Search

Precipitation variations mostly affect the water resource planning in semi-arid regions of northwest China. The objective of this study is to quantitatively explore the spatial and temporal variations of precipitation in different time scales in Xi'an city area. The Mann–Kendall test and wavelet analysis methods were applied to analyze the precipitation variability. In terms of temporal variation of precipitation, the results indicated that the annual precipitation exhibited a significant decreasing trend during 1951–2018. Except for summer precipitation representing a slightly increasing trend, the other seasonal precipitations had a similar decreasing trend to annual precipitation throughout 1951–2018. The monthly precipitation had different change trends, showing the precipitation from June to September could account for 58.4% of the total annual precipitation. In addition, it was clear that annual precipitation had a significant periodic change, with the periods of 6, 13, 19, and 27 years. For the spatial variation of precipitation during 1961–2018, the results showed that annual and seasonal precipitation exhibited obvious spatial differences, indicating an increasing spatial trend from north to south. Thus, understanding the precipitation variation in Xi'an city can provide a theoretical foundation of future water resources management for other cities in semi-arid regions of northwest China. HIGHLIGHTS There were different variation trends in annual, seasonal and monthly precipitation at a temporal scale.; The annual precipitation in Xi'an city had a significantly increasing trend during 1951–2018.; The annual and seasonal precipitation exhibited obvious spatial differences during 1961–2018.; Findings could guide future water resource management and allocation of other cities in semiarid regions of northwest China.

Because of the shortage of water resources, the phenomenon of groundwater over-extraction is widespread in many parts of the world, which has become a hot issue to be solved. The traditional idea of water resources management only considering blue water (stream flow) can't meet the demand of sustainable utilization of water resources. Blue water accounts for less than 40% of total rainfall, while green water (evapotranspiration) accounts for more than 60% of total rainfall. In the natural environment, vegetation growth mainly depends on green water, which is often neglected. Obviously, the traditional water resources management without considering green water has obvious deficiencies, which can't really reflect the regional water consumption situation in the water resources management. And only by limiting water consumption can achieve the real water saving. In addition, the mode of water resources development and utilization has changed from "supply according to demand" to "demand according to supply". In this background, for many regions with limited water resources, it is impossible to rely on excessive water intake for development, and sustainable development of regional can only be realized by truly controlling water demand. This paper chooses Shijin Irrigation District in the North China Plain as the research area, where agricultural water consumption is high and groundwater over-extraction is serious, and ecological environment is bad. In order to alleviate this situation, comprehensive regulation of water resources based ET is necessary. Therefore, this paper focuses on the concept of ET water resources management and includes green water into water resources assessment. Based on the principle of water balance, the target ET value of crops in the study area is calculated, and the ET value is taken as the target of water resources regulation. The actual water consumption is calculated by Penman-Monteith formula, and reduction of crop water consumption is obtained according to the difference between actual ET and target ET. The reduction in crop water consumption leads to a reduction in demand for water supply, which reduces groundwater extraction. The results of this study can provide necessary technical support for solving the problem of groundwater over-extraction and realizing real water saving.

Terrestrial vegetation growth activity plays pivotal roles on regional development, which has attracted wide attention especially in water resources shortage areas. The paper investigated the spatiotemporal change characteristics of vegetation growth activity using satellite-based Vegetation Health Indices (VHIs) including smoothed Normalized Difference Vegetation Index (SMN), smoothed Brightness Temperature (SMT), Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and VHI, based on 7-day composite temporal resolution and 16 km spatial resolution gridded data, and also estimated the drought conditions for the period of 1982–2016 in Jing-Jin-Ji region of China. The Niño 3.4 was used as a substitution of El Niño Southern Oscillation (ENSO) to reveal vegetation sensitivity to ENSO using correlation and wavelet analysis. Results indicated that monthly SMN has increased throughout the year especially during growing season, starts at approximate April and ends at about October. The correlation analysis between SMN and SMT, SMN and precipitation indicated that the vegetation growth was affected by joint effects of temperature and precipitation. The VCI during growing season was positive trends dominated and vice versa for TCI. The relationships between VHIs and drought make it possible to identify and quantify drought intensity, duration and affected area using different ranges of VHIs. Generally, the intensity and affected area of drought had mainly decreased, but the trends varied for different drought intensities, regions and time periods. Large-scale global climate anomalies such as Niño 3.4 exerted obvious impacts on the VHIs. The Niño 3.4 was mainly negatively correlated to VCI and positively correlated to TCI, and the spatial distributions of areas with positive (negative) correlation coefficients were mainly opposite. The linear relationships between Niño 3.4 and VHIs were in accordance with results of nonlinear relationships revealed using wavelet analysis. The results are of great importance to assess the vegetation growth activity, to monitor and quantify drought using satellite-based VHIs in Jing-Jin-Ji region.

Vegetation coverage and its dynamic response to climatic ecologic indicators are critical for the monitoring and management of terrestrial ecosystem. This paper aims to investigate the spatial and temporal variations and relationships between vegetation coverage and climatic factors during the growing season for the period of 2000–2017 in the Beijing–Tianjin–Hebei metropolitan region (BTH) of China, using the Normalized Difference Vegetation Index (NDVI) and related climate data. The multivariate linear regression model (MLR), support vector regression model (SVR), and gradient boosting regression tree model (GBRT) were applied to explore the predictability of NDVI, using climatic factors as predictors. The results showed that the mean NDVI during growing season has significantly increased in the BTH over the past 18 years, with about 66% of the total vegetation cover in the study area evidencing increasing trends. Significant increasing trends were mainly located in the northwest mountainous regions and southeastern plains, and the trends significantly decreased in big cities and the surroundings. The precipitation during growing season and summer has increased. However, no significant trend of the temperature was detected during growing season. NDVI was mainly positively correlated with precipitation at about 85.8% area of the BTH and negatively correlated with temperature during summer. Precipitation and temperature were the dominant influencing factors for vegetation growth in BTH, and therefore these factors were used as potential predictors to estimate NDVI during growing season. The comparative results of three prediction models showed that the prediction accuracy of GBRT was higher than other two models, indicating that it should be applicable to use GBRT model to predict the NDVI in the study area.

In water-scarce areas with low grain yields, people have to expand the farmland scale to gain more food. Rapid expansion of agricultural scale may lead to a higher risk of re-abandonment of wasteland due to waterless irrigation in water-deficient areas. Against this background, this study proposed a water resources regulation-allocation coupling model. The ‘regulation’ is to control the water consumption of agriculture and regulate the utilized scale with water resources as the main constraint; the ‘allocation’ is to optimize the water resources allocation among crops and arrange the crop planting system according to the water quota for different crops and the economic, social and ecological benefits brought by crops, so as to control the water resources among crops. Afterwards, taking an agro-pastoral ecotone in Northwest China as the study case, schemes of feasible agricultural scale and planting structure suitable for local conditions were obtained. Lastly, the matching patterns of land and water resources were raised gradually from ‘poor’ into ‘good’ degree from 2015 to 2030. This study is expected to provide a reference for controlling the ordered and balanced development of land and water resources of agriculture as well as effectively improving the sustainable development ability in water-deficient areas.

Increasing demand for water due to global population and economic growth will lead to severe global water shortage. Agricultural water consumes a large portion of water, and adjusting agricultural planting structure is one of the important means to realize agricultural water-saving. This paper took 2030 as the planning year and Shijin Irrigation District as the research area. Using the AquaCrop model, combined with field management, climatic and other conditions affecting crop growth and the yields of winter wheat, summer maize, and cotton under irrigation and rain-fed conditions were simulated in the planning year (2030). Taking the minimum consumption of agricultural irrigation water and the highest economic income as the goal, the planting structure adjustment plans of different hydrologic years are optimized under the limitation of water availability, cultivated land, and food security. The results showed that, after the adjustment of planting structure, the economy created by agriculture in the dry, normal, and wet year will increase by 0.1 billion yuan, 0.7 billion yuan, and 2.6 billion yuan, and the amount of irrigation is decreased by 42 million m3, 448 million m3, and 526 million m3, respectively.

The utilization of Regional Climate Methods (RCMs) to predict future regional climate is an important study under the changing environment. The primary objective of the paper is to correct the temperature and precipitation simulations for the period of 1980-2005 and 2026-2098 in the Wei River Basin (WRB), to evaluate the performance of RCMs for the period of 1980-2005, and further, to analyze the future changes of projected temperature and precipitation during 2026-2098. In this paper, the linear scaling method was used to correct the temperature simulations. Quantile mapping, local intensity scaling method and hybrid method were used to correct the precipitation simulations. The future changes of projected temperature and precipitation for the near-term (2026-2050), mid-term (2051-2075) and far-term (2076-2098), relative to the period of 1980-2005, were investigated under RCP 2.6 and RCP 8.5. Results indicate that: (1) The temperature biases were different spatial distributions, and the precipitation wet biases were detected in the WRB. After correction, HadGEM2-ES driven by RegCM4-4 had the best temperature reproducibility, and NCC-NorESM1-M driven by RegCM4-4 had the best precipitation reproducibility. (2) Under RCP 2.6, the projected annual, winter and spring temperature showed decreasing trends. The temperature was higher than that for the period of 1980-2005 except for the spring temperature decreases in the Beiluo River Basin. Under RCP 8.5, the temperature showed significantly increasing trends. The temperature for the near-term was similar to the period of 1980-2005, while the temperature increased significantly for the mid-term and far-term. (3) Under RCP 2.6, the precipitation had decreasing trends. Under RCP 8.5, precipitation trends were also spatially distributed. The relative deviation of winter precipitation was the largest. Relative to the period of 1980-2005, the light and moderate rain days showed little change for the period of 2026-2098, while the extreme rain days showed significantly increasing trends. (4) The results could be beneficial to the future climate projection, which provide references for the water resources management, the future hydrological process changes and attribution analysis in the WRB.

Yan, D.; Jiang, R.; Xie, J.; Zhu, J.; Liang, J., and Wang, Y., 2021. A multivariate and multistage streamflow prediction model based on signal decomposition techniques with deep learning. Journal of Coastal Research, 37(6), 1260–1270. Coconut Creek (Florida), ISSN 0749-0208. Accurate streamflow forecast plays an important role in the flood control operation of reservoir and water resource management, and how to build a prediction model with high accuracy is a research hotspot. In order to improve the prediction accuracy, this paper establishes a multivariate and multistage streamflow prediction model based on the combination of mode decomposition and deep learning models, including the variational mode decomposition (VMD), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMD), convolutional neural network (CNN), and long short-term memory (LSTM). Taking the Wei River Basin (WRB) of China as an example, streamflow in six hydrometric stations from the main stream of the WRB is used to validate the model. The results show that the proposed model have good prediction skills, and the prediction results of multistage models are better than single-stage models; however, the most complex models do not have the best results. The VMD also preformed better prediction skills than ICEEMD, and the optimal model was VCL (VMD-CNN-LSTM). The root-mean-square error, peak percentage of threshold statistic, and Nash-Sutcliffe efficiency coefficient of VCL at Huaxian station are 43.82 m3/s, 10.02%, and 0.94, respectively. The models proposed in this paper are suitable for streamflow forecasting, which provide reference for streamflow forecasting and sustainable watershed management.

Shallow groundwater has a great influence on crop growth. However, few studies have investigated the impact of different groundwater depth on crop growth. This paper mainly discusses the impact of the groundwater depth on summer maize growth and the groundwater contributions to summer maize evapotranspiration and yield via the AquaCrop crop growth model. Numerical simulation results showed that summer maize growth and soil water in the Shijin irrigation district were highly related to the groundwater depth. With the depletion of the groundwater, the crop coefficient, crop actual transpiration, crop yield and crop water-use efficiency tended to increase and then decrease. Considering the groundwater contribution, maize yield and water-use efficiency, the suitable groundwater table depth for summer maize growth in the Shijin irrigation district was 1.5 m, and the groundwater contribution was 58.8 % in this case. Thus, discussing the impact of groundwater on crop growth is highly pertinent, as it is important for arranging irrigation schedules.

The paper selected Shijin Irrigation District as the study area. Considering the future climate change, the paper studied the effects of climate change on summer maize growth. The effects of climate change on summer maize yield were quantitatively analyzed by combining the climate scenario Representative Concentration Pathway (RCP)4.5 and AquaCrop model. The results showed that under the RCP4.5 scenario, the future yield of summer maize in Shijin Irrigation District was mainly reduced, but the yield change showed an upward trend. When the direct impact of CO2 is not considered, the yield change ranged from − 6.72~− 3.04%, and the deforestation rate of summer maize is 10.795 kg for every 1 °C increase in growth period. The increase of summer maize is 3.35 kg for every 1 mm increase in rainfall. When considering the direct impact of CO2, the yield change ranged from − 4.04~+ 0.11%. It can be seen that CO2 can compensate for the production reduction effect caused by rising temperature.

Extreme precipitation index (EPI) is used to assess the extreme precipitation events, to further monitor and detect climate change impacts. The primary objective of the paper is to assess the extreme precipitation events and their spatiotemporal variability of 14 EPIs, and further investigate the linear and nonlinear relationships between typical EPIs and global climate anomalies El Nino Southern Oscillation (ENSO), using Nino 3.4 and SOI as climate indices, for the period of 1969–2016 in the Wei River Basin (WRB) of China. Observed daily precipitation data from 18 meteorological stations in the WRB were selected to calculate 14 EPIs derived or modified from core indices of the CCI/WCRP Expert Team for Climate Change Detection Monitoring and Indices (ETCCDMI). The trends of EPIs were estimated using Mann-Kendall non-parametric test. The teleconnections of ENSO to EPIs were revealed using principal component analysis (PCA), Pearson's correlation and wavelet analysis. Results indicate that: (1) The change patterns of extreme precipitation events varied for different EPIs and different meteorological stations for the period of 1969–2016 in the WRB. Four EPIs were negative trends dominated, and six EPIs were positive trends dominated, indicating that the extreme precipitation events had mainly increased for the past 48 years. (2) The spatial distributions of seven categories of trends were scattered and irregular for different EPIs and different areas of WRB. However, some of the EPIs had similar spatial distributions of trends. For example, the stations that had increasing trends mainly located at the northern part of WRB, especially in the Jing River Basin (JRB), for six precipitation intensity EPIs except R150, which was stationary trend dominated. (3) The nonlinear teleconnection relationships between typical EPIs and two climate anomalies revealed using wavelet analysis were consistent with linear relationships using correlation analysis. The Nino 3.4 and SOI had similar wavelet coherence but opposite phase difference with PC1 of typical EPIs. The phase differences between Nino 3.4 and PC1 of EPIs were anti-phase dominated, and in-phase dominated between SOI and PC1 of EPIs, indicating that the Nino 3.4 exerted negative climate impacts on the extreme precipitation events in the WRB and vice-versa for SOI. (4) The results should be beneficial to assess the extreme precipitation events, which provide references for the water resources planning and management in the WRB. It is possible to estimate and predict the future extreme precipitation events using climate models and global climate anomalies as potential predictors, which should be further studied in our future work.

Wang, Y.P.; Jiang, R.G.; Xie, J.C.; Zhao, Y., and Li, F.W., 2020. Water resources management under changing environment: A systematic review. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 29–41. Coconut Creek (Florida), ISSN 0749-0208.Under the influences of climate change, human activities and socioeconomic development, the water resources management (WRM) has become more complex and uncertain. The paper visualizes and quantifies the knowledge base, domain and structure of the WRM using the bibliometric analysis and knowledge map. The visualized knowledge maps are used to investigate the development trend, scientific collaboration, subject categories, core research topics and the newest research fronts of the WRM. 9028 publications on the WRM were retrieved from the Web of Science Core Collection databases for the period of 1990-2018. Results indicated that the research on the WRM maintained an increasing trend for the past 29 years. China contributed the most publication, and Germany cooperated the most with other countries/regions, which were two active contributors of the WRM research. The co-word analysis and burst detective analysis of keywords showed that climate change and ecosystem were the research hotspot of the WRM knowledge base. The co-occurrence analysis and burst detective analysis of subject categories found that the Environmental Sciences & Ecology and Green & Sustainable Science & Technology were the core direction of the WRM knowledge domain. Solving the practical problems through model simulation was the core of the WRM knowledge structure under the changing environment using the cluster analysis and document co-citation analysis. It is suggested that the WRM managers should take the sustainable and adaptive development as goal, ensure the health and integrity of the ecosystem, integrate stakeholders into the WRM, which should help to make the WRM more adaptable under changing environment.

Water resource carrying capacity (WRCC) is of great importance to assess the maximum capacity of water resource and scientifically evaluate for sustainable development of economic. This study proposes a chicken swarm optimization-projection pursuit evaluation (CSO-PPE) model, to assess the WRCC of Shaanxi province of China for the period of 2007–2016. The effectiveness of CSO was firstly demonstrated using four typical functions. Then, the CSO-PPM model is used to assess WRCC, and the results were compared with other three algorithms including the particle swarm optimization (PSO), firefly algorithm (FA), and seeker optimization algorithm (SOA). An evaluation index system was established based on the drivers-pressure-state-impact-response-management (DPSIRM) model. The results showed that the WRCC has gradually increased from 2007 to 2016. In addition, the southern and northern regions of Shaanxi province had higher water resource carrying capacity with respect to that of Guanzhong region of Shaanxi province. Additionally, the results also indicated that this evaluation method is feasible for assessing WRCC in Shaanxi province of China, which provide a scientific basis for assessing regional WRCC in other places.

Using wavelet theory, Mann-Kendall non-parametric test, principal component analysis, and correlation analysis, the study investigated the change characteristics and periodic variation of seasonal precipitation and their relationships with seven large-scale global climate indices including Nino 3.4, Southern Oscillation Index (SOI), Pacific Decadal Oscillation (PDO), Pacific North American Index (PNA), North Atlantic Oscillation (NAO), Arctic Oscillation (AO), and Eastern Asia/Western Russia (EA/WR), for the period of 1959–2016 in Jing-Jin-Ji metropolitan region. The results showed the following: (1) The change characteristics of Jing-Jin-Ji’s precipitation varied for different seasons. Only JJA precipitation showed statistically significant decreasing trend at 5% significance level. Precipitation in other three seasons had increasing trends. The trend of annual precipitation was mainly dominated by JJA precipitation. The results of fractal characteristics of precipitation indicated that the future trend of JJA precipitation would likely increase, but the future trends of annual precipitation and precipitation in other three seasons would likely decrease. The inapparent periodic variation of seasonal precipitation was revealed using adjacent-average smoothing method. (2) Wavelet theory was used to investigate the periodic variation of seasonal precipitation and seven large-scale climate indices. In general, the oscillations varied for different seasons and periods. The real part of the Morlet wavelet amplitude exhibited similar cycles using wavelet power spectrum. However, it is possible to estimate the future trends of seasonal precipitation according to the nested periodic positive and negative cycles. The periods of seven large-scale climate indices were scattered. (3) The PC1 of seasonal precipitation and climate indices had isolated coherence, which varied for different climate indices and periods. The phases between them also varied for different periods. As for different climate indices, the EA/WR had strong positive impacts on the seasonal precipitation at relatively large period, such as 8–16 years. The effects of climate indices on the seasonal precipitation varied for different PCs and climate indices. However, the results of correlation analysis were consistent with those of wavelet coherence and phase difference. The corrections varied for different time lags between climate indices and seasonal precipitation. The results should be beneficial to reveal the historical and future trends of seasonal precipitation in Jing-Jin-Ji metropolitan region. The linear and nonlinear relationships between seasonal precipitation and climate indices indicated that it might be possible to predict the seasonal precipitation using the climate indices as potential predictors. However, the mechanisms of climate change impacts on the precipitation need to be further elucidated, which are recommended in our future study.

Extreme climate index is one of the useful tools to monitor and detect climate change. The primary objective of this study is to provide more comprehensively the changes in precipitation and temperature extremes for the period of 1959–2015 in the Jing-Jin-Ji metropolitan region of China, including Beijing city, Tianjin city and Hebei province, which will hopefully provide scientific understanding of the precipitation and temperature-related natural hazards such as flood, drought and heat wave. Daily precipitation and temperature from 26 surface meteorological stations over the study area were used to calculate 14 extreme precipitation indices (EPIs) and 17 extreme temperature indices (ETIs) generated by the joint World Meteorological Organization Commission for Climatology (CCI)/World Climate Research Programme (WCRP) project on Climate Variability and Predictability (CLIVAR) Expect Team on Climate Change Detection, Monitoring and Indices (ETCCDMI). The EPIs and ETIs were generated using the RClimRex software, and the trends were analyzed using the Mann-Kendall non-parametric test and the Thiel-Sen’s slope estimator. The results showed the following: (1) The spatiotemporal variability for daily climate extremes varied for both the EPIs and ETIs between 1959 and 2015. In the case of the EPIs, most of the indices were negative trend-dominated, and for the ETIs, 13 ETIs were positive trend-dominated, and six ETIs were negative trend-dominated. (2) Six types of precipitation intensity EPIs including the R10mm–R250mm were especially defined to detect impacts of climate change on the precipitation events. The results showed that the spatial distributions of increasing trends for these EPIs were similar, mainly located at the south of the Hebei province. (3) In general, the trend magnitudes of fixed-threshold extreme indices were smaller than those of station-related thresholds and non-threshold extreme indices for both the EPIs and ETIs. (4) The spatial distributions of positive and negative trends for both the EPIs and ETIs were scattered. For several indices such as the ID0, SU25, WSDI and TX90p, stations that had statistically significant increasing/decreasing trends at 5% significance level were detected mainly located at the north of the Jing-Jin-Ji metropolitan region. More works on the physical mechanisms behind the varied changes and scattered spatial distributions of trends in the Jing-Jin-Ji metropolitan region are recommended as an extension of this study.

The Yellow River Basin (YRB) is an important economic zone in China. The issue of water ecology has attached widely attention by scholars and society. This paper systematically reviewed the status, hotspots and development tendency on water resources research in the YRB based on the China National Knowledge Infrastructure (CNKI) database from 1992 to 2019. The SATI4.0 was used to analyze the annual publication, journal sources and keywords. High-frequency keywords were extracted, and software including NetDraw, SPSS and CiteSpace were used to analyze the co-occurring keywords, clustering and research frontier. Results showed that: (1) The number of published papers had increased before 2005 and decreased after it. The quantity of paper has increased sharply since 2018, indicating that the research has a new direction. (2) The source of journals mainly concentrated on basic subjects and hydraulic engineering. (3) The cluster analysis shows that hydrologic features, water management and protection and the water rights system are hotspots. (4) The result of research frontier indicates that sensitivity, ecological protection and high-quality development of water resources have ample research perspectives. It is general trend to combine the scientific methods with traditional methods. The results are conducive to systematically understand research advances and trends of water resources under changing environment, thereby promoting the construction of ecological civilization in the YRB.

With climate change and inappropriate human behavior, the problem of urban water resources has become increasingly serious. The aim of this paper is to make insights into certain characteristics and landmark articles of urban water resources management (UWRM) and to analyze the future trends of this field. Bibliometric software is used to explore the spatial and temporal distribution, research hotspots, research frontiers and representative literature in the field. The number of publications in the field shown a steady growth trend for past 12 years. Among all the countries, Australia has published the largest quantity of articles than other countries, and Netherlands has the closest connection to others. The reference co-citation analysis was carried out to obtain the representative references. Based on keyword co-occurrence analysis, research hotspots mainly include system, climate change, impact, sustainability. Emerging keywords were obtained using keyword burst detection analysis, indicating that reducing environment footprint and sustainable rainwater management are the research frontiers in the last few years, and are expected to continue.

This paper reviews the literature on the Yellow River Basin’s water resources from 2000 to 2019 based on the Web of Science. The annual publication, source country and source journal are analyzed using SATI4.0. The nonhomogeneous linear difference equation, comprehensive score analysis and Bradford’s law are used for fundamental analysis. CiteSpace is used for co-occurrence analysis and burst detection of noun phrases. Finally, Carrot2 is used to cluster the literature to get a knowledge framework for the research. Results showed that: (1) The research process of water resources in the Yellow River Basin (YRB) can be divided into three stages. This topic will continue to flourish in the future. (2) The research covers a wide range of contents, which mainly includes runoff changes, runoff-sediment relationship, water resources allocation, disaster response, the impact of human activities and climate change, as well as ecological protection and sustainable development. Human activities and the impact of climate change are the focus of research. (3) In the future, scholars should adhere to the belief of green and sustainable development of YRB and focus on the study of the Loess Plateau.

Extreme climate index is one of the useful tools to monitor and detect climate change. The primary objective of this study is to provide a more comprehensively the changes in extreme precipitation between the periods of 1954–1983 and 1984–2013 in Shaanxi province under climate change, which will hopefully provide a scientific understanding of the precipitation-related natural hazards such as flood and drought. Daily precipitation from 34 surface meteorological stations were used to calculated 13 extreme precipitation indices (EPIs) generated by the joint World Meteorological Organization Commission for Climatology (CCI)/World Climate Research Programme (WCRP) project on Climate Variability and Predictability (CLIVAR) expect Team on climate change Detection, Monitoring and Indices (ETCCDMI). Two periods including 1954–1983 and 1984–2013 were selected and five types of precipitation days (R10mm-R100mm) were defined, to provide more evidences of climate change impacts on the extreme precipitation events, and specially, to investigate the changes in different types of precipitation days. The EPIs were generated using RClimRex software, and the trends were analyzed using Mann-Kendall nonparametric test and Sen’s slope estimator. The relationships between the EPIs and the impacts of climate anomalies on typical EPIs were investigated using correlation and composite analysis. The mainly results include: 1) Thirteen EPIs, except consecutive dry day (CDD), were positive trends dominated for the period of 1984–2013, but the trends were not obvious for the period of 1954–1983. Most of the trends were not statistically significant at 5 % significance level. 2) The spatial distributions of stations that exhibited positive and negative trends were scattered. However, the stations that had negative trends mainly distributed in the north of Shaanxi province, and the stations that had positive trends mainly located in the south. 3) The percentage of stations that had positive trends had increased from the period of 1954–1983 to 1984–2013 for all the 13 EPIs except CDD, indicating the possible climate change impacts on extreme precipitation events. 4) The correlations between annual total wet-day precipitation (PRCPTOT) and other 12 EPIs varied for different indices and stations. The composite analysis found that El Nino Southern Oscillation (ENSO) exerted greater impacts on PRCPTOT than other EPIs and greater in the Guanzhong Plain (GZP) than Qinling-Dabashan Mountains (QDM) and Shanbei Plateau (SBP) of Shaanxi province.

As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta's smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen's slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta's sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982-2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for different period of year ecologists might focus on.

A complex interwoven relationship exists between water resources and urbanization, which is of much interest in international water science research. To study the urban development in large cities facing water deficiency problems, it is important to identify rational thresholds of urbanization to achieve optimal utilization of limited water resources, and to promote sustainable economic and population growth and social development. A multi-objective optimization model is proposed to search for the optimal scale of urbanization of large cities with limited water resources. To solve a large-dimensional multi-objective optimization problem, the non-dominated sorting genetic algorithm (NSGA-II) is improved to the OENSGA-II based on the orthogonal generation method and the E-optimality method and applied to a typical arid city, Xi&rsquo, an of China, which underwent rapid urbanization in recent years. For Xi&rsquo, an, a statistically significant positive correlation is found between urbanization rate (Ur) and gross domestic product (GDP), domestic water, tertiary industry water, and ecological water. However, Ur is negatively correlated with the primary industry water. If the current urbanization trend continues, the water resources available are far from sufficient to support the future city of Xi&rsquo, an. In this work, it was found that, by implementing restrictive water resources management and water saving measures, the economic threshold of Xi&rsquo, an could be raised to 1890.3 and 2403.3 billion yuan, while the population threshold could be raised to 11.0 and 13.9 million, and Ur to 79.9% and 85.9% in 2020 and 2030, respectively. The corresponding maximum urban area to be constructed based on the projected population will be 964.81 and 1197.6 km2 in those years. It will be prudent for Xi&rsquo, an to practice strict water resource management, and to allocate its water resources among various water sectors effectively and equitably to avoid major water shortage problems in the future.

We investigated changes to precipitation and temperature of Alberta for historical and future periods. First, the Mann-Kendall test and Sen’s slope were used to test for historical trends and trend magnitudes from the climate data of Alberta, respectively. Second, the Special Report on Emissions Scenarios (SRES) (A1B, A2, and B1) of CMIP3 (Phase 3 of Coupled Model Intercomparison Project), projected by seven general circulation models (GCM) of the Intergovernmental Panel on Climate Change (IPCC) for three 30 years periods (2020s, 2050s, and 2080s), were used to evaluate the potential impact of climate change on precipitation and temperature of Alberta. Third, trends of projected precipitation and temperature were investigated, and differences between historical versus projected trends were estimated. Using the 50-km resolution dataset from CANGRD (Canadian Grid Climate Data), we found that Alberta had become warmer and somewhat drier for the past 112 years (1900–2011), especially in central and southern Alberta. For observed precipitation, upward trends mainly occurred in northern Alberta and at the leeward side of Canadian Rocky Mountains. However, only about 13 to 22 % of observed precipitation showed statistically significant increasing trends at 5 % significant level. Most observed temperature showed significant increasing trends, up to 0.05 °C/year in DJF (December, January, and February) in northern Alberta. GCMs’ SRES projections indicated that seasonal precipitation of Alberta could change from −25 to 36 %, while the temperature would increase from 2020s to 2080s, with the largest increase (6.8 °C) in DJF. In all 21 GCM-SRES cases considered, precipitation in both DJF and MAM (March, April, and May) is projected to increase, while temperature is consistently projected to increase in all seasons, which generally agree with the trends of historical precipitation and temperature. The SRES A1B scenario of CCSM3 might project more realistic future climate for Alberta, where its water resources can become more critical in the future as its streamflow is projected to decrease continually in the future.

The balance of water and soil resources directly influences the ecological environment and the potential of social and economic development in water shortage areas. Especially in the Mu Us Sandy Land, where was suffering extreme lack of water, when all kinds of agricultural development and utilization mode had caused obvious pressure on local water resources, if the mode and the existing agricultural planting structure were still continuing, disorderly expanding the developing scale, will undoubtedly made more serious ecological problems. In this study, taking Yulin District in the southern margin of Mu Us Sandy Land as the research object, the multi-objective optimization model of adaptive scale of development and utilization of the sandy land agriculture was established with the constraints of limited water resources and goals of economic, social, ecological benefits. Based on the NSGA-II algorithm, typical schemes including unitary planting structure, binary planting structure and three-component planting structure were proposed. After summing up and analyzing, seven different schemes including diversified agricultural planting pattern were recommended, also the planting areas of diverse crops were given. The final optimized schemes could provide references for the development, harnessing and utilization of sandy land, and improve the local ecological environment, expanding the effective cultivated land area of Yuyang District and promoting the coordinated development of regional water and soil resources.