Temporal Variability Of Soil Moisture Research Articles

Spatial-temporal simulation and prediction of root zone soil moisture based on Hydrus-1D and CNN-LSTM-Attention in the Yutian Oasis, Southern Xinjiang, China

Root zone soil moisture (RZSM) plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth. However, the correlation between RZSM and its associated variables, including surface soil moisture (SSM), often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques. Therefore, this study presents a CNN-LSTM-Attention (CLA) model for predicting RZSM. Owing to the scarcity of soil moisture observation data, the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatiotemporal vertical soil moisture. Meteorological data and MODIS vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model. The results of the CLA model for soil moisture prediction within the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models. This was particularly notable at depth of 80–100 cm, where the coefficient of determination (R²) reached nearly 0.9298. Moreover, the RMSE was reduced by 49% and 57% compared with those of the LSTM and CNN-LSTM models, respectively. This study demonstrates that the integration of physical modeling and deep learning techniques provides a more comprehensive and accurate understanding of spatial and temporal soil moisture variations in the root zone.

Distribution characteristics of soil moisture and temperature under different land use types in the deep profile of loess area in northern Shaanxi, China.

Understanding the effects of different land use modes on the spatial and temporal variations of soil moisture and temperature in the deep profile and revealing the regulatory effects of various vegetation covers on regional water and heat resources can provide a theoretical basis for the optimization of land management and vegetation restoration. Taking the advantage of different land use patterns in the Liudaogou watershed in the northern part of Loess Plateau, we monitored soil moisture content as well as temperature in the 0-1000 cm soil layer in 2022 to analyze the temporal variation and vertical profile distribution characteristics of soil moisture and temperature under four land use modes (woodland, grassland, farmland, and wild grassland). The results showed that soil moisture and temperature distributions varied significantly across different land use types. In the growing season (April-October), total soil water storage in the 0-1000 cm soil layer of the four land use types, in a descending order, was as follows: soybean farmland (1393 mm), wild grassland (1374 mm), Caragana korshinkii forest (1218 mm), and alfalfa grassland (557 mm). Soil moisture of C. korshinkii forest and soybean farmland changed obviously in the 0-300 cm soil layer, and that of wild grassland and alfalfa grassland was in 0-500 and 0-200 cm soil layers, respectively, while soil moisture of deep soil layers fluctuated little. The impact of land use modes on soil temperature was primarily manifested in the 0-200 cm soil layer, and the depth was up to 300 cm. The depth of precipitation infiltration replenishment of the four land use modes was approximately 200 cm. The depth of soil moisture depletion was 200 cm in both C. korshinkii forest and alfalfa grassland, and was 100 cm in soybean farmland and wild grassland. Soil hydrothermal processes in the deep profile varied across vegetation types.

Does afforestation increase soil water buffering? A demonstrator study on soil moisture variability in the Alpine Geroldsbach catchment, Austria

This study employed an operational monitoring network to measure soil moisture and runoff behaviour continuously in the Alpine catchment Geroldsbach-Götzens, Austria. We hypothesize that afforestation can have a positive impact on soil water buffering. To analyse the impact of soil properties and vegetation cover changes on soil water dynamics, four experimental plots were established on grassland and monitoring stations were installed in the forest. The rainfall test site is equipped with an automatic weather station to obtain meteorological observations, and weirs to measure surface runoff of natural occurring precipitation events and artificial rainfall simulations. In the plots, 200 soil moisture sensors were installed at five different depths, aimed to track and visualize infiltration and subsurface flow processes. Another twenty sensors monitored soil moisture at different afforestation stages in the forested part of the catchment. The measurements show that soils covered with young and old-growth forest have a higher and more stable soil moisture content than grassland and soils with a lack of vegetation throughout the seasons. We observed large spatial differences at plot scale, where the spatial variability of soil moisture increases with depth and is highest during convective precipitation. The initial conditions and rainfall characteristics play an important role in infiltration processes and soil water storage. Our rainfall test site demonstrated the challenges of innovative monitoring techniques and that it offers opportunities for more experiments to gather evidence-based data as input for flood models. Overall findings confirm the sponge effect of forest soils and indicate that afforestation as Nature-Based Solution reduces the temporal soil moisture variability, buffering soil water during precipitation events, which can be beneficial for runoff reduction in Alpine catchments.

Effects of vegetation restoration on the temporal variability of soil moisture in the humid karst region of southwest China

Study regiona typical humid karst region of southwestern China. Study focusTemporally stable soil moisture variability (SMV) helps clarify stress on vegetation growth. In the humid karst region of southwest China, large-scale vegetation restoration has affected the soil moisture. However, most studies here have focused on changes in the soil moisture content, and changes in the SMV have not been considered. New hydrological insights for the regionIn this study, changes to the SMV after vegetation restoration were analyzed using two indicators: the soil moisture coefficient of variation(SMCV) and soil moisture memory(SMM). The results showed that the SMCV decreased by an average of 22.3 % and the SMM increased by an average of 17.2 % in the Karst region of southwest China. the SMV had significantly decreased after vegetation restoration, and the overall decrease was larger on 15-day, monthly, and yearly scales and smaller on the weekly scale.When the effects of different vegetation types were considered, SMV greatly decreased in forestland and grassland on the 15-day and monthly scales, in shrubland on the annual scale, and in sparse woodland on the 15-day, monthly, and annual scales. Four geomorphic regions in particular showed large reductions in the SMV: the karst basin, peak cluster depression, peak forest plain, and non-karst region. In addition, changes in SMV after vegetation restoration have a significant positive influence on geochemical and climatic factors and a non-significant negative influence on geological structure factors. Among them, the significant influence factors of CaO content and MgO content are greater than SiO2 content by 0.71 and 0.63. precipitation and temperature are greater than vapor pressure difference by 0.51 and 0.4. Overall, vegetation restoration was found to make the SMV in the humid karst region more stable.

The hydrologic nature of swales uncovers remarkable influence of non-topographic factors on catchment-scale soil moisture variation

Concave hillslopes, also known as swales, play a pivotal role in facilitating the transfer of water and nutrients from hillslopes to riparian zones in headwater catchments by serving as pathways for surface and subsurface flow. Swales have long been recognized as hydrologic wet spots that contribute significantly to catchment runoff. However, long-term in situ observations have rarely confirmed this conventional understanding. In this study, we present a comprehensive analysis of three years of daily soil moisture measurements taken at multiple depths across 33 sites within a forested catchment to delineate key determinants of soil moisture variation at the catchment scale. Contrary to conventional understanding, our findings indicate that swales may represent areas of lower soil moisture, with those located on sunny hillslopes experiencing a higher frequency of dry conditions, amounting to 13.31% of the time during the study period, when compared to other topographical features such as slopes, valleys, and ridges. Topography alone accounted for less than half of the variation of soil moisture across the catchment. Whereas non-topographic factors (such as vegetation, soil porosity, soil temperature, and soil depth) strongly influenced soil moisture distribution in time and space. To better capture the nuances of soil moisture dynamics, we propose an enhancement to the Topographic Wetness Index (TWI) through the incorporation of non-topographic variables, which improves the model's representation of soil moisture variations. Our findings elucidate the complex interactions between topographic and non-topographic factors in shaping soil moisture dynamics at the catchment scale, offering valuable insights for refining process-based hydrological models and contributing to a deeper understanding of the spatial and temporal variability of soil moisture in headwater catchments.

Spatial and Temporal Variability of Soil Moisture and Its Driving Factors in the Northern Agricultural Regions of China

Soil moisture, as an important variable affecting water–heat exchange between land and atmosphere, is an important feedback to climate change. Soil moisture is of great concern in Northern China, where arable land is extensive, but water resources are distributed unevenly and extremely sensitive to climate change. Using measured soil moisture data collected by the China Meteorological Administration from 164 stations during 1980–2021, we explored the drivers of soil moisture variation by analyzing its spatiotemporal variability using linear regression, partial correlation analysis, and geostatistical methods. The results indicated that (1) soil moisture increased from northwest to southeast in Northern China, with the lowest soil moisture in the IM; (2) the overall trend of soil moisture in most regions decreased, especially in the arid northwest and northeast China. However, soil moisture in some regions began to increase gradually in recent years, such as in northwestern Xinjiang and the central-eastern part of IM; and (3) soil moisture in the whole region was negatively correlated with temperature and sunshine duration and positively correlated with precipitation and relative humidity. The results of the study can provide valuable guidance for timely agricultural irrigation and the adjustment of cropping structures, thereby ensuring agricultural production and food security.

Space-time dynamics and potential drivers of soil moisture and soil nutrients variation in a coal mining area of semi-arid, China

The spatial–temporal variability of soil moisture plays a pivotal role in the reconstruction and management of ecosystems in mining areas. However, the actual driving mechanisms behind long-term, continuous mining disturbances remain unclear due to a lack of continuous data. To address this gap, a network of instrumented sites, known as Disturbed Critical Zone Observatories (DCZO), has been established for ecological monitoring in mining areas. In this study, we used Sentinel-1A images of the Dafosi Mine from 2014 to 2022 and one year of continuous DCZO monitoring data. We employed Theil-Sen median trend analysis, empirical orthogonal function (EOF), and structural equation modeling (SEM) to quantitatively explore the factors influencing the variability of soil moisture and soil nutrients under continuous mining disturbances. The results showed: (1) Inter-annual variations in soil moisture differed among coal seams mined in various years, with the steepest decline observed in seams mined in 2021–2022 > 2018–2020 > 2015–2017 > 2013–2014. (2) Mining areas exhibited a significant reduction in Available Nitrogen (AN), Available Phosphorus (AP), and Available Potassium (AK), with decreases of 25.12 %, 25.38 %, and 22.59 %, respectively, compared to non-mining areas. (3) The first two EOFs explained 80.42 % of the observed spatiotemporal soil moisture pattern, 77.69 % of AN, 70.21 % of AP, and 88.32 % of AK, accounting for the total variability in soil moisture and soil nutrients. (4) The contribution rates of mining to soil moisture and nutrient variation were 27.31 % and 13.91 %, with subsidence having the most pronounced negative impact. Additionally, our study revealed that while soil moisture can partially recover through natural processes, soil nutrients require artificial reclamation during mining. This study offers valuable insights for sustainable mining management.

Spatial and Temporal Variations in Soil Moisture for a Tamarisk Stand under Groundwater Control in a Hyper-Arid Region

In hyper-arid regions, soil moisture’s role in ecohydrological processes can differ significantly from that in arid or semi-arid ecosystems. We investigated the spatial–temporal dynamics of soil moisture and its relationship with groundwater depths in a 200 m × 300 m phreatophytic tamarisk stand in the lower basin of the Tarim River, a hyper-arid zone in China. Soil moisture profiles, from the surface to the water table, were derived using drilling and oven-drying techniques. Over a three-year period, the soil moisture at multiple depths was continuously monitored in a specific plot using nine frequency domain reflectometry (FDR) sensors. Our results indicate a correlation between horizontal variations in soil moisture and groundwater depths (GWDs). Nevertheless, anomalies in this correlation were observed. Variations in horizontal soil moisture were strongly influenced by the clay content in the soil, with finer soils retaining more moisture. Despite varying GWDs, soil moisture profiles remained consistent, with no distinct correlation between them. Soil moisture exhibited stability across layers, with noticeable changes only adjacent to the water table. These results imply that in hyper-arid environments, soil texture primarily governs soil moisture distribution. However, the limited spatial and temporal scopes in our dataset, constrained by the region’s inhospitable conditions, necessitate further investigation. Future work should prioritize amalgamating diverse data sources to devise a region-specific soil moisture model for in-depth analysis of hyper-arid regions.

First implementation of a new cross-disciplinary observation strategy for heavy precipitation events from formation to flooding

Heavy Precipitation Events (HPE) are the result of enormous quantities of water vapor being transported to a limited area. HPE rainfall rates and volumes cannot be fully stored on and below the land surface, often leading to floods with short forecast lead times that may cause damage to humans, properties, and infrastructure. Toward an improved scientific understanding of the entire process chain from HPE formation to flooding at the catchment scale, we propose an elaborated event-triggered observation concept. It combines flexible mobile observing systems out of the fields of meteorology, hydrology and geophysics with stationary networks to capture atmospheric transport processes, heterogeneous precipitation patterns, land surface and subsurface storage processes, and runoff dynamics. As part of the Helmholtz Research Infrastructure MOSES (Modular Observation Solutions for Earth Systems), the effectiveness of our observation strategy is illustrated by its initial implementation in the Mueglitz river basin (210 km2), a headwater catchment of the Elbe in the Eastern Ore Mountains with historical and recent extreme flood events. Punctual radiosonde observations combined with continuous microwave radiometer measurements and back trajectory calculations deliver information about the moisture sources, and initiation and development of HPE. X-band radar observations calibrated by ground-based disdrometers and rain gauges deliver precipitation information with high spatial resolution. Runoff measurements in small sub-catchments complement the discharge time series of the operational network of gauging stations. Closing the catchment water balance at the HPE scale, however, is still challenging. While evapotranspiration is of less importance when studying short-term convective HPE, information on the spatial distribution and on temporal variations of soil moisture and total water storage by stationary and roving cosmic ray measurements and by hybrid terrestrial gravimetry offer prospects for improved quantification of the storage term of the water balance equation. Overall, the cross-disciplinary observation strategy presented here opens up new ways toward an integrative and scale-bridging understanding of event dynamics.

From rainfall to runoff: The role of soil moisture in a mountainous catchment

Soil moisture is essential in runoff generation, chemical transport, vegetation growth, and geological hazards like landslides in mountainous regions. However, due to its difficulty of measurement and its significant spatial heterogeneity and temporal variability, the dynamics and influence of soil moisture in the rainfall-runoff process are still not fully understood. In this study, precipitation, runoff, and soil moisture at multiple depths were measured and analyzed in a small, post-seismic mountainous catchment. Our results suggest that the dynamics of soil moisture could be explained by the characteristics of soil. The drastic change and the anisotropy of saturated hydraulic connectivity in the upper soil of the loose deposits in our catchment would lead to soil stratification at depths between 30 cm and 50 cm. The temporal variability of soil moisture was more highly correlated within the soil layers above and beneath the interface. Above the interface, besides vertical infiltration, occurrences of preferential flow were also frequent, transient perched water table may emerge during the wet period, making it a more active runoff contributor than the soil layers below the interface. When the level of rainfall reached the threshold, the correlation between rainfall and runoff became linear. This threshold response was more significant at soil layers above the interface, especially at the site with large topographic gradient. High antecedent soil moisture could skew the partitioning of precipitation into discharge, leading to an earlier flow peak. Our findings provide guidance on observation spot selection and the soil properties needed for the interpretation of future measurements, and may help develop flood early warnings system in small ungauged mountainous catchments with high flood risk.

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.

Soil Moisture Variations From Boreal Forests to the Tundra

AbstractSoil moisture has a profound influence on life on Earth, and this vital water resource varies across space and time. Here, we explored soil moisture variations in boreal forest and tundra environments, where comprehensive soil moisture datasets are scarce. We installed soil moisture sensors up to 14 cm depth at 503 measurement sites within seven study areas across northern Europe. We recorded 6,138,528 measurements to capture soil moisture variations of the snowless season from April to September 2020. We described the spatio‐temporal patterns of soil moisture, and test how these patterns are linked to topography and how these links vary in space and time. We found large spatial variation and often less pronounced temporal variation in soil moisture across the measurement sites within all study areas. We found that throughout the measurement periods both univariate and multivariate models with topographic predictors showed great temporal variation in their performance and in the relative influence of the predictors within and across the areas. We found that the soil moisture‐topography relationships are site‐specific, as the topography‐based models often performed poorly when transferred from one area to another. There was no general solution that would work well in all the study areas when modeling soil moisture variation with topography. This should be carefully considered before applying topographic proxies for soil moisture. Overall, these data and results highlight the strong spatio‐temporal heterogeneity of soil moisture conditions in boreal forest and tundra environments.

Land use in the Prairie Pothole Region influences the soil bacterial community composition and relative abundance of nitrogen cycling genes

The undulating topography of Prairie Pothole Region of North America creates spatial and temporal variability in soil moisture and nutrient levels, affecting microbial community processes and greenhouse gas emissions. By identifying differences in soil bacterial and archaeal community composition and the abundance of nitrogen cycling genes in permanent cover versus annual crop land over two growing seasons (2017 and 2018), we were able to assess the effects of topography and land use on the functional capacity of the soil microbiome. Permanent grassland cover was associated with higher bacterial diversity in upland positions and lower diversity in low-lying depressions. Bacterial community composition was also significantly different between cultivated and permanent cover at all points along the topographic slope, with the largest effects seen in the footslope and backslope positions. Compared to permanent cover, soil from annual cropland had consistently more abundant nitrifiers, including Nitrospira in the toeslope and backslope, and Nitrososphaeraceae in the shoulder and knoll samples while soils from permanent cover had a greater abundance of several Alphaproteobacteria from Rhodospirillales and Hyphomicrobiaceae across multiple upland positions. Upland soils from annual cropland also had consistently higher abundance of both bacterial and archaeal ammonia oxidizing ( amoA) genes and a higher ratio of nirK: nirS genes compared to those from permanent cover. These differences in microbial community composition were associated with higher N2O and CO2 emissions in upland soils in annual cropland; however, there were no differences in GHG emissions between the two systems in low-lying positions.

Hyperspectral response and monitoring study of soil moisture content based on the optimized spectral index

AbstractSoil moisture controls the exchange of energy between the land surface and the atmosphere and significantly affects plant growth and productivity. Hyperspectral (350–2500 nm) monitoring of soil moisture content (SMC) could provide a theoretical basis for the real‐time estimation of spatial and temporal variations in soil moisture. During this study, we have developed SMC prediction models in different soil moisture ranges by using the combination of spectral preprocessing methods and optimized spectral indices. Results indicated that first‐order derivative (FD) preprocessing method can highlight the effective information of the spectra and improve the correlation between spectral reflectance and SMC. The spectral reflectance exhibited a strong correlation with SMC near 1400, 1700, 1900, and 2200 nm. The FD‐NDSI‐W0‐model had the best SMC prediction and applicability (R2 = 0.977, root mean square error = 3.413%, relative percent deviation = 6.171) to predict SMCs. Overall, pretreatment methods, combined with the two‐band random combination to optimize the spectral index to build a prediction model of SMC, could be applied for accurate monitoring of SMC.

Real-Time Forecast of SMAP L3 Soil Moisture Using Spatial–Temporal Deep Learning Model with Data Integration

Soil moisture (SM) has significant impacts on the Earth’s energy and water cycle system. Remote sensing, such as the Soil Moisture Active Passive (SMAP) mission, has delivered valuable estimations of global surface soil moisture. However, it has a 2~3 days revisit time leading to gaps between SMAP areas. To achieve accurate and comprehensive real-time forecast of SM, we propose a spatial–temporal deep learning model based on the Convolutional Gated Recursive Units with Data Integration (DI_ConvGRU) to capture the spatial and temporal variation in SM simultaneously by modeling the influence of adjacent SM values in space and time. Experiments show that the DI_ConvGRU outperforms the ConvGRU with Linear Interpolation (interp_ConvGRU) and the Long Short-Term Memory with Data Integration (DI_LSTM). The best performance (Bias = 0.0132 m3/m3, ubRMSE = 0.022 m3/m3, R = 0.977) has been achieved through the use of spatial–temporal deep learning model and Data Integration term. In comparison with interp_ConvGRU and DI_LSTM, DI_ConvGRU has improved the model performance in 74.88% and 68.99% of the regions according to RMSE, respectively. The predictability of SM depends highly on SM memory characteristics. DI_ConvGRU can provide accurate spatial–temporal forecast for SM with missing data, making them potentially useful for applications such as filling observational gaps in satellite data.

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.

Investigating spatial relationship between vegetation cover and soil moisture using sentinel satellites: a case study of North Gondar Zone, Ethiopia

ABSTRACT Assessing spatial and temporal variations of soil moisture has a great role in different applications, such as natural resource management, flood-risk prediction, irrigation development, hydrology, and climatology. The current study is focusing on investigating the spatial relationship between soil moisture and vegetation cover. The Delta index model was applied using Sentinel-1 images to estimate soil moisture, and the Normalized Difference Vegetation Index was applied using Sentinel-2 images to assess vegetation cover. Besides, regression analysis was conducted to investigate the spatial relationship between soil moisture and vegetation cover. The derived soil moisture ranges from 0% to 48.4811%. Most areas with high elevation had high soil moisture, and low elevation had low soil moisture. In most part of the study area, vegetation cover and soil moisture have a direct relationship. Thus, higher vegetation cover was observed in areas with higher soil moisture; and lower vegetation cover was observed in areas with lower soil moisture.

The Patitapu Soil Moisture Network (PTSMN) dataset and its deployment in New Zealand’s hill country

A significant portion of New Zealand’s beef and sheep pastoral farming systems are located in the East Coast hill country of the North Island. In these landscapes, one of the main drivers of pasture growth is the highly variable soil water content. Decision making about fertiliser applications, stock and feed budgeting are guided by pasture growth models which rely on soil water information. However, due to the difficulties related to conventional soil moisture data collection, soil water dynamics are still poorly understood in hill country. To capture spatial and temporal soil moisture variability on diverse terrain positions, a soil moisture monitoring network was established at the field scale. The Patitapu Soil Moisture Network (PTSMN) is composed of twenty multi-sensor probes, collecting data from four consecutive depths at 0.1 m intervals. The paper describes the PTSMN deployment and the strategic selection of sensor locations through an extensive spatial analysis and discusses the challenges associated with the network deployment in an operating hill country farm. Additionally, we demonstrate the non-normal nature of soil moisture distribution and that Year 1 and Year 2 showed similarly shaped density curves in the 0.2 − 0.4 m soil depths indicating temporal stability. Similar magnitudes of temporal stability suggested the PTMSN is a temporally stable network although stability changed with depth. Results indicated that soil moisture variability decreased with depth and that higher mean soil moisture content was associated with low variability. Descending and ascending transition stages, i.e., drying, and rewetting periods resulted in the highest spatial variability. As PTSMN is a member of the International Soil Moisture Network, the calibrated, quality checked 2-year dataset has been made accessible online. The presented soil moisture dataset is expected to contribute to the research of near-surface and root-zone soil water dynamics and to assist with the establishment of future networks.

From point to field scale - indirect monitoring of soil moisture variations at the DWD test site in Falkenberg

Information regarding the spatial distribution of soil water content is key in many disciplines and applications including soil and atmospheric sciences, hydrology, and agricultural engineering. Thus, within the past decades various experimental methods and strategies have been developed to map spatial variations in soil moisture distribution and to monitor temporal changes. Our study examines the combination of electrical resistivity mapping and point observations of soil moisture to infer the spatial and the temporal variability of soil moisture. Over a period of around two years, we performed field measurements on six days to collect repeated electrical resistivity mapping data for a nine-hectare test site south-east of Berlin, Germany. Permanently installed TDR probes, temporary TDR measurements within permanently installed tubes, and gravimetric measurements using soil samples provided soil moisture data at various selected points. In addition, soil analysis and classification results are available for 132 regularly distributed positions up to depths of 1.2 m. We compare and link three-dimensional resistivity models obtained via data inversion to soil composition and soil moisture as provided by our point data. Both the soil samples and the resistivity models indicate a two-layer medium characterized by a sandy top layer with varying thickness and a loamy bottom soil. For all six field campaigns, we observe similar resistivity patterns reflecting the temporally stable influence of soil texture. While the overall patterns are stable, the range of resistivity values changes with soil moisture. Finally, to estimate spatial models of soil moisture, we link our soil moisture and resistivity data using empirical petrophysical models relying on a second order polynomial function. We observe a mean prediction error for soil moisture of +/- 0.034 m3 • m−3 using all observation points while we notice that point-specific models further reduce the error. Thus, we conclude that our experimental and data analysis strategies represent a reliable approach to establish site-specific models and to estimate three-dimensional moisture distribution including temporal variations.

Multi-depth evolution characteristics of soil moisture over the Tibetan Plateau in the past 70 years using reanalysis products

Soil moisture (SM) is a crucial component of the hydrological cycle. Both the spatial–temporal distribution and the variation characteristics of SM are effective indicators of regional land surface water resource homogeneity and heterogeneity. With consideration of the crucial role of the Tibetan Plateau (TP) in the hydrological process in Asia, this study investigated the fluctuation of multi-depth soil moisture across the TP during 1950–2020 on a monthly scale against the background of global warming by using ERA5 reanalysis datasets. The correlation and potential causality between soil moisture and associated driving factors were explored. Our research revealed that the soil moisture across the TP shows a slight wetting trend at 0–100 cm depth for the past 70 years against the background of climate warming and increasing precipitation. Additionally, the wetting region (variation trend ≥ 0.005 m3/m3 per decade) had sufficient water supplementation from precipitation and a mild soil temperature increase. By comparison, there is a noteworthy warming tendency and falling precipitation in the sparsely distributed drying region (variation trend ≤ −0.005 m3/m3 per decade). In terms of vertical variation features, the temporal dynamic fluctuation of soil moisture and soil temperature evidently decreases as the depth increases, suggesting high sensitivity of the surface layer soil to atmospheric conditions. Precipitation and snowmelt preliminarily proved to be the dominant drivers causing spatial and temporal variations in soil moisture (occupying over 70% of the TP region), and bidirectional causality (ranging between 15.52% and 50.56%) was found between soil moisture and these two parameters. In summary, this study explored the spatial–temporal fluctuation in the evolutionary characteristics of SM, which is expected to advance our understanding of soil moisture dynamics under the conditions of climate change across the TP.