Climate variability effects on spatial soil moisture dynamics

Soil moisture (SM) in the topsoil horizon is a key variable in terrestrial ecosystems, regulating water and energy exchange at the interface between the soil and the atmosphere. In forest ecosystems, SM exhibits pronounced spatio-temporal variability within the active root zone as a result of complex interactions among multiple factors including soil properties, topography, climate and vegetation. Formulating broadly reliable statements about spatio-temporal soil moisture characteristics and its effects remains challenging. Existing research is limited and sometimes contradictory regarding when and to what extent controls such as tree species influence topsoil SM variability across space and time.This study investigates spatio-temporal dynamics of topsoil SM and their controls in different forest ecosystems. The objectives are to quantify the spatial variability at the plot scale and its temporal evolution from event to seasonal scale, thereby improving our understanding of SM dynamics between wet and dry states and across different mono- and mixed-species forest stands. We investigate the variables governing spatial soil moisture variability and how they modulate SM patterns over time, with a focus on how ecohydrological processes amplify or mitigate SM variability.SM was recorded in four stands in the ECOSENSE forest in southwestern Germany, using 400 time domain transmissometry sensors (SMT100, Truebner GmbH, Germany) installed at 12 cm depth in a tree-centered design across stands of mixed and pure Douglas fir, Beech and Silver fir. A continuous 2.5-year dataset (2023 – 2025) was analysed using statistical and geostatistical approaches to identify dominating spatial SM patterns during wet and dry periods. Temporal stability was evaluated to determine the pattern persistence. Spatial SM differed significantly among plots during most of the observation period. Mean SM followed a similar annual cycle throughout all plots, with typical maxima in late winter and minima in early fall. Despite comparable soil properties and topography, the pure Beech and Douglas fir plots revealed significant seasonal differences in mean SM. Beech has more prominent autumn wetting, while Douglas fir has stronger spring drying, likely reflecting changes in evapotranspiration dynamics. The individual probability density functions of spatial soil moisture distribution transitioned between wet- or dry-preferential unimodal states and intermediate bimodal states. Across plots, spatial mean SM and the coefficient of variation exhibited an upward-convex relationship: variability was low under dry and wet ( 30% mean SM) and high under intermediate moisture conditions. The geostatistical variogram analyses showed short autocorrelation lengths, and pronounced spatial variability at few meters’ distance. Temporal stability of SM varied across plots with a range of persistently wet and dry spots. Individual locations deviated by up to 50% from temporally averaged SM at the Beech, Douglas fir and mixed plot, and up to 75% for the Silver fir plot.Combined with LiDAR derived canopy structure metrics, micro topographic maps, soil properties, and continuous ecohydrological- and meteorological observation, the presented soil moisture dataset provides a unique framework to investigate jointly modulating factors of soil moisture. It enables detailed analysis of wetting-drying cycles, including seasonal and species-specific differences.

[1] Land surface models of water and energy fluxes can benefit from the characterization of soil moisture variability provided by robust downscaling algorithms over a wide range of climatic settings. In this study, we present the application of a multifractal-based statistical downscaling scheme using 800 m aircraft-derived soil moisture data collected during three field campaigns in contrasting climatic regimes. The disaggregation scheme was tested in a previous work using data of the Southern Great Plains experiment in 1997 (SGP97) in a temperate region in Oklahoma. Here, we explore its capability on different climates by using data from two other campaigns: Soil Moisture Experiment in 2002 (SMEX02), in an agricultural region with subhumid climate in Iowa, and Soil Moisture Experiment in 2004 (SMEX04), conducted in two semiarid areas in Arizona and Sonora (Mexico). We first demonstrate the presence of multifractality in soil moisture fields over the scale range from 0.8 km (aircraft footprint) to 25.6 km (satellite footprint) over most wetness conditions. Next, we identify an empirical regional calibration relation linking model parameters with the spatial mean soil moisture and coarse-scale predictors that account for topography, soil texture, and land cover in each site. The downscaling model shows good performance in a broad range of conditions, except for a few cases where specific physiographic features introduce relevant spatial nonhomogeneity in the soil moisture field. The calibrated downscaling model is then applied to study the relation between spatial variability and mean soil moisture across the different climate settings. In such a way, we explain the diverse shapes presented in previous studies and suggest possible physical explanations for intraregional and interregional differences.

This paper reports on the analysis of a 2.5 year-long time series of ASAR wide swath mode (WSM) observations for characterizing the soil moisture dynamics. The employed ASAR WSM data set consists of 152 VV-polarized scenes acquired in the period between April 2005 and September 2007 over the Naqu river basin located on the Tibetan Plateau. For four different spatial domains, with areas of 30×30 km2, 5×5 km2 and (two domains of) 1×1 km2, the mean backscatter (σo) and the standard deviation (stdev) have been computed for each ASAR acquisition. Comparison of the mean σo values with the stdev values results in a specific triangular distribution of data points for all spatial domains. Analysis of the mean σo and stdev with respect to in-situ soil moisture measurements demonstrates that this triangular shaped distribution can be explained by soil moisture dynamics during monsoon and winter periods. This shows that the relationship between the spatial mean soil moisture and variability is not uniquely defined and may change throughout seasons. Downscaling of coarse resolution soil moisture products should, therefore, be ideally based on additional near real time data sources. In this context, the presented results could form a basis for the development of SAR-based soil moisture downscaling methodologies.

Abstract. Past studies on soil moisture spatial variability have been mainly conducted at catchment scales where soil moisture is often sampled over a short time period; as a result, the observed soil moisture often exhibited smaller dynamic ranges, which prevented the complete revelation of soil moisture spatial variability as a function of mean soil moisture. In this study, spatial statistics (mean, spatial variability and skewness) of in situ soil moisture, modeled and satellite-retrieved soil moisture obtained in a warm season (198 days) were examined over three large climate regions in the US. The study found that spatial moments of in situ measurements strongly depend on climates, with distinct mean, spatial variability and skewness observed in each climate zone. In addition, an upward convex shape, which was revealed in several smaller scale studies, was observed for the relationship between spatial variability of in situ soil moisture and its spatial mean when statistics from dry, intermediate, and wet climates were combined. This upward convex shape was vaguely or partially observable in modeled and satellite-retrieved soil moisture estimates due to their smaller dynamic ranges. Despite different environmental controls on large-scale soil moisture spatial variability, the correlation between spatial variability and mean soil moisture remained similar to that observed at small scales, which is attributed to the boundedness of soil moisture. From the smaller support (effective area or volume represented by a measurement or estimate) to larger ones, soil moisture spatial variability decreased in each climate region. The scale dependency of spatial variability all followed the power law, but data with large supports showed stronger scale dependency than those with smaller supports. The scale dependency of soil moisture variability also varied with climates, which may be linked to the scale dependency of precipitation spatial variability. Influences of environmental controls on soil moisture spatial variability at large scales are discussed. The results of this study should be useful for diagnosing large scale soil moisture estimates and for improving the estimation of land surface processes.

Spatial and temporal soil moisture and drought variability in the Upper Colorado River Basin

Our understanding of short‐ and long‐term dynamics of spatial soil moisture patterns is limited due to measurement constraints. Using new highly detailed data, this research aims to examine seasonal and event‐scale spatial soil moisture dynamics in the topsoil and subsoil of the small spruce‐covered Wüstebach catchment, Germany. To accomplish this, univariate and geo‐statistical analyses were performed for a 1 year long 4‐D data set obtained with the wireless sensor network SoilNet. We found large variations in spatial soil moisture patterns in the topsoil, mostly related to meteorological forcing. In the subsoil, temporal dynamics were diminished due to soil water redistribution processes and root water uptake. Topsoil range generally increased with decreasing soil moisture. The relationship between the spatial standard deviation of the topsoil soil moisture (SDθ) and mean water content ( ) showed a convex shape, as has often been found in humid temperate climate conditions. Observed scatter in topsoil SDθ( ) was explained by seasonal and event‐scale SDθ( ) dynamics, possibly involving hysteresis at both time scales. Clockwise hysteretic SDθ( ) dynamics at the event scale were generated under moderate soil moisture conditions only for intense precipitation that rapidly wetted the topsoil and increased soil moisture variability controlled by spruce throughfall patterns. This hysteretic effect increased with increasing precipitation, reduced root water uptake, and high groundwater level. Intense precipitation on dry topsoil abruptly increased SDθ but only marginally increased mean soil moisture. This was due to different soil rewetting behavior in drier upslope areas (hydrophobicity and preferential flow caused minor topsoil recharge) compared with the moderately wet valley bottom (topsoil water storage), which led to a more spatially organized pattern. This study showed that spatial soil moisture patterns monitored by a wireless sensor network varied with depth, soil moisture content, seasonally, and within single wetting and drying episodes. This was controlled by multiple factors including soil properties, topography, meteorological forcing, vegetation, and groundwater.

Soil moisture is the main limiting factor for crop growth and the sustainable development of oases in arid desert areas. Therefore, the temporal and spatial variation and infiltration laws of oasis soil moisture should be studied. The objective of this study is to reveal the influencing factors of the spatial–temporal variation of layered soil and sediment moisture and infiltration characteristics under irrigation in desert oases. Hydraulic conductivities were measured using the double-ring infiltrometer, while the regional and site soil moistures were measured and calibrated using weighted method and neutron moisture meter. Deterministic spatial interpolation methods, including multiquadric radial basis function, inverse distance weighted, and local polynomial regression isogram, were adopted to map the regional distribution of hydraulic conductivities, spatial soil moistures, and spatial–temporal isogram of the point site soil moistures in Yaoba Oasis, respectively. Results showed that the leading influencing factors of the (1) regional spatial soil moisture were soil and sediment permeability, stream link direction, microclimate, and dewfalls; (2) spatial layered soil and sediment moistures were microclimate and dew condensation; and (3) spatial–temporal variation at the point site profiles were soil texture, water requirement, and preferential flow. Under irrigation, soil moisture increased significantly, in which the maximum increase was 10.8 times the original state, while the recharging depth substantially increased up to 580 cm with the preferential flow. The spatial–temporal variation of the soil moisture under irrigation indicated that the best irrigation frequency should be 15 days per time. Moreover, the infiltration process can be divided into the preferential flow, piston flow, and balanced infiltration stages.

Applying fractal analysis to detect spatio-temporal variability of soil moisture content on two contrasting land use hillslopes

Regional scale spatio-temporal variability of soil moisture and its relationship with meteorological factors over the Korean peninsula

Intraseasonal dynamics of soil moisture variability within a small agricultural maize cropped field

We investigate the effect of changes in daily and interannual variability of temperature and precipitation on yields simulated by the CERES-Wheat model at two locations in the central Great Plains. Changes in variability were effected by adjusting parameters of the Richardson daily weather generator. Two types of changes in precipitation were created: one with both intensity and frequency changed; and another with change only in persistence. In both types mean total monthly precipitation is held constant. Changes in daily (and interannual) variability of temperature result in substantial changes in the mean and variability of simulated wheat yields. With a doubling of temperature variability, large reductions in mean yield and increases in variability of yield result primarily from crop failures due to winter kill at both locations. Reduced temperature variability has little effect. Changes in daily precipitation variability also resulted in substantial changes in mean and variability of yield. Interesting interactions of the precipitation variability changes with the contrasting base climates are found at the two locations. At one site where soil moisture is not limiting, mean yield decreased and variability of yield increased with increasing precipitation variability, whereas mean yields increased at the other location, where soil moisture is limiting. Yield changes were similar for the two different types of precipitation variability change investigated. Compared to an earlier study for the same locations wherein variability changes were effected by altering observed time series, and the focus was on interannual variability, the present results for yield changes are much more substantial. This study demonstrates the importance of taking into account change in daily (and interannual) variability of climate when analyzing the effect of climate change on crop yields.

Assessing the spatial and temporal dynamics of vegetation and soil moisture is essential for understanding ecosystem resilience under climate variability. Oromia, Ethiopia’s largest and most ecologically diverse region, faces recurrent droughts and land degradation that threaten livelihoods and natural resources. This study investigates vegetation dynamics, soil moisture variability, and drought-induced resilience patterns from 2001 to 2023 using integrated remote sensing datasets. Multi-temporal MODIS NDVI (2001–2023) and SMAP soil moisture (2015–2023) data were processed on the Google Earth Engine (GEE) platform. Annual composites were generated, zonal statistics were extracted for each administrative zone, and temporal trends were analyzed using linear regression and coefficients of variation (CV). The Vegetation Condition Index (VCI) was derived from NDVI to classify drought intensity, which was inversely interpreted as ecosystem resilience. Correlation analysis was used to quantify the relationship between NDVI and soil moisture, and final outputs were visualized as GeoTIFF layers for spatial interpretation. Results revealed distinct ecological gradients across Oromia. The western highlands (Ilubabor, Jimma, Kelem Wollega, and West Wollega) exhibited high NDVI, strong soil moisture retention, and high resilience (VCI > 60), reflecting stable vegetation and favorable rainfall. Conversely, the eastern and southern lowlands (Borena, East Shewa, East and West Hararghe) displayed low vegetation productivity, declining soil moisture, and very low resilience (VCI < 40), indicating recurring drought stress. NDVI and soil moisture both peaked between 2018 and 2020 but declined significantly afterward. Correlation analysis showed a strong positive association (R2 = 0.65, p < 0.01) between vegetation greenness and soil moisture, underscoring their ecological interdependence. The VCI-based resilience framework effectively captured spatial drought–resilience gradients across Oromia, offering a robust basis for regional adaptation planning. Policy measures should prioritize restoration and water conservation in low-resilience zones, invest in sustainable land management in moderately resilient areas, and protect high-resilience ecosystems as climate buffers. While the approach demonstrates strong potential for large-scale drought monitoring, the absence of ground validation and socioeconomic indicators limits full ecological interpretation. Future work should integrate ground-based observations and climate projections to enhance drought-resilience modeling accuracy.

Surface soil moisture dynamics was derived using microwave remote sensing, and employed to estimate soil physical and hydraulic properties. The L-band ESTAR radiometer was employed in an airborne campaign over the Little Washita watershed, Oklahoma during June 10-18, 1992. Brightness temperature (TB) data were employed in a soil moisture inversion algorithm which corrected for vegetation and soil effects. Analyses of spatial TB and soil moisture dynamics during the dry-down period revealed a direct relationship between changes in TB, soil moisture and soil texture. Extensive regression analyses were carried out which yielded statistically significant quantitative relationships between ratio of percent sand to percent clay (RSC, a term derived to quantify soil texture) and saturated hydraulic conductivity (Ksat) in terms of change components of TB and surface soil moisture. Validation of results indicated that both RSC and Ksat can be estimated with reasonable accuracy. These findings have potential applications for deriving spatial distributions of RSC and Ksat over large areas.

Chapter 12 - Hydropedology in the Ridge and Valley: Soil Moisture Patterns and Preferential Flow Dynamics in Two Contrasting Landscapes

Abstract. Interannual climatic and hydrologic variability has been substantial during the past decades in many regions. While climate variability and its impacts on precipitation and soil moisture have been studied intensively, less is known on subsequent implications for global food production. In this paper we quantify effects of hydroclimatic variability on global "green" and "blue" water availability and demand in global agriculture, and thus complement former studies that have focused merely on long-term averages. Moreover, we assess some options to overcome chronic or sporadic water scarcity. The analysis is based on historical climate forcing data sets over the period 1977–2006, while demography, diet composition and land use are fixed to reference conditions (year 2000). In doing so, we isolate the effect of interannual hydroclimatic variability from other factors that drive food production. We analyse the potential of food production units (FPUs) to produce a reference diet for their inhabitants (3000 kcal cap−1 day−1, with 80% vegetal food and 20% animal products). We applied the LPJmL vegetation and hydrology model to calculate the variation in green-blue water availability and the water requirements to produce that very diet. An FPU was considered water scarce if its water availability was not sufficient to produce the diet (i.e. assuming food self-sufficiency to estimate dependency on trade from elsewhere). We found that 24% of the world's population lives in chronically water-scarce FPUs (i.e. water is scarce every year), while an additional 19% live under occasional water scarcity (water is scarce in some years). Among these 2.6 billion people altogether, 55% would have to rely on international trade to reach the reference diet, while for 24% domestic trade would be enough. For the remaining 21% of the population exposed to some degree of water scarcity, local food storage and/or intermittent trade would be enough to secure the reference diet over the occasional dry years.