Assessing the hydrological and geomorphic behaviour of a landscape evolution model within a limits‐of‐acceptability uncertainty analysis framework

Abstract

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.

Living in the “Age of Humans”. Envisioning CAD Architecture for the Challenges of the Anthropocene—Energy, Environment, and Well-Being

“…This program was focused on the design of the adaptive built environment for Anthropocene societies and the envisioning and conceptualization of new architectural solutions, based on advanced digital technology. This research used several recent studies: The Anthropocene Working Group (AWG) and the Stockholm Resilience Centre (SRC) were found to be especially noteworthy [3][4][5][6][7]19,24,27,28,30,31,[35][36][37]39]. The AWG is an interdisciplinary research group dedicated to the study of the Anthropocene as a geological time unit, whilst the SRC is an international research center looking into resilience and sustainability science.…”

“…However, future climate change impact assessments are highly uncertain. For the first time, eleven global climate and hydrological models were used to systematically assess the hydrological response to climate change and forecast the future state of global resources of water [25][26][27][28].…”

“…Recently, strategies have been established to predict external environmental variations along with internal interactions between occupants, as an answer to the various weather phenomena of the global climate change era [26,28]. The new architecture and urbanism require a new way of thinking and visions of the built environment.…”

“…The first part of the research provided an answer as to how human activities have altered different landscapes at a variety of spatial and temporal scales i.e., plastic alluvial sediments from waste are an integral and environmentally sensitive component of geological data in the Age of Humans [4,7,9,[28][29][30]39,[46][47][48][49]. Landscape evolution models (LEMs) have the ability to characterize key aspects of geomorphological and hydrological processes.…”

Modelling urban sewer flooding and quantitative microbial risk assessment: A critical review

“…However, in many situations field‐based studies may not be efficient enough for appropriate data acquisition. Another important point is the use of uncertainty and sensitivity analysis, as this also can improve model outputs alongside model calibration (Liu et al, 2020; Petterson & Ashbolt, 2016; Savage et al, 2016; Thorndahl et al, 2008; Wong et al, 2021).…”