Soil Moisture Persistence Research Articles

Role of Initial Conditions and Meteorological Drought in Soil Moisture Drought Propagation: An Event‐Based Causal Analysis Over South Asia

AbstractThe role of meteorological droughts and initial conditions (land and atmosphere) in soil moisture drought (SMD) propagation are not yet fully understood. This work uses a drought event‐based causal framework to investigate the relative importance of meteorological drought (MD) duration and intensity and initial conditions that result in surface and rootzone SMD, considering their event‐level propagation time (PT) over South Asia. Initially, spatial variability of drought propagation is assessed by the Propagation Ratio (PR) computed based on MD counts that trigger SMD at various lags. PR depicts 2–3 months slower rootzone propagation than at surface. The gradual decrease in PR with increasing regional aridity indicates faster propagation over humid regions. The causal impact of initial conditions and MD parameters on propagating SMD are evaluated using normalized mutual information and a newly proposed normalized conditional mutual information. We found greater importance of triggering MD parameters followed by initial soil moisture condition on propagating SMD. This behavior is more evident for the surface layer propagation at shorter PT. There is a confounding effect of initial atmospheric conditions on drought propagation through initial soil moisture, depicting the significance of land‐atmosphere interactions prior to propagation. In the rootzone propagation, initial soil moisture has a greater influence on propagation, especially at longer PT, indicating the significance of soil moisture persistence. Stronger causal links obtained through the joint influence of MD parameters on SMD suggest the importance of accounting for MD duration and intensity simultaneously, which are not considered in drought index‐based propagation studies.

Temporal covariance of spatial soil moisture variations: A mechanistic error modeling approach

AbstractWhen estimating field‐scale average soil moisture from sensors measuring at fixed positions, spatial variability in soil moisture leads to “measurement errors” of the spatial mean, which may persist over time due to persistent soil moisture patterns resulting in autocorrelated measurement errors. The uncertainty of parameters that are derived from such measurements may be underestimated when they are assumed to be independent. Temporal autocorrelation models assume stationary random errors, but such error models are not necessarily applicable to soil moisture measurements. As an alternative, we propose a mechanistic error model that is based on the spatial variability of the water retention curve and assumes a uniform water potential. We tested whether spatial soil moisture variability and its temporal covariance could be predicted based on (1) mean soil moisture, (2) water retention variability, and (3) (co)variances of the van Genuchten parameters using a first‐order expansion of the retention curve. The proposed models were tested in a numerical and a field experiment. For the field experiment, in situ sensor measurements and water retention curves were obtained in a field plot. Both experiments showed that water retention variability under a uniform water potential is a good predictor for spatial soil moisture variability, and that soil moisture errors are strongly correlated in time and neglecting them would be an incorrect assumption. The temporal error covariance could be predicted as a function of the mean moisture contents at two observation times. Further research is required to assess the impact of these temporal correlations on soil moisture predictions.

Multiproduct Characterization of Surface Soil Moisture Drydowns in the United Kingdom

Abstract The persistence or memory of soil moisture (θ) after rainfall has substantial environmental implications. Much work has been done to study soil moisture drydown for in situ and satellite data separately. In this work, we present a comparison of drydown characteristics across multiple U.K. soil moisture products, including satellite-merged (i.e., TCM), in situ (i.e., COSMOS-UK), hydrological model [i.e., Grid-to-Grid (G2G)], statistical model [i.e., Soil Moisture U.K. (SMUK)], and land surface model (LSM) [i.e., Climate Hydrology and Ecology research Support System (CHESS)] data. The drydown decay time scale (τ) for all gridded products is computed at an unprecedented resolution of 1–2 km, a scale relevant to weather and climate models. While their range of τ differs (except SMUK and CHESS are similar) due to differences such as sensing depths, their spatial patterns are correlated to land cover and soil types. We further analyze the occurrence of drydown events at COSMOS-UK sites. We show that soil moisture drydown regimes exhibit strong seasonal dependencies, whereby the soil dries out quicker in summer than winter. These seasonal dependencies are important to consider during model benchmarking and evaluation. We show that fitted τ based on COSMOS and LSM are well correlated, with a bias of lower τ for COSMOS. Our findings contribute to a growing body of literature to characterize τ, with the aim of developing a method to systematically validate model soil moisture products at a range of scales. Significance Statement While important for many aspects of the environment, the evaluation of modeled soil moisture has remained incredibly challenging. Sensors work at different space and time scales to the models, the definitions of soil moisture vary between applications, and the soil moisture itself is subject to the soil properties while the impact of the soil moisture on evaporation or river flow is more dependent on its variation in time and space than its absolute value. What we need is a method that allows us to compare the important features of soil moisture rather than its value. In this study, we choose to study drydown as a way to capture and compare the behavior of different soil moisture data products.

Estimation of water required to recover from agricultural drought: Perspective from regression and probabilistic analysis methods

Agriculture is one of the sectors most directly and severely affected by droughts. Therefore, information regarding the water required (WR) to recover from agricultural drought and the probability of agricultural drought recovery under different precipitation scenarios is of great importance for drought risk management. In this study, we developed a quantitative evaluation method to estimate the WR of agricultural drought event recovery and a probabilistic drought recovery framework based on the copula function to calculate the probabilities of recovery from different grades of agricultural drought under given precipitation scenarios. The developed method and framework were demonstrated in the Yangtze River Basin (YRB) using the improved soil moisture anomaly percentage index (ISMAPI). The average duration of agricultural drought recovery in the YRB was 28 d. The difference in soil moisture persistence was the main factor affecting the spatial pattern of the grid mean drought recovery characteristics. For individual drought events, precipitation was the most important factor affecting drought recovery, followed by drought development intensity. There is a clear nonlinear relationship between the PCP value (percentage of cumulative precipitation during drought recovery to its corresponding climatological mean) and the drought recovery duration. In most grids, the three-parameter power function can be used to characterize this relationship and further estimate the WR to recover from an agricultural drought event under different expected recovery durations. We estimated the probabilities of extreme and severe agricultural drought recovery under different precipitation scenarios when the expected recovery durations were 10, 20, 30, 40, 50, and 60 d based on the developed probabilistic drought recovery framework. For example, when the cumulative precipitation is no less than its climatological mean within 30 d, the probability of recovering from extreme agricultural drought is 0.59. These findings are important for decision-making in water and drought management.

Spatiotemporal Dynamics of NDVI, Soil Moisture and ENSO in Tropical South America

We evaluated the coupled dynamics of vegetation dynamics (NDVI) and soil moisture (SMOS) at monthly resolution over different regions of tropical South America and the effects of the Eastern Pacific (EP) and the Central Pacific (CP) El Niño–Southern Oscillation (ENSO) events. We used linear Pearson cross-correlation, wavelet and cross wavelet analysis (CWA) and three nonlinear causality methods: ParrCorr, GPDC and PCMCIplus. Results showed that NDVI peaks when SMOS is transitioning from maximum to minimum monthly values, which confirms the role of SMOS in the hydrological dynamics of the Amazonian greening up during the dry season. Linear correlations showed significant positive values when SMOS leads NDVI by 1–3 months. Wavelet analysis evidenced strong 12- and 64-month frequency bands throughout the entire record length, in particular for SMOS, whereas the CWA analyses indicated that both variables exhibit a strong coherency at a wide range of frequency bands from 2 to 32 months. Linear and nonlinear causality measures also showed that ENSO effects are greater on SMOS. Lagged cross-correlations displayed that western (eastern) regions are more associated with the CP (EP), and that the effects of ENSO manifest as a travelling wave over time, from northwest (earlier) to southeast (later) over tropical South America and the Amazon River basin. The ParrCorr and PCMCIplus methods produced the most coherent results, and allowed us to conclude that: (1) the nonlinear temporal persistence (memory) of soil moisture is stronger than that of NDVI; (2) the existence of two-way nonlinear causalities between NDVI and SMOS; (3) diverse causal links between both variables and the ENSO indices: CP (7/12 with ParrCorr; 6/12 with PCMCIplus), and less with EP (5/12 with ParrCorr; 3/12 with PCMCIplus).

The Spatial-Temporal Characteristics of Soil Moisture and Its Persistence over Australia in the Last 20 Years

Persistence is an important feature of soil moisture, which affects many important processes such as land–air interaction and ecohydrological processes. Soil moisture datasets from reanalysis, remote-sensing observations and land surface models have been widely used in various ecohydrological studies, however, due to the complexity of hydrological processes, the essential features of soil moisture such as spatial-temporal characteristics and persistence still need to be further quantified. This study focused on the Australia region and used in situ observation from fourteen International Soil Moisture Network sites to evaluate soil moisture from six gridded products, including satellite remote-sensing records (ESA CCI), output of reanalysis (ERA5-Land) and land surface models (GLDAS and GLEAM). High correlation coefficients between observations and the other soil moisture datasets were gotten. Regional averaged inter-annual variations of soil moisture were relatively large with some dry periods (2002–2010, 2013–2016) and wet periods (2011–2012) indicated by these gridded products. General coherent spatial patterns were found in long-term soil moisture with large differences in the lateral inflow area of the Great Artesian Basin. The coefficient of variation of these soil moisture datasets generally decreased from northwest to southeast, but the enhanced vegetation index coefficient of variation was larger in the southwest corner, northeast (non-coastal areas) and the lateral inflow area. Persistence calculated from various soil moisture datasets had quite large differences compared with measurements. Meanwhile, little coherence was gotten among different surface soil moisture datasets, the persistence of deep soil moisture seemed to be significantly overestimated. Therefore, models still need to improve the temporal characteristics with the persistence rather than the correlation coefficient.

Distinct magnitude asymmetries of daily extreme anomalies in gross primary productivity between forests and non-forests

The magnitude asymmetry between positive and negative extreme anomalies in gross primary productivity (GPP) and the underlying mechanism have been investigated based on daily records from the data-driven and in situ datasets. Distinct magnitude asymmetries of daily GPP extreme anomalies (DGEAs) have been identified between forested and non-forested areas. Forested areas are dominated by negative asymmetries, manifesting as the magnitude of negative DGEAs significantly larger than that of their positive counterparts; while non-forested areas are dominated by positive asymmetries. These distinct asymmetric features are due to the different climatic constraints between forests and non-forests. We found that solar radiation is the dominant climatic controller of the daily variation in GPP of forests. Due to the existence of a light saturation point, vegetation productivity is less sensitive to extremely high values of solar radiation than it is to the extremely low, thus leading to the negative asymmetries. For the non-forested areas, however, the daily variation in GPP depends on the preceding 3‒6-day soil moisture conditions. Heavy precipitation is more effective at causing strong and persistent soil moisture anomalies, as soil water can penetrate to deeper soil layers. Therefore, strong positive asymmetry of precipitation anomalies results in the positive asymmetry of soil moisture anomalies and thus leads to the positive asymmetry of DGEAs. Due to the significant asymmetry in the DGEAs, the cumulative effect of DGEAs further rectifies the interannual variability of annual GPP, implying the importance of asymmetry in the DGEAs to changes of terrestrial carbon cycle.

Agricultural drought prediction in China based on drought propagation and large-scale drivers

Agricultural drought is generally defined as a deficit in soil moisture, and can affect plant growth and crop yields. Accurate prediction of agricultural drought with sufficient lead time can aid agricultural planning and reduce losses in agricultural production. In this study, the meta-Gaussian model was employed to predict agricultural drought (standardized soil moisture index, or SSI) during spring and summer in China based on the initial soil moisture conditions, antecedent meteorological drought (standardized precipitation index, or SPI) and large-scale drivers (El Niño-Southern Oscillation, or ENSO). Monthly precipitation and soil moisture data from Global Land Data Assimilation System, version 2 (GLDAS-2.0) were used to compute the meteorological and agricultural drought indicators. The conditional distribution of agricultural drought given multiple predictors was used for the statistical prediction. The autocorrelation of agricultural drought, the correlation between meteorological drought and agricultural drought, and the correlation between ENSO and agricultural drought were first evaluated to understand the predictors from soil moisture persistence, drought propagation, and large-scale drivers. We then employed the conditional distribution to predict agricultural drought over the period from 1948 to 2014, in which contributions of antecedent meteorological drought and large-scale drivers to the prediction performance were evaluated. Results showed that the prediction method performed well in semi-arid and sub-humid regions during spring, but did not perform well in humid regions during summer. In addition, the incorporation of ENSO provided useful predictability for long lead time prediction in certain regions (with significant influence of ENSO). The results obtained from this study can provide useful information for early agricultural drought warning across China.

Integrating Domain Knowledge in Data-Driven Earth Observation With Process Convolutions

The modelling of Earth observation data is a challenging problem, typically approached by either purely mechanistic or purely data-driven methods. Mechanistic models encode the domain knowledge and physical rules governing the system. Such models, however, need the correct specification of all interactions between variables in the problem and the appropriate parameterization is a challenge in itself. On the other hand, machine learning approaches are flexible data-driven tools, able to approximate arbitrarily complex functions, but lack interpretability and struggle when data is scarce or in extrapolation regimes. In this paper, we argue that hybrid learning schemes that combine both approaches can address all these issues efficiently. We introduce Gaussian process (GP) convolution models for hybrid modelling in Earth observation (EO) problems. We specifically propose the use of a class of GP convolution models called latent force models (LFMs) for EO time series modelling, analysis and understanding. LFMs are hybrid models that incorporate physical knowledge encoded in differential equations into a multioutput GP model. LFMs can transfer information across time-series, cope with missing observations, infer explicit latent functions forcing the system, and learn parameterizations which are very helpful for system analysis and interpretability. We consider time series of soil moisture from active (ASCAT) and passive (SMOS, AMSR2) microwave satellites. We show how assuming a first order differential equation as governing equation, the model automatically estimates the e-folding time or decay rate related to soil moisture persistence and discovers latent forces related to precipitation. The proposed hybrid methodology reconciles the two main approaches in remote sensing parameter estimation by blending statistical learning and mechanistic modeling.

Persistent effects of underground longwall coal mining on freshwater wetland hydrology

More than half of global wetlands have been lost because of anthropogenic disturbance, with the trend of decline continuing in the 21st century. While much of this loss relates to changes in surface flows, groundwater is also critical to sustaining wetland hydrology. Underground longwall mines extract coal seams, in turn fracturing the overlying stratigraphy, influencing aquifer connectivity and affecting surface flows via subsidence disturbance. Crucially, this subterranean disturbance may disrupt the hydrological processes that sustain freshwater wetlands at the surface. Here we present a new designed empirical study that compares the persistence of soil moisture after a rainfall event in wetlands subject to underground longwall coal mining to that in unmined reference wetlands. Accelerated Failure Time models showed that mined wetlands were persistently drier, retained water for shorter durations and exhibited less spatial differentiation than unmined wetlands. This quantitative evidence of severe, persistent hydrological change following resource extraction reinforces earlier observations and has important implications for biodiversity and provision of ecosystem services to a large urban population. If Ecologically Sustainable Development (ESD) outcomes and effective deployment of the mitigation hierarchy are to be achieved in line with current legislative and policy paradigms, our results highlight the need for more emphasis on impact avoidance and minimisation than restoration or offsetting to protect water and biodiversity values. Given severe constraints on restoration success, greater emphasis on avoidance in mine design and approval processes offers realistic opportunities for an improved balance between sustaining irreplaceable public assets and short-term benefits from non-renewable resource extraction.

Changes in Soil Moisture Persistence in China over the Past 40 Years under a Warming Climate

AbstractVariability in soil moisture has implications for regional terrestrial environments under a warming climate. This paper focuses on the spatiotemporal variability in the intra-annual persistence of soil moisture in China using the fifth-generation reanalysis dataset by the European Centre for Medium-Range Weather Forecasts for the period 1979–2018. The results show that in China, the mean intra-annual persistence in the humid to arid zones increased from 60 to 115 days in the lower layer but decreased from 19 to 13 days and from 25 to 14 days in the upper and root layers, respectively. However, these changes were strongly attenuated in extremely dry and wet regions due to the scarcity of soil moisture anomalies. Large changes in persistence occurred in the lower soil layer in dryland areas, with a mean difference of up to 40 days between the 2010s and the 1980s. Overall increasing trends dominated the large-scale spatial features, despite regional decreases in the eastern arid zone and the North and Northeast China plains. In the root layer, the two plains experienced an expanded decrease while on the Tibetan Plateau it was dominated by decadal variability. These contrasting changes between the lower and root layers along the periphery of the transition zone was a reflection of the enhanced soil hydrological cycle in the root layer. The enhanced persistence in drylands lower layer is an indication of the intensified impacts of soil moisture anomalies (e.g., droughts) on terrestrial water cycle. These findings may help the understanding of climate change impacts on terrestrial environments.

A new dynamic wetness index (DWI) predicts soil moisture persistence and correlates with key indicators of surface soil geochemistry

Commonly, the topographic influence on soil hydrology is calculated as a Topographic Wetness Index (TWI), which often correlates with surface soil properties, such as carbon and nitrogen, across broad spatial scales. However, traditional TWI methods can be ineffective at capturing finer scale variations when depression filling approaches are used and they do not incorporate localized soil texture controls on infiltration. We developed a new Dynamic Wetness Index (DWI) that attempts to account for the persistence of soil moisture over time at the microtopographic scale (~1 m2) by including inputs of measured soil texture, and information from the Dhara modeling framework that incorporates canopy process and surface-subsurface hydrologic models. DWI and TWI values were correlated with measured soil geochemical properties across six study sites (four agricultural sites, one restored prairie, and one forest site) within the Upper Sangamon River Basin, in central Illinois, USA. Relative to TWI, DWI improved correlations with certain measured soil surface geochemistry (pH R = −0.53), δ13C R = 0.13, δ15N R = 0.44) and certain lignin phenols (vanillyl, cinnamyl/vanillyl, syringyl-vanillyl-cinnamyl/substituted fatty acids). DWI positively correlated with indicators of lignin oxidation, indicating that wetter soils have higher potential for lignin decomposition. In this small dataset, relative to TWI the data show DWI increased significance and decreased the range of correlations with soil moisture and certain surface soil geochemistry parameters driving plant chemistry decay and nitrogen cycling.

Influence of persistence and oceanic forcing on global soil moisture predictability

Soil moisture is a key variable of the land surface and its variations are an important issue in climate studies. In this study, we employed the Community Earth System Model (CESM) for 20 ensemble member simulations of the 50-year period from 1965 to 2014 and used canonical correlation analysis (CCA) and multiple regression to analyze the spatiotemporal characteristics of soil moisture predictability. The effects of soil moisture persistence and sea surface temperature (SST) as an external forcing on its predictability were analyzed. Results show that the soil moisture predictability due to its persistence is 1–2 months and considering SST as an external forcing can significantly increase its predictability. Regions that exhibit a significant increase in predictability are mainly tropical regions, North America and Western Asia during winter and spring. In tropical regions, SST increases the predictability of soil moisture by influencing the local surface temperature and precipitation. In other regions, the effects of SST on wind speed, cloud cover, and surface evaporation also contribute significantly to the increase in soil moisture predictability. The results were validated through the analysis based on soil moisture data from land data assimilation system and observed precipitation.

Impact of radiations on the long-range correlation of soil moisture: A case study of the A’rou superstation in the Heihe River Basin

Analyses of the soil moisture evolution trend and the influence of different types of radiation on soil moisture are of great significance to the simulation and prediction of soil moisture. In this paper, soil moisture (2–60 cm) and various radiation data from 2014–2015 at the A’rou superstation were selected. The radiation data include the net radiation (NR), shortwave and longwave radiation (SR and LR). Using adaptive fractal analysis (AFA), the long–range correlation (LRC) of soil moisture and long-range cross correlation (LRCC) between moisture and three types of radiation were analyzed at different timescales and soil depths. The results show that: (1) Persistence of soil moisture and consistency between soil moisture and radiation mutate at 18-d and 6-d timescales, respectively. The timescale variation of soil moisture persistence is mainly related to the influence process of radiation on soil moisture; (2) Both the soil moisture persistence and soil moisture-radiation consistency vary substantially with soil depth. The soil depth variation of soil moisture persistence is related to the influence intensity of radiation; (3) From 2–6 day timescales, LR displays the strongest influence on soil moisture at depths of 2–10 cm through negative feedback of radiation on the soil temperature. The influence intensity decreases with depth from 2–15 cm. Therefore, the soil moisture persistence is weak and increases with depth from 2–15 cm; and (4) At more than 6 day timescales, SR and NR display a stronger influence on the soil moisture persistence at depths of 2–40 cm through positive feedback of radiation on the soil temperature, especially at depths of 2–10 cm. This influence also weakens with depth. The soil moisture persistence at depths of 2–10 cm is the weakest and increases with depth from 2–40 cm. The research results are instructive for determining timescales and soil depths related to soil water in hydrological models.

Spatiotemporal Analysis of Soil Moisture and Optimal Sampling Design for Regional‐Scale Soil Moisture Estimation in a Tropical Watershed of India

AbstractRegional‐scale soil moisture estimates are essential for several hydrological applications and for validating remote sensing‐based soil moisture products. Characterization of the regional‐scale soil moisture variability requires a robust in situ monitoring strategy at point scale to balance between representativeness and minimization of monitoring cost. In this study an optimal sampling design was determined to capture the spatiotemporal variability of soil moisture at watershed scale. The study was conducted for the typical Indian conditions of extreme seasonal variability that leads to very wet (during monsoon) to dry (during hot summer) in the eastern India. Soil moisture monitoring was done at 83 locations in an agricultural watershed of 500 km2 for 56 days of field campaigns across a year. Based on the analyses of 41,832 measurements collected during field campaigns, it was found that maximum numbers of required locations necessary to estimate watershed‐mean soil moisture within ±2% accuracy are 30. Moreover, five randomly selected locations were found to be sufficient for capturing the temporal variability of watershed‐mean soil moisture with an accuracy of ±3%. In addition, five most representative locations identified through time stability analysis can provide robust estimate of watershed‐mean soil moisture with accuracy of ±2%. Further, soil properties and topography are identified as significant physical parameters that jointly control the spatiotemporal persistence and variability of soil moisture in the Indian watershed. These findings will be quite useful to provide guidelines for optimizing short‐term soil moisture campaigns by sampling at few selected points representative of the watershed‐mean behavior.

Long‐Range Correlation Analysis of Soil Temperature and Moisture on A'rou Hillsides, Babao River Basin

AbstractStudying the tendency of soil temperature and moisture can provide scientific support for setting soil parameters for land surface hydrological model. Previous research has shown that there is long‐range correlation of soil temperature or moisture; however, driving factors of long‐range correlation variations and the cross correlation between temperature and moisture under multiple timescales have rarely been discussed. To explore the driving factors, we compared long‐range correlation between shady and sunny slopes. Adaptive fractal analysis was utilized to analyze long‐range cross correlation (LRCC) and long‐range correlation (LRC). Experimental data were collected between 9 August 2013 and 3 December 2014 at A'rou observation stations, Babao River Basin. Results show that two slopes have some common characteristics. (1) Soil temperature and moisture display the consistent change. (2) The persistence intensity of moisture, temperature, and their interaction intensity change at 9, 6, and 6‐day timescales, respectively. (3) LRC and LRCC indices demonstrate the stratification heterogeneity. Results also show that LRC and LRCC indices on two slopes have differences. The soil system was stratified into three layers to explain these differences over large timescales. (1) At 4‐ to 20‐cm depth, with significant influence of solar radiation and evapotranspiration on sunny slope, moisture shows weaker persistence. There is stronger antipersistence of temperature and stronger hydrothermal interaction on sunny slope. (2) At 20‐ to 80‐cm depth, there is weaker persistence of soil moisture on shady slope because of unstable groundwater and side runoff supply. (3) At 80‐ to 160‐cm depth, with seasonal melt‐water effect on sunny slope, persistence of moisture was considerably weaker.

Impact of spatially-variable soil thickness and texture on simulated hydrologic conditions in a semiarid watershed in northwest Mexico

Soil depth and texture exert strong controls on the spatial distribution of water and energy fluxes and states in semiarid watersheds. As a result, realistic representations of the spatial soil characteristics within watersheds are important for the improvement of process-based distributed hydrologic modeling applications. In this study, we evaluated the effects of combinations of soil thickness and soil texture products with varying spatial distributions to assess their effects on the simulated hydrologic response in the semiarid Sierra Los Locos watershed in Sonora, Mexico. The main findings of the hydrological simulations show that soil texture exerted a strong control on evapotranspiration and soil moisture patterns, while soil thickness was directly related with the magnitude and range of the simulated values. Furthermore, soil texture patterns were an important factor controlling the spatial and temporal persistence of soil moisture which is highly evident during the transition from dry to wet conditions in the North American monsoon region. Once vegetation cover increases in the watershed in response to seasonal rainfall, the influence of soil texture decreases for determining the spatial distribution of the simulated hydrologic response and soil thickness becomes more important. Spatially-variable soil thickness tends to create soil depressions that store and transmit subsurface water, leading to large spatial variations in soil moisture, evapotranspiration and runoff generation. The results of this study highlight the sensitivity of estimated water fluxes and states with respect to the spatial distribution of soil depth and texture as obtained in a distributed hydrologic model representative of modern approaches. Finally, this research work offers insight into the importance of field studies and remote sensing approaches to better characterize these watershed properties.

Long‐term predictability of soil moisture dynamics at the global scale: Persistence versus large‐scale drivers

AbstractHere we investigate factors that influence the long lead time predictability of soil moisture variability using standard statistical methods. As predictors we first consider soil moisture persistence only, using two independent global soil moisture data sets. In a second step we include three teleconnection indices indicative of the main northern, tropical, and southern atmospheric modes, i.e., the North Atlantic Oscillation (NAO), the Southern Oscillation Index (SOI), and the Antarctic Oscillation (AAO). For many regions results show significant skill in predicting soil moisture variability with lead times up to 5 months. Soil moisture persistence plays a key role at monthly to subseasonal time scales. With increasing lead times large‐scale atmospheric drivers become more important, and areas influenced by teleconnection indices show higher predictability. This long lead time predictability of soil moisture may help to improve early warning systems for important natural hazards, such as heat waves, droughts, wildfires, and floods.

Evaluation of soil moisture data products over Indian region and analysis of spatio-temporal characteristics with respect to monsoon rainfall

Soil moisture (SM) is an essential climate variable of greater relevance in the monsoon scenario, hence validation and understanding of its spatio-temporal variability over the Indian region is of high significance. In the present study, five SM products are evaluated against in situ SM measurements conducted by India Meteorological Department and the selected data product is used for spatio-temporal characterization of SM in relation to monsoon rainfall. The data products evaluated are: European Space Agency’s merged satellite SM, Modern-Era Retrospective analysis for Research and Applications (MERRA) Land SM, ECMWF’s ERA interim SM, Climate Forecast System Reanalysis SM, and Global Land Data Assimilation System Noah Land Surface Model SM. Comparisons show that seasonal SM patterns in all products generally follow the characteristics of rainfall, even though there are certain differences in details. The statistical estimates indicate fairly good agreement between in situ and the five products, with some variations among them and over the homogeneous rainfall regions. On comparison, MERRA SM is found appropriate for further analyses on spatio-temporal characteristics, which are then carried out with the 20year (1993–2012) SM data. Stability analyses revealed SM patterns indicative of relative SM variability as well as persistence. The spatial stability analysis depicts dry and wet patterns and their seasonal variations over different geographical locations in relation to all India spatial average. Large temporal variations are found over central, western and northern Indian regions caused by large intraseasonal variability in rainfall. In brief, intraseasonal and interannual soil moisture variations broadly follow the rainfall pattern, with long-term influences attributed to SM memory effects. The soil moisture persistence and dominant scales of variability are explored with autocorrelation and wavelet transform techniques. Seasonal persistence is large over regions receiving excessive rainfall, while drought years are noted to have large intraseasonal SM persistence compared to that of surplus year. Wavelet decomposition demonstrates that low periodicity (2–10days) SM modes match that of rainfall during initial periods of monsoon. The occurrence of significant higher periodicity modes are dominant in drought years compared to surplus, which also vary with geographical locations.

Assessing hydro-ecological vulnerability using microwave radiometric measurements from WindSat

Abstract The spatial distribution, magnitude and timing of precipitation events are being altered globally, often leading to extreme hydrologic conditions with serious implications to the environment and society. Motivated by the pressing need to understand, from a hydro-ecological perspective, the impact of the dynamic nature of the hydrologic cycle on the environment in water-stressed regions, we investigated how different habitats in East Africa behave under extreme hydrologic conditions. We assessed the hydro-ecological vulnerability of the region by studying the response of soil moisture and vegetation water content to precipitation deficiency. The spatial patterns and characteristics of the inter-relations among the three aforementioned hydrologic variables, as well as the sensitivity and resilience of vegetation water content and soil moisture, derived from WindSat, were investigated for different vegetation types during dry spells of varying duration, identified using the Tropical Rainfall Measuring Mission (TRMM), in 2003–2011. Forest/Woody Savanna (FWS) and Savanna/Grasslands (SG) are more sensitive to local hydrologic extremes, while Shrublands (SHR) and the soils that support it are the least impacted by these conditions. SG and FWS exhibit the highest vegetation water content resilience, whereas soil moisture persistence during dry spells is at its highest in SHR/SG. The environmental variability, illustrated by the spatial patterns of the aforementioned hydrologic properties, can potentially play a role in the enhancement of resilience. This study provides critical insight into the hydro-ecological vulnerability of East Africa using microwave remote sensing, and this information can be used towards advancing management and decision support systems that would improve societal well-being and economic development.