多源遥感驱动的SRM模型在缺资料地区的研究及应用

以MODIS雪盖、风云静止卫星降水、GLDAS气温等多源数据,作为传统SRM模型的输入参数,构建多源遥感驱动的SRM融雪径流模型,并在缺资料地区——青藏高原的年楚河流域进行融雪过程的径流模拟。研究表明融雪后期的瞬时降雪很大程度上影响了插值后积雪覆盖率的精度,在插值的时候考虑降水和气温,排除瞬时积雪干扰,改进线性插值获得每天的积雪覆盖率,可以提高模型模拟精度;遥感驱动的SRM模型在缺资料地区年楚河适用性较好,Nash-Sutcliffe系数(NSE)达到0.681,体积差(Dv)为-0.17%,均方根误差(RMSE)为9.678,模型模拟的精度较高。研究结果可为高寒地区生态水文模型研究提供重要参考,同时可为SRM模型在其他流域尤其是缺资料地区融雪径流计算中的应用提供有效支撑。     

Evaluating the hydro-estimator satellite rainfall algorithm over a mountainous region

This study investigates the performance of the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service (NOAA/NESDIS) operational rainfall estimation algorithm, called the hydro-estimator (HE), with and without its orographic correction method, in its depiction of the timing, intensity and duration of convective rainfall in general, and of the topography–rainfall relationship in particular. An event-based rainfall observation network in north-west Mexico, established as part of the North American monsoon experiment (NAME), provides gauge-based precipitation measurements with sufficient temporal and spatial sampling characteristics to examine the climatological structure of diurnal convective activity over north-west Mexico. In this study, rainfall estimates from the HE algorithm were evaluated against point observations collected from 49 rain gauges from August until the end of September in 2002 and from 79 gauges from August to September in 2003. While the HE with orographic correction to some extent captures the spatial distribution and timing of diurnal convective events, elevation-dependent biases exist, which are characterized by an underestimate in the occurrence of light precipitation at high elevations and an overestimate in the occurrence of precipitation at low elevations. The potential of the HE in providing high spatial and temporal resolution data is also evaluated using a hydrological model over the North American monsoon (NAM) region. The findings suggest that continued improvement to the HE orographic correction scheme is warranted in order to advance quantitative precipitation estimation in complex terrain regions and for use in hydrologic applications.     

Assessment and correction of three satellite rainfall estimate products for improving flood prevention in French Guiana

The French Guiana (80 000 km²) is highly vulnerable to flooding during the rainy season but the hydrological prevision is limited because the region cannot be cover by a dense network of rain gauges. Meteorological satellites could be an alternative for the measurement of precipitation. The objective of this paper was to evaluate and improve the accuracy of daily satellite rainfall estimates (SRE) throughout the French Guiana between April 2015 and March 2016. Validation data were composed by 70 rain gauges managed by France and Suriname. Three satellite-based rainfall estimates have been tested: TRMM-TMPA 3B42 (Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis) V7, IMERG (Integrated Multi-satellitE Retrievals) for GPM (Global Precipitation Measurement) and STAR Satellite rainfall estimates Hydro-Estimator (HE). Better SRE were obtained by GPM with a clearly higher probability of detection of rainy days (>70%). During the rainy season, biases remained important and SRE appeared inaccurate for the monitoring and forecasting of floods. Biases correction methods were applied, and the additive correction methods by interpolation of biases (ADD_IDW) obtained the better performance (absolute biases <8 mm day^?1; RMSE <12 mm day^?1) for each satellite products. This simple method proved to be very effective to reduce biases close to 0 throughout the year. After ADD_IDW correction, performance levels of TRMM, GPM, and HE products were relatively close and these three satellite products could be implemented into cascade chains in operational framework ensuring the provision of corrected SRE in real time and thus guarantee a reinforced hydrological monitoring in French Guiana.     

基于能量平衡的关川河流域蒸散发的遥感反演

蒸散发是水分循环的重要环节, 遥感技术为区域陆面水分蒸散发量估算提供了一种新的手段, 使用TM 数据和能量平衡模式估算流域蒸散发, 并给出了各个参量的遥感获取方法, 解决了由瞬时遥感数据估算全天蒸散发的问题。通过对关川河流域日蒸散发的模拟, 分析了该流域蒸散发的分布规律, 对陆面过程研究有一定的支持和指导意义。     

Comparative Analysis of Soil Water and Salt Dielectric Model

The soil dielectric constant，the basis of the microwave remote sensing inversion of soil water and salt，is one of the main parameters of microwave remote sensing research.It is very important to select the high precision soil water and salt dielectric model to improve the precision of soil water and salt inversion.However，the existing soil water and salt model still can’t quantitatively describe the effect of salt factor on soil permittivity.This paper simulates the complex permittivity of different texture，water content and salinity wet soil by Dobson model，Dobson\|S model，GRMDM model，HQR model and WYR model at L，Cand X bands when soil temperature equal 25 ℃.Comparison and analysis the simulation values with measured values by microwave vector network analyzer.The results show：(1)Dobson model and GRMDM model can accurately simulate the real part of dielectric constant of non\|saline soil，while the stimulated values of imaginary part is less than the measured values;(2)Dobson\|S model can well simulate the real part of the dielectric constant of saline soil，at L，C\nd Xbands the correlation coefficient R equal 0.97，the RMSE is less than 2.10.But for the imaginary part of the dielectric constant of saline soil，the Dobson\|S model，HQR model and WYR model with different simulation accuracy，when soil water content different.This study would benefit the choice of a suitable soil dielectric model for soil moisture and salinity retrieval.     

改进频率分布和风速纠正的青藏高原格点降水数据集（1980~2009年）

青藏高原格点降水数据是该地区气象、水文、生态等多方面研究的重要支撑。然而,因青藏高原地形和观测条件的影响,常规的格点降水资料处理方法通常难以表征格网内降水的真实统计参数,也并未将风速影响的器测误差考虑在内。针对此问题,对降水数据进行降水观测损失订正和降水频率分布优化形成新的数据集。由于考虑了风引起的观测损失且使用了不易受到台站密度影响的插值方案,该数据的均值比国际同类数据平均偏大20%,方差偏大2倍。更大的方差,意味着该数据可以更大程度地减少低密度观测网给格点数据带来的平滑效应,对于研究气候的变化特征更有优势。该数据覆盖时间范围从1980年1月1日至2009年12月31日,时间分辨率为1 d,水平空间分辨率10 km。数据适合作为数值模式降水和卫星遥感降水频率纠正的参考数据源,也可作为各类陆面水文模型的输入参数。     

Spatial downscaling of TRMM 3B43 precipitation considering spatial heterogeneity

The availability of accurate precipitation data with high spatial resolution is deemed necessary for many types of hydrological, meteorological, and environmental applications. The Tropical Rainfall Measuring Mission (TRMM) data sets can provide effective precipitation information, but at coarse resolution (0.25°), so it is very important to improve their resolution. There is a strong relationship between precipitation and other environment variables (e.g. vegetation and topography). The existing precipitation-downscaling methods attempt to describe this relationship by using a uniform empirical model. However, in the real world, the relationship is disturbed due to the influence of certain factors such as soil type, hydrological conditions, and human activities. In this study, a new downscaling method considering this spatial heterogeneity was proposed to downscale version 7 of the TRMM 3B43 precipitation product, which assumes that the relationship varies spatially but is the same in a local region. At a spatial resolution of 0.25°, the spatially varying relationship among TRMM, normalized difference vegetation index (NDVI), and digital elevation model (DEM) is explored by using a local regression analysis approach known as geographically weighted regression (GWR), but this relationship is the same in a pixel of 0.25° × 0.25°. The derived relationship is used to construct the precipitation downscaling model, which then produces 1 km downscaled precipitation data. The existing and proposed downscaling methods were both tested in North China for 2008–2011. The accuracy of the downscaled precipitation was validated by comparing it with observed precipitation data from 49 meteorological stations located in the study area. The results show that GWR is more suitable to capture the relationship among TRMM, DEM, and NDVI (minimum R² = 0.93). Compared with the existing downscaling method, the proposed method, which consistently showed increased R² (e.g. from 0.80 to 0.82 in 2011) and reduced RMSE (e.g. from 125.4 mm to 91 mm in 2011) in all four years, can more accurately produce downscaled precipitation data.     

An R package for assessment of statistical downscaling methods for hydrological climate change impact studies

Due to inherent bias the climate model simulated precipitation and temperature cannot be used to drive a hydrological model without pre-processing – statistical downscaling. This often consists of reducing the bias in the climate model simulations (bias correction) and/or transformation of the observed data in order to match the projected changes (delta change). The validation of the statistical downscaling methods is typically limited to the scale for which the transformation was calibrated and the driving variables (precipitation and temperature) of the hydrological model. The paper introduces an R package ”musica” which provides ready to use tools for routine validation of statistical downscaling methods at multiple time scales as well as several advanced methods for statistical downscaling. The musica package is used to validate simulated runoff. It is shown that using conventional methods for downscaling of precipitation and temperature often leads to substantial biases in simulated runoff at all time scales.     

Research of Satellite Precipitation Data Assimilation on WRF Model

Weather research and forecasting model and four\|dimensional variational(4Dvar)data assimilation system were used to assimilate Tropical Rainfall Measuring 3B42 precipitation dataset(TRMM 3B42),Global Precipitation Measurement dataset(GPM)and FY\|2G precipitation dataset during 1 July to 4 July 2015.The results showed that：(1)assimilation of the satellite precipitation datasets does improve the forecasting of precipitation,because all assimilation precipitation RMSE are in(0,1),and assimilating GPM dataset is superior than others;(2)the results of 2 m relative humidity from all experiments underestimated real observations,and 2 m relative humidity RMSE(units %)were in(10,50).Moreover,assimilating GPM provides an advantage in estimating various air moisture conditions;(3)Although the impact of assimilating precipitation datasets were complex for simulating 10 m wind speed,results of 10 m wind speed experiments were overestimated\|the real observation and the RMSE were in 1.5~3 m/s.In conclusion,GPM precipitation datasets assimilation was good for simulating precipitation,relative humidity and 10 m wind speed.     

Monthly Runoff Prediction of Zhengyixia in the Heihe River based on Singular Spectrum Analysis-grey Wolf Optimizer-support Vector Regression Hybrid Model

Hydrologic prediction is an important prerequisite for optimal allocation of water resources, but the traditional forecasting methods generally have the problem of low forecasting accuracy. To improve the accuracy of hydrologic prediction, a hybrid data-driven model is proposed for monthly runoff forecasting, namely, Singular Spectrum Analysis-Grey Wolf Optimizer-Support Vector Regression (SSA-GWO-SVR) model. The proposed model uses SSA to denoise the runoff data to improve the stability and predictability of runoff series, and uses GWO to optimize the parameters of SVR model to enhance the generalization ability of the model. This model is validated by monthly runoff prediction of Zhengyixia in the Heihe River Basin, and the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), correlation coefficient (R) and Nash-Sutcliffe Efficiency Coefficien (NSEC) are used as evaluation criteria. The experimental results show that the prediction accuracy of the proposed model is significantly higher than those of Autoregressive Integrated Moving Average model (ARIMA), Persistent Model (PM), Cross Validation(CV)-SVR and GWO-SVR models, and the can predict the runoff peak well, which indicates that the model is a reliable runoff forecasting model, can capture the intrinsic characteristics of hydrologic runoff more deeply, and provides a new method for hydrologic prediction based on data-driven model.     

Data Assimilation Experiment ofPoor Quality of RemoteSensing Image in Critical Phenology

In the process of data assimilation，influenced by the water vapor and cloud cover in the study area，the qualities of remote sensing images are poor in the key crop phenological phases.This will cause not to get the perfect remote sensing images for a long time.So we try to solve this problem by using an improved EnKF method to assimilate the WOFOST crop growth model and the terrible quality of remote sensing images to forecast the maize’s yield in the Red Star Farm in Heilongjiang province.In order to improve the accuracy of simulated time series curve of the LAI and yield production results，the consideration on quality evaluation of the remote sensing images is introduced by using expansion coefficient and adjustable factor.The results shows based on the improved EnKF method，time series curve of the LAI keeps a normal tendency of LAI rather than negative fluctuations，and it also avoids the serrated fluctuation to a certain extent.In addition，compared with the original EnKF method，in the field level R2 can increased to 0.67 from 0.59，RMSE is reduced to 92.23 kg/hm2 from 240.57 kg/hm2 and in the farm level R2 can increased to 0.61 from 0.52，RMSE is reduced to 122.44 kg/hm2 from 310.94 kg/hm2 between simulated yield and measured yield.     

基于奇异谱分析-灰狼优化-支持向量回归混合模型的黑河正义峡月径流预测

水文预报是水资源优化配置的重要前提,而传统预报方法普遍存在预测精度低的问题,为提高水文预报的准确性,提出了一种混合数据驱动模型用于月径流预测,即奇异谱分析-灰狼优化-支持向量回归(SSA-GWO-SVR)模型。该模型通过SSA对径流数据进行去噪处理来提高径流序列的平稳性和可预测性,采用GWO对SVR模型的参数进行联合选优,从而增强模型的泛化能力。通过黑河正义峡的月径流预测进行模型验证,以平均绝对误差(MAE)、均方根误差(RMSE)、相关系数(R)和纳什效率系数(NSEC)为模型评价标准。实验结果表明该模型的预测精度明显高于自回归积分滑动平均模型(ARIMA)、持续性模型(PM)、交叉验证-SVR(CV-SVR)和GWOSVR模型,并且它能很好地预测径流峰值,说明该模型是一种可靠的径流预测模型,能够更深入地捕获水文径流的内在特性,为基于数据驱动模型的水文预报提供了一种新方法。     

Atmospheric Correction of GF-1/WFV Image in Taihu Lake based on the 6S Model Pixel by Pixel

The spectral information of water is weak, and the commonly used radiation transfer model has poor accuracy in atmospheric correction of water body. Based on the Gaofen-1 WFV image (GF-1/WFV) and the synchronous in situ spectra covering Taihu Lake on 29^th, April, 2016, the sensitivity analysis of the input parameters in 6S model was first performed, and then the image was corrected using 6S model using the observation geometry calculated pixel-by-pixel, the partitioned aerosol type and the Aerosol Optical Depth (AOD) determined by the partitioned dark pixel and Spline interpolation. The experimental results show that the aerosol type has the greatest influence on the 6S atmospheric correction results. Compared with the FLAASH method, the 6S method using the observation geometry and aerosol parameters calculated pixel-by-pixel significantly improved the atmospheric correction accuracy, with the ARE (Average Relative Error) of the four bands reduced by 1.84%，7.78%，4.79%，17%. The 6S atmospheric correction method pixel by pixel with the input of accurate atmospheric parameters can improve the correction accuracy of the remote sensing reflectance above water surface.     

TSEB模型在复杂下垫面下模拟结果比较研究

地表蒸散发对干旱半干旱地区水文过程模拟以及水文平衡有重要影响,复杂地表更是对地表蒸散发模拟提出了新的挑战.利用TSEB(Two-Source Energy Balance)模型,分别以Landsat、MODIS卫星数据为驱动数据,得到黑河下游绿洲地表蒸散发时空分布格局,并利用大孔径闪烁仪和涡动相关仪的观测数据对模拟结果...     

Soil Heavy Metals Estimation based on Hyperspectral in Urban Residential

To explore the possibility of using soil spectral reflectance to estimate soil heavy metal content in urban residential area，this study chooses 30 soil samples of Cu，Pb and Zn in Minhang Residential area，Shanghai Province.Through the spectral factor transform to highlight its eigenvalues，constructed Multiple Linear Stepwise Regression(MLSR) model and Partial Least Squares Regression(PLSR) model based on spectral reflectance of soil heavy metals.The results show that the reciprocal first-order and the logarithmic first-order differential transformation can effectively enhance the heavy metal soil spectral characteristics.The best characteristic bands of Cu，Pb and Zn are 1 042.7 nm、706.84 nm and 1 404.8 nm.In terms of model stability and accuracy，PLSR model is better than MLSR model.The RMSE of Cu and Zn were only about 10% of the mean value of heavy metals in the study area，and the accuracy of the model was high.Compared with Cu and Zn，the R2 of Pb is between 0.64~0.88 which with higher model stability.By preprocessing the spectral data，the partial least-squares regression can effectively improve the accuracy of estimating the heavy metal content in urban residential areas. 〖WTHZ〗Key words:〖WT〗 Urban residential area;Soil heavy metals;Hyperspectral;Multivariate Linear Stepwise Regression(MLSR) model;Partial Least Squares Regression(PLSR) model 〖HT〗〖ST〗〖HJ〗〖WT〗〖JP〗〖LM〗     

An O2-A Band Atmospheric Correction Algorithm for Tower-based Platform based on Look-up Table

Tower-based spectral observation is an important connecting bridge between flux sites and satellite remote sensing data，and the effect of atmospheric absorption and scattering between horizontal surface and tower-based platform on the atmospheric absorption band such as O2-A is difficult to ignore.Firstly，the influence of atmospheric radiation transfer on the up-welling radiance and down-welling irradiance of the tower-based platform is analyzed，and the atmospheric correction method of based on upward and downward transmittance is established，that is，the influence of the upwelling radiance and down-welling irradiance is corrected by the direct transmittance and the total transmittance.Secondly，using the simulation data of MODTRAN model，the influence of AOD550 and radiative transfer path length on atmospheric transmittance is quantitatively analyzed，and the LUT of AOD550 is established based on the ratio of down-welling irradiance of near-infrared and red bands and solar zenith angle，as well as the upward and downward atmospheric transmittance LUT based on the AOD550 and the radiative transfer path length.Finally，using the canopy spectral data of different growth stages observed by the tower-based platform，the difference of the apparent reflectance between the inside and outside of the O₂-A band absorption line before and after atmospheric correction was analyzed.The results show that the atmospheric correction method based on LUT of AOD550 and radiative transfer path length proposed in this paper can better correct the influence of upwelling radiance and down-welling on the O₂-A absorption band of the tower-based platform，and provides important method support for applications such as SIF observation on the tower platform.     

二维量子力学效应对阈值电压的影响

在亚50nm的MOSFET中,沿沟道方向的量子力学效应严重影响了器件性能.基于WKB理论,考虑MOSFET中该效应对垂直沟道方向上能级的影响,引入了其时阈值电压的修正.继而对沟道方向的子带作了抛物线近似并进行了数值拟合,从而建立了一个考虑量子力学效应的全解析模型.由此模型可以得到二维量子力学修正和沟道长度以及其它器件参数的关系.与数值模拟结构比较可以得出如下结论:在亚50nm的MOSFET中,量子力学效应引入了阈值电压的修正是不可忽略的.且此全解析模型精度令人满意.     

Parameter uncertainty and temporal dynamics of sensitivity for hydrologic models: A hybrid sequential data assimilation and probabilistic collocation method

In this study, a hybrid sequential data assimilation and probabilistic collocation (HSDAPC) approach is proposed for analyzing uncertainty propagation and parameter sensitivity of hydrologic models. In HSDAPC, the posterior probability distributions of model parameters are first estimated through a particle filter method based on streamflow discharge data. A probabilistic collocation method (PCM) is further employed to show uncertainty propagation from model parameters to model outputs. The temporal dynamics of parameter sensitivities are then generated based on the polynomial chaos expansion (PCE) generated by PCM, which can reveal the dominant model components for different catchment conditions. The maximal information coefficient (MIC) is finally employed to characterize the correlation/association between model parameter sensitivity and catchment precipitation, potential evapotranspiration and observed discharge. The proposed method is applied to the Xiangxi River located in the Three Gorges Reservoir area. The results show that: (i) the proposed HSDAPC approach can generate effective 2nd and 3rd PCE models which provide accuracy predictions; (ii) 2^nd-order PCE, which can run nearly ten time faster than the hydrologic model, can capably represent the original hydrological model to show the uncertainty propagation in a hydrologic simulation; (iii) the slow (R_s) and quick flows (R_q) in Hymod show significant sensitivities during the simulation periods but the distribution factor (α) shows a least sensitivity to model performance; (iv) the model parameter sensitivities show significant correlation with the catchment hydro-meteorological conditions, especially during the rainy period with MIC values larger than 0.5. Overall, the results in this paper indicate that uncertainty propagation and temporal sensitivities of parameters can be effectively characterized through the proposed HSDAPC approach.     

A tool for downscaling weather data from large-grid reanalysis products to finer spatial scales for distributed hydrological applications

A downscaling tool was developed to provide sub-daily high spatial resolution surfaces of weather variables for distributed hydrologic modeling from NASA Modern Era Retrospective-Analysis for Research and Applications reanalysis products. The tool uses spatial interpolation and physically based relationships between the weather variables and elevation to provide inputs at the scale of a gridded hydrologic model, typically smaller (∼100 m) than the scale of weather reanalysis data (∼20–200 km). Nash-Sutcliffe efficiency (NSE) measures greater than 0.70 were obtained for direct tests of downscaled daily temperature and monthly precipitation at 173 SNOTEL sites. In an integrated test driving the Utah Energy Balance (UEB) snowmelt model, 80% of these sites gave NSE > 0.6 for snow water equivalent. These findings motivate use of this tool in data sparse regions where ground based observations are not available and downscaled global reanalysis products may be the only option for model inputs.     

基于环境信息和回归模型的青藏高原MODIS积雪面积比例产品制备

积雪面积比例（Fractional Snow Cover, FSC）是定量描述单位像元内积雪覆盖面积（Snow Cover Area, SCA）与像元空间范围的比值,可为区域气候模拟、水文模型等提供积雪分布的定量信息。MODIS FSC产品是根据经验模型计算得到,并没有考虑地形、植被和地表温度等环境因素的影响,在青藏高原的验证精度低。针对此问题,考虑青藏高原地区环境因素（地形、植被、地表温度）对FSC制备的影响,基于多元自适应回归模型（Multivariate Adaptive Regression Splines, MARS）和线性回归模型分别建立FSC制备的非参数回归模型和经验回归模型。用Landsat 8地表反射率的数据和SNOMAP算法制备FSC的参考数据集。选取一部分参考数据集作为模型的训练数据集,另一部分作为模型的检验数据集。研究结果表明：MARS方法估计FSC的精度明显高于线性回归模型和原有的MODIS FSC制备方法。MARS的总体R、RMSE、MAE分别为0.791、0.103、0.058。在线性回归模型中精度最高的总体R、RMSE、MAE分别为0.647、0.128、0.072。MODIS 原有FSC制图方法的总体R、RMSE、MAE分别为0.595、0.221、0.170。考虑了环境信息的MARS方法更加适用于青藏高原地区FSC制备。本研究为制备青藏高原地区更高精度的FSC数据提供了新思路。