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The precipitation recharge coefficient (PRC), representing the amount of groundwater recharge from precipitation, is an important parameter for groundwater resources evaluation and numerical simulation. It was usually obtained from empirical knowledge and site experiments in the 1980s. However, the environmental settings have been greatly modified from that time due to land use change and groundwater over-pumping, especially in the Beijing plain area (BPA). This paper aims to estimate and analyze PRC of BPA with the distributed hydrological model and GIS for the year 2011 with similar annual precipitation as long-term mean. It is found that the recharge from vertical (precipitation + irrigation) and precipitation is 291.0 mm/yr and 233.7 mm/yr, respectively, which accounts for 38.6% and 36.6% of corresponding input water. The regional mean PRC is 0.366, which is a little different from the traditional map. However, it has a spatial variation ranging from –7.0% to 17.5% for various sub-regions. Since the vadose zone is now much thicker than the evaporation extinction depth, the land cover is regarded as the major dynamic factor that causes the variation of PRC in this area due to the difference of evapotranspiration rates. It is suggested that the negative impact of reforestation on groundwater quantity within BPA should be well investigated, because the PRC beneath forestland is the smallest among all land cover types.

The northeastern China is an important commodity grain region in China, as well as a notable corn belt and major soybean producing area. It thus plays a significant role in the national food security system. However, large-scale land reclamation and non-optimum farming practices give rise to soil degradation in the region. This study analyzed the food security issues coupled with global climate change in the northeastern China during 1980–2000, which is the period of modern agriculture. The results of statistical data show that the arable land area shrank markedly in 1992, and then increased slowly, while food production generally continually increased. The stable grain yield was due to the increase of applied fertilizer and irrigated areas. Soil degradation in the northeastern China includes severe soil erosion, reduced soil nutrients, a thinner black soil layer, and deterioration of soil physical properties. The sustainable development of the northeastern China is influenced by natural-artificial binary disturbance factors which consist of meteorological conditions, climate changes, and terrain factors as well as soil physical and chemical properties. Interactions between the increasing temperature and decreasing precipitation in the region led to reduced accumulation of soil organic matter, which results in poor soil fertility. Human-induced factors, such as large-scale land reclamation and non-optimum farming practices, unsuitable cultivation systems, dredging, road building, illegal land occupation, and extensive use of fertilizers and pesticides, have led to increasingly severe soil erosion and destruction. Solutions to several problems of soil degradation in this region requiring urgent settlement are proposed. A need for clear and systematic recognition and recording of land use changes, land degradation, food production and climate change conditions is suggested, which would provide a reference for food security studies in the northeastern China.

Mechanism and modeling of the land subsidence are complex because of the complicate geological background in Beijing, China. This paper analyzed the spatial relationship between land subsidence and three factors, including the change of groundwater level, the thickness of compressible sediments and the building area by using remote sensing and GIS tools in the upper-middle part of alluvial-proluvial plain fan of the Chaobai River in Beijing. Based on the spatial analysis of the land subsidence and three factors, there exist significant non-linear relationship between the vertical displacement and three factors. The Back Propagation Neural Network (BPN) model combined with Genetic Algorithm (GA) was used to simulate regional distribution of the land subsidence. Results showed that at field scale, the groundwater level and land subsidence showed a significant linear relationship. However, at regional scale, the spatial distribution of groundwater depletion funnel did not overlap with the land subsidence funnel. As to the factor of compressible strata, the places with the biggest compressible strata thickness did not have the largest vertical displacement. The distributions of building area and land subsidence have no obvious spatial relationships. The BPN-GA model simulation results illustrated that the accuracy of the trained model during fifty years is acceptable with an error of 51% of verification data less than 20 mm and the average of the absolute error about 32 mm. The BPN model could be utilized to simulate the general distribution of land subsidence in the study area. Overall, this work contributes to better understand the complex relationship between the land subsidence and three influencing factors. And the distribution of the land subsidence can be simulated by the trained BPN-GA model with the limited available dada and acceptable accuracy.

It is important to understand how land use change impacts groundwater recharge, especially for regions that are undergoing rapid urbanization and there is limited surface water. In this study, the hydrological processes and recharge ability of various land use types in Guishui River Basin, China (in Beijing Municipality) were analyzed. The impact of land use change was investigated based on water balance modeling, WetSpass and GIS. The results indicate that groundwater recharge accounts for only 21.16% of the precipitation, while 72.54% is lost in the form of evapotranspiration. The annual-lumped groundwater recharge rate decreases in the order of cropland, grassland, urban land, and forest. Land use change has resulted in a decrease of 4 × 106 m3 of yearly groundwater recharge in the study area, with a spatially averaged rate of 100.48 mm/yr and 98.41 mm/yr in 1980 and 2005, respectively. This variation has primarily come from an increase of urban area and rural settlements, as well as a decrease of cropland.

Due to long-term over-exploitation of groundwater in Beijing Municipality,regional groundwater funnels have formed and land subsidence has been induced.By combining a groundwater monitoring network,GPS monitor-ing network data,radar satellite SAR data,GIS and other new technologies,a coupled process model based on the dy-namic variation of groundwater and the deformation response of land subsidence has been established.The dynamic variation of groundwater funnels and the land subsidence response process were analyzed systematically in Beijing.Study results indicate that current groundwater funnel areas are distributed mainly in the southwest of Shunyi District,the northeast of Chaoyang District and the northwest of Tongzhou District,with an average decline rate of groundwa-ter level of 2.66 m/yr and a maximum of 3.82 m/yr in the center of the funnels.Seasonal and interannual differences exist in the response model of land subsidence to groundwater funnels with uneven spatial and temporal distribution,where the maximum land subsidence rate was about-41.08 mm/yr and the area with a subsidence rate greater than 30 mm/yr was about 1637.29 km2.Although a consistency was revealed to exist between a groundwater funnel and the spatial distribution characteristics of the corresponding land subsidence funnel,this consistency was not perfect.The results showed that the response model of land subsidence to the dynamic variation of groundwater was more revealing when combining conventional technologies with InSAR,GIS,GPS,providing a new strategy for environmental and hydrogeological research and a scientific basis for regional land subsidence control.

Based on multi-source data, this paper evaluated the economic value of ecological services in the Yeyahu Wetland Nature Reserve, Beijing, China. The ecological services of wetland included gas regulation, water quality im- provement, biodiversity maintenance, erosion control, water supply, recreational opportunity, raw material supply and existence value. Multiple conventional evaluation methods were used to calculate the value of eight wetland services. The results showed that significant values came from biodiversity maintenance and recreational opportunity. The main reasons were as follows. Firstly, Yeyahu Wetland Nature Reserve was the habitat for migrant birds, and government had payed more efforts to protect precious birds. Secondly, the population is large in Beijing. People enjoyed going outside and enjoyed the natural and artificial wetland scenes. At the same time, most people were prepared to pay for wetland conservation. The decline of vegetation cover made the economic value of erosion control the lowest. While the shrink of water resource and the deteriorative water quality caused the economic value of water supply lower. The evaluation results could help decision-makers understand the present status of the Yeyahu Wetland Nature Reserve and provide a scientific basis for strategic decision.

Taking a typical inland wetland of Honghe National Nature Reserve (HNNR), Northeast China, as the study area, this paper studied the application of L-band Synthetic Aperture Radar (SAR) image in extracting eco-hydrological information of inland wetland. Landsat-5 TM and ALOS PALSAR HH backscatter images were first fused by using the wavelet-IHS method. Based on the fused image data, the classification method of support vector machines was used to map the wetland in the study area. The overall mapping accuracy is 77.5%. Then, the wet and dry aboveground biomass estimation models, including statistical models and a Rice Cloudy model, were established. Optimal parameters for the Rice Cloudy model were calculated in MATLAB by using the least squares method. Based on the validation results, it was found that the Rice Cloudy model produced higher accuracy for both wet and dry aboveground biomass estimation compared to the statistical models. Finally, subcanopy water boundary information was extracted from the HH backscatter image by threshold method. Compared to the actual water borderline result, the extracted result from L-band SAR image is reliable. In this paper, the HH-HV phase difference was proved to be valueless for extracting subcanopy water boundary information.

Air entrapment is an important consideration in environments with shallow water tables and sandy soil, like the condition of highly conductive sandy soils and flat topography in Florida, USA. It causes water table rises in soils, which are significantly faster and higher than those in soils without air entrapment. Two numerical models, Integrated Hydrologic Model (IHM) and HYDRUS-1D (a single-phase, one-dimensional Richards’ equation model) were tested at an area of west central Florida to help further understanding the shallow water table behavior during a long term air entrapment. This investigation employed field data with two modeling approaches to quantify the variation of air pressurization values. It was found that the air pressurization effect was responsible at time up to 40 cm of water table rise being recorded by the observation well for these two models. The values of air pressurization calculated from IHM and HYDRUS-1D match the previously published values. Results also indicated that the two numerical models did not consider air entrapment effect (as the predictive parameters remain uncertain) and thus results of depth to water table from these models did not compare to the observations for these selected periods. Incorporating air entrapment in prediction models is critical to reproduce shallow water table observations.

Wetland research has become a hot spot linking multiple disciplines presently. Wetland classification and mapping is the basis for wetland research. It is difficult to generate wetland data sets using traditional methods because of the low accessibility of wetlands, hence remote sensing data have become one of the primary data sources in wetland research. This paper presents a case study conducted at the core area of Honghe National Nature Reserve in the Sanjiang Plain, Northeast China. In this study, three images generated by airship, from Thematic Mapper and from SPOT 5 were selected to produce wetland maps at three different wetland landscape levels. After assessing classification accuracies of the three maps, we compared the different wetland mapping results of 11 plant communities to the airship image, 6 plant ecotypes to the TM image and 9 landscape classifications to the SPOT 5 image. We discussed the different characteristics of the hierarchical ecosystem classifications based on the spatial scales of the different images. The results indicate that spatial scales of remote sensing data have an important link to the hierarchies of wetland plant ecosystems displayed on the wetland landscape maps. The richness of wetland landscape information derived from an image closely relates to its spatial resolution. This study can enrich the ecological classification methods and mapping techniques dealing with the spatial scales of different remote sensing images. With a better understanding of classification accuracies in mapping wetlands by using different scales of remote sensing data, we can make an appropriate approach for dealing with the scale issue of remote sensing images.