture-dominated critical zone observatory (CZO) in the Ganga basin, India

Space-time variability of soil moisture (SM) and ground water plays a fundamental role in shaping hydrology of terrestrial ecosystem, best represented as the Critical Zone (CZ), which extends from top of vegetation canopy to the bottom of groundwater table. In several parts of the world, a network of instrumented sites, known as Critical Zone Observatories (CZOs), have been set up to understand the hydrodynamics of soil–water system in particular reference to natural and anthropogenic forcings. Here, we employed the empirical orthogonal function (EOF), random combination, and temporal stability approach to understand the in-situ space–time dynamics of SM and depth to groundwater table (DTGT) over an agriculture-dominated CZO in the Ganga basin. Our results showed that both the components exhibit a constant temporal coefficient of variation, suggesting a consistent seasonal changing pattern. Around 91 % of the observed DTGT spatial variation are explained by first two spatial EOFs while the first five EOFs explain only 67 % of the total SM variability. On an annual basis, the spatial patterns of SM and DTGT are driven by topography and soil texture (% clay) while monsoon rainfall and post-monsoon crop cycle appear to be the leading factors for temporal variability of both components. Furthermore, we have demonstrated that randomly selected four sampling locations and three monitoring wells within the CZO could capture the mean spatial variability of SM (RMSE = 3 % vol/vol) and DTGT (RMSE = 0.7 mgbl) respectively. In addition, temporal stability analysis indicates that four representative sites and a single monitoring well can provide robust catchment mean with an absolute error of ±2 % vol/vol and 0.36 mgbl respectively. Overall, this study provides an insight to the hydrodynamics and controls of SM and groundwater in an agricultural landscape with significant implications for upscaling and efficient water resource management in such regions.

Cross-Evaluation of Soil Moisture Based on the Triple Collocation Method and a Preliminary Application of Quality Control for Station Observations in China

Soil moisture (SM) measurements from ground stations are often after quality control (QC) in the operational system, but the QC flags may not be reliable in some cases when precipitation events or manual watering happen. This study applies the triple collocation (TC) method to conduct a cross-evaluation of SM data from ERA5 reanalysis estimates, ESA-CCI estimates, and ~2000 ground stations across the China domain. The results show that all datasets can capture the spatial pattern of SM in China. TC-based correlation coefficient (CC) and root mean square error (RMSE) show that the station data have worse performance in western and central China. For most stations, TC-based CC is between 0.6~0.9, and TC-based RMSE is between 0.01~0.06 m3/m3. In addition, TC-based metrics show good agreement with the CC between precipitation and SM, indicating that these metrics can reflect the quality of station data. We further selected typical stations (e.g., CC 0.2, RMSE 0.06 m3/m3) to check the quality of the QC procedure. The comparison shows that TC-based metrics can better represent the actual quality for these stations compared to raw QC flags. This study indicates that TC has the potential to detect problematic stations and could be a supplement to traditional QC of station observations.

Calibration and validation of soil water reflectometers

AbstractIn this study, we tested soil water reflectometers represented by the CS65x series soil water reflectometers. The objectives were to (a) quantify the sensing volume of the CS65x sensors, (b) evaluate common univariate and multivariate calibration models, and (c) assess the implications of using uncalibrated sensors. The support volume was determined by analyzing the response of the bulk dielectric permittivity to a changing amount of surrounding media in the radial and axial directions of the sensor. Calibration models were tested using a dataset of ∼300 soil samples spanning nine soil textural classes. A time series of rootzone soil water storage recorded in two contrasting soils and two intensive (n ∼300) spatial surveys of near‐surface soil moisture were used to quantify the implications of using uncalibrated sensors. The electromagnetic signal of CS655 was concentrated within a ∼3.5‐cm radius around the center of the sensor rods. Univariate and multivariate calibration equations reduced the mean absolute error (MAE) in soil moisture observations by ∼40% compared with factory default equations. Uncalibrated sensors did not change the pattern of soil moisture temporal dynamics but resulted in a strong bias in soil water storage in a silty clay loam soil and altered the soil moisture spatial patterns obtained in moderate to wet field conditions in a silty clay soil. The CS655 sensor with the Kargas and Soulis calibration equation (MAE = 0.026 cm3 cm−3) appears to be an acceptable candidate for deployment in soil moisture monitoring networks seeking to avoid site‐specific sensor calibrations.