本文選題：TRMM + 黃河源區(qū)��； 參考：《中國(guó)地質(zhì)大學(xué)(北京)》2017年碩士論文

【摘要】：地處青藏高原東部的黃河源區(qū)屬于典型的寒旱區(qū),近年來(lái)的觀測(cè)數(shù)據(jù)表明,在全球氣候變暖的條件下,該區(qū)域的降水特征產(chǎn)生了一定變化,因此研究降水對(duì)其徑流過(guò)程產(chǎn)生的影響對(duì)于揭示水文循環(huán)以及對(duì)氣候的響應(yīng)機(jī)制具有重要的科學(xué)意義。本文選用TRMM遙感降水?dāng)?shù)據(jù),采用相關(guān)系數(shù)、相對(duì)誤差和均方根誤差三個(gè)指標(biāo),從時(shí)間尺度和空間尺度分析了TRMM數(shù)據(jù)在黃河源區(qū)及周邊地區(qū)的適用性,并利用TRMM數(shù)據(jù)分析了源區(qū)降水的時(shí)空變化特征;在數(shù)據(jù)相關(guān)性分析的基礎(chǔ)上,選用了與TRMM降水?dāng)?shù)據(jù)具有較強(qiáng)相關(guān)性的MODIS NDVI植被數(shù)據(jù),通過(guò)降尺度方法將空間分辨率為0.25°的TRMM數(shù)據(jù)提高到1 km,同時(shí)利用比例指數(shù)法將降尺度的TRMM年數(shù)據(jù)轉(zhuǎn)化為月降水?dāng)?shù)據(jù);在以上工作的的基礎(chǔ)上,分別將降尺度的TRMM數(shù)據(jù)和分辨率為0.5°的格點(diǎn)降水?dāng)?shù)據(jù)作為輸入變量,運(yùn)用SRM融雪徑流模型對(duì)源區(qū)2010年的徑流進(jìn)行模擬,分析兩種不同降水?dāng)?shù)據(jù)對(duì)模型精度的影響;在模擬中,還運(yùn)用源區(qū)周邊站點(diǎn)降水、氣溫?cái)?shù)據(jù)改進(jìn)了模型參數(shù)臨界溫度。研究結(jié)果表明,TRMM降水?dāng)?shù)據(jù)整體高于氣象站點(diǎn)實(shí)測(cè)值,在溫度較高的4-10月的數(shù)據(jù)精度優(yōu)于其它低溫月份;隨著月、季、年時(shí)間尺度的增大,TRMM數(shù)據(jù)的精度也隨之提高;源區(qū)降水的空間分布特征表現(xiàn)為從東南向西北遞減,降水受局部地形的影響并不隨高程升高而線性增大;研究區(qū)內(nèi)NDVI和TRMM在年尺度的相關(guān)系數(shù)為0.81,且在分辨率為0.75°時(shí)具有最好的相關(guān)性;降尺度后TRMM降水?dāng)?shù)據(jù)經(jīng)檢驗(yàn)與氣象站觀測(cè)數(shù)據(jù)的相關(guān)系數(shù)略低于原始TRMM數(shù)據(jù),相對(duì)誤差和均方根誤差變化不大;利用比例指數(shù)法得到的降尺度的TRMM月數(shù)據(jù)經(jīng)檢驗(yàn)在瑪多、達(dá)日等多數(shù)站點(diǎn)的精度有一定程度的增大;以氣象站降水?dāng)?shù)據(jù)為輸入變量的SRM模擬結(jié)果優(yōu)于0.5°格點(diǎn)降水?dāng)?shù)據(jù)和降尺度TRMM數(shù)據(jù);模型在融雪期的模擬精度高于非融雪期,在融雪期內(nèi)徑流峰值的模擬精度較低;運(yùn)用改進(jìn)后的臨界溫度對(duì)模擬流量略有降低,說(shuō)明將臨界溫度調(diào)整后積雪轉(zhuǎn)化的徑流量變小,導(dǎo)致模型精度降低。
[Abstract]:The source region of the Yellow River located in the eastern part of the Qinghai-Xizang Plateau is a typical cold and arid region. The observed data in recent years show that under the condition of global warming, the precipitation characteristics of the region have changed to a certain extent. Therefore, it is of great scientific significance to study the influence of precipitation on the runoff process in order to reveal the hydrological cycle and the response mechanism to climate. In this paper, TRMM remote sensing precipitation data are used to analyze the applicability of TRMM data in the source region of the Yellow River and its surrounding areas from the time scale and the spatial scale, using the correlation coefficient, relative error and root mean square error. On the basis of data correlation analysis, MODIS NDVI vegetation data with strong correlation with TRMM precipitation data are selected, and the characteristics of temporal and spatial variation of precipitation in source region are analyzed by using TRMM data, and MODIS NDVI vegetation data with strong correlation with TRMM precipitation data are selected based on the data correlation analysis. The TRMM data with a spatial resolution of 0.25 擄are raised to 1 km by downscaling method, and the annual downscaling TRMM data are converted into monthly precipitation data by using the proportional index method. The downscale TRMM data and the grid precipitation data with a resolution of 0.5 擄are used as input variables, and the runoff of the source region in 2010 is simulated by using the SRM snowmelt runoff model. The effects of two different precipitation data on the accuracy of the model are analyzed. The critical temperature of the model parameters is improved by using the precipitation and temperature data around the source area. The results show that the precipitation data of TRMM are higher than the measured values of meteorological stations, and the precision of the data in April to October is better than that in other low temperature months, and the accuracy of TRMM data increases with the increase of monthly, seasonal, annual time scale, and so on. The spatial distribution of precipitation in the source region is decreasing from southeast to northwest, and the precipitation does not increase linearly with elevation. The correlation coefficient between NDVI and TRMM in the study area is 0.81at the annual scale and has the best correlation at the resolution of 0.75 擄, and the correlation coefficient between the TRMM precipitation data and the observational data of meteorological stations after downscaling is slightly lower than that of the original TRMM data. The relative error and root mean square error have little change, and the monthly data of downscaling TRMM obtained by the proportional index method have some degree of increase in the accuracy of most stations, such as Mador, Dadi and so on. The SRM simulation results using precipitation data of meteorological station as input variables are better than that of 0.5 擄lattice precipitation data and downscale TRMM data, and the simulation accuracy of the model in the snowmelt period is higher than that in the non-snowmelt period, and the simulation accuracy of the peak runoff value in the snowmelt period is lower. The modified critical temperature is used to reduce the simulated flow slightly, which shows that the runoff of snow conversion after adjusting the critical temperature is smaller, which leads to the decrease of model precision.
【學(xué)位授予單位】：中國(guó)地質(zhì)大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TV121

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本文編號(hào)：1922611