An Initial Inventory and Indexation of Groundwater Mega-Depletion Cases

The state of groundwater systems worldwide is presently not well defined, and in particular there is little context for agencies responsible for managing water resources to evaluate occurrences of groundwater depletion against other cases globally. In this study, an initial inventory of groundwater depletion problems is compiled and ranked to identify the world’s most critical cases, i.e. situations of groundwater mega-depletion. The ranking is based on an indexed approach that considers overdraft, drawdown and subsidence, plus the importance of the resources in terms of population-dependency and rates of extraction. The five most highly ranked depleted aquifers of the world include the shallow aquifers of the Hai River Plain (China), the Altiplano region (Spain), the Mexico Basin (Mexico), the Huang River basin (China) and the California Central Valley (USA). An abridged account of modelling to assess drawdown is described for the Hai River Plain, revealing that despite recharge in the order of 13,000 GL/yr, an overdraft of about 8,000 GL/yr is occurring to support the vast population of the region. This has led to up to 100 m of drawdown in places and reports of subsidence of several metres. The Hai River situation demonstrates that falling water levels may not act to alleviate pumping stresses; a symptom of unchecked extraction and an exemplary illustration of the tragedy of the commons. The causal factors leading to mega-depletion are varying across the globe and each mega-depletion case contains unique elements, although population appears to be an important factor.     

The Added Value of Understanding Informal Social Networks in an Adaptive Capacity Assessment: Explorations of an Urban Water Management System in Indonesia

Social networks play an important role in environmental governance regimes, and they are a key to the adaptive capacity of systems that deal with complex, contextual and multi-faceted issues. Urban water systems are typical examples of complex systems facing many pressures, such as increased population, water quality deterioration, and climate change. This paper explores social networks of the key stakeholders engaged in urban water management, in Makassar City, Indonesia, in the context of exploring ways to improve management of an increasingly complex urban water system. Three social networks were explored; those constituted by formal and informal interactions and networks perceived by stakeholders to be “ideal”. Formal networks were identified through an examination of the legislative instruments and government agencies’ documents relating to water provision in Makassar, while the informal and “ideal” networks were investigated in collaboration with the stakeholders. The research found that the informal social network was more extensive than were the formally required networks, and the investigation of informal networks created a potentially more robust and adaptive water management system than would have occurred through inclusion of formal institutional arrangements. We suggest that in examination of the adaptive capacity of an urban water system, one also considers the informal arrangements and linkages, as this additional information about the system is necessary to enhance our understanding of potential adaptation of water management and improved urban water systems.     

河南省农业水土资源空间匹配格局对产能的影响

通过对河南省农业水资源与耕地资源的利用态势进行模拟分析,构建区域水土资源匹配测算模型,将匹配结果与产能分布情况做相关性分析,探讨河南省农业水土资源匹配格局对粮食产能的影响．结果表明,河南省耕地资源优势明显,水资源匮乏,水土资源匹配较差;水土资源匹配程度与耕地产能的空间分布呈现出明显的错位现象,这是由于产能高产区垦殖率高,灌溉条件好,过度利用水资源引起的．     

Use of flow-normalization to evaluate nutrient concentration and flux changes in Lake Champlain tributaries, 1990–2009

The U.S. Geological Survey evaluated 20 years of total phosphorus (P) and total nitrogen (N) concentration data for 18 Lake Champlain tributaries using a new statistical method based on weighted regressions to estimate daily concentration and flux histories based on discharge, season, and trend as explanatory variables. The use of all the streamflow discharge values for a given date in the record, in a process called “flow-normalization”, removed the year-to-year variation due to streamflow and generated a smooth time series from which trends were calculated. This approach to data analysis can be of great value to evaluations of the success of restoration efforts because it filters out the large random fluctuations in the flux that are due to the temporal variability in streamflow. Results for the full 20 years of record showed a mixture of upward and downward trends for concentrations and yields of P and N. When the record was broken into two 10-year periods, for many tributaries, the more recent period showed a reversal in N from upward to downward trends and a similar reversal or reduction in magnitude of upward trends for P. Some measures of P and N concentrations and yields appear to be related to intensity of agricultural activities, point-source loads of P, or population density. Total flow-normalized P flux aggregated from the monitored tributaries showed a decrease of 30 metric tons per year from 1991 to 2009, which is about 15% of the targeted reduction established by the operational management plan for the Lake Champlain Basin.     

公路弃土场坡面产流特征及过程模拟

公路弃土场坡面径流的模拟对流域水资源评价和道路侵蚀模型的构建均具有重要意义。通过不同雨强(0.6～2.79mm/min)的人工降雨试验,对不同坡度(19.4%～78.1%)弃土场坡面及坡度为30.6%的弃土场施工道路的坡面径流过程进行了研究。结果表明:在小雨强下(0.6mm/min)径流显著受到土壤干容重及小区坡度的影响,而随雨强的增加,坡度及土壤干容重对径流的影响逐渐被降雨所覆盖。使用Green-Ampt入渗模型可以较为准确地计算公路弃土场坡面径流,模型有效系数达0.975。     

Hydrological Evolution of Wetland in Naoli River Basin and its Driving Mechanism

Naoli river basin(NRB), with an area of 24,863 km², is the largest basin and also the largest marsh distribution area in Sanjiang Plain, Heilongjiang, China. The hydrological evolution process of wetland in NRB has made a marked ecological responses for anthropic activities, also reflects the drying trend of the Sanjiang Plain, Northeast China. Global climate warming also contributes to the hydrological evolution process. The following key research results are obtained: (1) The monthly average water level of Naoli river at Caizuizi hydrological station in different ages showed a marked decline tendency, the annual mean water level dropped from 96.63 m during 1960–1969 to 95.59 m during 2000–2005, the water level drawdown is 1.04 m; (2) The annual runoff flowing into wetlands in NRB decreased. Duration of Naoli river and its tributaries being thoroughly frozen from riverbed to river-water-surface showed an prolonged trend, and the water level drawdown in frozen seasons increased. The water storage capacities of wetlands in NRB declined. (3) The interactions between ground water and surface water in wetland areas are close. The ground water level variation span is bigger than that of surface water level in wetland areas of NRB. The drawdown of ground water level promotes the surface water level to decline, correspondingly. In recent 20 years, the cultivated area extension of rice field in upstream NRB has made an adverse influence on the hydrological processes of wetlands. (4) The wetland area decrease and farmland area increase significantly contribute to the runoff depth decrease of wetlands in NRB. The runoff depth variability has been mostly posed by anthropic activities. (5) Reservoirs, ditches and dykes in NRB have greatly changed the runoff generation processes. Thickness of the seasonal frozen soil layer becoming thinner and the evaporation potential becoming bigger also contribute to the runoff depth reduction and the water level drawdown of rivers. The present study results will provide a scientific basic for developing an integrated watershed management program for NRB, especially, restoring the wetland hydrological processes, maintaining or improving the wetland structure and enhancing the wetland service functions.     

A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data

This study proposes a new four-component algorithm for land use and land cover (LULC) classification using RADARSAT-2 polarimetric SAR (PolSAR) data. These four components are polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms. First, polarimetric decomposition can be used to support the classification of PolSAR data. It is aimed at extracting polarimetric parameters related to the physical scattering mechanisms of the observed objects. Second, PolSAR interferometry is used to extract polarimetric interferometric information to support LULC classification. Third, the main purposes of object-oriented image analysis are delineating image objects, as well as extracting various textural and spatial features from image objects to improve classification accuracy. Finally, a decision tree algorithm provides an efficient way to select features and implement classification. A comparison between the proposed method and the Wishart supervised classification which is based on the coherency matrix was made to test the performance of the proposed method. The overall accuracy of the proposed method was 86.64%, whereas that of the Wishart supervised classification was 69.66%. The kappa value of the proposed method was 0.84, much higher than that of the Wishart supervised classification, which exhibited a kappa value of 0.65. The results indicate that the proposed method exhibits much better performance than the Wishart supervised classification for LULC classification. Further investigation was carried out on the respective contribution of the four components to LULC classification using RADARSAT-2 PolSAR data, and it indicates that all the four components have important contribution to the classification. Polarimetric information has significant implications for identifying different vegetation types and distinguishing between vegetation and urban/built-up. The polarimetric interferometric information extracted from repeat-pass RADARSAT-2 images is important in reducing the confusion between urban/built-up and vegetation and that between barren/sparsely vegetated land and vegetation. Object-oriented image analysis is very helpful in reducing the effect of speckle in PolSAR images by implementing classification based on image objects, and the textural information extracted from image objects is helpful in distinguishing between water and lawn. The decision tree algorithm can achieve higher classification accuracy than the nearest neighbor classification implemented using Definiens Developer 7.0, and the accuracy of the decision tree algorithm is similar with that of the support vector classification which is implemented based on the features selected using genetic algorithms. Compared with the nearest neighbor and support vector classification, the decision tree algorithm is more efficient to select features and implement classification. Furthermore, the decision tree algorithm can provide clear classification rules that can be easily interpreted based on the physical meaning of the features used in the classification. This can provide physical insight for LULC classification using PolSAR data.     

Remote sensing of canopy light use efficiency in temperate and boreal forests of North America using MODIS imagery

Light use efficiency (LUE) is an important variable characterizing plant eco-physiological functions and refers to the efficiency at which absorbed solar radiation is converted into photosynthates. The estimation of LUE at regional to global scales would be a significant advantage for global carbon cycle research. Traditional methods for canopy level LUE determination require meteorological inputs which cannot be easily obtained by remote sensing. Here we propose a new algorithm that incorporates the enhanced vegetation index (EVI) and a modified form of land surface temperature (T_m) for the estimation of monthly forest LUE based on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Results demonstrate that a model based on EVI × T_m parameterized from ten forest sites can provide reasonable estimates of monthly LUE for temperate and boreal forest ecosystems in North America with an R² of 0.51 (p < 0.001) for the overall dataset. The regression coefficients (a, b) of the LUE–EVI × T_m correlation for these ten sites have been found to be closely correlated with the average EVI (EVI_ave, R² = 0.68, p = 0.003) and the minimum land surface temperature (LST_min, R² = 0.81, p = 0.009), providing a possible approach for model calibration. The calibrated model shows comparably good estimates of LUE for another ten independent forest ecosystems with an overall root mean square error (RMSE) of 0.055 g C per mol photosynthetically active radiation. These results are especially important for the evergreen species due to their limited variability in canopy greenness. The usefulness of this new LUE algorithm is further validated for the estimation of gross primary production (GPP) at these sites with an RMSE of 37.6 g C m^? 2 month^? 1 for all observations, which reflects a 28% improvement over the standard MODIS GPP products. These analyses should be helpful in the further development of ecosystem remote sensing methods and improving our understanding of the responses of various ecosystems to climate change.     

Problems of spatial linkage of a geo-referenced Demographic and Health Survey (DHS) dataset to a population census: A case study of Egypt

GPS coordinates are increasingly available as spatial references on population surveys in the developing world, where high-resolution address and street mapping are absent. This potentially offers opportunities to enhance national census data by spatial linkage with survey sources. The paper explores the use of GPS-referenced Demographic and Health Survey (DHS) data in combination with census data in Egypt and identifies errors in coordinate referencing. The study develops a practical approach to the measurement of spatial uncertainty in this situation and assessment of its impact on data linkage. The analysis specifically addresses the analytical implications at three different spatial scales and is internationally relevant to the handling of GPS-referenced DHS data in GIS.     

POTENTIAL FOR VEGETATION‐BASED RIVER MANAGEMENT IN DRYLAND,SALINE CATCHMENTS

The approaches used to manage rivers have been developed and adapted to many different problems and settings. Because of their relatively low cost, vegetation‐based approaches implemented at the reach, landholder and catchment scales have become the foundation for river management in most landscapes. In many dryland agricultural catchments, secondary (anthropogenic) salinity caused by clearing native vegetation has resulted in rising saline groundwater, streamflow salinity values that exceed seawater and severe the degradation of riparian vegetation communities. The potential effectiveness of vegetation‐based strategies in these landscapes remains largely unknown, yet these strategies are still widely pursued. This study initially investigated the patterns of vegetation recovery and recolonization following a large flood in a saline river that disturbed the system. A conceptual model was developed to describe spatial patterns of where different vegetation types had regrown and recovered in relation to controls exerted by streamflow salinity, surface texture characteristics, topography and reach morphology. Using this model, vegetation‐based river management options for different reaches were developed, and their potential effectiveness in stabilizing reaches was investigated using a 1‐D hydraulic modelling approach. This study finds that in a dryland catchment with high stream salinity (20 000–93 000 mg L^?1), there is still a strong potential for successful vegetation‐based management, but only in selected reaches. Results showed that changes in stream power and channel velocity were not associated with the areas of most severe vegetation degradation. Rather, there is a complex interplay between channel morphology, channel slope and places of potential vegetation growth within a reach. This paper outlines an approach to evaluate the potential success of vegetation‐based river management in saline landscapes. It identifies the need to prioritize investment based on the following: an understanding of factors controlling revegetation potential, the likely impact of revegetation in mitigating adverse channel changes and the proximity of reaches to high‐value infrastructure and biodiversity assets. Copyright © 2011 John Wiley & Sons, Ltd.