MODFLOW is one of the most popular groundwater simulation tools available; however, the development of lake modules that can be coupled with MODFLOW is lacking apart from the LAK3 package. This study proposes a new approach for simulating lake - groundwater interaction under steady-state flow, referred to as the sloping lakebed method (SLM). In this new approach, discretization of the lakebed in the vertical direction is independent of the spatial discretization of the aquifer system, which can potentially solve the problem that the lake and groundwater are usually simulated at different scales. The lakebed is generalized by a slant at the bottom of each lake grid cell, which can be classified as fully submerged, dry, and partly submerged. The SLM method accounts for all lake sources and sinks, establishing a governing equation that can be solved using Newton's method. A benchmarking case study was conducted using a modified model setup in the LAK3 user manual. It was found that when there is a sufficient number of layers at the top of the groundwater model, SLM simulates an almost identical groundwater head as the LAK3-based model; when the number of layers decreases, SLM is unaffected while LAK3 may be at a risk of giving unrealistic results. Additionally, the SLM can reflect the relationship between the simulated lake surface area and lake water depth more accurately. Therefore, the SLM method is a promising alternative to the LAK3 package when simulating lake - groundwater interaction. 相似文献
The widely used groundwater flow model MODFLOW offers a range of software packages to simulate the interaction between streams and groundwater in aquifer systems. However, these existing packages lack a general method to address the chaotic simulation sequences of stream segments and require these segments to be ordered by modelers as input to the code. Therefore, it is challenging to simulate a stream network divided into a large number of segments such as a canal irrigation system. In this study, the Streamflow Automatic Routing (SAR) package was developed, and an effective method is proposed to automatically determine the segment simulation sequence. The stream segment order in the SAR input file is arbitrary, which allows modifications of the stream network by removing segments directly and adding segments at the end of the segment group. This mainly includes two processes: scanning all the outlet channels of the water system and calling the recursive algorithm for each outlet channel of the water system. The SAR package was tested using a hypothetical stream–aquifer system and applied to a complex flow field in Aiding Lake of Turpan Basin, China. In the results, a close fitting between the simulation and observations shows that the SAR package precisely simulated the exchange flux between the steams and aquifer. The SAR package can significantly improve the efficiency of simulations in a complex stream network, and it can be widely used as a subroutine package of MODFLOW in agricultural irrigation areas where rivers and canals are interlaced.
Object matching facilitates spatial data integration, updating, evaluation, and management. However, data to be matched often originate from different sources and present problems with regard to positional discrepancies and different levels of detail. To resolve these problems, this article designs an iterative matching framework that effectively combines the advantages of the contextual information and an artificial neural network. The proposed method can correctly aggregate one‐to‐many (1:N) and many‐to‐many (M:N) potential matching pairs using contextual information in the presence of positional discrepancies and a high spatial distribution density. This method iteratively detects new landmark pairs (matched pairs), based on the prior landmark pairs as references, until all landmark pairs are obtained. Our approach has been experimentally validated using two topographic datasets at 1:50 and 1:10k. It outperformed a method based on a back‐propagation neural network. The precision increased by 4.5% and the recall increased by 21.6%, respectively. 相似文献
With such significant advantages as all-day observation, penetrability and all-weather coverage, passive microwave remote
sensing technique has been widely applied in the research of global environmental change. As the satellite-based passive microwave
remote sensor, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) loaded on NASA’s (National Aeronautics
and Space Administration of USA) Aqua satellite has been popularly used in the field of microwave observation. The Microwave
Radiation Imager (MWRI) loaded on the Chinese FengYun-3A (FY-3A) satellite is an AMSR-E-like conical scanning microwave sensor,
but there are few reports about MWRI data. This paper firstly proposed an optimal spatial position matching algorithm from
rough to exact for the position matching between AMSR-E and MWRI data, then taking Northeast China as an example, comparatively
analyzed the microwave brightness temperature data derived from AMSR-E and MWRI. The results show that when the antenna footprints
of the two sensors are filled with either full water, or full land, or mixed land and water with approximate proportion, the
errors of brightness temperature between AMSR-E and MWRI are usually in the range from −10 K to +10 K. In general, the residual
values of brightness temperature between the two microwave sensors with the same spatial resolution are in the range of ±3
K. Because the spatial resolution of AMSR-E is three times as high as that of MWRI, the results indicate that the quality
of MWRI data is better. The research can provide useful information for the MWRI data application and microwave unmixing method
in the future. 相似文献