Assimilation of wide-swath altimetry water elevation anomalies to correct large-scale river routing model parameters
Assimilation of wide-swath altimetry water elevation anomalies to correct large-scale river routing model parameters
复制标题
同化宽幅测高水位异常以修正大规模河流演进模型参数
DOI：
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复制
发表时间：
2020
影响因子：
6.3
通讯作者：
C. David
中科院分区：
文献类型：
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作者：
C. Emery;S. Biancamaria;A. Boone;S. Ricci;M. Rochoux;V. Pedinotti;C. David
抽象。陆面模式结合河流演变模式被广泛用于研究水循环的大陆部分。他们给出了全球估计
水的流量和存储量，但他们不是没有不可忽视的不确定性，其中不准确的输入参数发挥了重要作用。的
即将发射的地表水和海洋地形（SWOT）卫星使命，计划于2021年发射，要求寿命至少为
3年，将致力于测量水面高程，宽度和水面坡度的河流宽超过100米，在
一个全球性的规模。SWOT将为河流水文学提供大量新的观测数据，并可能通过数据同化与
全球规模的模型，以纠正其输入参数，减少其相关的不确定性。将模拟水深与
然而，测量的水面高度仍然是一个挑战，并且可能在系统内引入大的偏差。一个很有前途的替代方案，
同化水面高程包括同化不依赖于参考面的水面高程异常。的
本文的目的是提出一个基于异步集合卡尔曼滤波（AEnKF）的数据同化平台，
水深和水位异常的综合SWOT观测，以校正大规模水文模型的输入参数，
一个21天的时间窗口。该研究应用于亚马逊流域的ISBA-CTRIP模式，重点是纠正亚马逊河流域的空间分布。
河流曼宁系数通过一系列观测系统模拟实验（OSSE）测试的数据同化算法能够
在一个同化周期的大部分时间内，恢复曼宁系数的真实值（流域平均曼宁系数均方根误差，RMSEn，为
在一个同化周期后，从33%减少到[1%-10%]，并显示出同化水异常的前景
（当同化超过1年的水面高程异常时，流域平均曼宁系数RMSEn从33%降低到[1%-2%]），这使得我们能够克服未知水深的问题。
Abstract. Land surface models combined with river routing models are widely used to study the continental part of the water cycle. They give global estimates
of water flows and storages, but they are not without non-negligible uncertainties, among which inexact input parameters play a significant part. The
incoming Surface Water and Ocean Topography (SWOT) satellite mission, with a launch scheduled for 2021 and with a required lifetime of at least
3 years, will be dedicated to the measuring of water surface elevations, widths and surface slopes of rivers wider than 100 m, at
a global scale. SWOT will provide a significant number of new observations for river hydrology and maybe combined, through data assimilation, with
global-scale models in order to correct their input parameters and reduce their associated uncertainty. Comparing simulated water depths with
measured water surface elevations remains however a challenge and can introduce within the system large bias. A promising alternative for
assimilating water surface elevations consists of assimilating water surface elevation anomalies which do not depend on a reference surface. The
objective of this study is to present a data assimilation platform based on the asynchronous ensemble Kalman filter (AEnKF) that can assimilate
synthetic SWOT observations of water depths and water elevation anomalies to correct the input parameters of a large-scale hydrologic model over
a 21 d time window. The study is applied to the ISBA-CTRIP model over the Amazon basin and focuses on correcting the spatial distribution of the
river Manning coefficients. The data assimilation algorithm, tested through a set of observing system simulation experiments (OSSEs), is able to
retrieve the true value of the Manning coefficients within one assimilation cycle much of the time (basin-averaged Manning coefficient root mean square error, RMSEn, is
reduced from 33 % to [1 %–10 %] after one assimilation cycle) and shows promising perspectives with assimilating water anomalies
(basin-averaged Manning coefficient RMSEn is reduced from 33 % to [1 %–2 %] when assimilating water surface elevation anomalies over 1 year), which allows us to overcome the issue of unknown bathymetry.
影响因子：
13.5
作者：
S. Biancamaria;M. Durand;K. Andreadis;P. Bates;A. Boone;N. Mognard;E. Rodríguez;D. Alsdorf;D. Lettenmaier;E. Clark
通讯作者：
S. Biancamaria;M. Durand;K. Andreadis;P. Bates;A. Boone;N. Mognard;E. Rodríguez;D. Alsdorf;D. Lettenmaier;E. Clark