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A severe agricultural drought swept Central Asia in 2021, causing mass die-offs of crops and livestock. The anthropogenic contribution to declines in soil moisture in this region over recent decades has remained unclear. Here we show from analysis of large ensemble simulations that the aggravation of agricultural droughts over southern Central Asia since 1992 can be attributed to both anthropogenic forcing and internal variability associated with the Interdecadal Pacific Oscillation (IPO). Although the negative-to-positive phase transition of IPO before 1992 offset human-induced soil moisture decline, we find that the positive-to-negative phase transition thereafter has doubled the externally forced rate of drying in the early growing season. Human-induced soil moisture loss will probably be further aggravated in the following century due to warming, albeit with increasing precipitation, and our simulations project that this trend will not be counterbalanced by the IPO phase change. Instead, this internal variability could modulate drying rates in the near term with an amplitude of −2 (+2) standard deviation of the IPO trend projected to amplify (weaken) the externally forced decrease in surface soil moisture by nearly 75% (60%). The findings highlight the need for the interplay between anthropogenic forcing and the natural variability of the IPO to be considered by policymakers in this climate-sensitive region.The interplay between anthropogenic forcing and internal variability associated with the Interdecadal Pacific Oscillation has exacerbated agricultural droughts over southern Central Asia since 1992, according to large ensemble simulations.

The objective of this work was to modify the HEC-HMS flood prediction for the karstic watershed of the Lijiang River, South China, through the quantitative inclusion into the model of the available reservoir capacity of karst (ARCK) as a case study. Due to the complexities caused by hidden drainage networks in karst hydrology, as a new approach, soil moisture accounting loss was used to reflect the ARCK in flood forecasting. The soil moisture loss was analyzed against daily rainfall runoff data across 1.5 years by using an artificial neural network via phyton programming. Through the correlations found among the amounts of soil moisture and river flow fluctuations in response to precipitation and its intervals, coefficients were introduced to the model for output modifications. ARCK analysis revealed that while heavy rainfalls with longer intervals (i.e., 174 mm/2d after 112 days of the dry season) may not cause considerable changes in the river flow magnitude (0.1–0.64 higher owing to high ARCK), relatively small rainfalls with higher frequency (i.e., 83 mm/4d during the wet season) can cause drastic raise of river flow (10–20 times greater at different stations) due to lower ARCK. Soil moisture accounting loss coefficients did enhance the model’s simulated hydrographs accuracy (NSE) up to 16% on average as compared to the initial forecasting via real data. However, the modifications were valid for flood events within a few years from the soil moisture observation period. Our result suggested that the inclusion of ARCK in modeling through soil moisture accounting loss can lead to increased prediction accuracy through consistent monitoring.

Soil moisture drydown patterns encode signatures of vegetation water‐use. Previous characterizations of the drydown patterns assume a static linear relationship between water‐limited transpiration and available moisture. However, ecohydrological studies show that vegetation exhibits a spectrum of responses to water availability, suggesting that soil moisture loss functions may be nonlinear. To represent these dynamics, we introduce a nonlinearity parameter to the loss function. Our analysis shows that the nonlinear loss model improves the characterization of the satellite‐observed soil moisture drydowns. Globally, functional responses of drydowns are dominated by convex nonlinearity, showing less ecosystem water loss in dry soils than the linear loss function predicts. We find distinct degrees of nonlinearity among different vegetation types; areas with non‐woody vegetation more frequently exhibit a concave nonlinearity, the signature of aggressive water‐use strategies. We propose the nonlinear loss function as a continuous and dynamic framework to represent vegetation water‐use under changing water availability.

Flash droughts feature rapid onsets of soil moisture drought events and result in severe impacts and damages, especially on agricultural and ecological systems. How the flash drought regime across China varies on multitemporal scales with climate change is not fully clear yet. In this study, we extended the flash drought definition to apply to arid regions by adding an absolute soil moisture variation criterion. Then, we detected flash drought events across China during 1981–2021 and characterized their frequency, duration, and affected area changes on seasonal, annual, and decadal scales, using soil moisture data from the European Center for Medium-Range Weather Forecasts climate reanalysis-Land. Results show that flash drought hotspots appeared in North China and the Yangtze River Basin. During 1981–2021, the hotspots, even nationwide, underwent significant increases in frequencies, durations, and affected areas of flash droughts. The increases held in the extremely high values of the frequencies and durations in the decadal comparisons. Especially, North China saw the most extensive and rapid increases. Seasonally, flash drought frequencies and durations intensified more during spring and autumn, and seasonal hotspots in eastern China shifted in phase with spatial patterns of soil moisture loss balanced by precipitation and evapotranspiration. Thus, flash droughts tended to amplify atmospheric aridity. These findings on the hotspot regions and the spatiotemporal evolutions of flash droughts across China would pinpoint soil moisture responses to climate change and prepare for climate change impacts on local ecosystems.

Wildfire activity and the hydrological cycle are strongly interlinked. While it is well known that wildfire occurrence and intensity are controlled by water availability, less is known about the effects of wildfire on plant and soil water cycling, especially at large scales. Here we investigate this by analysing fire impacts on the coupling between plant and soil water content, at the global scale, using remote sensing of soil moisture, vegetation water content and burned area. We find a strong effect of fire on plant–soil water relations, accelerating soil moisture loss by 17% and leading to faster gains in vegetation water content by 62%, both of which are positively related to fire severity and largest in forests. This effect is spatially extensive, with accelerated soil moisture loss found in 67%, and increased vegetation water content gain found in 67% of all analysed burned areas. After fire, plants also tended to have less control on their water content (that is, were more anisohydric). In summary, fire changes ecosystem functioning by increasing ecosystem water losses and shifting the relationship between soil and vegetation water budgets. With climate change, wildfire is likely to play an increasingly important role in ecosystem water cycling and subsequent ecosystem recovery. Fire affects the hydrological interactions between soil and vegetation, leading to faster soil moisture loss and accelerated vegetation water uptake, according to a global analysis of satellite observations on soil moisture, vegetation water content and burned area.

Global warming is amplified in the Arctic. However, numerical models struggle to represent key processes that determine Arctic weather and climate. To collect data that help to constrain the models, the HALO–(𝒜𝒞)3 aircraft campaign was conducted over the Norwegian and Greenland seas, the Fram Strait, and the central Arctic Ocean in March and April 2022. The campaign focused on one specific challenge posed by the models, namely the reasonable representation of transformations of air masses during their meridional transport into and out of the Arctic via northward moist- and warm-air intrusions (WAIs) and southward marine cold-air outbreaks (CAOs). Observations were made over areas of open ocean, the marginal sea ice zone, and the central Arctic sea ice. Two low-flying and one long-range, high-altitude research aircraft were flown in colocated formation whenever possible. To follow the air mass transformations, a quasi-Lagrangian flight strategy using trajectory calculations was realized, enabling us to sample the same moving-air parcels twice along their trajectories. Seven distinct WAI and 12 CAO cases were probed. From the quasi-Lagrangian measurements, we have quantified the diabatic heating/cooling and moistening/drying of the transported air masses. During CAOs, maximum values of 3 K h−1 warming and 0.3 g kg−1 h−1 moistening were obtained below 1 km altitude. From the observations of WAIs, diabatic cooling rates of up to 0.4 K h−1 and a moisture loss of up to 0.1 g kg−1 h−1 from the ground to about 5.5 km altitude were derived. Furthermore, the development of cloud macrophysical (cloud-top height and horizontal cloud cover) and microphysical (liquid water path, precipitation, and ice index) properties along the southward pathways of the air masses were documented during CAOs, and the moisture budget during a specific WAI event was estimated. In addition, we discuss the statistical frequency of occurrence of the different thermodynamic phases of Arctic low-level clouds, the interaction of Arctic cirrus clouds with sea ice and water vapor, and the characteristics of microphysical and chemical properties of Arctic aerosol particles. Finally, we provide a proof of concept to measure mesoscale divergence and subsidence in the Arctic using data from dropsondes released during the flights.

Drying of soil impacts land energy and water balance, influences the sustainability of vegetation growth, and modulates hydrological extremes including floods. While satellite soil moisture data are widely used for a range of environmental applications, systematic differences from regional in‐situ data prevent their optimal use as key physical signatures (such as soil moisture recession, also termed drydown) are represented differently. This study investigates differences in drydowns from the Soil Moisture Active Passive (SMAP) level 4 product with reference to in‐situ observations. A bivariate filtering alternative is proposed to minimize the disparity noted by modeling the relationship between the rate of drying and initial soil wetness and representing the same as in‐situ. Considerable improvements are observed in the resulting SMAP soil moisture filtered estimates. Although the algorithm assumes spatial stationarity, improvements exist across different soil properties and climatic conditions, providing a parsimonious alternative to better capture the dynamics of soil moisture loss. Plain Language Summary Soil drying affects the environment by changing how land uses energy and water. It also affects plant growth and can lead to extreme events like floods. Scientists use data from satellites to understand soil moisture, but this data sometimes differs from what's measured directly on the ground. Our study looks at these differences, focusing on how soil dries out, using data from a satellite program called the Soil Moisture Active Passive (SMAP). We suggest a new method to make satellite data closer to what's observed on the ground by adjusting it based on initial soil wetness and drying rates. This new approach showed better results and worked well in different types of soil and weather conditions. It helps us track how soil moisture decreases at ground level more accurately, which is important for understanding and managing our environment. Key Points Coarse‐scale satellite‐derived soil moisture dries faster than in‐situ measurements We propose a bivariate recursive filtering approach to characterize soil moisture drying rates and initial wetness conditions The proposed approach is applied to SMAP L4, eliminating systematic bias in drying rates for varied sand fractions and aridity profiles

Soil moisture is considered a key component for the structural integrity of engineered ecosystems, such as sea dikes. Although plants are important determinants of physical soil properties in dike greening, research lacks on the extent to which greater biodiversity can mitigate soil moisture loss during extreme weather events. This provided the motivation to investigate the influence of two plant communities of different species composition – namely, an herb-dominated vegetation area (‘Mix-Herb’) compared to a grass-dominated area (‘Mix-Grass’) – on soil physical conditions over the course of one year on a summer dike in northern Germany. Vegetation mapping, high-resolution measurements of soil temperature and moisture, and comprehensive precipitation data provided the framework for the investigations. It was found that species diversity (Shannon Index) declined over time from 2.7 to 2.3 for ‘Mix-Herb’ and from 2.2 to 2.0 for ‘Mix-Grass’. In-situ measurements of soil physical conditions revealed that the ‘Mix-Herb’ plant community moderated diurnal soil temperature variations more effectively than ‘Mix-Grass’. During a drought in June 2023, the ‘Mix-Herb’ vegetation area was also considerably less affected by soil heating and moisture deficit. However, after mowing, the thermal buffer effect reversed and greater diurnal temperature variations occurred in the soils of the herbaceous vegetation. During a second drought in September 2023, the’Mix-Grass‘soils exhibited higher moisture loss rates after mowing. These findings highlight the importance of the functional composition of plant communities and management practices such as mowing schedules, tailored spatially and temporally to ecological and climatic conditions, for regulating the soil microclimate on dike systems, with potential implications for dike’s resistance under climatic extremes.

Abstract Climate change and anthropogenic activities are affecting the entire earth, where urban areas are not an exception, being affected by extreme weather conditions and environmental disturbances. Urban expansion and industrial development have negatively affected the local climatic condition due to green space deficiency, soil moisture loss, soil erosion, land subsidence, high runoff, and low infiltration rate. Megacities are needed proper management and awareness for healthy ecosystem. The study investigated the properties of land alteration on the urban heat island (UHI) in the city of Seville, Spain. Earth observational Landsat 5 TM and 8 OLI/TIRS remote sensing datasets were used for generating the urban expansion and related land alteration. The study results indicate that built-up land increased by 139.2 Km2 while agricultural land decreased by 104.07 Km2. Open space and plantation areas also decreased by 62.33 Km2 and 30.76 Km2, respectively. The average temperature increase was around 0.13 °C per year between 1991 and 2021. Megacities need appropriate development, design, and supervision for sustainable urban development to avoid further UHI intensification. UHI map indicates that thermal variation increased from 2.21 °C (1991) to 3.42 °C (2021). The ecological disturbances also identified using UTFVI and the maps denoted that UTFVI values increased by 0.005 from 1991 to 2021. The present study outcomes are obliging for planners, researchers, and other participants for future evidence-based disaster planning and management.

Changing pathways of soil moisture loss, either directly from soil (evaporation) or indirectly through vegetation (transpiration), are an indicator of ecosystem and land hydrological cycle responses to the changing climate. Based on the ratio of transpiration to evaporation, this paper investigates soil moisture loss pathway changes across China using five reanalysis-type datasets for the past and Coupled Model Intercomparison Project Phase 6 (CMIP6) climate projections for the future. The results show that across China, the ratio of vegetation transpiration to soil evaporation has generally increased across vegetated land areas, except in grasslands and croplands in north China. During 1981–2014, there was an increase by 51.4 percentage points (pps, p , 0.01) on average according to the reanalyses and by 42.7 pps according to 13 CMIP6 models. The CMIP6 projections suggest that the holistic increasing trend will continue into the twenty-first century at a rate of 40.8 pps for SSP585, 30.6 pps for SSP245, and 21.0 pps for SSP126 shared socioeconomic pathway scenarios for the period 2015–2100 relative to 1981–2014. Major contributions come from the increases in vegetation transpiration over the semiarid and subhumid grasslands, croplands, and forestlands under the influence of increasing temperatures and prolonged growing seasons (with twin peaks in May and October). The future increasing vegetation transpiration ratio in soil moisture loss implies the potential of regional greening across China under global warming and the risks of intensifying land surface dryness and altering the coupling between soil moisture and climate in regions with water-limited ecosystems.