Anming Bao

Climate change impact on water resource extremes in a headwater region of the Tarim basin in China

The Tarim river basin in China is a huge inland arid basin, which is expected to be highly vulnerable to climatic changes, given that most water resources originate from the upper mountainous headwater regions. This paper focuses on one of these headwaters: the Kaidu river subbasin. The climate change impact on the surface and ground water resources of that basin and more specifically on the hydrological extremes were studied by using both lumped and spatially distributed hydrological models, after simulation of the IPCC SRES greenhouse gas scenarios till the 2050s. The models include processes of snow and glacier melting. The climate change signals were extracted from the grid-based results of general circulation models (GCMs) and applied on the station-based, observed historical data using a perturbation approach. For precipitation, the time series perturbation involves both a wet-day frequency perturbation and a quantile perturbation to the wet-day rainfall intensities. For temperature and potential evapotranspiration, the climate change signals only involve quantile based changes. The perturbed series were input into the hydrological models and the impacts on the surface and ground water resources studied. The range of impact results (after considering 36 GCM runs) were summarized in high, mean, and low results. It was found that due to increasing precipitation in winter, snow accumulation increases in the upper mountainous areas. Due to temperature rise, snow melting rates increase and the snow melting periods are pushed forward in time. Correspondence to: P. Willems (patrick.willems@bwk.kuleuven.be) Although the qualitive impact results are highly consistent among the different GCM runs considered, the precise quantitative impact results varied significantly depending on the GCM run and the hydrological model.

Effects of levofloxacin hydrochlordie on photosystem II activity and heterogeneity of Synechocystis sp.

Effects of LH on photosynthesis of Synechocystis sp. were investigated by a variety of in vivo chlorophyll fluorescence. O2 evolution and the photosystem II (PSII) activity were clearly inhibited by LH. Exposure to LH increased the proportion of PSIIbeta and this weakened the connectivity between PSII units and hindered excitation energy-transfer between PSII units. LH decreased the density of the active photosynthetic reaction centers, inhibited electron transport, and increased the dissipated energy flux per reaction center. The inhibitory effect of LH on Q(A)(-) reoxidation process could be divided into several stages. LH first inhibited the electron transfer from Q(A)(-) to Q(B) by weakening the connectivity between Q(A)(-) and Q(B), and PQ binding began taking part in Q(A)(-) reoxidation. At the second stage, the connectivity between Q(A)(-) and PQ pool was broken and inhibition on PQ binding occurred. At this stage, some Q(A)(-) began to be oxidized by S2(Q(A)Q(B))(-). Finally, when the connectivity between Q(A)(-) and Q(B) and PQ was completely broken, all Q(A)(-) was oxidized through charge recombination.

Vegetation dynamics and responses to climate change and human activities in Central Asia

Knowledge of the current changes and dynamics of different types of vegetation in relation to climatic changes and anthropogenic activities is critical for developing adaptation strategies to address the challenges posed by climate change and human activities for ecosystems. Based on a regression analysis and the Hurst exponent index method, this research investigated the spatial and temporal characteristics and relationships between vegetation greenness and climatic factors in Central Asia using the Normalized Difference Vegetation Index (NDVI) and gridded high-resolution station (land) data for the period 1984-2013. Further analysis distinguished between the effects of climatic change and those of human activities on vegetation dynamics by means of a residual analysis trend method. The results show that vegetation pixels significantly decreased for shrubs and sparse vegetation compared with those for the other vegetation types and that the degradation of sparse vegetation was more serious in the Karakum and Kyzylkum Deserts, the Ustyurt Plateau and the wetland delta of the Large Aral Sea than in other regions. The Hurst exponent results indicated that forests are more sustainable than grasslands, shrubs and sparse vegetation. Precipitation is the main factor affecting vegetation growth in the Kazakhskiy Melkosopochnik. Moreover, temperature is a controlling factor that influences the seasonal variation of vegetation greenness in the mountains and the Aral Sea basin. Drought is the main factor affecting vegetation degradation as a result of both increased temperature and decreased precipitation in the Kyzylkum Desert and the northern Ustyurt Plateau. The residual analysis highlighted that sparse vegetation and the degradation of some shrubs in the southern part of the Karakum Desert, the southern Ustyurt Plateau and the wetland delta of the Large Aral Sea were mainly triggered by human activities: the excessive exploitation of water resources in the upstream areas of the Amu Darya basin and oil and natural gas extraction in the southern part of the Karakum Desert and the southern Ustyurt Plateau. The results also indicated that after the collapse of the Soviet Union, abandoned pastures gave rise to increased vegetation in eastern Kazakhstan, Kyrgyzstan and Tajikistan, and abandoned croplands reverted to grasslands in northern Kazakhstan, leading to a decrease in cropland greenness. Shrubs and sparse vegetation were extremely sensitive to short-term climatic variations, and our results demonstrated that these vegetation types were the most seriously degraded by human activities. Therefore, regional governments should strive to restore vegetation to sustain this fragile arid ecological environment.

Soil moisture content retrieval based on apparent thermal inertia for Xinjiang province in China

In the arid to semi-arid regions of north-western China, soil moisture is the main hydrological driver for vegetation growth. With the launch of the Moderate Resolution Imaging Spectroradiometer (MODIS), the local MODIS reception capacity and the strong pressure on water resources in the province, the detection and mapping of soil moisture content (SMC) has become a major issue for the regional water management authorities of the province. In this article, we apply the apparent thermal inertia approach to quantify SMC in the soils of the province of Xinjiang using locally received MODIS data. We report on SMC mapping for the entire province for the year 2005. For the estimation, diurnal land surface temperature (LST (Tσ)), LST difference (ΔTσ) and broadband albedo (α0) were applied to determine the space–time variability of SMC. The retrieval of SMC was based on the rationale that high apparent thermal inertia (Iτ) values correspond to high SMCs and low Iτ values correspond to the minimal ones. To enable the application of the technique, a new classification of soil texture was established based on an existing Chinese soil type classification. Typically only topsoil surface moisture content is retrieved with thermal remote sensing (RS). However, SMC retrieval for a 1 m soil profile was performed by applying a semi-empirical modelling approach. The model uses a two-layer water balance equation, and its SMC (θg) input is based on its linear relationship with the soil moisture saturation index (θsi) at time t. For validation purposes, the automatic weather station and time domain reflectometry (TDR) monitoring network included eight sites in the province, including the Mosuowan and Tianshan snow sites and the Turpan, Bayangburk, Kuerle, Yinsu, Alagan and Yiganbujima TDR sites for which data for the year of 2005 were acquired by the Xinjiang Meteorological Bureau (XMB), the Tarim Management Bureau (TMB) and the Xinjiang Institute for Ecology and Geography (XIEG). When time series of SMC determined by using Iτ are compared with the measurements at the different validation sites, regression curve slopes of the validation relationships vary between 0.499 and 0.922. The R2 values vary between 0.25 and 0.83. The minimum and maximum root mean square errors (RMSEs) are 0.001 and 0.028, respectively. Results suggest that apparent thermal inertia application is quite suitable for θg retrieval of a 1 m soil moisture profile in an arid to semi-arid region. The Aqua MODIS 10-day mean soil moisture product is proven to deliver quantitatively correct SMC imagery representing seasonal changes quite realistically.

Evaluation of PERSIANN-CDR for Meteorological Drought Monitoring over China

In this paper, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) is analyzed for the assessment of meteorological drought. The evaluation is conducted over China at 0.5° spatial resolution against a ground-based gridded China monthly Precipitation Analysis Product (CPAP) from 1983 to 2014 (32 years). The Standardized Precipitation Index (SPI) at various time scales (1 month to 12 months) is calculated for detecting drought events. The results show that PERSIANN-CDR depicts similar drought behavior as the ground-based CPAP in terms of capturing the spatial and temporal patterns of drought events over eastern China, where the intensity of gauge networks and the frequency of droughts are high. 6-month SPI shows the best agreement with CPAP in identifying drought months. However, large differences between PERSIANN-CDR and CPAP in depicting drought patterns and identifying specific drought events are found over northwestern China, particularly in Xinjiang and Qinghai-Tibet Plateau region. Factors behind this may be due to the relatively sparse gauge networks, the complicated terrain and the performance of PERSIANN algorithm.

Response of vegetation to temperature and precipitation in Xinjiang during the period of 1998-2009

In this paper, 10-day spatio-temporal response of vegetation to the change of temperature and precipitation in spring, summer, autumn and whole year during the period of 1998―2009 was analyzed based on the data of SPOT VEGETATION-NDVI and 10-day average temperature or precipitation from 54 meteorological stations in Xinjiang. The results show that the response of 10-day NDVI to temperature was more significant than that to precipitation, and the maximal response of vegetation to temperature and precipitation lagged for two 10-day periods. Seasonally, the effect of temperature and precipitation on vegetation NDVI was most marked in autumn, then in spring, and it was not significant in summer. The response of vegetation to 10-day change of meteorological factors was positive with a long affecting duration in spring, and it had a relatively short affecting duration in autumn and summer. Spatially, the 10-day maximal response of NDVI to temperature in northern Xinjiang was higher than that in southern Xinjiang. The correlation between the 10-day NDVI in whole year and the temperature in the 0-8 10-day period was significantly higher than that between the annual NDVI and the annual temperature at all meteorological stations; the interannual change of NDVI was accordant well with the change of annual precipitation. However, the effect of precipitation within a year on NDVI was not strong. The results indicated that interannual change of temperature was not the dominant factor affecting the change of vegetation NDVI in Xinjiang, but the decrease of annual precipitation was the main factor resulting in the fluctuation of vegetation coverage. Ten-day average temperature was an important factor to promote vegetation growth in Xinjiang within a year, but the effect of precipitation on vegetation growth within a year was not strong.

A multistage simulation-based optimization model for water resources management in Tarim River Basin, China

In this study, a multistage simulation-based optimization model is developed for supporting water resources management under uncertainty. The system couples a lumped rainfall-runoff model with an inexact multistage stochastic program, where its random parameter is provided by the statistical analysis of simulation outcomes. Moreover, penalties are exercised with recourse against any infeasibility, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic consequences when the promised water-allocation targets are violated. The developed model can also reflect dynamic features of the system conditions through transactions at discrete points in time over a multistage context. The developed model is applied to a real case of planning water resources management in Tarim River Basin, which is one of the most serious water-shortage regions of China. A variety of policies associated with different water-allocation targets are analyzed. The results are helpful for decision makers identifying optimal water-allocation plans for mitigating the conflict among ecological protection, economic development, and regional sustainability.

Understanding the Spatial Temporal Vegetation Dynamics in Rwanda

Knowledge of current vegetation dynamics and an ability to make accurate predictions of ecological changes are essential for minimizing food scarcity in developing countries. Vegetation trends are also closely related to sustainability issues, such as management of conservation areas and wildlife habitats. In this study, AVHRR and MODIS NDVI datasets have been used to assess the spatial temporal dynamics of vegetation greenness in Rwanda under the contrasting trends of precipitation, for the period starting from 1990 to 2014, and for the first growing season (season A). Based on regression analysis and the Hurst exponent index methods, we have investigated the spatial temporal characteristics and the interrelationships between vegetation greenness and precipitation in light of NDVI and gridded meteorological datasets. The findings revealed that the vegetation cover was characterized by an increasing trend of a maximum annual change rate of 0.043. The results also suggest that 81.3% of the country’s vegetation has improved throughout the study period, while 14.1% of the country’s vegetation degraded, from slight (7.5%) to substantial (6.6%) deterioration. Most pixels with severe degradation were found in Kigali city and the Eastern Province. The analysis of changes per vegetation type highlighted that five types of vegetation are seriously endangered: The “mosaic grassland/forest or shrubland” was severely degraded, followed by “sparse vegetation,” “grassland or woody vegetation regularly flooded on water logged soil,” “artificial surfaces” and “broadleaved forest regularly flooded.” The Hurst exponent results indicated that the vegetation trend was consistent, with a sustainable area percentage of 40.16%, unsustainable area of 1.67% and an unpredictable area of 58.17%. This study will provide government and local authorities with valuable information for improving efficiency in the recently targeted countrywide efforts of environmental protection and regeneration.

Leaf and canopy water content estimation in cotton using hyperspectral indices and radiative transfer models

a b s t r a c t In present study some vegetation indices for estimating leaf EWT and EWTcanopy were investigated using simulations and field measurements. Leaf and canopy spectral reflectance as well as leaf EWT and EWTcanopy were measured in cotton during the growing seasons of 2010 and 2011. The PROSPECT-5 model was coupled with the SAILH model to explore the performance of water-related vegetation indices for leaf EWT and EWTcanopy estimation. The vegetation indices evaluated were published formulations and new simple ratio vegetation indices formulated with wavebands at 1060 nm and 1640 nm. The sen- sitivities of these indices to leaf internal structural N and LAI effects were assessed. Simulation results indicated that all of the water-related vegetation indices were insensitive to leaf internal structural N, with the highest coefficient of determination R 2 0.9; P 0.8; P < 0.001). Results obtained with field measurements were in agreement with simulation results, with the coefficient of determination R2 = 0.5 (P < 0.001) for leaf EWT and R2 = 0.57 (P < 0.001) for EWTcanopy by the new simple ratio indices. This study provides a new candidate for leaf EWT and EWTcanopy estimation using hyperspectral vegetation indices.

Modelling Snowmelt Runoff under Climate Change Scenarios in an Ungauged Mountainous Watershed, Northwest China

An integrated modeling system has been developed for analyzing the impact of climate change on snowmelt runoff in Kaidu Watershed, Northwest China. The system couples Hadley Centre Coupled Model version 3 (HadCM3) outputs with Snowmelt Runoff Model (SRM). The SRM was verified against observed discharge for outlet hydrological station of the watershed during the period from April to September in 2001 and generally performed well for Nash-Sutcliffe coefficient (EF) and water balance coefficient (RE). The EF is approximately over 0.8, and the water balance error is lower than ± 10%, indicating reasonable prediction accuracy. The Statistical Downscaling Model (SDSM) was used to downscale coarse outputs of HadCM3, and then the downscaled future climate data were used as inputs of the SRM. Four scenarios were considered for analyzing the climate change impact on snowmelt flow in the Kaidu Watershed. And the results indicated that watershed hydrology would alter under different climate change scenarios. The stream flow in spring is likely to increase with the increased mean temperature; the discharge and peck flow in summer decrease with the decreased precipitation under Scenarios 1 and 2. Moreover, the consideration of the change in cryosphere area would intensify the variability of stream flow under Scenarios 3 and 4. The modeling results provide useful decision support for water resources management.