Regions of Strong Coupling Between Soil Moisture and Precipitation

Uniformly elevated future heat stress in China driven by spatially heterogeneous water vapor changes

The wet bulb temperature (Tw) has gained considerable attention as a crucial indicator of heat-related health risks. Here we report south-to-north spatially heterogeneous trends of Tw in China over 1979-2018. We find that actual water vapor pressure (Ea) changes play a dominant role in determining the different trend of Tw in southern and northern China, which is attributed to the faster warming of high-latitude regions of East Asia as a response to climate change. This warming effect regulates large-scale atmospheric features and leads to extended impacts of the South Asia high (SAH) and the western Pacific subtropical high (WPSH) over southern China and to suppressed moisture transport. Attribution analysis using climate model simulations confirms these findings. We further find that the entire eastern China, that accommodates 94% of the country's population, is likely to experience widespread and uniform elevated thermal stress the end of this century. Our findings highlight the necessity for development of adaptation measures in eastern China to avoid adverse impacts of heat stress, suggesting similar implications for other regions as well.

Importance of soil moisture conservation in mitigating climate change

A troubling feedback loop, where drier soil contributes to hotter climates, has been widely recognized. This study, drawing on climate model simulations, reveals that maintaining current global soil moisture levels could significantly alleviate 32.9% of land warming under low-emission scenarios. This action could also postpone reaching critical warming thresholds of 1.5 °C and 2.0 °C by at least a decade. Crucially, preserving soil moisture at current levels could prevent noticeable climate change impacts across 42% of the Earth's land, a stark deviation from projections suggesting widespread impacts before the 2060s. To combat soil drying, afforestation in mid-to-low latitude regions within the next three decades is proposed as an effective strategy to increase surface water availability. This underscores the substantial potential of nature-based solutions for managing soil moisture, benefiting both climate change mitigation and ecological enhancement.

Weaker land-atmosphere coupling in global storm-resolving simulation

The debate on the sign of the soil moisture-precipitation feedback remains open. On the one hand, studies using global coarse-resolution climate models have found strong positive feedback. However, such models cannot represent convection explicitly. On the other hand, studies using km-scale regional climate models and explicit convection have reported negative feedback. Yet, the large-scale circulation is prescribed in such models. This study revisits the soil moisture-precipitation feedback using global, coupled simulations conducted for 1 y with explicit convection and compares the results to coarse-resolution simulations with parameterized convection. We find significant differences in a majority of points with feedback that is weaker and dominantly negative with explicit convection. The model with explicit convection is more often in a wet regime and prefers the triggering of convection over dry soil in the presence of soil moisture heterogeneity, in contrast to the coarse-resolution model. Further analysis indicates that the feedback not only between soil moisture and evapotranspiration but also between evapotranspiration and precipitation is weaker, in better agreement with observations. Our findings suggest that coarse-resolution models may not be well suited to study aspects of climate change over land such as changes in droughts and heatwaves.

Influence paradigms of soil moisture on land surface energy partitioning under different climatic conditions

Soil moisture (SM) directly controls the land surface energy partition which plays an important role in the formation of extreme weather events. However, its dependence on specific climatic conditions is not thoroughly understood due to the complexity of soil moisture effects. Here, we examine the relationship between SM and surface energy partitioning under different climate conditions, and identify the influence paradigms of soil moisture on surface energy partition. We find that temperature changes can explicitly determine the impact paradigm of different physical processes, i.e. evapotranspiration, soil freezing and thawing, and such influence paradigms are also affected by atmospheric aridity (VPD). Globally, there are five paradigms that effects on surface energy partitioning, including the warm-wet paradigm (WW), transitional paradigm (TP), warm-dry paradigm (WD), cool-wet paradigm (CW) and cold paradigm (CP). Since 1981, the global area proportion for TP is observed to increase pronouncedly. We also find that the critical SM threshold exhibits regional variations and the global average is 0.45 m3/m3. The identified paradigms and their long-term change trends provide new insights into the global intensification of land-atmosphere interaction, which has important implications for global warming and the formation of heatwaves.

Dryness stress weakens the sustainability of global vegetation cooling

Dryness stress can limit vegetation growth, and the cooling potential of vegetation will also be strongly influenced. However, it is still unclear how dryness stress feedback weakens the sustainability of vegetation-based cooling. Based on the long-time series of multi-source remote sensing product data for the period 2001-2020, the relative contribution rate, and the method of decoupling and boxing, we determined that greening will likely mitigate global warming by 0.065 ± 0.009 °C/a, but nearly 47 % of the area is unsustainable. This phenomenon is strongly related to dryness stress. The restricted area of soil moisture (SM: 68.35 %) to vegetation is larger than that of the atmospheric vapor pressure deficit (VPD: 34.19 %). With the decrease in SM, vegetation will decrease by an average of 14.9 %, and with the increase in VPD, vegetation will decrease by 3.8 %. With the continuous increase in the dryness stress area, the sustainability of the vegetation cooling effect will be threatened in an area of about 21.03 million km2, which is equivalent to the area of North America. Specifically, we found that with the decrease in SM and the increase in VPD, the contribution of vegetation to the cooling effect has been weakened by 10.8 %. This conclusion confirms that dryness stress will threaten the sustainability of vegetation-based climate cooling and provides further insight into the effect of dryness stress on vegetation cooling.

The negative-positive feedback transition thresholds of meteorological drought in response to agricultural drought and their dynamics

There are complex bidirectional feedback relationships among different types of droughts (e.g., meteorological and agricultural droughts). As agricultural drought intensifies, meteorological drought response to agricultural drought may be changed from negative to positive feedback. Nevertheless, the negative-positive feedback transition thresholds of meteorological drought in response to agricultural drought and their dynamics have remained unsolved. Herein, we proposed a new quantitative method to characterize the mutual feedback between meteorological drought and agricultural drought based on the vine copula function for the first time in this study. The negative-positive feedback transition threshold and the sensitivity of the feedback were quantified under certain drought conditions. In order to investigate the feedback relationship dynamics under a changing environment, the total study period was evenly divided into two stages: stage 1 (1982-1999) and stage 2 (2000-2018). Finally, the random forest method was used to explore the dominant factors on the transition threshold. Results indicate that: (1) the negative-positive feedback transition thresholds in August is generally lower than June and July in mainland China, the basin with large threshold is the Southwest River Basin; (2) the sensitivity of meteorological drought in response to agricultural drought was higher in positive feedback than in negative feedback; (3) the transition thresholds of stage 2 was mostly reduced, while the feedback sensitivity of positive feedback was mostly increased; and (4) compared with the single factor, the land-meteorological coupling strength (the correlation between precipitation and soil moisture) dominants the negative-positive feedback transition threshold. This study sheds new insights into droughts feedback.

Global diagnosis of land-atmosphere coupling based on water isotopes

Land-atmosphere coupling (LAC) plays a significant role in weather and climate and is related to droughts and heatwaves. We propose a simple and efficient LAC diagnosis method based on the analysis of water isotopes in atmospheric water vapour and precipitation. Using the method, we identify the primary LAC hotspot regions of the globe and reveal the seasonality of LAC strength. We find that LAC strength exhibits a relationship with latitude. Low latitudes present stronger LAC strength and contribute more significantly to the overall LAC area compared to boreal middle and high latitudes. It's important to note that LAC primarily manifests in the troposphere and is detected in the lower stratosphere of low latitudes, with limited influence observed in the stratosphere. However, the impact of LAC is noticeable in the upper stratosphere in boreal middle and high latitudes. Moreover, the seasonality of LAC strength is pronounced. On a global scale, the season with the strongest LAC is boreal autumn in the Northern Hemisphere but boreal summer in the Southern Hemisphere. Notably, this pattern does not exhibit a seesaw effect between the two hemispheres. Our isotope-based LAC diagnosis method captures the major LAC hotspots found in previous work and validates the seasonality of LAC within these hotspots. This substantiates the reliability and effectiveness of our isotope-based approach.

Soil moisture precipitation feedbacks in the Eastern European Alpine region in convection-permitting climate simulations

A novel convection permitting modelling framework that combines a pseudo-global warming approach with continuously forced deep soil moisture from prescribed perturbation storylines is applied in the Eastern European Alpine region and parts of the Pannonian Basin to investigate soil moisture precipitation (SMP) feedbacks on summertime precipitation and the feedbacks' role under changed climate conditions. A set of 1-year convection-permitting (3 km horizontal grid spacing) soil moisture sensitivity simulations with the regional climate model of the Consortium for Small-Scale Modelling in Climate Mode are conducted. In order to account for global warming, end-of-the-century climate change effects from four global climate models, projecting the greenhouse gas concentration scenario RCP 8.5, are imprinted. The simulations reveal that (1) the locations of precipitation events are highly sensitive to soil moisture modifications while intensities and the internal structure of precipitation events are nearly unaffected and (2) high precipitation intensities are more likely in combinations with positive temporal but distinctive (either strong positive or strong negative) spatial SMP coupling. Low precipitation intensities are in favour of combinations of negative temporal and positive spatial coupling. The analyses suggest that soil moisture at a given time acts as a guiding field for the location of the next precipitation event. Interestingly, this behaviour is independent of climate change, although the coupling strength's increase is 1.5-1.7 times larger than expected from linear climate change scaling when climate becomes 50% dryer. Finally, it is found that (1) local deviations in the climate change signal of summertime precipitation in the range of up to ±40% are caused by uncertainty in deep soil moisture in the range of ±10% and (2) these local deviations in the climate change signal are dominated by soil moisture uncertainty in future climate conditions.

A global daily evapotranspiration deficit index dataset for quantifying drought severity from 1979 to 2022

Droughts cause multiple ecological and social damages. Drought indices are key tools to quantify drought severity, but they are mainly limited to timescales of monthly or longer. However, shorter-timescale (e.g., daily) drought indices enable more accurate identification of drought characteristics (e.g., onset and cessation time) and help timely potential mitigation of adverse effects. Here, we propose a dataset of a daily drought index named daily evapotranspiration deficit index (DEDI), which is produced for global land areas from 1979 to 2022 using actual and potential evapotranspiration data. Validation efforts show that the DEDI dataset can well identify dry and wet variations in terms of spatial patterns and temporal evolutions when compared with other available drought indices on a daily scale. The dataset also has the capability to capture recent drying trends and to detect ecology- or agriculture-related droughts. Overall, the DEDI dataset is a step forward in facilitating drought monitoring and early warning at higher temporal resolution than other compared existing products.

Daily Soil Moisture Retrieval by Fusing CYGNSS and Multi-Source Auxiliary Data Using Machine Learning Methods

The Cyclone Global Navigation Satellite System (CYGNSS), a publicly accessible spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data, provides a new alternative opportunity for large-scale soil moisture (SM) retrieval, but with interference from complex environmental conditions (i.e., vegetation cover and ground roughness). This study aims to develop a high-accuracy model for CYGNSS SM retrieval. The normalized surface reflectivity calculated by CYGNSS is fused with variables that are highly related to the SM obtained from optical/microwave remote sensing to solve the problem of the influence of complicated environmental conditions. The Gradient Boost Regression Tree (GBRT) model aided by land-type data is then used to construct a multi-variables SM retrieval model with six different land types of multiple models. The methodology is tested in southeastern China, and the results correlate very well with the existing satellite remote sensing products and in situ SM data (R = 0.765, ubRMSE = 0.054 m3m-3 vs. SMAP; R = 0.653, ubRMSE = 0.057 m3 m-3 vs. ERA5 SM; R = 0.691, ubRMSE = 0.057 m3m-3 vs. in situ SM). This study makes contributions from two aspects: (1) improves the accuracy of the CYGNSS retrieval of SM based on fusion with other auxiliary data; (2) constructs the SM retrieval model with multi-layer multiple models, which is suitable for different land properties.

River interlinking alters land-atmosphere feedback and changes the Indian summer monsoon

Massive river interlinking projects are proposed to offset observed increasing droughts and floods in India, the most populated country in the world. These projects involve water transfer from surplus to deficit river basins through reservoirs and canals without an in-depth understanding of the hydro-meteorological consequences. Here, we use causal delineation techniques, a coupled regional climate model, and multiple reanalysis datasets, and show that land-atmosphere feedbacks generate causal pathways between river basins in India. We further find that increased irrigation from the transferred water reduces mean rainfall in September by up to 12% in already water-stressed regions of India. We observe more drying in La Niña years compared to El Niño years. Reduced September precipitation can dry rivers post-monsoon, augmenting water stress across the country and rendering interlinking dysfunctional. Our findings highlight the need for model-guided impact assessment studies of large-scale hydrological projects across the globe.

Soil moisture-atmosphere coupling accelerates global warming

Soil moisture-atmosphere coupling (SA) amplifies greenhouse gas-driven global warming via changes in surface heat balance. The Scenario Model Intercomparison Project projects an acceleration in SA-driven warming due to the 'warmer climate - drier soil' feedback, which continuously warms the globe and thereby exerts an acceleration effect on global warming. The projection shows that SA-driven warming exceeds 0.5 °C over extratropical landmasses by the end of the 21st Century. The likelihood of extreme high temperatures will additionally increase by about 10% over the entire globe (excluding Antarctica) and more than 30% over large parts of North America and Europe under the high-emission scenario. This demonstrates the high sensitivity of SA to climate change, in which SA can exceed the natural range of climate variability and play a non-linear warming component role on the globe.

Radiative controls by clouds and thermodynamics shape surface temperatures and turbulent fluxes over land

Land surface temperatures (LSTs) are strongly shaped by radiation but are modulated by turbulent fluxes and hydrologic cycling as the presence of water vapor in the atmosphere (clouds) and at the surface (evaporation) affects temperatures across regions. Here, we used a thermodynamic systems framework forced with independent observations to show that the climatological variations in LSTs across dry and humid regions are mainly mediated through radiative effects. We first show that the turbulent fluxes of sensible and latent heat are constrained by thermodynamics and the local radiative conditions. This constraint arises from the ability of radiative heating at the surface to perform work to maintain turbulent fluxes and sustain vertical mixing within the convective boundary layer. This implies that reduced evaporative cooling in dry regions is then compensated for by an increased sensible heat flux and buoyancy, which is consistent with observations. We show that the mean temperature variation across dry and humid regions is mainly controlled by clouds that reduce surface heating by solar radiation. Using satellite observations for cloudy and clear-sky conditions, we show that clouds cool the land surface over humid regions by up to 7 K, while in arid regions, this effect is absent due to the lack of clouds. We conclude that radiation and thermodynamic limits are the primary controls on LSTs and turbulent flux exchange which leads to an emergent simplicity in the observed climatological patterns within the complex climate system.

Chinese Soil Moisture Observation Network and Time Series Data Set for High Resolution Satellite Applications

High-quality ground observation networks are an important basis for scientific research. Here, an automatic soil observation network for high-resolution satellite applications in China (SONTE-China) was established to measure both pixel- and multilayer-based soil moisture and temperature. SONTE-China is distributed across 17 field observation stations with a variety of ecosystems, covering both dry and wet zones. In this paper, the average root mean squared error (RMSE) of station-based soil moisture for well-characterized SONTE-China sites is 0.027 m3/m3 (0.014~0.057 m3/m3) following calibration for specific soil properties. The temporal and spatial characteristics of the observed soil moisture and temperature in SONTE-China conform to the geographical location, seasonality and rainfall of each station. The time series Sentinel-1 C-band radar signal and soil moisture show strong correlations, and the RMSE of the estimated soil moisture from radar data was lower than 0.05 m3/m3 for the Guyuan and Minqin stations. SONTE-China is a soil moisture retrieval algorithm that can validate soil moisture products and provide basic data for weather forecasting, flood forecasting, agricultural drought monitoring and water resource management.

Climate Change Impacts on the Potential Distribution Pattern of Osphya (Coleoptera: Melandryidae), an Old but Small Beetle Group Distributed in the Northern Hemisphere

Exploring the development of species distribution patterns under climate change is the basis of biogeography and macroecology. However, under the background of global climate change, few studies focus on how the distribution pattern and the range of insects have or will change in response to long-term climate change. An old but small, Northern-Hemisphere-distributed beetle group Osphya is an ideal subject to conduct the study in this aspect. Here, based on a comprehensive geographic dataset, we analyzed the global distribution pattern of Osphya using ArcGIS techniques, which declared a discontinuous and uneven distribution pattern across the USA, Europe, and Asia. Furthermore, we predicted the suitable habitats of Osphya under different climate scenarios via the MaxEnt model. The results showed that the high suitability areas were always concentrated in the European Mediterranean and the western coast of USA, while a low suitability exhibited in Asia. Moreover, by integrating the analyses of biogeography and habitat suitability, we inferred that the Osphya species conservatively prefer a warm, stable, and rainy climate, and they tend to expand towards higher latitude in response to the climate warming from the past to future. These results are helpful in exploring the species diversity and protection of Osphya.

The Distribution Pattern and Species Richness of Scorpionflies (Mecoptera: Panorpidae)

The uneven distribution of species diversity on earth, with mountainous regions housing half of the high species diversity areas, makes mountain ecosystems vital to biodiversity conservation. The Panorpidae are ecological indicators, ideal for studying the impact of climate change on potential insect distribution. This study examines the impact of environmental factors on the distribution of the Panorpidae and analyzes how their distribution has changed over three historical periods, the Last Interglacial (LIG), the Last Glacial Maximum (LGM), and Current. The MaxEnt model is used to predict the potential distribution area of Panorpidae based on global distribution data. The results show that precipitation and elevation are the primary factors affecting species richness, and the suitable areas for Panorpidae are distributed in southeastern North America, Europe, and southeastern Asia. Throughout the three historical periods, there was an initial increase followed by a decrease in the area of suitable habitats. During the LGM period, there was a maximum range of suitable habitats for cool-adapted insects, such as scorpionflies. Under the scenarios of global warming, the suitable habitats for Panorpidae would shrink, posing a challenge to the conservation of biodiversity. The study provides insights into the potential geographic range of Panorpidae and helps understand the impact of climate change on their distribution.

A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset

The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data and is aimed at extrapolating SMAP-like quality SM back in time using previous satellite microwave platforms. CASM represents SM in the top soil layer, and it is defined on a global 25 km EASE-2 grid and for 2002-2020 with a 3-day temporal resolution. The seasonal cycle is removed for the neural network training to ensure its skill is targeted at predicting SM extremes. CASM comparison to 367 global in-situ SM monitoring sites shows a SMAP-like median correlation of 0.66. Additionally, the SM product uncertainty was assessed, and both aleatoric and epistemic uncertainties were estimated and included in the dataset. CASM dataset can be used to study a wide range of hydrological, carbon cycle, and energy processes since only a consistent long-term dataset allows assessing changes in water availability and water stress.

A 21-year dataset (2000-2020) of gap-free global daily surface soil moisture at 1-km grid resolution

Global soil moisture estimates from current satellite missions are suffering from inherent discontinuous observations and coarse spatial resolution, which limit applications especially at the fine spatial scale. This study developed a dataset of global gap-free surface soil moisture (SSM) at daily 1-km resolution from 2000 to 2020. This is achieved based on the European Space Agency - Climate Change Initiative (ESA-CCI) SSM combined product at 0.25° resolution. Firstly, an operational gap-filling method was developed to fill the missing data in the ESA-CCI SSM product using SSM of the ERA5 reanalysis dataset. Random Forest algorithm was then adopted to disaggregate the coarse-resolution SSM to 1-km, with the help of International Soil Moisture Network in-situ observations and other optical remote sensing datasets. The generated 1-km SSM product had good accuracy, with a high correlation coefficent (0.89) and a low unbiased Root Mean Square Error (0.045 m³/m³) by cross-validation. To the best of our knowledge, this is currently the only long-term global gap-free 1-km soil moisture dataset by far.

Soil moisture-evaporation coupling shifts into new gears under increasing CO2

When soil moisture (SM) content falls within a transitional regime between dry and wet conditions, it controls evaporation, affecting atmospheric heat and humidity. Accordingly, different SM regimes correspond to different gears of land-atmosphere coupling, affecting climate. Determining patterns of SM regimes and their future evolution is imperative. Here, we examine global SM regime distributions from ten climate models. Under increasing CO₂, the range of SM extends into unprecedented coupling regimes in many locations. Solely wet regime areas decline globally by 15.9%, while transitional regimes emerge in currently humid areas of the tropics and high latitudes. Many semiarid regions spend more days in the transitional regime and fewer in the dry regime. These imply that a larger fraction of the world will evolve to experience multiple gears of land-atmosphere coupling, with the strongly coupled transitional regime expanding the most. This could amplify future climate sensitivity to land-atmosphere feedbacks and land management.

Retrieving Soil Physical Properties by Assimilating SMAP Brightness Temperature Observations into the Community Land Model

This paper coupled a unified passive and active microwave observation operator-namely, an enhanced, physically-based, discrete emission-scattering model-with the community land model (CLM) in a data assimilation (DA) system. By implementing the system default local ensemble transform Kalman filter (LETKF) algorithm, the Soil Moisture Active and Passive (SMAP) brightness temperature TBp (p = Horizontal or Vertical polarization) assimilations for only soil property retrieval and both soil properties and soil moisture estimates were investigated with the aid of in situ observations at the Maqu site. The results indicate improved estimates of soil properties of the topmost layer in comparison to measurements, as well as of the profile. Specifically, both assimilations of TBH lead to over a 48% reduction in root mean square errors (RMSEs) for the retrieved clay fraction from the background compared to the top layer measurements. Both assimilations of TBV reduce RMSEs by 36% for the sand fraction and by 28% for the clay fraction. However, the DA estimated soil moisture and land surface fluxes still exhibit discrepancies when compared to the measurements. The retrieved accurate soil properties alone are inadequate to improve those estimates. The discussed uncertainties (e.g., fixed PTF structures) in the CLM model structures should be mitigated.

Interaction between dry and hot extremes at a global scale using a cascade modeling framework

Climate change amplifies dry and hot extremes, yet the mechanism, extent, scope, and temporal scale of causal linkages between dry and hot extremes remain underexplored. Here using the concept of system dynamics, we investigate cross-scale interactions within dry-to-hot and hot-to-dry extreme event networks and quantify the magnitude, temporal-scale, and physical drivers of cascading effects (CEs) of drying-on-heating and vice-versa, across the globe. We find that locations exhibiting exceptionally strong CE (hotspots) for dry-to-hot and hot-to-dry extremes generally coincide. However, the CEs differ strongly in their timescale of interaction, hydroclimatic drivers, and sensitivity to changes in the soil-plant-atmosphere continuum and background aridity. The CE of drying-on-heating in the hotspot locations reaches its peak immediately driven by the compounding influence of vapor pressure deficit, potential evapotranspiration, and precipitation. In contrast, the CE of heating-on-drying peaks gradually dominated by concurrent changes in potential evapotranspiration, precipitation, and net-radiation with the effect of vapor pressure deficit being strongly controlled by ecosystem isohydricity and background aridity. Our results help improve our understanding of the causal linkages and the predictability of compound extremes and related impacts.

Soil-vegetation moisture capacitor maintains dry season vegetation productivity over India

India receives more than 70% of its annual rainfall in the summer monsoon from June to September. The rainfall is scanty and scattered for the rest of the year. Combining satellite data and model simulations, we show that the soil-vegetation continuum works as a natural capacitor of water, storing the monsoon pulse and releasing the moisture to the atmosphere through evapotranspiration over approximately 135 days when the moisture supply from precipitation is less than the evapotranspiration losses. The total Gross Primary Productivity of vegetation in India during the capacitor period accounts for almost 35% of the total annual GPP value. It primarily depends on the soil moisture at the beginning of the period, a measure of moisture capacitance of soil, with a correlation of 0.6. Given that India is the second largest contributor to recent global greening, its soil-vegetation water capacitance plays a significant role in the global carbon balance.

Evaluation of Several Satellite-Based Soil Moisture Products in the Continental US

Satellite-based soil moisture products are suitable for large-scale regional monitoring due to the accessibility. Five soil moisture products including SMAP, ESA CCI, and AMSR2 (ascending, descending, and average) were selected in the continental United States (US) from 2016 to 2021. To evaluate the performance of the products and assess their applicability, ISMN (International Soil Moisture Network) data were used as the in situ measurement. PBIAS (Percentage of BIAS), R (Pearson correlation coefficient), RMSE (Root Mean Square Error), ubRMSE (unbiased RMSE), MAE (Mean Absolute Error), and MBE (Mean Bias Error) were selected for evaluation. The performance of five products over six observation networks and various land cover types was compared, and the differences were analyzed at monthly, seasonal, and annual scales. The results show that SMAP had the smallest deviation with the ISMN data because PBIAS was around -0.13, and MBE was around -0.02 m³/m³. ESA CCI performed the best in almost all aspects; its R reached around 0.7, and RMSE was only around 0.07 m³/m³ at the three time scales. The performance of the AMSR2 products varied greatly across the time scales, and increasing errors and deviations showed from 2016 to 2020. The PBO_H2O and USCRN networks could reflect soil moisture characteristics in the continental US, while iRON performed poorly. The evaluation of the networks was closely related to spatial distributions. All products performed better over grasslands and shrublands with R, which was greater than 0.52, and ubRMSE was around 0.1 m³/m³, while products performed worse over forests, where PBIAS was less than -0.62, and RMSE was greater than 0.2 m³/m³, except for ESA CCI. From the boxplot, SMAP was close to the ISMN data with differences less than 0.004 m³/m³ between the median and lower quartiles.

Effects of soil moisture, net radiation, and atmospheric vapor pressure deficit on surface evaporation fraction at a semi-arid grass site

Surface energy partitioning is one of the most important aspects of the land-atmosphere coupling. The objective of this study is to examine how soil moisture (SM) and atmospheric conditions (net radiation, Rn and vapor pressure deficit, VPD) affect surface evaporation fraction (EF, determined by LE/(LE + H), where LE and H are latent and sensible heat flux, respectively) with measurements at a semi-arid grass site in China during the mid-growing season, 2020. The three factors (SM, Rn, and VPD) were divided into different levels, and then their effects on EF were investigated qualitatively using a combinatorial stratification method and quantificationally using a path analysis. Generally, the results indicated that the effect of one factor of SM, Rn and VPD on EF was influenced by the other two factors. EF tended to increase with increasing SM. Increased VPD (Rn) enhanced (weakened) the SM-EF relationship. When soil was dry, EF tended to decrease with increasing VPD; when soil was wet, EF initially levelled off and then decreased with increasing VPD. Increased Rn enhanced (weakened) the positive (negative) effect of VPD on EF when soil was wet (dry). In terms of Rn effect, EF tended to decrease as Rn increases. Further, path analysis suggested that SM, Rn, and VPD not only directly affected EF, but also indirectly affected EF, mainly through canopy conductance (Gs) and temperature difference between land surface and air (∆T). The direct effect of SM accounted for >50 % of its total effect on EF, while the total effects of Rn and VPD on EF were dominated by their indirect effects. These observational evidences may have implications for improving representation of land-atmosphere coupling in atmospheric general circulation models over the semi-arid regions covered by grass.

Machine learning based estimation of field-scale daily, high resolution, multi-depth soil moisture for the Western and Midwestern United States

Background

High-resolution soil moisture estimates are critical for planning water management and assessing environmental quality. In-situ measurements alone are too costly to support the spatial and temporal resolutions needed for water management. Recent efforts have combined calibration data with machine learning algorithms to fill the gap where high resolution moisture estimates are lacking at the field scale. This study aimed to provide calibrated soil moisture models and methodology for generating gridded estimates of soil moisture at multiple depths, according to user-defined temporal periods, spatial resolution and extent.

Methods

We applied nearly one million national library soil moisture records from over 100 sites, spanning the U.S. Midwest and West, to build Quantile Random Forest (QRF) calibration models. The QRF models were built on covariates including soil moisture estimates from North American Land Data Assimilation System (NLDAS), soil properties, climate variables, digital elevation models, and remote sensing-derived indices. We also explored an alternative approach that adopted a regionalized calibration dataset for the Western U.S. The broad-scale QRF models were independently validated according to sampling depths, land cover type, and observation period. We then explored the model performance improved with local samples used for spiking. Finally, the QRF models were applied to estimate soil moisture at the field scale where evaluation was carried out to check estimated temporal and spatial patterns.

Results

The broad-scale QRF model showed moderate performance (R² = 0.53, RMSE = 0.078 m³/m³) when data points from all depth layers (up to 100 cm) were considered for an independent validation. Elevation, NLDAS-derived moisture, soil properties, and sampling depth were ranked as the most important covariates. The best model performance was observed for forest and pasture sites (R² > 0.5; RMSE < 0.09 m³/m³), followed by grassland and cropland (R² > 0.4; RMSE < 0.11 m³/m³). Model performance decreased with sampling depths and was slightly lower during the winter months. Spiking the national QRF model with local samples improved model performance by reducing the RMSE to less than 0.05 m³/m³ for grassland sites. At the field scale, model estimates illustrated more accurate temporal trends for surface than subsurface soil layers. Model estimated spatial patterns need to be further improved and validated with management data.

Conclusions

The model accuracy for top 0-20 cm soil depth (R² > 0.5, RMSE < 0.08 m³/m³) showed promise for adopting the methodology for soil moisture monitoring. The success of spiking the national model with local samples showed the need to collect multi-year high frequency (e.g., hourly) sensor-based field measurements to improve estimates of soil moisture for a longer time period. Future work should improve model performance for deeper depths with additional hydraulic properties and use of locally-selected calibration datasets.

Large-scale reforestation can increase water yield and reduce drought risk for water-insecure regions in the Asia-Pacific

Large-scale reforestation can potentially bring both benefits and risks to the water cycle, which needs to be better quantified under future climates to inform reforestation decisions. We identified 477 water-insecure basins worldwide accounting for 44.6% (380.2 Mha) of the global reforestation potential. As many of these basins are in the Asia-Pacific, we used regional coupled land-climate modeling for the period 2041-2070 to reveal that reforestation increases evapotranspiration and precipitation for most water-insecure regions over the Asia-Pacific. This resulted in a statistically significant increase in water yield (p < .05) for the Loess Plateau-North China Plain, Yangtze Plain, Southeast China, and Irrawaddy regions. Precipitation feedback was influenced by the degree of initial moisture limitation affecting soil moisture response and thus evapotranspiration, as well as precipitation advection from other reforested regions and moisture transport away from the local region. Reforestation also reduces the probability of extremely dry months in most of the water-insecure regions. However, some regions experience nonsignificant declines in net water yield due to heightened evapotranspiration outstripping increases in precipitation, or declines in soil moisture and advected precipitation.

Diminishing seasonality of subtropical water availability in a warmer world dominated by soil moisture-atmosphere feedbacks

Global warming is expected to cause wet seasons to get wetter and dry seasons to get drier, which would have broad social and ecological implications. However, the extent to which this seasonal paradigm holds over land remains unclear. Here we examine seasonal changes in surface water availability (precipitation minus evaporation, P–E) from CMIP5 and CMIP6 projections. While the P–E seasonal cycle does broadly intensify over much of the land surface, ~20% of land area experiences a diminished seasonal cycle, mostly over subtropical regions and the Amazon. Using land–atmosphere coupling experiments, we demonstrate that 63% of the seasonality reduction is driven by seasonally varying soil moisture (SM) feedbacks on P–E. Declining SM reduces evapotranspiration and modulates circulation to enhance moisture convergence and increase P–E in the dry season but not in the wet season. Our results underscore the importance of SM–atmosphere feedbacks for seasonal water availability changes in a warmer climate.

Here, the authors find increased dry–season and decreased wet–season water availability over subtropical regions and the Amazon. This is caused by seasonally varying soil moisture–atmosphere feedbacks under global warming.

The global spatiotemporal heterogeneity of land surface-air temperature difference and its influencing factors

The water and energy in the land surface and lower atmosphere have a strong coupling relationship. Apart from the land surface temperature (Ts) and air temperature (Ta), the land surface-air temperature difference (Ts-Ta) is also an essential parameter reflecting the coupling process. However, the global spatiotemporal variations and influencing factors of Ts-Ta remain not well explored. Here, ERA5-land reanalysis data, GIMMS NDVI data, and elevation data were used to analyze the global spatiotemporal heterogeneity and influencing factors of Ts-Ta. It was found that annual mean Ts-Ta exhibited a decreasing trend from the equator to polar areas. And the annual Ts-Ta increased at 0.009 °C/10a from 1981 to 2020. The variations of global net radiation mainly determined the spatiotemporal heterogeneity of global Ts-Ta. The different properties of the land surface and near-surface atmosphere were the main factors affecting the Ts-Ta, including soil moisture, vegetation, snow cover, and the water vapor content in the atmosphere. In addition, Ts and Ta also affected each other. These findings are conducive to a better understanding of the land-atmosphere coupling, and it is of great significance to take better measures to adapt the global climate change.

Drought propagation under global warming: Characteristics, approaches, processes, and controlling factors

Drought is a costly natural hazard with far-reaching impacts on agriculture, ecosystem, water supply, and socio-economy. While propagating through the water cycle, drought evolves into different types and affects the natural system and human society. Despite much progress made in recent decades, a synthesis of the characteristics, approaches, processes, and controlling factors of drought propagation is still lacking. We bridge this gap by reviewing the recent progress of drought propagation and discussing challenges and future directions. We first introduce drought propagation characteristics (e.g., response time scale, lag time), followed by different approaches, including statistical analysis and hydrological modeling. The recent progress in the propagation from meteorological drought to different types of drought (agricultural drought, hydrological drought, and ecological drought) is then synthesized, including the basic process, commonly used indicators, data sources, and main findings of drought propagation characteristics. Different controlling factors of drought propagations, including climate (e.g., aridity, seasonality, and anomalies of meteorological variables), catchment properties (e.g., slope, elevation, land cover, aquifer, baseflow), and human activities (e.g., reservoir operation and water diversion, irrigation, and groundwater abstraction), are then summarized. Challenges in drought propagation include the discrepancy in drought indicators (and approaches) and difficulty in characterizing the full propagation process and isolating influencing factors. Future analysis of drought propagation should shift from single indicators to multiple indicators, from individual drivers to combined drivers, from uni-directional analysis to feedbacks, from hazards to impacts, and from stationary to nonstationary assumptions. This review is expected to be useful for drought prediction and management across different regions under global warming.

Flash drought early warning based on the trajectory of solar-induced chlorophyll fluorescence

Flash drought often leads to devastating effects in multiple sectors and presents a unique challenge for drought early warning due to its sudden onset and rapid intensification. Existing drought monitoring and early warning systems are based on various hydrometeorological variables reaching thresholds of unusually low water content. Here, we propose a flash drought early warning approach based on spaceborne measurements of solar-induced chlorophyll fluorescence (SIF), a proxy of photosynthesis that captures plant response to multiple environmental stressors. Instead of negative SIF anomalies, we focus on the subseasonal trajectory of SIF and consider slower-than-usual increase or faster-than-usual decrease of SIF as an early warning for flash drought onset. To quantify the deviation of SIF trajectory from the climatological norm, we adopt existing formulas for a rapid change index (RCI) and apply the RCI analysis to spatially downscaled 8-d SIF data from GOME-2 during 2007-2018. Using two well-known flash drought events identified by the operational US Drought Monitor (in 2012 and 2017), we show that SIF RCI can produce strong predictive signals of flash drought onset with a lead time of 2 wk to 2 mo and can also predict drought recovery with several weeks of lead time. While SIF RCI shows great early warning potential, its magnitude diminishes after drought onset and therefore cannot reflect the current drought intensity. With its long lead time and direct relevance for agriculture, SIF RCI can support a global early warning system for flash drought and is especially useful over regions with sparse hydrometeorological data.

Advances in the Quality of Global Soil Moisture Products: A Review

Soil moisture is a crucial component of land–atmosphere interaction systems. It has a decisive effect on evapotranspiration and photosynthesis, which then notably impacts the land surface water cycle, energy transfer, and material exchange. Thus, soil moisture is usually treated as an indispensable parameter in studies that focus on drought monitoring, climate change, hydrology, and ecology. After consistent efforts for approximately half a century, great advances in soil moisture retrieval from in situ measurements, remote sensing, and reanalysis approaches have been achieved. The quality of soil moisture estimates, including spatial coverage, temporal span, spatial resolution, time resolution, time latency, and data precision, has been remarkably and steadily improved. This review outlines the recently developed techniques and algorithms used to estimate and improve the quality of soil moisture estimates. Moreover, the characteristics of each estimation approach and the main application fields of soil moisture are summarized. The future prospects of soil moisture estimation trends are highlighted to address research directions in the context of increasingly comprehensive application requirements.

A Deep-Learning Approach to Soil Moisture Estimation with GNSS-R

GNSS reflection measurements in the form of delay-Doppler maps (DDM) can be used to complement soil measurements from the SMAP Mission, which has a revisit rate too slow for some hydrological/meteorological studies. The standard approach, which only considers the peak value of the DDM, is subject to a significant amount of uncertainty due to the fact that the peak value of the DDM is not only affected by soil moisture, but also complex topography, inundation, and overlying vegetation. We hypothesize that information from the entire 2D DDM could help decrease uncertainty under various conditions. The application of deep-learning-based techniques has the potential to extract additional information from the entire DDM, while simultaneously allowing for the incorporation of additional contextual information from external datasets. This work explored the data-driven approach of convolutional neural networks (CNNs) to determine complex relationships between the reflection measurement and surface parameters, providing the groundwork for a mechanism to achieve improved global soil moisture estimates. A CNN was trained on CYGNSS DDMs and contextual ancillary datasets as inputs, with aligned SMAP soil moisture values as the targets. Data were aggregated into training sets, and a CNN was developed to process them. Predictions from the CNN were studied using an unbiased subset of samples, showing strong correlation with the SMAP target values. With this network, a soil moisture product was generated using DDMs from 2017–2019 which is generally comparable to existing global soil moisture products, and shows potential advantages in spatial resolution and coverage over regions where SMAP does not perform well. Comparisons with in-situ measurements demonstrate the correlation between the network predictions and ground truth with high temporal resolution.

Topographic and vegetation drivers of thermal heterogeneity along the boreal–grassland transition zone in western Canada: Implications for climate change refugia

Climate change refugia are areas that are relatively buffered from contemporary climate change and may be important safe havens for wildlife and plants under anthropogenic climate change. Topographic variation is an important driver of thermal heterogeneity, but it is limited in relatively flat landscapes, such as the boreal plain and prairie regions of western Canada. Topographic variation within this region is mostly restricted to river valleys and hill systems, and their effects on local climates are not well documented. We sought to quantify thermal heterogeneity as a function of topography and vegetation cover within major valleys and hill systems across the boreal–grassland transition zone.

Using iButton data loggers, we monitored local temperature at four hills and 12 river valley systems that comprised a wide range of habitats and ecosystems in Alberta, Canada (N = 240), between 2014 and 2020. We then modeled monthly temperature by season as a function of topography and different vegetation cover types using general linear mixed effect models.

Summer maximum temperatures (T_max) varied nearly 6°C across the elevation gradient sampled. Local summer mean (T_mean) and maximum (T_max) temperatures on steep, north‐facing slopes (i.e., low levels of potential solar radiation) were up to 0.70°C and 2.90°C cooler than highly exposed areas, respectively. T_max in incised valleys was between 0.26 and 0.28°C cooler than other landforms, whereas areas with greater terrain roughness experienced maximum temperatures that were up to 1.62°C cooler. We also found that forest cover buffered temperatures locally, with coniferous and mixedwood forests decreasing summer T_mean from 0.23 to 0.72°C and increasing winter T_min by up to 2°C, relative to non‐forested areas.

Spatial predictions of temperatures from iButton data loggers were similar to a gridded climate product (ClimateNA), but the difference between them increased with potential solar radiation, vegetation cover, and terrain roughness.

Species that can track their climate niche may be able to compensate for regional climate warming through local migrations to cooler microsites. Topographic and vegetation characteristics that are related to cooler local climates should be considered in the evaluation of future climate change impacts and to identify potential refugia from climate change.

Relative Strengths Recognition of Nine Mainstream Satellite-Based Soil Moisture Products at the Global Scale

Soil moisture (SM) is a crucial driving variable for the global land surface-atmosphere water and energy cycle. There are now many satellite-based SM products available internationally and it is necessary to consider all available SM products under the same context for comprehensive assessment and inter-comparisons at the global scale. Moreover, product performances varying with dynamic environmental factors, especially those closely related to retrieval algorithms, were less investigated. Therefore, this study evaluated and identified the relative strengths of nine mainstream satellite-based SM products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2), Chinese Fengyun-3B (FY3B), the Soil Moisture Active Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), and the European Space Agency (ESA) Climate Change Initiative (CCI) by using the Pearson correlation coefficient (R), R of SM seasonal anomalies (Ranom), unbiased Root Mean Square Error (ubRMSE), and bias metrics against ground observations from the International Soil Moisture Network (ISMN), as well as the Global Land Data Assimilation System (GLDAS) Noah model simulations, overall and under three dynamic (Land Surface Temperature (LST), SM, and Vegetation Optical Depth (VOD)) conditions. Results showed that the SMOS-INRA-CESBIO (IC) product outperformed the SMOSL3 product in most cases, especially in Australia, but it exhibited greater variability and higher random errors in Asia. ESA CCI products outperformed other products in capturing the spatial dynamics of SM seasonal anomalies and produced significantly high accuracy in croplands. Although the Chinese FY3B presented poor skills in most cases, it had a good ability to capture the temporal dynamics of the original SM and SM seasonal anomalies in most regions of central Africa. Under various land cover types, with the changes in LST, SM, and VOD, different products exhibited distinctly dynamic error characteristics. Generally, all products tended to overestimate the low in-situ SM content but underestimate the high in-situ SM content. It is expected that these findings can provide guidance and references for product improvement and application promotions in water exchange and land surface energy cycle.

Quantifying the contribution of evaporation from Lake Taihu to precipitation with an isotope-based method

Moisture recycling plays a crucial role in regional hydrological budgets. The isotopic composition of precipitation has long been considered as a good tracer to investigate moisture recycling. This study quantifies the moisture recycling fractions (f_r) in the Lake Taihu region using spatial variations of deuterium excess in precipitation (d_P) and surface water vapour flux (d_E). Results show that d_P at a site downwind of the lake was higher than that at an upwind site, indicating the influence of lake moisture recycling. Spatial variations in d_P after sub-cloud evaporation corrections were 2.3, 1.4 and 3.2 ‰, and d_E values were 27.4, 32.3 and 31.4 ‰ for the first winter monsoon, the summer monsoon and the second winter monsoon, respectively. Moisture recycling fractions were 0.48 ± 0.13, 0.07 ± 0.03 and 0.38 ± 0.05 for the three monsoon periods, respectively. Both using the lake parameterization kinetic fractionation factors or neglecting sub-cloud evaporation would decrease f_r, and the former has a larger influence on the f_r calculation. The larger f_r in the winter monsoon periods was mainly caused by lower specific humidity of airmasses but comparable moisture uptake along their trajectories compared to the summer monsoon period.

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).

Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation

Soil moisture is an essential parameter for a better understanding of water processes in the soil–vegetation–atmosphere continuum. Satellite synthetic aperture radar (SAR) is well suited for monitoring water content at fine spatial resolutions on the order of 1 km or higher. Several methodologies are often considered in the inversion of SAR signals: machine learning techniques, such as neural networks, empirical models and change detection methods. In this study, we propose two hybrid methodologies by improving a change detection approach with vegetation consideration or by combining a change detection approach together with a neural network algorithm. The methodology is based on Sentinel-1 and Sentinel-2 data with the use of numerous metrics, including vertical–vertical (VV) and vertical–horizontal (VH) polarization radar signals, the classical change detection surface soil moisture (SSM) index ISSM, radar incidence angle, normalized difference vegetation index (NDVI) optical index, and the VH/VV ratio. Those approaches are tested using in situ data from the ISMN (International Soil Moisture Network) with observations covering different climatic contexts. The results show an improvement in soil moisture estimations using the hybrid algorithms, in particular the change detection with the neural network one, for which the correlation increases by 54% and 33% with respect to that of the neural network or change detection alone, respectively.

New Investigation and Challenge for Spatiotemporal Drought Monitoring Using Bottom-Up Precipitation Dataset (SM2RAIN-ASCAT) and NDVI in Moroccan Arid and Semi-Arid Rangelands

Remotely sensed soil moisture products showed sensitivity to vegetation cover density and soil typology at regional dryland level. In these regions, drought monitoring is significantly performed using soil moisture index and rainfall data. Recently, rainfall and soil moisture observations have increasingly become available. This has hampered scientific progress as regards characterization of land surface processes not just in meteorology. The purpose of this study was to investigate the relationship between a newly developed precipitation dataset, SM2RAIN (Advanced SCATterometer (SM2RAIN-ASCAT), and NDVI (eMODIS-TERRA) in monitoring drought events over diverse rangeland regions of Morocco. Results indicated that the highest polynomial correlation coefficient and the lowest root mean square error (RMSE) between SM2RAIN-ASCAT and NDVI were found in a 10-year period from 2007 to 2017 in all rangelands (R = 0.81; RMSE = 0.05). This relationship was strong for degraded rangeland, where there were strong positive correlation coefficients for NDVI and SM2RAIN (R = 0.99). High correlations were found for sparse and moderate correlations for shrub rangeland (R = 0.82 and 0.61, respectively). The anomalies maps showed a very good similarity between SM2RAIN and Normalized Difference Vegetation Index (NDVI) data. The results revealed that the SM2RAIN-ASCAT and NDVI product could accurately predict drought events in arid and semi-arid rangelands.

A Comprehensive Evaluation of Gridded L-, C-, and X-Band Microwave Soil Moisture Product over the CZO in the Central Ganga Plains, India

Recent developments in passive microwave remote sensing have provided an effective tool for monitoring global soil moisture (SM) observations on a spatiotemporal basis, filling the gap of uneven in-situ measurement distribution. In this paper, four passive microwave SM products from three bands (L, C, and X) are evaluated using in-situ observations, over a dry–wet cycle agricultural (mostly paddy/wheat cycle crops) critical zone observatory (CZO) in the Central Ganga basin, India. The L-band and C/X-band information from Soil Moisture Active Passive (SMAP) Passive Enhanced Level 3 (SMAP-L3) and Advanced Microwave Scanning Radiometer 2 (AMSR2), respectively, was selected for the evaluation. The AMSR2 SM products used here were derived using the Land Parameter Retrieval Model (LPRM) algorithm. Spatially averaged observations from 20 in-situ distributed locations were initially calibrated with a single and continuous monitoring station to obtain long-term ground-based data. Furthermore, several statistical metrices along with the triple collocation (TC) error model were used to evaluate the overall accuracy and random error variance of the remote sensing products. The results indicated an overall superior performance of SMAP-L3 with a slight dry bias (−0.040 m3·m−3) and a correlation of 0.712 with in-situ observations. This also met the accuracy requirement (0.04 m3·m−3) during most seasons with a modest accuracy (0.059 m3·m−3) for the entire experimental period. Among the LPRM datasets, C1 and C2 products behaved similarly (R = 0.621) with a ubRMSE of 0.068 and 0.081, respectively. The X-band product showed a relatively poor performance compared to the other LPRM products. Seasonal performance analysis revealed a higher correlation for all the satellite SM products during monsoon season, indicating a strong seasonality of precipitation. The TC analysis indicated the lowest error variance (0.02 ± 0.003 m3·m−3) for the SMAP-L3. In the end, we introduced Spearman’s rank correlation to assess the dynamic response of SM observations to climatic and vegetation parameters.

Hydrothermal Factors Influence on Spatial-Temporal Variation of Evapotranspiration-Precipitation Coupling over Climate Transition Zone of North China

As a land–atmosphere coupling “hot spot”, the northern China climate transition zone has a sharp spatial gradient of hydrothermal conditions, which plays an essential role in shaping the spatial and temporal pattern of evapotranspiration-precipitation coupling, but whose mechanisms still remain unclear. This study analyzes the spatial and temporal variation in land–atmosphere coupling strength (CS) in the climate transitional zone of northern China and its relationship with soil moisture and air temperature. Results show that CS gradually transitions from strong positive in the northwest to negative in the southeast and northeast corners. The spatial distribution of CS is closely related to climatic hydrothermal conditions, where soil moisture plays a more dominant role: CS increases first, and then decreases with increasing soil moisture, with the threshold of soil moisture at 0.2; CS gradually transitions from positive to negative at soil moisture between 0.25 and 0.35; CS shows an exponential decreasing trend with increasing temperature. In terms of temporal variation, CS is strongest in spring and weakens sequentially in summer, autumn, and winter, and has significant interdecadal fluctuations. The trend in CS shifts gradually from significantly negative in the west to a non-significant positive in the east. Soil moisture variability dominates the intra-annual variability of CS in the study regions, and determines the interannual variation of CS in arid and semi-arid areas. Moreover, the main reason for the positive and negative spatial differences in CS in the study area is the different driving regime of evapotranspiration (ET). ET is energy-limited in the southern part of the study area, leading to a positive correlation between ET and lifting condensation level (LCL), while in most of the northern part, ET is water-limited and is negatively correlated with LCL; LCL has a negative correlation with P across the study area, thus leading to a negative ET-P coupling in the south and a positive coupling in the north.

Assessment of the importance of increasing temperature and decreasing soil moisture on global ecosystem productivity using solar-induced chlorophyll fluorescence

The accurate assessment of the global gross primary productivity (GPP) of vegetation is the key to estimating the global carbon cycle. Temperature (Ts) and soil moisture (SM) are essential for vegetation growth. It is acknowledged that the global Ts has shown an increasing trend, yet SM has shown a decreasing trend. However, the importance of SM and Ts changes on the productivity of global ecosystems remains unclear, as SM and Ts are strongly coupled through soil-atmosphere interactions. Using solar-induced chlorophyll fluorescence (SIF) as a proxy for GPP and by decoupling SM and Ts changes, our investigation shows Ts plays a more important role in SIF in 60% of the vegetation areas. Overall, increased Ts promotes SIF by mitigating the resistance from SM's reduction. However, the importance of SM and Ts varies, given different vegetation types. The results show that in the humid zone, the variation of Ts plays a more important role in SIF, but in the arid and semi-arid zones, the variation of SM plays a more important role; in the semi-humid zone, the disparity in the importance of SM and Ts is difficult to unravel. In addition, our results suggest that SIF is very sensitive to aridity gradients in arid and semi-arid ecosystems. By decoupling the intertwined SM-Ts impact on SIF, our study provides essential evidence that benefits future investigation on the factors the influence ecosystem productivity at regional or global scales.

Uncovering the critical soil moisture thresholds of plant water stress for European ecosystems

Understanding the critical soil moisture (SM) threshold (θ_crit ) of plant water stress and land surface energy partitioning is a basis to evaluate drought impacts and improve models for predicting future ecosystem condition and climate. Quantifying the θ_crit across biomes and climates is challenging because observations of surface energy fluxes and SM remain sparse. Here, we used the latest database of eddy covariance measurements to estimate θ_crit across Europe by evaluating evaporative fraction (EF)-SM relationships and investigating the covariance between vapor pressure deficit (VPD) and gross primary production (GPP) during SM dry-down periods. We found that the θ_crit and soil matric potential threshold in Europe are 16.5% and -0.7 MPa, respectively. Surface energy partitioning characteristics varied among different vegetation types; EF in savannas had the highest sensitivities to SM in water-limited stage, and the lowest in forests. The sign of the covariance between daily VPD and GPP consistently changed from positive to negative during dry-down across all sites when EF shifted from relatively high to low values. This sign of the covariance changed after longer period of SM decline in forests than in grasslands and savannas. Estimated θ_crit from the VPD-GPP covariance method match well with the EF-SM method, showing this covariance method can be used to detect the θ_crit . We further found that soil texture dominates the spatial variability of θ_crit while shortwave radiation and VPD are the major drivers in determining the spatial pattern of EF sensitivities. Our results highlight for the first time that the sign change of the covariance between daily VPD and GPP can be used as an indicator of how ecosystems transition from energy to SM limitation. We also characterized the corresponding θ_crit and its drivers across diverse ecosystems in Europe, an essential variable to improve the representation of water stress in land surface models.

Projection of the Near-Future PM2.5 in Northern Peninsular Southeast Asia under RCP8.5

Throughout the year, particularly during the dry season, the northern peninsula of Southeast Asia struggles with air pollution from PM2.5. In this study, we used the Nested Regional Climate and Chemistry Model (NRCM-Chem) to predict the PM2.5 concentrations over Southeast Asia’s northern peninsula during the years 2020–2029 under the Representative Concentration Pathway (RCP)8.5. In general, the model reasonably shows a good result, including temperature, precipitation, and PM2.5 concentration, compared to the observation with an Index of Agreement (IOA) in the range of 0.63 to 0.80. However, there were some underestimations for modeled precipitation and temperature and an overestimation for modeled PM2.5 concentration. As a response to changes in climatic parameters and the emission of PM2.5’s precursors, PM2.5 concentrations tend to increase across the region in the range of (+1) to (+35) µg/m3 during the dry season (November to April) and decline in the range of (−3) to (−30) µg/m3 during the wet season (May to October). The maximum increase in PM2.5 concentrations were found in March by >40 µg/m3.

Contrasting impacts of forests on cloud cover based on satellite observations

Forests play a pivotal role in regulating climate and sustaining the hydrological cycle. The biophysical impacts of forests on clouds, however, remain unclear. Here, we use satellite data to show that forests in different regions have opposite effects on summer cloud cover. We find enhanced clouds over most temperate and boreal forests but inhibited clouds over Amazon, Central Africa, and Southeast US. The spatial variation in the sign of cloud effects is driven by sensible heating, where cloud enhancement is more likely to occur over forests with larger sensible heat, and cloud inhibition over forests with smaller sensible heat. Ongoing forest cover loss has led to cloud increase over forest loss hotspots in the Amazon (+0.78%), Indonesia (+1.19%), and Southeast US (+ 0.09%), but cloud reduction in East Siberia (-0.20%) from 2002-2018. Our data-driven assessment improves mechanistic understanding of forest-cloud interactions, which remain uncertain in Earth system models.

How forests influence cloud cover in different regions is not well understood. Here, the authors use satellite data to show that forests enhance clouds over most temperate and boreal forests but inhibited clouds over forests of Amazon, Central Africa, and Southeast US relative to nonforest areas.

Inferring causal relations from observational long-term carbon and water fluxes records

Land, atmosphere and climate interact constantly and at different spatial and temporal scales. In this paper we rely on causal discovery methods to infer spatial patterns of causal relations between several key variables of the carbon and water cycles: gross primary productivity, latent heat energy flux for evaporation, surface air temperature, precipitation, soil moisture and radiation. We introduce a methodology based on the convergent cross-mapping (CCM) technique. Despite its good performance in general, CCM is sensitive to (even moderate) noise levels and hyper-parameter selection. We present a robust CCM (RCCM) that relies on temporal bootstrapping decision scores and the derivation of more stringent cross-map skill scores. The RCCM method is combined with the information-geometric causal inference (IGCI) method to address the problem of strong and instantaneous variable coupling, another important and long-standing issue of CCM. The proposed methodology allows to derive spatially explicit global maps of causal relations between the involved variables and retrieve the underlying complexity of the interactions. Results are generally consistent with reported patterns and process understanding, and constitute a new way to quantify and understand carbon and water fluxes interactions.

Land transpiration-evaporation partitioning errors responsible for modeled summertime warm bias in the central United States

Earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiment exhibit a well-known summertime warm bias in mid-latitude land regions - most notably in the central contiguous United States (CUS). The dominant source of this bias is still under debate. Using validated datasets and both coupled and off-line modeling, we find that the CUS summertime warm bias is driven by the incorrect partitioning of evapotranspiration (ET) into its canopy transpiration and soil evaporation components. Specifically, CMIP6 ESMs do not effectively use available rootzone soil moisture for summertime transpiration and instead rely excessively on shallow soil and canopy-intercepted water storage to supply ET. As such, expected summertime precipitation deficits in CUS induce a negative ET bias into CMIP6 ESMs and a corresponding positive temperature bias via local land-atmosphere coupling. This tendency potentially biases CMIP6 projections of regional water stress and summertime air temperature variability under elevated CO2 conditions.

Representation of Spatial Variability of the Water Fluxes over the Congo Basin Region

The Congo Basin, being one of the major basins in the tropics, is important to the global climate, yet its hydrology is perhaps the least understood. Although various reanalysis/analysis datasets have been used to improve our understanding of the basin's hydroclimate, they have been historically difficult to validate due to sparse in situ measurements. This study analyzes the impact of model resolution on the spatial variability of the Basin's hydroclimate using the Decorrelation Length Scale (DLCS) technique, as it is not subject to uniform model bias. The spatial variability within the precipitation (P), evaporation/evapotranspiration (E), and precipitation-minus-evaporation (P-E) fields were investigated across four spatial resolutions using reanalysis/analysis datasets from the ECMWF ranging from 9-75 km. Results show that the representation of P and P-E fields over the Basin and the equatorial Atlantic Ocean are sensitive to model resolution, as the spatial patterns of their DCLS results are resolution-dependent. However, the resolution-independent features are predominantly found in the E field. Furthermore, the P field is the dominant source of spatial variability of P-E, occurring over the land and the equatorial Atlantic Ocean, while over the Southern Atlantic, P-E is mainly governed by the E field, with both showing weak spatial variability.

Comparison of Long-Term Changes in Non-Linear Aggregated Drought Index Calibrated by MERRA–2 and NDII Soil Moisture Proxies

This study aimed at evaluating Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA–2) and Normalized Difference Infrared Index (NDII) soil moisture proxies in calibrating a comprehensive Non-linear Aggregated Drought Index (NADI). Soil moisture plays a critical role in temperature variability and controlling the partitioning of water into evaporative fluxes as well as ensuring effective plant growth. Long-term variability and change in climatic variables such as precipitation, temperatures, and the possible acceleration of the water cycle increase the uncertainty in soil moisture variability. Streamflow, temperature, rainfall, reservoir storage, MERRA–2, and NDII soil moisture proxies’ data from 1986 to 2016 were used to formulate the NADI. The trend analysis was performed using the Mann Kendall, SQ-MK was used to determine the point of trend direction change while Theil-Sen trend estimator method was used to determine the magnitude of the detected trend. The seasonal correlation between the NADI-NDII and NADI-MERRA–2 was higher in spring and autumn with an R2 of 0.9 and 0.86, respectively. A positive trend was observed over the 30 years period of study, NADI-NDII trend magnitude was found to be 0.02 units per year while that of NADI-MERRA–2 was 0.01 units. Wavelet analysis showed an in-phase relationship with negligible lagging between the NDII and MERRA–2 calibrated NADI. Although a robust comparison is recommended between soil moisture proxies and observed soil moisture, the soil moisture proxies in this study were found to be useful in monitoring long-term changes in soil moisture.