Quantifying land change dynamics, resilience and feedback: A comparative analysis of the lake Chad basin in Africa and Aral Sea basin in Central Asia.

The intricate interaction of natural and anthropogenic factors drives changes in land and water in response to societal demands and climate change. However, there has been insufficient information on the feedback effects in dryland hotspots altered by land change dynamics. This research compared two transboundary inland lakes, the Lake Chad basin (LCB) in Africa and the Aral Sea basin (ASB) in Central Asia, using remote sensing and geographic information system techniques to analyze and quantify present and future land cover dynamics, resilience, and their feedback effects. The study integrated Cellular Automata, Markov Chain, and Multilayer Perceptron models to predict LULC changes up to 2030. Descriptive statistics, ordinary least squares regression, hotspot Gi-Bin, trend analysis, and advanced geostatistical methods were utilized to identify relationships, patterns, magnitudes, and directions of observed changes in the feedback effects. From 2000 to 2030, the analysis unveils intriguing trends, including an increase in cropland from 48% to 51% and a decrease in shrubland from 18% to 15% in the LCB. The grassland increased from 21% to 22%, and the settlement expanded from 0.10 to 0.60% in the ASB. Water bodies remained stable at 1.60 % in LCB, while in ASB, it declined from 3% to 2%. These changes were significantly influenced by population, elevation, and temperature in both basins, with irrigation also playing a significant role in the ASB and slope in LCB. The study further revealed discernible shifts in normalized difference vegetation index, temperature, and precipitation linked to specific land cover conversions, suggesting alterations in surface properties and vegetation health. This study underscores the complex interplay between land cover dynamics, resilience, climate variability, and feedback mechanisms in LCB and ASB.

Modeling the effect of grazing on carbon and water use efficiencies in grasslands on the Qinghai-Tibet Plateau.

BACKGROUND: Carbon and water use efficiencies (CUE and WUE, respectively) are vital indicators of the adaptability of plants to environmental conditions. However, the effects of grazing and climate change on the spatiotemporal changes in CUE and WUE in Qinghai-Tibet Plateau grasslands (QTPG) are still unclear. RESULTS: Using the enhanced Biome-BGCMuSo model in combination with observed data, we estimated and analyzed the spatiotemporal variations in CUE and WUE and their responses to grazing in QTPG from 1979 to 2018. The mean annual CUE was 0.7066 in QTPG from 1979 to 2018 under the actual climate scenario. In general, the grassland CUE was low in the southeast and high in the northwest. Grazing generally decreased CUE in QTPG from 1979 to 2018, and there was an increasing trend in the difference in CUE between the grazing and nongrazing scenarios. The difference in CUE was generally greater in the northwest than in the southeast. The mean annual WUE was 0.5591 g C/kg H2O in QTPG from 1979 to 2018 under the actual climate scenario. After 2000, the grassland WUE exhibited a fluctuating upward trend. In general, the grassland WUE was greater in the southeast than in the northwest. Grazing generally decreased WUE in QTPG from 1979 to 2018, and there was an increasing trend in the difference in WUE between the grazing and nongrazing scenarios. The difference in WUE was generally greater in the northwest than in the southeast. CONCLUSIONS: The findings of this study suggested that the spatiotemporal changes in CUE and WUE in QTPG were closely related to changes in the natural environment and grazing management.

Recent impacts of water management on dryland's salinization and degradation neutralization.

Reducing soil salinization of croplands with optimized irrigation and water management is essential to achieve land degradation neutralization (LDN). The effectiveness and sustainability of various irrigation and water management measures to reduce basin-scale salinization remain uncertain. Here we used remote sensing to estimate the soil salinity of arid croplands from 1984 to 2021. We then use Bayesian network analysis to compare the spatial-temporal response of salinity to water management, including various irrigation and drainage methods, in ten large arid river basins: Nile, Tigris-Euphrates, Indus, Tarim, Amu, Ili, Syr, Junggar, Colorado, and San Joaquin. In basins at more advanced phases of development, managers implemented drip and groundwater irrigation and thus effectively controlled salinity by lowering groundwater levels. For the remaining basins using conventional flood irrigation, economic development and policies are crucial for establishing a virtuous circle of "improving irrigation systems, reducing salinity, and increasing agricultural incomes" which is necessary to achieve LDN.

Unintended consequences of combating desertification in China.

Since the early 2000s, China has carried out extensive "grain-for-green" and grazing exclusion practices to combat desertification in the desertification-prone region (DPR). However, the environmental and socioeconomic impacts of these practices remain unclear. We quantify and compare the changes in fractional vegetation cover (FVC) with economic and population data in the DPR before and after the implementation of these environmental programmes. Here we show that climatic change and CO2 fertilization are relatively strong drivers of vegetation rehabilitation from 2001-2020 in the DPR, and the declines in the direct incomes of farmers and herders caused by ecological practices exceed the subsidies provided by governments. To minimize economic hardship, enhance food security, and improve the returns on policy investments in the DPR, China needs to adapt its environmental programmes to address the potential impacts of future climate change and create positive synergies to combat desertification and improve the economy in this region.

Effects of land clearing for agriculture on soil organic carbon stocks in drylands: A meta-analysis.

Agricultural activities have been expanding globally with the pressure to provide food security to the earth's growing population. These agricultural activities have profoundly impacted soil organic carbon (SOC) stocks in global drylands. However, the effects of clearing natural ecosystems for cropland (CNEC) on SOC are uncertain. To improve our understanding of carbon emissions and sequestration under different land uses, it is necessary to characterize the response patterns of SOC stocks to different types of CNEC. We conducted a meta-analysis with mixed-effect model based on 873 paired observations of SOC in croplands and adjacent natural ecosystems from 159 individual studies in global drylands. Our results indicate that CNEC significantly (p < .05) affects SOC stocks, resulting from a combination of natural land clearing, cropland management practices (fertilizer application, crop species, cultivation duration) and the significant negative effects of initial SOC stocks. Increases in SOC stocks (in 1 m depth) were found in croplands which previously natural land (deserts and shrublands) had low SOC stocks, and the increases were 278.86% (95% confidence interval, 196.43%-361.29%) and 45.38% (26.53%-62.23%), respectively. In contrast, SOC stocks (in 1 m depth) decreased by 24.11% (18.38%-29.85%) and 10.70% (1.80%-19.59%) in clearing forests and grasslands for cropland, respectively. We also established the general response curves of SOC stocks change to increasing cultivation duration, which is crucial for accurately estimating regional carbon dynamics following CNEC. SOC stocks increased significantly (p < .05) with high long-term fertilizer consumption in cleared grasslands with low initial SOC stocks (about 27.2 Mg ha-1 ). The results derived from our meta-analysis could be used for refining the estimation of dryland carbon dynamics and developing SOC sequestration strategies to achieve the removal of CO2 from the atmosphere.

Net carbon flux from cropland changes in the Central Asian Aral Sea Basin.

The transboundary Aral Sea Basin (ASB) covers parts of the former Union of Soviet Socialist Republics (USSR), the central Asian portion of Kazakhstan (KAZ), Uzbekistan (UZB), Turkmenistan (TUK), Kyrgyzstan (KGZ), and Tajikistan (TAJ). During recent decades, the region has experienced widespread cropland changes and has therefore attracted attention. However, carbon flux caused by these changes, which is critical to understand the carbon cycle in the region and to develop strategies for carbon sequestration, has not been quantified. We applied the Bookkeeping Model to analyze carbon flux caused by cropland changes. We found that the cropland area expanded from 1975 to 2019. Prior to 1990, the net increment in cropland area was 64.47 kha yr-1; this decreased to 11.02-18.69 kha yr-1 after the collapse of the USSR (1991). Grasslands and bare lands with low carbon density were the main types of land that were converted to cropland, accounting for approximately 70.3-99.29% of the land cleared for cropland. In terms of cropland loss, grassland accounted for more than 40.69% of the cropland converted to other land cover types, followed by artificial surfaces, which accounted for 25.84-45.16%. The expansion of cropland served as a carbon sink owing to the increase in irrigation and fertilization. The overall potential carbon emission was -89.38 ± 35.34 Tg C, and contributions of the five countries were as follows: TUR (-33.65 ± 6.30 Tg C), UZB (-29.23 ± 25.35 Tg C), KAZ (-12.76 ± 12.16 Tg C), TAJ (-11.11 ± 5.47 Tg C), KGZ (-2.63 ± 7.83 Tg C). The net carbon flux was -2.04 ± 0.23 Tg C yr-1 before 1990, and it decreased to -1.87 ± 1.03 Tg C yr-1 during the post-Soviet era. Finally, we state sustainable agricultural management is urgently needed to deal with the environmental problems of the Aral Sea, which have been primarily caused by cropland expansion.

Spatiotemporal variations in aerosol optical depth and associated risks for populations in the arid region of Central Asia.

With the progress of urbanization, atmospheric pollution and physical health issues caused by the increase of aerosol optical depth (AOD) become more and more prominent. Hence, population exposure risk to AOD becomes a research hotspot. The arid Central Asia (ACA) has a generally high AOD and is a major source area for dust aerosols in the world. Only few studies have discussed population exposure risk to AOD in ACA. Based on multisource remote sensing data, and used population exposure risk model, this study evaluated population exposure risk to AOD in six ecological zones (Northern steppe region of ACA (NSCA), Aral Sea desert area (ASDA), Tianshan Mountains (TSMT), Junggar Basin desert area (JBDA), Tarim Basin desert area (TBDA) and Hexi corridor desert area (HCDA)). Generally, AOD in ACA was kept increasing from 2000 to 2015, and it increased mostly in HCDA and areas near the Aral Sea (p < 0.001). With respect to seasonal variations, the maximum AOD was observed in spring and autumn, and the minimum was in winter. Considering land use changes, AOD was mainly manifested by the reduction of water bodies and expansion of construction lands. This was the mostly significant in NSCA and ASDA (p < 0.01). The population exposure risk to AOD in ACA was increasing continuously from 2000 to 2015, and high-value regions (>9) concentrated in oases, specifically, in the Aral Sea basin and Tarim River basin.The Aral Sea basin became the major AOD source region in ACA due to the shrinking water area after unreasonable development and utilization of water resources. These further increase population exposure risk to AOD in the Aral Sea area. Hence, ecological restoration in terminal lakes of ACA will become the key to lower population exposure risk to AOD practically.

Human appropriation of net primary production estimates in the Xinjiang grasslands.

The human appropriation of net primary production (HANPP) was developed to estimate the intensity of human activities in natural ecosystems, which is still unclear in the Xinjiang grasslands. Using the Biome-Biogeochemical Cycle (Biome-BGC) grazing model in combination with field data, we assessed the HANPP and explored its spatiotemporal patterns in the Xinjiang grasslands. Our results showed that (1) the HANPP increased from 38 g C/m2/yr in 1979 to 88 g C/m2/yr in 2012, with an average annual increase of 1.47%. The HANPP was 80 g C/m2/yr, which represented 51% of the potential net primary production (NPPpot), and the HANPP efficiency was 70% in this region. (2) The areas with high HANPP values mainly occurred in northern Xinjiang and northwest of the Tianshan Mountains, while areas with low HANPP values mainly occurred in southern Xinjiang and southwest of the Tianshan Mountains. (3) Interannual variations in HANPP and NPPpot were significantly positively correlated (P<0.01). Interannual variations in HANPP efficiency and grazing intensity were negatively correlated (P<0.01). These results can help identify the complex impacts of human activities on grassland ecosystems and provide basic data for grassland management.

Large-scale rain-fed to paddy farmland conversion modified land-surface thermal properties in Cold China.

The recent acute evolution of cropland structure in Cold China could lead to rapid rice paddy expansion, potentially altering land-surface thermal processes and influencing climate. To address the issue, this study investigated the changes in cropland type, land-surface temperature (LST) and heat fluxes in the agricultural region of Cold China during 2000-2015 based on time-series of land-use data and MODIS LST product, using the split-window algorithms (SWA) model and the pixel component arranging and component algorithm (PCACA). The investigation revealed large-scale land transformation from rain-fed farmland to paddy field in Cold China during 2000-2015. Compared to the rain-fed farmland, lower LST was observed in paddy field throughout crop growing seasons, with the highest LST threshold found in June (7.17 ± 1.05 °C) and the lowest value found in August (1.04 ± 0.35 °C). The cooling effect of paddy-field ranged from 0.59 ± 0.06 °C, 0.77 ± 0.07 °C, and 1.08 ± 0.08 °C for the low-, medium-, and high-density paddies, respectively. Compared to other months, stronger cooling effect was found in May and June. Further analysis showed the conversion of a rain-fed farmland to paddy field reduced the sensible heat flux and soil heat flux by 52.94 W/m2 and 15.26 W/m2, respectively, while increased the latent heat flux and net radiation by 115.66 W/m2 and 47.34 W/m2, respectively. The findings from this study indicated the changes in cropland structure and management regime (e.g., irrigation) could profoundly modify land-surface thermal processes and local/regional climate, interfering the signals from global warming. Therefore, instrumental climate data that collected from areas experienced large-scale conversion between rain-fed and paddy farmland should be carefully screened and corrected to prevent land-use induced biases.

Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda.

The use of appropriate approaches to produce risk maps is critical in landslide disaster management. The aim of this study was to investigate and compare the stability index mapping (SINMAP) and the spatial multicriteria evaluation (SMCE) models for landslide risk modeling in Rwanda. The SINMAP used the digital elevation model in conjunction with physical soil parameters to determine the factor of safety. The SMCE method used six layers of landslide conditioning factors. In total, 155 past landslide locations were used for training and model validation. The results showed that the SMCE performed better than the SINMAP model. Thus, the receiver operating characteristic and three statistical estimators-accuracy, precision, and the root mean square error (RMSE)-were used to validate and compare the predictive capabilities of the two models. Therefore, the area under the curve (AUC) values were 0.883 and 0.798, respectively, for the SMCE and SINMAP. In addition, the SMCE model produced the highest accuracy and precision values of 0.770 and 0.734, respectively. For the RMSE values, the SMCE produced better prediction than SINMAP (0.332 and 0.398, respectively). The overall comparison of results confirmed that both SINMAP and SMCE models are promising approaches for landslide risk prediction in central-east Africa.

Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa.

Application of suitable methods to generate landslide susceptibility maps (LSM) can play a key role in risk management. Rwanda, located in centre-eastern Africa experiences frequent and intense landslides which cause substantial impacts. The main aim of the current study was to effectively generate susceptibility maps through exploring and comparing different statistical and probabilistic models. These included weights of evidence (WoE), logistic regression (LR), frequency ratio (FR) and statistical index (SI). Experiments were conducted in Rwanda as a study area. Past landslide locations have been identified through extensive field surveys and historical records. Totally, 692 landslide points were collected and prepared to produce the inventory map. This was applied to calibrate and validate the models. Fourteen maps of conditioning factors were produced for landslide susceptibility modeling, namely: elevation, slope degree, topographic wetness index (TWI), curvature, aspect, distance from rivers and streams, distance to main roads, lithology, soil texture, soil depth, topographic factor (LS), land use/land cover (LULC), precipitation and normalized difference vegetation index (NDVI). Thus, the produced susceptibility maps were validated using the receiver operating characteristic curves (ROC/AUC). The findings from this study disclosed that prediction rates were 92.7%, 86.9%, 81.2% and 79.5% respectively for WoE, FR, LR and SI models. The WoE achieved the highest AUC value (92.7%) while the SI produced a lowest AUC value (79.5%). Additionally, 20.42% of Rwanda (5048.07 km2) was modeled as highly susceptible to landslides with the western part the highly susceptible comparing to other parts of the country. Conclusively, the comparison of produced maps revealed that all applied models are promising approaches for landslide susceptibility studying in Rwanda. The results of the present study may be useful for landslide risk mitigation in the study area and in other areas with similar terrain and geomorphological conditions. More studies should be performed to include other important conditioning factors that exacerbate increases in susceptibility especially anthropogenic factors.

Satellite-observed vegetation stability in response to changes in climate and total water storage in Central Asia.

Ecosystems in arid and semi-arid regions are vulnerable to climatic and anthropogenic disturbances. However, our understanding of vegetation stability (including resistance and resilience, which are the abilities of ecosystems to resist perturbations and return to pre-disturbance structure or function, respectively) in response to environmental changes in dryland ecosystems remains insufficient, particularly in the absence of large-scale observations of water availability. Here we introduced GRACE monthly total water storage anomaly (TWSA) data into an autoregressive model with remote sensed EVI, air temperature and precipitation to investigate the short-term vegetation stability and its influencing factors in Central Asia (CA) during 2003-2015. The results showed that the grid-level vegetation resilience in CA increased logarithmically as mean annual precipitation (R2 = 0.33, P < 0.05) but decreased linearly with increasing mean annual temperature (R2 = 0.41, P < 0.05). Vegetation resilience was not correlated with TWSA, due to the decoupling of TWSA with precipitation both spatially and temporally in the majority of CA. At the biome level, vegetation resilience also increased as a logarithmical function of aridity index (R2 = 0.80, P < 0.05). Vegetation resistance to TWSA showed minor difference across biomes, while vegetation resistance to precipitation functioned as a parabolic curve along the aridity gradient (R2 = 0.59, P < 0.05). Our results suggest that accounting for the effects of total water column instead of precipitation only is critical in understanding vegetation-water relationships in drylands. The steep decrease in vegetation resilience in areas with high temperature and low water availability implies a high risk of collapse for these water-limited ecosystems if there are severe droughts. Furthermore, reduction in total water storage, induced by, e.g., large-scale extraction of surface runoff or shallow-layer groundwater for irrigation, can result in negative influences to natural biomes in dryland regions.

Landslide Susceptibility Assessment Using Spatial Multi-Criteria Evaluation Model in Rwanda.

Landslides susceptibility assessment has to be conducted to identify prone areas and guide risk management. Landslides in Rwanda are very deadly disasters. The current research aimed to conduct landslide susceptibility assessment by applying Spatial Multi-Criteria Evaluation Model with eight layers of causal factors including: slope, distance to roads, lithology, precipitation, soil texture, soil depth, altitude and land cover. In total, 980 past landslide locations were mapped. The relationship between landslide factors and inventory map was calculated using the Spatial Multi-Criteria Evaluation. The results revealed that susceptibility is spatially distributed countrywide with 42.3% of the region classified from moderate to very high susceptibility, and this is inhabited by 49.3% of the total population. In addition, Provinces with high to very high susceptibility are West, North and South (40.4%, 22.8% and 21.5%, respectively). Subsequently, the Eastern Province becomes the peak under low susceptibility category (87.8%) with no very high susceptibility (0%). Based on these findings, the employed model produced accurate and reliable outcome in terms of susceptibility, since 49.5% of past landslides fell within the very high susceptibility category, which confirms the model's performance. The outcomes of this study will be useful for future initiatives related to landslide risk reduction and management.

Robustness and Uncertainties of the "Temperature and Greenness" Model for Estimating Terrestrial Gross Primary Production.

Terrestrial gross primary production (GPP) plays a vital role in offsetting anthropogenic CO2 emission and regulating global carbon cycle. Various remote sensing approaches for estimating GPP have attracted considerable scientific attentions, yet their robustness and uncertainties remain unclear. Here we evaluate the performance of the "temperature and greenness" (TG) model, a representative remote sensing model in estimating GPP, using the global FLUXNET GPP based on parameter sensitive analysis and optimization strategies. The results show that the minimum (xn) and optimum (xo) temperatures for photosynthesis are sensitive parameters but maximum temperature (xm) not. Optimized xn and xo differ largely from their defaults for more than half of 12 plant functional types (PFTs). Parameter optimization significantly improves the TG's performance in forest ecosystems where temperature or solar radiation has significant contribution to GPP. For water-limited ecosystems where GPP are strongly dependent of EVI and EVI are sensitive to precipitation, parameter optimization has limited effects. These results imply that the TG model, and most likely for other kind of GPP models using same methodology, can't be significantly improved for all PFTs through parameter optimization only, and other key climatic variables should be incorporated into the model for better predicting terrestrial ecosystem GPP.

Carbon stock and its responses to climate change in Central Asia.

Central Asia has a land area of 5.6 × 10(6) km(2) and contains 80-90% of the world's temperate deserts. Yet it is one of the least characterized areas in the estimation of the global carbon (C) stock/balance. This study assessed the sizes and spatiotemporal patterns of C pools in Central Asia using both inventory (based on 353 biomass and 284 soil samples) and process-based modeling approaches. The results showed that the C stock in Central Asia was 31.34-34.16 Pg in the top 1-m soil with another 10.42-11.43 Pg stored in deep soil (1-3 m) of the temperate deserts. They amounted to 18-24% of the global C stock in deserts and dry shrublands. The C stock was comparable to that of the neighboring regions in Eurasia or major drylands around the world (e.g. Australia). However, 90% of Central Asia C pool was stored in soil, and the fraction was much higher than in other regions. Compared to hot deserts of the world, the temperate deserts in Central Asia had relatively high soil organic carbon density. The C stock in Central Asia is under threat from dramatic climate change. During a decadal drought between 1998 and 2008, which was possibly related to protracted La Niña episodes, the dryland lost approximately 0.46 Pg C from 1979 to 2011. The largest C losses were found in northern Kazakhstan, where annual precipitation declined at a rate of 90 mm decade(-1) . The regional C dynamics were mainly determined by changes in the vegetation C pool, and the SOC pool was stable due to the balance between reduced plant-derived C influx and inhibited respiration.

Biomass allocation patterns across China's terrestrial biomes.

Root to shoot ratio (RS) is commonly used to describe the biomass allocation between below- and aboveground parts of plants. Determining the key factors influencing RS and interpreting the relationship between RS and environmental factors is important for biological and ecological research. In this study, we compiled 2088 pairs of root and shoot biomass data across China's terrestrial biomes to examine variations in the RS and its responses to biotic and abiotic factors including vegetation type, soil texture, climatic variables, and stand age. The median value of RS (RSm) for grasslands, shrublands, and forests was 6.0, 0.73, and 0.23, respectively. The range of RS was considerably wide for each vegetation type. RS values for all three major vegetation types were found to be significantly correlated to mean annual precipitation (MAP) and potential water deficit index (PWDI). Mean annual temperature (MAT) also significantly affect the RS for forests and grasslands. Soil texture and forest origin altered the response of RS to climatic factors as well. An allometric formula could be used to well quantify the relationship between aboveground and belowground biomass, although each vegetation type had its own inherent allometric relationship.

Growing season net ecosystem CO2 exchange of two desert ecosystems with alkaline soils in Kazakhstan.

Central Asia is covered by vast desert ecosystems, and the majority of these ecosystems have alkaline soils. Their contribution to global net ecosystem CO2 exchange (NEE) is of significance simply because of their immense spatial extent. Some of the latest research reported considerable abiotic CO2 absorption by alkaline soil, but the rate of CO2 absorption has been questioned by peer communities. To investigate the issue of carbon cycle in Central Asian desert ecosystems with alkaline soils, we have measured the NEE using eddy covariance (EC) method at two alkaline sites during growing season in Kazakhstan. The diurnal course of mean monthly NEE followed a clear sinusoidal pattern during growing season at both sites. Both sites showed significant net carbon uptake during daytime on sunny days with high photosynthetically active radiation (PAR) but net carbon loss at nighttime and on cloudy and rainy days. NEE has strong dependency on PAR and the response of NEE to precipitation resulted in an initial and significant carbon release to the atmosphere, similar to other ecosystems. These findings indicate that biotic processes dominated the carbon processes, and the contribution of abiotic carbon process to net ecosystem CO2 exchange may be trivial in alkaline soil desert ecosystems over Central Asia.

Effect of land use history and pattern on soil carbon storage in arid region of Central Asia.

The purpose of this study is to investigate variations in soil organic carbon (SOC) in arid areas due to differences in the cultivation history, land use, and soil salinization. The study area is the lower Sangong River basin on the piedmont of the northern TianShan mountains, which experiences heavy land-use activities. In 1982 and 2005,127(152) and 74 (161) samples in old (new) oasis were collected from each site at the surface soil (i.e., 0-20 cm). The data reveal that the mean value of the surface soil organic carbon content of the old oasis was higher than that of the new oasis by 4.01 g/kg in 1982 and 3.79 g/kg in 2005. Additionally, the soil organic carbon content decreased more rapidly in the newly reclaimed oasis than in the old oasis from 1982 to 2005. The spatial pattern of the SOC content was correlated with the exploitation time in the new oasis, the agricultural land use history, and the SOC content. The decreasing trend is clearer in the high SOC content area than in the low SOC content area. Farmland is the largest carbon pool in both the new and old oases. The carbon density of the old oasis was higher than that of the new oasis by 4.01 and 3.79 g/kg in 1982 and 2005 respectively. The loss of SOC in the agricultural watershed of the arid region in NW China is obvious. Improvements of land management practices, such as no tillage, straw returning to soil, and balanced fertilization techniques, should be adopted to increase the SOC content.

Integrated assessment of heavy metal contamination in sediments from a coastal industrial basin, NE China.

The purpose of this study is to investigate the current status of metal pollution of the sediments from urban-stream, estuary and Jinzhou Bay of the coastal industrial city, NE China. Forty surface sediment samples from river, estuary and bay and one sediment core from Jinzhou bay were collected and analyzed for heavy metal concentrations of Cu, Zn, Pb, Cd, Ni and Mn. The data reveals that there was a remarkable change in the contents of heavy metals among the sampling sediments, and all the mean values of heavy metal concentration were higher than the national guideline values of marine sediment quality of China (GB 18668-2002). This is one of the most polluted of the world's impacted coastal systems. Both the correlation analyses and geostatistical analyses showed that Cu, Zn, Pb and Cd have a very similar spatial pattern and come from the industrial activities, and the concentration of Mn mainly caused by natural factors. The estuary is the most polluted area with extremely high potential ecological risk; however the contamination decreased with distance seaward of the river estuary. This study clearly highlights the urgent need to make great efforts to control the industrial emission and the exceptionally severe heavy metal pollution in the coastal area, and the immediate measures should be carried out to minimize the rate of contamination, and extent of future pollution problems.

[Dynamics of ecosystem services value in Aksu River watershed in 1960 -2008].

By using GIS-RS techniques and the table 'Equivalent weight of China terrestrial ecosystems services value', in combining with the local grain yield and grain purchasing price, this paper analyzed the changes of ecosystem services value in Aksu River watershed in 1960-2008, and explored the responses of ecosystem services value change in arid trans-boundary river watershed to land use/cover change. Overall, the ecosystem services value in Aksu River watershed in 1960-2008 changed slightly, with the total value increased after an initial decrease. The main cause of the initial decrease was the area decrease of wetland, woodland, and grassland. The area increase of cropland and water body partly offset the loss of the total value, but the loss was still larger than the gain. The contribution of each ecosystem service value to the total ecosystem services value had less change, and the service value from soil formation and protection together with waste treatment contributed most. There was a significant spatial heterogeneity in the magnitude and the variation of ecosystem services value, with the downstream of the watershed changed significantly. Sensitivity analysis indicated that the estimated total ecosystem services value in the study area was inelastic to the services value coefficients, and the results shown in this paper were robust.