Variation trend of global soil moisture and its cause analysis

Deng, Yuanhong; Shi-jie, Wang; Bai, Xiaoyong; Luo, Geping; Wu, Luhua; Cao, Yue; Li, Huiwen; Li, Chao‐Jun; Yang, Yujie; Hu, Zhang; Tian, Shiqi

doi:10.1016/j.ecolind.2019.105939

Cited by 83 publications

(51 citation statements)

References 54 publications

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“…The data required for the spatial quantification of ecosystem health and its influencing factors include land use and NDVI, GIMMS NDVI3g is produced by NASA's Global Inventory Modeling and Mapping Studies (GIMMS) team based on the Advanced Very High Resolution Radiometer (AVHRR) images on NOAA series satellites, which are processed with strict quality control to generate maximum synthetic products (Deng et al, 2019; Tian et al, 2015). The data used for the analysis of the influencing factors of ecosystem health include annual rainfall, temperature, drought index, soil moisture, population density, GDP, etc, the climate dataset are from NOAA climate prediction centre with a horizontal resolution of 0.5° × 0.5° longitude.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of temporal and spatial changes of global ecosystem health

et al. 2020

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Understanding the spatial distribution pattern and driving factors behind ecosystem health is beneficial to ecosystem management and restoration. However, the literature shows little in‐depth exploration of regional heterogeneity and the factors that influence ecosystem health on a global scale. This study assesses global ecosystem health for the period 2000–2015 based on the vigor, organization, resilience, and service (VORS) model, and it explores the factors that influence regional differences in ecosystem health. Our results show that: (a) regions with high levels of ecosystem health are distributed mainly near the Equator and within the north–south regression line (0°N–13°N, 0°S–18°S); (b) seven critical zones with high levels of ecosystem health are identified (13°N–18°S, 45°N–65°N, 35°S–56°S, 47°W–80°W, 120°W–130°W, 8°E–37°E, 92°E–157°E); and (c) average annual precipitation and soil moisture play a key role in ecosystem health globally, with correlations of 0.574 and 0.399, respectively. Socioeconomic factors act as bridges, linking and reinforcing the influence of other factors in areas with medium to low levels of ecosystem health. Our study contributes to better understanding of ecosystem health, fills gaps in global ecosystem health diagnosis, and provides reference points for management and recovery of ecosystems.

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Section: Methodsmentioning

confidence: 99%

Evaluation of temporal and spatial changes of global ecosystem health

et al. 2020

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“…Under the context of global climate change, temperature (Ts) presents a notable increasing trend (IPCC, 2013), while soil moisture (SM) suggests otherwise, showing a decreasing trend (Albergel et al, 2013; Deng et al, 2020) with heightened groundwater vulnerability (Nistor, 2019). Suitable Ts and sufficient SM are the basic conditions for vegetation growth.…”

Section: Introductionmentioning

confidence: 99%

“…Studies have shown that the Ts usually fails to reach its optimum value in ecosystems (Huang et al, 2019), and an increasing Ts boosts photosynthesis. However, SM is currently showing a decreasing trend (Deng et al, 2020) and decreasing SM can limit photosynthesis (Rogers et al, 2017). Therefore, better knowledge of global ecosystem productivity is much needed under the context of increased Ts coupled with decreased SM.…”

Section: Introductionmentioning

confidence: 99%

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

Dang

Shao

Huang

et al. 2022

Global Change Biology

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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.

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“…The spatial resolution of the ESA CCI SM data is too coarse for some applications and downscaling techniques have been explored to obtain a finer spatial resolution (e.g., [42]). Several validation/prediction exercises of the ESA CCI SM data [26,[41][42][43][44][45] have been done since the product was made available, for example, in drought studies [36,46], land-atmosphere interactions studies [47], climate trend analyses [40,48], detection of irrigated areas [49] and crop growth modelling [50]. Enlarging the geographical distribution of studies by applying the product certainly helps to widen the validation of SM estimates [51], especially in regions where the existing network of reference ground stations is sparse, as is the case in the western Iberian Peninsula [43].…”

Section: Introductionmentioning

confidence: 99%

Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Figueiredo

Royer

Fonseca

et al. 2020

Water

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The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

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Variation trend of global soil moisture and its cause analysis

Cited by 83 publications

References 54 publications

Evaluation of temporal and spatial changes of global ecosystem health

Evaluation of temporal and spatial changes of global ecosystem health

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

Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

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