High-frequency monitoring of hydrological and biogeochemical fluxes in forested catchments of southern Chile

The variability of rainfall-dependent streamflow at catchment scale modulates many ecosystem processes in wet temperate forests. Runoff in small mountain catchments is characterized by a quick response to rainfall pulses which affects biogeochemical fluxes to all downstream systems. In wet-temperate climates, water erosion is the most important natural factor driving downstream soil and nutrient losses from upland ecosystems. Most hydrochemical studies have focused on water flux measurements at hourly scales, along with weekly or monthly samples for water chemistry. Here, we assessed how water and element flows from broad-leaved, evergreen forested catchments in southwestern South America, are influenced by different successional stages, quantifying runoff, sediment transport and nutrient fluxes during hourly rainfall events of different intensities. Hydrograph comparisons among different successional stages indicated that forested catchments differed in their responses to high intensity rainfall, with greater runoff in areas covered by secondary forests (SF), compared to old-growth forest cover (OG) and dense scrub vegetation (CH). Further, throughfall water was greatly nutrient enriched for all forest types. Suspended sediment loads varied between successional stages. SF catchments exported 455 kg of sediments per ha, followed by OG with 91 kg/ha and CH with 14 kg/ha, corresponding to 11 rainfall events measured from December 2013 to April 2014. Total nitrogen (TN) and phosphorus (TP) concentrations in stream water also varied with rainfall intensity. In seven rainfall events sampled during the study period, CH catchments exported less nutrients (46 kg/ha TN and 7 kg/ha TP) than SF catchments (718 kg/ha TN and 107 kg/ha TP), while OG catchments exported intermediate sediment loads (201 kg/ha TN and 23 kg/ha TP). Further, we found significant effects of successional stage attributes (vegetation structure and soil physical properties) and catchment morphometry on runoff and sediment concentrations, and greater nutrients retention in OG and CH catchments. We conclude that in these southern hemisphere, broad-leaved evergreen temperate forests, hydrological processes are driven by multiple interacting phenomena, including climate, vegetation, soils, topography, and disturbance history.     

The isotopic signature of monsoon conditions,cloud modes,and rainfall type

This work provides a comprehensive physically based framework for the interpretation of the north Australian rainfall stable isotope record (δ¹⁸O and δ²H). Until now, interpretations mainly relied on statistical relationships between rainfall amount and isotopic values on monthly timescales. Here, we use multiseason daily rainfall stable isotope and high resolution (10 min) ground‐based C‐band polarimetric radar data and show that the five weather types (monsoon regimes) that constitute the Australian wet season each have a characteristic isotope ratio. The data suggest that this is not only due to changes in regional rainfall amount during these regimes but, more importantly, is due to different rain and cloud types that are associated with the large scale circulation regimes. Negative (positive) isotope anomalies occurred when stratiform rainfall fractions were large (small) and the horizontal extent of raining areas were largest (smallest). Intense, yet isolated, convective conditions were associated with enriched isotope values whereas more depleted isotope values were observed when convection was widespread but less intense. This means that isotopic proxy records may record the frequency of which these typical wet season regimes occur. Positive anomalies in paleoclimatic records are most likely associated with periods where continental convection dominates and convection is sea‐breeze forced. Negative anomalies may be interpreted as periods when the monsoon trough is active, convection is of the oceanic type, less electric, and stratiform areas are wide spread. This connection between variability of rainfall isotope anomalies and the intrinsic properties of convection and its large‐scale environment has important implications for all fields of research that use rainfall stable isotopes.     

初轨计算中的病态分析

本文对现有初轨计算方法进行病态性分析与误差分析；研究结果表明：病态对现有初轨算法的影响，主要来源于法方程系数中包含观测误差．系数行列式愈大，定轨精度的损失愈多，当■被随机误差项△μ淹盖时，现有初轨算法将失效．此外，仿真结果还显示：■与△μ的大小还极大地依赖观测弧段的空间位置，当观测弧段包含近站点作为中点时，■最大，而■小，此时定轨精度较高；当观测弧段位于近站点的某一侧时，■小，而■大，此时定轨精度较低，观测弧段愈偏离近站点，病态影响愈大；因而在观测时，应尽量使观测弧段与近站点对称（此时μ值较大），这是提高短弧定轨的一种有效途径．     

CHANGE OF NUTRIENT IMPORT AND EXPORT IN PROCESS OF RAINFALL IN AILAO MOUNTAIN OF YUNNAN PROVINCE

ＣＨＡＮＧＥ　ＯＦ　ＮＵＴＲＩＥＮＴ　ＩＭＰＯＲＴ　ＡＮＤ　ＥＸＰＯＲＴ　ＩＮ　ＰＲＯＣＥＳＳ　ＯＦ　ＲＡＩＮＦＡＬＬ　ＩＮ　ＡＩＬＡＯ　ＭＯＵＮＴＡＩＮ　ＯＦ　ＹＵＮＮＡＮ　ＰＲＯＶＩＮＣＥＧａｎＪｉａｎｍｉｎ（甘健民）；ＸｕｅＪｉｎｇｙｉ（薛敬意...     

关于改进的Encke方法中参考轨道的选择和校正

对于改进的Ｅｎｃｋｅ方法，选择适当的参考轨道是一个关键．然而，对于人造地球卫星长弧轨道计算，目前所给出的几种参考轨道均需要逐段校正，这将给定轨问题带来附加的复杂性．本文将仔细探讨如何选择参考轨道和减少校正次数．     

小波分析理论在GPS技术中的应用

在GPS数据处理中 ,存在着误差影响、影响波的干扰、周跳和数据量大等问题。误差影响和影响波的干扰实质是在接收卫星信号时受到其它因素的影响 ;周跳是由于卫星信号的失锁而造成信号的不连续 ;数据量大是因为GPS观测需要采样间隔小又连续观测所致。由于小波理论具有时频分析、波形分解、特征提取和快速小波变换等特性 ,应用小波变换和波形分解可以解决误差影响和影响波的干扰的问题 ;应用特征提取可以解决周跳检测问题 ;应用快速小波变换可进行数据压缩     

Atmospheric radiative transfer simulation for atmospheric correction of remote sensing data

IntroductionThe radiance leaving the earth-atmosphere sys-tem which can be sensed by a satellite borne ra-diometer is the sum of radiation emission fromtheearth surface and each atmospheric level that aretransmittedtothe top of the atmosphere.The radia-tion emissionfromthe earthsurface andthe radianceof each atmospheric level can be separated fromtheradiance at the top the atmospheric level a satellitemeasured.Thus,the earth surface parameters willbe retrieved from the surface radiance after a…     

差分法周跳探测与修复方法改进

在目前常用的周跳探测与修复方法基础上 ,提出了首先将观测资料按照观测历元不连续分成若干小弧段 ,然后利用差分法进行周跳探测 ,根据差分后周跳放大的特性判断周跳和野值 ,并确定其位置利用宽带组合和电离层组合的方法解算周跳大小。通过实例验证了其有效性。     

格网DEM中基于坡度信息的粗差检测与剔除方法试验

提出了一种基于格网DEM的粗差检测及剔除方法,其基本思想是对每个表面上的点,在坡度上,高程或突变量引起的形状不连续,可能被怀疑有误差,通过坡度上每个点,应用坡度逼近或改变量来计算,考虑坡度变化的相对值,并以这些相对值计算一个统计值为判断该点合法性的阈值,使计算结果更为可靠。     

Identification of physical processes inherent in artificial neural network rainfall runoff models

The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black‐box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall‐runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall‐runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall‐runoff process. The results demonstrate that the hidden neurons in the ANN rainfall‐runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. Copyright © 2004 John Wiley & Sons, Ltd.