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1.
Normalized Difference Vegetation Index (NDVI) is widely recognized as a good indicator of vegetation productivity. Diagnosing the NDVI trend and understanding climatic factors influences on NDVI can predict the productivity changes under different climatic scenarios. This paper examined NDVI dynamic and its response to climate factors during a 10 year period (1998–2008) in Inner Mongolia. The main findings are as follows: (1) The NDVI multi-scale characters can be revealed well by wavelet transform, and the average NDVI and the NDVI amplitude show a gradually decreased trend from northeast to southwest in Inner Mongolia during the past 10 years, furthermore, this trend is consistent with the heat and water distribution caused by latitude difference in north–south direction and Asia monsoon effect in east–west direction. (2) The relation between NDVI and temperature is the most close, followed by precipitation, sunshine hours and relative humidity. Different vegetation cover types show different strengths in correlation between NDVI and climate variables with the correlation values decreasing from forest, meadow steppe to desert steppe in whole. (3) The precipitation and temperature have the same change cycle, both nearly 290 days in the 20 selected stations. The NDVI has the same change cycle with the precipitation and temperature or either 10 days earlier or later than precipitation and temperature, which supports the significant correlation between NDVI and its climatic factors from a new perspective. The nearly 290 days change cycle implies that the vegetation growth cycle is nearly 10 months and there are no obvious differences change cycles in different vegetations. (4) Vegetation dynamic is significantly correlated to the temperature and precipitation at the time scale of 10, 20, 40, 80, 160, and 320-day, respectively, and the S3 scale (i.e., the time scale of 80-day), nearly 3 months (one season), is most significant and suitable for evaluating the vegetation dynamic to climatic factors.  相似文献   

2.
For better prediction and understanding of land-atmospheric interaction, in-situ observed meteorological data acquired from the China Meteorological Administration (CMA) were assimilated in the Weather Research and Forecasting (WRF) model and the monthly Green Vegetation Coverage (GVF) data, which was calculated using the Normalized Difference Vegetation Index (NDVI) of the Earth Observing System Moderate-Resolution Imaging Spectroradiometer (EOS-MODIS) and Digital Elevation Model (DEM) data of the Shuttle Radar Topography Mission (SRTM) system. Furthermore, the WRF model produced a High-Resolution Assimilation Dataset of the water-energy cycle in China (HRADC). This dataset has a horizontal resolution of 25 km for near surface meteorological data, such as air temperature, humidity, wind vectors and pressure (19 levels); soil temperature and moisture (four levels); surface temperature; downward/upward short/long radiation; 3-h latent heat flux; sensible heat flux; and ground heat flux. In this study, we 1) briefly introduce the cycling 3D-Var assimilation method and 2) compare results of meteorological elements, such as 2 m temperature and precipitation generated by the HRADC with the gridded observation data from CMA, and surface temperature and specific humidity with Global Land Data Assimilation System (GLDAS) output data from the National Aeronautics and Space Administration (NASA). We find that the simulated results of monthly 2 m temperature from HRADC is improved compared with the control simulation and has effectively reproduced the observed patterns. The simulated special distribution of ground surface temperature and specific humidity from HRADC are much closer to GLDAS outputs. The spatial distribution of root mean square errors (RMSE) and bias of 2 m temperature between observations and HRADC is reduced compared with the bias between observations and the control run. The monthly spatial distribution of surface temperature and specific humidity from HRADC is consistent with the GLDAS outputs over China. This study could improve the land surface parameters by utilizing remote sensing data and could further improve atmospheric elements with a data assimilation system. This work provides an effective attempt at combining multi-source data with different spatial and temporal scales into numerical simulations, and the simulated results could be used in further research on the long-term climatic effects and characteristics of the water-energy cycle over China.  相似文献   

3.
The Mongolian Plateau (MP) steppe is one of the largest steppe environments in the world. To monitor the terrestrial vegetation dynamics on the MP and to ascertain what the driving forces, this study examined the vegetation dynamics in Republic of Mongolia (M) and the Inner Mongolia Autonomous Region (IM) of China from the period 1982 to 2011, based on the satellite-derived GIMMS NDVI3g (Normalized Difference Vegetation Index) data across three biomes (desert, grassland and forest). The results are as followed: (1) Vegetation coverage in IM was generally greater than that in M. Before 2002, time series of NDVI over the MP increased at an average rate of 0.05% yr−1. Additionally, after 2002, the NDVI increased at a rate of 0.21% yr−1. From 1982 to 2011, the area of IM and M with positive anomalies in the NDVI increased at a separate rate of 1.82% yr−1 and 1.76% yr−1, respectively. (2) At the biome scale, the inter-annual forest NDVI variation in IM and desert NDVI for the entire MP had a significant increasing trend (0.06% yr−1 and 0.04% yr−1, respectively). (3) Climate forcing was a dominant controlling factor affecting the vegetation, and the anthropogenic behavior exhibited no significant value in the whole region. However, overgrazing was the most important reason for the regional degradation, particularly in IM. (4) In the future, the forest biome will go to recovery, whereas both the grassland and desert biomes are predicted to degrade continuously.  相似文献   

4.
The structure, capabilities and performance of a distributed parameter hydrologic model are described. The model, called Topog-Yield, permits a transient analysis of unsaturated-saturated flow and evapotranspiration to be performed across complex terrain using a one-dimensional framework. It is applied to a 0.32 km2 mountain ash (Eucalyptus regnans) forest catchment in the central Victorian highlands, Australia. We compare observed and predicted daily runoff values for the site over a continuous 12 year period (1972–1983) when the catchment vegetation was in an undisturbed climax condition. All input parameter values were based on published or measured data, although some variables were adjusted within the range of known variability to yield a best fit between predicted and observed streamflow in the first year of simulation, 1972. Although the model was ‘calibrated’ for the first year, all variables other than climatic inputs remained fixed for the following 11 years. Modelled and observed daily runoff values compare well throughout the period of simulation, despite a wide range of climatic conditions. When modelled daily runoff values were lumped on a monthly basis, the model was able to explain 87% of the variation in observed monthly streamflows over the 12 year period. Modelled annual runoff was within ±5% of observed values for 6 of the 12 years of record. Annual runoff prediction errors exceeded ±10% of observed values in only 2 of the 12 years. By the end of the 12 year simulation, the model had over-predicted runoff by less than 5%. Input data requirements and model results are discussed in the light of a preliminary sensitivity analysis.  相似文献   

5.
Jing Fu  Jun Niu  Bellie Sivakumar 《水文研究》2018,32(12):1814-1827
Vegetation cover plays an important role in linking the atmosphere, water, and land and is deemed as a key indicator in the terrestrial ecological system. Therefore, it is of great importance to monitor vegetation dynamics and understand the mechanisms of vegetation change, including that driven by climate change. This study examines (a) the evolution of vegetation dynamics over the Heihe River Basin in the typical arid zone in north‐western China using nonparametric Mann–Kendall test and Thiel Sen's slope; (b) the relationships between remotely sensed vegetation indices (normalized difference vegetation index [NDVI] and enhanced vegetation index [EVI]) and hydroclimatic variables based on correlation analysis; and (c) the prediction of vegetation anomalies using a multiple linear regression model. For the analysis, the Moderate Resolution Imaging Spectroradiometer NDVI/EVI product and the gridded daily meteorological data at a spatial resolution of 0.125° over the period 2001–2010 are considered. The results indicate that vegetation cover improved over a large proportion during 2001–2010, with a significant trend towards warm and wet, characterized by an increase in average annual temperature and precipitation by 0.042 °C/year and 5.8 mm/year, respectively. We test the feasibility of NDVI and EVI in quantifying the responses of vegetation anomaly to climate change and develop a statistical model to predict vegetation dynamics in the basin. The NDVI‐based model is found to be more reliable than the EVI‐based model, partly due to the vegetation characteristics and geomorphologic properties of the study region. The proposed model performs well when there is no lag time between meteorological factors and vegetation indices for grassland and cropland, whereas 1‐month lead time prediction is found to be best for forest. The soil water content is introduced as an extra explanatory variable, which effectively improves the prediction accuracy for different land use types. In general, the predictive ability of the proposed model is stable and satisfactory, and the model can provide useful early warning information for regional water resources management under changing climate.  相似文献   

6.
From 2011 to 2019, mercury (Hg) stores and fluxes were studied in the small forested catchment Lesní potok (LES) in the central Czech Republic using the watershed mass balance approach together with internal measurements. Mean input fluxes of Hg via open bulk deposition, beech throughfall and spruce throughfall during the periodwere 2.9, 3.9 and 7.6 μg m−2 year−1, respectively. These values were considerably lower than corresponding deposition Hg fluxes reported in the early years of the 21st century from catchments in Germany. Current bulk precipitation inputs at unimpacted Czech mountainous sites were lower than those in Germany. The largest Hg inputs to the catchment were via litterfall, averaging 22.6 and 17.8 μg m−2 year−1 for beech and spruce stands. The average Hg input, based on the sum of mean litterfall and throughfall deposition, was 23.0 μg m−2 year−1, compared to the estimated Hg output in runoff of 0.5 μg m−2 year−1, which is low compared to other reported values. Thus, only ~2% of Hg input is exported in stream runoff. Stream water Hg was only weakly related to dissolved organic carbon (DOC) but both concentrations were positively correlated with water temperature. The estimated total soil Hg pool averaged 47.5 mg m−2, only 4% of which was in the O-horizon. Thus Hg in the O-horizon pool represents 72 years of deposition at the current input flux and 3800 years of export at the current runoff flux. Age-dating by 14C suggested that organic soil contains Hg from recent deposition, while mineral soil at 40–80 cm depth contained 4400-year old carbon, suggesting the soil had accumulated atmospheric Hg inputs through millennia to reach the highest soil Hg pool of the soil profile. These findings suggest that industrial era intensification of the Hg cycle is superimposed on a slower-paced Hg cycle during most of the Holocene.  相似文献   

7.
基于遥感特征指数的地表水体自动提取技术研究   总被引：4，自引：2，他引：2       下载免费PDF全文
为了从海量遥感数据中有效地提取地表水体信息,并提高自动化提取效率,提出了一种基于遥感特征指数的地表水体自动提取方法.该方法选取归一化植被指数(NDVI)、归一化建筑指数(NDBI)和修正归一化水体指数(MNDWI)作为遥感特征指数集,并根据这些指数构建了遥感特征指数曲线.通过分析,发现地表水体在特征曲线中单调上升,植被在特征曲线中单调下降,而其它地物并无此特征.因此,根据这一规律,利用ERDAS IMAGINE软件建立了自动化提取模型.通过与其他方法对比,表明所建立的模型在精度和自动化方面都明显优于其他方法,可用于海量数据地表水体的自动提取.最后,在ARCGIS环境下,并通过决策树模型初步实现了地表水体的自动分类.  相似文献   

8.
《水文科学杂志》2013,58(3):513-525
Abstract

The Water Erosion Prediction Project (WEPP) model was calibrated and evaluated for estimation of runoff and sediment yield in the data-scarce conditions of the Indian Himalaya. The inputs derived from remote sensing and geographic information system technologies were combined in the WEPP modelling system to simulate surface runoff and sediment yield from the hilly Kaneli watershed. The model parameters were calibrated using measured data on runoff volumes and sediment yield. The calibrated model was validated by producing the monthly runoff and sediment yield simulations and comparing them with data that were not used in calibration. The model was also used to make surface runoff and sediment yield simulations for each of the individual watershed elements, comprising 18 hillslopes and seven channels, and the detailed monthly results for each are presented. Although, no field data on hillslope runoff and sediment yield are currently available for the validation of distributed results produced by the model, the present investigation has demonstrated clearly the applicability of the WEPP model in predicting hydrological variables in a data-scarce situation.  相似文献   

9.
ABSTRACT

An appropriate streamflow forecasting method is a prerequisite for implementation of efficient water resources management in the water-limited, arid regions that occupy much of Iran. In the current research, monthly streamflow forecasting was combined with three data-driven methods based on large input datasets involving 11 precipitation stations, a natural streamflow, and four climate indices through a long period. The major challenges of rainfall–runoff modelling are generally attributed to complex interacting processes, the large number of variables, and strong nonlinearity. The sensitivity of data-driven methods to the dimension of input/output datasets would be another challenge, so large datasets should be compressed into independently standardized principal components. In this study, three pre-processing techniques were applied: singular value decomposition (SVD) provided more efficient forecasts in comparison to principal component analysis (PCA) and average values of inputs in all networks. Among the data-driven methods, the multi-layer perceptron (MLP) with 1-month lag-time outperformed radial basis and fuzzy-based networks. In general, an increase in monthly lag-time of streamflow forecasting resulted in a decline in forecasting accuracy. The results reveal that SVD was highly effective in pre-processing of data-driven evaluations.  相似文献   

10.
Agricultural soil erosion is largely attributed to arable intensification and increased mechanization. Runoff from arable land and intensively managed grassland transports sediment and contaminants across the landscape and into watercourses, causing crop loss, land degradation, and water quality issues. One low-cost and low-maintenance nature-based mitigation approach is the implementation of vegetated buffer strips (VBS): grassland sited along field margins to trap sediment and contaminants, reducing transportation and diffuse pollution rates. GIS modelling using remotely sensed landscape indices and land parcel data can provide an efficient means of identifying priority areas for intervention at sub-catchment or farm system scales. We develop and test a scalable runoff risk model in the lower Rother catchment, West Sussex. The model uses the Normalized Difference Vegetation Index (NDVI) applied to satellite images as an erodibility proxy and identifies locations along pathways that are conceivably at greatest risk of sediment accumulation and transfer, guided by field observations. We assess current and historical field boundaries near high-risk locations, evaluating the potential capacity of their margins to contribute to runoff risk reduction using an innovative ranking system. Recommendations are made for VBS implementation and the value of historical field boundary and margin restoration is discussed. Our method offers a rapid approach with minimal data requirements to identify high-risk sediment runoff locations and priority sites for intervention. The tool has the potential to guide decision-makers responsible for targeting and implementing soil erosion and runoff control measures such as VBS, while also maximizing agri-environmental and cultural benefits.  相似文献   

11.
Mangrove forests dominate many tropical coastlines and are one of the most bio‐diverse and productive environments on Earth. However, little is known of the large‐scale dynamics of mangrove canopies and how they colonize intertidal areas. Here we focus on a fringe mangrove forest located in the Mekong River Delta, Vietnam; a fast prograding shoreline where mangroves are encroaching tidal flats. The spatial and temporal evolution of the mangrove canopy is studied using a time series of Landsat images spanning two decades as well as Shuttle Radar Topography Mission (SRTM) elevation data. Our results show that fast mangrove expansion is followed by an increase in Normalized Difference Vegetation Index (NDVI) in the newly established canopy. We observe three different dynamics of the mangrove fringe: in the southwest part of the fringe, near a deltaic distributary where the fringe boundary is linear, the canopy expands uniformly on the tidal flats with a high colonization rate and high NDVI values. In the northeast part of the fringe, near another distributary, the canopy expands at a much lower rate with low NDVI values. In the fringe center, far from the river mouths, the fringe boundary is highly irregular and mangroves expansion in characterized by sparse vegetated patches displaying low NDVI values. We ascribe these different dynamics to wave action and southwest longshore transport triggered by energetic northeasterly monsoons during winter. We further link the large‐scale dynamics of the fringe to small‐scale physical disturbances (waves, erosion and deposition) that might prevent the establishment of mangrove seedlings. Based on these results, we include mangrove encroachment in an already published conceptual model of progradation of the Mekong River Delta. We conclude that high NDVI values and a constantly linear vegetation–water interface are indicative of stable mangrove canopies undergoing fast expansion, probably triggered by sediment availability at the shore. Our results can be applied more generally to mangrove forests growing in minerogenic and high tidal range environments with high sediment inputs. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

12.
Evapotranspiration (ET) is an important parameter in hydrologic processes and modelling. In agricultural watersheds with competing uses of fresh water including irrigated agriculture, estimating crop evapotranspiration (ETc) accurately is critical for improving irrigation system and basin water management. The use of remote sensing-based basal crop coefficients is becoming a common method for estimating crop evapotranspiration for multiple crops over large areas. The Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI), based on reflectance in the red and near-infrared bands, are commonly used for this purpose. In this paper, we examine the effects of row crop orientation and soil background darkening due to shading and soil surface wetness on these two vegetation indices through modelling, coupled with a field experiment where canopy reflectance of a cotton crop at different solar zenith angles, was measured with a portable radiometer. The results show that the NDVI is significantly more affected than the SAVI by background shading and soil surface wetness, especially in north–south oriented rows at higher latitudes and could lead to a potential overestimation of crop evapotranspiration and irrigation water demand if used for basal crop coefficient estimation. Relationships between the analysed vegetation indices and canopy biophysical parameters such as crop height, fraction of cover and leaf area index also were developed for both indices.  相似文献   

13.
滇池蓝藻水华光谱特征、遥感识别及暴发气象条件   总被引：3，自引：0，他引：3  
通过研究滇池蓝藻水华在可见光、红外谱段的光谱特征,并利用假彩色合成法以及归一化植被指数(NDVI)法进行了滇池蓝藻水华信息的遥感识别和提取,进而对提取结果进行了对比分析.结果表明:假彩色合成图的绿色区域和NDVI值大于-0.1的区域,为蓝藻水华区域.-0.1≤NDVI≤0.2时,轻度水华,像元内水华覆盖度为0-30%;0.2NDVI≤0.4时,中度水华,像元内蓝藻水华覆盖度为31%-80%;NDVI0.4时,重度水华,水华浓厚,像元内蓝藻水华覆盖度为81%-100%.同时研究了激励滇池蓝藻水华暴发的关键气象因子和指标.滇池蓝藻水华暴发的关键时期是6-9月份,影响滇池蓝藻暴发的关键因子是日照和风速.6-9月份连续4-5h的光照,且风速≤2m/s的气象条件组合极易引起蓝藻水华暴发.  相似文献   

14.
Water budget analyses are important for the evaluation of the water resources in semiarid and arid regions. The lack of observed data is the major obstacle for hydrological modelling in arid regions. The aim of this study is the analysis and calculation of the natural water resources of the Western Dead Sea subsurface catchment, one which is highly sensitive to rainfall resulting in highly variable temporal and spatial groundwater recharge. We focus on the subsurface catchment and subsequently apply the findings to a large‐scale groundwater flow model to estimate the groundwater discharge to the Dead Sea. We apply a semidistributed hydrological model (J2000g), originally developed for the Mediterranean, to the hyperarid region of the Western Dead Sea catchment, where runoff data and meteorological records are sparsely available. The challenge is to simulate the water budget, where the localized nature of extreme rainstorms together with sparse runoff data results in few observed runoff and recharge events. To overcome the scarcity of climate input data, we enhance the database with mean monthly rainfall data. The rainfall data of 2 satellites are shown to be unsuitable to fill the missing rainfall data due to underrepresentation of the steep hydrological gradient and temporal resolution. Hydrological models need to be calibrated against measured values; hence, the absence of adequate data can be problematic. Therefore, our calibration approach is based on a nested strategy of diverse observations. We calculate a direct surface runoff of the Western Dead Sea surface area (1,801 km2) of 3.4 mm/a and an average recharge (36.7 mm/a) for the 3,816 km2 subsurface drainage basin of the Cretaceous aquifer system.  相似文献   

15.
Soil salinization is one of the most predominant environmental hazards responsible for agricultural land degradation, especially in the arid and semi-arid regions.An accurate spatial prediction and modeling of soil salinity in agricultural land are so important for farmers and decision-makers to develop the appropriate mechanisms to prevent the loss of fertile soil and increase crop production.El Outaya plain is marked by soil salinity increases due to the excessive use of poor groundwater quality for irrigation. This study aims to compare the performance of simple kriging, cokriging(SCOK), multilayer perceptron neural networks(MLP-NN), and support vector machines(SVM)in the prediction of topsoil and subsoil salinity. The field covariates including geochemical properties of irrigation groundwater and physical properties of soil and environmental covariates including digital elevation model and remote sensing derivatives were used as input candidates to SCOK, MLP-NN, and SVM. The optimal input combination was determined using multiple linear stepwise regression(MLSR). The results revealed that the SCOK using field covariates including water electrical conductivity(ECw) and sand percentage(sand %), and environmental covariates including land surface temperature(LST), topographic wetness index(TWI), and elevation could significantly increase the accuracy of soil salinity spatial prediction. The comparison of the prediction accuracy of the different modeling techniques using the Taylor diagram indicated that MLP-NN using LST, TWI, and elevation as inputs were more accurate in predicting the topsoil salinity [ECs(TS)] with a mean absolute error(MAE) of 0.43, root mean square error(RMSE) of 0.6 and correlation coefficient of 0.946. MLP-NN using ECw and sand % as inputs were more accurate in predicting the subsoil salinity [ECs(SS)] with MAE of 0.38, RMSE of0.6, and R of 0.968.  相似文献   

16.
Accurate snow accumulation and melt simulations are crucial for understanding and predicting hydrological dynamics in mountainous settings. As snow models require temporally varying meteorological inputs, time resolution of these inputs is likely to play an important role on the model accuracy. Because meteorological data at a fine temporal resolution (~1 hr) are generally not available in many snow‐dominated settings, it is important to evaluate the role of meteorological inputs temporal resolution on the performance of process‐based snow models. The objective of this work is to assess the loss in model accuracy with temporal resolution of meteorological inputs, for a range of climatic conditions and topographic elevations. To this end, a process‐based snow model was run using 1‐, 3‐, and 6‐hourly inputs for wet, average, and dry years over Boise River Basin (6,963 km2), which spans rain dominated (≤1,400 m), rain–snow transition (>1,400 and ≤1,900 m), snow dominated below tree line (>1,900 and ≤2,400 m), and above tree line (>2,400 m) elevations. The results show that sensitivity of the model accuracy to the inputs time step generally decreases with increasing elevation from rain dominated to snow dominated above tree line. Using longer than hourly inputs causes substantial underestimation of snow cover area (SCA) and snow water equivalent (SWE) in rain‐dominated and rain–snow transition elevations, due to the precipitation phase mischaracterization. In snow‐dominated elevations, the melt rate is underestimated due to errors in estimation of net snow cover energy input. In addition, the errors in SCA and SWE estimates generally decrease toward years with low snow mass, that is, dry years. The results indicate significant increases in errors in estimates of SCA and SWE as the temporal resolution of meteorological inputs becomes coarser than an hour. However, use of 3‐hourly inputs can provide accurate estimates at snow‐dominated elevations. The study underscores the need to record meteorological variables at an hourly time step for accurate process‐based snow modelling.  相似文献   

17.
Rainfall–runoff models are widely used to predict flows using observed (instrumental) time series of air temperature and precipitation as inputs. Poor model performance is often associated with difficulties in estimating catchment‐scale meteorological variables from point observations. Readily available gridded climate products are an underutilized source of temperature and precipitation time series for rainfall–runoff modelling, which may overcome some of the performance issues associated with poor‐quality instrumental data in small headwater monitoring catchments. Here we compare the performance of instrumental measured and E‐OBS gridded temperature and precipitation time series as inputs in the rainfall–runoff models “PERSiST” and “HBV” for flow prediction in six small Swedish catchments. For both models and most catchments, the gridded data produced statistically better simulations than did those obtained using instrumental measurements. Despite the high correspondence between instrumental and gridded temperature, both temperature and precipitation were responsible for the difference. We conclude that (a) gridded climate products such as the E‐OBS dataset could be more widely used as alternative input to rainfall–runoff models, even when instrumental measurements are available, and (b) the processing applied to gridded climate products appears to provide a more realistic approximation of small catchment‐scale temperature and precipitation patterns needed for flow simulations. Further research on this issue is needed and encouraged.  相似文献   

18.
Potential evapotranspiration (PET) is a key input to hydrological models. Its estimation has often been via the Penman–Monteith (P–M) equation, most recently in the form of an estimate of reference evapotranspiration (RET) as recommended by FAO‐56. In this paper the Shuttleworth–Wallace (S–W) model is implemented to estimate PET directly in a form that recognizes vegetation diversity and temporal change without reference to experimental measurements and without calibration. The threshold values of vegetation parameters are drawn from the literature based on the International Geosphere–Biosphere Programme land cover classification. The spatial and temporal variation of the LAI of vegetation is derived from the composite NOAA‐AVHRR normalized difference vegetation index (NDVI) using a method based on the SiB2 model, and the Climate Research Unit database is used to provide the required meteorological data. All these data inputs are publicly and globally available. Consequently, the implementation of the S–W model developed in this study is applicable at the global scale, an essential requirement if it is to be applied in data‐poor or ungauged large basins. A comparison is made between the FAO‐56 method and the S–W model when applied to the Yellow River basin for the whole of the last century. The resulting estimates of RET and PET and their association with vegetation types and leaf area index (LAI) are examined over the whole basin both annual and monthly and at six specific points. The effect of NDVI on the PET estimate is further evaluated by replacing the monthly NDVI product with the 10‐day product. Multiple regression relationships between monthly PET, RET, LAI, and climatic variables are explored for categories of vegetation types. The estimated RET is a good climatic index that adequately reflects the temporal change and spatial distribution of climate over the basin, but the PET estimated using the S–W model not only reflects the changes in climate, but also the vegetation distribution and the development of vegetation in response to climate. Although good statistical relationships can be established between PET, RET and/or climatic variables, applying these relationships likely will result in large errors because of the strong non‐linearity and scatter between the PET and the LAI of vegetation. It is concluded that use of the implementation of the S–W model described in this study results in a physically sound estimate of PET that accounts for changing land surface conditions. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

19.
Vegetation processes are seldom considered in lumped conceptual rainfall–runoff (RR) models although they have significant impacts on runoff via the control of evapotranspiration. This paper incorporates the remotely-sensed the moderate resolution imaging spectrometer mounted on the polar-orbiting terra satellite-leaf area index (MODIS-LAI) data into Xinanjiang rainfall–runoff model and assesses the model performance on 210 catchments in south-east Australia. The results show that the inclusion of LAI data improves both the model calibration results as well as the daily runoff prediction in ungauged catchments. It is likely that more significant improvements to the model structure to integrate the remotely-sensed vegetation and other data can further reduce the uncertainty in runoff prediction in ungauged catchments.  相似文献   

20.
基于频率域峰值属性的河道砂体定量预测及应用(英文)   总被引：1，自引：1，他引：0  
河道砂体是陆相含油气盆地最重要的储集类型之一,其边界识别和厚度定量预测是储层预测的热点难题。本文在总结现有方法技术的基础上,提出一种利用频率域峰值属性进行河道砂体边界识别和厚度定量预测的新方法。对典型河道薄砂体地震反射进行了正演模拟,构造了一种新的地震属性——峰值频率-振幅比,研究表明:峰值频率属性对地层厚度变化敏感,振幅属性对地层岩性变化敏感,两者比值突出河道砂体的边界,同时,借助峰值频率与薄层厚度间存在的定量关系进行薄砂体厚度计算。实际数据应用表明,地震峰值频率属性可以较好的刻画河道的平面展布特征;峰值频率-振幅比属性可以提高对河道砂体边界的识别能力;利用频率域地震属性进行砂体边界识别及厚度定量预测是可行的。  相似文献   

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