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A dynamic transition in soil hydrologic states through meteorological variability and terrestrial feedback governs soil‐vegetation‐climate (SVC) interactions, constrained by critical soil moisture (SM) thresholds. However, observational and scaling constraints limit critical SM threshold estimation at the remote‐sensing (RS) footprint scale. Using global surface SM (θRS) from NASA’s Soil Moisture Active Passive (SMAP) satellite, we characterize the seasonal preferential hydrologic states of θRS and derive three tipping characteristics to estimate the intensity (Mean Tipping Depth, ε‾$\\overline{\\boldsymbol{\\varepsilon }}$ ), frequency (Tipping Count, η), and duration (Mean Tipped Time, τ‾$\\overline{\\boldsymbol{\\tau }\\,}$ ) of the excursion of θRS from wet‐ to dry‐average conditions. The preferential state provides the seasonally dominant hydrological states of θRS, while tipping characteristics capture the ecosystem linkages of the dynamic transition in θRS hydrologic states. Globally, θRS predominantly exhibits a (unimodal) dry‐preferential state, especially over arid/semi‐arid drylands and a unimodal wet‐preferential θRS state in high‐latitude boreal forests and tundra biomes. Prevalence of (bimodal) bistable θRS state overlaps with regions of strong positive SM‐precipitation coupling and monsoonal climate in semi‐arid/subhumid climates. Seasonal preferential hydrologic states co‐vary with the regional variability in plant water stress threshold and land‐atmospheric coupling strength. Tipping characteristics of θRS show sensitivity to intra‐biome variability in SVC coexistence patterns and display high skill in partitioning global ecoregions. While ε‾$\\overline{\\boldsymbol{\\varepsilon }}$and η are climate‐controlled, τ‾$\\overline{\\boldsymbol{\\tau }\\,}$is moderated by soil and vegetation through their influence over θRS drydown during water‐limited evapotranspiration. Preferential states and tipping characteristics find applications in quantifying SVC coexistence patterns, climate model diagnosis, and assessing ecosystem sensitivity to climate change. Key Points Global surface soil moisture dynamics is categorized into dry‐preferential, wet‐preferential and bistable hydrologic states Tipping characteristics are defined to capture intensity, frequency, and duration of surface soil moisture excursions from wet‐to dry‐average state Soil moisture tipping characteristics capture soil‐vegetation‐climate coexistence patterns within global biomes

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 soil microbial carbon models may change considerably soil carbon projections under global warming, underscoring the need to improve parameterization of microbial processes.

The coupling between soil moisture (SM) and evapotranspiration (ET) governs key dynamics of Earth's climate and biosphere productivity. Yet, prevailing statistical models fall short of capturing the physics of water–energy exchange across diverse hydroclimates. In this study, we introduce an optimal transport framework based on the hypothesis that hydroclimates regulate SM–ET coupling near a quasi‐optimum state. This state is characterized by least action principle, defined by dynamic convolution between the water potential gradient (Δω ${\\Delta }\\omega $) driving land‐to‐atmosphere moisture flux and the time weighted mass flux (referred as the SM‐ET coupling metric, λSM−ET ${\\lambda }_{SM-ET}$). Global validation of this framework using decadal (2010–2019) SM and ET remote sensing data reveals widespread convergence toward the least action state across hydroclimatic zones, supporting the notion of emergent climatic regulation in SM–ET coupling. As a corollary to the proposed hypothesis, we estimate two emergent properties of the SM–ET coupling: active root zone depth supporting ET, and the characteristic transit timescales over which SM is lost to atmosphere. Our root depth estimates show strong correspondence with in situ measurements (correlation >0.86) across biomes, underscoring the framework's physical realism. Notably, dynamic transit times are also validated against isotope measurements and findings suggest that SM perturbations often cycle back into the atmosphere within 3–7 days, calling into question traditional metrics of bulk residence time, that often overestimates the actual turnover. Overall, this framework provides a physically grounded way to study water–energy interactions across diverse environments.

A lack of high‐density rootzone soil moisture (θRZ) observations limits the estimation of continental‐scale, space‐time contiguous θRZ dynamics. We derive a proxy of daily θRZ dynamics — active rootzone degree of saturation (SRZ) — by recursive low‐pass (LP) filtering of surface soil moisture (θS) within a terrestrial water‐energy coupling (WEC) framework. We estimate the LP filter parameters and WEC thresholds for the piecewise‐linear coupling between SRZ and evaporative fraction (EF) at remote sensing and field scale over the Contiguous U.S. We use θS from the Soil Moisture Active‐Passive (SMAP) satellite and 218 in‐situ stations, with EF from the Moderate Resolution Imaging Spectroradiometer. The estimated SRZ compares well against SMAP Level‐4 estimates and in‐situ θRZ, at the corresponding scale. The instantaneous hydrologic state (SRZ) vis‐à‐vis the WEC thresholds is proposed as a rootzone soil moisture stress index (SMSRZ) for near‐real‐time operational agricultural drought monitoring and agrees well with established drought metrics. Plain Language Summary Rootzone soil moisture plays a vital role in agricultural, hydrological, and ecosystem processes. The available spaceborne satellites for monitoring soil moisture can only capture variability in a shallow soil layer at the surface, typically limited to the top 5 cm. Hence, spatiotemporally continuous estimation of rootzone soil moisture dynamics typically relies on soil moisture estimates from land‐surface models, which are subject to errors in the surface meteorological forcing data, process formulations, and model parameters. Some studies suggest that the rootzone soil moisture dynamics can be estimated by filtering the high‐frequency variability in the surface soil moisture. However, such “filters” require observed rootzone data (often unavailable at high spatial density) for calibration. This study uses the relationship between surface soil moisture and evaporative fraction derived using spaceborne observations from the Soil Moisture Active Passive mission and the Moderate Resolution Imaging Spectroradiometer to estimate rootzone soil moisture dynamics for the Contiguous U.S. at 9 km grid resolution. We further demonstrate that this approach can be extended into a near‐real‐time agricultural drought monitor to assess drought impacts on vegetation using surface soil moisture observations. Key Points Terrestrial water‐energy coupling is used to parameterize low‐pass filter to estimate rootzone dynamics from surface soil moisture Rootzone degree of saturation and water‐energy coupling thresholds are estimated using evaporative fraction and surface soil moisture SMAP‐based rootzone degree of saturation can used for operational, near‐real‐time agricultural drought monitoring over Contiguous U.S

The contrast between the point‐scale nature of current ground‐based soil moisture instrumentation and the ground resolution (typically >102 km2) of satellites used to retrieve soil moisture poses a significant challenge for the validation of data products from current and upcoming soil moisture satellite missions. Given typical levels of observed spatial variability in soil moisture fields, this mismatch confounds mission validation goals by introducing significant sampling uncertainty in footprint‐scale soil moisture estimates obtained from sparse ground‐based observations. During validation activities based on comparisons between ground observations and satellite retrievals, this sampling error can be misattributed to retrieval uncertainty and spuriously degrade the perceived accuracy of satellite soil moisture products. This review paper describes the magnitude of the soil moisture upscaling problem and measurement density requirements for ground‐based soil moisture networks. Since many large‐scale networks do not meet these requirements, it also summarizes a number of existing soil moisture upscaling strategies which may reduce the detrimental impact of spatial sampling errors on the reliability of satellite soil moisture validation using spatially sparse ground‐based observations. Key Points Satellite soil moisture retrievals are obtained at coarse spatial resolutions It is difficult to validate them using point‐scale ground observations Credible soil moisture upscaling strategies exist to address the problem

Nitrate-N (NO3 -- N) is one of the most pervasive contaminants in groundwater. Nitrate in groundwater exhibits long-term behavior due to complex interactions at multiple scales among various geophysical factors, such as sources of nitrate-N, characteristics of the vadose zone and aquifer attributes. To minimize contamination of nitrate-N in groundwater, it is important to estimate hot spots (>10 mg/L of NO3 -- N), trends and persistence of nitrate-N in groundwater. To analyze the trends and persistence of nitrate-N in groundwater at multiple spatio-temporal scales, we developed and used an entropy-based method along with the Hurst exponent in two different hydrogeologic settings: the Trinity and Ogallala Aquifers in Texas at fine (2 km × 2 km), intermediate (10 km × 10 km) and coarse (100 km × 100 km) scales. Results show that nitrate-N exhibits long-term persistence at the intermediate and coarse scales. In the Trinity Aquifer, overall mean nitrate-N has declined with a slight increase in normalized marginal entropy (NME) over each decade from 1940 to 2008; however, the number of hot spots has increased over time. In the Ogallala Aquifer, overall mean nitrate-N has increased with slight moderation in NME since 1940; however, the number of hot spots has significantly decreased for the same period at all scales.

Daily evapotranspiration (ET) estimated from an unoccupied aerial system (UAS) could help improve irrigation practices, but its spatial resolution needs to be upscaled to coarser pixel resolutions before applying surface energy balance models. The purpose of this study was to evaluate the impact of various energy balance-based aggregation schemes on generating spatially distributed latent heat flux (LE), and, in comparison, to existing occupied aircraft and satellite remote sensing platforms. In 2017, UAS multispectral and thermal imagery, along with ground truth data, were collected at various cotton growth stages. These data sources were combined to model LE using a Two-Source Energy Balance Priestley–Taylor (TSEB-PT) model. Several UAS aggregation schemes were tested, including the mode of aggregation (i.e., input image and output flux) as well as the averaging scheme (i.e., simple aggregation vs. Box–Cox). Results indicate that output flux aggregation with Box–Cox averaging produced the lowest relative upscaling pixel-scale variability in LE and the lowest absolute prediction errors (relative to eddy covariance flux tower measurements). Output flux aggregation with simple averaging was also more accurate in reproducing LE from occupied aircraft and satellite imagery. Although results are limited to a single site, UAS LE estimates were reliably aggregated to coarser pixel resolutions, which made for faster image processing for operational applications.

A framework is proposed for understanding the efficacy of the microwave radiative transfer model (RTM) of soil moisture with different support scales, seasonality (time), hydroclimates, and aggregation (scaling) methods. In this paper, the sensitivity of brightness temperature TB (H- and V-polarization) to physical variables (soil moisture, soil texture, surface roughness, surface temperature, and vegetation characteristics) is studied. Our results indicate that the sensitivity of brightness temperature (V- or H-polarization) is determined by the upscaling method and heterogeneity observed in the physical variables. Under higher heterogeneity, the TB sensitivity to vegetation and roughness followed a logarithmic function with an increasing support scale, while an exponential function is followed under lower heterogeneity. Surface temperature always followed an exponential function under all conditions. The sensitivity of TB at H- or V- polarization to soil and vegetation characteristics varied with the spatial scale (extent and support) and the amount of biomass observed. Thus, choosing an H- or V-polarization algorithm for soil moisture retrieval is a tradeoff between support scales, and land surface heterogeneity. For largely undisturbed natural environments such as SGP’97 and SMEX04, the sensitivity of TB to variables remains nearly uniform and is not influenced by extent, support scales, or an upscaling method. On the contrary, for anthropogenically-manipulated environments such as SMEX02 and SMAPVEX12, the sensitivity to variables is highly influenced by the distribution of land surface heterogeneity and upscaling methods.

Regenerative agriculture is pivotal for mitigating climate change, with no-tillage practices on cropland being generally effective at raising soil organic carbon (SOC). Yet, our understanding of the compound impact of soil and environmental factors on SOC gain potential after transitioning to no-till practices is still developing. Using imbalanced machine learning classification, here we quantify key thresholds to hierarchically classify SOC gain potential by switching from conventional tillage to long-term no-tillage with residue retention. Our findings reveal that antecedent SOC level exerts the primary influence, with a reduced gain potential for antecedent SOC exceeding 50 tonnes per hectare. Wet climate (Dryness Index < 1.5) and low productivity (net annual primary productivity < 5.5 tonnes per hectare) could further lessen the effectiveness of SOC sequestration. These key thresholds identify vast areas across Africa, Australia, South Asia, Southern Europe, and parts of North and South America as high-potential croplands for carbon sequestration and offer guidelines for assessing the reliability of regenerative agriculture in local and regional contexts.

Escherichia coli ( E. coli ) contamination of groundwater (GW) and surface water (SW) occurs significantly through the subsurface from onsite wastewater treatment systems (OWTSs). However, E. coli transport in the subsurface remains inadequately characterized at the field scale, especially within the vadose zone. Therefore, the aim of this research is to investigate the impact of groundwater fluctuations (e.g., recharging, discharging conditions) and variable conditions in the vadose zone (e.g., pulses of E. coli flux) by characterizing E. coli fate and transport in a linked surface water-soil water-groundwater system (SW-SoW-GW). In particular, this study characterizes the impact of flow regimes on E. coli transport in the subsurface and evaluates the sensitivity of parameters that control the transport of E. coli in the SW-SoW-GW system. This study was conducted in Lake Granbury, which is an important water supply in north-central Texas providing water for over 250,000 people. Results showed that there was less removal of E. coli during groundwater recharge events as compared to GW discharge events. Also, groundwater and surface water systems largely control E. coli transport in the subsurface; however, temporal variability of E. coli can be explained by linking the SW-SoW-GW system. Moreover, sensitivity analysis revealed that saturated water content of the soil, total retention rate coefficient, and hydraulic conductivity are important parameters for E. coli transport in the subsurface.