Soil Moisture Variability Research Articles

Temperature and soil moisture manipulation yields evidence of drought-induced pollen limitation in bee-pollinated squash.

Climate change alters environmental conditions in ways that directly and indirectly affect plants. Flowering plants, for example, modify reproductive allocation in response to heat and drought stress, and such changes can in turn affect pollinator visitation and, ultimately, plant reproduction. Although the individual effects of warming and drought on plant reproductive allocation are well known, these factors may interact to influence reproduction. Here, we conducted a fully crossed temperature by irrigation manipulation in squash (Cucurbita pepo) to test how temperature and soil moisture variation affect pollinator-mediated reproduction. To tease apart the direct and indirect effects of temperature and soil moisture, we compared hand-pollinated plants to bee-pollinated plants and restricted bee foraging (i.e., pollen transfer) to one experimental group per day. Temperature and soil-moisture limitation acted independently of one another: warming decreased flower size and increased pollen production, whereas the effects of soil-moisture limitation were uniformly inhibitory. While treatments did not change squash bee (Xenoglossa spp.) behavior, floral visitation by the honey bee (Apis mellifera) increased with temperature in male flowers and decreased with soil moisture in female flowers. Pollen deposition by bees was independent of plant soil moisture, yet reducing soil moisture increased pollen limitation. This result stemmed at least in part from the effects of soil-moisture limitation on pollen viability; seed set declined with increasing deposition of fluorescent pigment (a proxy for pollen) from plants experiencing decreased soil moisture. These findings suggest that the transfer of lower-quality pollen from plants experiencing soil-moisture limitation led to drought-induced pollen limitation. Similar effects may occur in a wide variety of flowering plant species as climate warming and drought increasingly impact animal-pollinated systems.

Interannual Variability of Water and Heat Fluxes in a Woodland Savanna (Cerrado) in Southeastern Brazil: Effects of Severe Drought and Soil Moisture

The Brazilian Cerrado biome is known for its high biodiversity, and the role of groundwater recharge and climate regulation. Anthropogenic influence has harmed the biome, emphasizing the need for science to understand its response to climate and reconcile economic exploration with preservation. Our work aimed to evaluate the seasonal and interannual variability of the surface energy balance in a woodland savanna (Cerrado) ecosystem in southeastern Brazil over a period of 19 years, from 2001 to 2019. Using field micrometeorological measurements, we examined the variation in soil moisture and studied its impact on the temporal pattern of energy fluxes to distinguish the effects during rainy years compared to a severe drought spell. The soil moisture measures used two independent instruments, cosmic ray neutron sensor CRNS, and FDR at different depths. The measures were taken at the Pé de Gigante (PEG) site, in a region of well-defined seasonality with the dry season in winter and a hot/humid season in summer. We gap-filled the energy flux measurements with a calibrated biophysical model (SiB2). The long-term averages for air temperature and precipitation were 22.5 °C and 1309 mm/year, respectively. The net radiation (Rn) was 142 W/m2, the evapotranspiration (ET) and sensible heat flux (H) were 3.4 mm/d and 52 W/m2, respectively. Soil moisture was marked by a pronounced negative anomaly in the 2014 year, which caused an increase in the Bowen ratio and a decrease in Evaporative fraction, that lasted until the following year 2015 during the dry season, despite the severe meteorological drought of 2013/2014 already ending, which was corroborated by the two independent measurements. The results showed the remarkable influence of precipitation and soil moisture on the interannual variability of the energy balance in this Cerrado ecosystem, aiding in understanding how it responds to strong climate disturbances.

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.

Characteristics, Sources, and Mechanisms of Soil Respiration under Simulated Rainfall in a Native Karst Forest in Southwestern China

Rainfall significantly affects soil respiration rates by altering microbial activity and organic matter decomposition. In karst regions, it also impacts carbonate dissolution and precipitation, further influencing soil CO2 flux. Investigating the mechanism of rainfall’s impact on soil respiration is essential for accurately evaluating and predicting changes in terrestrial ecosystems. However, our understanding of the interaction between rainfall and soil respiration in the extensive karst ecosystems of southwestern China remains limited. This study conducted field-based simulated rainfall experiments to examine variations in soil respiration rates and elucidate the associated control mechanisms through stable carbon isotope composition analysis. Simulated rainfall significantly increased the CO2 release via soil respiration. We observed significant differences in the δ13C value of soil-respired CO2 before and after simulated rainfall. Following the rain, the δ13C of soil-respired CO2 was enriched compared to that before the rain. Through isotope data analysis, we found that the increased soil CO2 emissions were primarily driven by heterotrophic respiration, likely stimulated via changes in soil moisture, affecting microbial growth conditions. Furthermore, the variation in soil moisture affected carbonate dissolution and precipitation, potentially increasing the soil CO2 release after rainfall. In conclusion, these findings expand our understanding of rainfall’s effects on soil respiration in the native karst forests of southwestern China, contributing to the prediction of carbon cycling processes in such ecosystems. The data from this study have significant implications for addressing the release of greenhouse gases in efforts to combat climate change.

The role of commercial agriculture in meeting sustainable development goals in South Africa: Evidence from municipal-level total factor productivity analysis

Improving agricultural productivity is critical, both locally and globally, particularly in the pursuit of the Sustainable Development Goals (SDGs) of eradicating poverty (SDG 1), increasing food production for food security (SDG 2), and promoting efficient use of agricultural resources and sustainable farming practices (SDG 12). This study examines and compares total factor productivity efficiency (TFPE) in the South African commercial agricultural sector, along with its drivers, using local municipal-level data. We used the Färe-Primont index to assess municipalities' TFPE levels, considering both traditional (economic) factors (labour, land, and operating expenses) and environmental variables (temperature, rainfall, and soil moisture index). Our TFPE index results are not only lower, but they show more variation across municipalities than the economic TFPE index, which is commonly used in agricultural productivity research. This implies that ignoring environmental variables may bias analysis; therefore, future studies should consider including environmental factors in their analyses. We then used hierarchical clustering to group municipalities with similar TFPE levels and components, followed by fractional regression to identify the drivers of efficiency levels. The cluster analysis results reveal that the best-performing municipalities are in cluster 4, comprising municipalities from the Western Cape Winelands specialising in horticulture production, municipalities near urban areas with better market proximity, those engaged in the global value chain, and those with a high proportion of farmers who own most of their farmland. Furthermore, the fractional regression results show that age, gender, race, market proximity, land use types, and production diversification are significant drivers of TFPE and its components. Our study offers insights into the divergent agricultural TFPE performance among municipalities and the underlying factors causing these disparities. The findings can inform the development of targeted strategies, particularly at the local level, aimed at enhancing agricultural productivity and making significant contributions to achieving the SDGs.

Temporal and Spatial Soil Moisture–Precipitation Coupling Relationships Over the Tibetan Plateau

AbstractSoil moisture can significantly influence weather and climate via land‒atmosphere interactions over the Tibetan Plateau. However, the temporal and spatial preferences of precipitation for soil moisture anomalies and the underlying mechanisms over the plateau have not been determined. Using multiple satellite data sets (including Global Precipitation Measurement precipitation data and Soil Moisture Active Passive and Advanced SCATterometer soil moisture data) and ERA5 reanalysis data, the temporal and spatial soil moisture–precipitation coupling (SMPC) relationships in seven summers during 2015–2021 over the plateau are quantified based on a percentile‐based method. The satellite observations show prevalent positive temporal SMPC across the plateau, indicating that wetter‐than‐normal soil conditions tend to lead to more afternoon precipitation. While ERA5 generally aligns with satellite findings, it underestimates areas with positive temporal SMPC. Both the satellite and ERA5 data show that spatial SMPC relationships are usually statistically insignificant, but a few regions show significant positive relationships, that is, precipitation is more likely to occur over soils wetter than the surrounding soils. Moreover, the satellite observations suggest an inter‐event positive correlation between the temporal and spatial SMPC relationships. ERA5 agrees with the satellite‐based results over the western plateau but shows discrepancies over the eastern plateau. The temporal and spatial variations in soil moisture modulate the partitioning of surface heat fluxes, planetary boundary layer height, and lifting condensation level, promoting moist convection and afternoon precipitation. The findings from this study shed new light on SMPC and have important implications for precipitation forecasting over the plateau.

Recent multispecies tree-growth decline reveals a severe aridity change in Mediterranean Chile

Soil moisture (SM) is a crucial factor in the water cycle, sustaining ecosystems and influencing local climate patterns by regulating the energy balance between the soil and atmosphere. Due to the absence of long-term, in-situ measurements of SM, studies utilizing satellite-based data and tree-ring analysis have become valuable in assessing variations of SM at regional and multi-century scales, as well as determining its effects on tree growth. This information is particularly pertinent in biodiversity hotspots made up of semi-arid ecosystems currently threatened by climate change. In the Mediterranean Chile region (MC; 30°–37° S), an ongoing megadrought since 2010 has resulted in a significant decline in the forest throughout the area. However, the impact of SM on tree growth at a multi-species and regional level remains unexplored. We analyzed a new network of 22 tree-ring width chronologies across the MC to evaluate the main spatiotemporal tree-growth patterns of nine woody species and their correlation with SM, using PCA. We also reconstructed the SM variations over the past four centuries and assessed its connection with large-scale climate forcings. Our results indicate that the primary growth patterns (PC1) explained 27% of the total variance and displayed a significant relationship with SM between 1982–2015 (r = 0.91), accurately reflecting the current megadrought. The tree-ring SM reconstruction covers the period 1616–2018 and shows a strong decrease around the year 2007, revealing an unprecedented recent change in aridity with respect to the last four centuries. The intensity of the South Pacific subtropical anticyclone, which primarily owe their existence to the subsiding branch of the Hadley Cell, appears as the primary climatic mechanism correlated with the reconstruction and the present aridity conditions in MC. The current SM conditions align with anticipated aridity changes in MC, providing a bleak perspective of future regional climate.

Assessment of Long-term spatiotemporal soil moisture variation in the lower Mahanadi River basin: a hydrological modeling based approach

Assessment of Long-term spatiotemporal soil moisture variation in the lower Mahanadi River basin: a hydrological modeling based approach

Effect of strip tillage widths on soil moisture, soil temperature and soil structure in northeast China

Strip tillage is an effective tillage technique, which included the strip seedbed tilled area (ST-IR) and the straw-covered area between the seedbeds (ST-BR). However, soil hydrothermal conditions and soil disturbance varies with strip tillage widths, which might affect crop growth and yields. Therefore, this study explored the effect of strip tillage width on the soil hydrothermal conditions and soil structure of ST-IR and ST-BR, to determine the optimal strip tillage width. A field experiment with three replicates was conducted to analyze soil moisture and soil temperature variation and to clarify the difference of soil structure in the IR and BR. Three strip tillage widths, 20 cm (ST20), 30 cm (ST30), and 40 cm (ST40), were examined in this study. Soil temperature in both the ST-IR and ST-BR increased as the strip tillage width increased, and the average soil temperature of ST30-IR and ST40-IR in the seedling stage was 2.11°C and 2.62°C higher than that of ST20-IR, respectively. Moreover, the daily soil temperature range in both the IR and BR was greater for ST40 than for ST20 and ST30 in the seedling stage; soil temperature differences in other crop growth stages were small. No significant difference in soil temperature was observed between ST30-IR and ST40-IR. Conversely, soil moisture was greater in ST20-IR and ST30-IR than in ST40-IR; soil moisture was also greater in ST30-BR and ST40-BR than in ST20-BR during the monitoring period. Soil moisture in ST40-IR and ST20-BR increased and decreased more rapidly during rainfall events than in the other treatments. Moreover, soil temperature was higher in the ST-IR than in the ST-BR for all 3 strip tillage widths, and the opposite results were observed for soil moisture. In addition, strip tillage widths had no significant differences on soil structure. In conclusion, a 30 cm strip tillage width had higher soil temperature and remained more soil moisture than other strip tillage widths, so this strip tillage width is thus recommended for use by farmers in Northeast China.

Growth, Productivity, Yield Components and Seasonality of Different Genotypes of Forage Clover Lotus corniculatus L. under Varied Soil Moisture Contents.

This study aimed to evaluate the response to water deficit of different ecotypes and a variety of Lotus corniculatus on growth, productivity, and yield components, through seasonal times. A randomized block experimental design in a 2 × 5 factorial arrangement with three replicates was used. The first variation factor was soil moisture contents: field capacity (FC) was 26.5% ± 1.5, and water deficit (WD) was 85% of FC (22.5% ± 1.5); the second variation factor was the ecotypes identified with the codes 255301, 255305, 202700 and 226792 and of the variety Estanzuela Ganador. The best responses in plant cover and weight of accumulated fresh biomass were obtained in the ecotype 202700 under WD, with values of 1649.0 cm2 and 583.7 g plant-1, and 1661.2 cm2 and 740.1 g plant-1 in ecotype 255305 under FC. The leaf clover was the main component of yield during the summer and autumn seasons. Ecotype 226792 was tolerant to low temperatures during the winter season with better leaf development. Ecotype 202700 is the best option for forage clover production when water is limited, and ecotype 255305 when water is not resource-limited, but these preliminary conclusions need to be confirmed in field studies.

The effect of soil moisture content and soil texture on fast in situ pH measurements with two types of robust ion-selective electrodes

Abstract. In situ soil pH measurements with ion-selective electrodes (ISEs) are receiving increasing attention in soil mapping for precision agriculture as they can avoid time-consuming sampling and off-site laboratory work. However, unlike the standard laboratory protocol, in situ pH measurements are carried out at lower and varying soil moisture contents (SMCs), which can have a pronounced effect on the sensor readings. In addition, as the contact with the soil during in situ measurements should be relatively short, effects of soil texture could be expected because texture controls the migration of protons to the electrode interface. This may be exacerbated by the fact that the electrodes used for in situ measurements are made of less sensitive but more robust materials as compared to the standard glass electrode. Therefore, the aim of the present study was to investigate the effect of soil moisture and soil texture on pH measurements using robust antimony and epoxy-body ISEs pressed directly into the soil for 30 s. The SMC was gradually increased from dry conditions to field capacity. A wide range of soil texture classes were included, with sand, silt, and clay contents ranging from 16 % to 91 %, 5 % to 44 %, and 4 % to 65 %, respectively. An exponential model was fitted to the data to quantify the relationship between SMC and pH. The results show that an increase in SMC causes a maximum increase in pH of approximately 1.5 pH units, regardless of the type of pH ISE used. Furthermore, for sandy soil textures, a rather linear relationship between pH and SMC was observed, whereas, with decreasing mean particle diameter (MPD), the model had a pronounced exponential shape, i.e., a greater pH increase at low SMC and a plateau effect at high SMC. With increasing SMC, the pH values asymptotically approached the standard pH measured with a glass electrode in 0.01 M CaCl2 (soil : solution ratio of 1:2.5). Thus, at high SMC, subsequent calibration of the sensor pH values to the standard pH value is negligible, which may be relevant for using the sensor pH data for lime requirement estimates. The pH measurement error decreases exponentially with increasing soil moisture and increases with decreasing MPD. Using a knee point detection, reliable pH values were obtained for SMC > 11 %, irrespective of the pH ISE used. An analysis of the regression coefficients of the fitted exponential model showed that the maximum pH increase also depends on soil texture; i.e., the influence of soil moisture variation on the pH value increases with decreasing MPD. Moreover, the concavity of the exponential curve increases with decreasing MPD.

Enhancing a machine learning model for predicting agricultural drought through feature selection techniques

This study aims to determine the crucial variables for predicting agricultural drought in various climates of Iran by employing feature selection methods. To achieve this, two databases were used, one consisting of ground-based measurements and the other containing six reanalysis products for temperature (T), root zone soil moisture (SM), potential evapotranspiration (PET), and precipitation (P) variables during the 1987–2019 period. The accuracy of the global database data was assessed using statistical criteria in both single- and multi-product approaches for the aforementioned four variables. In addition, five different feature selection methods were employed to select the best single condition indices (SCIs) as input for the support vector regression (SVR) model. The superior multi-products based on time series (SMT) showed increased accuracy for P, T, PET, and SM variables, with an average 47%, 41%, 42%, and 52% reduction in mean absolute error compared to SSP. In hyperarid climate regions, PET condition index was found to have high relative importance with 40% and 36% contributions to SPEI-3 and SPEI-6, respectively. This suggests that PET plays a key role in agricultural drought in hyperarid regions because of very low precipitation. Additionally, the accuracy results of different feature selection methods show that ReliefF outperformed other feature selection methods in agricultural drought modeling. The characteristics of agricultural drought indicate the occurrence of drought in 2017 and 2018 in various climates in Iran, particularly arid and semi-arid climates, with five instances and an average duration of 12 months of drought in humid climates.

Modeling Yield of Irrigated and Rainfed Bean in Central and Southern Sinaloa State, Mexico, Based on Essential Climate Variables

The goal was to model irrigated (IBY) and rainfed (RBY) bean yields in central (Culiacán) and southern (Rosario) Sinaloa state as a function of the essential climate variables soil moisture, temperature, reference evapotranspiration, and precipitation. For Sinaloa, for the period 1982–2013 (October–March), the following were calculated: (a) temperatures, (b) average degree days for the bean, (c) cumulative reference evapotranspiration, and (d) cumulative effective precipitation. For essential climate variables, (e) daily soil moisture obtained from the European Space Agency and (f) IBY and RBY from the Agrifood and Fisheries Information Service were used. Multiple linear regressions were significant for predicting IBY–RBY (dependent variables) as a function of essential climate variables (independent variables). The four models obtained were significantly predictive: IBY–Culiacán (Pearson correlation (PC) = 0.590 > Pearson critical correlation (CPC) = |0.349|), RBY–Culiacán (PC = 0.734 > CPC = |0.349|), IBY–Rosario (PC = 0.621 > CPC = |0.355|), and RBY–Rosario (PC = 0.532 > CPC = |0.349|). Due to the lack of irrigation depth data, many studies only focus on modeling RBY; this study is the first in Sinaloa to predict IBY and RBY based on essential climate variables, contributing to the production of sustainable food.

Evaluation of long-term spatio-temporal soil moisture variation over the lower Mahanadi River Basin of Odisha, India: a case study

Evaluation of long-term spatio-temporal soil moisture variation over the lower Mahanadi River Basin of Odisha, India: a case study

Predicting Soil Organic Matter, Available Nitrogen, Available Phosphorus and Available Potassium in a Black Soil Using a Nearby Hyperspectral Sensor System.

Black soils, which play an important role in agricultural production and food security, are well known for their relatively high content of soil organic matter (SOM). SOM has a significant impact on the sustainability of farmland and provides nutrients for plants. Hyperspectral imaging (HSI) in the visible and near-infrared region has shown the potential to detect soil nutrient levels in the laboratory. However, using portable spectrometers directly in the field remains challenging due to variations in soil moisture (SM). The current study used spectral data captured by a handheld spectrometer outdoors to predict SOM, available nitrogen (AN), available phosphorus (AP) and available potassium (AK) with different SM levels. Partial least squares regression (PLSR) models were established to compare the predictive performance of air-dried soil samples with SMs around 20%, 30% and 40%. The results showed that the model established using dry sample data had the best performance (RMSE = 4.47 g/kg) for the prediction of SOM, followed by AN (RMSE = 20.92 mg/kg) and AK (RMSE = 22.67 mg/kg). The AP was better predicted by the model based on 30% SM (RMSE = 8.04 mg/kg). In general, model performance deteriorated with an increase in SM, except for the case of AP. Feature wavelengths for predicting four kinds of soil properties were recommended based on variable importance in the projection (VIP), which offered useful guidance for the development of portable hyperspectral sensors based on discrete wavebands to reduce cost and save time for on-site data collection.

Comparative analysis of SAOCOM and Sentinel-1 data for surface soil moisture retrieval using a change detection method in a semiarid region (Douro River’s basin, Spain)

The growing interest in low-frequency SAR for soil parameter retrieval has led to the development of new active L-band satellites, that will provide novel surface soil moisture products and retrieval possibilities; however, due to data unavailability so far, limited applications have investigated the use of change detection models using L-band satellite SAR data. Since July 2020, high revisit time, high-resolution acquisitions by the Satélite Argentino de Observación COn Microondas (SAOCOM) Argentinian-Italian constellation have become accessible over Europe. Therefore, this research presents an investigation of the potential of multi-temporal L-band SAOCOM-1 for monitoring soil moisture variations underneath low and sparse agricultural vegetation. Moreover, it proposes a procedure for the mitigation of roughness contribution, by exploiting the entropy parameter derived from the dual-polarimetric decomposition. L-band sensitivity to soil moisture has been jointly evaluated in respect of Sentinel-1 C-band data by (1) comparing the temporal profiles of the backscattering coefficient, γ0, at VV and VH polarization, with the support of decomposition parameters (entropy and ᾱ), NDVI and precipitation data; (2) regression analysis with in situ soil moisture measurements, obtained by the REMEDHUS network in the Douro River basin (Spain); 3) evaluating the soil moisture retrievals obtained at C- and L- band using a change detection method. Finally, the effectiveness of the roughness normalization procedure for SAOCOM data has been validated using in situ data. L-band co-polarized γ0 has proved to be the best configuration for soil moisture inversion, being relatively insensitive to vegetation, as demonstrated by decomposition results and trend interpretation. Overall, regressions detected an R2 22% higher at L-band than C-band, with values up to 0.74 for VV (R̄2=0.32) and up to 0.47 for the VH band (R̄2=0.14). Co-polarized data obtained R2 on average 62.1% and 74.7% higher for SAOCOM and Sentinel-1. The retrieval models show an ubRMSD of 7.1% for SAOCOM data and 8.3% for Sentinel-1. The application of the proposed roughness normalization procedure to SAOCOM led to an ubRMSD of 6.7% improving the retrieved soil moisture trend by 7.9%. This exploratory analysis demonstrated SAOCOM data potential for soil moisture mapping and would serve as a foundation for more advanced retrieval procedures.

Within‐field soil moisture variability and time‐invariant spatial structures of agricultural fields in the US Midwest

AbstractUnderstanding soil moisture variability and estimating high‐resolution soil moisture at subfield to field scales is critical for agricultural research and applications. However, systematic investigation of subfield scale soil moisture variability over cropland is still lacking from both measurement and satellite remote sensing. In this study, we aim to investigate (1) the characteristics of within‐field soil moisture distribution over typical cropland in the US Midwest and (2) the capabilities of satellite remote sensing in capturing the spatiotemporal variabilities of soil moisture at subfield scale. Specifically, we conducted soil moisture field experiments in three typical commercial agricultural fields (∼85 acres per field) in central Illinois, representing typical commercial farmlands in the US Midwest, and compared the soil moisture measurements with satellite remote sensing data from optical and active microwave sensors. In each field, dense soil moisture samples (spaced at 50–60 m) were obtained for two dry down events in May and July 2021, and multiple long‐term soil moisture stations were installed. We found prominent time‐invariant spatial structures of soil moisture at within‐field scales both during the dry down period and over longer time scales, and the stability is minimally affected by plant water use during the growing season. Comparing the field campaign measurements with satellite remote sensing data, we found that surface reflectance of shortwave infrared bands, such as SWIR1 (1610 nm) from Sentinel‐2, can capture relative surface soil moisture patterns at within‐field scales, but their relationships with soil moisture are field specific. These findings and the improved understanding of within‐field soil moisture dynamics could potentially help future research on high‐resolution soil moisture estimation with multi‐source remote sensing data.

Historical Soil Moisture Variability in High‐Latitude Humid Regions: Insights From a Paleoclimate Data‐Model Comparison

AbstractUnderstanding historical soil moisture (SM) variations and their relationship with temperature in high‐latitude humid regions is essential for predicting hot droughts under widespread warming. This paper presents the first‐ever annual‐resolution summer surface SM reconstruction (1736–2006 CE) in Sweden, located in northern Europe (NE). The reconstruction utilizes the paleoclimate proxy, tree‐ring δ18O, which exhibits a strong correlation with reanalysis SM data during 1948–2007 CE (r = −0.67, p < 0.001). In contrast to an unprecedented trend suggested by previous studies, our reconstruction exhibits a wetting trend in NE since the 1960s falling within the historical spectrum of trends. Both reanalysis SM data and our reconstruction show significant negative correlations with temperature at interannual timescales. However, Coupled Model Intercomparison Project Phase 6 climate models failed to capture the significant SM‐temperature relationship during the historical (1850–1947 CE) and observation periods (1948–2006 CE). This proxy‐model comparison represents a critical step toward enhancing our understanding of SM dynamics and the reliability of SM projections in high‐latitude humid regions.

Long-term species-level observations indicate the critical role of soil moisture in regulating China's grassland productivity relative to phenological and climatic factors

As a sensitive indicator of climate change and a key variable in ecosystem surface-atmosphere interaction, vegetation phenology, and the growing season length, as well as climatic factors (i.e., temperature, precipitation, and sunshine duration) are widely recognized as key factors influencing vegetation productivity. Recent studies have highlighted the importance of soil moisture in regulating grassland productivity. However, the relative importance of phenology, climatic factors, and soil moisture to plant species-level productivity across China's grasslands remains poorly understood. Here, we use nearly four decades (1981 to 2018) of in situ species-level observations from 17 stations distributed across grasslands in China to examine the key mechanisms that control grassland productivity. The results reveal that soil moisture is the strongest determinant of the interannual variability in grassland productivity. In contrast, the spring/autumn phenology, the length of vegetation growing season, and climate factors have relatively minor impacts. Generally, annual aboveground biomass increases by 3.9 to 25.3 g∙m2 (dry weight) with a 1 % increase in growing season mean soil moisture across the stations. Specifically, the sensitivity of productivity to moisture in wetter and colder environments (e.g., alpine meadows) is significantly higher than that in drier and warmer environments (e.g., temperate desert steppes). In contrast, the sensitivity to the precipitation of the latter is greater than the former. The effect of soil moisture is the most pronounced during summer. Dominant herb productivity is more sensitive to soil moisture than the others. Moreover, multivariate regression analyses show that the primary climatic factors and their attributions to variations in soil moisture differ among the stations, indicating the interaction between climate and soil moisture is very complex. Our study highlights the interspecific difference in the soil moisture dependence of grassland productivity and provides guidance to climate change impact assessments in grassland ecosystems.

Strong coupling between soil moisture and temperature intensifies warming and humidification on the Tibetan Plateau: Evidence from 200-year tree ring records

In recent times, the warming and humidification of the Tibetan Plateau have become more pronounced. However, research on soil moisture on the Tibetan Plateau has been limited by the late initiation of data acquisition, thereby constraining the study of its driving mechanisms. Hence, there is an urgent need for historical time series to fill this research gap. To address this issue, we analyzed four spruce (Picea likiangensis) chronologies collected in high-altitude regions of the Tibetan Plateau, and we found that soil moisture during the dry season was a critical limiting factor for tree growth. The first principal component of a principal component analysis, accounting for 56.21% of the variance in all chronologies, was then used to reconstruct soil moisture variations from October of the previous year to May of the current year in the southeastern Tibetan Plateau from 1778 to 2020. We found significant relationships between soil moisture changes and the El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Variability (AMV). We also found a strong linkage between dry season soil moisture and summer temperature, suggesting that this relationship contributes to the intensification of warming and humidification on the Tibetan Plateau.