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Precipitation (PPT) patterns in semiarid grasslands are characterized by infrequent and small PPT events; however, plants and soil microorganisms are adapted to use the unpredictable small pulses of water. Several studies have shown short-term responses of carbon and nitrogen mineralization rates (called the priming effect or the Birch effect) stimulated by wet-dry cycles; however, dynamics, drivers, and the contribution of the “priming effect” to the annual C balance is poorly understood. Thus, we analysed six years of continuous net ecosystem exchange measurements to evaluate the effect of the PPT periodicity, magnitude of individual PPT events on the daily/annual ecosystem C balance (NEE) in a semiarid grassland. We included the period between PPT events, a priori daytime NEE rate and a priori soil moisture content as the main drivers of the priming effect. Ecosystem respiration (ER) responded within few hours following a PPT event whereas it took five-nine days for gross ecosystem exchange (GEE; such as –NEE = GEE + ER) to respond. Precipitation events as low as 0.25 mm increased ER, but cumulative PPT > 40 mm that infiltrated deep into the soil profile stimulated GEE. Overall, ER fluxes following PPT events were related to the change of soil water content at shallow depth and previous soil conditions (e.g., previous NEE rate, previous soil water content) and the size of the stimulus (e.g., PPT event size). Carbon effluxes from priming effect accounted for less than 5% of ecosystem respiration but were significatively high respect to the carbon balance. In the long-term, changes in PPT regimes to more intense and less frequent PPT events, as expected by the climate change effect, could convert the semiarid grassland from a slight C sink to a C source. [ABSTRACT FROM AUTHOR]

Soil moisture is an important parameter that regulates multiple ecosystem processes and provides important information for environmental management and policy decision-making. Spaceborne sensors provide soil moisture information over large areas, but information is commonly available at coarse resolution with spatial and temporal gaps. Here, we present a modular spatial inference framework to downscale satellite-derived soil moisture using terrain parameters and test the performance of two modeling methods (Kernel-Weighted K-Nearest Neighbor and Random Forest ). We generate monthly and weekly gap-free spatial predictions on soil moisture at 1 km using data from the European Space Agency Climate Change Initiative (ESA-CCI; version 6.1) over two regions in the conterminous United States. RF was the method that performed better in cross-validation when comparing with the reference ESA-CCI data, but KKNN showed a slightly higher agreement with ground-truth information as part of independent validation. We postulate that more heterogeneous landscapes (i.e., high topographic variation) may be more challenging for downscaling and predicting soil moisture; therefore, moisture networks should increase monitoring efforts across these complex landscapes. Future opportunities for development of modular cyberinfrastructure tools for downscaling satellite-derived soil moisture are discussed. [ABSTRACT FROM AUTHOR]

Over the past decade, Brazil has experienced severe droughts across its territory, with important implications for soil moisture dynamics. Soil moisture variability has a direct impact on agriculture, water security and ecosystem services. Nevertheless, there is currently little information on how soil moisture across different biomes responds to drought. In this study, we used satellite soil moisture data from the European Space Agency, from 2009 to 2015, to analyze differences in soil moisture responses to drought for each biome of Brazil: Amazon, Atlantic Forest, Caatinga, Cerrado, Pampa and Pantanal. We found an overall soil moisture decline of -0.5 % yr -1 (p<0.01) at the national level. At the biome level, Caatinga presented the most severe soil moisture decline (-4.4 % yr -1), whereas the Atlantic Forest and Cerrado biomes showed no significant trend. The Amazon biome showed no trend but had a sharp reduction of soil moisture from 2013 to 2015. In contrast, the Pampa and Pantanal biomes presented a positive trend (1.6 % yr -1 and 4.3 % yr -1 , respectively). These trends are consistent with vegetation productivity trends across each biome. This information provides insights into drought risk reduction and soil conservation activities to minimize the impact of drought in the most vulnerable biomes. Furthermore, improving our understanding of soil moisture trends during periods of drought is crucial to enhance the national drought early warning system and develop customized strategies for adaptation to climate change in each biome. [ABSTRACT FROM AUTHOR]

Over the last 100 years a shifting human population has seen large proportions of people relocating from rural agricultural regions to urban and suburban areas. Extensive development within these modified lands has altered soils and natural vegetative cover which directly affects many biophysical processes. The current study examines over 4 years of continuous (i.e., hourly) CO2 measurements within a suburban forest floor and lawn to elucidate daily, seasonal and annual patterns of biogenic CO2 emissions while identifying the importance of forcing biophysical drivers (e.g., temperature, moisture). On an annual basis, the suburban forest floor and lawn emitted 587 ± 15.5 g C m−2 yr−1 and 1220 ± 31.5 g C m−2 yr−1, respectively. Overall, temperature was a primary driver of CO2 emissions within both forest and lawn, yet the forest was more complex with additional influence from tree canopy phenology and soil moisture. Diurnal CO2 emission patterns were different between land use types in their response to temperature as we found a counterclockwise hysteresis in forest and an in-sync response in the lawn. In all cases, the strength of biophysical drivers and observed CO2 emission rates were different depending on land use type and throughout the different seasons. Our findings provide insights for modeling and benchmarking biogenic CO2 dynamics across suburban ecosystems. [ABSTRACT FROM AUTHOR]

Soil moisture is key for quantifying soil-atmosphere interactions. We provide a soil moisture pattern recognition framework to increase the spatial resolution and fill gaps of the ESA-CCI (European Space Agency-Climate Change Initiative v4.5) soil moisture dataset, which contains more than 40 years of satellite soil moisture global grids with a spatial resolution of ~27 km. We use terrain parameters coupled with bioclimatic and soil type information to predict the finer-grained satellite soil moisture. We assess the impact of terrain parameters on the prediction accuracy by cross-validating the pattern recognition of soil moisture with and without the support of bioclimatic and soil type information. The outcome is a new dataset of gap-free global mean annual soil moisture and uncertainty for 28 years (1991-2018) across 15 km grids. We use independent in situ records from the International Soil Moisture Network (ISMN, n = 13376) and in situ precipitation records (n = 4909) only for evaluating the new dataset. Cross-validated correlation between observed and predicted soil moisture values varies from r = 0.69 to r = 0.87 with root mean squared errors (RMSE, m3/m3) around 0.03 and 0.04. Our soil moisture predictions improve: (a) the correlation with the ISMN (when compared with the original ESA-CCI dataset) from r = 0.30 (RMSE = 0.09, ubRMSE = 0.37) to r = 0.66 (RMSE = 0.05, ubRMSE = 0.18); and (b) the correlation with local precipitation records across boreal (from r = < 0.3 up r = 0.49) or tropical areas (from r = < 0.3 to r = 0.46) which are currently poorly represented in the ISMN. Temporal trends show a decline of global annual soil moisture using: (a) data from the ISMN (-1.5 [-1.8, -1.24] %, (b) associated locations from the original ESA-CCI dataset (-0.87 [-1.54, -0.17] %), (c) associated locations from predictions based on terrain parameters (-0.85 [-1.01, -0.49] %), and (d) associated locations from predictions including bioclimatic and soil type information (-0.68 [-0.91, -0.45] %). We provide a new soil moisture dataset that has no gaps and a finer resolution together with validation methods and a modeling approach that can be applied worldwide (Guevara, et al., 2020, https://doi.org/10.4211/hs.9f981ae4e68b4f529cdd7a5c9013e27e). [ABSTRACT FROM AUTHOR]

Over the past decade, Brazil has experienced severe droughts across its territory, with important implications for soil moisture dynamics. Soil moisture variability has a direct impact on agriculture, water security, and ecosystem services. Nevertheless, there is currently little information on how soil moisture across different biomes respond to drought. In this study, we used satellite soil moisture data from the European Space Agency, from 2009 to 2015, to analyze differences in soil moisture responses to drought for each biome of Brazil: The Amazon, Atlantic Forest, Caatinga, Cerrado, Pampas and Pantanal. We found an overall soil moisture decline of -0.5 %/year (p < 0.01) at the national level. At the biome-level, Caatinga presented the most severe soil moisture decline (-4.4 % per year); whereas Atlantic Forest and Cerrado biomes showed no significant trend. The Amazon biome showed no trend but a sharp reduction of soil moisture from 2013 to 2015. In contrast, Pampas and Pantanal presented a positive trend (1.6 and 4.3 %/year, respectively). This information provides insights for drought risk reduction and soil conservation activities to minimize the impact of drought in the most vulnerable biomes. Furthermore, improving our understanding of soil moisture trends during periods of drought is crucial to enhance the national drought early warning system and develop customized strategies for adaptation to climate change in each biome. [ABSTRACT FROM AUTHOR]

Annual soil moisture estimates are useful to characterize trends in the climate system, in the capacity of soils to retain water and for predicting land and atmosphere interactions. The main source of soil moisture spatial information across large areas (e.g., continents) is satellite-based microwave remote sensing. However, satellite soil moisture datasets have coarse spatial resolution (e.g., 25–50 km grids); and large areas from regional-to-global scales have spatial information gaps. We provide an alternative approach to predict soil moisture spatial patterns (and associated uncertainty) with higher spatial resolution across areas where no information is otherwise available. This approach relies on geomorphometry derived terrain parameters and machine learning models to improve the statistical accuracy and the spatial resolution (from 27km to 1km grids) of satellite soil moisture information across the conterminous United States on an annual basis (1991–2016). We derived 15 primary and secondary terrain parameters from a digital elevation model. We trained a machine learning algorithm (i.e., kernel weighted nearest neighbors) for each year. Terrain parameters were used as predictors and annual satellite soil moisture estimates were used to train the models. The explained variance for all models-years was >70% (10-fold cross-validation). The 1km soil moisture grids (compared to the original satellite soil moisture estimates) had higher correlations (improving from r2 = 0.1 to r2 = 0.46) and lower bias (improving from 0.062 to 0.057 m3/m3) with field soil moisture observations from the North American Soil Moisture Database (n = 668 locations with available data between 1991–2013; 0-5cm depth). We conclude that the fusion of geomorphometry methods and satellite soil moisture estimates is useful to increase the spatial resolution and accuracy of satellite-derived soil moisture. This approach can be applied to other satellite-derived soil moisture estimates and regions across the world. [ABSTRACT FROM AUTHOR]

Soil moisture is key for quantifying soil-atmosphere interactions and the ESA-CCI (European Space Agency Climate Change Initiative) provides historical (> 30 years) satellite soil moisture gridded data at the global scale. We evaluate an alternative approach to increase the spatial resolution of the original ESA-CCI soil moisture measurements from 27 km to 15 km grids by coupling machine learning (ML) algorithms with information from digital terrain analysis at the global scale. We modeled mean annual ESA-CCI soil moisture values across 26 years of available data (1991–2016) using a ML based kernel method and multiple terrain parameters (e.g., slope, wetness index) as prediction factors. We used ground information from the International Soil Moisture Network (ISMN, n = 13376) for evaluating soil moisture predictions. We provide gap-free mean annual soil moisture predictions, which increase by nearly 50 % the spatial resolution of ESA-CCI soil moisture product. Our predictions showed a statistical accuracy varying 0.69–0.87 % and 0.04 m³/m³ of cross-validated explained variance and root mean squared error (RMSE). We found no significant differences between the ESA-CCI and our predictions, but we found discrepancy between multiple evaluation metrics (e.g., bias vs efficiency) comparing the ESA-CCI with the ISMN. We found a negative bias (−0.01 to −0.08 m³/m³) between the values of ISMN when comparing with the ESA-CCI and our predictions across the analyzed years. A temporal analysis, using a robust trend detection strategy (i.e., Theil-Sen estimator), suggests a decline of soil moisture at the global scale that is consistent in both gridded datasets and field measurements of soil moisture varying from −0.7[−0.77, −0.62] % in the ESA-CCI product, −0.9[−1.01, −0.8] % in the downscaled predictions, and −1.6 [−1.7, −1.5] % in the ISMN. [ABSTRACT FROM AUTHOR]

Forest soils are an important component of CO2 and CH4 fluxes at the global scale, but the magnitude of these fluxes varies greatly in space and time within a landscape. Understanding the spatial and temporal distributions of these fluxes across complex landscapes remains a major challenge for researchers and land managers alike. We investigated the spatiotemporal variability of soil-atmosphere CO2 and CH4 fluxes and the relationships of these fluxes to chemical and physical soil properties distributed across a topographically-heterogeneous landscape. Soil CO2 and CH4 fluxes were measured along with soil temperature, moisture, bulk density, texture, carbon, sorption capacity, and dissolved organic matter quality over 2 years along hillslope transects spanning valley bottom, transition zone, and upland landscape positions in a temperate forest watershed. Transition zone soil CO2 efflux was 54-160% higher than low-lying valley bottoms, and 15-54% higher than uplands. Net seasonal CH4 uptake was 58-150% higher in transition zone soils than in uplands, while valley bottoms were occasionally large net sources (up to 19 nmol CH4 m−2 s−1). Soil CO2 efflux and net CH4 uptake were both positively associated with seasonal temperature, and were highest in soils with relatively high carbon and clay content, and relatively low bulk density, moisture, and sorption capacity. We concluded that: (1) transition zone soils act as landscape hotspots for net CH4 uptake in addition to CO2 efflux, and (2) that this spatial distribution is more consistent across seasons for net CH4 uptake than for CO2 efflux. [ABSTRACT FROM AUTHOR]

Aims: Soils provide key ecosystem services and directly control ecosystem functions; thus, there is a need to define the reference state of soil functionality. Most common functional classifications are vegetation-centered, such as plant functional types (PFTs), and neglect soil characteristics and processes. We propose Soil Functional Types (SFTs) as a conceptual approach to represent and describe the functionality of soils based on characteristics of their greenhouse gas (GHG) flux dynamics.Methods: We used automated measurements of CO2, CH4 and N2O soil fluxes in a forested area to define SFTs as surface areas with similar GHG dynamics. We performed mixed effects models, and independent cluster analyses of environmental variables and SFT classifications.Results: Unique groupings based on SFTs, but not environmental variables, supported the hypothesis that SFTs provide additional insights on the spatial variability of soil functionality beyond information represented by commonly measured soil parameters (e.g., soil moisture, soil temperature, litter biomass).Conclusions: This approach could complement vegetation-based functional classifications to better represent the broad range of ecosystem functions. A global application of the proposed SFT framework will only be possible if there is a community-wide effort to share data and create a global database of GHG emissions from soils. [ABSTRACT FROM AUTHOR]

Background and aims: We conducted a mesocosm study to investigate the extent to which the process of hydraulic redistribution of soil water by plant roots is affected by mycorrhizosphere disturbance. Methods: We used deuterium-labeled water to track the transfer of hydraulically lifted water (HLW) from well-hydrated donor oaks ( Quercus agrifolia Nee.) to drought-stressed receiver seedlings growing together in mycorrhizal or fungicide-treated mesocosms. We hypothesized that the transfer of HLW from donor to receiver plants would be enhanced in undisturbed (non-fungicide-treated) mesocosms where an intact mycorrhizal hyphal network was present. Results: Contrary to expectations, both upper soil and receiver seedlings contained significantly greater proportions of HLW in mesocosms where the abundance of mycorrhizal hyphal links between donor and receiver roots had been sharply reduced by fungicide application. Reduced soil hyphal density and viability likely hampered soil moisture retention properties in fungicide-treated mesocosms, thus leading to faster soil water depletion in upper compartments. The resulting steeper soil water potential gradient between taproot and upper compartments enhanced hydraulic redistribution in fungicide-treated mesocosms. Conclusions: Belowground disturbances that reduce soil hyphal density and viability in the mycorrhizosphere can alter the patterns of hydraulic redistribution by roots through effects on soil hydraulic properties. [ABSTRACT FROM AUTHOR]

Precipitation regimes are predicted to become more variable with more extreme rainfall events punctuated by longer intervening dry periods. Water-limited ecosystems are likely to be highly responsive to altered precipitation regimes. The bucket model predicts that increased precipitation variability will reduce soil moisture stress and increase primary productivity and soil respiration in aridland ecosystems. To test this hypothesis, we experimentally altered the size and frequency of precipitation events during the summer monsoon (July through September) in 2007 and 2008 in a northern Chihuahuan Desert grassland in central New Mexico, USA. Treatments included (1) ambient rain, (2) ambient rain plus one 20 mm rain event each month, and (3) ambient rain plus four 5 mm rain events each month. Throughout two monsoon seasons, we measured soil temperature, soil moisture content ( θ), soil respiration ( R), along with leaf-level photosynthesis ( A), predawn leaf water potential (Ψ), and seasonal aboveground net primary productivity (ANPP) of the dominant C grass, Bouteloua eriopoda. Treatment plots receiving a single large rainfall event each month maintained significantly higher seasonal soil θ which corresponded with a significant increase in R and ANPP of B. eriopoda when compared with plots receiving multiple small events. Because the strength of these patterns differed between years, we propose a modification of the bucket model in which both the mean and variance of soil water change as a consequence of interannual variability from 1 year to the next. Our results demonstrate that aridland ecosystems are highly sensitive to increased precipitation variability, and that more extreme precipitation events will likely have a positive impact on some aridland ecosystem processes important for the carbon cycle. [ABSTRACT FROM AUTHOR]

Soil moisture plays a key role in the Earth's water and carbon cycles, but acquisition of continuous (i.e., gap-free) soil moisture measurements across large regions is a challenging task due to limitations of currently available point measurements. Satellites offer critical information for soil moisture over large areas on a regular basis (e.g., European Space Agency Climate Change Initiative (ESA CCI), National Aeronautics and Space Administration Soil Moisture Active Passive (NASA SMAP)); however, there are regions where satellite-derived soil moisture cannot be estimated because of certain conditions such as high canopy density, frozen soil, or extremely dry soil. We compared and tested three approaches, ordinary kriging (OK), regression kriging (RK), and generalized linear models (GLMs), to model soil moisture and fill spatial data gaps from the ESA CCI product version 4.5 from January 2000 to September 2012, over a region of 465,777 km2 across the Midwest of the USA. We tested our proposed methods to fill gaps in the original ESA CCI product and two data subsets, removing 25% and 50% of the initially available valid pixels. We found a significant correlation (r = 0.558, RMSE = 0.069 m3m−3) between the original satellite-derived soil moisture product with ground-truth data from the North American Soil Moisture Database (NASMD). Predicted soil moisture using OK also had significant correlation with NASMD data when using 100% (r = 0.579, RMSE = 0.067 m3m−3), 75% (r = 0.575, RMSE = 0.067 m3m−3), and 50% (r = 0.569, RMSE = 0.067 m3m−3) of available valid pixels for each month of the study period. RK showed comparable values to OK when using different percentages of available valid pixels, 100% (r = 0.582, RMSE = 0.067 m3m−3), 75% (r = 0.582, RMSE = 0.067 m3m−3), and 50% (r = 0.571, RMSE = 0.067 m3m−3). GLM had slightly lower correlation with NASMD data (average r = 0.475, RMSE = 0.070 m3m−3) when using the same subsets of available data (i.e., 100%, 75%, 50%). Our results provide support for using geostatistical approaches (OK and RK) as alternative techniques to gap-fill missing spatial values of satellite-derived soil moisture. [ABSTRACT FROM AUTHOR]

Highlights: [•] Wavelet transform was applied to examine spectral characteristics of ET. [•] ET power spectra display a −1 power law at less than an hour time-scale. [•] The cospectra of ET and soil moisture show temporal correlation at 5-days. [•] ET synchronizes with net radiation, and leads vapor pressure deficit for ∼2.0h. [•] The phase relationship between ET and soil moisture is influenced by irrigation. [ABSTRACT FROM AUTHOR]

In nearly all large-scale terrestrial ecosystem models, soil respiration is represented as a function of soil temperature. However, the relationship between soil respiration and soil temperature is highly variable across sites and there is often a pronounced hysteresis in the soil respiration-temperature relationship over the course of the growing season. This phenomenon indicates the importance of biophysical factors beyond just temperature in controlling soil respiration. To identify the potential mechanisms of the seasonal soil respiration-temperature hysteresis, we developed a set of numerical models to demonstrate how photosynthesis, soil moisture, and soil temperature, alone and in combination, affect the hysteresis relationship. Then, we used a variant of the model informed by observations of soil respiration, soil temperature, photosynthesis, and soil moisture from multiple mesic and semi-arid ecosystems to quantify the frequency of hysteresis and identify its potential controls. We show that the hysteresis can result from the seasonal cycle of photosynthesis (which supplies carbon to rhizosphere respiration), and soil moisture (which limits heterotrophic respiration when too low or too high). Using field observations of soil respiration, we found evidence of seasonal hysteresis in 9 out of 15 site-years across 8 diverse biomes. Specifically, clockwise hysteresis occurred when photosynthesis preceded seasonal soil temperature and counterclockwise hysteresis occurred when photosynthesis lagged soil temperature. We found that across all sites, much of the respiration-temperature lag was explained by the decoupling of photosynthesis and temperature, highlighting the importance of recently assimilated carbon to soil respiration. An analysis of observations from 129 FLUXNET sites revealed that time lags between gross primary productivity (a proxy for canopy photosynthesis) and soil temperature were common phenomena, which would tend to drive counterclockwise hysteresis at low-latitude sites and clockwise hysteresis at high-latitude sites. Collectively, our results show that incorporating photosynthesis and soil moisture in the standard exponential soil respiration-temperature model (i.e., Q 10 model) improves the explanatory power of models at local scales. [ABSTRACT FROM AUTHOR]