Articles | Volume 30, issue 12
https://doi.org/10.5194/hess-30-3825-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-3825-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Jun 2026
Research article |
| 24 Jun 2026
| 24 Jun 2026
Scale-dependent transition in soil moisture memory and its environmental controls in complex mountain terrain
Jun Zhang
Key Laboratory of Mountain Hazards and Engineering Resilience, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
University of Chinese Academy of Sciences, Beijing 100049, China
Key Laboratory of Mountain Hazards and Engineering Resilience, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
Key Laboratory of Mountain Hazards and Engineering Resilience, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
Yuan Xue
State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Related authors
Jun Zhang and Yong Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-206, https://doi.org/10.5194/essd-2026-206, 2026
Revised manuscript under review for ESSD
Short summary
Short summary
PlanetGSD 1.0, the first standardized cross-planetary grain-size database, provides 6,527 harmonized measurements (Earth, Moon, Mars) via the Unified Grain Size Distribution (UGSD) function. Openly available at Figshare, it enables robust regolith comparison, simulant benchmarking, and landing site assessment.
Jun Zhang and Yong Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-206, https://doi.org/10.5194/essd-2026-206, 2026
Revised manuscript under review for ESSD
Short summary
Short summary
PlanetGSD 1.0, the first standardized cross-planetary grain-size database, provides 6,527 harmonized measurements (Earth, Moon, Mars) via the Unified Grain Size Distribution (UGSD) function. Openly available at Figshare, it enables robust regolith comparison, simulant benchmarking, and landing site assessment.
Songtang He, Zhenhong Shen, Jeffrey Neal, Zongji Yang, Jiangang Chen, Daojie Wang, Yujing Yang, Peng Zhao, Xudong Hu, Yongming Lin, Youtong Rong, Yanchen Zheng, Xiaoli Su, and Yong Kong
EGUsphere, https://doi.org/10.5194/egusphere-2025-3004, https://doi.org/10.5194/egusphere-2025-3004, 2025
Preprint archived
Short summary
Short summary
We explored why landslides still happen in areas with dense vegetation. Using data from a mountainous region in China, we combined large-scale mapping with detailed field analysis. We found that while plants can help prevent landslides, their weight and interaction with rainfall and wind can sometimes make slopes more unstable. This research highlights the complex role of vegetation and helps improve landslide prediction and prevention in green mountain areas.
Pengcheng Su, Jingjing Liu, Yong Li, Wei Liu, Yang Wang, Chun Ma, and Qimin Li
Hydrol. Earth Syst. Sci., 25, 5879–5903, https://doi.org/10.5194/hess-25-5879-2021, https://doi.org/10.5194/hess-25-5879-2021, 2021
Short summary
Short summary
We identified ± 150 glacial lakes in the Poiqu River basin (central Himalayas), and we explore the changes in five lakes over the last few decades based on remote sensing images, field surveys, and satellite photos. We reconstruct the lake basin topography, calculate the water capacity, and propose a water balance equation (WBE) to explain glacial lake evolution in response to local weather conditions. The WBE also provides a framework for the water balance in rivers from glacierized sources.
Cited articles
Anderson, S.: Soils: Genesis and geomorphology, Cambridge University Press, https://doi.org/10.1017/CBO9780511815560, 2005.
An, H., Ouyang, C., and Chen, X.: Real-time estimation of SMAP soil moisture in mountainous areas and its impact on rainfall-runoff simulation, J. Hydrol., 133487, https://doi.org/10.1016/j.jhydrol.2025.133487, 2025.
Blanka-Végi, V., Tobak, Z., Sipos, G., Barta, K., Szabó, B., and Van Leeuwen, B.: Estimation of the spatiotemporal variability of surface soil moisture using machine learning methods integrating satellite and ground-based soil moisture and environmental data, Water Resour. Manag., 39, 2317–2334, https://doi.org/10.1007/s11269-024-04069-3, 2025.
Blöschl, G. and Sivapalan, M.: Scale issues in hydrological modelling: a review, Hydrol. Process., 9, 251–290, https://doi.org/10.1002/hyp.3360090305, 1995.
Bogaard, T. and Greco, R.: Invited perspectives: Hydrological perspectives on precipitation intensity-duration thresholds for landslide initiation: proposing hydro-meteorological thresholds, Nat. Hazards Earth Syst. Sci., 18, 31–39, https://doi.org/10.5194/nhess-18-31-2018, 2018.
Bogaard, T. A. and Greco, R.: Landslide hydrology: from hydrology to pore pressure, WIREs Water, 3, 439–459, https://doi.org/10.1002/wat2.1126, 2016.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Brocca, L., Morbidelli, R., Melone, F., and Moramarco, T.: Soil moisture spatial variability in experimental areas of central Italy, J. Hydrol., 333, 356–373, https://doi.org/10.1016/j.jhydrol.2006.09.004, 2007.
Brocca, L., Ponziani, F., Moramarco, T., Melone, F., Berni, N., and Wagner, W.: Improving landslide forecasting using ASCAT-derived soil moisture data: A case study of the Torgiovannetto landslide in central Italy, Remote Sens., 4, 1232–1244, https://doi.org/10.3390/rs4051232, 2012.
Cai, J. S., Yeh, T. C. J., Yan, E. C., Tang, R. X., Hao, Y. H., Huang, S. Y., and Wen, J. C.: Importance of variability in initial soil moisture and rainfalls on slope stability, J. Hydrol., 571, 265–278, https://doi.org/10.1016/j.jhydrol.2019.01.046, 2019.
Chen, S. J., Tang, B. H., Ma, X., He, Z. W., Fu, W., and Chen, J.: Spatiotemporal variations and driving factors of evapotranspiration in the Yunnan-Guizhou Plateau from 2003 to 2020, J. Water Clim. Change, 15, 5587–5605, https://doi.org/10.2166/wcc.2024.424, 2024.
Cho, E. and Choi, M.: Regional scale spatio-temporal variability of soil moisture and its relationship with meteorological factors over the Korean peninsula, J. Hydrol., 516, 317–329, https://doi.org/10.1016/j.jhydrol.2013.12.053, 2014.
Coe, J. A., Kinner, D. A., and Godt, J. W.: Initiation conditions for debris flows generated by runoff at Chalk Cliffs, central Colorado, Geomorphology, 96, 270–297, https://doi.org/10.1016/j.geomorph.2007.03.017, 2008.
Copernicus Climate Change Service, Climate Data Store: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023.
Crow, W. T., Berg, A. A., Cosh, M. H., Loew, A., Mohanty, B. P., Panciera, R., de Rosnay, P., Ryu, D., and Walker, J. P.: Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite soil moisture products, Rev. Geophys., 50, RG2002, https://doi.org/10.1029/2011RG000372, 2012.
Cui, Y., Xu, C., Chen, H., Cui, Y., Xue, L., and Qin, S.: Startup mechanism of locked segment-dominated rockslides: Insights from a physical model experiment replicating natural infiltration conditions, Eng. Geol., 108494, https://doi.org/10.1016/j.enggeo.2025.108494, 2025.
Dahal, R. K. and Hasegawa, S.: Representative rainfall thresholds for landslides in the Nepal Himalaya, Geomorphology, 100, 429–443, https://doi.org/10.1016/j.geomorph.2008.01.014, 2008.
Dobriyal, P., Qureshi, A., Badola, R., and Hussain, S. A.: A review of the methods available for estimating soil moisture and its implications for water resource management, J. Hydrol., 458, 110–117, https://doi.org/10.1016/j.jhydrol.2012.06.021, 2012.
Dong, J. and Ochsner, T. E.: Soil texture often exerts a stronger influence than precipitation on mesoscale soil moisture patterns, Water Resour. Res., 54, 2199–2211, https://doi.org/10.1002/2017WR021692, 2018.
Dymond, S. F., Wagenbrenner, J. W., Keppeler, E. T., and Bladon, K. D.: Dynamic hillslope soil moisture in a Mediterranean montane watershed, Water Resour. Res., 57, e2020WR029170, https://doi.org/10.1029/2020WR029170, 2021.
Entin, J. K., Robock, A., Vinnikov, K. Y., Hollinger, S. E., Liu, S., and Namkhai, A.: Temporal and spatial scales of observed soil moisture variations in the extratropics, J. Geophys. Res.-Atmos., 105, 11865–11877, https://doi.org/10.1029/2000JD900051, 2000.
Fang, X., Zhao, W., Wang, L., Feng, Q., Ding, J., Liu, Y., and Zhang, X.: Variations of deep soil moisture under different vegetation types and influencing factors in a watershed of the Loess Plateau, China, Hydrol. Earth Syst. Sci., 20, 3309–3323, https://doi.org/10.5194/hess-20-3309-2016, 2016.
Gao, J., Shi, Y., Zhang, H., Chen, X., Zhang, W., Shen, W., Xiao, T., and Zhang, Y.: China regional 250 m fractional vegetation cover data set (2000–2024), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/Terre.tpdc.300330, 2022.
Ghannam, K., Nakai, T., Paschalis, A., Oishi, C. A., Kotani, A., Igarashi, Y., Kumagai, T., and Katul, G. G.: Persistence and memory timescales in root-zone soil moisture dynamics, Water Resour. Res., 52, 1427–1445, https://doi.org/10.1002/2015WR017983, 2016.
Hansun, S.: A new approach of moving average method in time series analysis, in: 2013 Conference on New Media Studies (CoNMedia), IEEE, 1–4, https://doi.org/10.1109/conmedia.2013.6708545, 2013.
Hersbach, H., Comyn-Platt, E., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J.-N., Cagnazo, C., and Cucchi, M.: ERA5 post-processed daily-statistics on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.4991cf48, 2023.
Hu, W., Xu, Q., Wang, G. H., Van Asch, T. W. J., and Hicher, P. Y.: Sensitivity of the initiation of debris flow to initial soil moisture, Landslides, 12, 1139–1145, https://doi.org/10.1007/s10346-014-0529-2, 2015.
Huang, F., Chen, J., Liu, W., Huang, J., Hong, H., and Chen, W.: Regional rainfall-induced landslide hazard warning based on landslide susceptibility mapping and a critical rainfall threshold, Geomorphology, 408, 108236, https://doi.org/10.1016/j.geomorph.2022.108236, 2022.
Kantelhardt, J. W., Koscielny-Bunde, E., Rego, H. H., Havlin, S., and Bunde, A.: Detecting long-range correlations with detrended fluctuation analysis, Physica A, 295, 441–454, https://doi.org/10.1016/S0378-4371(01)00144-3, 2001.
Kantelhardt, J. W., Koscielny-Bunde, E., Rybski, D., Braun, P., Bunde, A., and Havlin, S.: Long-term persistence and multifractality of precipitation and river runoff records, J. Geophys. Res.-Atmos., 111, D01106, https://doi.org/10.1029/2005JD005881, 2006.
Kim, S.: ppcor: an R package for a fast calculation to semi-partial correlation coefficients, Communications for Statistical Applications and Methods, 22, 665–674, https://doi.org/10.5351/CSAM.2015.22.6.665, 2015.
Kirchner, J. W.: Catchments as simple dynamical systems: Catchment characterization, rainfall-runoff modeling, and doing hydrology backward, Water Resour. Res., 45, W02429, https://doi.org/10.1029/2008WR006912, 2009.
Kursa, M. B., Jankowski, A., and Rudnicki, W. R.: Boruta – a system for feature selection, Fund. Inform., 101, 271–285, https://doi.org/10.3233/FI-2010-288, 2010.
Liu, Y., Ge, J., Guo, W., Cao, Y., Chen, C., Luo, X., Yang, L., and Wang, S.: Revisiting biophysical impacts of greening on precipitation over the Loess Plateau of China using WRF with water vapor tracers, Geophys. Res. Lett., 50, e2023GL102809, https://doi.org/10.1029/2023GL102809, 2023.
Martínez-Fernández, J., González-Zamora, A., and Almendra-Martín, L.: Soil moisture memory and soil properties: An analysis with the stored precipitation fraction, J. Hydrol., 593, 125622, https://doi.org/10.1016/j.jhydrol.2020.125622, 2021.
McColl, K. A., Alemohammad, S. H., Akbar, R., Konings, A. G., Yueh, S., and Entekhabi, D.: The global distribution and dynamics of surface soil moisture, Nat. Geosci., 10, 100–104, https://doi.org/10.1038/ngeo2868, 2017.
Mirus, B. B., Becker, R. E., Baum, R. L., and Smith, J. B.: Integrating real-time subsurface hydrologic monitoring with empirical rainfall thresholds to improve landslide early warning, Landslides, 15, 1909–1919, https://doi.org/10.1007/s10346-018-0995-z, 2018.
Moragoda, N., Kumar, M., and Cohen, S.: Representing the role of soil moisture on erosion resistance in sediment models: Challenges and opportunities, Earth-Sci. Rev., 229, 104032, https://doi.org/10.1016/j.earscirev.2022.104032, 2022.
Nicolai-Shaw, N., Gudmundsson, L., Hirschi, M., and Seneviratne, S. I.: Long-term predictability of soil moisture dynamics at the global scale: Persistence versus large-scale drivers, Geophys. Res. Lett., 43, 8554–8562, https://doi.org/10.1002/2016GL069847, 2016.
Pan, H.-L., Jiang, Y.-J., Wang, J., and Ou, G.-Q.: Rainfall threshold calculation for debris flow early warning in areas with scarcity of data, Nat. Hazards Earth Syst. Sci., 18, 1395–1409, https://doi.org/10.5194/nhess-18-1395-2018, 2018.
Parada, L. M. and Liang, X.: A stochastic modeling approach for characterizing the spatial structure of L band radiobrightness temperature imagery, J. Geophys. Res.-Atmos., 108, 4706, https://doi.org/10.1029/2003JD003567, 2003.
Peng, C., Zeng, J., Chen, K. S., Li, Z., Ma, H., Zhang, X., Shi, P., Wang, T., Yi, L., and Bi, H.: Global spatiotemporal trend of satellite-based soil moisture and its influencing factors in the early 21st century, Remote Sens. Environ., 291, 113569, https://doi.org/10.1016/j.rse.2023.113569, 2023.
Percival, D. B. and Walden, A. T.: Spectral analysis for physical applications, Cambridge University Press, https://doi.org/10.1017/CBO9780511622762, 1993.
Pickett, S. T. A.: Space-for-time substitution as an alternative to long-term studies, in: Long-term studies in ecology: approaches and alternatives, edited by: Likens, G. E., Springer, 110–135, https://doi.org/10.1007/978-1-4615-7358-6_5, 1989.
Ponziani, F., Pandolfo, C., Stelluti, M., Berni, N., Brocca, L., and Moramarco, T.: Assessment of rainfall thresholds and soil moisture modeling for operational hydrogeological risk prevention in the Umbria region (central Italy), Landslides, 9, 229–237, https://doi.org/10.1007/s10346-011-0287-3, 2012.
Rahmati, M., Amelung, W., Brogi, C., Dari, J., Flammini, A., Bogena, H., Franz, T., Heye, A., Lücke, A., Neuwirth, M., Vereecken, H., and Verhoef, A.: Soil moisture memory: State-of-the-art and the way forward, Rev. Geophys., 62, e2023RG000828, https://doi.org/10.1029/2023RG000828, 2024.
Ran, Q., Su, D., Li, P., and He, Z.: Experimental study of the impact of rainfall characteristics on runoff generation and soil erosion, J. Hydrol., 424, 99–111, https://doi.org/10.1016/j.jhydrol.2011.12.035, 2012.
Rodriguez-Iturbe, I., Porporato, A., Ridolfi, L., Isham, V., and Coxi, D. R.: Probabilistic modelling of water balance at a point: the role of climate, soil and vegetation, P. R. Soc. London A, 455, 3789–3805, https://doi.org/10.1098/rspa.1999.0477, 1999.
Salvucci, G. D. and Entekhabi, D.: Equivalent steady soil moisture profile and the time compression approximation in water balance modeling, Water Resour. Res., 30, 2737–2749, https://doi.org/10.1029/94WR00948, 1994.
Schönauer, M., Ågren, A. M., Katzensteiner, K., Hartsch, F., Arp, P., Drollinger, S., and Jaeger, D.: Soil moisture modeling with ERA5-Land retrievals, topographic indices, and in situ measurements and its use for predicting ruts, Hydrol. Earth Syst. Sci., 28, 2617–2633, https://doi.org/10.5194/hess-28-2617-2024, 2024.
Schreiber, T. and Schmitz, A.: Surrogate time series, Physica D, 142, 346–382, https://doi.org/10.1016/S0167-2789(00)00043-9, 2000.
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil moisture–climate interactions in a changing climate: A review, Earth-Sci. Rev., 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004, 2010.
Song, P., Zhang, Y., Guo, J., Shi, J., Zhao, T., and Tong, B.: A 1 km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003–2019, Earth Syst. Sci. Data, 14, 2613–2637, https://doi.org/10.5194/essd-14-2613-2022, 2022.
Valavi, R., Elith, J., Lahoz-Monfort, J. J., and Guillera-Arroita, G.: blockCV: An R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models, Methods Ecol. Evol., 10, 225–232, https://doi.org/10.1111/2041-210X.13107, 2019.
Van Genuchten, M. T.: A closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sci. Soc. Am. J., 44, 892–898, https://doi.org/10.2136/sssaj1980.03615995004400050002x, 1980.
Varga, C. and Csiszér, L.: The influence of slope aspect on soil moisture, Acta Universitatis Sapientiae, Agriculturae and Environment, 12, 82–93, https://doi.org/10.2478/ausae-2020-0007, 2020.
Wei, L., Song, D., Cui, P., Su, L., Zhou, G. G. D., Hu, K., Wei, F., Hong, Y., Ou, G., Zhang, J., Kang, Z., Guo, X., Zhong, W., Li, X., Zhang, Y., Shi, C., and Tang, H.: A long-term dataset of debris-flow and hydrometeorological observations from 1961 to 2024 in Jiangjia Ravine, China, Earth Syst. Sci. Data, 17, 7331–7358, https://doi.org/10.5194/essd-17-7331-2025, 2025.
Western, A. W., Zhou, S. L., Grayson, R. B., McMahon, T. A., Blöschl, G., and Wilson, D. J.: Spatial correlation of soil moisture in small catchments and its relationship to dominant spatial hydrological processes, J. Hydrol., 286, 113–134, https://doi.org/10.1016/j.jhydrol.2003.09.014, 2004.
Wicki, A., Lehmann, P., Hauck, C., Seneviratne, S. I., Waldner, P., and Stähli, M.: Assessing the potential of soil moisture measurements for regional landslide early warning, Landslides, 17, 1881–1896, https://doi.org/10.1007/s10346-020-01400-y, 2020.
Wicki, A., Jansson, P.-E., Lehmann, P., Hauck, C., and Stähli, M.: Simulated or measured soil moisture: which one is adding more value to regional landslide early warning?, Hydrol. Earth Syst. Sci., 25, 4585–4610, https://doi.org/10.5194/hess-25-4585-2021, 2021.
Xie, P., Joyce, R., Wu, S., Yoo, S.-H., Yarosh, Y., Sun, F., and Lin, R.: NOAA Climate Data Record (CDR) of CPC Morphing Technique (CMORPH) High Resolution Global Precipitation Estimates, Version 1, NOAA National Centers for Environmental Information [data set], https://doi.org/10.25921/w9va-q159, 2019.
Yang, H., Hu, K., Zhang, S., and Liu, S.: Feasibility of satellite-based rainfall and soil moisture data in determining the triggering conditions of debris flow: The Jiangjia Gully (China) case study, Eng. Geol., 315, 107041, https://doi.org/10.1016/j.enggeo.2023.107041, 2023.
Yin, L., Dai, E., Zheng, D., Wang, Y., Ma, L., and Tong, M.: What drives the vegetation dynamics in the Hengduan Mountain region, southwest China: Climate change or human activity?, Ecol. Indic., 112, 106013, https://doi.org/10.1016/j.ecolind.2019.106013, 2020.
Zhang, B., Tian, L., He, C., and He, X.: Response of erosive precipitation to vegetation restoration and its effect on soil and water conservation over China's Loess Plateau, Water Resour. Res., 59, e2022WR033382, https://doi.org/10.1029/2022WR033382, 2023.
Zhang, J.: Daily soil moisture and its driving factors (static and dynamic) in three watersheds: Dali River Basin, Anning River Basin, and Jiangjia Ravine (2003–2022), Zenodo [data set], https://doi.org/10.5281/zenodo.17510469, 2025a.
Zhang, J.: Code for: “Scale-Dependent Soil Moisture Memory and Its Driving Mechanisms in Hazard-Prone Mountain Watersheds”, Zenodo [code], https://doi.org/10.5281/zenodo.17510622, 2025b.
Zhang, J., Li, Y., Yang, T., Liu, D., Liu, X., and Jiang, N.: Spatiotemporal variation of moisture in rooted-soil, Catena, 200, 105144, https://doi.org/10.1016/j.catena.2021.105144, 2021.
Zhang, J., Wu, Z., Li, Y., Qin, C., and Cui, J.: Memory character and predictive period of soil moisture in the root-zone and along hillslope, J. Hydrol., 133428, https://doi.org/10.1016/j.jhydrol.2025.133428, 2025.
Zhu, X., Fraedrich, K., Liu, Z., and Blender, R.: A demonstration of long-term memory and climate predictability, J. Climate, 23, 5021–5029, https://doi.org/10.1175/2010JCLI3370.1, 2010.
Short summary
To better predict mountain hazards like landslides, we studied how long soil retains rain moisture. Using 20 years of satellite data from China, we found a control shift at about five years. Short-term memory is governed by weather and plants, while long-term persistence is locked in by soil and terrain. This creates a lasting "background" wetness, especially in humid forests, pre-conditioning slopes for years.
To better predict mountain hazards like landslides, we studied how long soil retains rain...
