Comparison of groundwater storage changes over losing and gaining aquifers of China using GRACE satellites, modeling and in-situ observations.

Groundwater depletion in intensively exploited aquifers of China has been widely recognized, whereas an overall examination of groundwater storage (GWS) changes over major aquifers remains challenging due to limited data and notable uncertainties. Here, we present a study to explore GWS changes over eighteen major aquifers covering an area of 1,680,000 km2 in China using data obtained from the Gravity Recovery and Climate Experiments (GRACE), global models, and in-situ groundwater level observations. The analysis aims to reveal the discrepancy in annual trends, amplitudes, and phases associated with GWS changes among different aquifers. It is found that GWS changes in the studied aquifers represent a spatial pattern of 'Wet-gets-more, Dry-gets-less'. An overall decreasing trend of -4.65 ± 0.34 km3/yr is observed by GRACE from 2005 to 2016, consisting of a significant (p < 0.05) increase of 47.28 ± 3.48 km3 in 7 aquifers and decrease of 103.56 ± 2.4 km3 (∼2.6 times the full storage capacity of the Three Gorges Reservoir) in 10 aquifers summed over the 12 years. The annual GWS normally reaches a peak in late July with an area-weighted average annual amplitude of 19 mm, showing notable discrepancy in phases and amplitudes between the losing aquifers (12 mm in middle August) in northern China and gaining aquifers (28 mm in early July) mostly in southern China. GRACE estimates are generally comparable, but can be notably different, with the results obtained from model simulations and in-situ observations at aquifer scale, with the area-weighted average correlation coefficients of 0.6 and 0.5, respectively. This study highlights different GWS changes of losing and gaining aquifers in response to coupled impacts of hydrogeology, climate and human interventions, and calls for divergent adaptions in regional groundwater management.

Investigating socio-ecological vulnerability to climate change via remote sensing and a data-driven ranking algorithm.

The necessity for extensive historical data, variables, and weight determination still presents challenges and complexity, notwithstanding the growth in research on socio-ecological vulnerability to climate change. In order to fill in these gaps, this study used China's Fujian Province as a case study to propose a unique strategic approach for studying socio-ecological vulnerability to climate change from 2000 to 2020 by utilizing remote sensing and the framework of the Intergovernmental Panel on Climate Change. In a GIS scenario, this method employs a comprehensive framework with a wide variety of indicators and a data-driven ranking algorithm. The findings of this study revealed a moderate degree of socio-ecological vulnerability throughout the coast, with significant regional heterogeneity in its spatial distribution. Furthermore, throughout the course of the two-decade, the highly vulnerable zones expanded by 6.04%, outpacing the low-risk areas by 1116 km2 (61.41%) and 2066 km2 (123.39%), respectively, with the majority of the increase taking place in Fuzhou and Ningde. These changes in vulnerability were shown to be principally influenced by changes in vegetation, precipitation, GDP, and land use (LULC). The major influence of precipitation was highlighted further in the spatial autocorrelation analysis, which demonstrated a close correlation between growing socio-ecological vulnerability and increased precipitation. To conclude, this study's methodology differs from other socio-ecological vulnerability studies in that it is flexible and self-sufficient, offering users a choice of weight application. It also gives a more useful, accurate, and suggestive model to enable decision-makers or stakeholders build strategies or ideas for constructing more resilient coastal systems.

Measuring and evaluating SDG indicators with Big Earth Data.

The United Nations 2030 Agenda for Sustainable Development provides an important framework for economic, social, and environmental action. A comprehensive indicator system to aid in the systematic implementation and monitoring of progress toward the Sustainable Development Goals (SDGs) is unfortunately limited in many countries due to lack of data. The availability of a growing amount of multi-source data and rapid advancements in big data methods and infrastructure provide unique opportunities to mitigate these data shortages and develop innovative methodologies for comparatively monitoring SDGs. Big Earth Data, a special class of big data with spatial attributes, holds tremendous potential to facilitate science, technology, and innovation toward implementing SDGs around the world. Several programs and initiatives in China have invested in Big Earth Data infrastructure and capabilities, and have successfully carried out case studies to demonstrate their utility in sustainability science. This paper presents implementations of Big Earth Data in evaluating SDG indicators, including the development of new algorithms, indicator expansion (for SDG 11.4.1) and indicator extension (for SDG 11.3.1), introduction of a biodiversity risk index as a more effective analysis method for SDG 15.5.1, and several new high-quality data products, such as global net ecosystem productivity, high-resolution global mountain green cover index, and endangered species richness. These innovations are used to present a comprehensive analysis of SDGs 2, 6, 11, 13, 14, and 15 from 2010 to 2020 in China utilizing Big Earth Data, concluding that all six SDGs are on schedule to be achieved by 2030.

Active water management brings possibility restoration to degraded lakes in dryland regions: a case study of Lop Nur, China.

Protecting and restoring the degraded arid lakes are globally urgent issues. We document a potential recovery of the dried salt-lake, Lop Nur called "the Sea of Death" which is located at the terminus of the largest inland basin in China, the Tarim River Basin. The changes and relationship of surface water with climate parameters and groundwater in the basin over the last 30 years are analyzed, by using satellite remote sensing and land data assimilation products. We find that with increased surface water in the basin, the groundwater level in Lop Nur began to show an obvious positive response in 2015; and the rate of decline of the groundwater level is slowing down. We argue that after a balance is achieved between regional groundwater recharge and evapotranspiration, the Lop Nur ecosystem will gradually recover. This study shows an encouraging case for the protection and restoration of degraded lakes in dryland regions around the world.

Domino effect of a natural cascade alpine lake system on the Third Pole.

Third Pole natural cascade alpine lakes (NCALs) are exceptionally sensitive to climate change, yet the underlying cryosphere-hydrological processes and associated societal impacts are largely unknown. Here, with a state-of-the-art cryosphere-hydrology-lake-dam model, we quantified the notable high-mountain Hoh-Xil NCALs basin (including Lakes Zonag, Kusai, Hedin Noel, and Yanhu, from upstream to downstream) formed by the Lake Zonag outburst in September 2011. We demonstrate that long-term increased precipitation and accelerated ice and snow melting as well as short-term heavy precipitation and earthquake events were responsible for the Lake Zonag outburst; while the permafrost degradation only had a marginal impact on the lake inflows but was crucial to lakeshore stability. The quadrupling of the Lake Yanhu area since 2012 was due to the tripling of inflows (from 0.25 to 0.76 km3/year for 1999 to 2010 and 2012 to 2018, respectively). Prediction of the NCALs changes suggests a high risk of the downstream Qinghai-Tibet Railway, necessitating timely adaptions/mitigations.