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

Cristián Frêne  Juan J. Armesto  Freddy Véliz  Fernando D. Alfaro  Kathleen C. Weathers 《水文研究》2021,35(5):e14175

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.  相似文献   

The isotopic signature of monsoon conditions,cloud modes,and rainfall type          下载免费PDF全文

Costijn Zwart  Niels C. Munksgaard  Alain Protat  Naoyuki Kurita  Dionisia Lambrinidis  Michael I. Bird 《水文研究》2018,32(15):2296-2303

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.  相似文献   

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

甘健民  薛敬意  赵恒康 《中国地理科学(英文版)》1996,6(2):155-165

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

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

Ashu Jain  K. P. Sudheer  Sanaga Srinivasulu 《水文研究》2004,18(3):571-581

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.  相似文献   

企鹅珍珠贝人工苗生长的初步观察   总被引：3，自引：0，他引：3  

梁飞龙  毛勇  余祥勇 《广东海洋大学学报》2001,21(1):6-9

为了解企鹅珍珠贝生长规律，观察了壳高2.5～5.0mm的出池幼苗，按月测量其生长参数和成活率，以及环境因子。结果表明，企鹅珍珠贝生长最快的月份为7～11月和次年4～6月，壳长，壳高和壳宽月均增加量分别为3.8～13.0mm、4.7～11.2mm、2.3～3.8mm，月成活率97.8%～98.6%。企鹅珍珠贝生长最慢月份是11月至次年3月。  相似文献   

Extreme Rainfall Events and Associated Natural Hazards in Alaknanda Valley, Indian Himalayan Region   总被引：2，自引：0，他引：2  

JOSHI Varun KUMAR Kireet 《山地科学学报》2006,3(3):228-236

Introduction The Himalaya is considered to be the youngest mountains on the earth, and is tectonically very active, and hence inherently (geologically) vulnerable to hazards. Extreme rainfall events, landslides, debris flows, torrents and flash floods due…  相似文献   

Neural network prediction of nitrate in groundwater of Harran Plain, Turkey   总被引：2，自引：0，他引：2  

M. Irfan Yesilnacar  Erkan Sahinkaya  Muhsin Naz  Bestamin Ozkaya 《Environmental Geology》2008,56(1):19-25

Monitoring groundwater quality by cost-effective techniques is important as the aquifers are vulnerable to contamination from the uncontrolled discharge of sewage, agricultural and industrial activities. Faulty planning and mismanagement of irrigation schemes are the principle reasons of groundwater quality deterioration. This study presents an artificial neural network (ANN) model predicting concentration of nitrate, the most common pollutant in shallow aquifers, in groundwater of Harran Plain. The samples from 24 observation wells were monthly analysed for 1 year. Nitrate was found in almost all groundwater samples to be significantly above the maximum allowable concentration of 50 mg/L, probably due to the excessive use of artificial fertilizers in intensive agricultural activities. Easily measurable parameters such as temperature, electrical conductivity, groundwater level and pH were used as input parameters in the ANN-based nitrate prediction. The best back-propagation (BP) algorithm and neuron numbers were determined for optimization of the model architecture. The Levenberg–Marquardt algorithm was selected as the best of 12 BP algorithms and optimal neuron number was determined as 25. The model tracked the experimental data very closely (R = 0.93). Hence, it is possible to manage groundwater resources in a more cost-effective and easier way with the proposed model application.  相似文献   

Spatial Variability of Rock Depth in Bangalore Using Geostatistical, Neural Network and Support Vector Machine Models     

T. G. Sitharam  Pijush Samui  P. Anbazhagan 《Geotechnical and Geological Engineering》2008,26(5):503-517

Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 km². In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore, where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced level of rock has been presented and discussed.  相似文献   

强降雨条件下土质边坡瞬态稳定性分析   总被引：9，自引：1，他引：8  

吴长富  朱向荣  尹小涛  刘雪梅 《岩土力学》2008,29(2):386-391

基于实际降雨气象资料,设计了单峰降雨和8个不同时间间隔的双峰降雨计算方案,利用非饱和土力学理论,对边坡的瞬态稳定性进行了计算和分析,研究了水分在坡体内的运移对边坡稳定性的时间空间影响效应,同时考察了降雨入渗造成的土性渗透特性的改变。分析发现：一次降雨的影响历时约12 d,降雨后约0.5 d该类土质边坡最危险;不同时间间隔的两次降雨对边坡稳定的影响比单峰降雨的最小安全系数滞后约0.3 d～0.8 d,影响历时基本保持不变;土体物理力学性质、边坡最危险滑动面及其对应的安全系数随水分在坡体内渗透运移而变化。  相似文献   

浙江省小流域山洪灾害临界雨量确定方法分析   总被引：12，自引：2，他引：10  

叶勇  王振宇  范波芹 《水文》2008,28(1):56-58

小流域山洪灾害具有突发性,预测预警难度较大.本文结合浙江省小流域山洪灾害防御的实践经验,研究提出了以水位反推法计算临界雨量,简单实用,具有较好的实践价值和推广意义.  相似文献