Assessing the hydrological and geomorphic behaviour of a landscape evolution model within a limits-of-acceptability uncertainty analysis framework,Earth Surface Processes and Landforms

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Assessing the hydrological and geomorphic behaviour of a landscape evolution model within a limits-of-acceptability uncertainty analysis framework
Earth Surface Processes and Landforms ( IF 2.8 ) Pub Date : 2021-04-27 , DOI: 10.1002/esp.5140
Jefferson S. Wong _{1,

2} , Jim E. Freer _{1,

3,

4} , Paul D. Bates _{1,

3} , Jeff Warburton ₅ , Tom J. Coulthard ₆

Affiliation

Landscape evolution models (LEMs) have the capability to characterize key aspects of geomorphological and hydrological processes. However, their usefulness is hindered by model equifinality and paucity of available calibration data. Estimating uncertainty in the parameter space and resultant model predictions is rarely achieved as this is computationally intensive and the uncertainties inherent in the observed data are large. Therefore, a limits-of-acceptability (LoA) uncertainty analysis approach was adopted in this study to assess the value of uncertain hydrological and geomorphic data. These were used to constrain simulations of catchment responses and to explore the parameter uncertainty in model predictions. We applied this approach to the River Derwent and Cocker catchments in the UK using a LEM CAESAR-Lisflood. Results show that the model was generally able to produce behavioural simulations within the uncertainty limits of the streamflow. Reliability metrics ranged from 24.4% to 41.2% and captured the high-magnitude low-frequency sediment events. Since different sets of behavioural simulations were found across different parts of the catchment, evaluating LEM performance, in quantifying and assessing both at-a-point behaviour and spatial catchment response, remains a challenge. Our results show that evaluating LEMs within uncertainty analyses framework while taking into account the varying quality of different observations constrains behavioural simulations and parameter distributions and is a step towards a full-ensemble uncertainty evaluation of such models. We believe that this approach will have benefits for reflecting uncertainties in flooding events where channel morphological changes are occurring and various diverse (and yet often sparse) data have been collected over such events.

中文翻译：

在可接受限度的不确定性分析框架内评估景观演变模型的水文和地貌行为

景观演化模型 (LEM) 能够表征地貌和水文过程的关键方面。然而，模型等价性和可用校准数据的缺乏阻碍了它们的实用性。估计参数空间中的不确定性和由此产生的模型预测很少实现，因为这是计算密集型的，并且观测数据中固有的不确定性很大。因此，本研究采用可接受限度（LoA）不确定性分析方法来评估不确定水文和地貌数据的价值。这些用于限制流域响应的模拟并探索模型预测中的参数不确定性。我们使用 LEM CAESAR-Lisflood 将这种方法应用于英国的德文特河和科克流域。结果表明，该模型通常能够在水流的不确定性限制内产生行为模拟。可靠性指标范围从 24.4% 到 41.2%，并捕获了高强度低频沉积物事件。由于在流域的不同部分发现了不同的行为模拟集，因此在量化和评估点行为和流域空间响应方面评估 LEM 性能仍然是一个挑战。我们的结果表明，在不确定性分析框架内评估 LEM，同时考虑到不同观测的不同质量会限制行为模拟和参数分布，并且是对此类模型进行全集成不确定性评估的一步。

更新日期：2021-04-27

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