141 citations as recorded by crossref.

1. Basin-informed flood frequency analysis using deep learning exhibits consistent projected regional patterns over CONUS R. Lazin et al. https://doi.org/10.1038/s41598-025-97610-2
2. A hybrid model with a physics-constrained neural network to improve hydrodynamic prediction W. Liu et al. https://doi.org/10.1016/j.envsoft.2025.106699
3. Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al. https://doi.org/10.5194/hess-29-5233-2025
4. A data-driven runoff-sediment prediction system with explainable hybrid deep learning models M. Zhang et al. https://doi.org/10.1016/j.catena.2026.110446
5. Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification M. Fan et al. https://doi.org/10.1016/j.envsoft.2023.105849
6. Challenges and opportunities of ML and explainable AI in large-sample hydrology L. Slater et al. https://doi.org/10.1098/rsta.2024.0287
7. A Hybrid Muskingum–Machine Learning Flood Forecasting Model: Application and Evaluation in the Tarim River Basin P. Wang et al. https://doi.org/10.3390/w18091077
8. A physically guided and interpretable SWAT-BiLSTM framework with Bayesian optimization for bias correction in daily streamflow forecasting L. Jin et al. https://doi.org/10.1016/j.jconhyd.2026.104952
9. Advancing Sub-Seasonal to Seasonal Streamflow Forecasting in Canada: A Review of Conventional and Emerging Approaches for Operational Applications D. Nguyen et al. https://doi.org/10.1016/j.rineng.2025.106345
10. Enhancing streamflow predictions with machine learning and Copula-Embedded Bayesian model averaging A. Sattari et al. https://doi.org/10.1016/j.jhydrol.2024.131986
11. Accelerating urban flood prediction using a dual-stream Transformer‑CNN model with spatiotemporal feature fusion and uncertainty quantification W. Gao et al. https://doi.org/10.1016/j.jhydrol.2026.135119
12. A stochastic multi-objective calibration framework for semi-distributed rainfall–runoff models in transboundary river basins T. Nguyen et al. https://doi.org/10.1007/s00477-026-03321-z
13. Towards a global spatial machine learning model for seasonal groundwater level predictions in Germany S. Kunz et al. https://doi.org/10.5194/hess-29-3405-2025
14. Fully differentiable, fully distributed rainfall-runoff modeling F. Scholz et al. https://doi.org/10.5194/hess-29-6257-2025
15. A heterogeneous multi-graph spatio-temporal network for runoff forecasting X. Zhou et al. https://doi.org/10.1016/j.engappai.2026.114967
16. Enhancing runoff predictions in data-sparse regions through hybrid deep learning and hydrologic modeling S. Chen et al. https://doi.org/10.1038/s41598-024-77678-y
17. Evaluating the parameter sensitivity and impact of hydrologic modeling decisions on flood simulations A. Alexander et al. https://doi.org/10.1016/j.advwatres.2023.104560
18. Unstructured mesh-based graph neural networks for estimating the spatiotemporal distribution of a human-induced chemical in freshwater S. Kim et al. https://doi.org/10.1016/j.wroa.2025.100367
19. Enhancing probabilistic hydrological predictions with mixture density Networks: Accounting for heteroscedasticity and Non-Gaussianity D. Li et al. https://doi.org/10.1016/j.jhydrol.2024.131737
20. Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets F. Hasan et al. https://doi.org/10.3390/w16131904
21. Uncertainty quantification of machine learning models to improve streamflow prediction under changing climate and environmental conditions S. Liu et al. https://doi.org/10.3389/frwa.2023.1150126
22. Uncertainty in Environmental Micropollutant Modeling H. Ahkola et al. https://doi.org/10.1007/s00267-024-01989-z
23. Bayesian deep learning for probabilistic aquifer vulnerability and uncertainty prediction T. Mengistu et al. https://doi.org/10.1038/s41598-025-32612-8
24. A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting E. Snieder & U. Khan https://doi.org/10.5194/hess-29-785-2025
25. The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) J. Mai et al. https://doi.org/10.5194/hess-26-3537-2022
26. Machine Learning-Based Reconstructions of Historical Daily and Monthly Runoff for the Laurentian Great Lakes R. Gupta et al. https://doi.org/10.1038/s41597-026-07000-0
27. A probabilistic machine learning framework for daily extreme events forecasting A. Sattari et al. https://doi.org/10.1016/j.eswa.2024.126004
28. Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model A. Tursun et al. https://doi.org/10.1016/j.ejrh.2024.101744
29. Technical note: How many models do we need to simulate hydrologic processes across large geographical domains? W. Knoben et al. https://doi.org/10.5194/hess-29-2361-2025
30. Estimating uncertainty in flood model outputs using machine learning informed by Monte Carlo analysis M. Nguyen et al. https://doi.org/10.1016/j.jhydrol.2025.133928
31. Evidential uncertainty quantification with multiple deep learning architectures for spatiotemporal drought forecasting A. Ferchichi et al. https://doi.org/10.1007/s00521-025-11026-7
32. Machine learning in stream and river water temperature modeling: a review and metrics for evaluation C. Corona & T. Hogue https://doi.org/10.5194/hess-29-2521-2025
33. Multi-site deep learning for groundwater level prediction across global datasets: toward scalable applications under data scarcity A. Nolte et al. https://doi.org/10.2166/hydro.2025.095
34. Application of Deep Learning in Drainage Systems Monitoring Data Repair—A Case Study Using Con-GRU Model L. He et al. https://doi.org/10.3390/w15081635
35. Bayesian deep learning framework for pesticide transport modeling with uncertainty quantification L. Xiang & P. Qu https://doi.org/10.1016/j.ress.2026.112692
36. Interpretable machine-learning intensity-duration-frequency (IDF) surface modeling in a data-scarce Ethiopian Rift Valley Lake Basin A. Tesfaye et al. https://doi.org/10.1016/j.ejrh.2026.103463
37. Data reformation – A novel data processing technique enhancing machine learning applicability for predicting streamflow extremes V. Tran et al. https://doi.org/10.1016/j.advwatres.2023.104569
38. Zero-shot forecasting of streamflow using time series foundation models: are we there yet? A. Sun & A. Sun https://doi.org/10.1088/3049-4753/ae4982
39. Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al. https://doi.org/10.3390/w16152161
40. Optimized decomposition and deep learning with bias correction for reliable runoff point-interval prediction H. Ma et al. https://doi.org/10.1038/s41598-025-33713-0
41. Deep Learning-Based Monthly Runoff Simulation in Changing Environments: Enhancing Accuracy by Reducing Data Redundancy S. Liu et al. https://doi.org/10.1007/s11269-026-04680-6
42. Response to hydrometeorological changes and evaluation of habitat quality in the Dongting Lake basin, China H. Wang et al. https://doi.org/10.2166/wcc.2024.179
43. Use of one-dimensional CNN for input data size reduction in LSTM for improved computational efficiency and accuracy in hourly rainfall-runoff modeling K. Ishida et al. https://doi.org/10.1016/j.jenvman.2024.120931
44. Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations Y. Zhang et al. https://doi.org/10.5194/hess-27-4529-2023
45. A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven https://doi.org/10.1002/wat2.1761
46. On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration S. Wi & S. Steinschneider https://doi.org/10.5194/hess-28-479-2024
47. Improvement and application of the Green-Ampt model integrating mechanism correction and intelligent parameter prediction L. Xu et al. https://doi.org/10.1016/j.advwatres.2026.105439
48. RETRACTED: A Physics‐Aware Machine Learning‐Based Framework for Minimizing Prediction Uncertainty of Hydrological Models A. Roy et al. https://doi.org/10.1029/2023WR034630
49. ML-DPIE: comparative evaluation of machine learning methods for drought parameter index estimation: a case study of Türkiye Ö. Çoban et al. https://doi.org/10.1007/s11069-023-06233-1
50. How confident and reliable are deep learning models for streamflow prediction under flood conditions M. Albared et al. https://doi.org/10.1007/s11069-025-07527-2
51. Uncertainty Reduction in Streamflow Prediction Using Mixture Density Networks N. Kumar & T. Roy https://doi.org/10.1061/JHYEFF.HEENG-6781
52. Exploring soil moisture dynamics and variability across scales and geological settings using gaussian mixture-long short-term memory networks B. Bischof et al. https://doi.org/10.1016/j.jhydrol.2025.134364
53. Bayesian extreme learning machines for hydrological prediction uncertainty J. Quilty et al. https://doi.org/10.1016/j.jhydrol.2023.130138
54. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. https://doi.org/10.1029/2022WR034352
55. Learning landscape features from streamflow with autoencoders A. Bassi et al. https://doi.org/10.5194/hess-28-4971-2024
56. Monthly Runoff Prediction Via Mode Decomposition-Recombination Technique X. Yang et al. https://doi.org/10.1007/s11269-023-03668-w
57. Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins K. Ma et al. https://doi.org/10.1007/s11442-024-2235-x
58. Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell E. Acuña Espinoza et al. https://doi.org/10.5194/hess-29-1749-2025
59. Deep transfer learning based on transformer for flood forecasting in data-sparse basins Y. Xu et al. https://doi.org/10.1016/j.jhydrol.2023.129956
60. Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff D. Althoff & G. Destouni https://doi.org/10.1016/j.oneear.2023.08.002
61. Streamflow prediction with aleatoric uncertainty quantification in the Yala river basin via Fokker–Planck equation-based frameworks S. Houénafa et al. https://doi.org/10.1016/j.ejrh.2025.102921
62. Uncertainty-aware deep learning for wildfire danger forecasting S. Kondylatos et al. https://doi.org/10.1088/3049-4753/ae89b9
63. Uncertainty Assessment in Deep Learning-based Plant Trait Retrievals from Hyperspectral data E. Cherif et al. https://doi.org/10.5194/bg-23-2235-2026
64. Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions K. Tripathy & A. Mishra https://doi.org/10.1016/j.jhydrol.2023.130458
65. A deep learning modeling framework with uncertainty quantification for inflow-outflow predictions for cascade reservoirs V. Ngoc Tran et al. https://doi.org/10.1016/j.jhydrol.2024.130608
66. Diagnosing the Added Value of Remote Sensing and Gridded Precipitation for Daily Runoff Forecasting Under Strong Antecedent Runoff Control R. Song et al. https://doi.org/10.3390/su18147494
67. Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States K. Hunt et al. https://doi.org/10.5194/hess-26-5449-2022
68. Hydro–Meteorological Coupled Runoff Forecasting Using Multi-Model Precipitation Forecasts Z. Zhu et al. https://doi.org/10.3390/w18050638
69. Multi-step-ahead forecasting of daily reference evapotranspiration using hybrid deep learning models for the Taklamakan Desert oasis M. Tang et al. https://doi.org/10.1016/j.ejrh.2025.102663
70. Hybrid Lake Model (HyLake) v1.0: unifying deep learning and physical principles for simulating lake-atmosphere interactions Y. He & X. Yang https://doi.org/10.5194/gmd-18-9257-2025
71. A variational approach at uncertainty estimation in data-driven rainfall-runoff modeling M. Álvarez Chaves et al. https://doi.org/10.1088/3049-4753/ae89ba
72. How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent? N. Mangukiya et al. https://doi.org/10.1002/hyp.14936
73. A data-centric perspective on the information needed for hydrological uncertainty predictions A. Auer et al. https://doi.org/10.5194/hess-28-4099-2024
74. Locally relevant streamflow reanalysis using ensemble data assimilation and a gauge-based adaptive localization scheme V. Prakash & M. Saharia https://doi.org/10.1016/j.jhydrol.2026.136277
75. Simulating hydrologic pathway contributions in fluvial and karst settings: An evaluation of conceptual, physically-based, and deep learning modeling approaches A. Husic et al. https://doi.org/10.1016/j.hydroa.2022.100134
76. A deep learning-based probabilistic approach to flash flood warnings in mountainous catchments Y. Zhao et al. https://doi.org/10.1016/j.jhydrol.2025.132677
77. Attributing terrestrial water storage changes on the Tibetan Plateau to climate and human drivers using a hybrid deep learning approach Z. Yang et al. https://doi.org/10.1016/j.jhydrol.2026.135599
78. Deep Learning zur Optimierung der Trinkwasserversorgung aus Talsperren G. Johnen et al. https://doi.org/10.1007/s35152-025-1982-z
79. WATER LEVEL PREDICTIONS THAT VISUALIZE UNCERTAINTY USING LSTM COUPLED WITH ALTERNATIVE METHODS IN BAYESIAN INFERENCE AT A RESERVOIR NEAR A DRAINAGE PUMPING STATION N. KIMURA et al. https://doi.org/10.2208/jscejj.23-22011
80. Adapting cities to the surge: A comprehensive review of climate-induced urban flooding G. Dharmarathne et al. https://doi.org/10.1016/j.rineng.2024.102123
81. Advancing Machine Learning-Based Streamflow Prediction Through Event Greedy Selection, Asymmetric Loss Function, and Rainfall Forecasting Uncertainty S. Tofighi et al. https://doi.org/10.3390/app152111656
82. A novel hybrid fine-tuning method for supercharging deep learning model development for hydrological prediction M. Jahangir et al. https://doi.org/10.1016/j.envsoft.2026.106978
83. Streamflow forecast model using ANN D. Sonowal et al. https://doi.org/10.1080/23249676.2024.2310098
84. Karhunen–Loève deep learning method for surrogate modeling and approximate Bayesian parameter estimation Y. Wang et al. https://doi.org/10.1016/j.advwatres.2025.105024
85. Pooling local climate and donor gauges with deep learning for improved reconstructions of streamflow in ungauged and partially gauged basins S. Wi et al. https://doi.org/10.1016/j.jhydrol.2025.133764
86. Hydropower flexibility valuation tool for flow requirement evaluation M. Roni et al. https://doi.org/10.1016/j.egyr.2022.11.172
87. Global prediction of extreme floods in ungauged watersheds G. Nearing et al. https://doi.org/10.1038/s41586-024-07145-1
88. EARLS: a runoff reconstruction dataset for Europe D. Klotz et al. https://doi.org/10.5194/essd-18-5485-2026
89. Is smart sampling worth it? Impact of training data selection on the performance of LSTMs in streamflow prediction B. Heudorfer & R. Loritz https://doi.org/10.2166/nh.2026.119
90. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al. https://doi.org/10.5194/hess-28-4187-2024
91. A hybrid data-driven approach to analyze the drivers of lake level dynamics M. Somogyvári et al. https://doi.org/10.5194/hess-28-4331-2024
92. Multi-Step Ahead Probabilistic Forecasting of Daily Streamflow Using Bayesian Deep Learning: A Multiple Case Study F. Ghobadi & D. Kang https://doi.org/10.3390/w14223672
93. Coupling hydrological, geotechnical and machine learning models to enhance landslide prediction for an early warning system: application to Upper Garonne River Basin, Pyrenees, Spain F. Asurza et al. https://doi.org/10.1007/s10346-025-02685-7
94. UQEF-Dynamic: A scalable open-source tool for forward uncertainty quantification and global sensitivity analysis of time-varying model outputs I. Buha et al. https://doi.org/10.1016/j.envsoft.2026.107088
95. A Case Study of Tidal Analysis Using Theory-Based Artificial Intelligence Techniques for Disaster Management in Taehwa River, South Korea K. Kareem et al. https://doi.org/10.3390/w14142172
96. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. https://doi.org/10.5194/hess-26-3377-2022
97. Improving streamflow simulation through machine learning-powered data integration and its potential for forecasting in the Western U.S. Y. Yang et al. https://doi.org/10.5194/hess-29-5453-2025
98. Ensemble post-processing of sub-seasonal to seasonal precipitation forecasts based on a novel probabilistic double machine learning method S. Zhan et al. https://doi.org/10.1016/j.jhydrol.2025.133484
99. Remote Sensing and Artificial Intelligence in Flood Prediction: Progress, Challenges, and Prospects S. ShravanKumar et al. https://doi.org/10.1007/s11831-025-10466-7
100. LSTM-Based Model for Predicting Inland River Runoff in Arid Region: A Case Study on Yarkant River, Northwest China J. Li et al. https://doi.org/10.3390/w14111745
101. Predictive Performance of Ensemble Learning Boosting Techniques in Daily Streamflow Simulation D. Chandran & N. Chithra https://doi.org/10.1007/s11269-024-04029-x
102. Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty https://doi.org/10.1016/j.jhydrol.2023.130498
103. Harnessing Novel Data‐Driven Techniques for Precise Rainfall–Runoff Modeling S. Sammen et al. https://doi.org/10.1111/jfr3.70013
104. Streamflow forecasting with deep learning models: A side-by-side comparison in Northwest Spain J. Farfán-Durán & L. Cea https://doi.org/10.1007/s12145-024-01454-9
105. Physics-aware machine learning revolutionizes scientific paradigm for process-based modeling in hydrology Q. Xu et al. https://doi.org/10.1016/j.earscirev.2025.105276
106. Enhancing long-term reservoir inflow forecasting: an integrated approach combining switch prediction method, ensemble rainfall forecasts, and machine learning techniques H. Lin et al. https://doi.org/10.5194/nhess-26-3085-2026
107. Uncertainties in different leak localization methods for water distribution networks: a review P. Mohan Doss et al. https://doi.org/10.1080/1573062X.2023.2229301
108. Enhancing streamflow simulation in large and human-regulated basins: Long short-term memory with multiscale attributes A. Tursun et al. https://doi.org/10.1016/j.jhydrol.2024.130771
109. Explainable Deep Ensemble Bias Correction of GloFAS-ERA5 Streamflow Across Snow-Influenced Transboundary Basins of Central Asia T. Honcharenko et al. https://doi.org/10.3390/w18162055
110. Flood forecasting with machine learning models in an operational framework S. Nevo et al. https://doi.org/10.5194/hess-26-4013-2022
111. Physics-guided networks for probabilistic hydrodynamic forecasting in canal systems W. Liu et al. https://doi.org/10.1016/j.ese.2026.100703
112. Hybrid modeling for streamflow prediction in ungauged rivers with uncertainty quantification: a comparative analysis Y. Zeng et al. https://doi.org/10.2166/hydro.2025.011
113. Multi-step ahead streamflow and uncertainty forecasting using a HyMoLAP rainfall-runoff model-based framework integrated with Bayesian neural networks in the Ouémé river basin, Benin S. Ezéckiel Houénafa et al. https://doi.org/10.1371/journal.pone.0333590
114. Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what is the best way to leverage regionalised information? S. Chidepudi et al. https://doi.org/10.5194/hess-29-841-2025
115. Characterizing the influence of remotely sensed wetland and lake water storage on discharge using LSTM models M. Vanderhoof et al. https://doi.org/10.1080/02626667.2025.2593333
116. Evaluation of daily stream temperature predictions (1979–2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm J. Diaz et al. https://doi.org/10.1016/j.envsoft.2025.106655
117. A dynamic-gated Mixture-of-Experts framework improves and interprets daily streamflow simulation W. Yuan et al. https://doi.org/10.1038/s43247-026-03799-z
118. Performance of LSTM over SWAT in Rainfall-Runoff Modeling in a Small, Forested Watershed: A Case Study of Cork Brook, RI S. Shrestha & S. Pradhanang https://doi.org/10.3390/w15234194
119. Operational flood forecasting with adaptive dual-correction bayesian model averaging in the Huai river basin S. Jiang et al. https://doi.org/10.1016/j.ejrh.2026.103437
120. Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al. https://doi.org/10.1016/j.jhydrol.2024.132003
121. A hybrid deep learning rainfall-runoff forecasting model Incorporating spatiotemporal information from multi-source data W. Liu et al. https://doi.org/10.1016/j.eswa.2025.129974
122. Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks G. Nearing et al. https://doi.org/10.5194/hess-26-5493-2022
123. Deep learning for the probabilistic prediction of semi-continuous hydrological variables – An application to streamflow prediction across CONUS J. Quilty & M. Jahangir https://doi.org/10.1016/j.jhydrol.2026.134986
124. Using Entity-Aware LSTM to Enhance Streamflow Predictions in Transboundary and Large Lake Basins Y. Park et al. https://doi.org/10.3390/hydrology12100261
125. An explainable physics-aware deep learning framework with improved spatiotemporal dependence matrices and signal decomposition for multi-station uncertainty daily runoff simulation W. Wu et al. https://doi.org/10.1016/j.jhydrol.2026.135625
126. EEG-Based Seizure Prediction via Model Uncertainty Learning C. Li et al. https://doi.org/10.1109/TNSRE.2022.3217929
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130. Self-training approach to improve the predictability of data-driven rainfall-runoff model in hydrological data-sparse regions S. Yoon & K. Ahn https://doi.org/10.1016/j.jhydrol.2024.130862
131. Comparing Robust Optimization Approaches for Addressing Hydrologic Model Uncertainty in Infrastructure Planning: A Green Infrastructure Example J. Smith et al. https://doi.org/10.1061/JWRMD5.WRENG-6729
132. MC-ANN: A Mixture Clustering-Based Attention Neural Network for Time Series Forecasting Y. Li & D. Anastasiu https://doi.org/10.1109/TPAMI.2025.3565224
133. A Bayesian evidential learning framework for safety and performance prediction in thermo-hydro-mechanical coupled deep mine geothermal systems L. Zhang et al. https://doi.org/10.1016/j.jrmge.2025.11.022
134. Low-Cost Electrodynamic Pluviometers for Flood and Debris Flow Monitoring C. Fidani & M. Siciliani https://doi.org/10.3390/su17219662
135. Process guided graph-based transformer learning for streamflow predictions in data-sparse river basins V. Budamala et al. https://doi.org/10.1016/j.jhydrol.2026.135672
136. Can discharge be used to inversely correct precipitation? A. Manoj J et al. https://doi.org/10.5194/hess-29-6115-2025
137. Quantum reservoir computing for daily China precipitation: non-stationary echo state property constraints and quantile prediction Z. Ke & L. Pei https://doi.org/10.1007/s40435-026-02263-2
138. Multi-step ahead probabilistic runoff forecasting with SHAP interpretability: a GPR-enhanced deep learning ensemble approach integrating teleconnection factors F. Zhu et al. https://doi.org/10.1016/j.envsoft.2025.106647
139. Performance and uncertainty analysis in deep learning frameworks for streamflow forecasting via Monte Carlo dropout technique X. Le et al. https://doi.org/10.1016/j.ejrh.2025.102668
140. A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al. https://doi.org/10.5194/hess-28-2871-2024
141. Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems V. Srikrishnan et al. https://doi.org/10.1029/2021EF002644