Terrestrial ecosystem and climate system are closely related to each other. Faced with the unavoidable global climate change, it is important to investigate terrestrial ecosystem responding to climate change. In inland river basin of arid and semi-arid regions in China, sensitivity difference of vegetation responding to climate change from 1998 to 2007 was analyzed in this paper. (1) Differences in the global spatio-temporal distribution of vegetation and climate are obvious. The vegetation change shows a slight degradation in this whole region. Degradation is more obvious in densely vegetated areas. Temperature shows a general downward trend with a linear trend coefficient of -1.1467. Conversely, precipitation shows an increasing trend with a linear trend coefficient of 0.3896. (2) About the central tendency response, there are similar features in spatial distribution of both NDVI responding to precipitation (NDVI-P) and NDVI responding to AI (NDVI-AI), which are contrary to that of NDVI responding to air temperature (NDVI-T). Typical sensitivity region of NDVI-P and NDVI-AI mainly covers the northern temperate arid steppe and the northern temperate desert steppe. NDVI-T typical sensitivity region mainly covers the northern temperate desert steppe. (3) Regarding the fluctuation amplitude response, NDVI-T is dominated by the lower sensitivity, typical regions of the warm temperate shrubby, selui-shrubby, bare extreme dry desert, and northern temperate meadow steppe in the east and temperate semi-shrubby, dwarf arboreous desert in the north are high response. (4) Fluctuation amplitude responses between NDVI-P and NDVI-AI present a similar spatial distribution. The typical sensitivity region mainly covers the northern temperate desert steppe. There are various linear change trend responses of NDVI-T, NDVI-P and NDVI-AI. As to the NDVI-T and NDVI-AI, which are influenced by the boundary effect of semi-arid and semi-humid climate zones, there is less correlation of their linear change tendency along the border. There is stronger correlation in other regions, especially in the NDVI-T in the northern temperate desert steppe and NDVI-AI in the warm temperate shrubby, selui-shrubby, bare, extreme and dry desert.

Land surface emissivity is one of the important parameters in temperature inversion from thermal infrared remote sensing. Using MOD11C3 of Terra-MODIS L3 level products, spatio-temporal data sets of land surface emissivity in China for 10 years from 2001 to 2010 are obtained. The results show that the land surface emissivity in the northwest desert region is the lowest in China, with little seasonal variations. In contrast, there are significant seasonal variations in land surface emissivity in northeast China and northern Xinjiang, the Qinghai- Tibet Plateau, the Yangtze River Valley and the eastern and southern China. In winter, the land surface emissivity in the northeast China and northern Xinjiang is relatively high. The land surface emissivity of the Qinghai-Tibet Plateau region is maintained at low value from November to March, while it becomes higher in other months. The land surface emissivity of the Yangtze River Valley, eastern and southern China, and Sichuan Basin varies from July to October, and peaks in August. Land surface emissivity values could be divided into five levels: low emissivity (0.6163-0.9638), moderate-low emissivity (0.9639-0.9709), moderate emissivity (0.9710-0.9724), moderate-high emissivity (0.9725-0.9738), and high emissivity (0.9739-0.9999). The percentages of areas with low emissivity, moderate-low emissivity and moderate emissivity are, respectively, about 20%, 10% and 20%. The moderate-high emissivity region makes up 40%-50% of China's land surface area. The inter-annual variation of moderate-high emissivity region is also very clear, with two peaks (in spring and autumn) and two troughs (in summer and winter). The inter-annual variation of the high emissivity region is very significant, with a peak in winter (10%), while only 1% or 2% in other seasons. There is a clear association between the spatio-temporal distribution of China's land surface emissivity and temperature: the higher the emissivity, the lower the temperature, and vice versa. Emissivity is an inherent property of any object, but the precise value of its emissivity depends very much on its surrounding environmental factors.

A total of 12 indices of temperature extremes and 11 indices of precipitation extremes at 111 stations in southwestern China at altitudes of 285-4700 m were examined for the period 1961-2008. Significant correlations of temperature extremes and elevation included the trends of diurnal temperature range, frost days, ice days, cold night frequency and cold day frequency. Regional trends of growing season length, warm night frequency, coldest night and warmest night displayed a statistically significant positive correlation with altitude. These characteristics indicated the obvious warming with altitude. For precipitation extreme indices, only the trends of consecutive dry days, consecutive wet days, wet day precipitation and the number of heavy precipitation days had significant correlations with increasing altitude owing to the complex influence of atmospheric circulation. It also indicated the increased precipitation mainly at higher altitude areas, whereas the increase of extreme precipitation events mainly at lowers altitude. In addition, the clearly local influences are also crucial on climate extremes. The analysis revealed an enhanced sensitivity of climate extremes to elevation in southwestern China in the context of recent warming.

This study examines the hydrological and meteorological data of the source region of the Yellow River from 1956 to 2010 and future climate scenarios from regional climate model (PRECIS) during 2010-2020. Through analyzing the flow variations and revealing the climate causes, it predicts the variation trend for future flows. It is found that the annual mean flow showed a decreasing trend in recent 50 years in the source region of the Yellow River with quasi-periods of 5a, 8a, 15a, 22a and 42a; the weakened South China Sea summer monsoon induced precipitation decrease, as well as evaporation increase and frozen soil degeneration in the scenario of global warming are the climate factors, which have caused flow decrease. Based on the regional climate model PRECIS prediction, the flows in the source region of the Yellow River are likely to decrease generally in the next 20 years.

Based on the data of eight meteorological stations from the 1950s to 2007, current cropping patterns, field water moisture management, we use the Mann-Kendall and the Rescaled Range Analysis methods to research the changes of humidity and crop irrigation water requirements in the Lancang River Basin. The results show that the annual and dry season average temperatures significantly increased, and the dry season rainfall increased while wet season rainfall decreased. Evaportranspiration (ET₀) increased during both dry and wet seasons at all stations except Dali, Jianchuan and Gengma, and the aridity-humidity index decreased at most of the stations. The turning points of weather factors, ET₀, the aridity- humidity index, paddy irrigation requirements and total agricultural water requirements occurred from the 1960s to the 1990s. The spatial changing tendency of paddy irrigation quota increased with the increase of altitude and latitude, and the correlation coefficients are 0.513 and 0.610, respectively. The maximum value is observed in Weixi, while the minimum in Mengla.

In this study, a monthly dataset of temperature time series (1961-2010) from 12 meteorological stations across the Three-River Headwater Region of Qinghai Province (THRHR) was used to analyze the climate change. The temperature variation and abrupt change analysis were examined by using moving average, linear regression, Spline interpolation, Mann-Kendall test and so on. Some important conclusions were obtained from this research, which mainly contained four aspects as follows. (1) There were several cold and warm fluctuations for the annual and seasonal average temperature in the THRHR and its three sub-headwater regions, but the temperature in these regions all had an obviously rising trend at the statistical significance level, especially after 2001. The spring, summer, autumn and annual average temperature increased evidently after the 1990s, and the winter average temperature exhibited an obvious upward trend after entering the 21st century. Except the standard value of spring temperature, the annual and seasonal temperature standard value in the THRHR and its three sub-headwater regions increased gradually, and the upward trend for the standard value of winter average temperature indicated significantly. (2) The tendency rate of annual average temperature in the THRHR was 0.36℃10a^-1, while the tendency rates in the Yellow River Headwater Region (YERHR), Lancangjiang River Headwater Region (LARHR) and Yangtze River Headwater Region (YARHR) were 0.37℃10a^-1, 0.37℃10a^-1 and 0.34℃10a^-1 respectively. The temperature increased significantly in the south of Yushu County and the north of Nangqian County. The rising trends of temperature in winter and autumn were higher than the upward trends in spring and summer. (3) The abrupt changes of annual, summer, autumn and winter average temperature were found in the THRHR, LARHR and YARHR, and were detected for the summer and autumn average temperature in the YERHR. The abrupt changes of annual and summer average temperatures were mainly in the late 1990s, while the abrupt changes of autumn and winter average temperatures appeared primarily in the early 1990s and the early 21st century respectively. (4) With the global warming, the diversities of altitude and underlying surface in different parts of the Tibetan Plateau were possibly the main reasons for the high increasing rate of temperature in the THRHR.

Palaeo-hydrological field investigation was carried out in the middle reaches of the Jinghe River. A set of palaeoflood slackwater deposit beds was identified in the Holocene loess-soil sequence in the riverbanks. The sediment samples were collected from the profile, and the particle-size distribution, magnetic susceptibility, loss-on-ignition were analyzed in laboratory. The analytical results showed that the palaeoflood slackwater deposits have recorded extraordinary flood events in the Jinghe River valley. According to stratigraphic correlation and OSL dating, the palaeoflood events were dated to 4100-4000 a BP. The palaeoflood peak discharges were estimated to be 19,410-22,280 m³/s by using the hydrological model and checked by different approaches. These results have the flood data sequence of the Jinghe River extended to 10,000-year time-scale. It provided significant data for hydraulic engineering and for mitigation of flood hazards in the Jinghe River drainage basin.

Based on the analysis of ion chemical composition of lake water and shallow groundwater in the Badain Jaran Desert, this paper discussed the characteristics of ion chemical composition, spatial variation of lake water, and possible supply sources of lake water and groundwater in the desert areas. The results show that the pH, salinity, TDS and electrical conductivity of the lake water are greater than those of groundwater. The ion contents of water samples are dominated by Na⁺ and Cl^-. Most of the higher salinity lakes are Na (K)-Cl-(SO₄) type, and a few of low salinity lakes belong to the Na-(Mg)-(Ca)-Cl-(SO₄)-(HCO₃) type. Most of the groundwater is Na-(Ca)-(Mg)-Cl-(SO₄)-(HCO₃) type, attributed to subsaline lake, and only a few present the Na-Cl-SO₄ type, flowing under saline lake. The pH, salinity, TDS and electrical conductivity in the southeastern lakes are relatively low, and there are slightly alkaline lakes. The pH, salinity, TDS and electrical conductivity in the northern lakes are much greater than those of the southeastern lakes, and the northern lakes are moderately alkaline and saline ones. In the southeastern part of the Badain Jaran Desert, the ion chemical characteristics of the lake water from south to north show a changing trend of subsaline →saline→hypersaline. The changing trend of chemical compositions of ions in recent 9 years indicates that most of the ion contents have a shade of reduction in Boritaolegai, Badain, Nuoertu and Huhejilin lakes, which state clearly that the amount of fresh water supply is increasing in the 9-year period. The ion chemical composition of the lake water reveals that the flow direction of lake water is from southeast to northwest in the Badain Jaran Desert. The ion chemical composition, moisture content of sand layer water level height and hierarchical cluster analysis of the lake water and groundwater demonstrate that the lake water is mainly supplied by local rainfall and infiltration of precipitation in Yabulai Mountains and Heishantou Mountain, and the supply from the Qilian Mountains is almost impossible.

This paper presents the structure and contents of a standardized layered classification system of digital geomorphology for China. This digital classification method combines landforms characteristics of morphology with genesis. A total of 15 categories of exogenic and endogenic forces are divided into two broad categories: morpho-genetic and morpho-structural landforms. Polygon patches are used to manage the morpho-genetic types, and solitary points, lines and polygons are used to manage the morpho-structural types. The classification method of digital morpho-genetic types can be divided into seven layers, i.e. basic morphology and altitude, genesis, sub-genesis, morphology, micro-morphology, slope and aspect, material and lithology. The method proposes combinations of matrix forms based on layered indicators. The attributes of every landform types are obtained from all or some of the seven layers. For the 15 forces categories, some classification indicators and calculation methods are presented for the basic morphology, the morphologic and sub-morphologic landforms of the morpho-genetic types. The solitary polygon, linear and point types of morpho-structural landforms are presented respectively. The layered classification method can meet the demands of scale-span geomorphologic mapping for the national primary scales from 1:500,000 to 1:1,000,000. The layers serve as classification indicators, and therefore can be added and reduced according to mapping demands, providing flexible expandability.

Tropical cyclone, a high energy destructive meteorological system with heavy rainfall and gale triggered massive landslides and windstorms, poses a significant threat to coastal areas. In this paper we have developed a Tropical Cyclone Potential Impact Index (TCPI) based on the air mass trajectories, disaster information, intensity, duration, and frequency of tropical cyclones. We analyzed the spatial pattern and interannual variation of the TCPI over the period 1949–2009, and taking the Super Typhoon Saomai as an example have examined the relationship between the TCPI and direct economic losses, total rainfall, and maximum wind speed. The results reveal that China's TCPI appears to be a weak decreasing trend over the period, which is not significant overall, but significant in some periods. Over the past 20 years, the TCPI decreased in the southern China coastal provinces of Hainan, Guangdong and Guangxi, while it increased in the southeastern coastal provinces of Zhejiang, Fujian and Taiwan. The highest values of TCPI are mainly observed in Taiwan, Hainan, the coastal areas of Guangdong and Fujian and Zhejiang’s southern coast. The TCPI has a good correlation (P=0.01) with direct economic loss, rainfall, and maximum wind speed.

The regional hydrological system is extremely complex because it is affected not only by physical factors but also by human dimensions. And the hydrological models play a very important role in simulating the complex system. However, there have not been effective methods for the model reliability and uncertainty analysis due to its complexity and difficulty. The uncertainties in hydrological modeling come from four important aspects: uncertainties in input data and parameters, uncertainties in model structure, uncertainties in analysis method and the initial and boundary conditions. This paper systematically reviewed the recent advances in the study of the uncertainty analysis approaches in the large-scale complex hydrological model on the basis of uncertainty sources. Also, the shortcomings and insufficiencies in the uncertainty analysis for complex hydrological models are pointed out. And then a new uncertainty quantification platform PSUADE and its uncertainty quantification methods were introduced, which will be a powerful tool and platform for uncertainty analysis of large-scale complex hydrological models. Finally, some future perspectives on uncertainty quantification are put forward.

This study was performed at three eutrophic rivers in Southeast China aiming to determine the magnitude and patterns of dissolved N₂O concentrations and fluxes over a seasonal (in 2009) and diurnal (24 h) temporal scale. The results showed that N₂O concentrations varied from 0.28 to 0.38 (mean 0.32±0.04), 0.29 to 0.46 (mean 0.37±0.07), and 2.07 to 3.47 (mean 2.84±0.63) μg N-N₂O L^-1 in the Fengle, Hangbu and Nanfei rivers, respectively, in the diurnal study performed during the summer of 2008. The study found that mean N₂O concentration and estimated N₂O flux (67.89 ± 6.71 μg N-N₂O m^-2 h^-1) measured from the Nanfei River with serious urban wastewater pollution was significantly higher than those from the Fengle and the Hangbu Rivers with agricultural runoff. In addition, the seasonal study during June and December of 2009 also showed that the mean N₂O concentration (10.59±14.67 μg N-N₂O L^-1) and flux (236.87±449.74 μg N-N₂O m^-2 h^-1) observed from the Nanfei River were significantly higher than those from the other two rivers. Our study demonstrated both N₂O concentrations and fluxes exhibited seasonal and diurnal fluctuations. Over three consecutive days during the summer of 2008, N₂O accumulation rates varied within the range of 3.91-7.21, 2.76-15.71, and 3.23-30.03 μg N-N₂O m^-2 h^-1 for the Fengle, Hangbu and Nanfei Rivers, respectively, and exponentially decreased with time.