china meteorological forcing dataset

基于多源数据的安吉白茶产量遥感估算探究摘要：安吉白茶是一种以中国浙江安吉县为产区的珍贵茶叶，其生长环境多变，特殊是受到气候变化的影响较大，因此对其产量的准确估算显得尤为重要。

本探究基于多源数据，包括植被指数、地表温度、降水等参数数据，通过遥感技术进行安吉白茶产量的遥感估算。

起首利用Landsat8 OLI遥感影像提取出安吉白茶种植区域的陆地遮盖数据，结合MODIS地表温度与植被指数数据对种植区域进行土地遮盖类型分类，接受谷粒多普勒雷达图像实现了地形高程信息的得到。

其次，利用Meteorological Forcing Dataset for Land Surface Model (MFDLSM)得到了安吉县的土地利用类型和地表温度、降水数据，融合在一起得到不同月份的作物生长期的土地利用类型影像，通过NDVI、EVI等指数的时空差异性，依据多元线性回归模型反演得到安吉白茶产量，并验证了该方法的准确性和可行性。

关键词：安吉白茶，遥感技术，多源数据，地表温度，降水，产量估算Abstract:Anji white tea is a precious tea with Anji County, Zhejiang Province, China being its primary production area. Due to its changeable growth environment, especially the impact of climate change, the accurate estimation of its yield is particularly important. Based on multi-source data, including vegetation index, land surface temperature, precipitation and other parameter data, this study carried out remote sensing estimation of Anji white tea yield. Firstly, the land cover data of the Anji white tea planting area was extracted from Landsat8 OLI remote sensing image. Combined with MODIS land surface temperature and vegetation index data, we classified the land cover type of the planting area, and obtained thetopographic elevation information by means of grain Doppler radar images. Secondly, using the Meteorological Forcing Dataset for Land Surface Model (MFDLSM), we obtained the land use type and land surface temperature and precipitation data in Anji County. We fused them together to obtain land use type images during the crop growth period of different months. The yield of Anji white tea was then determined by multi-linear regression model inversion according to the temporal and spatial differences of NDVI and EVI indexes, and the accuracy and feasibility of the method were verified.Keywords: Anji white tea; remote sensing technology; multi-source data; land surface temperature; precipitation; yield estimation。

人教版必修一·U4第四课时随堂训练（解析版）一、单项选择（共8小题）1. I can never understand the reason _______ he explained to me.A．why B．that C．when D．Where2. It’s such a difficult problem _______ nobody can work out.A．as B．that C．which D．so that3. He bought a new building ________top is different from those of the others around.A．what B．which C．that D．Whose4. It is in the hall can house 10,000 people the opening ceremony of the Global 5G Technology Summit will be held.A．which;that B．where;that C．which;who D．where; whom5. The book tells stories of the earthquake through the eyes of those lives were affectedA．whose B．that C．who D．Which6. As President, he appointed men to high government positions ________ he considered most capable, ________ some of them openly defied his authority.A．which … as though B．/ … thoughC．which … even if D．whom … as though7. The movie Jurassic Park was a big hit, with its novel idea ___________ a mixture of fear and excitement into people’s hearts.A．strikes B．struck C．to strike D．Striking8. It ________ me that nobody is in favour of the new changes.A．catches B．bears C．charges D．strikes【答案与解析】1.考查定语从句。

中国气象辐射资料年册 ANNUAL SOLAR RADIATION DATA OF CHINA2001年国家气象中心NATIONAL METEOROLOGICAL CENTER前言太阳辐射是地球大气运动的主要能源，也是地球气候形成的最重要因子。

多年来的实践证明，太阳辐射观测资料在对大气运动规律研究、气候预测、农作物产量评估、气候资源的开发利用等研究领域是必不可少的基础数据，《中国气象辐射资料年册》是国家气象中心定期出版的气象资料产品之一。

随着社会的发展和人类的进步，包括气候变化在内的环境问题越来越受到国际社会和广大民众的关注，与气候有关的资源问题已经成为制约可持续发展的因素之一，天气气候与国民经济和人们的日常生活更加密切。

愿我们的气象信息资源在国民经济建设中发挥出更大的作用，为您提供更全面更完美的气象信息服务。

本期责任编辑：王颖资料审核：孙化南杨燕茹终审：王颖目录(LIST)前言(Intrduction)说明(Explanatory Note)z北京(Beijing)54511 北京(Beijng) (1)z天津(Tianjin)54527 天津(Tianjin) (2)z河北(Hebei)54539 乐亭(Leting) (3)z山西(Sanxi)53487 大同(Datong) (51)53772 太原(Taiyuan) (4)53963 侯马(Houma) (51)z内蒙古(Naimenggu)50527 呼伦贝尔盟(海拉尔)(Hailaer) (5)50834 索伦(Sulen) (52)52267 额济纳旗(Ejiniqi) (6)53068 二连浩特(Erlianhaote) (7)53336 乌拉特中方旗海流图(Hailiutu) (52)53543 伊克昭盟东胜(Dongshen) (53)54102 锡林郭勒盟（锡林浩特）(Xilinhaote) (53)54135 通辽(Tongliao) (54)z辽宁(Liaoning)54324 朝阳(Chaoyang) (54)54342 沈阳(Shenyang) (8)54662 大连(dalian) (55)z吉林(Jiling)54161 长春(changchun) (9)54292 延吉(Yuanjie) (55)z黑龙江(Heilongjiang)50136 漠河(Mohe) (10)50468 黑河(Heihe) (11)50742 富裕(Fuyu) (56)50873 佳木斯(Jiamusi) (56)50953 哈尔滨(Haerbing) (12)z上海(Shanghai)58362 上海(Shanghai) (13)z江苏(Jiangsu)58144 清江(Qingjiang) (57)58238 南京(Nanjing) (14)58265 吕泗(Lusi) (57)z浙江(Zhijiang)58457 杭州(Hangzhou) (15)58665 洪家(Hongjia) (58)z安徽(Anhui)58321 合肥(Hefei) (16)58531 屯溪(Tunxi) (58)z福建(Fujian)58737 建瓯(Jianou) (59)58847 福州(Fuzhou) (17)z江西(Jiangxi)57993 赣州(Gangzhou) (59)58606 南昌(Nanchang) (18)z山东(Shandong)54764 烟台(Yantai) (19)54823 济南(Jinan) (20)54936 莒县(Juxian) (60)z河南(Henan)57083 郑州(Zhengzhou) (21)57178 南阳(Nanyyang) (60)58208 固始(Gushi) (61)z湖北(hubei)57461 宜昌(Yichang) (61)57494 武汉(Wuhan) (22)z湖南(Hunan)57649 吉首(Jishou) (62)57687 长沙(Changsha) (23)57874 常宁(Changning) (62)z广东(Guangdong)59287 广州(Guangzhou) (24)59316 汕头(Santou) (25)z广西(Guanxi)57957 桂林(Guailing) (26)59431 南宁(Nanning) (27)59644 北海(Baihai) (63)z海南(Hainan)59758 海口(Haikou) (28)59948 三亚(Sanya) (29)59981 西沙(Xisha) (63)z重庆(Chongqing)57516 重庆(Chongqing) (30)z四川(Sichuan)56146 甘孜(Gangzi) (64)56173 红原(Hongyuan) (64)56196 绵阳(Miangyang) (65)56385 峨眉山(Emeishan) (65)56666 攀枝花(Panzhihua) (66)57602 泸州(Luzhou) (66)z贵州(Guizhou)57816 贵阳(Guiyang) (32)z云南(Yunnan)56651 丽江(Lijiang) (67)56739 腾冲(Tengcong) (67)56778 昆明(Kunming) (33)56959 景洪(Jinghong) (34)56985 蒙自(Mengzi) (68)z西藏(Xizang)55228 噶尔(Geer) (35)55299 那曲(Naque) (68)55591 拉萨(Lasa) (36)56137 昌都(Changduo) (37)z陕西(Shanxi)53845 延安(Yanan) (69)57036 西安(Xian) (38)57245 安康(Ankang) (69)z甘肃(Gansu)52418 敦煌(Dunhuang) (39)52533 酒泉(Jiuquan) (70)52681 民勤(Minqing) (70)52889 兰州(Lanzhou) (40)z青海(Qinghai)52754 刚察(Gangcha) (71)52818 格尔木(Geermu) (41)52866 西宁(xining) (42)56029 玉树(Yushu) (71)56043 果洛(Guolu) (72)z宁夏(ningxia)53614 银川(Yinchuan) (43)53817 固原(Guyuan) (72)z新疆(Xinjiang)51076 阿勒泰(Aletai) (44)51133 塔城(Tacheng) (45)51431 伊宁(Yining) (46)51463 乌鲁木齐(Urumuqi) (47)51567 焉耆(Yanqi) (73)51573 吐鲁番(Tuolufan) (73)51628 阿克苏(Akesuo) (74)51709 喀什(Kashi) (48)51828 和田(Hetian) (49)52203 哈密(Hami) (50)z台湾(暂缺)(Tianwan)附录(Appendix)…………………………………………………………………… (75-76)说 明<<中国气象辐射资料年册>>的资料来源于气象辐射信息化资料,其资料具有一定的代表性(除台湾省资料暂缺外),能较准确地、全面地反映我国太阳和地球的辐射基本状况,有助于了解我国全年不同时段内的能量收支情况,更好地为国民经济服务。

剑桥雅思阅读8原文翻译及答案(test3)店铺为大家整理收集了剑桥雅思阅读8真题解析：test3阅读原文解析，希望对各位考生的备考有所帮助,祝每位烤鸭考试顺利，都能取得好成绩!剑桥雅思阅读8原文(test3)READING PASSAGE 1You should spend about 20 minutes on Questions 1-13, which are based on Reading Passage 1 below.Striking Back at LightningWith LasersSeldom is the weather more dramatic than when thunderstorms strike. Their electrical fury inflicts death or serious injury on around 500 people each year in the United States alone. As the clouds roll in, a leisurely round of golf can become a terrifying dice with death — out in the open, a lone golfer may be a lightning bolt’s most inviting target. And there is damage to property too. Lightning damage costs American power companies more than $100 million a year.But researchers in the United States and Japan are planning to hit back. Already in laboratory trials they have tested strategies for neutralising the power of thunderstorms, and this winter they will brave real storms, equipped with an armoury of lasers that they will be pointing towards the heavens to discharge thunderclouds before lightning can strike.The idea of forcing storm clouds to discharge their lightning on command is not new. In the early 1960s, researchers tried firing rockets trailing wires into thunderclouds to set up an easy discharge path for the huge electric charges that these clouds generate. The technique survives to this day at a test site inFlorida run by the University of Florida, with support from the Electrical Power Research Institute (EPRI), based in California. EPRI, which is funded by power companies, is looking at ways to protect the United States’ power grid from lightning strikes. ‘We can cause the lightning to strike where we want it to usin g rockets,’ says Ralph Bernstein, manager of lightning projects at EPRI. The rocket site is providing precise measurements of lightning voltages and allowing engineers to check how electrical equipment bears up.Bad behaviourBut while rockets are fine for research, they cannot provide the protection from lightning strikes that everyone is looking for. The rockets cost around $1,200 each, can only be fired at a limited frequency and their failure rate is about 40 per cent. And even when they do trigger lightning, things still do not always go according to plan. ‘Lightning is not perfectly well behaved,’ says Bernstein. ‘Occasionally, it will take a branch and go someplace it wasn’t supposed to go.’And anyway, who would want to fire streams of rockets in a p opulated area? ‘What goes up must come down,’ points out Jean-Claude Diels of the University of New Mexico. Diels is leading a project, which is backed by EPRI, to try to use lasers to discharge lightning safely —and safety is a basic requirement since no one wants to put themselves or their expensive equipment at risk. With around $500,000 invested so far, a promising system is just emerging from the laboratory.The idea began some 20 years ago, when high-powered lasers were revealing their ability to extract electrons out of atoms and create ions. If a laser could generate a line of ionisation in the air all the way up to a storm cloud, thisconducting path could be used to guide lightning to Earth, before the electric field becomes strong enough to break down the air in an uncontrollable surge. T o stop the laser itself being struck, it would not be pointed straight at the clouds. Instead it would be directed at a mirror, and from there into the sky. The mirror would be protected by placing lightning conductors close by. Ideally, the cloud-zapper (gun) would be cheap enough to be installed around all key power installations, and portable enough to be taken to international sporting events to beam up at brewing storm clouds.A stumbling blockHowever, there is still a big stumbling block. The laser is no nifty portable: it’s a monster that takes up a whole room. Diels is trying to cut down the size and says that a laser around the size of a small table is in the offing. He plans to test this more manageable system on live thunderclouds next summer.Bernstein says that Diels’s system is attracting lots of interest from the power companies. But they have not yet come up with the $5 million that EPRI says will be needed to develop a commercial system, by making the lasers yet smaller and cheaper. ‘I cannot say I have money yet, but I’m working on it,’ says Bernstein. He reckons that the forthcoming field tests will be the turning point —and he’s hoping for good news. Bernstein predicts ‘an avalanche of interest and support‘ if all goes well. He expects to see cloud-zappers eventually costing $50,000 to $100,000 each.Other scientists could also benefit. With a lightning ‘switch’ at their fingertips, materials scientists could find out what happens when mighty currents meet matter. Diels also hopes to see the birth of ‘interactive meteorology’ —not justforecasting the weather but controlling it. ‘If we could discharge clouds, we might affect the weather,’ he says.And perhaps, says Diels, we’ll be able to conf ront some other meteorological menaces. ‘We think we could prevent hail by inducing lightning,’ he says. Thunder, the shock wave that comes from a lightning flash, is thought to be the trigger for the torrential rain that is typical of storms. A laser thunder factory could shake the moisture out of clouds, perhaps preventing the formation of the giant hailstones that threaten crops. With luck, as the storm clouds gather this winter, laser-toting researchers could, for the first time, strike back.Questions 1-3Choose the correct letter, A, B, C or D.Write the correct letter in boxes 1-3 on your answer sheet.1 The main topic discussed in the text isA the damage caused to US golf courses and golf players by lightning strikes.B the effect of lightning on power supplies in the US and in Japan.C a variety of methods used in trying to control lightning strikes.D a laser technique used in trying to control lightning strikes.2 According to the text, every year lightningA does considerable damage to buildings during thunderstorms.B kills or injures mainly golfers in the United States.C kills or injures around 500 people throughout the world.D damages more than 100 American power companies.3 Researchers at the University of Florida and at the University of New MexicoA receive funds from the same source.B are using the same techniques.C are employed by commercial companies.D are in opposition to each other.Questions 4-6Complete the sentences below.Choose NO MORE THAN TWO WORDS from the passage for each answer.Write your answers in boxes 4-6 on your answer sheet.4 EPRI receives financial support from ..................... .5 The advantage of the technique being developed by Diels is that it can be used.....................6 The main difficulty associated with using the laser equipment is related to its.....................Questions 7-10Complete the summary using the list of words, A-I, below.Write the correct letter, A-I, in boxes 7-10 on your answer sheet.In this method, a laser is used to create a line of ionization by removing electrons from 7 ..................... . This laser is then directed at 8 ..................... in order to control electrical charges, a method which is less dangerous than using 9..................... . As a protection for the lasers, the beams are aimed firstly at10 ..................... .A cloud-zappersB atomsC storm cloudsD mirrorsE techniqueF ionsG rockets H conductors I thunderQuestions 11-13Do the following statements agree with the information given in Reading Passage 1?In boxes 11-13 on your answer sheet writeYES if the statement agrees with the claims of the writerNo if the statement contradicts the claims of the writerNOT GIVEN if it is impossible to say what the writer thinks about this11 Power companies have given Diels enough money to develop his laser.12 Obtaining money to improve the lasers will depend on tests in real storms.13 Weather forecasters are intensely interested in Diels’s system.READING PASSAGE 2You should spend about 20 minutes on Questions 14-26, which are based on Reading Passage 2 below.The Nature of GeniusThere has always been an interest in geniuses and prodigies. The word ‘genius’, from the Latin gens (= family) and the term ‘genius’, meaning ‘begetter’, comes from the ea rly R o m a n c u l t o f a d i v i n i t y a s t h e h e a d o f t h e f a m i l y . I n i t s e a r l i e s t f o r m , g e n i u s w a s c o n c e r n e d w i t h t h e a b i l i t y o f t h e h e a d o f t h e f a m i l y , t h e p a t e r f a m i l i a s , t o p e r p e t u a t e h i m s e l f . G r a d u a l l y , g e n i u s c a m e t o r e p r e s e n t a p e r s o n s c h a r a c t e r i s t i c s a n d t h e n c e a n i n d i v i d u a l s h i g h e s t a t t r i b u t e s d e r i v e d f r o m h i s g e n i u s o r g u i d i n g s p i r i t . T o d a y , p e o p l e s t i l l l o o k t o s t a r s o r g e n e s , a s t r o l o g y o r g e n e t i c s , i n t h e h o p e o f f i n d i n g t h e s o u r c e o f e x c e p t i o n a l a b i l i t i e s o r p e r s o n a l c h a r a c t e r i s t i c s . / p > p b d s f i d = " 1 3 4 " > 0 0 T h e c o n c e p t o f g e n i u s a n d o f g i f t s h a s b e c o m e p a r to f o u r f o l k c u l t u r e , a n d a t t i t u d e s a r e a m b i v a l e n t t o w a r d s t h e m . W e e n v y t h e g i f t e d a n d m i s t r u s t t h e m . I n t h e m y t h o l o g y o f g i f t e d n e s s , i t i s p o p u l a r l y b e l i e v e d t h a t i f p e o p l e a r e t a l e n t e d i n o n e a r e a , t h e y m u s t b e d e f e c t i v e i n a n o t h e r , t h a t i n t e l l e c t u a l s a r e i m p r a c t i c a l , t h a t p r o d i g i e s b u r n t o o b r i g h t l y t o o s o o n a n d b u r n o u t , t h a t g i f t e d p e o p l e a r e e c c e n t r i c , t h a t t h e ya r e p h y s i c a l w e a k l i n g s , t h a t t h e r e s a t h i n l i n eb e t w e e n g e n i u s a n d m a d n e s s , t h a t g e n i u s r u n s i n f a m i l i e s , t h a t t h e g i f t e d a r e s oc l e v e r t h e yd o n t ne e d s p e c i a l h e l p , t h a t g if t e d n e s s i s t h e s a m e a s h a v i ng ahi g h I Q , t h a t s o m e r a c e s a r e m o r e i n t e l l i g e n t o r m u s i c a l o r m a t h e m a t i c a l t h a n o t h e r s , t h a t g e n i u s g o e s u n r e c o g n i s e d a n d u n r e w a r d e d , t h a t a d v e r s i t y m a k e s m e n w i s e o r t h a t p e o p l e w i t h g i f t s h a v e a r e s p o n s i b i l i t y t o u s e t h e m . L a n g u a g e h a s b e e n e n r i c h e d w i t h s u c h t e r m s a s h i g h b r o w , e g g h e a d , b l u e - s t o c k i n g , w i s e a c r e , k n o w - a l l , b o f f i n a n d , f o r m a n y , i n t e l l e c t u a l i s a t e r m o f d e n i g r a t i o n . / p > p b d s f i d = " 1 3 5 " > 0 0 T h e n i n e t e e n t h c e n t u r y s a w c o n s i d e r a b l e i n t e r e s t i n t h e n a t u r e o f g e n i u s , a n d p r o d u c e d n o t a f e w s t u d i e s o f f a m o u s p r o d i g i e s . P e r h a p s f o r u s t o d a y , t w o o f t h e m o s t s i g n i f i c a n t a s p e c t s o f m o s t o f t h e s e s t u d i e s o f g e n i u s a r e t h e f r e q u e n c y w i t h w h i c h e a r l y e n c o u r a g e m e n t a n d t e a c h i n g b y p a r e n t s a n d t u t o r s h a d b e n e f i c i a l e f f e c t s o n t h e i n t e l l e c t u a l , a r t i s t i c o r m u s i c a l d e v e l o p m e n t o f t h e c h i l d re n b u t c a u s e d g r e a t d if f i c u l t i e s o f a d j u s t m e n t l a t e r i n t h e i r l i v e s , a n d t h e f r e q u e n c y w i t h w h i c h a b i l i t i e s w e n t u n r e c og n i s e d b y t e a ch e r s a n d s c h o o l s . H o w e v e r , t h e di f f i c u l t y w i t h t h e e v i d e n c e p r o d u c e d b y t h e s e s t u d i e s , f a s c i n a t i n g a s t h e y a r e i n c o l l e c t i n g t o g e t h e r a n e c d o t e s a n d a p p a r e n t s i m i l a r i t i e s a n d e x c e p t i o n s , i s t h a t t h e y a r e n o t w h a t w e w o u l d t o d a y c a l l n o r m - r e f e r e n c e d . I n o t h e r w o r d s , w h e n , f o r i n s t a n c e , i n f o r m a t i o n i s c o l l a t e d a b o u t e a r l y i l l n e s s e s , m e t h o d s o f u p b r i n g i n g , s c h o o l i n g , e t c . , w e m u s t a l s o t a k e i n t o a c c o u n t i n f o r m a t i o n f r o m o t h e r h i s t o r i c a l s o u r c e s a b o u t h o w c o m m o n o r e x c e p t i o n a l t h e s e w e r e a t t h e t i m e . F o r i n s t a n c e , i n f a n t m o r t a l i t y w a s h i g h a n d l i f e e x p e c t a n c y m u c h s h o r t e r t h a n t o d a y , h o m e t u t o r i n g w a s c o m m o n i n t h e f a m i l i e s o f t h e n o b i l i t y a n d w e a l t h y , b u l l y i n g a n d c o r p o r a l p u n i s h m e n t w e r e c o m m o n a t t h e b e s t i n d e p e n d e n t s c h o o l s a n d , f o r t h e m o s t p a r t , t h e c a s e s s t u d i e d w e r e m e m b e r s o f t h e p r i v i l e g e d c l a s s e s . I t w a s o n l y w i t h t h e g r o w t h o f p a e d i a t r i c s a n d p s y c h o l o g y i n t h e t w e n t i e t h c e n t u r y t h a t s t u d i e s c o u l d b e c a r r i e d o u t o n a m o r e o bj e c t i v e , i f s t i l l n o t a l w a y s v e r y s c i e n t i f i c , b a s i s . / p > p b d s f i d = " 1 3 6 " > 0 0 G e n i u s e s , h o w e v e r t h e y a r e d e f i n e d , a r e b u t t h e p e ak s w h i c h s t a n d o u t t h r o u gh t h e m i s t o f h i s t o r y a n d a r e v i s i b l e t o t h e p a r ti c u l a r o b s e r v e r f r o m h i s o r h e r p a r t i c u l a r v a n t a g e p o i n t . C h a n g e t h e o b s e r v e r s a n d t h e v a nt a g e p o i n t s , c l e a r a w a y s o m e o f t h e m i s t , a n d a d i f f e r e n t l o t o f p e a k s a p p e a r . G e n i u s i s a t e r m w e a p p l y t o t h o s e w h o m w e r e c o g n i s e f o r t h e i r o u t s t a n d i n g a c h i e v e m e n t s a n d w h o s t a n d n e a r t h e e n d o f t h e c o n t i n u u m o f h u m a n a b i l i t i e s w h i c h r e a c h e s b a c k t h r o u g h t h e m u n d a n e a n d m e d i o c r e t o t h e i n c a p a b l e . T h e r e i s s t i l l m u c h t r u t h i n D r S a m u e l J o h n s o n s o b s e r v a t i o n , T h e t r u e g e n i u s i s a m i n d o f l a r g e g e n e r a l p o w e r s , a c c i d e n t a l l y d e t e r m i n e d t o s o m e p a r t i c u l a r d i r e c t i o n . W e m a y d i s a g r e e w i t h t h e g e n e r a l , f o r w e d o u b t i f a l l m u s i c i a n s o f g e n i u s c o u l d ha v eb ec o m e s c i e n t i s t s o f g e n i u s o r v i c e v e r s a ,b u t t h e r e i s n o d o u b t i n g t h e ac c ide n t a l d e t e r m i n a t i o n w h i c h n u r t u r e d o r t r i g g e r e d t h e i r g if t s i n t o t h o s e c h a n n e l s i n t o w h i c h t h e y h a v e p o u r e d t h e i r p o w e r s s o s u c c e s s f u l l y . A l o ng th e c o n ti n u u m o f a b i l i t i e s a r e h u n d r e d s o f t h o u s a n d s o f g i f t e d m e n a n d w o m e n , b o y s a n d g i r l s . / p > p b d s f i d = " 1 3 7 " > 0 0 W h a t w e a p p r e c i a t e , e nj o y o r m a r v e l a t i n t h e w o rk s o f g e n i u s o r t h e a c h i e v e m e n t s o f p r o d i g i e s a r e t h e m a n i f e s t a t i o n s o f s k il l s o r a b i l i t i e s w h i c h a r e s im i l a r t o , b u t s o m u c h s u p e r i o r t o , o u r o wn . B u t t h a t t h e i r m i n d s a r e no t d i f f e r e n t f r o m o u r o w n i s d e m o n s t r a t e d b y t h e f a c t t h a t t h e h a r d - w o n d i s c o v e r i e s o f s c i e n t i s t s l i k e K ep l e r o r E i n s t e i n b e c o m e t h e c o m m o n p l a c e k n o w l e d g e o f s c h o o l c h i l d r e n a n d t h e o n c e o u t r a g e o u s s h a p e s a n d c o l o u r s o f a n a r t i s t l i k e P a u l K l e e s o s o o n a p pe a r o n t h ef a b r i c s w e w e a r . T h i s d o e s n o t m i n i m i s e t h e s u p r e m a c y o f t h e i r a c h i e v e m e n t s , w h i c h o u t s t r i p o u r o w n a s t h e s u b - f o u r - m i n u t e m i l e r s o u t s t r i p o u r j og g i n g . / p > p b d s f i d = " 1 3 8 " > 0 0 T o thi n k o f g e n i u s e s a n d t h e g i f t e d a s h a v i n g u n i q u e l y d i f f e r e n t b r a i n s i s o n l y r e a s o n a b l e i f w e a c c e p t t h a t e a c h h u m a n b r a i n i s u n i q u e l y d i f f e r e n t . T h e p u r p o s e o f i n s t r u c t i o n i s t o m a k e u s e v e n m o r e d i f f e r e n t f r o m o n e a n o t h e r , a n d i n t h e p r o c e s s o f b e i n g e d u c a t e d w e c a n l e a r n f r o m t h e a c h i e v e m e n t s o f t h o s e m o r e g i f t e d t h a n o u r s e l v e s . B u t b e f o r e w e t r y t o e m u l a t e g e n i u s e s o r e n c o u r a g e o u r c h i l d r e n t o d o s o w e s h o u l d n o t e t h a t s o m e o f t h e t h i n g s w e l e a r n f r o m t h e m m a y p r o v e u n p a l a t a b l e . W e m a y e n v y t h e i r a c h i e v e m e n t s a n d f a m e , b u t w e s h o u l d a l s o r e c o g n i s e t h e p r i c e t h e y m a y h a v e p a i d i n t e r m s o f p e r s e v e r a n c e , s i n g l e - m i n d e d n e s s , d e d i c a t i o n , r e s t r i c t i o n s o n t h e i r p e r s o n a l l i v e s , t h e d e m a n d s u p o n t h e i r e n e r g i e s a n d t i m e , a n d h o w o f t e n t h e y h a d t o d i s p l a y g r e a t c o u r a g e t o p r e s e r v e t h e i r i n t e g r i t y o r t o m a k e t h e i r w a y t o t h e t o p . / p > p b d s f i d = " 1 3 9 " > 0 0 G e n i u s a n d g i f t e d n e s s a r e r e l a t i v e d e s c r i p t i v e t e r m s o f n o r e a l s u b s t a n c e . W e m a y , a t b e s t , g i v e t h e m s o m e p r e c i s i o n b y d e f i n i n g t h e m a n d p l a c i n g t h e m i n a c o n t e x t b u t , w h a t e v e r w e d o , w e s h o u l d n e v e r d e l u d e o u r s e l v e s i n t o b e l i e v i n g t h a t g i f t e d c h i l d r e n o r g e n i u s e s a r e d i f f e r e n t f r o m t h e r e s t o f h u m a n i t y , s a v e i n t h e d e g r ee t o w h i c h t h e y h a v e d e v e l o p e d t h e p e rf o r m a n c e o f t h e i r a b i l i t i e s . / p > p b d s f i d = " 1 4 0 " > 0 0 Q u e s t i o n s 1 4 - 1 8 / p > p b d s f i d = " 1 4 1 " > 0 0 C h o o s e F I V E l e t t e r s , A - K . / p > p b d s f i d = " 1 4 2 " > 0 0 W r i t e t h e c o r r e c t l e t t e r s i n b o x e s 1 4 - 1 8 o n y o u r a n s w e r s h e e t . / p > p b d s f i d = " 1 4 3 " > 0 0 N B Y o u r a n s w e r s m a y b eg i v e n i n a n y o r d e r . / p > p b d s f i d = " 1 4 4 " > 0 0 B e l o w a r e l i s t e d s o m e p o p u l a r be l i ef s a b o u tg e n i u s a n d g i f t e d n e s s . / p > p b d sf i d = " 1 4 5 " > 0 0 W h i c h F I V E o f t h e s e b e l i e f s a r e r e p o r t e d b y t h e w r i t e r o f t h e t e x t ? / p > p b d s f i d = " 1 4 6 " > 0 0 A T r u l yg i f t e d p e o p l e a r e t a l e n t ed i n a l l a re a s . / p > p b d sf i d = " 1 4 7 " > 0 0 B T h e t a le n t s ofg e n i u s e s a r e s o o n e xh a u s t e d . / p > p b d s fi d = " 1 4 8 " > 0 0 C G i f t e d p e o p l e s h o u l d u s e t h e i r g i f t s . / p > p b d s f i d = " 1 4 9 " > 0 0 D A g e n i u s a p p e a r s o n c e i n e v e r y g e n e r a t i o n . / p > p b d s f i d = " 1 5 0 " > 0 0 E G e n i u s c a n b e e a s i l y d e s t r o y e d b yd i s c o u r a ge m e n t . / p > p b d sf i d = " 1 5 1 " > 0 0 F Ge n i u s i s i n h e r i t e d . / p > p b d sf i d = " 1 5 2 " > 0 0 G G i f t e d p e o p l e a r e v e r y h a r d t o l i v e w i t h . / p > p b d s f i d = " 1 5 3 " > 0 0 H P e o p l e n e v e r a p p r e c i a t e t r u eg e n i u s . / p > p b d s f i d = " 1 5 4 " > 0 0 I G e n i u s e s a r e n a t u r a l l e a d e r s . / p > p b d s f i d = " 1 5 5 " > 0 0 J G i f t e d p e o p l e d e v e l o p th ei r g r e a t n e s s t h r o u g h d i f f i c u l t i e s . / p > p b d s f i d = " 1 5 6 " > 0 0 K G e n i u s w i l l a l w a y s r e v e a l i t s e l f . / p > p b d s f i d = " 1 5 7 " > 0 0 Q u e s t i o n s 1 9 - 2 6 / p > p b d s f i d = " 1 5 8 " >0 0 D o t h e f o l l o w i n g s t a t e m e n t s a g r e e w i t h t he i nf o r m a t i o ng i v e n i n R e a d i n g P a s s a g e 2 ? / p >p b d s f i d = " 1 5 9 " > 0 0 I n b o x e s 1 9 - 2 6 o n y o u r a n s w e r s h e e t , w r i t e / p > p b d s f i d = " 1 6 0 " > 0 0 T R U E i f t h e s t a t e m e n t a g r e e s w i t h t h e i n f o r m a t i o n / p > p b d s f i d = " 1 6 1 " > 0 0 F A L S E i f t h e s t a t e m e n t c o n t r a d i c t s t h e i n f o r m a t i o n / p > p b d s f i d = " 1 6 2 " > 0 0 N O T G I V E N i f t h e r e i s n o i n f o r m a t i o n o n t h i s / p > p b d s f i d = " 1 6 3 " > 0 0 1 9 N i n e t e e n t h - c e n t u r y s t u d i e s o f t h e n a t u r e o f g e n i u s f a i l e d t o t a k e i n t o a c c o u n t t h e u n i q u e n e s s o f t h e pe r s o n s u p b r i n g i n g . / p > p b d sf i d = " 1 6 4 " > 0 0 20 N i n e t e e n t h - c e n t u r y s t u d i e s o f g e n i u s l a c ke d b o t h o b j e c t i v i t y a n d a p r o p e r s c i e n t if i c a p p r o a c h . / p > p b d s f i d = " 1 6 5 " > 0 0 2 1 A t r u eg e n i u sh a s g e n e r a l p o w e r s c a p a b l e o f e x c e l l e n c ei n a n y a r e a . / p > p b d s f i d = " 1 6 6 " > 0 0 2 2 T h e s k i l l s o f o r d i n a r y i n d i v i d u a l s a r e i n e s s e n c e t h e sa m e a s t h e s k i l l s o f p r o d i g i e s . / p > pb d s f i d = "1 6 7 " > 0 023 T h e e a s e w i t h w h i c h t r u l y g r e a t i de a s a r e a c c e p t e d a n d t a k e nf o rg r a n t e d f a i l s t o l e s s e n th ei r s i g n i f i c a n c e . / p > p b d s f i d = " 1 68 " > 0 0 2 4 G i f t e d n e s s a n d g e n i u s d e s e r v e p r o pe r s c i e n t if i c r e s e a r c h i n t o t h e i r t r u e n a t u r e s o t h a t a l l t a l e n t m a y b e r e t a i n e d f o r t h e h u m a n r a c e . / p > p b d s f i d = " 1 6 9 " > 0 0 2 5 G e n i u s e s o f t e n p a y a h igh p ri c e t o a c h i e v e g r e a t n e s s . / p > p b d s f i d = " 1 7 0 " > 0 0 2 6 T o b e a g e n i u s i s w o r t h t h e h i g h p e r s o n a l c o s t . / p > p b d s f i d = " 1 7 1 " > 0 0 R E A D I N G P A S S A G E 3 / p > p b d s f i d = " 1 7 2 " > 0 0 Y o u s h o u l d s p e n d a b o u t 2 0 m i n u t e s o n Q u e s t i o n s 2 7 - 4 0 , w h i c h a r e b a s e d o n R e a d i n g P a s s a g e3 o n t h e f o l l o w i n g p a g e s . / p > p b d s f i d = " 1 7 3 " >0 0 Q u e s t i o n s 2 7 - 3 2 / p > p b d s f i d = " 1 7 4 " > 0 0 Re a d i n g P a s s a g e 3 h a s s e v e n p a r a g r a p h s , A - G . / p > p b d sf i d = " 1 7 5 " > 0 0 C h o o s e t h e c o r r e c t h e a d i ng f o r p a r a g r a ph s B - G f r o m t h e li s t o f h e a d i n g s b e l o w . / p > p b d s f i d = " 1 7 6 " > 0 0 W r i t e t h e c o r r e c t n u m b e r , i - x , i n b o x e s 2 7 - 3 2 o n y o u r a n s w e r s h e e t . / p > p b d s f i d = " 1 7 7 " > 0 0 L i s t o f H e a d i n g s / p > p b d s f i d = " 1 7 8 " > 0 0 i T h e b i o l o g i c a l c l o c k / p > p b d s f i d = " 1 7 9 " > 0 0 i i W h y d y i n g i s b e n e f i c i a l / p > p b d s f i d = " 1 8 0 " > 0 0 i i i T h e a g e i n g p r o c e s s o f m e n a n d w o m e n / p > p b d s f i d = " 1 8 1 " > 0 0 i v P r o l o n g i n g y o u r l i f e / p > p b d s f i d = " 1 8 2 " > 0 0 v L i m i t a t i o n s o f l i f e s p a n / p > p b d s f i d = " 1 8 3 " > 0 0 v i M o d e s o f d e v e l o p m e n t o f d i f f e r e n t s p e c i e s / p > p b d s f i d = " 1 8 4 " > 0 0 v i i A s t a b l e l i f e s p a n d e s p i t e i m p r o v e m e n t s / p > p b d s f i d = " 1 8 5 " > 0 0 v i i i E n e r g y c o n s u m p t i o n / p > p b d s f i d = " 1 8 6 " > 0 0 i x F u n d a m e n t a l d i f f e r e nc e s i n a g e i n g o f o b j e c t s a nd o r g a n i s m s / p > p bd s f i d = " 1 8 7 " > 0 0 x Re p a i r ofg e n e t i c m a t e r i a l / p > p b d s f i d = " 1 8 8 " > 0 0 E x a m p l e A n s w e r / p > p b d s f i d = " 1 8 9 " > 0 0 P a r a g r a ph A v / p > p b d s fi d = " 1 9 0 " > 0 0 2 7 P a r a g r a p h B / p > p b d s f i d = " 1 9 1 " > 0 0 2 8 P a r a g r a p h C / p > p b d s f i d = " 1 9 2 " > 0 0 29 P a r a g r a p h D / p > p b d s f i d = " 1 9 3 " > 0 0 3 0 P a r ag r a p h E / p > p b d s f i d = " 1 9 4 " > 0 0 3 1 P a r a g r a p h F / p > p b d s f i d = " 1 9 5 " > 0 0 3 2 P a r a g r a p h G / p > p b d s f i d = " 1 9 6 " > 0 0 H O W D O E S T H E B I O L O G I C A L C L O C K T I C K ? / p > p b d s f i d = " 1 9 7 " > 0 0 A O u r l if e s p a n i s r e s t r i c t e d . E v e r y o n e a c c e p t s t h i s a s b i o l og i c a l l y o b v i o u s . N o thi n g l i v e s f o r e v e r !H o w e v e r , i n t h i s s t a t e m e n t w e t h i n k o f a r t i f i c i a l l y p r o d u c e d , t e c h n i c a l o b j e c t s , p r o d u c t s w h i c h a r e s u b j e c t e d t o n a t u r a l w e a r a n d t e a r d u r i n g u s e . T h i s l e a d s t o t h e r e s u l t t h a t a t s o m e t i m e o r o t h e r t h e o b j e c t s t o p s w o r k i n g a n d i s u n u s a b l e ( d e a t h i n t h e b i o l o g i c a l s e n s e ) . B u t a r e t h e w e a r a n d t e a r a n d l o s s o f f u n c t i o n o f t e c h n i c a l o b j e c t s a n d t h e d e a t h o f l i v i n g o r g a n i s m s r e a l l y s i m i l a r o r c o m p a r a b l e ? / p > p b d s f i d = " 1 9 8 " > 0 0 B O u r d e a d p r o d u c t s a r e s t a t i c , c l o s e d s y s t e m s . I t i s a l w a y s t h e b a s i c m a t e r i a l w h i c h c o n s t i t u t e s t h e o b j e c t a n d w h i c h , i n t h e n a t u r a l c o u r s e o f t h i n g s , i s w o r n d o w n a n d b e c o m e s o l d e r . A g e i n g i n t h i s c a s e m u s t o c c u r a c c o r d i n g t o t h e l a w s o f p h y s i c a l c h e m i s t r y a n d o f t h e r m o d y n a m i c s . A l t h o u g h t h e s a m e l a w h o l d s f o r a l i v i n g o r g a n i s m , t h e r e s u l t o f t h i s l a w i s n o t i n e x o r a b l e i n t h e s a m e w a y . A t l e a s t a s l o n g a s a b i o l o g i c a l s y s t e m h a s t h e a b i l i t y t o r e n e w i t s e l f i t c o u l d a c t u a l l y b e c o m e o l d e r w i t h o u t a g e i n g ; a n o r g a n i s m i s a n o p e n , d y n a m i c s y s t e m t h r o u g h w h i c h n e w m a t e r i a l c o n t i n u o u s l y f l o w s . D e s t r u c t i o n o f o l d m a t e r i a l a n d f o r m a t i o n o f n e w m a t e r i a l a r e t h u s i n p e r m a n e n t d y n a m i c e q u i l i b r i u m . T h e m a t e r i a l o f w h i c h t h e o r g a n i s m i s f o r m e d c h a n g e s c o n t i n u o u s l y . T h u s o u r b o d i e s c o n t i n u o u s l y e x c h a n g e o l d s u b s t a n c e f o r n e w , j u s t l i k e a s p r i n g w h i c h m o r。

第39卷第5期2015年9月大气科学Chinese Journal of Atmospheric SciencesV ol. 39 No. 5Sep. 2015董丹宏, 黄刚. 2015. 中国最高、最低温度及日较差在海拔高度上变化的初步分析 [J]. 大气科学, 39 (5): 000–000. Dong Danhong, Huang Gang. 2015. Relationship between altitude and variation characteristics of the maximum temperature, minimum temperature, and diurnal temperature range in China [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 39 (5): 000–000, doi:10.3878/j.issn.1006-9895. 1501.14291.中国最高、最低温度及日较差在海拔高度上变化的初步分析董丹宏1, 4黄刚2, 31成都信息工程大学大气科学学院，成都6102252中国科学院大气物理研究大气科学和地球流体力学数值模拟国家重点实验室，北京1000293全球变化研究协同创新中心，北京1008754中国科学院大气物理研究所季风系统研究中心，北京100029摘要本文利用中国740个气象台站1963～2012年均一化逐日最高温度和最低温度资料，分析了中国地区最高、最低气温和日较差变化趋势的区域特征及其与海拔高度的关系。

结果表明：近50年气温的变化趋势无论是年或季节变化，最低温度的增温幅度都高于最高温度，且其增温显著区域都对应我国高海拔地区。

除了春季，其他季节最高、最低温度及日较差的升温幅度随着海拔高度的升高而增大，其中最高温度的变化趋势与海拔高度的相关性最好。

同一海拔高度上，最高、最低温度在不同年代的增幅具有不一致性：20世纪80年代，二者变化幅度最小；20世纪90年代，二者增幅最大，尤以低海拔地区最为明显。

不同陆面模式对我国地表温度模拟的适用性评估孙帅;师春香;梁晓;韩帅;姜志伟;张涛【摘要】基于CLDAS大气驱动数据驱动CLM3.5陆面模式和3种不同参数化方案下的Noah-MP陆面模式模拟得到的地表温度,利用中国气象局2009 2013年2000多个国家级地面观测站地表温度进行质量评估.结果表明:从时间分布看,模拟地表温度与观测的偏差及均方根误差均呈季节性波动;从空间分布看,模拟地表温度与观测的偏差及均方根误差在中国东部地区相对于中国西部地区更小.选择Noah-MP陆面模式3种不同参数化方案模拟结果进行对比,结果表明:Noah-MP模式的非动态植被方案不变时,考虑植被覆盖度的二流近似辐射传输方案的Noah-MP陆面模式模拟的地表温度优于考虑太阳高度角和植被三维结构的二流近似辐射传输方案Noah-MP陆面模式模拟的地表温度;选择动态植被方案的Noah-MP陆面模式模拟的地表温度优于选择非动态植被方案的Noah-MP陆面模式;总体而言,考虑动态植被方案的Noah-MP陆面模式模拟的地表温度优于其他两种参数化方案的Noah-MP陆面模式以及CLM3.5陆面模式模拟的地表温度.%As an important physical quantity in the land surface process,the ground temperature plays an important role in climate change research,agricultural production and ecological environment.A set of simulation experiments are carried out,in which ground temperature are simulated by Community Land Model 3.5 (CLM3.5) land surface model and Noah-Multi Parameterization Land Surface Model (Noah-MP) of three different parameterization schemes,forced by China Meteorological Administration Land Data Assimilation System (CLDAS) atmosphere forcing data containing high-quality temperature,pressure,humidity,wind speed,precipitation and solarshortwave radiation.The different model-simulated ground temperature is verified by 2000 national ground temperature observation stations of China Meteorological Administration from 2009 to 2013.Results show that errors of different model-simulated ground temperature compared with observations behave seasonal fluctuations from the error analysis of time series.And the ground temperature simulated by CLM3.5 land surface model and Noah-MP land surface model can better represent the spatial distribution of ground temperature of China in seasonal climate state.The ground temperature is underestimated in general,and the underestimation in spring and autumn is smaller than that in summer and winter.On the spatial distribution,the error of the model-simulated ground temperature in the eastern China is smaller than that in the western China,and in the northeastern China and northern Xinjiang the error is even greater.Three different parameterization schemes of Noah-MP land surface model are selected to compare the simulation result.Results show that when the non-dynamic vegetation scheme remain unchanged,considering different radiation transferring schemes,the two-stream approximation radiative transferring scheme considering vegetation coverage of Noah-MP land surface model performs better than the radiative transferring scheme considering the solar altitude angle and vegetation 3D structures of Noah-MP surface land model.When the default two-stream approximation radiative transferring scheme in Noah-MP land model doesn't change,considering the dynamic vegetation scheme of Noah-MP land surface model,the result shows that the ground temperature choosing thedynamic vegetation scheme of Noah-MP land surface model is better than the non-dynamic vegetation scheme named of NoahMP land model.Above all,the ground temperature simulated by the dynamic vegetation scheme of NoahMP land surface model is better than the other two parameterization schemes of Noah-MP land model and the CLM3.5 land surface model.【期刊名称】《应用气象学报》【年(卷),期】2017(028)006【总页数】13页(P737-749)【关键词】CLDAS;CLM3.5;Noah-MP;地表温度;站点观测【作者】孙帅;师春香;梁晓;韩帅;姜志伟;张涛【作者单位】南京信息工程大学地理与遥感学院,南京210044;国家气象信息中心,北京100081;国家气象信息中心,北京100081;国家气象信息中心,北京100081;国家气象信息中心,北京100081;国家气象信息中心,北京100081【正文语种】中文基于CLDAS大气驱动数据驱动CLM3.5陆面模式和3种不同参数化方案下的Noah-MP陆面模式模拟得到的地表温度，利用中国气象局2009—2013年2000多个国家级地面观测站地表温度进行质量评估。

第22卷　第2期2024年4月中国水土保持科学Science of Soil and Water ConservationVol.22　No.2Apr.2024　D OI :10.16843/j.sswc.2023084基于SWAT 模型的渠江流域径流侵蚀功率时空规律分析黄　幸1,2,莫淑红2†,李平治2,乔殿新3,李斌斌3(1.河海大学水文水资源学院,210098,南京;2.西安理工大学省部共建西北旱区生态水利国家重点实验室,710048,西安;3.水利部水土保持监测中心,100053,北京)摘要:探明流域径流侵蚀功率的演变规律对重点侵蚀区识别和土壤侵蚀防治至关重要㊂以嘉陵江右岸支流渠江流域为研究区,基于SWAT 模型模拟计算流域径流侵蚀功率,分析其时空分布特征与空间尺度效应,并通过聚类分析㊁相关性分析揭示其对流域气象㊁地形㊁土壤等因素的关系㊂结果表明:渠江流域年尺度的径流侵蚀功率大于季尺度,其中第3季度为土壤侵蚀重点防治时段;全年和第3季度的多年平均径流侵蚀功率均呈现出北部大南部小㊁西部大东部小㊁上游大下游小的空间分布特征;渠江干流与其支流大通江的多年平均径流侵蚀功率和流域控制面积之间均呈幂指数关系,且其变化规律存在空间阈值,在年尺度干流和大通江的阈值面积分别为8549.4和8504.4km 2,在第3季度干流和大通江的阈值面积分别为4834.9和6223.5km 2;气象因子㊁地形因子和流域形态因子为渠江流域径流侵蚀功率的主要影响因素㊂研究结果可为制订渠江流域土壤侵蚀治理方案提供决策依据㊂关键词:SWAT 模型;径流侵蚀功率;时空分布;空间尺度效应;渠江流域中图分类号:S157.1;P333文献标志码:A文章编号:2096⁃2673(2024)02⁃0025⁃09引用格式:黄幸,莫淑红,李平治,等.基于SWAT 模型的渠江流域径流侵蚀功率时空规律分析[J].中国水土保持科学,2024,22(2):25-33.HUANG Xing,MO Shuhong,LI Pingzhi,et al.Spatial and temporal analysis of runoff ero⁃sion power in Qujiang River Basin based on SWAT model[J].Science of Soil and Water Conservation,2024,22(2):25-33.收稿日期:20230508　修回日期:20240110项目名称:国家自然科学基金 基于侵蚀能量过程的集合式流域水土流失预报模型”(U2040208), 陕北黄土高原区人 水耦合系统互馈及协同进化机理研究”(52179024)第一作者简介:黄幸(2000 ),女,硕士研究生㊂主要研究方向:水文水资源㊂E⁃mail:809319042@†通信作者简介:莫淑红(1972 ),女,博士,教授㊂主要研究方向:旱区水文及水资源㊂E⁃mail:moshuhong@Spatial and temporal analysis of runoff erosion powerin Qujiang River Basin based on SWAT modelHUANG Xing 1,2,MO Shuhong 2,LI Pingzhi 2,QIAO Dianxin 3,LI Binbin 3(1.College of Hydrology and Water Resources,Hohai University,210098,Nanjing,China;2.State Key Laboratory of Eco⁃hydraulics in Northwest Arid Region,Xi′an University of Technology,710048,Xi′an,China;3.The Center of Soil and Water Conservation Monitoring,Ministry of Water Resources,100053,Beijing,China)Abstract :[Background ]Soil erosion destroys soil and water resources,exacerbates natural disasters such as droughts and floods,and threatens human survival and development.Qujiang River Basin is severely affected by soil erosion,and runoff erosion power can reflect dynamic characteristics of water erosion better than rainfall erosion force.Therefore,it’s important to use runoff erosion power theory to study erosion in Qujiang River Basin to reveal mechanism of water⁃sand response.[Methods ]This paper中国水土保持科学2024年took Qujiang River Basin as a research object,calculating seasonal as well as annual runoff erosion power based on runoff,which was simulated by SWAT model in terms of utilizing meteorological forcing data such as precipitation,temperature,wind etc.In addition,features of spatial⁃temporal pattern and effects of spatial scale were analyzed.Cluster and correlation method were adopted for investigation into relationships between runoff erosion power with meteorological,topographic and soil conditions. [Results]1)The constructed SWAT model had high accuracy in runoff simulation and good applicability in Qujiang River Basin.R2and NS coefficients were0.69and above,while PBIAS coefficient was below 16.72%in both parameter rate setting period and validation period.2)In aspect of time,annual runoff erosion power outweighed that in season.However,erosion during seasonⅢwas more serious than that in other season,which requires more attention on soil erosion prevention and control.Besides,runoff erosion power for whole year and for season III demonstrated a decreasing trend from north to south,west to east and up to down inspace.3)The thresholds of drainage control area for whole year in Qujiang river and Datong river were8549.4and8504.4km2respectively,while those for season III were4834.9and 6223.5km2respectively,indicating runoff erosion power decreased smoothly with increasing area when the area was larger than spatial thresholds,then gradually tended to a stable value.4)Meteorological, topographic and watershed morphological characters were main factors influenced runoff erosion power in Qujiang River Basin.Erosion in upstream area of basin presented greater performance than downstream, due to the steep topography,uneven precipitation distribution and morphological ease of runoff generation and flow concentration processes in upper reaches.[Conclusions]This paper illustrated feasibility of SWAT model and its simulated outcome in Qujiang River Basin.The spatial⁃temporal runoff erosion power characteristics together with impacts are closely related to meteorological constituents,terrain and basin shape.Therefore,the results contribute to effective identification of key sand producing areas in watershed,and also provide supports for soil erosion prevention,ecology restoration and environmental carrying capacity enhancement.Keywords:SWAT model;runoff erosion power;temporal and spatial distribution;spatial scale effect; Qujiang River Basin 土壤侵蚀是全球公认的最严重的环境问题之一,其破坏水土资源,加剧旱涝等自然灾害,威胁人类的生存和发展㊂嘉陵江流域地质地貌条件复杂㊁降水丰富㊁土壤可蚀性强,且不合理生产活动多,使得流域内水土流失面积和土壤侵蚀总量曾常居长江流域前列,水土流失治理刻不容缓[1]㊂当前有许多量化土壤侵蚀对流域影响的模型,如通用土壤流失方程㊁修正土壤流失方程和中国坡面土壤流失方程等[2]㊂降雨侵蚀力反映降雨及其产生的径流剥离和携带土壤颗粒的能力,其作为水蚀动力指标被广泛应用于土壤侵蚀模型及土壤侵蚀分析中[34]㊂但是降雨侵蚀力的计算通常需要高精度且不易获取的长序列场次降雨数据,资料处理较繁琐,且其仅通过雨滴的击溅效应表征土壤侵蚀作用,并未体现水蚀动力过程中的径流侵蚀和径流输沙作用[56],具有一定的局限性㊂与降雨侵蚀力相比,径流侵蚀功率能更好的反映水蚀动力特性,对于侵蚀动力机制的反应更敏感,数据要求更低[7]㊂目前已有众多学者基于径流侵蚀功率理论研究黄土高原地区的土壤侵蚀空间分布特征及尺度效应,取得较好成果[89]㊂长江流域的降水特征及下垫面条件与黄河流域不同,其侵蚀产沙和主要驱动机制也会有所差异[10],而此类研究在长江流域相对较少㊂因此,利用径流侵蚀功率理论研究长江流域地区的侵蚀情况对揭示其水沙响应机理具有重要意义㊂笔者以嘉陵江子流域渠江流域为例,基于SWAT(Soil and Water Assessment Tool)模型模拟流域径流,计算并分析径流侵蚀功率的时空分布特征㊁空间尺度效应及主要影响因子,以期为流域有效识别重点产沙区㊁合理开发利用水土资源等做出贡献㊂1　研究区概况渠江流域面积为3.92万km2(E106°28′~ 109°00′,N30°00′~32°48′),属亚热带湿润季风气62　第2期黄幸等:基于SWAT模型的渠江流域径流侵蚀功率时空规律分析候㊂其雨季集中于7 9月,多年平均降雨量为1078mm,输沙量主要来自汛期,多年平均输沙模数为347t/km2㊂流域地势东北高西南低,源头地势坡度较大,至下游浅丘区比降逐渐减小,土壤类型以棕壤㊁黄棕壤和紫色土为主㊂渠江流域水系及水文站点分布见图1㊂图1　渠江流域水系及水文站点分布图Fig.1　Distribution of river system and hydrological stations in Qujiang River Basin2　数据与方法2.1　数据来源笔者采用的数据主要包括摘自‘长江流域水文年鉴“的2008 2018年渠江流域上㊁中㊁下游巴中㊁七里沱和罗渡溪水文站实测日流量资料,由中国大气同化驱动数据集整理的逐日降水㊁风速㊁温度等气象数据,以及中科院地理空间数据云㊁中科院数据中心遥感影像㊁世界土壤数据库提供的30m分辨率地形数据㊁1∶10万土地覆盖数据和1∶100万土壤类型等空间数据㊂2.2　径流侵蚀功率计算2.2.1　SWAT模型构建与应用　笔者使用SWAT 模型[11]模拟渠江流域2009 2018年月径流,进而分析计算流域径流侵蚀功率㊂先依据集水面积㊁坡度等级㊁土地利用与土壤类型将渠江流域划分为77个子流域和1377个水文响应单元㊂然后,选取2008年作为预热期进行参数预调,2009 2013年作为率定期,2014 2018年作为验证期㊂在SWAT-CUP中选择径流相关参数进行参数敏感性分析和参数率定,并运用决定系数R2(R-Square)㊁纳什效率系数NS(Nash-Sutcliffe efficiency coefficient)㊁偏差比例PBIAS(percent bias)作为评价指标检验径流模拟精度㊂一般认为R2>0.6㊁NS>0.5㊁PBIAS≤±25%时,结果较好㊂满足精度要求时,输出渠江各子流域出口的模拟月径流过程㊂2.2.2　径流侵蚀功率计算　径流侵蚀功率属于经验模型指标中的侵蚀动力因子,其计算原理及应用详见文献[7]㊂依此推出的季径流侵蚀功率和年径流侵蚀功率计算公式分别见式(1)和式(2)㊂E s=Q s H s㊂(1)式中:E s为季径流侵蚀功率,m4/(km2㊃s);Q s为季最大月流量模数,m3/(km2㊃s);H s为季径流深,m㊂E y=Q y H y㊂(2)式中:E y为年径流侵蚀功率,m4/(km2㊃s);Q y为年最大月流量模数,m3/(km2㊃s);H y为年径流深,m㊂2.3　径流侵蚀功率影响因素分析选用系统聚类方法[12]和斯皮尔曼相关系数[13]对径流侵蚀功率影响因子进行合并归类与相关性分析㊂斯皮尔曼相关系数的绝对值越大,指标之间相关性越高,其表达式见式(3)㊂ρ=1-6∑n i=1d2in(n2-1)㊂(3)式中:ρ为斯皮尔曼相关系数;n为系列长度;d为72中国水土保持科学2024年按升序或降序排列后,同次序指标间的差值,量纲均为1㊂3　结果与分析3.1　模型率定与验证结果率定期㊁验证期巴中㊁七里沱和罗渡溪水文站模拟与实测月径流量值对比见图2~4㊂分析可知,巴中水文站率定期R 2=0.96㊁NS =0.92㊁PBIAS =16.2%,验证期R 2=0.70㊁NS =0.69㊁PBIAS =16.43%,率定结果较好,验证结果评级为可信;七里沱水文站率定期R 2=0.95㊁NS =0.93㊁PBIAS =-0.3%,验证期R 2=0.82㊁NS =0.80㊁PBIAS =-14.81%,率定结果极好,验证结果评级为较好;罗渡溪水文站率定期R 2=0.93㊁NS =0.92㊁PBIAS =-3.5%,验证期R 2=0.87㊁NS =0.84㊁PBIAS =-16.72%,率定结果极好,验证结果评级为较好㊂综上所述,在率定期和验证期内,3个水文站模拟所得月径流过程与实测月径流过程吻合度较高,表明所构建的SWAT 模型能较真实地反映渠江流域水文情势㊂图2　巴中水文站月径流量模拟值与实测值对比图Fig.2　Comparison of simulated and measured monthly runoff at Bazhong station图3　七里沱水文站月径流量模拟值与实测值对比图Fig.3　Comparison of simulated and measured monthly runoff at Qilituo station3.2　径流侵蚀功率的时空分布基于SWAT 模型得到2009 2018年渠江77个子流域季尺度和年尺度的多年平均径流侵蚀功率,其空间分布见图5和6㊂从时间角度看,全流域多年平均年径流侵蚀功率为0.049m 4/(km 2㊃s),总体上大于各季度多年平均径流侵蚀功率;在季尺度,第3季度多年平均径流侵蚀功率最大(0.028m 4/(km 2㊃s)),其次为第2季度㊁第4季度和第1季度,其变化周期与水文周期一致㊂显然,第3季度的径流侵蚀功率对全年贡献最大,应当重视该季度的水土流失治理㊂在空间尺度上,流域全年和第3季度的多年平均径流侵蚀功率均呈现出 北部大㊁南部小;西部大㊁东部小;上游大㊁下游小”的空间分布特征㊂第4季度的空间分布虽在南北和上下游部分与全年相似,但在东西部表现出 东部大㊁西部小”的特征㊂第1季度和第2季度的空间分布82　第2期黄幸等:基于SWAT模型的渠江流域径流侵蚀功率时空规律分析图4　罗渡溪水文站月径流量模拟值与实测值对比图Fig.4　Comparison of simulated and measured monthly runoff at Luoduxi station与全年相反,表现为 南部大㊁北部小;东部大㊁西部小;下游大㊁上游小”,但因其径流侵蚀功率较小,对年尺度空间分布影响也较小㊂3.3　径流侵蚀功率的空间尺度效应渠江流域的径流侵蚀功率空间差异明显,可能存在空间尺度效应,即径流侵蚀功率随流域控制面积变化可能呈现出一定规律㊂首先利用SWAT 模型计算2009 2018年渠江77个子流域出口断面及以上控制面积的全年和第3季度的多年平均径流侵蚀功率,结果见图7㊂由图7可知,对全年和第3季度,渠江各子流域出口断面及以上控制区域的多年平均径流侵蚀功率总体呈现出 上游大,下游小;干流大,支流小”的分布特点㊂为量化径流侵蚀功率与流域控制面积的相关关系,选取侵蚀较严重的渠江干流和支流大通江为研究对象,将流域控制面积分别与全年和第3季度的多年平均径流侵蚀功率进行拟合分析㊂干流的拟合结果分别见式(4)㊁式(5)和图8,大通江的拟合结果分别见式(6)㊁式(7)和图9㊂E my =0.0761A -0.158,R 2=0.67;(4)E ms =0.0438A-0.137,R 2=0.52;(5)E my 1=0.0771A -0.153,R 2=0.46;(6)E ms 1=0.0510A-0.165,R 2=0.49㊂(7)式中:E my ㊁E my 1分别为干流和大通江多年平均年径流侵蚀功率,m 4/(km 2㊃s);E ms ㊁E ms 1分别为干流和大通江多年平均季径流侵蚀功率,m 4/(km 2㊃s);A 为流域控制面积,103km 2㊂由图8和9可知,渠江干流和支流大通江存在较显著的空间尺度效应,其多年平均径流侵蚀功率与流域控制面积之间均呈幂指数关系,且干流相关关系优于支流大通江,全年相关系数与第3季度接近㊂随着流域控制面积的增大,多年平均径流侵蚀功率逐渐减小并趋于平缓,减小速率由大变小,说明存在空间阈值㊂为确定阈值,对式(4)~(7)求导,导数方程见式(8)~(11)㊂E′my =-0.0120A -1.158;(8)E′ms =-0.0060A-1.137;(9)E′my 1=-0.0118A -1.153;(10)E′ms 1=-0.0084A-1.165㊂(11)经计算可知,渠江干流在全年和第3季度的空间阈值分别为8549.4和4834.9km 2,大通江在全年和第3季度的空间阈值分别为8504.4和6223.5km 2㊂即当流域控制面积小于空间阈值时,|E |≥0.001,多年平均径流侵蚀功率随流域控制面积增加而迅速减小,反之则变化平缓,并逐渐减小至某一稳定值㊂其中干流的全年和第3季度径流侵蚀功率稳定值分别为0.04和0.02m 4/(km 2㊃s),大通江的全年和第3季度径流侵蚀功率稳定值分别为0.056和0.035m 4/(km 2㊃s)㊂3.4　径流侵蚀功率影响因素为探究渠江流域径流侵蚀功率时空分布及空间尺度效应的主要驱动因素,选用SPSS (statistical product and service solutions)软件对多年平均的年径流侵蚀功率进行聚类分析㊂本文共选取地形(75°以上坡度面积比例)㊁流域形态(圆度)㊁气象(各子流域上的多年平均降水量)㊁侵蚀动力(多年平均径流侵蚀功率)㊁土地利用(林草地面积比例)和土壤(易受侵蚀土壤面积比例)6类聚类因子,经分析将渠江77个子流域划分为3种聚类类型,其空间分布见图10㊂可知,第1聚类与第2聚类主要分布于上游地区,其面积所例分别为26.0%和19.3%,多年平均径流侵蚀功率分别为0.05892中国水土保持科学2024年　4个季度的季径流侵蚀功率量级不同,值越大侵蚀越严重,其总体范围为0.00001≤E s≤0.08000㊂下同㊂Magnitude of seasonal runoff erosion power varies among four periods,with larger values indicating more severe erosion.The over⁃all range is0.00001≤E s≤0.08000.The same below.图5　渠江流域多年平均季径流侵蚀功率空间分布图Fig.5　Spatial distribution of multi⁃year average runoff erosion power at season scale in Qujiang River Basin 和0.052m4/(km2㊃s),均大于多年平均径流侵蚀功率中值0.049m4/(km2㊃s),该地区径流侵蚀能量较大,受侵蚀程度较剧烈㊂第3聚类是渠江流域的主导聚类,主要分布在中下游地区,其面积比例为54.7%,流域内多年平均径流侵蚀功率为0.046m4/(km2㊃s),小于多年平均径流侵蚀功率中值,该地区植被类型多样,下垫面条件较好,受侵蚀程度较小㊂03　第2期黄幸等:基于SWAT模型的渠江流域径流侵蚀功率时空规律分析图6　渠江流域多年平均年径流侵蚀功率空间分布图Fig.6　Spatial distribution of multi⁃year average runoff erosionpower at year scale in Qujiang River Basin 统计侵蚀动力因子与其余聚类因子的斯皮尔曼相关系数见表1,结果表明气象因子㊁地形因子和流域形态因子为渠江流域径流侵蚀功率排名前3的主图7　渠江各子流域出口断面及以上控制面积的多年平均径流侵蚀功率空间分布图Fig.7　Spatial distribution of multi⁃year average runoff erosion power for control area at and abovethe outlet cross⁃section of each Qujiang River subbasin要影响因子㊂且在显著水平为0.05时,第1聚类中的气象因子与径流侵蚀功率呈显著相关,说明降雨的空间分布不均性对该地区径流侵蚀能量影响较大;第2聚类中气象因子㊁地形因子和流域形态因子与径流侵蚀功率呈显著相关,说明该地区75°以上坡度面积比例很大,流域内地势陡峭,降雨空间分布差异较显著,流域形态偏圆形,产汇流过程较快,能显著影响径流侵蚀功率的空间分布和规律;第3聚类中流域形态因子与气象因子与径流侵蚀功率呈一般相关,其对该聚类地区径流侵蚀功率的影响远不如第2聚类地区,说明该地区整体地势较平缓,各子流域多年平均降雨量差距不大,流域形态较狭长,产汇流过程相对较慢㊂4　结论1)所构建的SWAT 模型径流模拟精度较高,在渠江流域适用性好㊂巴中㊁七里沱㊁罗渡溪水文站率定期R 2㊁NS 系数均在0.92及以上,PBIAS 系数均<16.20%;验证期R 2㊁NS 系数均在0.69及以上,PBIAS 系数均在16.72%以下㊂这说明该模型能较真实地反映渠江流域水文情势㊂2)渠江流域多年平均年径流侵蚀功率总体上大于季径流侵蚀功率,其中第3季度>第2季度>第4季度>第1季度㊂流域全年和第3季度的多年平均径流侵蚀功率空间分布均呈现出 北部大㊁南部小;西部大㊁东部小;上游大㊁下游小”的特征;第4季度的空间分布在东西部呈现出 东部大㊁西部小”的特征,其余部分与全年相似;第1㊁第2季度的空间分布与全年分布规律相反㊂ 3)渠江干流及其支流大通江的径流侵蚀功率具有较为显著的空间尺度效应,全年和第3季度的多年平均径流侵蚀功率与流域控制面积之间呈13中国水土保持科学2024年图8　渠江干流多年平均年和第3季度径流侵蚀功率与流域控制面积关系图Fig.8　Fitting results of multi⁃year average runoff erosionpower and watershed control area at year and seasonscale(season Ⅲ)in the main stream of Qujiang River图9　渠江支流大通江多年平均年和第3季度径流侵蚀功率与流域控制面积关系图Fig.9　Fitting results of multi⁃year average runoff erosionpower and watershed control area at year and seasonscale(season Ⅲ)in Datong River图10　渠江流域聚类空间分布图Fig.10　Clustering spatial distribution of Qujiang River Basin幂指数均关系㊂当流域控制面积分别>8549.4和4834.9km 2时,干流全年和第3季度的多年平均径流侵蚀功率随着面积增加变化幅度极小,并逐渐稳定于0.04和0.02m 4/(km 2㊃s);当流域控制面积分别大于8504.4和6223.5km 2时,大通江全年和第3季度的多年平均径流侵蚀功率随着面积增加变化幅度极小,并逐渐稳定于0.056和0.035m 4/(km 2㊃s)㊂表1　斯皮尔曼相关系数计算结果表Tab.1　Spearman correlation coefficient calculated results table聚类名Cluster name地形因子Topographical factor流域形态因子Watershed morphology气象因子Meteorology 土地利用因子Land use 土壤因子Soil第1聚类Cluster Ⅰ-0.451-0.4840.621*-0.280-0.022第2聚类Cluster Ⅱ0.636*0.622*0.678*0.441-0.336第3聚类Cluster Ⅲ0.0680.349*0.336*-0.2610.072　注:*表示在0.05水平上相关性显著㊂Notes:*indicates significant correlation at the 0.05level. 4)气象因子㊁地形因子和流域形态因子对渠江流域径流侵蚀功率的分布有主要影响作用㊂流域上游区因地势陡峭㊁降水分布不均㊁形态易于产汇流而表现出较大的径流侵蚀功率;下游地区因地势平坦,产汇流过程缓慢受侵蚀情况较轻㊂5　参考文献[1]　万彩兵,程冬兵,李昊.水土保持法修订实施十年来长江流域水土流失治理成效[J].中国水土保持,2021(6):1.WAN Caibing,CHENG Dongbing,LI Hao.Effect of soil and water loss control in the Yangtze River Basin since the revision and implementation of soil and water conser⁃vation law for ten years [J].Soil and Water Conservation in China,2021(6):1.[2]　史志华,刘前进,张含玉,等.近十年土壤侵蚀与水土保持研究进展与展望[J ].土壤学报,2020,57(5):1117.SHI Zhihua,LIU Qianjin,ZHANG Hanyu,et al.Study on soil erosion and conservation in the past 10years:Pro⁃23　第2期黄幸等:基于SWAT模型的渠江流域径流侵蚀功率时空规律分析gress and prospects[J].Acta Pedologica Sinica,2020,57(5):1117.[3]　许功伟,徐立荣,张云苹,等.1970 2019年大汶河流域降雨侵蚀力时空变化分析[J].水土保持研究,2022,29(6):45.XU Gongwei,XU Lirong,ZHANG Yunping,et al.Spati⁃otemporal variation of rainfall erosivity in the Dawen RiverBasin during1970-2019[J].Research of Soil and Wa⁃ter Conservation,2022,29(6):45.[4]　WISCHMEIER W H,SMITH D D.Predicting rainfallerosion losses:A guide to conservation planning[M].Washington:Department of Agriculture,1978:26.[5]　徐晶,时延锋,徐征和,等.1961 2020年沂蒙山区降雨侵蚀力时空变化特征[J].水土保持研究,2022,29(6):36.XU Jing,SHI Yanfeng,XU Zhenghe,et al.Spatiotem⁃poral variation characteristics of rainfall erosivity in theYimeng Mountain area from1961to2020[J].Researchof Soil and Water Conservation,2022,29(6):36. [6]　TU Anguo,ZENG Jianling,LIU Zhao,et al.Effect ofminimum inter⁃event time for rainfall event separationon rainfall properties and rainfall erosivity in a humidarea of southern China[J].Geoderma,2023,431:116332.[7]　鲁克新.黄土高原流域生态环境修复中的水沙响应模拟研究[D].西安:西安理工大学,2006:18.LU Kexin.Study on runoff⁃sediment response simulationin watershed eco⁃environmental rehabilitation on loessplateau[D].Xi′an:Xi′an University of Technology,2006:18.[8]　张译心,徐国策,李占斌,等.不同时间尺度下流域径流侵蚀功率输沙模型模拟精度[J].水土保持研究,2020,27(3):1.ZHANG Yixin,XU Guoce,LI Zhanbin,et al.Simula⁃tion accuracy of runoff erosion power⁃sediment transfermodel at different time scales[J].Research of Soil andWater Conservation,2020,27(3):1.[9]　杨光,鲁克新,李鹏,等.基于SWAT模型的清水河流域年径流侵蚀功率空间分布[J].水土保持研究,2022,29(6):90.YANG Guang,LU Kexin,LI Peng,et al.Spatial distri⁃bution of annual runoff erosion power in Qingshui RiverBasin based on SWAT model[J].Research of Soil andWater Conservation,2022,29(6):90. [10]CHANG Enhao,LI Peng,LI Zhanbin,et al.The impactof vegetation successional status on slope runoff erosion inthe Loess Plateau of China[J].Water,2019,11(12):2614.[11]HUSSAINZADA W,LEE H S.Effect of an improved ag⁃ricultural irrigation scheme with a hydraulic structure forcrop cultivation in arid northern Afghanistan using the Soiland Water Assessment Tool(SWAT)[J].Scientific Re⁃ports,2022,12(1):5186.[12]倪鹏,包为民,张乾,等.基于主成分分析的系统聚类分析方法在洪水预报中的应用[J].三峡大学学报(自然科学版),2018,40(1):1.NI Peng,BAO Weimin,ZHANG Qian,et al.Applica⁃tion of hierarchical cluster analysis to flood forecastingbased on principal component analysis[J].Journal ofChina Three Gorges University(Natural Sciences),2018,40(1):1.[13]张钰荃,高成,陈旭东,等.西江上游流域年径流系数变化规律及其影响因素分析[J].水电能源科学,2021,39(5):37.ZHANG Yuquan,GAO Cheng,CHEN Xudong,et al.Variation of runoff coefficient and its influencing factors inupper reaches of Xijiang River Basin[J].Water Re⁃sources and Power,2021,39(5):37.33。

Journal of Northeast Agricultural University东北农业大学学报第52卷第1期52(1):82~892021年1月January 2021呼兰河流域网格化降水产品评估研究邢贞相，袁泽，段维义，喻熠，纪毅，刘昊奇，付强（东北农业大学水利与土木工程学院，哈尔滨150030）摘要：以中国逐日网格降水量实时分析系统数据集（CGDPA ）为参考值，基于时空分布特征评估中国区域地面气象要素数据集降水产品（CMFD_P ）、全球高分辨率陆面模拟系统降水产品（GLDAS_P ）、中国陆面数据同化系统降水产品（CLDAS_P ）、中国自动站和CMORPH 融合网格降水量数据集（CMPA ）在呼兰河流域精度。

结果表明，①CMFD_P 与GLDAS_P 整体高估多年平均降水量，CLDAS_P 与CMPA 整体低估多年平均降水量。

②4套数据捕捉年内大部分降水集中在汛期，但相对于其他数据，GLDAS_P 年内分配表现坦化特征，汛期偏低，非汛期偏高。

③日降水精度由优到劣为CMPA>CMFD_P>CLDAS_P>GLDAS_P 。

④各套产品可捕捉降水量自东北向西南递减总体空间分布特征。

关键词：网格化降水产品评估；陆面同化数据；时空特征分析；呼兰河流域中图分类号：P339文献标志码：A文章编号：1005-9369（2021）01-0082-08邢贞相,袁泽,段维义,等.呼兰河流域网格化降水产品评估研究[J].东北农业大学学报,2021,52(1):82-89.DOI ：10.19720/ki.issn.1005-9369.2021.01.0010.Xing Zhenxiang,Yuan Ze,Duan Weiyi,et al.Evaluation of grid precipitation products in Hulanhe Basin[J].Journal of North-east Agricultural University,2021,52(1):82-89.(in Chinese with English abstract)DOI ：10.19720/ki.issn.1005-9369.2021.01.0010.Evaluation of grid precipitation products in Hulanhe Basin/XING Zhenxiang,YUAN Ze,DUAN Weiyi,YU Yi,JI Yi,LIU Haoqi,FU Qiang(School of Water Conservancy and CivilEngineering,Northeast Agricultural University,Harbin 150030,China)Abstract:China Gauge-based Daily Precipitation Analysis (CGDPA)was selected as a referencevalue,the accuracy of Precipitation product of China Meteorological Forcing Dataset (CMFD_P),Precipitation product of Global Land Data Assimilation System (GLDAS_P),Precipitation product of China Land Data Assimilation System (CLDAS_P),and CMPA hourly Precipitation product in the Hulanhe Basin was evaluated based on the temporal and spatial distribution characteristics.The results showed that:①The multi-year average annual precipitation of CMFD_P and GLDAS_P was overall overestimated,while that of CLDAS_P and CMPA was overall underestimated.②The four sets of data could capture that most of the precipitation in the year was concentrated in the flood season,but compared with other three sets of data,the distribution of GLDAS_P in a year showed the characteristics of flattening,with low precipitation in flood season and high precipitation in non-flood season.③The descending order of daily precipitation accuracy was CMPA>CMFD_P>CLDAS_P>GLDAS_P.④Each set of precipitation products could capture the overall spatial distribution characteristics of precipitation decreasing from northeast to southwest.Key words:evaluation of gridded precipitation products;land surface assimilation data;spatio-temporal characteristics analysis;Hulanhe Basin基金项目：十三五国家重点研发计划课题（2017YFC0406004，2018YFC0407303）；国家自然科学基金（51979038，51909033）；黑龙江省自然科学基金（E2015024，LH2019E010）作者简介：邢贞相（1976-），男，教授，博士，博士生导师，研究方向为水文水资源不确定性分析。

中国55年来地面水汽压网格数据集的建立及精度评价沈艳1 熊安元1施晓晖2刘小宁11. 国家气象信息中心，北京, 1000812. 中国气象科学研究院灾害天气国家重点实验室，北京, 100081摘要对气象要素网格化是气候变化研究中避免空间抽样误差的有效方法之一。

文中采用薄盘光滑样条插值法（ANUSPLIN），在考虑站点经度、纬度和海拔高度的基础上，对中国55年来地面水汽压站点资料进行空间插值，得到了中国陆地水汽压年和月平均值1°×1°网格数据集。

精度检验表明：中国年水汽压插值误差普遍小于0.3 hPa；而月水汽压的插值误差由于受水汽压周期变化的影响，表现出周期性变化的特点。

一般夏季较大，最大误差在0.5 hPa 左右，冬季较小,约为0.2 hPa。

在考虑站点海拔与对应网格DEM差值大小的基础上，建立实测水汽压值与对应网格水汽压值年序列，并进一步分析二者的相关关系，表明：（1）二者具有很好的相关性，相关系数为 0.88 —0.96；（2）能很好地模拟地形影响，得到的网格水汽压可以较好地代表实测水汽压的变化趋势。

由此建立了中国近55年来地面水汽压的年序列。

其趋势表明：近55年来中国年平均水汽压呈增加趋势，其线性趋势为0.52 hPa/(100 a)，其中西部增加趋势大于东部，且以夏季的增大趋势最为显著。

结合近50年来气温的变化趋势说明：在中国，气温每增加1 ℃，大气中年平均水汽含量约增加3.15%。

关键词网格数据集，地面水汽压，气候变化，趋势系数资助课题：国家级气象科学数据资源建设(2005DKA31700-02)，中国气象局气象新技术推广项目(CMATG2006Z03)。

作者简介：沈艳,主要从事气候变化研究.Email: sheny@2007-05-28收稿,2007-07-30改回.中图法分类号P468.0+2Development of the grid based ground water vapor pressure over China in recent 55 yearsand its accuracy evaluationSHEN Yan1XIONG Anyuan1 SHI Xiaohui2 LIU Xiaoning11. National Meteorological Information Center, CMA, Beijing 100081, China2. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaShen Yan,Xiong Anyuan,Shi Xiaohui,Liu Xiaoning.2008..AbstractThe detection of large-area average changes of meteorological data is always hampered because of the different record series of long-term in-situ measurements, which can often cause the spatial sampling errors. Fortunately, more and more scientists explore that the grid-based dataset can reduce the spatial sampling error through given interpolation method. Substantial progress has been made in the last two decades in quantitatively documenting analysis of different meteorological factors. Ground water vapor pressure is an important meteorological factor that can control some physiological, ecological and water balance process in ecosystem. In this article, using the quality controlled observational monthly and yearly mean ground water vapor pressure data series over China Mainland, through the ANUSPLIN software developed by the Australian National University based on the thin-plate smooth spline method, the datasets of yearly and monthly grid-based ground water vapor pressure are established over China in recent 55 years from the 1951 to 2005. Cross-validation tests show that this gauge-based analysis has highquantitative quality, which annual interpolation error is typically less than 0.3 hPa except from 1951 to 1953 and monthly error has periodic variation with biggest in summer and smallest in winter. In spring and autumn its monthly error value is between the others. The research results include: (1) The relationship between measured and its corresponding grid cell indicates that they are a good linear correlation passing the 0.01-level confidence check. The grid value can represent the pattern of the measured one. (2) Using the 30-year normal from the 1971 to 2000, annual and seasonal variation based on the grid dataset suggests that average annual change shows increasing trend over the 55 years with its linear trend 0.52 hPa per 100-year. The increasing trend in western China is more obvious than that in eastern China. For the seasonal scales, the summer trend is the most dramatic, which is estimated linear trend 0.98 hPa per 100-year over the whole China. While the spring's is lowest which is 0.42 hPa per 100-year. Considering the temperature rising in recent 50 years, 3.15% of water vapor will be increased when temperature warms up 1 degree over China, which is lower than the globe average value. There are two reasons for it, one is the stronger warming trend in China than in the world the other maybe arises from drought enhancement in China. This developed datasets are helpful to explore the spatial and temporal distributions of the ground water vapor pressure. It can be used in a wide range of applications, including weather/climate monitoring, climate analysis, numerical model verifications, ecological assessment, and hydrological studies.Key words Grid dataset, Ground water vapor pressure, Climate change, Trend coefficient。

Soil Moisture Drought in China,1950–2006A IHUI W ANGNansen-Zhu International Research Centre,Institute of Atmospheric Physics,Beijing,ChinaD ENNIS P.L ETTENMAIERDepartment of Civil and Environmental Engineering,University of Washington,Seattle,WashingtonJ USTIN S HEFFIELDDepartment of Civil and Environmental Engineering,Princeton University,Princeton,New Jersey (Manuscript received17March2010,infinal form19November2010)ABSTRACTFour physically based land surface hydrology models driven by a common observation-based3-hourly meteorological dataset were used to simulate soil moisture over China for the period1950–2006.Monthly values of total column soil moisture from the simulations were converted to percentiles and an ensemble method was applied to combine all model simulations into a multimodel ensemble from which agricultural drought severities and durations were estimated.A cluster analysis method and severity–area–duration (SAD)algorithm were applied to the soil moisture data to characterize drought spatial and temporal vari-ability.For drought areas greater than150000km2and durations longer than3months,a total of76droughts were identified during the1950–2006period.The duration of50of these droughts was less than6months.The five most prominent droughts,in terms of spatial extent and then duration,were identified.Of these,the drought of1997–2003was the most severe,accounting for the majority of the severity–area–duration enve-lope of events with areas smaller than5million km2.The1997–2003drought was also pervasive in terms of both severity and spatial extent.It was also found that soil moisture in north central and northeastern China had significant downward trends,whereas most of Xinjiang,the Tibetan Plateau,and small areas of Yunnan province had significant upward trends.Regions with downward trends were larger than those with upward trends(37%versus26%of the land area),implying that over the period of analysis,the country has become slightly drier in terms of soil moisture.Trends in drought severity,duration,and frequency suggest that soil moisture droughts have become more severe,prolonged,and frequent during the past57yr,especially for northeastern and central China,suggesting an increasing susceptibility to agricultural drought.1.IntroductionDrought is recognized as one of the costliest of natural disasters.Severe drought can affect large regions and can persist for decades,as in the case of the1930s‘‘Dust Bowl’’drought in the United States and the Sahel drought of the1980s,among many others.While many regions in the world have suffered from droughts,China has expe-rienced frequent severe droughts during the second half of twentieth century.Studies based on climate station data show that much of northern China has experienced droughts since the1950s,with the most severe and pro-longed droughts having occurred since1990(Qian et al. 2003;Wang et al.2003;Zou et al.2005;Xin et al.2006; Zhai et al.2010).For instance,Zou et al.(2005)calculated the Palmer Drought Severity Index(PDSI)for the period 1951–2003over China and found that almost every year had more than25%of the country under drought threat with the threshold of PDSI,21.0.Zhai et al.(2010)also found large increases in dryness over northern China after 1990.The2008/09winter drought in northeastern China was one of the worst in the past50years,resulting in an estimated16billion Chinese yuan(2.3billion U.S.dol-lars)in economic losses and subjecting more than10Corresponding author address:Aihui Wang,Nansen-Zhu In-ternational Research Centre,Institute of Atmospheric Physics, Chinese Academy of Sciences,P.O.Box9804,Beijing100029, China.E-mail:wangaihui@DOI:10.1175/2011JCLI3733.1Ó2011American Meteorological Societymillion people to water shortages.This drought is still de-veloping(for more information,see http://news.xinhuanet. com/english/2009-02/04/content_10759053.htm).Drought has especially impacted the agricultural areas of northern China.For instance,in2000drought dam-aged more than40million hectares of crops in northern China(Song et al.2005).Wang et al.(2003),based on analysis of precipitation data,showed that the area in drought in northern China increased during1950–2000, and that drought variations displayed multiple time scales and seasonal differences.The Yellow River has experi-enced prolonged periods of below normalflows in recent decades,resulting in complete drying of the river in some locations and periods.For example,a severe drought over northern China in1997resulted in226days of zeroflow in the Yellow River from Henan to Shandong provinces. The total length of the river with zeroflow was about 687km(Liu and Zhang2002;Xu2004;Cong et al.2009). Droughts have also frequently occurred in the Yangtze River basin during the past50years(e.g.,Su et al.2008; Zhai et al.2010).The occurrence of severe droughts such as those mentioned here is usually related to a combina-tion of precipitation and/or temperature anomalies,hy-drological anomalies(e.g.,low soil moisture or groundwater from previous seasons or years),terrestrial ecosystem con-ditions,and/or human activities(Woodhouse and Overpeck 1998;Understanding drought characteristics,including their duration,areal extent,and possible causes,is critical to understanding the nature of future droughts and eventually for forecasting of droughts,a science that is currently in its infancy(Svoboda et al.2002;Sheffield et al.2009).Soil moisture modulates both the land surface water and energy cycles.Changes in soil moisture directly affect plant water availability,and in turn plant productivity and crop yields;hence,soil moisture deficits have critical implications for both agriculture and water supply.China lies within the Asian monsoon regime,and the summer and winter monsoons control precipitation variations in space and time over most of the country.The climate becomes wetter from north to south and from west to east.The northwestern regions are extremely arid,with some areas having no more than100mm of precipitation annually.Retention of soil moisture is especially impor-tant for agriculture in these dry areas.For this reason,soil moisture anomalies are often used as an index of agri-cultural drought.Unfortunately,long-term measurements of soil mois-ture are only available at a few sites globally(e.g.,Robock et al.2000),and the density of stations is far too sparse and the time span of observations is too short to support drought studies.Remote sensing provides an alternative source of soil moisture measurements.However,satellite sensors provide estimates of the water content only of the upper few centimeters of soil(depending on their wave-length).While the recent(November2009)launch of the European Soil Moisture and Ocean Salinity(SMOS) mission and planned launch of the U.S.Soil Moisture Active Passive(SMAP)mission will provide observations targeted directly at soil moisture,the current generation of satellite sensors has wavelengths too short to provide a viable source of soil moisture information over larger areas,and short record lengths will continue to be an issue even in the SMOS and SMAP era.As an alternative to in situ and remotely sensed soil moisture,numerical model simulations have been used as a source of soil moisture information for drought studies(Sheffield et al.2004,2009;Sheffield and Wood 2007;Andreadis et al.2005;Wang et al.2009).Land surface models(LSMs)coupled with atmospheric models(e.g.,Schubert et al.2008)or driven offline by observed meteorological forcings can reproduce land sur-face water and energy variations.For example,Sheffield et al.(2004)derived a hydrologically based drought index based on simulated soil moisture and showed that it was able to reproduce drought occurrence and severity char-acteristics over the continental United States.Andreadis et al.(2005)developed an approach that they termed se-verity–area–duration(SAD)analysis,an adaptation of the depth–area–duration approaches widely used in design storm analysis,to characterize drought over the continental United States for the period1916–2003.They used total column soil moisture simulations from the variable in-filtration capacity(VIC)model.Wang et al.(2009)showed that plausible results were produced when the same methods were applied to other LSMs.Sheffield et al.(2009) extended the SAD approach to investigate global drought during1950–2000using total column soil moisture derived using the VIC model.The accuracy with which hydrologic variables,including soil moisture,can be reproduced using offline applications of LSMs is strongly dependent on the quality of the me-teorological forcing data and the physical parameteriza-tions in the LSM.Given the same meteorological forcings and land surface properties(e.g.,soils,topography,vege-tation),simulations from different models can show parison of model-simulated soil moisture with in situ measurements generally shows that models poorly simulate actual soil moisture,but the models are able to reproduce anomalies and seasonal variability(Entin et al. 2000;Guo and Dirmeyer2006).Because the representa-tions of soil hydrology differ from model to model,the variability of soil moisture is highly model dependent.On the other hand,when appropriately normalized to have the same range and variability,model-derived soil moisture is generally much more consistent across models.Wang et al. (2009)investigated the performance of six LSMs withrespect to their ability to reproduce agricultural(soil moisture)drought characteristics over the continental United States for a simulation period of almost100years. Their approach to standardizing the soil moisture output was to express each model’s monthly soil moisture as a percentile relative to that month’s historical simula-tions;hence,all values were reformulated as uniformly distributed(between0and1)variables.The results showed a general consistency in the representation of major droughts and thus the value of using the multi-model ensemble as a means of combining the estimates from the individual models.In this paper,we utilize the multimodel approach de-veloped in Wang et al.(2009)to reconstruct soil moisture in China,with particular emphasis on droughts.The pa-per is organized as follows:section2describes the models and data used in this study;the data analysis and statistical methods are described in section3;section4investigates drought spatial and temporal variability as estimated from the multimodel ensemble derived soil moisture;and section5provides a summary and conclusions.2.Models and data descriptionThe soil moisture variables used in this paper come from four land surface models:1)VIC(Liang et al.1994);2)the Community Land Model,version3.5(CLM3.5;Oleson et al.2007,2008);3)Noah version2.7(Mitchell et al.2001) with an updated snow albedo scheme(Livneh et al.2010); and4)a hybrid of CLM3.5with the VIC soil hydrology scheme(CLM-VIC;Wang et al.2008).The details of the structure and model physical processes are described in the references given above.All models were forced at the land surface with precipitation,surface air temperature, surface wind,vapor pressure deficit,and downward solar and longwave radiation.The models close the surface water budget by producing as prognostic variables runoff and evapotranspiration,with snow water equivalent and soil moisture as state variables.They close the surface energy budget concurrently by solving for the effective surface temperature(and depth profile of temperature)as a state variable,with reflected shortwave and emitted longwave radiation as prognostic variables.The perfor-mance of the four models have been extensively studied at both single stations and over larger areas or watersheds (globally in some cases;e.g.,Chen et al.1997;Nijssen et al. 2001;Maurer et al.2002;Ek et al.2003;Dickinson et al. 2006;Oleson et al.2007;Wang et al.2008,2009).Soil column representations and the parameteriza-tions used for soil water movement differ considerably among the models.The total soil depth in CLM3.5is fixed at3.43m and is divided into10layers with in-creasing soil layer thickness from upper to lower layers. In VIC and CLM-VIC,the layer depths differ from grid cell to grid cell.The total soil column in these two models can be as deep as3m[values extracted from the VIC global parameter set constructed by Nijssen et al. (2001)].In the Noah model,the deepest total soil col-umn depth isfixed at2m,and the layer depths arefixed at10cm,30cm,60cm,and1m,respectively.The dif-ferent soil column depths result in disparities in the soil water holding capacity among models and lead to large differences in the simulated mean soil moisture amount. All of the models require that certain soil and vegetation parameters that describe the land system(e.g.,soil water holding capacities,hydraulic conductivities,thermal conductivities and capacities,vegetation types,etc.)be specified,and these parameters differ from model to model.We have used herein‘‘off the shelf’’model pa-rameters that have been estimated or calibrated for the model domain in previous studies.We did so to avoid influencing uncertainties in each model’s representation of drought by introducing potentially inappropriate parameter values.While our general approach is similar to that used in Wang et al.(2009),the specific data sources vary somewhat.For CLM3.5,soil data were derived from the International Geosphere–Biosphere Programme(IGBP)soil dataset(Bonan et al.2002), with vegetation parameters derived from Moderate Res-olution Imaging Spectroradiometer(MODIS;Lawrence and Chase2007)imagery.For the VIC model,soils data were derived from the Food and Agriculture Association (FAO)–United Nations Educational,Scientific and Cul-tural Organization(UNESCO)digital soil map of the world with the World Inventory of Soil Emission Poten-tials(WISE),using methods described in Nijssen et al. (2001).For the Noah model,parameters were taken from multiple sources:soil data,green vegetation fraction,and snow-free albedo were retrieved from the global dataset of Matthews(1984,1985)and vegetation type was taken from the University of Maryland1-km vegetation class dataset(Hansen et al.2000).Because CLM-VIC is CLM3.5 merged with the VIC soil hydrology scheme,the CLM-VIC model used the same soil data as the VIC model. The vegetation scheme in CLM-VIC is identical to that in CLM3.5and so the same vegetation data were used in both models.All models were driven by a common meteorological dataset,which is a hybrid of data from the National Centers for Environmental Prediction(NCEP)–National Center for Atmospheric Research(NCAR)reanalysis (Kalnay et al.1996)and a suite of global observation-based products.Details of the dataset are described in Sheffield et al.(2006).The dataset has been used to evaluate the global terrestrial water budget(Sheffield and Wood2007)and also to drive the VIC model for explo-ration of global drought characteristics(Sheffield et al.2009).The dataset used in this paper was extended to 2006using the same approach as in Sheffield et al.(2006) to produce the original dataset,which spanned the period 1948–2000.The extension was based on updated versions (to2006)of the underlying observations for precipitation, temperature(CRU3.0;Mitchell and Jones2005),and radiation(SRB3.0;Gupta et al.2006).The horizontal resolution of the original Sheffield et al.(2006)dataset was18318,with a temporal resolution of3h.In this study,wefirst interpolated the global data to0.58spatial resolution using a bilinear interpolation method,and we then extracted the forcing data within the study domain. At0.58spatial resolution,the Chinese land area consisted of3880grid cells.We recognize that the raw(station)data that underlie the gridded dataset are somewhat sparser than the0.58resolution we used.On the other hand,the topographic,soil,and vegetation data have considerably finer spatial resolution.Our choice to use0.58as our model resolution represents a compromise between these two considerations.Sheffield et al.(2006)demonstrated that when the data were interpolated from28to18using the same bilinear interpolation method that we applied, some biases appeared in the foothill regions of major mountain ranges.We therefore compared the0.58ele-vation data interpolated from18to0.58dataset and found no prominent differences except in the vicinity of the southern foot of the Himalayas,which constitutes a small part of the entire domain(not shown).On this basis,we decided not to adjust the interpolated data.It is also worth noting that for drought studies such as this one,modest spatial variations in precipitation are somewhat less im-portant than for other applications,since droughts tend to cover relatively large areas and to reflect accumulated precipitation deficits rather than short-term variations. Our simulations were performed using a1-h time step and0.58spatial resolution(the hourly data were linearly interpolated from the original3-h time step data).To eliminate model initialization effects,all models werefirst run for10yr by cycling the forcing data for1948initial-ized with a specified intermediate soil wetness.The models were then run from1948to2006,initialized with thefinal year of the spinup run.The annual precipitation for1948 over much of China is slightly higher than the mean cli-matology(not shown),so the initialization described above might give a slightly wet initial condition.However,our analysis started with1950,which should help to dissipate any modest initialization influences.3.MethodologyFollowing Andreadis et al.(2005)and Sheffield et al. (2009),we defined drought as occurring when soil mois-ture values(percentiles)fall below a threshold value continuously in time over a contiguous area.We then used monthly soil moisture percentiles from the four LSMs to form a multimodel ensemble dataset,as de-scribed below.Drought events were identified from the multimodel dataset using the same cluster analysis ap-proach as in Andreadis et al.(2005).Their method searches for temporally continuous and spatially con-tiguous areas in drought.SAD analysis is then applied to the space–time characteristics of these events.We also analyzed droughts on a grid cell by grid cell basis in terms of long-term trends in drought characteristics, such as duration and severity of soil moisture deficits.a.Multimodel ensemble schemeAs noted above,previous work(e.g.,Koster et al. 2009;Wang et al.2009)indicates the intermodel varia-tions are considerably reduced when the simulated soil moisture values are appropriately normalized.Our ap-proach to doing so consisted of three steps.First,simu-lated(total column)soil moisture was converted to percentiles using the time series values of total column soil moisture for each model for each month in the pe-riod1950–2006.Second,the median values of the monthly percentiles derived from the four models were computed at each grid cell to form a unified percentile time series for that grid cell.We found that one of the models(CLM3.5) had some anomalous drift in very dry areas(caused by the model’s very small interannual range in total column soil moisture in very dry areas).For this reason,we used the multimodel median,rather than the mean,notwith-standing that an exploratory analysis showed that this choice makes little difference to the character of the in-ferred multimodel drought.Finally,at each grid cell and for each month,the median(over models)percentile time series was again converted to a new percentile according to the empirical probability distribution of the median percentiles.This third step had only a minor effect on midrange soil moisture values;however,it corrects the tails of the averaged percentile distribution,which oth-erwise would deemphasize extremes.Stated otherwise, this step assures that the multimodel variable has a uni-form distribution—without this step,extreme low or high values do not occur as often as in the percentile series from the individual models.Finally,the ensemble soil moisture percentiles were used to identify droughts as follows.A drought was defined as any percentile below 20%,as in previous drought identification work(Andreadis et al.2005;Sheffield et al.2009;Wang et al.2009).It is also a threshold used in the U.S.Drought Monitor(see http:// /dm/archive/99/classify.htm).We note that notwithstanding this step,the multimodel ensemble is ex-pected to be smoother in both space and time than the space–time variations of the individual models.We viewthis as a desirable outcome of the multimodel ensemble processing.b.Clustering algorithm and severity–area–durationanalysis for drought identificationThe clustering algorithm developed by Andreadis et al.(2005)was used to identify drought spatial and temporal variations.The algorithm combines both spa-tially and temporally contiguous regions with soil mois-ture percentiles below the specified(20th percentile) value.For a specified minimum area,droughts are al-lowed to break up to form subdroughts or merge to form new larger droughts with time.The minimum area threshold was taken as25000km2in previous work over the continental U.S.domain(Andreadis et al.2005; Wang et al.2009)and500000km2for a study of global land areas by Sheffield et al.(2009).Sheffield et al. (2009)found that drought clusters with smaller(e.g., 25000km2)thresholds could shrink to a few grid cells and persist many years through tenuous spatial con-nectivity.To avoid this situation,we set a minimum threshold of150000km2.In section4d,we examine the sensitivity of the results to this threshold.We used the severity–area–duration analysis approach of Andreadis et al.(2005)as a supplementary tool to characterize drought events.SAD is based on the widely used depth–area–duration(DAD)technique(Grebner and Roesch1997)used for design storm analysis.We applied the method as in Andreadis et al.(2005);the only difference is that our percentile variants were the percentiles of the multimodel medians computed as described above,whereas in Andreadis et al.the per-centiles were from a single model.In brief,the severity (S)in SAD is defined as S5(12S P/t),where in our case P is the monthly ensemble median soil moisture percentile and S P is their sum over a duration of t months.The severity was calculated at each grid cell within the drought event as identified by the cluster analysis,implemented as in Andreadis et al.(2005),and then averaged over the drought area.The averaged drought severity for an event was calculated for time intervals within the lifetime of the event(3,6,12,24,and 48months,where the specific period within the event lifetime was picked to maximize the severity)and for subareas within the spatial domain of the event(starting from150000km2,which is about60grid cells—also selected so as to maximize the severity for the given subarea).Within each drought cluster,SAD treats the grid cell with the maximum severity to be the center of the drought and then adjoining grid cells with the next largest severity are added to form an intermediate drought cluster.In our application,this procedure con-tinued in area increments of20grid cells(or50000km2)until the maximum spatial extent of the drought event was reached for a specified average severity over the specified drought length.The SAD algorithm provides a way to estimate an absolute drought magnitude with-out being constrained to an individual basin or area (Sheffield et al.2009).As in Andreadis et al.(2005), following identification of all drought events during the study period,the maximum severities of all events at each area increment were selected to form SAD enve-lope curves,which represent the most severe events during the period of record for each area and duration.c.Trend analysis and estimation offield significance Nonparametric trend tests have been widely used in hydrology.The Mann–Kendall(MK)test(Mann1945), for instance,is extensively used for testing of monotonic (e.g.,linear)trends.In its classical form,the MK test requires an assumption of independence,which is often approximately met for annual(but not seasonal)data. On the other hand,soil moisture usually has substantial memory at monthly andfiner time intervals;that is,the monthly soil moisture is usually autocorrelated.Hamed and Rao(1998)proposed a modified MK test for auto-correlated data.Hirsch et al.(1982)proposed an alter-native formulation of the MK test for use with seasonal data.Their test has been applied both to observed cli-mate and hydrological variables(e.g.,Lettenmaier et al. 1994)and to model-derived hydrological variables(e.g., Andreadis and Lettenmaier2006).We applied the Hirsch et al.(1982)method to the monthly time series of soil moisture,drought severity,duration,and frequency as in Andreadis and Lettenmaier(2006).Spatial correlation among climate variables is another complication that reduces the degrees of freedom when assessing regional trends.Livezey and Chen(1983) proposed a method wherein both a local significance (significance level of a test if applied at a single location individually)and afield significance(which pertains to the number of locations at which the null hypothesis has to be rejected at the local significance level).Thefield significance level is calculated based on Monte Carlo procedures as follows.We generated500time series of soil moisture by resampling the original monthly series for each month using the same sequence for all the grid cells.This ensured that the time series of each sample retained the spatial correlation structure and seasonal variations of the original datasets.The area of significant local trends was then calculated for each resampled se-ries and the95th percentile calculated from the total sample.If the area of significant trends in the original data is greater than this percentile value then it isfield signifiing this method with a5%local signifi-cance level,Andreadis and Lettenmaier(2006)calculatedthat for a field significance level of 5%,the fraction of rejections needed to exceed 20.4%for soil moisture and 14.1%for runoff over the continental United States for the analysis period 1920–2003.We followed the same approach in our analysis of reconstructed soil moisture over China.4.Resultsa.Consistency of simulated soil moistureTo compare the regional disparities among different models,we divided the domain into seven subregions as indicated in the top panel in Fig.1.The subregions are referred to as NE,N,SE,ENW,SW,WNW,and Tibet.(Note:Taiwan and Hainan both belong to SE,but we did not perform simulations over those areas because of inadequacies of forcing data for these relatively small islands.)The bottom panel in Fig.1shows 13-month moving averaged soil moisture percentiles for the en-semble percentiles over the seven subregions and over the entire domain.The shading indicates the envelope of percentiles from the four individual models.With the exception of WNW and part of the ENW region,the envelopes do not exhibit large discrepancies in most regions,implying that the models generally are consis-tent with each other.For some regions (e.g.,SE and WNW)there seems to be large disparity between models at the start of the time period,indicatingthatF IG .1.(top)Location of regions (‘‘China’’refers to the entire domain);(bottom)13-month moving averages of soil moisture percentiles from the ensemble median (dark lines)and range of individual models (gray shading)for the seven regions and China.there may be some lingering initialization effects.Figure 1does not show frequent drought occurrence (i.e.,per-centiles below the 20th)because of the smoothing in-herent in the temporal moving average and the large areas over which the spatial averages were computed.Furthermore,while an entire region may not be in drought at a particular time,this does not preclude some part of the region being in drought.Figure 2shows the time series of averaged percentiles,averaged severity,and the percentage area in drought averaged over the subregions and the entire domain.For the entire domain,the averaged percentiles are about 50%,and the percentage of area in drought is typically about 25%,which is consistent with the PDSI data of Zou et al.(2005).The values of the average severities are comparable to the values of the averaged percen-tiles.Among all subregions,the temporal variation of the three drought indicators over SE and SW shows greater high-frequency variability than for the other regions.The high frequency of soil moisture variations over south China might relate to the variations of the East Asian monsoon precipitation (Zuo and Zhang 2007).With respect to timing of droughts,the lowest averaged percentiles are in the late 1960s in the N re-gion,and after 2000in the WNW region.On the other hand,the maximum severity appears in the early 2000s in the N region and the late 1980s for WNW.Based on the time series shown in Fig.2,Table 1summarizes the top five months ranked by the averaged severity,area extent,and a combined metric of severity and area.The combined metric was computed as the averaged severity over the drought area multiplied by the total area in drought for each month.The most se-vere drought occurred in June 2003over the northeast with a severity value of about 68%.For all five selected months,the average severity was larger than 63%.For the months ranked by spatial extent (second column in Table 1),the top five months all occurred in thesecondF IG .2.The 13-month moving averages of soil moisture percentiles,percentage of region in drought,and average severity of area in drought averaged over each region and China.The percentage area in drought is the ratio of areas with soil moisture percentiles below 20%to total land areas within the China domain (except the regions without data).The average se-verity is the average soil moisture deficit with respect to the 20%in each month.。

A change to its population policy for the world`s most populated country,that is where we start this new week on CNN STUDENT NEWS.China introduced its so-called one-child policy in the late1970s.It`s been credited with helping to control China`s population growth.It`s also been criticized for forcing parents to make difficult personal choices,or in some cases face huge fines.The policy said that in urban areas,parents could only have one child,although there were some exceptions.The new rule says that if either parent is an only child,then they are eligible to have two children of their own.One reason for the change,economics.In China,many people care for their elderly relatives,so a single child could end up being financially responsible for parents and grandparents.This new policy could help with that.Another reason,China says it wants to improve human rights.That`s also why it says it`s getting rid of its labor camps.Since 1957,Chinese authorities could hold people in these camps without a trial.Now,China`s government is expected to shut the camps down.改变世界`人口政策最密集的国家，这是我们开始新的一周美国有线电视新闻网学生新闻。

我国全球大气再分析(CRA-40)卫星遥感资料的收集和预处理王旻燕;姚爽;姜立鹏;刘志权;师春香;胡开喜;张涛;张志森;刘景卫【摘要】卫星资料对数值天气预报准确率的影响非常显著.可用于1979年以来全球大气再分析的卫星资料,来源于80余颗卫星的约60种星载传感器,卫星观测资料数据格式的标准化、长时间序列数据使用和均一化处理技术难点多.在推进我国全球大气再分析(CRA-40,1979—2018年)研发时,已优先开展并全面完成了83.5TB 的1979年以来53种115个子类气象卫星资料的收集整合.介绍了面向我国全球大气再分析所用卫星资料的收集整合情况、对应于10年再分析试制产品CRA-Interim(2007—2016年)的数据格式标准化和卫星资料数据质量预评估等观测资料预处理情况.近年发布的重处理卫星数据产品已替换了原同期业务产品.所收集整合的这些历史和实时卫星资料,已逐步通过全国综合气象信息共享平台(CIMISS)归档和提供应用服务.【期刊名称】《气象科技进展》【年(卷),期】2018(008)001【总页数】6页(P158-163)【关键词】再分析;CRA-40;卫星遥感资料;收集整合;预处理;同化【作者】王旻燕;姚爽;姜立鹏;刘志权;师春香;胡开喜;张涛;张志森;刘景卫【作者单位】国家气象信息中心,北京 100081;国家气象信息中心,北京 100081;国家气象信息中心,北京 100081;美国国家大气研究中心,博尔德 80301,美国;国家气象信息中心,北京 100081;国家气象信息中心,北京 100081;国家气象信息中心,北京100081;国家气象信息中心,北京 100081;国家气象信息中心,北京 100081【正文语种】中文0 引言从20世纪90年代中期开始，美国、欧盟和日本等先后组织和实施了一系列全球大气再分析计划，这些计划生成的再分析产品使用价值和获得的应用效益，已远远超过观测资料本身。

VIC水文模型入门攻略VIC的数据和输入文件准备VIC（Variable Infiltration Capacity）是一种用于模拟陆地表面和水文过程的水文模型。

它被广泛应用于流域水文、气候变化、洪水预报等领域。

在使用VIC模型进行模拟前，需要准备相应的数据和输入文件。

下面是VIC水文模型入门攻略，包括VIC模型的数据和输入文件准备。

1.数据准备：VIC模型所需的输入数据包括气象数据、土壤参数、地形数据和植被数据等。

-气象数据：包括降水量、温度、相对湿度、风速等。

可以利用气象站点观测数据、卫星遥感数据或气象模型输出数据。

通常需要根据模拟需求选择适当的时段和空间分辨率，并将数据格式转换为VIC所能接受的格式，例如ASCII格式。

-土壤参数：包括土壤类型、含水量曲线、渗透性等。

可以使用土壤数据库或通过野外调查获取。

不同土壤类型具有不同的水文特性，需要根据实际情况进行选择和参数化。

- 地形数据：包括高程、坡度、坡向等。

可以使用数字高程模型（Digital Elevation Model, DEM）数据进行提取和计算。

地形数据对于确定流域的水文特征和水动力过程具有重要影响。

-植被数据：包括植被类型、覆盖率、叶面积指数等。

可以使用植被遥感数据或植被地图进行提取和参数化。

植被数据用于计算蒸散发和蒸腾过程等。

2.输入文件准备：VIC模型需要准备多个输入文件，包括全局控制文件、网格化参数文件、气象驱动文件等。

- 全局控制文件（global parameter file）：用于配置模型运行的全局参数，包括模拟时段、时间步长、输出设置等。

该文件是VIC模型运行的主控文件，需要根据实际需求进行参数的设置。

- 网格化参数文件（vegetation parameter file、soil parameter file）：用于描述不同网格单元的土壤、植被和地形等参数。

该文件需要根据实际情况进行参数设置和空间分布的描述。

- 气象驱动文件（meteorological forcing file）：包含模拟时段内的气象数据，用于驱动VIC模型的运行。

数据简介中国区域地面气象要素数据集是中国科学院青藏高原研究所开发的一套近地面气象与环境要素再分析数据集。

该数据集是以国际上现有的 Princeton 再分析资料、GLDAS 资料、GEWEX-SRB 辐射资料，以及 TRMM 降水资料为背景场，融合了中国气象局常规气象观测数据制作而成。

目前已完成并公开的数据为此数据集 1.0 版的 B-01 子集，其时间分辨率为 3 小时，水平空间分辨率 0.1°，包含近地面气温、近地面气压、近地面空气比湿、近地面全风速、地面向下短波辐射、地面向下长波辐射、地面降水率，共 7 个要素（变量）。

具体地：ITP 中国区域地面气象要素数据产品之各个子数据集时空分辨率及范围：数据产品大类数据产品子类时间属性空间属性时间分辨率时间覆盖范围水平空间分辨率水平空间覆盖范围A A-01 1 小时2004.01-2008.12（5 年）0.1 度70°E-140°E, 15°N-55°N中国陆地区域A-02 1 小时2004.01-2008.12（5 年）0.25 度70°E-140°E, 15°N-55°N中国陆地区域B B-01 3 小时1981.01-2008.12（28 年）0.1 度70°E-140°E, 15°N-55°N中国陆地区域B-02 3 小时1981.01-2008.12（28 年）0.25 度70°E-140°E, 15°N-55°N中国陆地区域C C-01 1 天1951.01-2008.12（58 年）0.1 度70°E-140°E, 15°N-55°N中国陆地区域C-02 1 天1951.01-2008.12（58 年）0.25 度70°E-140°E, 15°N-55°N中国陆地区域各变量的物理意义：更多信息，请参见随数据一同发布的《User’s Guide for China Meteorological Forcing Dataset》。

基于Delta分位数映射法的青藏高原中东部IMERG卫星降水误差订正杜娟;于晓晶;黎小东;敖天其【期刊名称】《高原气象》【年(卷),期】2024(43)2【摘要】可靠的降水资料对理解青藏高原水量平衡和水循环过程尤为重要。

IMERG(Integrated MultisatellitE Retrievals for Global Precipitation Measurement)是新一代卫星降水产品,具有更广的覆盖范围与更高的时空分辨率,但在高原复杂地形条件下仍然存在较大的不确定性。

鉴于此,本研究应用Delta分位数映射法(Quantile Delta Mapping,QDM),对IMERG日降水数据进行偏差订正,使用2001-2010年的中国区域地面气象要素数据集(China Meteorological Forcing Dataset,CMFD)降水数据和IMERG日降水产品分季节建立传递函数,对2011-2014年的IMERG逐日降水进行订正。

研究结果表明:(1)Delta分位数映射法能够有效订正IMERG的降水频率、数值和空间分布,对极端降水和负偏差较大区域的订正效果更为明显。

订正后的IMERG降水概率分布更加接近观测概率分布,降水偏差也更符合正态分布,改进了对全年和季节降水空间分布的刻画,提高了月降水的精度。

(2)订正后日降水量均方根误差由1.49 mm·d^(-1)降低到1.26 mm·d^(-1),精度提高了15.44%;订正后的日降水在不同降水量级的临界成功指数CSI、命中率POD、误报率FAR、准确率Precision和F评分Fscore均有提高,降低了微量和暴量降水的空报率。

(3)对极端降水的订正效果显著,降水强度SDII以及极强降水量R95p和R99p的均值更接近观测值;有效提高了对极端降水空间分布的表征,极端降水偏差从30%以上降低到5%以内;SDII、R95p和R99p的均方根误差从1.59 mm·d^(-1)、6.54 mm·d^(-1)、14.89 mm·d^(-1)降为0.65 mm·d^(-1)、3.01 mm·d^(-1)、8.99 mm·d^(-1),精度分别提高了59.12%、53.98%和39.62%。

大气强迫日变化对东中国海海温模拟的影响于洋;高会旺;史洁【期刊名称】《中国海洋大学学报（自然科学版）》【年(卷),期】2017(047)004【摘要】The diurnalatmosphericforcing has ability to change the horizontal gradient of sea surface temperature (SST) and enhance the vertical mixing between the surface and subsurface.Itis non-ignorable for the precise description of the upper ocean.In this study,the Regional Ocean Modeling System (ROMS) Model with the 4 times a dayNCEP/NCAR forcing data has been used to investigate these kinds of impacts on the temperature simulation in the shelf seas ofparing with the daily mean forcing,the diurnal forcing makesthe model havea betterperformance.The diurnal forcing causes a decreasein net surface heat flux from the ocean about 13 W · m-2 in summer andan increase of it about 10 W · m-2 in winter,increasingthe temporal mean heat absorption in the shelf seas of China about 1.4.The high frequency diurnal forcinginfluences the vertical structure of the sea temperaturebythe thermal and dynamic processes of the sea air interface.It leads to an increase of the mixed layer depth (MLD) about 10％,an increase of the mean temperature of the Yellow Sea Cold Water Mass (YSCWM) about 0.5 ℃ and a decrease of the meanvolume of the YSCWM about 1/3.%大气强迫的日变化可以减弱海表温度的水平梯度加强表层与次表层间的垂向混合,从而影响海温的模拟.本文基于区域海洋模式ROMS,利用NCEP/NCAR发布的6h1次的10 m风速、短波辐射、海表气压等再分析资料,研究了大气强迫的日变化对东中国海温度模拟的影响.通过与观测资料比对发现,相比于日均大气强迫下的模拟,日变化大气强迫下的模拟与观测更为接近.大气强迫的日变化对东中国海的海-气热通量具有显著影响,能够使东中国海年均海气热通量增加1.4 W/m2,夏季增加13 W/m2,冬季减小10 W/m2.大气强迫的日变化通过海-气界面的热力和动力过程影响水温的垂直结构,加强东中国海的上层混合,使东中国海混合层厚度(MLD)增加约10％;夏季黄海冷水团的平均温度升高0.5℃,体积减少约1/3.【总页数】9页(P106-113,131)【作者】于洋;高会旺;史洁【作者单位】中国海洋大学环境科学与工程学院,山东青岛266100;中国海洋大学环境科学与工程学院,山东青岛266100;中国海洋大学海洋环境与生态教育部重点实验室,山东青岛266100;中国海洋大学环境科学与工程学院,山东青岛266100;中国海洋大学海洋环境与生态教育部重点实验室,山东青岛266100【正文语种】中文【中图分类】P732.6【相关文献】1.利用大气环流模式模拟北大西洋海温异常强迫响应 [J], 李建;周天军;宇如聪2.不同时间频次的外强迫对全球大洋海温模拟的影响 [J], 史珍;李响;刘娜3.全球海洋模式中不同海表热力与动力强迫对海温模拟的影响 [J], 史珍;李响;凌铁军;刘娜4.夏季赤道中东太平洋海温和北极海冰异常对大气环流影响的数值模拟 [J], 杨修群;谢倩5.热带西太平洋海温异常对大气影响的数值模拟 [J], 徐启春因版权原因，仅展示原文概要，查看原文内容请购买。

全球气压气温模型在中国地区的精度分析施宏凯;何秀凤;王俊杰【摘要】为检验全球气压气温模型在中国地区的精度,基于我国29个探空站2015-03～2016-02期间实测的气压和气温数据,比较分析分别由GPT模型及最新的GPT2w_5和GPT2w_1模型导出的气压和气温结果.实验结果表明,中国地区3种气象模型均有明显的季节性且对纬度变化敏感,GPT2w_5与GPT2w_1模型精度相当,二者的气压和气温精度较GPT模型均有明显提高,但对于个别异常值,GPT2w 1模型较GPT2w_5模型表现出更好的鲁棒性.%The global pressure and temperature model (GPT) is an empirical model providing pressure and temperature at any site in the vicinity of the earth's surface.Aiming at mitigating the shortages of GPT,the GPT2w model is presented,which provides more meteorological data,with a higher horizontal resolution of 5° (GPT2w_5 model) and 1° (GPT2w_1 model).In order to test the accuracy of pressure and temperature of GPT,GPT2w_5 and GPT2w_1 model,the performance of the three models are compared and analyzed at selected sites,based on 1 year (2015.3 to 2016.2) high-precision sounding data from 29 sounding sites in China.The results show that all three models have obvious seasonality and sensitivity to latitude change;the GPT2w_1 model performs as well as the GPT2w_5 model and significantly better than the GPT model;but compared with the GPT2w_5 model,the GPT2w_1 model is more robust.【期刊名称】《大地测量与地球动力学》【年(卷),期】2017(037)008【总页数】5页(P841-844,848)【关键词】气压;气温;GPT模型;GPT2w模型;精度分析【作者】施宏凯;何秀凤;王俊杰【作者单位】河海大学地球科学与工程学院,南京市佛城西路8号,211100;河海大学地球科学与工程学院,南京市佛城西路8号,211100;河海大学地球科学与工程学院,南京市佛城西路8号,211100【正文语种】中文【中图分类】P228对流层延迟由对流层中干空气成分和水汽造成，是影响精密单点定位精度的主要因素之一，也是GPS气象学中重要的研究对象[1]。