Hydrologic Interpretation of Machine Learning Models for 10-daily streamflow simulation in Climate sensitive Upper Indus Catchments
Abstract. Machine learning for hydrologic modeling has seen significant development and has been suggested as a valuable augmentation to physical hydrological modeling, especially in data scarce catchments. In Pakistan, surface water flows predominantly originate from the transboundary Upper Indus sub-catchments of Chenab, Jhelum, Indus and Kabul rivers. These are high elevation data scarce catchments and generated streamflows are highly seasonal and prone to climate change. Given the catchment characteristics, there is utmost need to develop machine learning models that are hydrologically robust. Thus, the current study besides evaluating the potential of three machine learning models for streamflow simulation also focused on the hydrologic interpretation of machine learning models using SHapley Additive exPlananations(SHAP).XGBOOST, RandomForest and Classification and Regression Trees(CART) were evaluated. All of these models performed well and range of R 2 and Nash Efficiency for all three models lies between 0.65 to 0.90. Our study’s most crucial contribution is SHapley Additive exPlananations (SHAP) method which gives extensive insights into the influence of each variable on simulated streamflow. SHAP analysis highlighted the significance of minimum temperature in high elevation zones for both Indus and Chenab catchment where streamflows are dominated by snow and glacier melt. We strongly believe that the findings of this study will promote the use of SHAP analysis for streamflow forecasting in data scarce and high elevation catchments in Pakistan.
Haris Mushtaq et al.
Status: final response (author comments only)
RC1: 'Comment on hess-2022-213', Anonymous Referee #1, 29 Oct 2022
- AC1: 'Reply on RC1', Haris Mushtaq, 08 Feb 2023
RC2: 'Comment on hess-2022-213', Elena Toth, 16 Dec 2022
- AC2: 'Reply on RC2', Haris Mushtaq, 09 Feb 2023
Haris Mushtaq et al.
Haris Mushtaq et al.
Viewed (geographical distribution)
This study applied several machine learning models to streamflow forecast and investigated the model explanation with SHAP. The study is of potential value due to the unique features of hydrological process in Pakistan basins. However, the writing requires careful revision. And the contribution of this study should be further strengthened. I have the following comments for the authors’ consideration.
Line 4-5ã“Given the catchment characteristics, there is an utmost need to develop machine learning models that are hydrologically robust”ï¼ The logic is not convincing. Why there is an utmost need to develop machine learning models under the described catchment characteristics?
Line 5-8 Please split it into two short sentences.
Line 12 “We strongly believe….”. In my personal opinion, it’s better to remove emotional word.
Line 43 “Data-driven”. The initial should be lower-case.
Line 68 “These streamflows are highly seasonal, variant, and prone to extreme events and climate change”. I agree with the authors. The catchments in Pakistan have some special features. Development of machine learning for these catchments provides good chance to explore the potential of machine learning technique. It’s better if the authors could expand the challenges of machine learning on modeling the streamflow in these catchments. In other words, does the machine learning have the potential to forecast streamflow which may be highly seasonal, variant, and prone to extreme events and climate? It is an interesting point to attract the readers and an important contribution for hydrological modeling.
Line 69 Please correct the citation format
Line 87-117 The authors summarized the existing studies of several machine learning models. It will be better if the features (advantages and disadvantages) of these models could be briefly summarized.
Line 118 It is weird to set a “1.1 Hydrologic interpretation” in the Introduction section. I suggest to remove the subtitle.
Line 139 There is typo
Line 131-137 These two paragraphs can be merged.
Line 177-185 Please merge these paragraphs.
Line 187 Please reorganize this sentence.
Line 363-366 Please reorganize this long sentence.
Figure 6 Please reorganize this figure to make it more formal.
Figure 7-9 Is it possible to specifically display the comparison under extreme events and climate (or other unique conditions in Pakistan) during 2005-2014? The long-term comparison in one figure may cover some interesting streamflow patterns, which however can be the new finding of this study.