Spatio-temporal evaluation of global gridded precipitation datasets across Iran   总被引：1，自引：0，他引：1  

Seyed-Mohammad Hosseini-Moghari  Kumars Ebrahimi 《水文科学杂志》2013,58(11):1669-1688

ABSTRACT

In this work, the accuracy of four gridded precipitation datasets – Climatic Research Unit (CRU), Global Precipitation Climatology Centre (GPCC), PERSIANN-Climate Data Record (PCDR) and University of Delaware (UDEL) – is evaluated across Iran to find an alternative source of precipitation data. Monthly, seasonal and annual precipitation data from 85 synoptic stations for the period 1984–2013 were used as the basis for the evaluations. Our results indicate that all datasets underestimate and overestimate precipitation in stations with annual precipitation greater than 600 and less than 100 mm, respectively. However, all datasets correctly recognize regimes of precipitation, but with a bias in amount of precipitation. Our spatio-temporal assessments show that GPCC is the most suitable dataset to be used over Iran. Both UDEL and CRU can be considered as the second and third most suitable datasets, while PCDR showed the weakest performance among the studied datasets.  相似文献   

Estimation of joint trace length probability distribution function in igneous,sedimentary, and metamorphic rocks     

Jamal Zadhesh  Seyed-Mohammad Esmaeil Jalali  Ahmad Ramezanzadeh 《Arabian Journal of Geosciences》2014,7(6):2353-2361

To predict the behavior of structures in and on jointed rock masses, it is necessary to characterize the geomechanical properties of joints and intact rock. Among geometry properties of joints, trace length has a vital importance, because it affects rock mass strength and controls the stability of the rock structures in jointed rock masses. Since joint length has a range of values, it is useful to have an understanding of the distribution of these values in order to predict how the extreme values may be compared to the values obtained from a small sample. For this purpose, three datasets of joint systems from nine exposures of igneous, metamorphic, and sedimentary rocks are studied. Joint trace length is one of the most difficult properties to measure accurately, but it may be possible to record other geometrical properties of exposed joints accurately; thereby, support vector machine (SVM) model is used to predict the joint trace length. SVM is a novel machine learning method, which is a powerful tool used to solve the problem characterized by small sample and non-linearity with a good generalization performance. Consequently, goodness-of-fit (GOF) tests were applied on these data. According to these GOF tests, the lognormal distribution was found to be the best probability distribution function for representing a joint trace length distribution.  相似文献