基于FFT的快速SAR分布目标回波模拟算法

大面积分布目标的合成孔径雷达 (SAR)回波模拟需要大量的运算 ,文中提出了一种合成孔径雷达回波模拟的快速算法 ,算法利用时域插值和FFT来缩减运算量 ,对于大面积目标回波模拟时 ,该算法有很高的效率。文中详细分析sinc函数插值所带来的误差以及补偿方法 ,并在次基础上提出了一种利用增采样插值方法 ,该方法以增加少量运算为代价 ,使得模拟精度的大幅度提高。文中比较了传统方法和基于FFT的快速SAR分布目标回波模拟算法的模拟结果 ,证明了这种快速算法确实行之有效     

利用案例推理（CBR）方法对雷达图像进行土地利用分类

提出了基于案例推理 (CBR)的遥感分类的新方法。基于规则的专家系统被用来提高遥感分类的效率。但所涉及的规则可能多达上百或上千条 ,有的问题根本无法用规则来表达。CBR只是根据以往的案例进行推理 ,克服了基于规则的推理方法的不足 ,可以用来解决一些复杂的资源环境问题。利用CBR和模糊数学结合 ,并通过分层随机采样来控制案例在空间上的分布 ,以反映复杂环境所造成的光谱变化 ,由此解决遥感分类中的“同物异谱”现象。实验表明 ,所建立的案例库可以被重复多次使用 ,分类的效果比监督分类和非监督分类要好。     

Impact of land-cover and climate changes on runoff of the source regions of the Yellow River

After dividing the source regions of the Yellow River into 38 sub-basins, the paper made use of the SWAT model to simulate streamflow with validation and calibration of the observed yearly and monthly runoff data from the Tangnag hydrological station, and simulation results are satisfactory. Five land-cover scenario models and 24 sets of temperature and precipitation combinations were established to simulate annual runoff and runoff depth under different scenarios. The simulation shows that with the increasing of vegetation coverage annual runoff increases and evapotranspiration decreases in the basin. When temperature decreases by 2oC and precipitation increases by 20%, catchment runoff will increase by 39.69%, which is the largest situation among all scenarios.     

Green''''s functions for uniformly distributed loads acting on an inclined line in a poroelastic layered site

Based on one type of practical Biot's equation and the dynamic-stiffness matrices of a poroelastic soil layer and half-space, Green's functions were derived for uniformly distributed loads acting on an inclined line in a poroelastic layered site. This analysis overcomes significant problems in wave scattering due to local soil conditions and dynamic soil-structure interaction. The Green's functions can be reduced to the case of an elastic layered site developed by Wolf in 1985. Parametric studies are then carried out through two example problems.     

Ground deformation of Nisyros Volcano (Greece) for the period 1995–2002: Results from DInSAR and DGPS observations

Differential GPS (DGPS) and Differential Interferometric Synthetic Aperture Radar (DInSAR) analyses were applied to the Kos-Yali-Nisyros Volcanic Field (SE Hellenic Volcanic Arc) to quantify the ground deformation of Nisyros Volcano. After intense seismic activity in 1996, a GPS network was installed in June 1997 and re-occupied annually up to 2002. A general uplift ranging from 14 to 140 mm was determined at all stations of the network. The corresponding horizontal displacements ranged from 13 to 53 mm. The displacement vectors indicate that the island is undergoing extension towards the East, West and South. A two-source “Mogi” model combined with assumed motion along the Mandraki Fault was constructed to fit the observed deformation. The best-fit model assumes sources at a depth of 5500 m NW of the centre of the island and at 6500 m offshore ESE of Yali Island. DInSAR analysis using four pairs of images taken between May 1995 and September 2000 suggests that deformation was occurring during 1995 before the start of the seismic crisis. An amplitude of at least 56 mm along the slant range appeared for the period 1996 through 1999. This deformation is consistent with the two-source model invoked in DGPS modelling. Surface evidence of ground deformation is expressed in the contemporaneous reactivation of the Mandraki Fault. In addition, a 600 m long N-S trending irregular rupture in the caldera floor was formed between 2001 and 2002. This rupture is interpreted as the release of surface stress in the consolidated epiclastic and hydrothermal sediments of the caldera floor.     

黄土区人类活动影响下的 产汇流模拟研究

日益频繁的人类活动改变了流域下垫面条件,对流域产汇流产生很大的影响。本文以黄河中游典型支流岔巴沟为研究区域,提出利用基于DEM的分布式水文模拟技术,探讨流域人类活动过程中的产汇流模拟,避免了经验公式的概化和由此引起的局限。模拟的结果证实了该方法的可行性。采用网格滞蓄的方法可以在子网格上体现人类活动引起的下垫面的变化及其对产汇流的影响,反映各个时期的产汇流条件,对降雨做出合理响应。     

Development of a soil moisture‐based distributed hydrologic model for determining hydrologically based critical source areas

A simple grid cell‐based distributed hydrologic model was developed to provide spatial information on hydrologic components for determining hydrologically based critical source areas. The model represents the critical process (soil moisture variation) to run‐off generation accounting for both local and global water balance. In this way, it simulates both infiltration excess run‐off and saturation excess run‐off. The model was tested by multisite and multivariable evaluation on the 50‐km² Little River Experimental Watershed I in Georgia, U.S. and 2 smaller nested subwatersheds. Water balance, hydrograph, and soil moisture were simulated and compared to observed data. For streamflow calibration, the daily Nash‐Sutcliffe coefficient was 0.78 at the watershed outlet and 0.56 and 0.75 at the 2 nested subwatersheds. For the validation period, the Nash‐Sutcliffe coefficients were 0.79 at the watershed outlet and 0.85 and 0.83 at the 2 subwatersheds. The per cent bias was less than 15% for all sites. For soil moisture, the model also predicted the rising and declining trends at 4 of the 5 measurement sites. The spatial distribution of surface run‐off simulated by the model was mainly controlled by local characteristics (precipitation, soil properties, and land cover) on dry days and by global watershed characteristics (relative position within the watershed and hydrologic connectivity) on wet days when saturation excess run‐off was simulated. The spatial details of run‐off generation and travel time along flow paths provided by the model are helpful for watershed managers to further identify critical source areas of non‐point source pollution and develop best management practices.     

Effects of forest roads on the hydrological response of a small‐scale mountain watershed in Greece

The effects of land use changes on the ecology and hydrology of natural watersheds have long been debated. However, less attention has been given to the hydrological effects of forest roads. Although less studied, several researchers have claimed that streamflow changes related to forest roads can cause a persistent and pervasive effect on hillslope hydrology and the functioning of the channel system. The main potential direct effects of forest roads on natural watersheds hydrologic response are runoff production on roads surfaces due to reduced infiltration rates, interruption of subsurface flow by road cutslopes and rapid transfer of the produced runoff to the stream network through roadside ditches. The aforementioned effects may significantly modify the total volume and timing of the hillslope flow to the stream network. This study uses detailed field data, spatial data, hydro‐meteorological records, as well as numerical simulation to investigate the effects of forest roads on the hydrological response of a small‐scale mountain experimental watershed, which is situated in the east side of Penteli Mountain, Attica, Greece. The results of this study highlight the possible effects of forest roads on the watersheds hydrological response that may significantly influence direct runoff depths and peak flow rates. It is demonstrated that these effects can be very important in permeable watersheds and that more emphasis should be given on the impact of roads on the watersheds hydrological response. Copyright © 2014 John Wiley & Sons, Ltd.     

Including spatial distribution in a data‐driven rainfall‐runoff model to improve reservoir inflow forecasting in Taiwan

Multi‐step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3‐h warning to be issued before any reservoir releases are made. However, the complex spatial and temporal heterogeneity of typhoon rainfall, coupled with a remote and mountainous physiographic context, makes the development of real‐time rainfall‐runoff models that can accurately predict reservoir inflow several hours ahead of time challenging. Consequently, there is an urgent, operational requirement for models that can enhance reservoir inflow prediction at forecast horizons of more than 3 h. In this paper, we develop a novel semi‐distributed, data‐driven, rainfall‐runoff model for the Shihmen catchment, north Taiwan. A suite of Adaptive Network‐based Fuzzy Inference System solutions is created using various combinations of autoregressive, spatially lumped radar and point‐based rain gauge predictors. Different levels of spatially aggregated radar‐derived rainfall data are used to generate 4, 8 and 12 sub‐catchment input drivers. In general, the semi‐distributed radar rainfall models outperform their less complex counterparts in predictions of reservoir inflow at lead times greater than 3 h. Performance is found to be optimal when spatial aggregation is restricted to four sub‐catchments, with up to 30% improvements in the performance over lumped and point‐based models being evident at 5‐h lead times. The potential benefits of applying semi‐distributed, data‐driven models in reservoir inflow modelling specifically, and hydrological modelling more generally, are thus demonstrated. Copyright © 2012 John Wiley & Sons, Ltd.     

Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models

This paper analyses the effect of rain data uncertainty on the performance of two hydrological models with different spatial structures: a semidistributed and a fully distributed model. The study is performed on a small catchment of 19.6 km² located in the north‐west of Spain, where the arrival of low pressure fronts from the Atlantic Ocean causes highly variable rainfall events. The rainfall fields in this catchment during a series of storm events are estimated using rainfall point measurements. The uncertainty of the estimated fields is quantified using a conditional simulation technique. Discharge and rain data, including the uncertainty of the estimated rainfall fields, are then used to calibrate and validate both hydrological models following the generalized likelihood uncertainty estimation (GLUE) methodology. In the storm events analysed, the two models show similar performance. In all cases, results show that the calibrated distribution of the input parameters narrows when the rain uncertainty is included in the analysis. Otherwise, when rain uncertainty is not considered, the calibration of the input parameters must account for all uncertainty in the rainfall–runoff transformation process. Also, in both models, the uncertainty of the predicted discharges increase in similar magnitude when the uncertainty of rainfall input increase.