自然资源遥感, 2023, 35(2): 16-24 doi: 10.6046/zrzyyg.2022143

综述

遥感估算河道流量研究进展

李和谋,1,2,3, 白娟3, 甘甫平,3, 李贤庆1,2, 王泽坤1,2,3

1.中国矿业大学(北京)煤炭资源与安全开采国家重点实验室,北京 100083

2.中国矿业大学(北京)地球科学与测绘工程学院,北京 100083

3.中国自然资源航空物探遥感中心,北京 100083

River discharge estimation based on remote sensing

LI Hemou,1,2,3, BAI Juan3, GAN Fuping,3, LI Xianqing1,2, WANG Zekun1,2,3

1. State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology(Beijing), Beijing 100083, China

2. College of Geoscience and Surveying Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China

3. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China

通讯作者: 甘甫平(1971-),男,研究员,主要从事遥感技术方法及地学应用研究。Email:fpgan@aliyun.com。

责任编辑: 陈理

收稿日期: 2022-04-14   修回日期: 2022-09-13  

基金资助: 自然资源部航空地球物理与遥感地质重点实验室课题“多平台遥感数据估算河流流量方法研究”(2020YFL22)
高分专项(民用)项目“高分航空载荷自然资源调查应用示范”(04-H30G01-9001-20/22-01-08)

Received: 2022-04-14   Revised: 2022-09-13  

作者简介 About authors

李和谋(1998-),男,硕士研究生,主要从事水文学及水资源研究。Email: j710714068@163.com。

摘要

鉴于全球径流数据的可获得性逐年下降,替代水文站点实测河道流量的反演算法正变得越来越重要。当前卫星遥感技术不断发展,估算河道流量的方法亦逐渐丰富,为此,该文对遥感技术反演河道流量的方法进行系统总结,并归纳了与河道流量估算密切相关的水力遥感要素反演方法及其进展情况。首先,梳理基于水文模型和基于经验回归方程2 类算法的方法原理和应用现状; 然后,总结不同方法的适用条件和存在不足; 最后,展望未来通过卫星遥感技术反演世界范围内河道流量的发展趋势: ①积极开发先进卫星遥感数据同化技术; ②集成新的传感器产品; ③优化与创新算法。

关键词： 河道流量; 遥感; 水位; 河宽; 水力特征

Abstract

Since the availability of global runoff data decrease year by year, the inversion algorithms, as substitutes for the river discharge measured at hydrological stations, have become increasingly important. With the continuous development of satellite remote sensing technology, the methods for estimating river discharge have increased in number. This study systematically summarized the remote sensing-based inversion methods for river discharge, as well as the inversion methods for hydraulic remote sensing elements that are closely related to the estimation of river discharge and the progress made in them. Moreover, this study reviewed the methods, principles, and application status of two types of algorithms based on hydrological models and empirical regression equations and summarized the applicable conditions and shortcomings of different methods. Finally, this study predicted the worldwide development trends of the river discharge inversion based on the satellite remote sensing technology, including ① actively developing the advanced data assimilation technology for satellite remote sensing data; ② integrating new sensor products; ③ optimizing and innovating algorithms.

Keywords： river discharge; remote sensing; water level; river width; hydraulic characteristics

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本文引用格式

李和谋, 白娟, 甘甫平, 李贤庆, 王泽坤. 遥感估算河道流量研究进展[J]. 自然资源遥感, 2023, 35(2): 16-24 doi:10.6046/zrzyyg.2022143

LI Hemou, BAI Juan, GAN Fuping, LI Xianqing, WANG Zekun. River discharge estimation based on remote sensing[J]. Remote Sensing for Land & Resources, 2023, 35(2): 16-24 doi:10.6046/zrzyyg.2022143

0 引言

河道流量作为水文循环的重要表征指标和水量平衡的基本组成要素[1⇓⇓-4],是水资源管理和生态环境保护措施制定的主要依据,现有的世界径流数据库对全球流域的时空覆盖范围十分有限,因此关于如何实时、精准、高效地获取河道流量的水文学研究一直在推进。从流量数据源获取手段来讲,目前主要分为站点实测和遥感反演。传统依靠水文站点监测河道流量的方法,从测站的建设到维护运行都需要大量的人力物力,对于气候恶劣和地形复杂地区通过水文站点监测河道流量更加困难,以上情况都导致全球水文观测网难以收集储存流量数据。此外,如今地球上观测网络的覆盖范围正在逐渐缩小[5],不同地区流量数据也存在不同步分享的限制。因此,在过去几十年遥感技术成为缺资料地区或无资料地区信息的唯一来源,河道流量遥感作为一个水文学科新兴领域得到快速发展[6],国内外水文学家致力利用无人机和卫星遥感数据来估算河道流量[7⇓⇓-10]。

遥感技术不仅能获取河流宽度、水面高程、水面流速和河流流速等流域水文变量,并且获得的信息具有实时性、宏观性、持续性等优点,所以遥感信息在缺资料和无资料地区的流量估算中发挥着重要作用。此外基于遥感数据应用再分析、统计插值、数据融合和数据同化等技术,构建全球范围长时间序列的高质量水文气象数据[11],结合水文数值模拟方法能够很大程度上解决缺资料地区的流量估算问题[12⇓-14],进而实现对流域水文循环过程时空变化规律的精准把握。

目前利用遥感技术估算河道流量的方法主要分为基于水文模型和基于经验回归方程2类方法,基于水文模型方法通过建立数学模型主要用于估算整个流域长时间段的日径流量或月径流量,而基于经验回归方程方法则是建立水力特征参数与实测流量经验回归方程主要估算河道特定断面的瞬时流量。其中基于经验回归方程方法根据涉及的水力特征参数个数,可以分为单水力特征参数经验法和多水力特征参数经验法,单水力特征参数经验法包括水位-流量曲线法、河宽-流量曲线法、C/M(calibration/measurement)信号法和多站水力几何(at mang stations hydraulic geometry,AMHG)法。

为了说明遥感技术对反演河道流量的重要作用,本文梳理了各种利用遥感信息估算河道流量方法的原理和研究进展,特别是过去10 a中开展的工作,还详细讨论了不同方法的应用挑战。在此基础上介绍了与河道流量估算密切相关的水力遥感要素反演进展,最后展望了基于遥感信息反演河道流量的未来发展趋势。

1 基于水文模型估算流量

水文模型是用于预测水文要素和理解水文过程的数值模型,在大陆尺度到全球尺度应用的水文模型主要分为地表模型(land surface model,LSM)和水量平衡模型(water balance model,WBM)[15]。LSM通常基于热量和质量平衡方程来定量模拟地表与大气界面间的水和能量通量交换; WBM主要基于水量平衡方程从流域整体估算长期河道流量。遥感技术对水文模型的推动作用主要表现在2个方面: 一是使用遥感获取的信息作为水文模型的驱动数据; 二是使用遥感观测的地表空间信息和河流水力信息校准水文模型参数。水量平衡方程公式为:

R=P-ET-TWCS,

式中: R为流域出口流量; P为降水量; ET为蒸散发量; TWCS为流域总储水变化。

遥感技术能够提供与水文要素密切相关的观测数据,帮助水文模型估算水量收支以反演河道流量。Li等[16]结合GRACE总水储量变化、GPCP降水和GLDAS蒸散发遥感产品,利用水文模型成功反演出黄河流域河道流量; Simons等[17]比较了5种卫星蒸散发产品的精度并对其进行融合,结合TRMM卫星降水产品成功评估了红河流域水储量变化和多年流量,研究表明通过对不同卫星遥感产品数据进行同化处理,有助于提高WBM反演精度,并验证了利用全球卫星数据产品和分布式WBM估算流量在缺资料地区的适用性; Laiolo等[18]研究同化不同遥感土壤水分数据对水文模型河道流量模拟的影响,结果表明即使采用简单的同化技术,对于所有土壤水分数据,模型流量估算精度也有普遍改进; Zhang等[19]使用5 种常用的卫星降水产品作为水文模型的驱动数据,用于估算中国湿润地区赣江流域河道径流,得出结论水文模型的选择比卫星降水产品的选择更重要,因为水文模型能够通过参数校准抵消不同降水输入对径流模拟的影响。

水文模型通常需要参数校准才能正常使用,在缺资料地区遥感数据被视为校准水文参数、验证水文模型的重要来源。Kittel等[20]采用了一种基于水文特征相似性和有观测数据流域与无资料流域的空间邻近性相结合的校准策略,即使用有流量观测数据的流域模型参数率定结果推算无资料流域的模型参数,并在非洲3个流域内成功应用,此方法有效提高了水文模型在无资料地区应用的可靠性和覆盖范围; Getirana等[21]将ENVISAT星载雷达测高数据用于南美洲亚马孙河的水文模型参数率定,结果表明在水文模型参数率定时采用雷达测高数据能得到模型合理参数; Liu等[22]使用河流水位和河流宽度对美国北部红河进行水文模型参数率定,分析表明采用此方法能够估算大型无资料地区河流的流量; Sun等[23] 探讨了青藏高原雅砻江上游流域采用遥感河流宽度校准水文模型的可靠性,得出结论对于中型无资料地区,仅基于高精度遥感河宽数据校准的水文模型能够估算河道流量; Wongchuig-Correa等[24]通过数据同化研究了SWOT(Surface Water and Ocean Topography)卫星校准水文模型的潜力,结果表明SWOT 数据可以改善水文模型的径流模拟; Huang等[25]使用类SWOT数据校准水文模型,估算了雅鲁藏布江和拉萨河的流量,纳什效率系数(Nash-Sutcliffe efficiency coefficient,NSE)分别达到0.85和0.75。

水文模型对于大尺度流域估算长时间序列的河道流量具有较好适用性,并且能够解析流域范围内的水文循环过程。总体上采用水文模型进行河道流量反演的不足体现在3个方面,即输入不确定性、模型不确定性和参数不确定性。尽管目前卫星遥感技术能够提供高时间分辨率的水文模型驱动数据,但空间分辨率往往较低,且不同卫星数据产品精度、分辨率存在差异,导致该方法反演结果的误差是各驱动数据源的累积,在量化每个卫星遥感产品的误差方面都存在挑战; 其次,因为各模型结构、流域形态的刻画、水文过程描述和物理基础概念的不同,导致各模型最终模拟结果存在很大差异,在如今各卫星遥感数据精度和分辨率不断提高的背景下,模型本身对反演精度的影响越来越明显,定量分析各模型适用区域范围存在难度; 最后,WBM参数校准要求很高,高度依赖地面观测数据进行参数率定,并且良好的校准参数可移植性低,不能保证在邻近地理区域以及气候或下垫面发生显著变化时流量的演算精度。

2 基于经验回归方程估算流量

在单站水力几何(at a stations hydraulic geometry, AHG)中,存在宽度和流量、深度和流量、流速和流量3种经验关系[26],基于经验回归方程估算流量本质即根据可获取得的遥感观测数据估算上述3个要素,利用估算的要素推算流量。河流宽度、深度和流速与流量的经验公式分别为:

w=aQb
d=cQf
v=kQm

式中: w为河流宽度; d为河流深度; v为河流流速,Q为河流流量; a,b,c,f,k和m为经验参数。

2.1 水位-流量经验曲线法

传统水文站监测河流流量,通常是在不同时期多次测量过水断面面积和断面平均流速进而得到实测流量,随后建立观测水位与观测流量之间的经验关系曲线,这样在以后工作中,只需要观测河流水位,就可以通过水位-流量关系曲线估算河道流量。当前可以通过遥感技术观测水面高程,与附近地面实测流量数据结合反演河道流量。

Kouraev和Zakharova等[27-28]先后选取了鄂毕河与亚马孙河的4个水文站点,建立了T/P (TOPEX/Poseidon)卫星高程测量数据导出的水位与水文站实测流量的经验关系式,结果表明T/P卫星测高数据可以成功应用于大型流域的部分河道流量演算; Zakharova等[29]还在勒拿河的研究中,利用河道支流的观测数据和中游的Jason-2/3卫星测高数据相结合,建立了3 个虚拟测站水位-流量曲线,估算了流域下游的月流量和年流量,提高了演算空间覆盖范围度; Birkinshaw等[30]利用ERS-2和ENVISAT测高卫星数据,结合地面实测河道断面估算了湄公河流量,并明确指出卫星测高数据对河道流量估算和提高估算精度有巨大潜力; Papa等[31-32]结合了T/P,ERS-2和ENVISAT卫星测高数据导出的河流高度,以及水文站实测日径流量,得出了1993—2008年恒河—雅鲁藏布江的月平均流量数据集,在此基础上使用Jason-2测高数据将该数据集拓展到2011年; Junqueira等[33]利用常规水文站水位和Jason-2测高数据导出的水位,结合Planet CubeSats卫星数据,开发了一种基于水位反演流量的半自动方法,与常规方法相比反演精度更高并能监测地表水域变化。

基于河流断面水位遥感估算河道流量的不足主要为适用范围局限、精度不高和时间分辨率低。首先水位-流量关系曲线依赖于现场水文资料观测,如前所述全球水文测站的减少与数据共享的限制,此方法演算的流量数据往往是作为有水文资料区域的补充; 另一个限制是由于目前卫星高度计的空间分辨率,该方法仍然局限于上千米宽的河流估算。其次,影响水位-流量经验关系的因素较多,如泥沙沉淀改变河床形态或人类活动修建水库等,都会导致演算精度的降低,可能需要经常重新测量水深和流量校准经验公式。另外,反演河流水位的测高卫星重返周期较长,如Topex/Poseidon和Jason重返周期为10 d以及ERS-2和ENVISAT重返周期为35 d,无法估算更精细的时间尺度流量。

2.2 河宽-流量经验曲线法

相比于河流水位,河流宽度更容易从多源遥感数据中获得,如合成孔径雷达(synthetic aperture Radar,SAR)和光学卫星等。利用遥感测量得到的河流宽度或平均河流宽度,结合地面测站数据,可以得到河宽与流量之间的经验关系,进而估算河道流量。

Smith 等[34⇓-36]基于ERS-1 SAR影像和MODIS影像提取的河流宽度建立宽度-流量关系曲线反演出4 条北半球高纬度地区辫状河流量,发现采用此方法可以有效预测偏远地区大型河流的流量,并且同一段河流的上下游水面宽度变化有较强的相关性,在河流长度足够长时,可以将建立的河宽-流量关系曲线延用到河流其他位置; Pavelsky等[37]针对SAR不能频繁采样和光学传感器易受云层影响的问题,提出了一种从空间不连续图像提取河流宽度估算流量,在美国塔纳诺河应用的相对误差仅6.7%; Elmi等[38]提出基于分位数函数的河流流量遥感估算方法,通过实测流量和计算河宽的分位数函数来构建河宽-流量曲线,这种方法不需要流量数据与卫星图像同步观测,在非洲尼日尔河的验证结果估算均方根误差为10%。

该算法不足在于依然需要地面实测径流数据,并且对于平坦地形上的河流和中小河流,河流宽度随流量变化幅度微小时也无法采用此算法。对于算法需要的SAR和光学卫星影像都有自身的局限性,SAR影像会受到植被散射影响,光学影像会受到云层和植被的限制。

2.3 C/M信号法

在一些河流中水面宽度随流量的变化很小,需要足够高空间分辨率的卫星传感器反复观测,这一点通常难以达到。然而河道水面面积和河道水面宽度为正相关关系,所以河段地表水区域的成像能替代河流水面宽度作为流量反演的近似指标。Brakenridge等[39]基于36.5 GHz的微波扫描辐射计(AMSR-E)波段,根据不受河流影响的陆地像元(C像元)与位于河道的水体像元(M像元)二者的亮温比值,提出估算河道流量的C/M信号法。要求选取的C像元位于河流附近始终不被水体淹没的陆地上,M像元部分或完全覆盖河道,其亮温随水体变化影响较大。据此得到C/M比,结合地面实测径流建立线性回归方程反演河道流量。C/M方法简单、具有鲁棒性,已应用于“全球洪水探测系统”。

Brakenridge等[39]将C/M时间序列法在全球多个流域进行应用,采用AMSR-E被动微波辐射数据在美国2条实测径流数据充足的河流可以实现日尺度径流模拟,在实测数据有限的欧洲、亚洲和非洲也能进行月尺度流量模拟; Tarpanelli等[40]采用空间分辨率更高的MODIS光学图像提取每个水文站周围的C/M比估算了意大利北部波河流量,结果优于采用AMSR-E数据的C/M信号法反演结果; 随后Tarpanelli等[41]根据尼日尔河上游4种MODIS卫星产品(由Terra和Aqua 2颗卫星派生)采用C/M信号法预测出4 d后尼日尔河下游流量,结果表明不同卫星产品对预测结果影响不大,但时间分辨率更高的影像预测结果更稳定。

Revilla⁃Romero等[42]在非洲、亚洲、欧洲、北美洲和南美洲不同气候和土地覆盖类型的河流应用了C/M信号法,发现气候类型、土地覆盖和流域面积是模拟效果的主要影响因素; 许继军等[43]在中国七大典型流域应用C/M信号法,得到相似结论,即气候类型、断面河宽等是影响C/M信号法模拟效果的主要因素; Van Dijk等[44]同时采用被动微波和光学影像应用C/M信号法模拟了全球范围内442 条河流的月径流量,结果表明采用光学影像的模拟效果总体上优于采用被动微波的模拟效果,但光学卫星传感器会受到云层和植被覆盖的影响,且热带地区的模拟精度高于干旱地区和温带地区; Kim等[45]提出了考虑地形及其对流域内流量影响来改进C/M信号法,利用地形湿度指数作为定性指标提取澳大利亚代表不同气候、流域大小和海拔的179 个水文站的C/M值,结果表明采用此方法提取的C/M值反演河道流量有明显改进; Li等[46]为克服C/M信号法仅适用于河宽在200~300 m河流的局限性,基于C/M信号法发展出多像素比(multiple pixel ratio,MPR)法,相比C/M信号法采用的单像素反射率,此方法采用多个像素的平均反射率,并采用此方法对黑河上游的2条子流域流量进行估算,结果表明MPR法不仅在空间分辨率上比C/M信号法更精细,而且估算结果具有更高稳定性。国内针对C/M信号法等典型微波亮温河道径流模拟方法的研究也仅处于起步阶段。

C/M信号法反演河道流量的数据来源主要为光学影像和被动微波辐射。其中光学影像的应用局限性为: ①云层覆盖等大气因素会限制水陆面积提取; ②植被覆盖会影响水体面积变化的识别。被动微波辐射虽然不受气候和地理条件限制,对云层和地表植被的穿透能力较强,但空间分辨率太低,如AMSR-E空间分辨率为15 km。

2.4 AMHG法

Gleason等[47]在AHG基础上研究发现了AMHG,即在天然河流中河宽、水深和平均流速的系数参数与其指数参数沿着河道存在对数-线性相关关系,该方法并不需要任何地面观测或者先验信息,仅根据河流宽度、水深和流速的时空变化就可以计算得到河流流量,其中河流宽度信息可以通过不同时相的Landsat影像获得。Gleason等[48]将AMHG方法应用在全球34条不同气候条件的大型河流,估算流量与实测流量的相对均方根误差在26%~41%之间; Rao等[49]结合AMHG方法与遗传算法演算了印度4条主要河流的流量, NSE都在0.8以上,分析表明此方法对低和中等流量的河流具有更好的适应性,但不适用于自然剖面为矩形的人工河道和洪水状态的河流; Mengen等[50]利用不受气候影响的Sentinel-1A/1B时间序列河宽测量数据,采用等分阈值法,即将估算流量值聚合于特定范围的流量时间序列之中,改进了Gleason的AMHG算法,不仅弥补了Landsat卫星多云雨季无法观测河流宽度的不足,并且时间分辨率从16 d提高至6 d,相对均方根误差降低为19.5%; Hagemann等[51]提出了利用河流宽度、水位和坡度的BAM(Bayesian AMHG-manning)算法,此算法精度相比AMHG法有所提高,结合SWOT卫星数据可以估算无资料地区河流流量。

河宽、水深和水流平均速度都符合AMHG规律,由于水面宽度是卫星遥感观测中能够直接获得的数据且观测精度较高,因此目前主要基于AMHG河宽法估算流量。AMHG方法的不足主要是应用范围不够全面,如在辫状河、宽度小于100 m河流以及汛期流量的估算不够精确; 时间分辨率低,受观测卫星重返周期限制。

2.5 多水力特征参数经验法

相比于通过单一水力特征参数与流量的经验关系估算河道流量,使用多个水力特征参数的流量经验反演算法考虑到了更多的河道变异性,反演结果具有更高精度[52]。针对不同河道的断面特征,根据曼宁公式或其他明渠流流量方程,综合河流宽度、水深、坡度和流速等变量,构建包含多变量的河道流量方程,再利用遥感技术观测的水力特征参数估算河道流量。其公式分别为:

Q=ewgdh
Q=iwjdlsn

式中: s为河道坡度; e,g,h,i,j,l和n为未知参数。

Birkinshaw等[53]使用ERS-2和ENVISAT卫星高度计测量出的河流水位和河流平均坡度以及Landsat数据导出的平均河流宽度来估算湄公河与鄂毕河的流量,NSE介于0.86~0.9之间; Bjerklie等[54]使用Jason-2雷达卫星测量水面高度、坡度和Landsat卫星观测的水体面积确定有效水面宽度,采用曼宁公式和普朗特卡门公式2种物理流阻方程估算育空河的流量; Sichangi等[55]利用多卫星测高数据与MODIS数据导出的水位和有效河流宽度用于修正曼宁公式中的未知参数,结合实地径流数据估算了全球8条河流的流量,NSE介于0.60~0.97之间,证明了该算法的有效性,并且发现同时使用水位和河流有效宽度的反演算法始终优于仅使用水位反演流量的算法; Huang等[56]使用Landsat和Sentinel-1/2数据导出的水面宽度结合Jason-2/3和SARAL/Altika测高卫星的水位数据得出相似结论,基于多水力特征参数估算结果优于单水力参数估算结果; Kebede等[57]根据卫星遥感数据估算出河流宽度、流速、水位、坡度和河道粗糙系数,进而利用修正的曼宁方程估算了长江下游的流量变化,选用2个水文站验证NSE为0.5和0.76,虽然反演精度还有待提升,但是该方法能够在完全不使用地面观测数据的情况下估算河道流量,可以应用于无水文测站的大型河道; Garambois等[58]在综合SWOT数据的基础上考虑河流宽度、水位和坡度,基于圣维南方程反演河道等效水深和摩擦系数,再估算河道流量; Yang等[4,59]提出采用更灵活的航空遥感(无人机)技术观测河道水力特征参数来估算缺资料地区或无资料地区的河道流量,在青藏高原和准噶尔盆地流域结合无人机数据与坡度-面积法估算,NSE为0.97,在博尔塔拉河利用无人机观测的动石速度转化为河流平均流速来估算流量; Wufu等[60]将无人机绘制的断面图与现场采集的流速数据相结合,估算天山地区19 条河段的流量,NSE为0.98,这些应用表明航空遥感技术在反演缺资料地区或无资料地区河道流量方面也具有巨大潜力。

以上文献的研究对象基本上都是大型河道,对于中小河道的应用还需要进一步研究。该方法不足在于要求反演地区存在多源重叠遥感数据集,实际应用中由于不同卫星任务轨道和重返周期的不同,卫星获得的水力特征数据重叠极其有限,导致该算法适用范围小; 多水力特征参数的引入虽然能够提高精度,但也会造成多数据源的误差累积,结果精度受限于最小分辨率的数据源。

3 水力要素遥感反演

利用遥感技术估算河道流量的方法,都依赖于河道水力要素遥感反演,卫星观测的水力要素一方面与水文模型结合能够提高估算精度,另一方面直接与实测流量拟合经验回归方程可以直接估算流量。目前卫星遥感技术可以观测与河道流量估算密切相关的水力要素包括河流水位、河流宽度、河流流速和河道坡度。

河流水位主要通过星载雷达高度计测量,雷达高度计首先利用一定频率的微波脉冲信号测量出卫星轨道高度,即卫星高度计相对于参考椭球面的高度; 然后利用卫星轨道高度减去卫星距离水面的距离; 最终经过计算确定基于参考椭球面的水面高程,精度为厘米级别。目前已发射的雷达高度计主要针对监测海洋水面进行设计和优化[61],如TOPEX-Poseidon、ENVISAT、ERS系列、Jason系列和ICEsat系列等,所以在处理陆域水体测高数据时存在挑战。SWOT卫星作为第一个专门用于陆域水体观测的卫星发射后,将极大提高星载雷达高度计的全球空间覆盖范围。光学遥感技术是目前河流水深测量的主要方法,基于光线对水体的透射原理,由水底反射回光学遥感器的信息能够直接估算出河流水深。

卫星遥感测量河流宽度主要通过雷达或光学影像,如QuickBird,IKONOS,WorldView,MODIS,Landsat,ENVISAT和Sentinel系列等。对于高空间分辨率影像可直接测量河道断面两岸陆水交接点的距离作为河流宽度; 对于中低空间分辨影像,因为存在陆水混合像元,直接测量河流宽度的误差较大,则可以提取一定范围内水体面积除以河道长度作为平均河流宽度反演河道流量。

河流流速也是估算河道流量的重要水力要素,实地河流流量测量通常需要对与河流流向正交的横断面使用流速仪对垂直剖面流速进行空间测量[62]。基于卫星遥感测量河流流速较为成功的方法是使用合成孔径雷达干涉测量(interferometric synthetic aperture Radar,InSAR),对同一地区拍摄2幅时间差在毫秒级的SAR影像,利用2幅影像之间的相位差估算水面流速[63-64]。也有学者通过遥感技术追踪河流冰屑、悬浮沉积物等物体的运动,估算相应的水面速度[65]。通过卫星遥感技术只能获取河流表面流速,在进行流量估算时转化为断面平均流速存在挑战。

河流坡度也是能够通过卫星遥感反演用于河道流量估算的水力要素。根据数字高程模型(digital elevation model,DEM)提取的河道高程差可以直接计算河流坡度,但采用此方法反演的静态河面坡度不能反映河流流量的动态变化,所以部分学者采用星载雷达高度计观测不同断面间的水面高程差来估算河面坡度[54]。

表1对上述不同方法估算河流流量的研究进行了总结。

表1   利用遥感估算河流流量的相关研究综述

Tab.1  A review of related researches on estimating river flows using remote sensing

研究方法研究人员及年份使用数据/空间分辨
率/重返周期		研究流域主要结论
基于水文模型Getirana等[21]
(2013年)		ENVISAT/350 m/35 d南美洲亚马孙河在水文模型参数率定时采用雷达测高数据能得到模型合理参数
Liu等[22](2015年)Landsat/30 m/16 d
ENVISAT/350 m/35 d		北美红河此方法能够估算大型无资料地区河流的流量
Sun等[23](2018年)QuickBird/0.6 m/4~6 d
IKONOS/0.58 m/3 d
WorldView-1/0.81 m/1.7 d		中国雅砻江仅基于高精度遥感河宽数据校准的水文模型能够估算河道流量
基于经验回归方程水位-流量经验曲线法Kouraev等[27]
(2004年)		TOPEX-Poseidon/600 m/10 d北极鄂毕河卫星测高数据可以估算大型流域的部分河道流量演算
Birkinshaw等[30]
(2010年)		ERS-2/30 m/35 d
ENVISAT/350 m/35 d		亚洲湄公河NSE介于0.823~0.935之间
Papa等[31]
(2012年)		Jason-2/12.5 m/10 d亚洲恒河和雅鲁藏布江平均误差为13%和6.5%
河宽-流量经验曲线法Smith等[36](2008年)MODIS/250 m/8 d俄罗斯勒拿河在河流长度足够长时,可以将建立的河宽-流量关系曲线延用到河流其他位置
Pavelsky等[37]
(2014年)		RapidEye/5 m/1 d北美塔纳诺河相对误差为6.7%
Elmi等[38]
(2015年)		MODIS/250 m/8 d非洲尼日尔河改进河宽-流量经验曲线算法不需要流量数据与卫星图像同步观测
C/M信号法Brakenridge等[39]
(2007年)		AMSR-E/25 km/16 d全球57 条河流基于被动微波遥感亮度温度的C/M信号法能够估算河流流量
Tarpanelli等[40]
(2013年)		MODIS/250 m/8 d欧洲波河基于光学遥感数据的C/M信号法可以估算中型流域流量
Li等[46](2019年)Landsat/30 m/16 d中国黑河基于C/M信号法发展出MPR法,能够估算小河流流量
AMHG法Gleason等[48]
(2014年)		Landsat/30 m/16 d全球34 条河流相对均方根误差介于26%~41%之间
Rao等[49](2020年)ResourceSat/23 m/24 d
Landsat/30 m/16 d		印度4 条河流NSE介于0.8~0.89之间
Mengen等[50]
(2020年)		Sentinel-1/10 m/6,12 d亚洲湄公河采用SAR卫星遥感数据,相对均方根误差为19.5%
多水力特征参数经验法Birkinshaw等[53]
(2012年)		ERS-2/30 m/35 d
ENVISAT/350 m/35 d
Landsat/30 m/16 d		亚洲湄公河和北极鄂毕河联合水位、河宽和河道坡度估算流量,NSE介于0.86~0.9之间
Sichangi等[55]
(2016年)		MODIS/250 m/8 d
10 个测高卫星数据		全球8 条河流使用卫星反演水位和有效河宽估算流量,NSE介于0.60~0.97之间
Bjerklie等[54]
(2018年)		Jason-2/12.5 m/10 d
ICESat/70 m/91 d
Landsat/30 m/16 d		北美育空河采用曼宁公式和普朗特卡门公式2种物理流阻方程估算流量
Yang等[4]
(2019年)		航空遥感(无人机)中国新疆10 条河流坡度-面积法与无人机遥感技术结合,能够估算无资料地区河流流量

新窗口打开| 下载CSV


4 结论与展望

总体上遥感技术对拓展新的反演河道流量方法和提高现有方法预测能力具有巨大潜力,遥感观测的多样化,为反演流量算法的数据获取提供了坚实保障,光学传感器可以提供更高观测频率和分辨率的地表空间数据,SAR能够实现无资料地区的全天候监测; 此外,卫星遥感能够提供土地利用/土地覆盖类型、河网水系分布和河道水力信息等地表空间信息,一方面可以有效确定物理参数,强化算法物理基础,另一方面动态变化监测可作为约束算法的率定参数,减少不确定性。虽然遥感技术估算河道流量比实地监测流量存在明显优势,但必须明确的是遥感流量数据永远不能取代实测流量数据,只有将2种形式的河道流量监测结合,才能更加全面地理解全球水文循环。河道流量遥感未来可以考虑从以下几个方面深入研究:

1)积极开发先进遥感数据同化技术。不同数据源(SAR和光学传感器)不同传感器都有各自优势和限制,将多尺度、多传感器的遥感数据进行联合同化,克服自身局限性,获得具有更高精度的观测产品,降低各算法数据输入的不确定性。

2)集成新的传感器产品。提高各水文变量和水力变量的监测能力,在现阶段基础上进一步提高各观测数据的时空分辨率,实现各观测目标的同步观测,提供更多的校准方向选择,不仅能够提高现有算法精度,还能在无资料地区进行河道反演。如2022 年底将要发射的SWOT卫星能通过观测湖泊、水库和大型河道的水储量变化来衡量全球地表水分收支,并以前所未有的时空分辨率观测全球范围内河流的水位、宽度、坡度。

3)优化与创新算法。遥感技术反演河道流量的潜力取决于利用多源遥感数据估算流量和流量相关参数算法的发展,优化或创新算法以约束估算流量和流量相关参数的不确定性,并允许对算法本身的不确定性进行量化和处理,以达到更高预测精度。

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Estimating river discharges in ungauged catchments using the slope-area method and unmanned aerial vehicle

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River discharge is of great significance in the development of water resources and ecological protection. There are several large ungauged catchments around the word still lacking sufficient hydrological data. Obtaining accurate hydrological information from these areas is an important scientific issue. New data and methods must be used to address this issue. In this study, a new method that couples unmanned aerial vehicle (UAV) data with the classical slope–area method is developed to calculate river discharges in typical ungauged catchments. UAV data is used to obtain topographic information of the river channels. In situ experiments are carried out to validate the river data. Based on slope–area method, namely the Manning–Strickler formula (M–S), Saint-Venant system of equivalence (which has two definitions, S-V-1 and S-V-2), and the Darcy–Weisbach equivalence (D–W) are used to estimate river discharge in ten sections of the Tibet Plateau and Dzungaria Basin. Results show that the overall qualification rate of the calculated discharge is 70% and the average Nash–Sutcliffe efficiency coefficient is 0.97, indicating strong practical application in the study area. When the discharge is less than 10 m3⁄s, D–W is the most appropriate method; M–S and S-V-1 are better than other methods when the discharge is between 10 m3⁄s and 50 m3⁄s. However, if the discharge is greater than 50 m3⁄s, S-V-2 provides the most accurate results. Furthermore, we found that hydraulic radius is an important parameter in the slope–area method. This study offers a quick and convenient solution to extract hydrological information in ungauged catchments.

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Gleason C J, Durand M T.

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Remote sensing of river discharge (RSQ) is a burgeoning field rife with innovation. This innovation has resulted in a highly non-cohesive subfield of hydrology advancing at a rapid pace, and as a result misconceptions, mis-citations, and confusion are apparent among authors, readers, editors, and reviewers. While the intellectually diverse subfield of RSQ practitioners can parse this confusion, the broader hydrology community views RSQ as a monolith and such confusion can be damaging. RSQ has not been comprehensively summarized over the past decade, and we believe that a summary of the recent literature has a potential to provide clarity to practitioners and general hydrologists alike. Therefore, we here summarize a broad swath of the literature, and find after our reading that the most appropriate way to summarize this literature is first by application area (into methods appropriate for gauged, semi-gauged, regionally gauged, politically ungauged, and totally ungauged basins) and next by methodology. We do not find categorizing by sensor useful, and everything from un-crewed aerial vehicles (UAVs) to satellites are considered here. Perhaps the most cogent theme to emerge from our reading is the need for context. All RSQ is employed in the service of furthering hydrologic understanding, and we argue that nearly all RSQ is useful in this pursuit provided it is properly contextualized. We argue that if authors place each new work into the correct application context, much confusion can be avoided, and we suggest a framework for such context here. Specifically, we define which RSQ techniques are and are not appropriate for ungauged basins, and further define what it means to be ‘ungauged’ in the context of RSQ. We also include political and economic realities of RSQ, as the objective of the field is sometimes to provide data purposefully cloistered by specific political decisions. This framing can enable RSQ to respond to hydrology at large with confidence and cohesion even in the face of methodological and application diversity evident within the literature. Finally, we embrace the intellectual diversity of RSQ and suggest the field is best served by a continuation of methodological proliferation rather than by a move toward orthodoxy and standardization.

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This study assesses the suitability of five popular satellite-based precipitation products in modeling water balance in a humid region of China during the period 1998–2012. The satellite-based precipitation products show similar spatial patterns with varying degrees of overestimation or underestimation, compared with the gauged precipitation. A distributed hydrological model is used to evaluate the suitability of satellite-based precipitation products in simulating streamflow, evapotranspiration and soil moisture. The simulations of streamflow and evapotranspiration forced by the MSWEP precipitation perform best among the five satellite-based precipitation products, where the Kling-Gupta efficiency (KGE) between the simulated and observed streamflow ranges from 0.75 to 0.91, and the KGE between the simulated and observed evapotranspiration ranges from 0.46 to 0.61. However, the KGE between the simulated and observed soil moisture is negative, indicating that the performance of soil moisture simulation forced by satellite-based precipitation is poor. In addition, this study finds the spatial pattern of simulated streamflow is dominated by the distribution of precipitation, whereas the distribution of evapotranspiration and soil moisture is controlled by the parameters of the hydrological model. This study is useful for the improvement of hydrological modeling based on remote sensing and the monitoring of regional water resources.

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Due to the rapid decline of in situ observations on river discharge in Arctic regions, evaluation of the continental freshwater input to the Arctic Ocean has become problematic and necessitates the development of alternative approaches based on remote sensing. Radar altimetric satellites have demonstrated high potential for estimation of river water discharge. Compared to polar orbiting altimeters, non-polar orbit satellites have an advantage in temporal sampling. Their greatest drawback, however, is spatial coverage: observations do not cover the low reaches of most parts of Arctic rivers. In this study of the Lena River, we demonstrate a way to overcome this limitation by using a combination of in situ observations from tributaries and satellite observations in the middle river reaches. The water discharge as well as monthly and annual water flow were evaluated using three virtual stations. Direct combination of the water level from these virtual stations was not possible because of the difference in seasonal amplitude. However, the combination of altimetric discharge from the three independently processed tracks significantly improves the flow retrievals. The accuracy of the monthly water flow estimates at the river outlet is 23%. It increases with the integration time giving 7% for annual flow.

Birkinshaw S J, O’Donnell G M, Moore P, et al.

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[J]. Hydrological Processes, 2014, 28(6):3035-3040.

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[C]// Proceedings of the Geoscience and Remote Sensing Symposium, 2015:727-730.

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Revilla-Romero B, Thielen J, Salamon P, et al.

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[J]. Hydrology and Earth System Sciences, 2014, 18(11):4467-4484.

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. One of the main challenges for global hydrological modelling is the limited availability of observational data for calibration and model verification. This is particularly the case for real-time applications. This problem could potentially be overcome if discharge measurements based on satellite data were sufficiently accurate to substitute for ground-based measurements. The aim of this study is to test the potentials and constraints of the remote sensing signal of the Global Flood Detection System for converting the flood detection signal into river discharge values. The study uses data for 322 river measurement locations in Africa, Asia, Europe, North America and South America. Satellite discharge measurements were calibrated for these sites and a validation analysis with in situ discharge was performed. The locations with very good performance will be used in a future project where satellite discharge measurements are obtained on a daily basis to fill the gaps where real-time ground observations are not available. These include several international river locations in Africa: the Niger, Volta and Zambezi rivers. Analysis of the potential factors affecting the satellite signal was based on a classification decision tree (random forest) and showed that mean discharge, climatic region, land cover and upstream catchment area are the dominant variables which determine good or poor performance of the measure\\\\-ment sites. In general terms, higher skill scores were obtained for locations with one or more of the following characteristics: a river width higher than 1km; a large floodplain area and in flooded forest, a potential flooded area greater than 40%; sparse vegetation, croplands or grasslands and closed to open and open forest; leaf area index > 2; tropical climatic area; and without hydraulic infrastructures. Also, locations where river ice cover is seasonally present obtained higher skill scores. This work provides guidance on the best locations and limitations for estimating discharge values from these daily satellite signals.\n

许继军, 屈星, 曾子悦, 等.

基于高精度遥感亮温的典型流域河道径流模拟分析

[J]. 水科学进展, 2021, 32(6):877-889.

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Van Dijk A I, Brakenridge G R, Kettner A J, et al.

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[C]// Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(13):4788.

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Mengen D, Ottinger M, Leinenkugel P, et al.

Modeling river discharge using automated river width measurements derived from Sentinel-1 time series

[J]. Remote Sensing, 2020, 12(19):3236.

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Against the background of a worldwide decrease in the number of gauging stations, the estimation of river discharge using spaceborne data is crucial for hydrological research, river monitoring, and water resource management. Based on the at-many-stations hydraulic geometry (AMHG) concept, a novel approach is introduced for estimating river discharge using Sentinel-1 time series within an automated workflow. By using a novel decile thresholding method, no a priori knowledge of the AMHG function or proxy is used, as proposed in previous literature. With a relative root mean square error (RRMSE) of 19.5% for the whole period and a RRMSE of 15.8% considering only dry seasons, our method is a significant improvement relative to the optimized AMHG method, achieving 38.5% and 34.5%, respectively. As the novel approach is embedded into an automated workflow, it enables a global application for river discharge estimation using solely remote sensing data. Starting with the mapping of river reaches, which have large differences in river width over the year, continuous river width time series are created using high-resolution and weather-independent SAR imaging. It is applied on a 28 km long section of the Mekong River near Vientiane, Laos, for the period from 2015 to 2018.

Hagemann M W, Gleason C J, Durand M T.

BAM:Bayesian AMHG-manning inference of discharge using remotely sensed stream width,slope,and height

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Sichangi A W, Wang L, Yang K, et al.

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An improved approach to monitoring Brahmaputra River water levels using retracked altimetry data

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Kebede M G, Wang L, Yang K, et al.

Discharge estimates for ungauged rivers flowing over complex high-mountainous regions based solely on remote sensing-derived datasets

[J]. Remote Sensing, 2020, 12(7):1064.

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Reliable information about river discharge plays a key role in sustainably managing water resources and better understanding of hydrological systems. Therefore, river discharge estimation using remote sensing techniques is an ongoing research goal, especially in small, headwater catchments which are mostly ungauged due to environmental or financial limitations. Here, a novel method for river discharge estimation based entirely on remote sensing-derived parameters is presented. The model inputs include average river width, estimated from Landsat imagery by using the modified normalized difference water index (MNDWI) approach; average depth and velocity, based on empirical equations with inputs from remote sensing; channel slope from a high resolution shuttle radar topography mission digital elevation model (SRTM DEM); and channel roughness coefficient via further analysis and classification of Landsat images with support of previously published values. The discharge of the Lhasa River was then estimated based on these derived parameters and by using either the Manning equation (Model 1) or Bjerklie equation (Model 2). In general, both of the two models tend to overestimate discharge at moderate and high flows, and underestimate discharge at low flows. The overall performances of both models at the Lhasa gauge were satisfactory: comparisons with the observations yielded Nash–Sutcliffe efficiency coefficient (NSE) and R2 values ≥ 0.886. Both models also performed well at the upper gauge (Tanggya) of the Lhasa River (NSE ≥ 0.950) indicating the transferability of the methodology to river cross-sections with different morphologies, thus demonstrating the potential to quantify streamflow entirely from remote sensing data in poorly-gauged or ungauged rivers on the Tibetan Plateau.

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Inferrence of effective river properties from remotely sensed observations of water surface

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Yang S, Li C, Lou H, et al.

Performance of an unmanned aerial vehicle (UAV) in calculating the flood peak discharge of ephemeral rivers combined with the incipient motion of moving stones in arid ungauged regions

[J]. Remote Sensing, 2020, 12(10):1610.

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Ephemeral rivers are vital to ecosystem balance and human activities as essential surface runoff, while convenient and effective ways of calculating the peak discharge of ephemeral rivers are scarce, especially in ungauged areas. In this study, a new method was proposed using an unmanned aerial vehicle (UAV) combined with the incipient motion of stones to calculate the peak discharge of ephemeral rivers in northwestern China, a typical arid ungauged region. Two field surveys were conducted in dry seasons of 2017 and 2018. Both the logarithmic and the exponential velocity distribution methods were examined when estimating critical initial velocities of moving stones. Results reveal that centimeter-level orthoimages derived from UAV data can demonstrate the movement of stones in the ephemeral river channel throughout one year. Validations with peak discharge through downstream culverts confirmed the effectiveness of the method. The exponential velocity distribution method performs better than the logarithmic method regardless of the amount of water through the two channels. The proposed method performs best in the combination of the exponential method and the river channel with evident flooding (>20 m3/s), with the relative accuracy within 10%. In contrast, in the river channel with a little flow (around 1 m3/s), the accuracies are weak because of the limited number of small moving stones found due to the current resolution of UAV data. The poor performance in the river channel with a little flow could further be improved by identifying smaller moving stones, especially using UAV data with better spatial resolution. The presented method is easy and flexible to apply with appropriate accuracy. It also has great potential for extensive applications in obtaining runoff information of ephemeral rivers in ungauged regions, especially with the quick advance of UAV technology.

Wufu A, Chen Y, Yang S, et al.

Changes in glacial meltwater runoff and its response to climate change in the Tianshan region detected using unmanned aerial vehicles (UAVs) and satellite remote sensing

[J]. Water, 2021, 13(13):1753.

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The Tianshan Mountains, known as the “water tower” of Central Asia, are the major source of water for the most part of Xinjiang and oasis region of Central Asia. However, climate warming has amplified the discharges of glacial meltwater in the Tianshan Mountains. In this study, we calculated river discharge by integrating cross-sections mapped using unmanned aerial vehicles (UAV) and water velocity data collected in the field. Multiple remote sensing images, such as Landsat and Sentinel-2 imagery, were applied to estimate the long-term discharge of 19 river sections in ungauged regions of the Tianshan Mountains. River discharge variations under climate change were also examined. Using our in-situ measured discharges as reference, the UAV derived discharge results have an NSE (Nash–Sutcliffe efficiency) of 0.98, an RMSE (root mean square error) of 8.49 m3/s, and an average qualification rate of 80%. The monthly discharge of glacial meltwater-dominated river sections showed an average decrease of 2.46% during 1989–2019. The shrinking and even disappearance of mountain glaciers (approximately −4.98 km2/year) was the main reasons for the decrease trend. However, the precipitation-dominated river sections showed an average increase of 2.27% for the same period. The increase in precipitation (approximately 1.93 mm/year) was the key cause for the increase tendency. This study highlights remote sensing hydrological station technology and its application in the long-term prediction of river discharge, which is critical for decision-making regarding integrated water resource management in alpine regions.

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