本文關(guān)鍵詞： 微波遙感 土壤濕度 數(shù)據(jù)同化 誤差估計(jì) 周期性誤差 集合卡爾曼平滑 半分布式水文模型　出處：《武漢大學(xué)》2016年博士論文　論文類(lèi)型：學(xué)位論文

【摘要】：近年來(lái),人口增長(zhǎng)與全球變化大環(huán)境下,干旱、洪水、熱浪等極端天氣事件頻發(fā),氣候?yàn)?zāi)害不斷加劇。從全球尺度來(lái)看,地球的氣候系統(tǒng)與水循環(huán)過(guò)程,十分龐大且極其復(fù)雜。作為大氣與陸表過(guò)程耦合的重要狀態(tài)變量,土壤濕度的精準(zhǔn)時(shí)空刻畫(huà)對(duì)于加深地球氣候系統(tǒng)認(rèn)知起著關(guān)鍵作用。隨著衛(wèi)星傳感器與計(jì)算機(jī)技術(shù)的日臻完善,基于衛(wèi)星平臺(tái)的遙感手段能夠探測(cè)或反演多種區(qū)域氣候變量,而主被動(dòng)微波遙感已被廣泛應(yīng)用于全球尺度的土壤濕度觀測(cè)。盡管目前全球多源衛(wèi)星微波土壤濕度產(chǎn)品能夠提供長(zhǎng)達(dá)近四十年的長(zhǎng)時(shí)序數(shù)據(jù)集,然而數(shù)據(jù)質(zhì)量參差不齊,針對(duì)多源衛(wèi)星微波土壤濕度反演的誤差評(píng)估與精度刻畫(huà)顯得十分必要。同時(shí),觀測(cè)僅能夠提供瞬時(shí)真值,對(duì)于水文過(guò)程研究與預(yù)報(bào)預(yù)警而言,仍需結(jié)合水文模型。由此,本文針對(duì)衛(wèi)星土壤濕度觀測(cè)精度以及水文數(shù)據(jù)同化框架展開(kāi)理論與方法的研究,主要內(nèi)容如下：(1)以數(shù)據(jù)同化框架為主線,從觀測(cè)、模型、算法三大數(shù)據(jù)同化基本要素出發(fā),分別系統(tǒng)地總結(jié)當(dāng)前衛(wèi)星微波土壤濕度觀測(cè)、水文過(guò)程數(shù)值模型、數(shù)據(jù)同化算法三個(gè)方面的國(guó)內(nèi)外研究現(xiàn)狀與基礎(chǔ)理論方法。通過(guò)分析其各自在實(shí)際應(yīng)用所存在的優(yōu)勢(shì)與不足,從而引出本文的研究目標(biāo)與基本任務(wù)。(2)多源衛(wèi)星微波遙感土壤濕度來(lái)源豐富,通過(guò)衛(wèi)星升降軌能夠提供當(dāng)?shù)貢r(shí)上午與下午兩個(gè)時(shí)刻的觀測(cè),然而具體應(yīng)用時(shí)往往主要選用晚間或凌晨時(shí)段所獲取的觀測(cè)。本文首先針對(duì)多源衛(wèi)星微波遙感土壤濕度的觀測(cè)精度進(jìn)行研究,包括主動(dòng)與被動(dòng)微波來(lái)源。以觀測(cè)獲取時(shí)刻為基本劃分,分析不同時(shí)刻地表溫度與植被冠層溫度差異、植被含水量等觀測(cè)條件對(duì)于土壤濕度反演誤差的影響。選取美國(guó)大陸為研究區(qū)域,將主動(dòng)、被動(dòng)微波以及陸面過(guò)程模式所分別得到的表層土壤濕度觀測(cè)組合在一起,采用空間大尺度的三重組合法,分析觀測(cè)時(shí)刻對(duì)于誤差的影響。進(jìn)一步結(jié)合地面站點(diǎn)量測(cè),運(yùn)用直接對(duì)比法進(jìn)行交叉驗(yàn)證。通過(guò)上述兩組獨(dú)立估計(jì)方法,揭示衛(wèi)星微波遙感土壤濕度觀測(cè)誤差與獲取時(shí)刻、遙感手段、地表覆被及反演算法間的關(guān)聯(lián)關(guān)系,呈現(xiàn)不同地表覆被條件下多源衛(wèi)星土壤濕度觀測(cè)精度的基本空間分布。(3)衛(wèi)星數(shù)據(jù)驗(yàn)證與實(shí)際運(yùn)用時(shí),通常假定土壤濕度觀測(cè)僅具有高斯分布的隨機(jī)誤差,例如水文數(shù)據(jù)同化。然而受地表覆被空間異質(zhì)性、衛(wèi)星周期性采樣與產(chǎn)品處理方式等因素的影響,基于衛(wèi)星的土壤濕度反演可能存在周期性誤差。針對(duì)這一問(wèn)題,本文從頻率域角度分析被動(dòng)微波土壤濕度周期性誤差產(chǎn)生的基本物理機(jī)制。通過(guò)模擬實(shí)驗(yàn)、單點(diǎn)驗(yàn)證、全球評(píng)估等多角度探究周期性誤差出現(xiàn)的原因,結(jié)合站點(diǎn)土壤濕度觀測(cè)與高空間分辨率的土地利用數(shù)據(jù),揭示周期性誤差與地表覆被空間異質(zhì)性間的關(guān)系。選取當(dāng)前連續(xù)觀測(cè)時(shí)段最長(zhǎng)的被動(dòng)微波土壤濕度數(shù)據(jù)作為研究對(duì)象,運(yùn)用平均周期圖法估計(jì)土壤濕度高頻與低頻的功率譜密度,采用本文所提出的高頻峰值檢測(cè)算法,從而獲取全球周期性誤差的空間分布圖。根據(jù)土壤濕度反演算法的物理機(jī)制,以衛(wèi)星直接獲取的亮溫觀測(cè)為數(shù)據(jù)源,提出基于亮溫觀測(cè)派生參量且能夠刻畫(huà)地表覆被特性的空間異質(zhì)性指數(shù),從而對(duì)周期性誤差可能存在的區(qū)域進(jìn)行預(yù)測(cè)分析。(4)水文數(shù)據(jù)同化通過(guò)將觀測(cè)與具有物理機(jī)制的水文模型相結(jié)合,能夠緩解多源異質(zhì)來(lái)源數(shù)據(jù)的不確定性,獲取具有物理一致性、時(shí)空連續(xù)的陸表水文過(guò)程狀態(tài)估計(jì)與預(yù)測(cè)。盡管目前集合卡爾曼濾波及其變種算法己在數(shù)據(jù)同化領(lǐng)域得到充分的實(shí)驗(yàn)論證,具體應(yīng)用中仍存在一定問(wèn)題。本文選取中國(guó)黑河上游八寶河流域作為研究區(qū)域,通過(guò)組織模型基礎(chǔ)地理與驅(qū)動(dòng)輸入數(shù)據(jù),校準(zhǔn)水文模型相關(guān)敏感性參數(shù),建立數(shù)據(jù)同化的模型基礎(chǔ)。通過(guò)不同來(lái)源驅(qū)動(dòng)數(shù)據(jù),包括站點(diǎn)觀測(cè)與天氣預(yù)報(bào)模型再分析資料,構(gòu)建觀測(cè)系統(tǒng)模擬試驗(yàn)框架。針對(duì)集合卡爾曼平滑算法與復(fù)雜水文模型的數(shù)據(jù)同化問(wèn)題,以半分布式水文模型為模式依托,引入膨脹因子與局地化改進(jìn)方法,改善背景場(chǎng)誤差協(xié)方差矩陣估計(jì),提升集合卡爾曼平滑算法處理高維狀態(tài)估計(jì)的效用。通過(guò)表層土壤濕度觀測(cè)的數(shù)據(jù)同化,改進(jìn)深層與根區(qū)土壤濕度以及關(guān)鍵水文變量的估計(jì)值。聯(lián)合分析空間異質(zhì)的輸入數(shù)據(jù)與參數(shù)以及數(shù)據(jù)同化改進(jìn)算法,揭示多重因素對(duì)數(shù)據(jù)同化效果的影響,包括降水、土壤類(lèi)型以及土地利用數(shù)據(jù)等。
[Abstract]:In recent years, population growth and global change environment, droughts, floods, heat waves and other extreme weather events and frequent climate disasters intensified. From the view of global scale climate system and the water cycle process of the earth is very large and extremely complex. As an important state variable atmospheric and land surface process coupling, the precise temporal soil moisture the characterization plays a key role in the global climate system. With the deepening cognition of satellite sensor and computer technology is improving, the means of satellite platform remote sensing can detect or inversion of multiple regional climate variables based on soil moisture observation and passive microwave remote sensing has been widely used in the global scale. Despite the current global multi satellite microwave soil moisture products to provide the long time-series data for nearly forty years, however, the uneven quality of data, aiming at Multi-source Satellite Microwave soil moisture retrieval It is necessary to describe the evaluation accuracy and error. At the same time, the observation can only provide instantaneous true value for research and prediction of hydrological process, still need to combine with the hydrological model. Thus, the research of satellite soil moisture observation accuracy and hydrological data assimilation framework theory and method, the main contents are as follows: (1) to the data assimilation framework as the main line, from the observation model, three algorithms based on data assimilation of basic elements, were systematically summarized the current satellite microwave soil moisture observation, numerical model of hydrological processes, the three aspects of Data Assimilation Algorithm Research Status and basic theory methods. Through the analysis of their respective advantages and existed in the practical application which leads to insufficient, and basic tasks of the research goal of this paper. (2) multi satellite microwave remote sensing of soil moisture rich source, via satellite to provide local rail lift The observation of the morning and in the afternoon the two time, however, specific applications are often observed using the evening or early morning hours are obtained. This paper firstly according to the observation accuracy of multi satellite microwave remote sensing of soil moisture, including active and passive microwave sources. To observe the time for obtaining the basic division, to analyze the different time land surface temperature and vegetation canopy the temperature difference effect of vegetation water content observation conditions for soil moisture inversion error. Select the United States, as the study area, the active, passive microwave and land surface models were obtained from the surface soil moisture observations together with the three combination method of large scale space, analysis of the influence of observation time for the error. Combining with the ground station measured by direct comparison method of cross validation. Through the above two groups of independent estimation methods, revealing the satellite microwave Time, and access to remote sensing soil moisture observation error of remote sensing, land cover and the relationship between the inversion algorithm, different land cover distribution of space observation accuracy of soil moisture under the condition of multi satellite. (3) satellite data validation and practical application, usually assume that the soil moisture observation has only the random error of Gauss distribution for example, the hydrological data assimilation. However the land cover spatial heterogeneity, the influence factors of periodic sampling and processing satellite products such as satellite, soil moisture inversion may exist periodic error based on. To solve this problem, the basic physical mechanism based on frequency domain analysis of passive microwave soil moisture cycle error through the simulation experiment, single point verification, global assessment of multiple perspectives to explore the causes of periodic error, combined with soil moisture observations with high spatial resolution The land use data, reveal periodic error and ground cover relationship between spatial heterogeneity. Selection of passive microwave soil moisture data is currently the longest continuous observation period as the research object, using the average periodogram estimation of soil moisture in the high-frequency and low-frequency power spectral density, high frequency peak detection algorithm proposed in this paper, space in order to obtain the distribution map of global periodic error. According to the physical mechanism of soil moisture inversion algorithm, with direct access to the satellite brightness temperature observations as the data source, the brightness temperature observations derived parameters and can depict surface cover characteristics of spatial heterogeneity index based on the prediction analysis and periodic error may exist in the region. (4) hydrologic data assimilation by hydrological observation and model with physical mechanism combining multi-source and heterogeneous data sources can alleviate the uncertainty, get out Physical consistency, continuous spatio-temporal land surface hydrological process state estimation and prediction. Although Calman set filtering algorithm and its variants have been demonstrated fully in the field of data assimilation, some problems still exist in the specific application. This paper selects eight Chinese upstream of Heihe River Basin as the study area, through the organization model of geography and drive input data, calibration of hydrological model sensitivity parameters, based on the data assimilation model. Data driven by different sources, including site observation and weather forecast model reanalysis data, build observation system simulation experiment framework. According to the data assimilation problem sets Calman smoothing algorithm and the complex hydrological model, the semi distributed hydrological model for model based on the introduction of improved method of local factors and expansion, improve the background error covariance matrix estimation, improve collection card Coleman smoothing algorithm for state estimation of utility. Through data assimilation of surface soil moisture observations, improved estimates of deep soil moisture and root zone and key hydrological variable. The improved algorithm combined with analysis of spatial heterogeneity of input data and parameters and data assimilation, uncovering the influence of multiple factors, shown on the effect of data assimilation including precipitation. Soil types and land use data.

【學(xué)位授予單位】：武漢大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：S152.71;S127

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