the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A clustering approach to reduce computational expense in land surface models: a case study using JULES vn5.9
Rich Ellis
Eleanor Blyth
Simon Dadson
Abstract. Land surface models such as JULES (the Joint UK Land Environment Simulator) are usually run on a regular, rectilinear grid, resulting in gridded outputs for variables such as soil moisture and water fluxes. Here we investigate a method of clustering grid cells with similar characteristics together in JULES. Clustering grid cells has the potential to reduce computational expense as well as providing an alternative to tiling approaches for capturing sub-grid heterogeneity. In this study, we cluster grid cells exclusively in the land surface part of modelling, i.e., separate from river routing. We compare gridded and clustered soil moisture outputs from JULES with measurements from the UK Centre for Ecology and Hydrology (UKCEH) COSMOS-UK network and show that the clustering approach can model soil moisture well while reducing computational expense. However, soil moisture results are dependent on the characteristics used to create the clusters. We investigate the effect of using clusters on predicted river flows, and compare routed JULES outputs with NRFA gauge data in the catchment. We show that less expensive JULES clustered outputs give similar river flow results to standard gridded outputs when routed at the grid resolution, and are able to match observed river flow better than gridded outputs when routed at higher resolution.
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Elizabeth Cooper et al.
Status: open (until 16 Oct 2023)
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RC1: 'Comment on egusphere-2023-1596', Anonymous Referee #1, 12 Sep 2023
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This paper presents a case study where the JULES land model is run with a vector-based spatial configuration, instead of the more typical grid-based setup. The main benefit of such an approach is that it can lead to computational efficiency, if grid cells with similar hydroclimatic behaviour can be grouped into a single computational unit. As the authors already acknowledge, this approach is not new (in addition to the already existing mention of Swenson et al., 2019, see also for example Gharari et al., 2020). The main novelties in this paper are application to the JULES model, as well as a comparison with observations of three soil moisture observation stations. I believe this is within scope of EGU journals, though HESS may be more appropriate than GMD because the tools the authors use, as well as the general concept, already exist.Â
I do think that the paper needs to be clarified in multiple places (see comments in the uploaded .pdf). Briefly, the paper currently assumes more background knowledge from the reader (both on terminology, as well as on the JULES model) than I think is appropriate. Additionally, methods need to be clarified in multiple places, with a particular focus on explaining to the reader why various choices are appropriate. I particularly want to highlight the choice to run JULES at a daily time step. This seems an uncommon choice for land surface models, and it is unclear to me to what extent results derived from a daily-step model configuration have practical relevance for JULES applications at sub-daily time steps. Given that these methodological choices underpin the analysis, I believe another round of reviews after these clarifications are made may be appropriate.
I further think that the presented analysis on clustering outcomes and similarity between vector-based and gridded setups (section 3.1.1) would benefit from extra analysis. Currently the reader is only presented with two sets of plots for a single time step (out of a 3-year run), and only the most minimal statistics for (I believe) a spatial comparison only. More in-depth analysis is needed to support the statements that:
- Land use and soil type are the most important covariants,
- 1000 vector-based clusters produce sufficiently similar results to a more high-resolution gridded setupFinally, the main conclusion of the paper is that similar JULES performance can be obtained by using a clustering-based spatial discretization scheme as one can get with a traditional gridded setup. First, the paper focus heavily on aggregated efficiency metrics (KGE, NSE, MAE, etc) to compare the gridded and cluster-based setups. While these aggregated scores are indeed similar between both setups, the time series plot in the paper make it very clear that these similar scores are obtained as the result of very different internal model dynamics. I believe the conclusions need to be more nuanced to reflect this. Second and related, I believe that more investigation of these internal dynamics would strengthen the paper. This can involve more detailed analysis of model states and fluxes, as well as comparison to additional external data sources (such as ET) to determine if either of these two setups (gridded or cluster-based) is closer to reality - this would add a new dimension to the paper, in the sense that we would then better understand whether reducing the computational demand of running JULES also comes with a trade-off in model realism. Third (and this is more of a suggestion), the application domain of this test case seems somewhat small to me in both time and space. I believe the paper would be strengthened if the model domain would be larger and/or the simulation times were longer.
Please see the uploaded pdf for further comments.
Elizabeth Cooper et al.
Elizabeth Cooper et al.
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