Relative importance of climate factors and human activities in impacting vegetation dynamics during 2000–2015 in the Otindag Sandy Land,northern China

In recent years, there has been increasing research interests in differentiating the relative importance of climate factors and human activities in impacting vegetation dynamics. In this study, based on residual trend method, we used MOD13A3（MODIS vegetation index product）, MCD12Q1（MODIS land cover product） and meteorological datasets to differentiate the relative importance of climate factors and human activities in impacting vegetation dynamics during 2000–2015 in the Otindag Sandy Land, northern China. Results show that during the study period（2000–2015）, the overall vegetation condition had improved in the Otindag Sandy Land. The driving forces of vegetation dynamics differed spatially in the whole study area over the study period. The area with vegetation degradation solely resulted from human activities accounted for 8.23% of the study area, while the area with vegetation degradation resulted from others（including climate factors and combination of climate factors and human activities） occupied 1.53%. The area with vegetation recovery benefitted from human activities occurred over 26.02% of the study area; the area benefitted from climate factors accounted for 23.69%; and the area benefitted from both climate factors and human activities occupied 37.74%. All in all, impacts of climate factors and human activities on vegetation dynamics varied at the county/city/banner scales and locality-specific measures should be adopted to protect the environments.

Science & Technology for Sustainable Water in China A Perspective of the IAC Water Program and Studies in China

Water is a big issue in the world. As we enter the 21st century, a global water crisis threatens the security, stability and environmental sustainability of all nations, particularly those in the developing world. The Inter-Academy Council (IAC) proposed to undertake a study of the current and emerging challenges and opportunities for sustainable water resources management at its 2009 Board meeting.This paper gives a perspective of the IAC Water Program, and the case studies conducted by China Working Group of the IAC Water Program on three key issues, namely climate change & water adaptive management, agricultural water & ecology, and urban water & environment. The purpose is to show the role of science & technology for sustainable water in China. These studies are the 1st phase of the IAC Water Program in China. Perspectives of new challenges and opportunities on this Program for the water future in the world and China are also given in the paper.

Promoting a Research Hub of Water Resources by Pooling Forces from CAS Institutes

CAS inaugurated its Center for Water Resources Research in 2006 with an objective to bring together R&D resources housed in different CAS institutes so as to play a major role in the comprehensive, strategic, interdisciplinary and foresighted studies of water resources at the national level and in key regions. In light of China’s strategic demands,

Runoff of arid and semi-arid regions simulated and projected by CLM-DTVGM and its multi-scale fluctuations as revealed by EEMD analysis

Runoff is a major component of the water cycle, and its multi-scale fluctuations are important to water resources management across arid and semi-arid regions. This paper coupled the Distributed Time Variant Gain Model （DTVGM） into the Community Land Model （CLM 3.5）, replacing the TOPMODEL-based method to simulate runoff in the arid and semi-arid regions of China. The coupled model was calibrated at five gauging stations for the period 1980-2005 and validated for the period 2006-2010. Then, future runoff （2010-2100） was simulated for different Representative Concentration Pathways （RCP） emission scenarios. After that, the spatial distributions of the future runoff for these scenarios were discussed, and the multi-scale fluctuation characteristics of the future annual runoff for the RCP scenarios were explored using the Ensemble Empirical Mode Decomposition （EEMD） analysis method. Finally, the decadal variabilities of the future annual runoff for the entire study area and the five catchments in it were investigated. The results showed that the future annual runoff had slowly decreasing trends for scenarios RCP 2.6 and RCP 8.5 during the period 2010-2100, whereas it had a non-monotonic trend for the RCP 4.5 scenario, with a slow increase after the 2050s. Additionally, the future annual runoff clearly varied over a decadal time scale, indicating that it had clear divisions between dry and wet periods. The longest dry period was approximately 15 years （2040-2055） for the RCP 2.6 scenario and 25 years （2045-2070） for the RCP 4.5 scenario. However, the RCP 8.5 scenario was predicted to have a long dry period starting from 2045. Under these scenarios, the water resources situation of the study area will be extremely severe. Therefore, adaptive water management measures addressing climate change should be adopted to proactively confront the risks of water resources.

Quantifying key model parameters for wheat leaf gas exchange under different environmental conditions

The maximum carboxylation rate of Rubisco(Vcmax)and maximum rate of electron transport(Jmax)for the biochemical photosynthetic model,and the slope(m)of the Ball-Berry stomatal conductance model influence gas exchange estimates between plants and the atmosphere.However,there is limited data on the variation of these three parameters for annual crops under different environmental conditions.Gas exchange measurements of light and CO2 response curves on leaves of winter wheat and spring wheat were conducted during the wheat growing season under different environmental conditions.There were no significant differences for Vcmax,Jmax or m between the two wheat types.The seasonal variation of Vcmax,Jmax and m for spring wheat was not pronounced,except a rapid decrease for Vcmax and Jmax at the end of growing season.Vcmax and Jmax show no significant changes during soil drying until light saturated stomatal conductance(gssat)was smaller than 0.15 mol m^–2 s^–1.Meanwhile,there was a significant difference in m during two different water supply conditions separated by gssat at 0.15 mol m^–2 s^–1.Furthermore,the misestimation of Vcmax and Jmax had great impacts on the net photosynthesis rate simulation,whereas,the underestimation of m resulted in underestimated stomatal conductance and transpiration rate and an overestimation of water use efficiency.Our work demonstrates that the impact of severe environmental conditions and specific growing stages on the variation of key model parameters should be taken into account for simulating gas exchange between plants and the atmosphere.Meanwhile,modification of m and Vcmax(and Jmax)successively based on water stress severity might be adopted to simulate gas exchange between plants and the atmosphere under drought.

Effects of the Rainfall-Triggered Lisse Effect on the Stability of Loess Slopes

This paper coupled a water-air two-phase hydrodynamic(WATPH)model with the Iverson’s method to analyze the influence of the Lisse effect on the fast groundwater pressure(P_(w))response and the slope stability.Furthermore,the sensitivities of the driving force and loess soil parameters were investigated.Results showed that the WATPH model simulated the height and rise of the depth to the water table reasonably well.The depth to water table before rainfall(H0)had a significant impact on the Lisse effect and the slope stability.When the H_(0) was less than approximately 1 m,the rainfall triggered a significant Lisse effect and decreased the slope factor of safety(F_(s)).When the rainfall intensity(R_(i))was higher than the saturated hydraulic conductivity(K_(s)),the Lisse effect and the F_(s) slightly changed with the increase of the R_(i),and the slope tended to be unstable with continuous rainfall.With increasing K_(s),the Lisse effect noticeably increased,and the minimum F_(s) quickly decreases.The analysis of the normalized sensitivity coefficient revealed that H_(0) had a dramatic impact on the Lisse effect and loess slope stability.The different R_(i) and K_(s) values had prominent influences on the Lisse effect and slight impacts on F_(s).

Quantitative Assessment of the Effects of Climate Change and Human Activities on Grassland NPP in Altay Prefecture

Grassland degradation in Altay Prefecture is of considerable concern as it is a threat that hinders the sustainable development of the local economy and the stable operation of the livestock industry.Quantitative assessment of the relative contributions of climate change and human activities,which are considered as the dominant triggers of grassland degradation,to grassland variation is crucial for understanding the grassland degradation mechanism and mitigating the degraded grassland in Altay Prefecture.In this paper,the Carnegie-Ames-Stanford Approach model and the Thornthwaite memorial model were adopted to simulate the actual net primary productivity(NPP_(A))and potential net primary productivity(NPP_(P))in the Altay Prefecture from 2000 to 2019.Meanwhile,the difference between potential NPP and actual NPP was employed to reflect the effects of human activities(NPP_(H))on the grassland.On this basis,we validated the viability of the simulated NPP using the Pearson correlation coefficient,investigated the spatiotemporal variability of grassland productivity,and established comprehensive scenarios to quantitatively assess the relative roles of climate change and human activities on grassland in Altay prefecture.The results indicate three main points.(1)The simulated NPP_(A) was highly consistent with the MOD17 A3 dataset in spatial distribution.(2)Regions with an increased NPP_(A) accounted for 70.53% of the total grassland,whereas 29.47% of the total grassland area experienced a decrease.At the temporal scale,the NPP_(A) presented a slightly increasing trend(0.83 g C m^(-2) yr^(-1))over the study period,while the trends of NPP_(P) and NPP_(H) were reduced(-1.31 and-2.15 g C m^(-2) yr^(-1)).(3)Compared with climate change,human activities played a key role in the process of grassland restoration,as 66.98% of restored grassland resulted from it.In contrast,inter-annual climate change is the primary cause of grassland degradation,as it influenced 55.70% of degraded grassland.These results could shed light on the mechanisms of grassland variation caused by climate change and human activities,and they can be applied to further develop efficient measures to combat desertification in Altay Prefecture.

Spatio-temporal trend and statistical distribution of extreme precipitation events in Huaihe River Basin during 1960-2009

Based on the daily precipitation data of 27 meteorological stations from 1960 to 2009 in the Huaihe River Basin, spatio-temporal trend and statistical distribution of extreme precipitation events in this area are analyzed. Annual maximum series （AM） and peak over threshold series （POT） are selected to simulate the probability distribution of extreme pre- cipitation. The results show that positive trend of annual maximum precipitation is detected at most of used stations, only a small number of stations are found to depict a negative trend during the past five decades, and none of the positive or negative trend is significant. The maximum precipitation event almost occurred in the flooding period during the 1960s and 1970s. By the L-moments method, the parameters of three extreme distributions, i.e., Gen- eralized extreme value distribution （GEV）, Generalized Pareto distribution （GP） and Gamma distribution are estimated. From the results of goodness of fit test and Kolmogorov-Smirnov （K-S） test, AM series can be better fitted by GEV model and POT series can be better fitted by GP model. By the comparison of the precipitation amounts under different return levels, it can be found that the values obtained from POT series are a little larger than the values from AM series, and they can better simulate the observed values in the Huaihe River Basin.

Variation in vegetation greenness along China's land border

Fourteen countries share about 22000 km land border with China, but not much is known about the variation in vegetation in such a large diverse area. By employing the remotely-sensed vegetation indices the vegetation greenness along the border was discussed. Our results show that since the early 21 st century, similar trends in vegetation greenness have occurred along most of China's border, but differences occurred on either side of the border. Along the border with North Korea and South Asian nations, greater increasing trend in vegetation greenness occurred inside China's border, suggesting that China's vegetation protection programs have been successful. Spatial and temporal variations in vegetation greenness trends were observed along China's border with Russia, Mongolia, and Central Asian nations. Vegetation variation was lower inside China, along the Russian border, and China's eastern border with Mongolia. Along most borders with Central Asian nations, rates of vegetation change inside China's border during the growing season were higher than the rates outside the border. The results suggest that social customs, resource exploitation patterns, and national environmental conservation programs may profoundly affect vegetation greenness.

An efficient global sensitivity analysis approach for distributed hydrological model

Sensitivity analysis of hydrological model is the key for model uncertainty quantification. However, how to effectively validate model and identify the dominant parameters for distributed hydrological models is a bottle-neck to achieve parameters optimization. For this reason, a new approach was proposed in this paper, in which the support vector machine was used to construct the response surface at first. Then it integrates the SVM-based response surface with the Sobol＇ method, i.e. the RSMSoboI＇ method, to quantify the parameter sensi- tivities. In this work, the distributed time-variant gain model （DTVGM） was applied to the Huaihe River Basin, which was used as a case to verify its validity and feasibility. We selected three objective functions （i.e. water balance coefficient WB, Nash-Sutcliffe efficiency coefficient NS, and correlation coefficient RC） to assess the model performance as the output responses for sensitivity analysis. The results show that the parameters gl and g2 are most important for all the objective functions, and they are almost the same to that of the classical approach. Furthermore, the RSMSobol method can not only achieve the quantification of the sensitivity, and also reduce the computational cost, with good accuracy compared to the classical approach. And this approach will be effective and reliable in the global sensitivity analysis for a complex modelling system.