Conditional simulation of remotely sensed rainfall data using a non-Gaussian v-transformed copula

Quantification of rainfall and its spatial and temporal variability is extremely important for reliable hydrological and meteorological modeling. While rain gauge measurements do not provide reasonable areal representation of rainfall, remotely sensed precipitation estimates offer much higher spatial resolution. However, uncertainties associated with remotely sensed rainfall estimates are not well quantified. This issue is important considering the fact that uncertainties in input rainfall are the main sources of error in hydrologic processes. Using an ensemble of rainfall estimates that resembles multiple realizations of possible true rainfall, one can assess uncertainties associated with remotely sensed rainfall data. In this paper, ensembles are generated by imposing rainfall error fields over remotely sensed rainfall estimates. A non-Gaussian copula-based model is introduced for simulation of rainfall error fields. The v-transformed copula is employed to describe the dependence structure of rainfall error estimates without the influence of the marginal distribution. Simulations using this model can be performed unconditionally or conditioned on ground reference measurements such that rain gauge data are honored at their locations. The presented model is implemented for simulation of rainfall ensembles across the Little Washita watershed, Oklahoma. The results indicate that the model generates rainfall fields with similar spatio-temporal characteristics and stochastic properties to those of observed rainfall data.     

利用NOAA/AVHRR数据获取地表特征参数的方法研究

地表特征参数的正确与否直接影响到区域陆面蒸散量估算精度,因此在区域非均匀陆面蒸散研究中,地表特征参数的获取方法是一个值得探讨的问题．与传统的方法相比,卫星遥感技术在求取地表特征参数时有其独特的优势．NOAA气象卫星AVHRR资料以其时间分辨率高、覆盖面广、价格低廉等优点广泛应用于非均匀陆面蒸散研究和应用中．本文建立了NOAA／AVHRR计算地表特征参数的参数化模型,选取中国东北松嫩平原西部地区2000年7月8日的AVHRR资料,试算了研究区地表温度、地表发射率、地表反照率、NDVI等主要地表特征参数,并且参照2000年研究区土地利用数据对各参数的空间分布特征及合理性进行了分析。     

Prediction of vegetation anomalies over an inland river basin in north‐western China

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.     

Effects of land-use/cover change on hydrological processes using a GIS/RS-based integrated hydrological model: case study of the East River,China

Abstract

An integrated model, combining a surface energy balance system, an LAI-based interception model and a distributed monthly water balance model, was developed to predict hydrological impacts of land-use/land-cover change (LUCC) in the East River basin, China, with the aid of GIS/RS. The integrated model is a distributed model that not only accounts for spatial variations in basin terrain, rainfall and soil moisture, but also considers spatial and temporal variation of vegetation cover and evapotranspiration (ET), in particular, thus providing a powerful tool for investigating the hydrological impact of LUCC. The model was constructed using spatial data on topography, soil types and vegetation characteristics together with time series of precipitation from 170 stations in the basin. The model was calibrated and validated based on river discharge data from three stations in the basin for 21 years. The calibration and validation results suggested that the model is suitable for application in the basin. The results show that ET has a positive relationship with LAI (leaf area index), while runoff has a negative relationship with LAI in the same climatic zone that can be described by the surface energy balance and water balance equation. It was found that deforestation would cause an increase in annual runoff and a decrease in annual ET in southern China. Monthly runoff for different land-cover types was found to be inversely related to ET. Also, for most of the scenarios, and particularly for grassland and cropland, the most significant changes occurred in the rainy season, indicating that deforestation would cause a significant increase in monthly runoff in that season in the East River basin. These results are important for water resources management and environmental change monitoring.

Editor Z.W. Kundzewicz     

Estimation of potential evapotranspiration over the Yellow River basin: reference crop evaporation or Shuttleworth–Wallace?

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.     

Near-real-time one-kilometre Soil Moisture Active Passive soil moisture data product

The coarse resolution soil moisture (SM) data from NASA SMAP mission have been steadily produced with the expected performance since April 2015. These coarse resolution observations could be downscaled to fine resolution using fine scale observations of SM sensitive quantities from existing satellite sensors. For operational users who need near-real-time (NRT) high resolution SM data, the downscaling approach should be feasible for operational implementation, requiring limited ancillary information and primarily depending on readily available satellite observations. Based on these principles, nine potential candidate downscaling schemes were selected for developing an optimal downscaling strategy. Using remotely sensed land surface temperature (LST) and enhanced vegetation index (EVI) observations, the optimal downscaling approach was tested for operational producing a NRT 1 km SM data product from SMAP. Comprehensive assessments on the 1 km SM product were conducted based on agreement statistics with in-situ SM measurements. Statistical results show that the accuracy of the original coarse spatial resolution SMAP SM product can be significantly improved by 8% by the downscaled 1 km SM. With respect to the in-situ measurements, the 1 km SM mapping capability developed here presents a clear advantage over the SMAP/Sentinel SM data product; and it also provides better data availability for users. This study suggests that a NRT 1 km SMAP SM data product could be routinely generated from SMAP at the centre for Satellite Applications and Research of NOAA NESDIS for operational users.     

INCORPORATING RADIATION INPUTS INTO THE SNOWMELT RUNOFF MODEL

Process-based, distributed-area snowmelt runoff models operating at small scales are essential to understand subtle effects of climate change, but require data not commonly available. Temperature index models operating over large areas provide realistic simulations of basin runoff with operationally available data, but lack rigorous physically based algorithms. A compromise between the two types of models is required to provide realistic evaluations of basin response to environmental changes in cold regions. One adaptation that is uniformly required for snowmelt models is the use of remotely sensed data, either as input or in model validation. At a minimum, snowmelt forecasting models need to incorporate snowcover extent information, which is currently obtained operationally. As more remote sensing capabilities come on line, models should accept upgraded information on snow water equivalent; additional remotely sensed information on landcover, frozen soil, soil moisture, cloudiness and albedo would also be useful. Adaptations to the semi-distributed snowmelt runoff model (SRM) are underway to make it more physically based for use in large area studies. A net radiation index has been added to the model, which formerly used only a temperature (degree–day) index to melt snow from a basin's elevation zones. The addition of radiation to the SRM allows the basin to be subdivided into hydrological response units by general aspect (orientation) as well as elevation. Testing of the new radiation-based SRM with measured radiation from a small research basin is the first step towards large scale simulations. Results from the W-3 research basin in Vermont, USA are promising. In the radiation version, the factor that multiplies the degree–day index is estimated independently of model output and is held constant throughout the season, in contrast with the degree–day version, where the corresponding factor is allowed to increase throughout the season. Without calibrating or optimizing on this important parameter, the goodness-of-fit measure R² is improved in two out of six test years when the radiation version of the SRM is used in place of the degree–day version in melt season simulations. When the accumulation of error is eliminated with periodic updating of streamflow, more significant improvement is noted with radiation included.     

Assessing the Sensitivity of a Model of Runoff Formation in the Ussuri River Basin

A physically based model of runoff formation with daily resolution has been developed for the upper part of the Ussuri basin with an area of 24400 km² based on ECOMAG hydrological modeling platform. Two versions of the hydrological model have been studied: (1) a crude version with the spatial schematization of the drainage area and river network based on DEM 1 × 1 km with the use of soil and landscape maps at a scale of 1: 2500000 and (2) a detailed version with DEM 80 × 80 m and soil and landscape maps of the scale of 1: 100000. Each version of the model has been tested for two variants of meteorological inputs: (1) meteorological forcing data (temperature, air humidity, precipitation) at eight weather stations and (2) with the involvement of additional data on precipitation collected at 15 gages in the basin. The model has been calibrated and validated over a 34-year period (1979–2012) with the use of runoff data for the Ussuri R. and its tributaries. The results of numerical experiments for assessing the sensitivity of model hydrological response to the spatial resolution of land surface characteristics and the density of precipitation gaging stations are discussed.     

On the usefulness of remote sensing input data for spatially distributed hydrological modelling: case of the Tarim River basin in China

The ecological situation of the Tarim River basin in China seriously declined since the early 1950s, mainly due to a strong increase in water abstraction for irrigation purposes. To restore the ecological system and support sustainable development of the Tarim River basin region in China, more hydrological studies are demanded to properly understand the processes of the watershed and efficiently manage the water resources. Such studies are, however, complicated due to the limited data availability, especially in the mountainous headwater regions of the Tarim River basin. This study investigated the usefulness of remote sensing (RS) data to overcome that lack of data in the spatially distributed hydrological modelling of the basin. Complementary to the conventional station‐based (SB) data, the RS products that are directly used in this study include precipitation, evapotranspiration and leaf area index. They are derived from raw image data of the Chinese Fengyun meteorological satellite and from the Moderate Resolution Imaging Spectroradiometer (MODIS). The MODIS land surface temperature was used to calculate the atmospheric temperature lapse rate to describe the temperature dependency on topographical variations. Moreover, MODIS‐based snow cover images were used to obtain model initial conditions and as validation reference for the snow model component. Comparison of model results based on RS input versus conventional SB input exhibited similar results in terms of high and low river runoff extremes, cumulative runoff volumes in both runoff and snow melting seasons and spatial and temporal variability of snow cover. During summer time, when the snow cover shrinks in the permanent glacier region, it was found that the model resolution influences the model results dramatically, hence, showing the importance of detailed (RS based) spatially distributed input data. Copyright © 2011 John Wiley & Sons, Ltd.     

Modeling Water and Heat Balance Components for Large Agricultural Region Utilizing Information from Meteorological Satellites

The method has been developed to evaluate water and heat balance components for vegetation covered area of regional scale based on the refined physical-mathematical model of vertical water and heat exchange between land surface and atmosphere (Land Surface Model, LSM) for vegetation season adapted to satellite information on land surface and meteorological conditions. The LSM is accommodated for utilizing satellite-derived estimates of vegetation and meteorological characteristics as model parameters and input variables. Estimates of these characteristics presented as distributions of their values over the study area have been obtained from AVHRR/NOAA, MODIS/EOS Terra and Aqua, SEVIRI/Meteosat-9, -10 data. To build such estimates methods and technologies have been developed and refined using results of thematic processing measurement data from these sensors. Among them the original Multi Threshold Method (MTM) has been developed and tested to calculate daily precipitation sums using rainfall intensity estimates retrieved from AVHRR and SEVIRI data with subsequent replacement of ground-measured rainfall amounts by these daily rainfalls. All technologies have been adapted to the study area with square of 227300 km² being the part of the Central Black Earth Region of European Russia. Developed earlier procedures of utilizing satellitederived estimates of vegetation and meteorological characteristics (including precipitation) in the model have been refined and verified. Final result of modeling is the fields of soil water content, evapotranspiration and other water and heat balance components of the region under study for years 2012–2014 vegetation seasons.     

Spatial Quantification of Groundwater Abstraction in the Irrigated Indus Basin

Groundwater abstraction and depletion were assessed at a 1‐km resolution in the irrigated areas of the Indus Basin using remotely sensed evapotranspiration (ET) and precipitation; a process‐based hydrological model and spatial information on canal water supplies. A calibrated Soil and Water Assessment Tool (SWAT) model was used to derive total annual irrigation applied in the irrigated areas of the basin during the year 2007. The SWAT model was parameterized by station corrected precipitation data (R) from the Tropical Rainfall Monitoring Mission, land use, soil type, and outlet locations. The model was calibrated using a new approach based on spatially distributed ET fields derived from different satellite sensors. The calibration results were satisfactory and strong improvements were obtained in the Nash‐Sutcliffe criterion (0.52 to 0.93), bias (?17.3% to ?0.4%), and the Pearson correlation coefficient (0.78 to 0.93). Satellite information on R and ET was then combined with model results of surface runoff, drainage, and percolation to derive groundwater abstraction and depletion at a nominal resolution of 1 km. It was estimated that in 2007, 68 km³ (262 mm) of groundwater was abstracted in the Indus Basin while 31 km³ (121 mm) was depleted. The mean error was 41 mm/year and 62 mm/year at 50% and 70% probability of exceedance, respectively. Pakistani and Indian Punjab and Haryana were the most vulnerable areas to groundwater depletion and strong measures are required to maintain aquifer sustainability.     

Estimation of future precipitation change in the Yangtze River basin by using statistical downscaling method

In this study, the applicability of the statistical downscaling model (SDSM) in downscaling precipitation in the Yangtze River basin, China was investigated. The investigation includes the calibration of the SDSM model by using large-scale atmospheric variables encompassing NCEP/NCAR reanalysis data, the validation of the model using independent period of the NCEP/NCAR reanalysis data and the general circulation model (GCM) outputs of scenarios A2 and B2 of the HadCM3 model, and the prediction of the future regional precipitation scenarios. Selected as climate variables for downscaling were measured daily precipitation data (1961–2000) from 136 weather stations in the Yangtze River basin. The results showed that: (1) there existed good relationship between the observed and simulated precipitation during the calibration period of 1961–1990 as well as the validation period of 1991–2000. And the results of simulated monthly and seasonal precipitation were better than that of daily. The average R ² values between the simulated and observed monthly and seasonal precipitation for the validation period were 0.78 and 0.91 respectively for the whole basin, which showed that the SDSM had a good applicability on simulating precipitation in the Yangtze River basin. (2) Under both scenarios A2 and B2, during the prediction period of 2010–2099, the change of annual mean precipitation in the Yangtze River basin would present a trend of deficit precipitation in 2020s; insignificant changes in the 2050s; and a surplus of precipitation in the 2080s as compared to the mean values of the base period. The annual mean precipitation would increase by about 15.29% under scenario A2 and increase by about 5.33% under scenario B2 in the 2080s. The winter and autumn might be the more distinct seasons with more predicted changes of precipitation than in other seasons. And (3) there would be distinctive spatial distribution differences for the change of annual mean precipitation in the river basin, but the most of Yangtze River basin would be dominated by the increasing trend.     

Use of multi‐platform,multi‐temporal remote‐sensing data for calibration of a distributed hydrological model: an application in the Arno basin,Italy

Images from satellite platforms are a valid aid in order to obtain distributed information about hydrological surface states and parameters needed in calibration and validation of the water balance and flood forecasting. Remotely sensed data are easily available on large areas and with a frequency compatible with land cover changes. In this paper, remotely sensed images from different types of sensor have been utilized as a support to the calibration of the distributed hydrological model MOBIDIC, currently used in the experimental system of flood forecasting of the Arno River Basin Authority. Six radar images from ERS‐2 synthetic aperture radar (SAR) sensors (three for summer 2002 and three for spring–summer 2003) have been utilized and a relationship between soil saturation indexes and backscatter coefficient from SAR images has been investigated. Analysis has been performed only on pixels with meagre or no vegetation cover, in order to legitimize the assumption that water content of the soil is the main variable that influences the backscatter coefficient. Such pixels have been obtained by considering vegetation indexes (NDVI) and land cover maps produced by optical sensors (Landsat‐ETM). In order to calibrate the soil moisture model based on information provided by SAR images, an optimization algorithm has been utilized to minimize the regression error between saturation indexes from model and SAR data and error between measured and modelled discharge flows. Utilizing this procedure, model parameters that rule soil moisture fluxes have been calibrated, obtaining not only a good match with remotely sensed data, but also an enhancement of model performance in flow prediction with respect to a previous calibration with river discharge data only. Copyright © 2006 John Wiley & Sons, Ltd.     

Assessment of calanchi and rill–interrill erosion susceptibilities using terrain analysis and geostochastics: A case study in the Oltrepo Pavese,Northern Apennines,Italy

Soil erosion is one of the most important environmental problems distributed worldwide. In the last decades, numerous studies have been published on the assessment of soil erosion and the related processes and forms using empirical, conceptual and physically based models. For the prediction of the spatial distribution, more and more sophisticated stochastic modelling approaches have been proposed – especially on smaller spatial scales such as river basins. In this work, we apply a maximum entropy model (MaxEnt) to evaluate badlands (calanchi) and rill–interrill (sheet erosion) areas in the Oltrepo Pavese (Northern Apennines, Italy). The aim of the work is to assess the important environmental predictors that influence calanchi and rill–interrill erosion at the regional scale. We used 13 topographic parameters derived from a 12 m digital elevation model (TanDEM-X) and data on the lithology and land use. Additional information about the vegetation is introduced through the normalized difference vegetation index based on remotely sensed data (ASTER images). The results are presented in the form of susceptibility maps showing the spatial distribution of the occurrence probability for calanchi and rill–interrill erosion. For the validation of the MaxEnt model results, a support vector machine approach was applied. The models show reliable results and highlight several locations of the study area that are potentially prone to future soil erosion. Thus, coping and mitigation strategies may be developed to prevent or fight the soil erosion phenomenon under consideration. © 2020 John Wiley & Sons, Ltd.     

A spatial analysis of hydro‐climatic and vegetation condition trends in the Yellow River basin

Stream‐gauge data indicate that the flow of the Yellow River has declined during the past several decades. Zero flow in sections of the river channel, i.e. the Yellow River drying‐up phenomenon, has occurred since the 1970s. In this paper we present an analysis of changes in the spatial patterns of climatic and vegetation condition data in the Yellow River basin based on data from meteorological stations and satellites. The climatic data are from 1960 to 2000 and the vegetation condition data are from 1982 to 2000. The angular‐distance‐weighted interpolation method is used to get climatic data coverage from station observations. The spatial distribution of tendency is detected with Student's t‐test. The spatial patterns of climatic and vegetation condition change was analysed together with the statistical data on human activities. The analysis indicates that the precipitation decreases and temperature increases in most parts of the Yellow River basin, the evaporative demand of the atmosphere decreases in the upper reaches and increases in the lower reaches, and human activities have improved the vegetation condition in the irrigation districts. The Loess Plateau, the Tibetan Plateau, and the irrigation districts are respectively suggested as precipitation, temperature, and human activity hot spots of the Yellow River drying‐up phenomenon. Copyright © 2007 John Wiley & Sons, Ltd.     

An intercomparison of remotely sensed soil moisture products at various spatial scales over the Iberian Peninsula

Soil moisture (SM) can be retrieved from active microwave (AM), passive microwave (PM) and thermal infrared (TIR) observations, each having unique spatial and temporal coverages. A limitation of TIR‐based retrievals is a dependence on cloud‐free conditions, whereas microwave retrievals are almost all weather proof. A downside of SM retrievals from PM is the coarse spatial resolution. Although SM retrievals at coarse spatial resolution proved to be valuable for global‐scale and continental‐scale studies, their value for regional‐scale studies remains limited. To increase the use of SM retrievals from PM observations, an existing method to enhance their spatial resolution was applied. We present an intercomparison study over the Iberian Peninsula for three SM products on two different spatial sampling grids. The remotely sensed SM products were also compared with in situ observations from the Remedhus network. Variations between ground data and satellite‐based SM are observed; all three remotely sensed SM products show good agreement to the ground observations. The comparison shows that these ground observations and satellite data are consistent, based on the correlation coefficient (R) and root mean square error (RMSE). The remotely sensed products were intercompared after sampling at 25 × 25 km² and after applying the smoothing filter‐based intensity modulation (SFIM) downscaling technique at 10 × 10 km² grids. After the application of the SFIM technique, the SM retrievals from PM observations show better agreement with the other remotely sensed SM products for approximately 40% of the study area. For another 40% of the study area, we found a similar agreement between these product combinations, whereas in extreme environments, both arid and densely vegetated regions, the agreement decreases after the application of the SFIM technique. Agreement between retrievals of absolute SM content from PM and TIR observations is generally high (R = 0.77 for semi‐arid areas). This study enhances our understanding of the remotely sensed SM products for improvements of SM retrieval and merging strategies. Copyright © 2013 John Wiley & Sons, Ltd.     

基于随机森林模型的GRACE数据3种空间降尺度对比

陆地水储量是赋存在陆地上各种形式水的综合体现,研究其时空变化对认识区域水循环过程和水资源调控等具有重要意义。然而现有陆地水储量变化数据实际分辨率较低,限制了其在中小流域或地区中的应用。针对这一问题,本文基于GRACE重力卫星和其后续卫星GRACE-FO反演的陆地水储量变化数据,首先采用随机森林模型,分别基于格点、区域(流域)和区域(全国)3种空间降尺度思路将GRACE数据降尺度至0.25°×0.25°,后结合GLDAS模型数据,基于水量平衡原理计算得到地下水储量变化数据,最后基于降尺度模型模拟效果和实测地下水位数据评估3种降尺度思路在全国的适用性。结果表明:随机森林模型能够较好地模拟驱动数据(降水、气温、植被条件指数和土壤水储量)与GRACE数据的统计关系,验证期格点降尺度思路的平均相关系数总体在0.6左右,区域降尺度思路的平均纳什效率系数、相关系数和均方根误差分别>0.5、>0.75和<6.6 cm,3种空间降尺度思路的模拟精度均满足基本要求;2003—2021年间,GRACE数据、格点降尺度、区域降尺度(流域)和区域降尺度(全国)得到的我国陆地水储量亏缺量分别约为...     

Estimating tree abundance from remotely sensed imagery in semi-arid and arid environments: bringing small trees to the light

The analysis of remotely sensed images provides a powerful method for estimating tree abundance. However, a number of trees have sizes that are below the spatial resolution of remote sensing images, and as a result they cannot be observed and classified. We propose a method for estimating the number of such sub-resolution trees on forest stands. The method is based on a backwards extrapolation of the size-class distribution of trees as observed from the remotely sensed images. We apply our method to a tree database containing around 13,000 tree individuals to determine the number of sub-resolution trees. While the proposed method is formulated for estimating tree abundance from remotely sensed images, it is generally applicable to any database containing tree canopy surface area data with a minimum size cut-off.     

卫星遥感藏北积雪分布及影响因子分析

利用1993~2004年SSM/I被动微波辐射仪反演的雪深资料,1996~2004年NOAA/AVHRR可见光和红外反演的积雪覆盖面积资料,1966~2003年藏北地区6个地面台站的积雪观测资料来检验卫星资料的可用性,并研究近年来藏北积雪的时空分布和影响因素.结果表明,SSM/I, NOAA/AVHRR和实际观测的积雪资料具一致性.从积雪时间变化看：季节尺度上,藏北地区秋冬季积雪迅速增加,但春季(3~5月)融雪速度不快,呈现正反馈特征;年际尺度上,藏北地区20世纪60年代末期起积雪开始减少,80年代积雪增加,90年代起到2003年积雪总体上减少,呈现出减少—增加—减少趋势.采用小波分析发现积雪振荡周期存在着一个准2~3年,准9年和13年的周期,从20世纪70年代初到90年代中期还有一个5年的周期.积雪空间上看,藏北地区积雪主要集中在东部地区,该区每个冬春年积雪覆盖旬数超过15旬,显著高于西部少雪区,大部分积雪集中在4900~5600 m的高度左右;藏北高原积雪变动的显著区位于藏北中东部的安多和聂荣地区.利用藏北地区1966~2003年的地面温度和降水资料建立回归方程模拟年累积雪日,结果表明模拟值与实测值之间的相关系数达0.74.积雪时空分布受温度、降水等因子影响明显.1998~2003年藏北积雪的减少与全球变暖有关,但降水的减少可能是导致近年来藏北积雪减少的更主要因素.     

Changing properties of precipitation concentration in the Pearl River basin, China

In this paper, precipitation concentrations across the Pearl River basin and the associated spatial patterns are analyzed based on daily precipitation data of 42 rain gauging stations during the period 1960–2005. Regions characterized by the different changing properties of precipitation concentration index (CI) are identified. The southwest and northeast parts of the Pearl River basin are characterized by lower and decreasing precipitation CI; the northwest and south parts of the study river basin show higher and increasing precipitation CI. Higher but decreasing precipitations CI are found in the West and East River basin. Comparison of precipitation CI trends before and after 1990 shows that most parts of the Pearl River basin are characterized by increasing precipitation CI after 1990. Decreasing precipitation CI after 1990 (compared to precipitation CI changes before 1990) is observed only in a few stations located in the lower Gui River and the lower Yu River. Significant increasing precipitation CI after 1990 is detected in the West River, lower North River and upper Beipan River. These changes of precipitation CI in the Pearl River basin are likely to be associated with the consequences of the well-evidenced global warming. These findings can contribute to basin-scale water resource management and conservation of ecological environment in the Pearl River basin.