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.     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

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...     

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…     

应用ATA的对数周期馈源方案于FAST的可能性探讨

介绍应用于美国凤凰计划的Allen望远镜阵(ATA)的对数周期馈源(LPA)概况，初步给出此种馈源的基本参数及性能，仿真估算了其驻波比和方向图，并结合正在进行中的我国500m孔径球面射电望远镜(FAST)计划，探讨了应用这种宽带馈源的可能性及其限制。     

A qualitative and quantitative evaluation of experimental model catchment evolution

Due to the geological time scales required for observation of catchment evolution, surrogates or analogues of field data are necessary to understand long‐term processes. To investigate long‐term catchment behaviour, two experimental model catchments that developed without rigid boundaries under controlled conditions are examined and a qualitative and quantitative analysis of their evolution is presented. Qualitatively, the experimental catchments have the visual appearance of field scale data. Observation demonstrates that changes in catchment shape and network form are conservative. Quantitative analysis suggests that the catchments reach an equilibrium form while a reduction in the channel network occurs. While the catchments are laboratory scale models, the results provide insights into field scale behaviour. Copyright © 2003 John Wiley & Sons, Ltd.     

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.     

THE SIMULATED STORAGE CAPACITY OF FLOOD AND WATERLOGGING IN THE TYPICAL AGRICULTURAL REGION IN JIANGHAN PLAIN

１ＩＮＴＲＯＤＵＣＴＩＯＮＪｉａｎｇｈａｎＰｌａｉｎｉｓｏｎｅｏｆｔｈｅｉｍｐｏｒｔａｎｔｂａｓｅｓｏｆＣｈｉｎａ′ｓａｇｒｉｃｕｌｔｕｒａｌｐｒｏｄｕｃｔｉｖｉｔｙｌｏｃａｔｅｄｉｎｔｈｅｍｉｄｄｌｅＣｈａｎｇｊｉａｎｇＲｉｖｅｒｖａｌｌｅｙｏｆｃｅｎｔｒａｌＣｈｉｎａ．ＴｈｅＪｉａｎｇｈａｎｐｌａｉｎａｐｐｒｏｘｉｍａｔｅｌｙ６．６×１０４ｋｍ２ｉｎａｒｅａａｎｄｏｖｅｒ３０ｍｉｌｌｉｏｎｉｎｐｏｐｕｌａｔｉｏｎｉｓｔｈｅｒｅｎｏｗｎｅｄ“ｃｏｕｎｔｒｙｏｆｆｉｓｈａｎｄｒｉｃｅ＂ｉｎ…     

重力插值方法研究

为了根据离散观测数据构制连续空间重力变化图像，分析和讨论了３种数值插值方法，计算结果表明多面函数方法插值精度最高。由于逐步回归分析筛选核函数中心点的计算繁琐，文中提出根据分形理论和Ｓｈａｎｎｏｎ取样定理来确定核函数中心点。对滇西试验场进行模拟试算，插值精度可达到４～５（１０－８ｍｓ－２）。