Spatiotemporal changes of ecological environment quality and climate drivers in Zoige Plateau.

Ecological environment is the essential material basis of human survival and connects regional economy with socially sustainable development. However, climate changes characterized by global climate warming have caused a series of ecological environmental problems in recent years. Few studies have discussed various climate factors affecting the ecological environment, and the spatial non-stationary effects of different climate factors on the ecological environment are still unclear. Dynamically monitoring ecological environment changes in fragile areas and identifying its climate-driving mechanism are essential for ecological protection and environmental repair. Taking Zoige Plateau as a case, this paper simulated the eco-environmental quality during 1987-2020 using remote sensing data, utilized Geodetector method to identify the contributions of various climate drivers to ecological environment quality, and then adopted the Geographically Weighted Regression model to explore the spatial non-stationary impacts of climate factors on ecological environment quality. The results showed that the ecological quality in the middle regions of the Zoige Plateau was slightly better than in the surrounding marginal areas. For the whole area of Zoige Plateau, the average ecological environment quality index was 54.92, 53.99, 56.17, 57.88, 63.44, 56.93, 59.43, and 59.76 in 1987, 1992, 1997, 2001, 2006, 2013, 2016 and 2020, respectively, which indicated that eco-environmental quality witnessed several fluctuations during the study period but showed a generally increasing trend. Among five climate factors, the temperature was the dominant climate factor affecting the ecological environment quality (q value: 0.11-0.19), sunshine duration (0.03-0.17), wind speed (0.03-0.11), and precipitation (0.03-0.08) were the main climate drivers, while the explanatory power of relative humidity to ecological environment quality was relatively small. Such various climate factors impacting the ecological environment quality demonstrated distinct spatial non-stationary and the range of driving impact varied with time. Temperature, sunshine duration, wind speed, and relative humidity promoted ecological environment quality in most regions (regression coefficients > 0), while precipitation mainly had a negative inhibitory impact (regression coefficients < 0). Meanwhile, the greater impacts of these five climate factors were concentrated in high-elevation regions of the south and west or the northern areas. The appropriate enhancement of climate warming and air humidity was beneficial to the improvement of the ecological environment, but the excessive precipitation would result in landslides and exhibit inhibition of vegetation growth. Therefore, selecting cold-tolerant herbs and shrubs, and strengthening climate monitoring and early warning systems (such as drought and excessive precipitation) are essential for ecological restoration.

Impact-based probabilistic modeling of hydro-morphological processes in China (1985-2015).

Hydro-morphological processes (HMP, any natural phenomenon contained within the spectrum defined between debris flows and flash floods) pose a relevant threat to infrastructure, urban and rural settlements and to lives in general. This has been widely observed in recent years and will likely become worse as climate change will influence the spatio-temporal pattern of precipitation events. The modelling of where HMP-driven hazards may occur can help define the appropriate course of actions before and during a crisis, reducing the potential losses that HMPs cause in their wake. However, the probabilistic information on locations prone to experience a given hazard is not sufficient to depict the risk our society may incur. To cover this aspect, modeling the loss information could open up to better territorial management strategies. In this work, we made use of the HMP catalogue of China from 1985 to 2015. Specifically, we implemented the Light Gradient Boosting (LGB) classifier to model the impact level that locations across China have suffered from HMPs over the thirty-year record. We obtained six impact levels as a combination of financial and life losses, whose classes we used as separate target variables for our LGB. In doing so, we estimated spatial probabilities of certain HMP impact, something that has yet to be tested in the natural hazard community, especially over such a large spatial domain. The results we obtained are encouraging, with each of the six impact categories being separately classified with excellent to outstanding performance (the worst case corresponds to a mean AUC = 0.862, whereas the best case corresponds to a mean AUC of 0.915). The good predictive performance our model produced suggest that the cartographic output could be useful to inform authorities of locations prone to human and infrastructural losses of specific magnitudes.

An integrated model chain for future flood risk prediction under land-use changes.

Flood is a very destructive natural disaster in the world that is strongly influenced by land-use change. Therefore, a comprehensive flood risk modeling considering the change in land-use is essential for understanding, predicting, and mitigating flood risk. However, most existing single modeling ignored the derivative effect of land-use change, which may reduce the reality of results. To further address the issue, this study presented an integrated model chain by coupling the Markov-FLUS model, the multiple linear regression and the improved TOPSIS model. By applying it in Guangdong Province, the future land-use simulation, spatialization of hazard-bearing bodies, and determination of flood risk were realized. The results show that the coupled model chain allows for good prediction of flood risk under different scenarios, which could be expressed by flood risk composite index (FRSI). In the natural growth scenario, the flood risk will show markedly increasing trend from 2020 to 2030 (FRSI = 2.06), with the high and highest risk zones will expand significantly. Spatially, these increased high flood risk zones mainly distributed on the periphery of existing built-up lands. On the contrary, the flood risk in ecological protection scenario tends to stabilize (FRSI = 1.98), which may be a reference for alternative development paths. These dynamic information identified by this model chain provides a deeper insight into the spatiotemporal characteristics of future high flood risk areas, which can facilitate reasonable flood mitigation measures to be developed at the most critical locations in the region. In further applications, more efficient spatialization models and climate factor are suggested to be introduced.

Tropical volcanic eruptions reduce vegetation net carbon uptake on the Qinghai-Tibet Plateau under background climate conditions.

The vegetation carbon uptake plays an important role in the terrestrial carbon cycle on the Qinghai-Tibet Plateau (QTP), while it is extremely sensitive to the impact of natural external forcings. Until now, there is limited knowledge on the spatial-temporal patterns of vegetation net carbon uptake (VNCU) after the force that caused by tropical volcanic eruptions. Here, we conducted an exhaustive reconstruction of VNCU on the QTP over the last millennium, and used a superposed epoch analysis to characterize the VNCU response of the QTP after the tropical volcanic eruptions. We then further investigated the divergent changes of VNCU response across different elevation gradients and vegetation types, and the impact of teleconnection forcing on VNCU after volcanic eruptions. Within a climatic background, we found that VNCU of the QTP tends to decrease after large volcanic eruptions, lasting until about 3 years, with a maximum decrease value occurring in the following 1 year. The spatial and temporal patterns of the VNCU were mainly driven by the post-eruption climate and moderated by the negative phase trends of El Niño-Southern Oscillation and the Atlantic multidecadal oscillation. In addition, elevation and vegetation types were undeniable driving forces associated with VNCU on QTP. Different water-heat conditions and vegetation types contributed to significant differences in the response and recovery processes of VNCU. Our results emphasized the response and recovery processes of VNCU to volcanic eruptions without the strong anthropogenic forcings, while the influence mechanisms of natural forcing on VNCU should receive more attention.

Health Risk and Resilience Assessment with Respect to the Main Air Pollutants in Sichuan.

Rapid urbanization and industrialization in developing countries have caused an increase in air pollutant concentrations, and this has attracted public concern due to the resulting harmful effects to health. Here we present, through the spatial-temporal characteristics of six criteria air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) in Sichuan, a human health risk assessment framework conducted to evaluate the health risk of different age groups caused by ambient air pollutants. Public health resilience was evaluated with respect to the risk resulting from ambient air pollutants, and a spatial inequality analysis between the risk caused by ambient air pollutants and hospital density in Sichuan was performed based on the Lorenz curve and Gini coefficient. The results indicated that high concentrations of PM2.5 (47.7 µg m-3) and PM10 (75.9 µg m-3) were observed in the Sichuan Basin; these two air pollutants posed a high risk to infants. The high risk caused by PM2.5 was mainly distributed in Sichuan Basin (1.14) and that caused by PM10 was principally distributed in Zigong (1.01). Additionally, the infants in Aba and Ganzi had high health resilience to the risk caused by PM2.5 (3.89 and 4.79, respectively) and PM10 (3.28 and 2.77, respectively), which was explained by the low risk in these two regions. These regions and Sichuan had severe spatial inequality between the infant hazard quotient caused by PM2.5 (G = 0.518, G = 0.493, and G = 0.456, respectively) and hospital density. This spatial inequality was also caused by PM10 (G = 0.525, G = 0.526, and G = 0.466, respectively), which is mainly attributed to the imbalance between hospital distribution and risk caused by PM2.5 (PM10) in these two areas. Such research could provide a basis for the formulation of medical construction and future air pollution control measures in Sichuan.