Understanding the global soil moisture (SM) dynamics and its governing controls beyond Darcy Scale is critical for various hydrologic, meteorological, agricultural, and environmental applications. In this study, we parameterize the pathways of the seasonal drydowns using global surface soil moisture (θ_RS) observation from SMAP satellite (between 2015 and 2019) at 36km X 36km. We develop a new data-driven non-parametric approach to identify the canonical shapes of θ_RS drydown, followed by a non-linear least-squares parameterization of the seasonal drydown pathways at each SMAP footprint. The derived parameters provide the effective soil water retention parameters (SWRPeff), land-atmospheric coupling strength, soil hydrologic regimes for SMAP footprint. Depending on footprint heterogeneity, climate and season, the characteristics curves comprising different drydown phases are discovered at SMAP footprints. Drydown curves respond to the within-footprint changes in the meteorological drivers, land-surface characteristics and the soil-vegetative and atmospheric dynamics. Drydown parameters display high inter-seasonal variability, especially in grasslands, croplands and savannah landscapes due to significant changes in the landscape characteristics and moisture patterns at the subgrid-scale. Soil texture exert influence on the characteristics soil water retention and drydown parameters only when the footprint mean θ_RS is low, specifically in arid and sparsely vegetated regions. The influence of soil texture on the inter-seasonal variability of SWRPeff is low compared to landuse and climate at RS-footprint scale. The global understanding of characteristics SM drydown features at SMAP footprints provides a significant step towards a scale-specific, effective soil hydrologic parameterization for various applications.

Carbon, water, and energy cycles are key processes controlling land-atmosphere interactions. Carbon and energy fluxes are both coupled with soil moisture (SM), leading to water-limited and energy-limited regimes. Functional relationships between carbon, water, and energy are key to understanding feedbacks between the land surface and atmosphere and give an estimate of the land-atmosphere coupling strength. Current carbon, water, and energy coupling relationships are commonly estimated at the point or satellite scale. These coupling relationships are expected to vary across scales. We investigated the carbon, water, and energy coupling relationships and the limiting thresholds from the field to satellite scale. We also investigated the effect of weather, seasons, and land cover on carbon-water-energy coupling strength. Carbon (carbon dioxide: CO2), energy (evapotranspiration: ET), and water (SM) were estimated using Eddy covariance and soil moisture monitoring systems at various FLUXNET sites in the Continental United States. These field measurements were used together with LANDSAT, MODIS, SMAP remote sensing satellites estimates of ET, CO2, and SM, respectively. Weather variables were obtained from Daymet (a gridded daily surface weather data product) and weather monitoring systems at the FLUXNET sites. This analysis provided insights to the spatial and temporal variations of the carbon, energy, and SM at different scales.

Flash droughts are characterized by an abrupt onset and swift intensification. Global surface soil moisture (θRS) from NASA’s Soil Moisture Active Passive (SMAP) satellite can facilitate a near-real-time assessment of emerging flash droughts at 36-km footprint. However, a robust flash drought monitoring using θRS must account for the i) short observation record of SMAP, ii) non-linear geophysical controls over θRS dynamics, and, iii) emergent meteorological drivers of flash droughts. We propose a new method for near-real-time characterization of droughts using Soil Moisture Stress (SMS, drought stress) and Relative Rate of Drydown (RRD, drought stress intensification rate) ─ developed using SMAP θRS (March 2015-2019) and footprint-scale seasonal soil water retention parameters and land-atmospheric coupling strength. SMS and RRD are nonlinearly combined to develop Flash Drought Stress Index (FDSI) to characterize emerging flash droughts (FDSI ≥ 0.71 for moderate to high RRD and SMS). Globally, FDSI shows high correlation with concurrent meteorological anomalies. A retrospective evaluation of select droughts is demonstrated using FDSI, including a mechanistic evaluation of the 2017 flash drought in the Northern Great Plains. About 5.2% of earth’s landmass experienced flash droughts of varying intensity and duration during 2015-2019 (FDSI ≥ 0.71 for >30 consecutive days), majorly in global drylands. FDSI shows high skill in forecasting vegetation health with a lead of 0-2 weeks, with exceptions in irrigated croplands and mixed forests. With readily available parameters, low data latency, and no dependence on model simulations, we provide a robust tool for global near-real-time flash drought monitoring using SMAP.

While hydraulic groundwater theory has been understood as a viable approach for representing the role of the aquifer(s) in the surface-subsurface hydrologic cycle, the integrated modeling community still lacks a proper hydrologic structure to utilize the well-studied theory for large-scale hydrologic predictions. This study aims to present a novel hydrologic modeling framework that enables the Boussinesq equation-based depiction of hillslope-channel connectivity for applying hydraulic groundwater theory to large-scale model configurations. We integrated the BE3S’s [Hong et al., 2020] representation scheme of the catchment-scale Boussinesq aquifer into the National Water Model (NWM) and applied the NWM-BE3S model to three major basins in Texas (i.e., the Trinity, Brazos, and Colorado River basins). Since the NWM currently relies on a single reservoir model for baseflow estimation, theory-based evaluation was performed as the efficacies that the Boussinesq aquifer has relative to the single reservoir model should be consistent with hydraulic groundwater theory. We identified that the implemented Boussinesq aquifer(s) showed ‘more’ pronouced improvements in capturing streamflow dynamics than the original NWM as aquifers exhibited higher nonlinearities in the observed recessions. The varying degree of improvements in streamflow outputs according to the recession nonlinearities demonstrates (1) the applicability of the theory-based depiction of hillslope-channel connectivity and (2) the technical enhancement of model structure. We also examined the river states of all the reaches based on the represented bidirectional lateral hydraulic connections between the stream-aquifer and thus identified the dominant processes between the stream-aquifer (i.e., either river infiltration or baseflow) were spatially variable roughly following climatic gradients.

Soil carbon cycling and ecosystem functioning can strongly depend on how microbial communities regulate their metabolism and adapt to changing environmental conditions to improve their fitness. Investing in extracellular enzymes is an important strategy for the acquisition of resources, but the principle behind the trade-offs between enzyme production and growth is not entirely clear. Here we show that the enzyme production rate per unit biomass may be regulated in order to maximize the biomass specific growth rate. Based on this optimality hypothesis, we derive mathematical expressions for the biomass specific enzyme production rate and the microbial carbon use efficiency, and verify them with experimental observations. As a result of this analysis, we also find that the optimal enzyme production rate decays hyperbolically with the soil organic carbon content. We then show that integrating the optimal extracellular enzyme production into microbial models may change considerably soil carbon projections under global warming, underscoring the need to improve parameterization of microbial processes.

The redox potential of soils is critical in understanding the structure and function of ecosystems. Soil redox state strongly governs the speciation, bioavailability, and solubility of limiting nutrients like nitrogen and phosphorous in soil. It also drives the reactivity, mobility, and toxicity of redox-sensitive elements such as anthropogenic contaminants, affecting soil and groundwater quality by altering or retaining undesirable metals. Although these factors are highly variable among different landcovers and soil depths, limited studies try to link the redox-sensitive elements with soil physical and chemical properties in various depths and with different landcovers. With designed experiments in Brazos River corridor in Texas, we (1) evaluate the effect of different land use and land covers on the concentrations of electron acceptors (O2, NO3, and SO4), reduced products (Mn(II), Fe(II)), and C, N, and P pools in the surface and deep soils, (2) determine effects of climatic gradient on redox biogeochemistry in deep soils, and (3) investigate the effects of soil physical and hydraulic properties on redox biogeochemistry. Soil physical and chemical properties were determined from varying soil depths (land surface up to 15 m) in different landcovers (grassland, forest, and salt marsh). Higher carbon and nitrogen content were observed in the surface soils due to carbon mineralization in all land covers. However, the phosphorous content was higher at 15-30 cm soil depth, because of the co-existing high iron and aluminum oxides concentrations that provide high surface area for phosphorous adsorption. C, N, P, and other redox-sensitive elements were positively correlated to clay content at various depths. The biogeochemical properties, including ammonium, ferric iron and sulfate, were disproportionally higher at the interface of soil layers where soil texture and hydraulic properties change. This finding reflects the role of soil layers as hot spots of biogeochemical processes in the subsurface. With the climate gradient across the study river basin, our data indicates C, N, P, and other redox-sensitive elements are more profound in the salt marsh and forest covers with higher annual mean temperature and precipitation as these factors stimulate microbial activity and thus influence redox processes.

The increasing frequency and severity of flash droughts pose a threat to global food and water security and seasonal climate forecasts. We introduce a new tool for near-real-time global flash drought monitoring with SMAP leveraging the footprint-scale thresholds of soil hydrologic regimes (energy-limited wet phase, moisture limited transitional, and dry phase) and land-atmospheric coupling strength. We define two complementary indices based on SMAP soil moisture for measuring the severity and the rate of intensification of drought, namely, Soil Moisture Stress (SMS) and Relative Rate of Drydown (RRD), respectively. SMS and RRD are non-linearly combined to provide FDSI (Flash Drought Stress Index) ─ a composite indicator used for global flash drought monitoring. Several advantages of FDSI include non-reliance on long-term soil moisture records, sensitivity to changing land-surface heterogeneity, land-atmospheric interactions, and evolving meteorological anomalies. FDSI is extensively validated globally across multiple timescales (daily, weekly, and monthly) using a suite of vegetation and meteorological drought indices. We demonstrate the application of FDSI in the mechanistic evaluation of select recent flash droughts across the globe (Northern Great Plains in 2017, South Africa in 2015-2016, and Eastern Australia in 2019-2020), and the onset of the ongoing (since 2020) heatwave induced drought in the western U.S. Through this presentation, we introduce the viewers to the open-source web-based resources for accessing global FDSI estimates and related geospatial parameters.

The past six decades has seen an explosive growth in remote sensing data across air, land, and water dramatically improving predictive capabilities of physical models and machine-learning (ML) algorithms. Physical models, however, suffer from rigid parameterization and can lead to incorrect inferences when little is known about the underlying physical process. ML models, conversely, sacrifice interpretation for enhanced predictions. Geostatistics are an attractive alternative since they do not have strong assumptions like physical models yet enable physical interpretation and uncertainty quantification. In this work, we propose a novel multiscale multi-platform geostatistical algorithm which can combine big environmental datasets observed at different spatio-temporal resolutions and over vast study domains. As a case study, we apply the proposed algorithm to combine satellite soil moisture data from Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) with point data from U.S Climate Reference Network (USCRN) and Soil Climate Analysis Network (SCAN) across Contiguous US for a fifteen-day period in July 2017. Using an underlying covariate-driven spatio-temporal process, the effect of dynamic and static physical controls—vegetation, rainfall, soil texture and topography—on soil moisture is quantified. We successfully validate the fused soil moisture across multiple spatial scales (point, 3 km, 25 km and 36 km) and compute five-day soil moisture forecasts across Contiguous US. The proposed algorithm is general and can be applied to fuse many other environmental variables.

For regional sustainability, spatio-temporal variability of groundwater level (GWL) in tropical savanna climatic region with heavily stressed aquifers needs future projection skills by taking hydrological, geological, and climatic (HGC) controls into consideration. This study analyzed the spatio-temporal variability of quarterly GWL and the HGC controls regulating it during the 1995-2015 period over a data-scarce tropical savanna region in India. Using data mining techniques, the study evaluated land use land cover (LULC), geomorphology, lithology, topography and rainfall as HGC controls for GWL variability. The analysis revealed that this region has high intra-annual spatial variability characterized by higher GWL variability in the drier period of the year than wet period. The temporal analysis of GWL demarcated the distinct regions with highly significant rising and declining trends with magnitude ranging from -0.51 to 0.42 m/year. It was discovered that the LULC could explain the observed GWL variability at the highest degree compared to the other considered HGC controls. Subsequently, through principal component analysis (PCA) six representative components covering more than 90% of the variance in 2002 LULC dataset were used for training the random forest (RF) learning algorithm to develop four prediction models corresponding to four temporal quarters. The PCA-RF based trained prediction models showed adequate accuracy during testing using the 2005, 2010, and 2015 LULC datasets. The developed models were further used to make short- and long-term GWL predictions in the study region. The developed models can contribute to regional-scale groundwater planning and management in data-scarce tropical regions.