Investigation of climate change impacts on food security has become a global hot spot. Even so, efforts to mitigate these issues in arid regions have been insufficient. Thus, in this paper, further research is discussed based on data obtained from various crop and climate models. Two DSSAT crop models (CMs) (CERES-Wheat and N-Wheat) were calibrated with two wheat cultivars (Gemiza9 and Misr1). A baseline simulation (1981-2010) was compared with different scenarios of simulations using three Global Climate Models (GCMs) for the 2030s, 2050s and 2080s. Probable impacts of climate change were assessed using the GCMs and CMs under the high emission Representative Concentration Pathway (RCP8.5). Results predicted decreased wheat grain yields by a mean of 8.7%, 11.4% and 13.2% in the 2030s, 2050s and 2080s, respectively, relative to the baseline yield. Negative impacts of climatic change are probable, despite some uncertainties within the GCMs (i.e., 2.1%, 5.0% and 8.0%) and CMs (i.e., 2.2%, 6.0% and 9.2%). Changing the planting date with a scenario of plus or minus 5 or 10 days from the common practice was assessed as a potentially effective adaptation option, which may partially offset the negative impacts of climate change. Delaying the sowing date by 10 days (from 20 November to 30 November) proved the optimum scenario and decreased further reduction in wheat yields resulting from climate change to 5.2%, 6.8% and 8.5% in the 2030s, 2050s and 2080s, respectively, compared with the 20 November scenario. The planting 5-days earlier scenario showed a decreased impact on climate change adaptation. However, the 10-days early planting scenario increased yield reduction under projected climate change. The cultivar Misr1 was more resistant to rising temperature than Gemiza9. Despite the negative impacts of projected climate change on wheat production, water use efficiency would slightly increase. The ensemble of multi-model estimated impacts and adaptation uncertainties of climate change can assist decision-makers in planning climate adaptation strategies.
The Linfen rift is a Cenozoic extensional rift with significant seismicity and seismic hazards. Studies of this rift shed light on deep dynamic processes and seismogenic mechanisms relevant to crustal structure and seismic activity. We first conducted a joint inversion of receiver functions and surface wave dispersion on waveform data collected from 27 broadband seismic stations to image the crustal S-wave velocity in the Linfen rift and its surroundings. We then relocated the source parameters for 10 earthquake events with depths>20 km and studied the relationship between crustal S-wave velocity and seismicity. The results show that low-velocity zones of different scales exist in the middle-lower crust, and that the depth of the seismogenic layer gradually increases from ~25 km in the south to ~34 km in the north, roughly corresponding to the bottom of the low-velocity zone. We found that most of the relocated earthquakes occurred in the low-velocity zone at depths of 18 km to 34 km, with the deepest at 32 km. Two of the greatest historic earthquakes, Linfen (Ms 7.75) in 1695 and Hongtong (Ms 8.0) in 1303, occurred at the bottom of the high-velocity zone at depths of 12 km to 18 km. Our results, combined with previous studies, suggest that the upwelling mantle material below the rift did not remarkably affect the velocity structure from the bottom of the seismogenic layer down to the uppermost mantle nor heat the crust. It is likely that neither crustal-scale faults nor mantle earthquakes exist in the Linfen rift.
Following flooding disasters, satellite images provide valuable information required for generating flood inundation maps. Multispectral or optical imagery can be used for generating flood maps when the inundated areas are not covered by clouds. We propose a rapid mapping method for identifying inundated areas based on the increase in the water index value between the pre- and post-flood satellite images. Values of the Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) will be higher in the post-flood image for flooded areas compared to the pre-flood image. Based on a threshold value, pixels corresponding to the flooded areas can be separated from non-flooded areas. Inundation maps derived from differencing MNDWI values accurately captured the flooded areas. However the output image will be influenced by the choice of the pre-flood image, hence analysts have to avoid selecting pre-flood images acquired in drought or earlier flood years. Also the inundation maps generated using this method have to be overlaid on the post-flood satellite image in order to orient personnel to landscape features. Advantages of the proposed technique are that flood impacted areas can be identified rapidly, and that the pre-existing water bodies can be excluded from the inundation maps. Using pairs of other satellite data, several maps can be generated within a single flood which would enable emergency response agencies to focus on newly flooded areas.
Landslides, seriously threatening human lives and environmental safety, have become some of the most catastrophic natural disasters in hilly and mountainous areas worldwide. Hence, it is necessary to forecast landslide deformation for landslide risk reduction. This paper presents a method of predicting landslide displacement, i.e., the improved multi-factor Kalman filter (KF) algorithm. The developed model has two advantages over the traditional KF approach. First, it considers multiple external environmental factors (e.g., rainfall), which are the main triggering factors that may induce slope failure. Second, the model includes random disturbances of triggers. The proposed model was constructed using a time series which consists of over 16-month of data on landslide movement and precipitation collected from the Miaodian loess landslide monitoring system and nearby meteorological stations in Shaanxi province, China. Model validation was performed by predicting movements for periods of up to 7 months in the future. The performance of the developed model was compared with that of the improved single-factor KF, multi-factor KF, multi-factor radial basis function, and multi-factor support vector regression approaches. The results show that the improved multi-factor KF method outperforms the other models and that the predictive capability can be improved by considering random disturbances of triggers.
Arizona residents have been dealing with the suspended particulate matter caused health issues for a long time due to Arizona’s arid climate. The state of Arizona is vulnerable to dust storms, especially in the monsoon season because of the anomalies in wind direction and magnitude. In this study, a high-resolution Weather Research and Forecasting (WRF) model coupled with a chemistry module (WRF-Chem) was simulated to compute the particulate matter spatiotemporal distribution as well as the climatic parameters for the state of Arizona. Subsequently, Ordinary Least Square (OLS), spatial lag, spatial error, and Geographically Weighted Regression (GWR) techniques were utilized to develop predictive models based on the climatic indicators that impacted the formation and dispersion of the particulate matter during dust storms. Census tracts were adopted to create local spatial averages for the chosen variables. Terrain height, temperature, wind speed, and vegetation fraction were designated as the most significant variables, whereas base state and perturbation pressures, planetary boundary layer height and soil moisture were adopted as supplementary variables. The determination coefficient for OLS, spatial lag, spatial error, and GWR models peaked at 0.92, 0.93, 0.96, and 0.97, respectively. These models provide a better understanding of the current distribution of the particulate matter and can be used to forecast future trends.
In rural north-western China, the tension between economic growth and ecological crises demonstrates the limitations of dominant top-down approaches to water management. In the 1990s, the Chinese government adopted the Integrated Water Resources Management (IWRM) approach to combat the degradation of water and ecological systems throughout its rural regions. While the approach has had some success at reducing desertification, water shortage, and ecological deterioration, there are important limitations and obstacles that continue to impede optimum outcomes in water management. As the current IWRM approach is instituted through a top-down centralized bureaucratic structure, it often fails to address the socio-political context in which water management is embedded and therefore lacks a complete treatment of how power is embedded in the bureaucracy and how it articulates through economic growth imperatives set by the Chinese state. The approach has relied on infrastructure heavy and technocratic solutions to govern water demand, which has worked to undermine the focus on integration and public participation. Finally, the historical process through which water management mechanisms have been instituted are fraught with bureaucratic fragmentation and processes of centralization that work against some of its primary goals such as reducing uncertainty and risk in water management systems. This article reveals the historical, social, political, and economic processes behind these shortcomings in water management in rural north-western China by focusing on the limitations of a top-down approach that rely on infrastructure, technology, and quantification, and thereby advances a more holistic, socio-political perspective for water management that considers the state-society dynamics inherent in water governance in rural China.
The snowpack is changing across the globe, as the climate warms and changes. We used daily snow water equivalent (SWE) niveograph (time series of SWE) data from 458 snow telemetry (SNOTEL) stations for the period 1982 through 2012. Nineteen indices based on amount, timing, time length, and rates were used to describe the annual temporal evolution in SWE accumulation and ablation. The trends in these annual indices were computed over the time period for each station using the Theil-Sen slope. These trends were then clustered into four groups to determine the spatial pattern of SWE trends. Temperature and precipitation data were extracted from the PRISM data set, due to the shorter time period of temperature measurement at the SNOTEL stations.
Results show that SNOTEL stations can be clustered in four clusters according to the observed trends in snow indices. Cluster 1 stations are mostly located in the Eastern- and South-eastern most parts of the study area and they exhibit a generalized decrease in the indices related with peak SWE and snow accumulation. Those stations recorded a negative trend in precipitation and an increase in temperature. Cluster 4 that is mostly restricted to the North and North-west of the study area shows an almost opposite pattern to cluster 1, due to months with positive trends and a more moderate increase of temperature. Stations grouped in clusters 2 and 3 appear mixed with clusters 1 and 4, in general they show very little trends in the snow indices.
In semi-arid regions, air temperatures have increased in the last decades more than in many other parts of the world. Mongolia has an arid/semi-arid climate and much of the population are herders whose livelihoods depend upon limited water resources that fluctuate with a variable climate. Herders were surveyed to identify their observations of changes in climate extremes for two soums of central Mongolia, Ikh-Tamir in the forest steppe north of the Khangai Mountains and Jinst in the desert steppe south of the mountains. The herders’ indigenous knowledge of changes in climate extremes mostly aligned with the station-based analyses of change. Temperatures were warming with more warm days and nights at all stations. There were fewer cool days and nights observed at the mountain stations both in the summer and winter, yet more cool days and nights were observed in the winter at the desert steppe station. The number of summer days is increasing while the number of frost days is decreasing at all stations. The results of this study support further use of local knowledge and meteorological observations to provide more holistic analysis of climate change in different regions of the world.
Grasslands play a key role in both carbon and water cycles. In semi-arid and arid grassland areas, the frequency and intensity of droughts are increasing. However, the influence of a drought on grassland carbon cycling is still unclear. In this paper, the relationship between drought and grassland carbon cycling is described from the perspective of drought intensity, frequency, duration, and timing. Based on a large amount of literature, we determined that drought is one of the most prominent threats to grassland carbon cycling, although the impacts of different drought conditions are uncertain. The effects of a drought on grassland carbon cycling are more or less altered by drought-induced disturbances, whether individually or in combination. Additionally, a new conceptual model is proposed to better explain the mechanism of droughts on grassland carbon cycling. At present, evaluations of the effects of droughts on grassland carbon cycling are mainly qualitative. A data fusion model is indispensable for evaluating the fate of carbon cycling in a sustainable grassland system facing global change. In the future, multi-source data and models, based on the development of single and multiple disturbance experiments at the ecosystem level, can be utilized to systematically evaluate drought impacts on grassland carbon cycling at different timescales. Furthermore, more advanced models should be developed to address extreme drought events and their consequences on energy, water, and carbon cycling.
Qinghai Province is one of the four largest pastoral regions in China. Timely monitoring of grass growth and accurate estimation of grass yields are essential for its ecological protection and sustainable development. To estimate grass yields in Qinghai, we used the normalized difference vegetation index (NDVI) time-series data derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) and a pre-existing grassland type map. We developed five estimation approaches to quantify the overall accuracy by combining four data pre-processing techniques (original, Savitzky-Golay (SG), Asymmetry Gaussian (AG) and Double Logistic (DL)), three metrics derived from NDVI time series (VImax, VIseason and VImean) and four fitting functions (linear, second-degree polynomial, power function, and exponential function). The five approaches were investigated in terms of overall accuracy based on 556 ground survey samples in 2016. After assessment and evaluation, we applied the best estimation model in each approach to map the fresh grass yields over the entire Qinghai Province in 2016. Results indicated that: 1) For sample estimation, the cross-validated overall accuracies increased with the increasing flexibility in the chosen fitting variables, and the best estimation accuracy was obtained by the so called “fully flexible model” with R2 of 0.57 and RMSE of 1140 kg/ha. 2) Exponential models generally outperformed linear and power models. 3) Although overall similar, strong local discrepancies were identified between the grass yield maps derived from the five approaches. In particular, the two most flexible modeling approaches were too sensitive to errors in the pre-existing grassland type map. This led to locally strong overestimations in the modeled grass yields.