Near-surface Soil Moisture Research Articles

The Synergistic Effect of the Same Climatic Factors on Water Use Efficiency Varies between Daily and Monthly Scales

The coupled processes of ecosystem carbon and water cycles are usually evaluated using the water use efficiency (WUE), and improving WUE is crucial for maintaining the sustainability of ecosystems. However, it remains unclear whether the WUE in different ecosystem responds synchronously to the synergistic effect of the same climate factors at daily and monthly scales. Therefore, we employed a machine learning-driven factor analysis method and a geographic detector model, and we quantitatively evaluated the individual effects and the synergistic effect of climate factors on the daily mean water use efficiency (WUED) and monthly mean water use efficiency (WUEM) in different ecosystems in China. Our results showed that (1) among the 10 carbon flux monitoring sites in China, WUED and WUEM exhibited the highest positive correlations with the near-surface air humidity and the highest negative correlation with solar radiation. The correlation between WUEM and climate factors was generally greater than that between WUED and climate factors. (2) There were significant differences in the order of importance and degree of impact of the same climate factors on WUED and WUEM in the different ecosystems. Among the 10 carbon flux monitoring sites in China, the near-surface air humidity imposed the greatest influence on the WUED and WUEM changes, followed by the near-surface water vapor pressure. (3) There were significant differences in the synergistic effects of the same climate factors on WUED and WUEM in the different ecosystems. Among the 10 carbon flux monitoring sites in China, the WUED variability was most sensitive to the synergistic effect of solar radiation and photosynthetically active radiation, while the WUEM variability was most sensitive to the synergistic effect of the near-surface air humidity and soil moisture. The research results indicated that synchronous responses of the WUE in very few ecosystems to the same climate factors and their synergistic effect occurred at daily and monthly scales. This finding enhances the understanding of sustainable water resource use and the impact of climate change on water use efficiency, providing crucial insights for improving climate-adaptive ecosystem management and sustainable water resource utilization across different ecosystems.

The characterization, mechanism, predictability, and impacts of the unprecedented 2023 Southeast Asia heatwave

In April and May 2023, Southeast Asia (SEA) encountered an exceptional heatwave. The Continental SEA was hardest hit, where all the countries broke their highest temperature records with measurements exceeding 42 °C, and Thailand set the region’s new record of 49 °C. This study provides a comprehensive analysis of this event by investigating its spatiotemporal evolution, physical mechanisms, forecast performance, return period, and extensive impacts. The enhanced high-pressure influenced by tropical waves, moisture deficiency and strong land-atmosphere coupling are considered as the key drivers to this extreme heatwave event. The ECMWF exhibited limited forecast skills for the reduced soil moisture and failed to capture the land-atmosphere coupling, leading to a severe underestimation of the heatwave’s intensity. Although the return period of this heatwave event is 129 years based on the rarity of temperature records, the combination of near-surface drying and soil moisture deficiency that triggered strong positive land-atmosphere feedback and rapid warming was extremely uncommon, with an occurrence probability of just 0.08%. These analyses underscore the exceptional nature of this unparalleled heatwave event and its underlying physical mechanisms, revealing its broad impacts, including significant health repercussions, a marked increase in wildfires, and diminished agricultural yields.

Near-surface soil hydrothermal response feedbacks landslide activity and mechanism

Surface moisture has recently been reported to be used in regional-scale landslide early warning. Nevertheless, near-surface multi-depth hydrothermal measurements as a hillslope scale are often less concerned and rarely linked to landslide kinematics. In this paper, we selected two neighboring landslides with different deformation mechanisms as case studies. Using in-situ multi-source sensors, we monitored real-time soil temperature and moisture at specific depths within approximately 1.5 m. The measurements span two complete monsoon seasons, representing concurrent dry and wet hydrological extremes. Statistical Pearson correlation analysis was employed to quantify the relationships between landslide activity and environmental variables such as soil temperature and moisture content. The results indicate that the near-surface soil temperatures and moisture contents contribute to a better understanding of the factors controlling landslide activity, in which variations synergistically reflect hydrothermal interaction and potential deformation mechanisms. These soil temperatures and moisture contents at certain depths (specifically at 20, 50, and even 100 cm) show moderate to strong correlations (with Pearson correlation coefficient values ranging from 0.4 to 0.8) with landslide deformation. In cases where discrete daily rainfall data exhibited unsatisfactory correlations due to their data attributes, soil temperature and moisture effectively served as alternative indicators for rainfall inputs, aiding in the analysis. Overall, this work emphasizes the critical influence of soil moisture and temperature on landslide dynamics. This study also highlights the need for comprehensive monitoring and forecasting strategies that consider a wide range of environmental factors to mitigate landslide risks associated with climate change, particularly in the context of intensified extreme weather events.

Relevance of near-surface soil moisture vs. terrestrial water storage for global vegetation functioning

Abstract. Soil water availability is an essential prerequisite for vegetation functioning. Vegetation takes up water from varying soil depths depending on the characteristics of its rooting system and soil moisture availability across depth. The depth of vegetation water uptake is largely unknown across large spatial scales as a consequence of sparse ground measurements. At the same time, emerging satellite-derived observations of vegetation functioning, surface soil moisture, and terrestrial water storage present an opportunity to assess the depth of vegetation water uptake globally. In this study, we characterize vegetation functioning through the near-infrared reflectance of vegetation (NIRv) and compare its relation to (i) near-surface soil moisture from the ESA's Climate Change Initiative (CCI) and (ii) total water storage from the Gravity Recovery and Climate Experiment (GRACE) mission at a monthly timescale during the growing season. The relationships are quantified through partial correlations to mitigate the influence of confounding factors such as energy- and other water-related variables. We find that vegetation functioning is generally more strongly related to near-surface soil moisture, particularly in semi-arid regions and areas with low tree cover. In contrast, in regions with high tree cover and in arid regions, the correlation with terrestrial water storage is comparable to or even higher than that of near-surface soil moisture, indicating that trees can and do make use of their deeper rooting systems to access deeper soil moisture, similar to vegetation in arid regions. At the same time, we note that this comparison is hampered by different noise levels in these satellite data streams. In line with this, an attribution analysis that examines the relative importance of soil water storage for vegetation reveals that they are controlled by (i) water availability influenced by the climate and (ii) vegetation type reflecting adaptation of the ecosystems to local water resources. Next to variations in space, the vegetation water uptake depth also varies in time. During dry periods, the relative importance of terrestrial water storage increases, highlighting the relevance of deeper water resources during rain-scarce periods. Overall, the synergistic exploitation of state-of-the-art satellite data products to disentangle the relevance of near-surface vs. terrestrial water storage for vegetation functioning can inform the representation of vegetation–water interactions in land surface models to support more accurate climate change projections.

Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions

In this study, we develop a machine-learning (ML)-enabled strategy for selecting hillslope-scale ecohydrological monitoring sites within snow-dominated mountainous watersheds, with a particular focus on snow-soil–plant interactions. Data layers rely on spatial data layers from both remote sensing and hydrological model simulations. Specifically, a Landsat-based foresummer drought sensitivity index is used to define the dependency of the annual peak plant productivity on the Palmer drought severity index in the early growing season. Hydrological simulations provide the spatiotemporal dynamics of near-surface soil moisture and snow depth. In this framework, a regression analysis identifies the key hydrological variables relevant to the spatial heterogeneity of drought sensitivity. We then apply unsupervised clustering to these key variables, using the Gaussian mixture model, to group hillslopes into several zones that have divergent relationships regarding soil moisture, snow dynamics, and drought sensitivity. Using the datasets collected in the East River Watershed (Crested Butte, Colorado, United States), results show that drought sensitivity is significantly correlated with model-derived soil moisture and snow-free timing over space and time. The relationship is, however, non-linear, such that the correlation decreases above a threshold elevation and in a heavy snow year due to large snowpacks, lateral flow, and soil storage limitations. Clustering is then able to define the zones that have high or low sensitivity to drought, as well as the mid-elevation regions where sensitivity is associated with the topographic aspect and net potential radiation. In addition, the algorithm identifies the most representative hillslopes with road/trail access within each zone for installing monitoring sites. Our method also aims to significantly increase the use of ML and model-simulation results to guide critical zone and watershed monitoring activities.

A Forward Model and Inversion Algorithm for Near-Surface Soil Moisture Estimation With GNSS Refraction Pattern Technique

A Forward Model and Inversion Algorithm for Near-Surface Soil Moisture Estimation With GNSS Refraction Pattern Technique

Comparison Between Thermal-Optical and L-Band Passive Microwave Soil Moisture Remote Sensing at Farm Scales: Towards UAV-Based Near-Surface Soil Moisture Mapping

Comparison Between Thermal-Optical and L-Band Passive Microwave Soil Moisture Remote Sensing at Farm Scales: Towards UAV-Based Near-Surface Soil Moisture Mapping

A cross-resolution transfer learning approach for soil moisture retrieval from Sentinel-1 using limited training samples

Synthetic Aperture Radar (SAR) data is increasingly popular as a data source for global near-surface soil moisture mapping, but large-scale applications are still challenging due to the complex scattering process and the cumbersome data preprocessing. The emergence of deep learning methods has allowed advances in the remote sensing of large-scale surface parameters, but its application in SAR soil moisture retrieval has suffered from the availability of ground soil moisture measurements. Accordingly, this study proposed a cross-resolution transfer learning framework, with the assumption that sophisticated models for different spatial resolutions share a similar model architecture and trainable parameters. A robust high-resolution model can thus be trained with fewer samples by using coarse models. Accordingly, 25 deep learning models were pre-trained taking ∼387,000 Soil Moisture Active Passive (SMAP) Level-3 9 km enhanced passive soil moisture measurements as the truth, with an average validation RMSE of 0.03 m3/m3. They were then transferred to finer grids of 0.1–1 km using a small number of in-situ samples. A total of ∼190,000 daily soil moisture measurements from the international soil moisture network (ISMN) were used to evaluate the proposed framework in three scenarios. The results show that 1) 5000–6000 random samples are sufficient to achieve a target RMSE of 0.06 m3/m3; 2) training samples from a short period (2 or 4 months for Sentinel-1) of 2021 resulted in an overall RMSE of ∼0.068 m3/m3 in an independent period of 2016–2020; 3) the transfer learning also improved the retrieval accuracy (10–30% in relative) over areas without ground samples used for training but failed to yield an acceptable accuracy over mountainous areas. The promising results from this study confirmed the effectiveness of using “pre-trained models + scenario specific models” for regional to global soil moisture retrieval from Sentinel-1.

Interannual land cover and vegetation variability based on remote sensing data in the HTESSEL land surface model: implementation and effects on simulated water dynamics

Abstract. Vegetation largely controls land surface–atmosphere interactions. Although vegetation is highly dynamic across spatial and temporal scales, most land surface models currently used for reanalyses and near-term climate predictions do not adequately represent these dynamics. This causes deficiencies in the variability of modeled water and energy states and fluxes from the land surface. In this study we evaluated the effects of integrating spatially and temporally varying land cover and vegetation characteristics derived from satellite observations on modeled evaporation and soil moisture in the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model. Specifically, we integrated interannually varying land cover from the European Space Agency Climate Change Initiative and interannually varying leaf area index (LAI) from the Copernicus Global Land Services (CGLS). Additionally, satellite data on the fraction of green vegetation cover (FCover) from CGLS were used to formulate and integrate a spatially and temporally varying effective vegetation cover parameterization. The effects of these three implementations on model evaporation fluxes and soil moisture were analyzed using historical offline (land-only) model experiments at the global scale, and model performances were quantified with global observational products of evaporation (E) and near-surface soil moisture (SMs). The interannually varying land cover consistently altered the evaporation and soil moisture in regions with major land cover changes. The interannually varying LAI considerably improved the correlation of SMs and E with respect to the reference data, with the largest improvements in semiarid regions with predominantly low vegetation during the dry season. These improvements are related to the activation of soil moisture–evaporation feedbacks during vegetation-water-stressed periods with interannually varying LAI in combination with interannually varying effective vegetation cover, defined as an exponential function of LAI. The further improved effective vegetation cover parameterization consistently reduced the errors of model effective vegetation cover, and it regionally improved SMs and E. Overall, our study demonstrated that the enhanced vegetation variability consistently improved the near-surface soil moisture and evaporation variability, but the availability of reliable global observational data remains a limitation for complete understanding of the model response. To further explain the improvements found, we developed an interpretation framework for how the model development activates feedbacks between soil moisture, vegetation, and evaporation during vegetation water stress periods.

On the Physics of High CAPE

Abstract Large values of convective available potential energy (CAPE) are an important ingredient for many severe convective storms, yet there has been comparatively little research on how, physically, such large values arise or why they take on the observed values and climatology. Here we build on recently published observational and theoretical work to construct a simple, one-dimensional coupled soil–atmosphere model of preconvective boundary layer growth, driven by a single diurnal cycle of prescribed net surface radiation. Based on this model and previously published research, we suggest that high CAPE (>∼1000 J kg−1) results when air masses that have been significantly modified by passage over dry, lightly vegetated soils are advected over moist and/or moderately vegetated soils and then exposed to surface solar heating. Several diurnal cycles may be needed to raise the moist static energy of the boundary layer to levels consistent with high CAPE. The production of CAPE and erosion of convective inhibition (CIN) are strongly affected by the potential temperature of the desert-modified air mass, the level of near-surface soil moisture (and root-zone soil moisture if significant vegetation is present), the type of soil, and the characteristics of the vegetation. Consequently, CAPE production and severe convective weather may be significantly affected by regional-scale land-use changes and by climate change. Significance Statement The energy available for severe convective storms depends strongly on the properties of the underlying soil and vegetation and the temperature of air masses formed over dry terrain upstream. This implies that the severity of convective storms can be strongly affected by changes in land use and by climate change.

Environmental controls on observed spatial variability of soil pore water geochemistry in small headwater catchments underlain with permafrost

Abstract. Soil pore water (SPW) chemistry can vary substantially across multiple scales in Arctic permafrost landscapes. The magnitude of these variations and their relationship to scale are critical considerations for understanding current controls on geochemical cycling and for predicting future changes. These aspects are especially important for Arctic change modeling where accurate representation of sub-grid variability may be necessary to predict watershed-scale behaviors. Our research goal is to characterize intra- and inter-watershed soil water geochemical variations at two contrasting locations in the Seward Peninsula of Alaska, USA. We then attempt to identify the key factors controlling concentrations of important pore water solutes in these systems. The SPW geochemistry of 18 locations spanning two small Arctic catchments was examined for spatial variability and its dominant environmental controls. The primary environmental controls considered were vegetation, soil moisture and/or redox condition, water–soil interactions and hydrologic transport, and mineral solubility. The sampling locations varied in terms of vegetation type and canopy height, presence or absence of near-surface permafrost, soil moisture, and hillslope position. Vegetation was found to have a significant impact on SPW NO3- concentrations, associated with the localized presence of nitrogen-fixing alders and mineralization and nitrification of leaf litter from tall willow shrubs. The elevated NO3- concentrations were, however, frequently equipoised by increased microbial denitrification in regions with sufficient moisture to support it. Vegetation also had an observable impact on soil-moisture-sensitive constituents, but the effect was less significant. The redox conditions in both catchments were generally limited by Fe reduction, seemingly well-buffered by a cache of amorphous Fe hydroxides, with the most reducing conditions found at sampling locations with the highest soil moisture content. Non-redox-sensitive cations were affected by a wide variety of water–soil interactions that affect mineral solubility and transport. Identification of the dominant controls on current SPW hydrogeochemistry allows for qualitative prediction of future geochemical trends in small Arctic catchments that are likely to experience warming and permafrost thaw. As source areas for geochemical fluxes to the broader Arctic hydrologic system, geochemical processes occurring in these environments are particularly important to understand and predict with regards to such environmental changes.

Development of a Multiphase Numerical Modeling Approach for Hydromechanical Behavior of Clay Embankments Subject to Weather-Driven Deterioration

Clay embankments used for road, rail, and flood defense infrastructure experience a suite of weather-driven deterioration processes that lead to a progressive loss of hydromechanical performance: micro-scale deformation (e.g., aggregation and desiccation), changes in soil-water retention, loss of strength, and macro-scale deformation. The objective of this study was to develop a numerical modeling approach to simulate the construction and long-term, weather-driven hydromechanical behavior of clay embankments. Subroutines within a numerical modeling package were developed to capture deterioration processes: (1) strength reduction due to wet-dry cycles; (2) bimodality of the near-surface hydraulic behavior; (3) soil-water and soil-gas retentivity functions considering void ratio dependency; and (4) hydraulic and gas conductivity functions considering void ratio dependency. Uniquely, the modeling approach was comprehensively validated using laboratory tests and nine years of field measurements from a full-scale embankment. The modeling approach captured the variation of near-surface soil moisture and matric suction over the monitored period in response to weather cycles. Further, the developed model approach could successfully simulate weather-driven deterioration processes in clay embankments. The model predictions manifested the ability of the modeling approach in capturing deterioration features such as irrecoverable increases in void ratio and hydraulic permeability near surface. The developed and validated numerical modeling approach enables forecasting the long-term performance of clay embankments under a range of projected climate conditions.

Heterogeneity of Surface Heat Exchange of Slopes and Potential Drivers of the Initiation of Thaw Slump, Qinghai-Tibet Plateau

In the mountainous permafrost area, most thaw slumps are distributed in north or northeast-facing shady slope areas. It is commonly known that there is a heterogeneity in permafrost between different slope aspects, but there has been a lack of detailed measured data to quantitatively evaluate their relationships, and in-depth understandings on how the slope aspects are linked to the distribution of thaw slumps. This study examined the heterogenous thermal regime, soil moisture content, and surface radiation at two slope sites with opposing aspects in a warming permafrost region on the Qinghai-Tibet Plateau (QTP). The results indicate that similar air temperatures (Ta) were monitored on the two slopes, but there were significant differences in ground temperature and moisture content in the active layer from 2016 to 2021. The sunny slope exhibited a higher mean annual ground surface temperature (Ts), and over the five years the mean annual temperature at the top of permafrost was 1.3–1.4℃ warmer on the sunny slope than the shady slope. On the contrary, the near-surface soil moisture content was about 10–13% lower on the sunny slope (~22–27%) than the shady slope (~35–38%) during the thawing season (June–September). Radiation data indicate that significantly higher shortwave downward radiation (DR) appeared at the sunny slope site. However, due to the greater surface albedo, the net radiation (Rn) was lower on the sunny slope. Slope aspect also affects the ground ice content due to its influence on ground temperature, freeze-thaw cycles, and soil moisture. Shady slopes have a shallower burial of ice-rich permafrost compared to sunny slopes. The results highlight greatly different near-surface ground thermal conditions at the two slope sites with different aspects in a mountainous permafrost region. This helps identify the slope-related causes of increasing thaw slumps and provides a basis for predicting their future development.

Drivers of accelerated warming in Mediterranean climate-type regions

The near-surface temperature in Mediterranean climate-type regions has increased overall similarly or more rapidly than the global mean rates. Although these regions have comparable climate characteristics and are located at similar latitudes, recent warming acceleration is most pronounced in the Mediterranean Basin. Here, we investigate the contributions of several climate drivers to regional warming anomalies. We consider greenhouse gases, aerosols, solar irradiance, land–atmosphere interactions, and natural climate variability modes. Our results highlight the dominant role of anthropogenic greenhouse gas radiative forcing in all Mediterranean climate-type regions, particularly those in the northern hemisphere. In the Mediterranean Basin, the recent warming acceleration is largely due to the combined effect of declining aerosols and a negative trend in near-surface soil moisture. While land-atmosphere feedbacks are also important in other locations (e.g., California and Southern Africa), this synergy is unique in the Mediterranean Basin. These two regional climate drivers have natural and anthropogenic components of equivalent importance. Such feedbacks are not fully resolved in the current regional climate projections.

Estimating ecosystem evaporation and transpiration using a soil moisture coupled two-source energy balance model across FLUXNET sites

The two-source energy balance model coupled with soil moisture (TSEB-SM) was evaluated against observations from a global set of 57 eddy covariance (EC) sites, part of the FLUXNET2015 dataset. In addition, modeled soil evaporation (E) and transpiration (T) were compared with the values obtained from the Transpiration Estimation Algorithm (TEA) and underlying water use efficiency (uWUE) approaches. The TSEB-SM model framework using near-surface soil moisture improved the agreement to EC-observed sensible and latent heat fluxes, reducing mean absolute percentage error (MAPE) by about 30% and root mean square error (RMSE) by about 44 W/m2 across all sites. The results show that the advantage of the TSEB-SM model, with respect to the original TSEB, becomes more evident as the ratio of actual to potential evapotranspiration (AET/PET) decreases. The E and T produced by TSEB-SM has better correlation with the results of uWUE partitioning than TSEB, especially under low soil water content condition. Likewise, TSEB-SM is superior to TSEB in simulating T when compared with sap flow measurements derived from the SAPFLUXNET database. These results imply that the development and application of TSEB-SM has made significant advances in modeling surface water fluxes, even though uncertainties remain. The approach used in TSEB-SM, driving the model with an extensive remotely sensed parameter set, gives valuable information on water use and provides an alternative to Global Climate Models where complex interactions of ecosystems are parametrized. Thus, TSEB-SM provides a unique insight into the flow of energy and the role of surface fluxes in the global water cycle.

Near-Surface Soil Moisture Characterization in Mississippi’s Highway Slopes Using Machine Learning Methods and UAV-Captured Infrared and Optical Images

Near-surface soil moisture content variation is a major factor in the frequent shallow slope failures observed on Mississippi’s highway slopes built on expansive clay. Soil moisture content variation is monitored generally through borehole sensors in highway embankments and slopes. This point monitoring method lacks spatial resolution, and the sensors are susceptible to premature failure due to wear and tear. In contrast, Unmanned/Uncrewed Aerial Vehicles (UAVs) have higher spatial and temporal resolutions that enable more efficient monitoring of site conditions, including soil moisture variation. The current study focused on developing two methods to predict soil moisture content (θ) using UAV-captured optical and thermal combined with machine learning and statistical modeling. The first method used Red, Green, and Blue (RGB) color values from UAV-captured optical images to predict θ. Support Vector Machine for Regression (SVR), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR) models were trained and evaluated for predicting θ from RGB values. The XGB model and MLR model outperformed the SVR model in predicting soil moisture content from RGB values. The R2 values for the XGB and MLR models were >0.9 for predicting soil moisture when compared to SVR (R2 = 0.25). The Root Mean Square Error (RMSE) for XGB, SVR, and MLR were 0.009, 0.025, and 0.01, respectively, for the test dataset, affirming that XGB was the best-performing model among the three models evaluated, followed by MLR and SVR. The better-performing XGB and MLR models were further validated by predicting soil moisture using unseen input data, and they provided good prediction results. The second method used Diurnal Land Surface Temperature variation (ΔLST) from UAV-captured Thermal Infrared (TIR) images to predict θ. TIR images of vegetation-covered areas and bare ground areas of the highway embankment side slopes were processed to extract ΔLST amplitudes. The underlying relationship between soil surface thermal inertia and moisture content variation was utilized to develop a predictive model. The resulting single-parameter power curve fit model accurately predicted soil moisture from ΔLST, especially in vegetation-covered areas. The power curve fit model was further validated on previously unseen TIR, and it predicted θ with an accuracy of RMSE = 0.0273, indicating good prediction performance. The study was conducted on a field scale and not in a controlled environment, which aids in the generalizability of the developed predictive models.

Observing the recent floods and drought in the Lake Victoria Basin using Earth observations and hydrological anomalies

The Lake Victoria Basin, home of Africa’s largest freshwater lake, experienced extensive floods in 2019–2020 and anomalous drought conditions in early 2022. Both antithetical hydrological events raised concerns for an estimated 35 million people within the basin who are vulnerable to these continually recurrent hydrometeorological extremes. While there is a need for high-resolution spatial and temporal hydrological monitoring, in-situ observations are limited and insufficient within the basin. This study assesses the capability of publicly available Earth observations and models to capture the occurrence of recent hydrological extremes within the Lake Victoria Basin. Spatial and temporal comparisons of the following hydrological variables are conducted within the Lake Victoria Basin from June 2002 – June 2022: precipitation, evapotranspiration, runoff, soil moisture, lake height, and total water storage anomaly (TWSA). Results from the time series and spatial analyses confirm that the anomalous wet and dry events respectively occurred from October 2019 – March 2020 and November 2021 – March 2021. The observations also indicated a quick recovery from the drought event by June 2022. Additionally, a comparison of 9-km soil moisture and 1-km downscaled near-surface soil moisture products improves understanding of wetness spatial variability within the basin. These results were validated using in-situ soil moisture data available through the International Soil Moisture Network, making this the first study to validate the downscaled 1-km soil moil moisture product within the African continent. Methods from this study demonstrate the utility of a diverse array of publicly available Earth observation and model data to improve understanding of recent hydrological anomalies and extreme events within the Lake Victoria Basin. This can be applied to water resources management, particularly in regions lacking sufficient in-situ hydrological data.

Assessing the Potential of Combined SMAP and In-Situ Soil Moisture for Improving Streamflow Forecast

Soil moisture is an essential hydrological variable for a suite of hydrological applications. Its spatio-temporal variability can be estimated using satellite remote sensing (e.g., SMOS and SMAP) and in-situ measurements. However, both have their own strengths and limitations. For example, remote sensing has the strength of maintaining the spatial variability of near-surface soil moisture, while in-situ measurements are accurate and preserve the dynamics range of soil moisture at both surface and larger depths. Hence, this study is aimed at (1) merging the strength of SMAP with in-situ measurements and (2) exploring the effectiveness of merged SMAP/in-situ soil moisture in improving ensemble streamflow forecasts. The conditional merging technique was adopted to merge the SMAP-enhanced soil moisture (9 km) and its downscaled version (1 km) separately with the in-situ soil moisture collected over the au Saumon watershed, a 1025 km2 watershed located in Eastern Canada. The random forest machine learning technique was used for downscaling of the near-surface SMAP-enhanced soil moisture to 1 km resolution, whereas the exponential filter was used for vertical extrapolation of the SMAP near-surface soil moisture. A simple data assimilation technique known as direct insertion was used to update the topsoil layer of a physically-based distributed hydrological model with four soil moisture products: (1) the merged SMAP/in-situ soil moisture at 9 and 1 km resolutions; (2) the original SMAP-enhanced (9 km), (3) downscaled SMAP-enhanced (1 km), and (4) interpolated in-situ surface soil moisture. In addition, the vertically extrapolated merged SMAP/in-situ soil moisture and subsurface (rootzone) in-situ soil moisture were used to update the intermediate layer of the model. Results indicate that downscaling of the SMAP-enhanced soil moisture to 1 km resolution improved the spatial variability of soil moisture while maintaining the spatial pattern of its original counterpart. Similarly, merging of the SMAP with in- situ soil moisture preserved the dynamic range of in-situ soil moisture and maintained the spatial heterogeneity of SMAP soil moisture. Updating of the top layer of the model with the 1 km merged SMAP/in-situ soil moisture improved the ensemble streamflow forecast compared to the model updated with either the SMAP-enhanced or in-situ soil moisture alone. On the other hand, updating the top and intermediate layers of the model with surface and vertically extrapolated SMAP/in-situ soil moisture, respectively, did not further improve the accuracy of the ensemble streamflow forecast. Overall, this study demonstrated the potential of merging the SMAP and in-situ soil moisture for streamflow forecast.

Multi-model based soil moisture simulation approach under contrasting weather conditions

We developed and tested a multi-model based soil moisture simulation approach for improving parameterization scheme and its transferability under contrasting weather conditions. Three popular hydrologic models in combination with three optimization schemes were adapted to consider uncertainties in physical and optimization model structures for estimating near-surface soil moisture dynamics. In order to improve parameterization and its transferability under contrasting weather conditions, a multiple set of weights based on different land surface wetness conditions (wet, normal, and dry) were used using a Multiple Weighting Algorithm (MWA). A Differential Split Sample Testing (DSST) scheme was used to test the transferability of parameterizations in the optimization and physical model domains. Data from three experimental sites (Little Washita, LW13 in Oklahoma, Everglades in Florida and Bondville/Olney sites in Illinois) were used to verify our proposed approach. Our findings indicated that the multi-model outputs were highly influenced both by the physical and optimization model structures. Overall, our approach performed well in the test of transferability under different weather conditions. However, we confirmed that the overfitted parameters (to the in-situ measurements during the calibration period) due to the optimization model structures can undermine the transferability of multi-model approach. Also, our findings indicate that the estimated soil hydraulic properties during the dry years can be limited in representing the soil wetness conditions for the wet years.

Study on Optimal Sampling Analysis of Soil Moisture at Field Scale for Remote Sensing Applications

With the rapid development of soil moisture estimation techniques involving remote sensing technology, the sampling designs used in soil moisture research are very important. To estimate the rational sample number for measuring near-surface soil moisture (0–20 cm), a random combination method was used to study the relationship between the average measured soil moisture contents and the true values at given scales. Compared to classic statistics and stratified sampling, the random combination method easily obtained precision estimates from a small number of samples. Moreover, the random combination method was upscaled to further discuss the influence of the coefficient of variation and study-region scale on the rational sample numbers at different scales (2, 10, 20, 40, 80, and 160 m). The results showed that the rational sample numbers for measuring near-surface soil moisture at the 2, 10, 20, 40, 80, and 160 m scales were 2, 5, 5, 8, 20, and 42, respectively, under the relative error of 10% at the 95% confidence level. The rational sample numbers at different scales were proportional to the coefficient of variation and the regional scale.