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61.
ABSTRACTA rainfall–streamflow model is proposed, in which a downscaled rainfall series and its wavelet-based decomposed sub-series at optimum lags were used as covariates in GAMLSS (Generalized Additive Model in Location, Scale and Shape). GAMLSS is applied in climate change impact assessment using CMIP5 general climate model to simulate daily streamflow in three sub-catchments of the Onkaparinga catchment, South Australia. The Spearman correlation and Nash-Sutcliffe efficiency between the observed and median simulated streamflow values were high and comparable for both the calibration and validation periods for each sub-catchment. We show that the GAMLSS has the capability to capture non-stationarity in the rainfall–streamflow process. It was also observed that the use of wavelet-based decomposed rainfall sub-series with optimum lags as covariates in the GAMLSS model captures the underlying physics of the rainfall–streamflow process. The development and application of an empirical rainfall–streamflow model that can be used to assess the impact of catchment-scale climate change on streamflow is demonstrated. 相似文献
62.
以2003年5月29日福州市LandsmETM＋影像为数据源,对2种地表温度空间降尺度算法——EM算法和HUTS算法进行实验、比较与分析,EM算法是利用高空间分辨率的地表比辐射率对低空间分辨率的亮度温度影像进行调节,从而达到提高热红外影像空间分辨率的目的;HUTS算法则是引入了归一化差异植被指数NDVI和地表反照率d,通过在低空间分辨率拟合二者与地表温度LST之间的关系,然后根据其尺度不变性,将该关系应用到高空间分辨率的影像上,从而达到提高热红外影像空间分辨率的目的．研究结果表明：1）2种算法所得结果影像都能在有效提高空间分辨率的同时较好地保持了原始地表温度影像的空间分布特征,达到了较好的降尺度效果;2）以RMSE为定量评价指标,HUTS算法要略优于EM算法,其中,EM算法的RMSE为1．2494,而HUTS算法仅为0．9869． 相似文献
63.
Shuang-Ye Wu 《水文科学杂志》2013,58(8):1251-1263
Abstract This study aims to assess the potential impact of climate change on flood risk for the city of Dayton, which lies at the outlet of the Upper Great Miami River Watershed, Ohio, USA. First the probability mapping method was used to downscale annual precipitation output from 14 global climate models (GCMs). We then built a statistical model based on regression and frequency analysis of random variables to simulate annual mean and peak streamflow from precipitation input. The model performed well in simulating quantile values for annual mean and peak streamflow for the 20th century. The correlation coefficients between simulated and observed quantile values for these variables exceed 0.99. Applying this model with the downscaled precipitation output from 14 GCMs, we project that the future 100-year flood for the study area is most likely to increase by 10–20%, with a mean increase of 13% from all 14 models. 79% of the models project increase in annual peak flow. Citation Wu, S.-Y. (2010) Potential impact of climate change on flooding in the Upper Great Miami River Watershed, Ohio, USA: a simulation-based approach. Hydrol. Sci. J. 55(8), 1251–1263. 相似文献
64.
Deepti Joshi Andre St-Hilaire Taha B. M. J. Ouarda Anik Daigle Nathalie Thiemonge 《水文科学杂志》2013,58(11):1996-2010
ABSTRACTThis work explores the ability of two methodologies in downscaling hydrological indices characterizing the low flow regime of three salmon rivers in Eastern Canada: Moisie, Romaine and Ouelle. The selected indices describe four aspects of the low flow regime of these rivers: amplitude, frequency, variability and timing. The first methodology (direct downscaling) ascertains a direct link between large-scale atmospheric variables (the predictors) and low flow indices (the predictands). The second (indirect downscaling) involves downscaling precipitation and air temperature (local climate variables) that are introduced into a hydrological model to simulate flows. Synthetic flow time series are subsequently used to calculate the low flow indices. The statistical models used for downscaling low flow hydrological indices and local climate variables are: Sparse Bayesian Learning and Multiple Linear Regression. The results showed that direct downscaling using Sparse Bayesian Learning surpassed the other approaches with respect to goodness of fit and generalization ability.
Editor D. Koutsoyiannis; Associate editor K. Hamed 相似文献
65.
D.L. De Luca 《水文科学杂志》2013,58(8):1536-1558
AbstractThe spatial and temporal variability of the scaling properties and correlation structure of a data set of rainfall time series, aggregated over different temporal resolutions, and observed in 70 raingauges across the Basilicata and Calabria regions of southern Italy, is investigated. Two types of random cascade model, namely canonical and microcanonical models, were used for each raingauge and selected season. For both models, different hypotheses concerning dependency of parameters on time scale and rainfall height can be adopted. In particular, a new approach is proposed which consists of several combinations of models with a different scale dependence of parameters for different temporal resolutions. The goal is to improve the modelling of the main features of rainfall time series, especially for cases where the variability of rainfall changes irregularly with temporal aggregation. The results obtained with the new methodology showed good agreement with the observed data, in particular, for the summer months. In fact, during this season, rainfall heights aggregated at fine temporal resolutions (from 5 to 20 min) are more similar (relative to the winter season) to the values cumulated on 1 or 3 h (due to convective phenomena) and, consequently, the process of rainfall breakdown is nearly stationary for a range of finer temporal resolutions.
Editor D. Koutsoyiannis; Associate editor A. Montanari 相似文献
66.
遥感地表温度降尺度方法比较————性能对比及适应性评价 总被引：1，自引：0，他引：1
在归纳现有遥感地表温度降尺度方法的基础上, 选取3种代表性方法:Normalized Difference Vegetation Index (NDVI)、Pixel Block Intensity Modulation (PBIM)和Linear Spectral Mixture Model (LSMM)方法进行实验比较, 并建立了一种纹理相似性度量指标CO-RMSE (Co-Occurrence Root Mean Square Error)。结果表明:(1)NDVI方法受季节影响最严重, 不适于春、冬季, 其次为PBIM方法;(2)LSMM方法受分辨率限制最大, 低分辨率时丢失大量纹理信息, NDVI方法在较高分辨率时优于PBIM方法, 较低分辨率时则相反;(3)3种方法的适用区域分别为植被与裸土像元并存区域, 山区和反照率变化较大区域, 以及类别间温差较大区域;(4)NDVI方法操作最简单, LSMM方法最复杂。分析认为, 尺度因子是决定方法性能的关键, 应根据季节、分辨率、地表覆盖、应用目的和操作性等综合选择。 相似文献
67.
Cambodia is one of the most vulnerable countries to climate change impacts such as floods and droughts. Study of future climate change and drought conditions in the upper Siem Reap River catchment is vital because this river plays a crucial role in maintaining the Angkor Temple Complex and livelihood of the local population since 12th century. The resolution of climate data from Global Circulation Models (GCM) is too coarse to employ effectively at the watershed scale, and therefore downscaling of the dataset is required. Artificial neural network (ANN) and Statistical Downscaling Model (SDSM) models were applied in this study to downscale precipitation and temperatures from three Representative Concentration Pathways (RCP 2.6, RCP 4.5 and RCP 8.5 scenarios) from Global Climate Model data of the Canadian Earth System Model (CanESM2) on a daily and monthly basis. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were adopted to develop criteria for dry and wet conditions in the catchment. Trend detection of climate parameters and drought indices were assessed using the Mann-Kendall test. It was observed that the ANN and SDSM models performed well in downscaling monthly precipitation and temperature, as well as daily temperature, but not daily precipitation. Every scenario indicated that there would be significant warming and decreasing precipitation which contribute to mild drought. The results of this study provide valuable information for decision makers since climate change may potentially impact future water supply of the Angkor Temple Complex (a World Heritage Site). 相似文献
68.
统计降尺度方法在北京月尺度预测中的应用 总被引：1，自引：0，他引：1
利用SDSM(statistical downscaling method)方法对北京47年(1961-2007年)的最低、最高气温和降水变化情况进行模拟评估,在此基础上对2008年北京奥运期间和2009年国庆期间天气变化进行实际预测应用。结果表明,SDSM方法具备模拟气温和降水等要素的能力。从年际变化模拟的情况上看,SDSM模型对气温模拟的效果好于降水,其中对于月平均最低(最高)气温模拟的效果好于最低(最高)气温极值的模拟。模型模拟的逐年极端最高(最低)气温结果在整体上偏低于实况气温,体现出气温极值模拟能力的不足。SDSM模型模拟的降水量整体上小于实测值,对降水极大值模拟能力更弱。对奥运会和国庆期间北京天气预测结果表明,模型对日最高、最低气温和降水的数值预测能力较差,预测值偏低于实际值,但升温和降温过程发生的时段能够准确的预测。 相似文献
69.
Can Artificial Climate Trends in Global Reanalysis be Reduced by Dynamical Downscaling:A Case Study of China 
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下载免费PDF全文 In this study,the ability of dynamical downscaling for reduction of artificial climate trends in global reanalysis is tested in China.Dynamical downscaling is performed using a 60-km horizontal resolution Regional Integrated Environmental Model System (RIEMS) forced by the NCEP-Department of Energy (DOE) reanalysis II (NCEP-2).The results show that this regional climate model (RCM) can not only produce dynamically consistent fine scale fields of atmosphere and land surface in the regional domain,but it also has the ability to minimize artificial climate trends existing in the global reanalysis to a certain extent.As compared to the observed 2-meter temperature anomaly averaged across China,our model can simulate the observed inter-annual variation and variability as well as reduce artificial climate trends in the reanalysis by approximately 0.10 C decade 1 from 1980 to 2007.The RIEMS can effectively reduce artificial trends in global reanalysis for areas in western China,especially for regions with high altitude mountains and deserts,as well as introduce some new spurious changes in other local regions.The model simulations overestimated observed winter trends for most areas in eastern China with the exception of the Tibetan Plateau,and it greatly overestimated observed summer trends in the Sichuan Basin located in southwest China.This implies that the dynamical downscaling of RCM for long-term trends has certain seasonal and regional dependencies due to imperfect physical processes and parameterizations. 相似文献
70.
针对动力气候模式对区域或更小空间尺度内的日降水预测技巧偏低的问题,应用最优子集回归 (OSR) 方法对国家气候中心业务化的月动力气候模式 (DERF) 输出的高度场、风场和海平面气压场进行降尺度处理用于降水预测,旨在提高预测准确率。1982—2006年交叉检验结果表明:OSR方法能显著提高降水预测技巧,其中11~40 d改善效果最为显著。在此基础上,应用一步法和两步法两种统计降尺度方法预测极端降水日数,交叉检验结果表明:两种方法均优于随机预测,冬季两步法预测技巧略高于一步法,夏季一步法略优于两步法。综合认为OSR,OSR结合随机天气发生器 (WG) 两种统计降尺度方法对月尺度降水或极端降水日数的预测均具有较高的技巧,可作为短期气候预测的重要参考信息。 相似文献