Publications from ML4HES

Zhong, L.; Lei, H.; Li, Z.; Jiang, S.: Advancing streamflow prediction in data-scarce regions through vegetation-constrained distributed hybrid ecohydrological models. Journal of Hydrology 645 (PART A), 132165 (2024)

Song, W.; Jiang, S.; Camps-Valls, G.; Williams, M.; Zhang, L.; Reichstein, M.; Vereecken, H.; He, L.; Hu, X.; Shi, L.: Towards data-driven discovery of governing equations in geosciences. Communications Earth & Environment 5, 589 (2024)

Jiang, S.; Sweet, L.-b.; Blougouras, G.; Brenning, A.; Li, W.; Reichstein, M.; Denzler, J.; Shangguan, W.; Yu, G.; Huang, F. et al.; Zscheischler, J.: How interpretable machine learning can benefit process understanding in the geosciences. Earth's Future 12 (7), e2024EF004540 (2024)

Feng, J.; Li, J.; Xu, C.; Wang, Z.; Zhang, Z.; Wu, X.; Lai, C.; Zeng, Z.; Tong, H.; Jiang, S.: Viewing soil moisture flash drought onset mechanism and their changes through XAI lens: A case study in Eastern China. Water Resources Research 60 (6), e2023WR036297 (2024)

Wang, C.; Jiang, S.; Zheng, Y.; Han, F.; Kumar, R.; Rakovec, O.; Li, S.: Distributed hydrological modeling with physics‐encoded deep learning: A general framework and its application in the Amazon. Water Resources Research 60 (4), e2023WR036170 (2024)

Jiang, S.; Tarasova, L.; Yu, G.; Zscheischler, J.: Compounding effects in flood drivers challenge estimates of extreme river floods. Science Advances 10 (13), eadl4005 (2024)

Jiang, S.; Tarasova, L.; Yu, G.; Zscheischler, J.: The importance of compounding drivers for large river floods. Research Square (2024)