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Assessment of Simulated Soil Moisture from WRF Noah, Noah-MP, and CLM Land Surface Schemes for Landslide Hazard Application

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Assessment of Simulated Soil Moisture from WRF Noah, Noah-MP, and CLM Land Surface Schemes for Landslide Hazard Application Zhuo, L., Dai, Q., Han, D., Chen, N., & Zhao, B. (2019). Assessment of simulated soil moisture from WRF Noah, Noah-MP, and CLM land surface schemes for landslide hazard application. Hydrology and Earth System Sciences, 23, 4199-4218. https://doi.org/10.5194/hess- 23-4199-2019 Publisher's PDF, also known as Version of record License (if available): CC BY Link to published version (if available): 10.5194/hess-23-4199-2019 Link to publication record in Explore Bristol Research PDF-document This is the final published version of the article (version of record). It first appeared online via European Geosciences Union at https://www.hydrol-earth-syst-sci.net/23/4199/2019/ . Please refer to any applicable terms of use of the publisher. University of Bristol - Explore Bristol Research General rights This document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/red/research-policy/pure/user-guides/ebr-terms/ Hydrol. Earth Syst. Sci., 23, 4199–4218, 2019 https://doi.org/10.5194/hess-23-4199-2019 © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. Assessment of simulated soil moisture from WRF Noah, Noah-MP, and CLM land surface schemes for landslide hazard application Lu Zhuo1,2,3, Qiang Dai1,2,4, Dawei Han2, Ningsheng Chen5, and Binru Zhao2,6 1Key Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing, China 2WEMRC, Department of Civil Engineering, University of Bristol, Bristol, UK 3Department of Civil and Structural Engineering, University of Sheffield, Sheffield, UK 4Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China 5The Institute of Mountain Hazards and Environment (IMHE), Sichuan, China 6College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, China Correspondence: Qiang Dai ([email protected]) Received: 22 February 2019 – Discussion started: 22 March 2019 Revised: 2 September 2019 – Accepted: 16 September 2019 – Published: 18 October 2019 Abstract. This study assesses the usability of Weather Re- performance is based on the landslide forecasting accuracy search and Forecasting (WRF) model simulated soil mois- using 45 selected rainfall events between 2014 and 2015. ture for landslide monitoring in the Emilia Romagna re- Contingency tables, statistical indicators, and receiver oper- gion, northern Italy, during the 10-year period between 2006 ating characteristic analysis for different threshold scenar- and 2015. In particular, three advanced land surface ios are explored. The results have shown that, for landslide model (LSM) schemes (i.e. Noah, Noah-MP, and CLM4) in- monitoring, Noah-MP at the surface soil layer with 30 % ex- tegrated with the WRF are used to provide detailed multi- ceedance probability provides the best landslide monitoring layer soil moisture information. Through the temporal evalu- performance, with its hit rate at 0.769 and its false alarm rate ation with the single-point in situ soil moisture observations, at 0.289. Noah-MP is the only scheme that is able to simulate the large soil drying phenomenon close to the observations during the dry season, and it also has the highest correlation coefficient and the lowest RMSE at most soil layers. It is also demon- 1 Introduction strated that a single soil moisture sensor located in a plain area has a high correlation with a significant proportion of Landslide is a recurring geological hazard during rainfall the study area (even in the mountainous region 141 km away, seasons, which causes massive destruction, loss of life, and based on the WRF-simulated spatial soil moisture informa- economic damage worldwide (Klose et al., 2014). The ac- tion). The evaluation of the WRF rainfall estimation shows curate prediction and monitoring of the spatio-temporal oc- there is no distinct difference among the three LSMs, and currence of the landslide is key to preventing and reducing their performances are in line with a published study for the casualties and damage to properties and infrastructure. One central USA. Each simulated soil moisture product from the of the most widely adopted methods for landslide prediction three LSM schemes is then used to build a landslide predic- is based on rainfall threshold and relies on building the rain- tion model, and within each model, 17 different exceedance fall intensity–duration curve using the information from past probability levels from 1 % to 50 % are adopted to determine landslide events (Chae et al., 2017). However, such a method the optimal threshold scenario (in total there are 612 scenar- is in many cases insufficient for landslide hazard assessment ios). Slope degree information is also used to separate the (Posner and Georgakakos, 2015), because in addition to rain- study region into different groups. The threshold evaluation fall, the initial soil moisture condition is one of the main trig- gering factors of the events (Glade et al., 2000; Crozier, 1999; Published by Copernicus Publications on behalf of the European Geosciences Union. 4200 L. Zhuo et al.: Assessment of simulated soil moisture from WRF Noah, Noah-MP, and CLM Tsai and Chen, 2010; Hawke and McConchie, 2011; Bittelli from hydrological modelling (Srivastava et al., 2013a, 2015), et al., 2012; Segoni et al., 2018b; Valenzuela et al., 2018; drought studies (Zaitchik et al., 2013), and flood investiga- Bogaard and Greco, 2018). tions (Leung and Qian, 2009), to regional weather prediction For landslide applications, one potential soil moisture esti- (Stéfanon et al., 2014). Therefore, NWP-based soil moisture mation method is through satellite remote sensing technolo- datasets could provide valuable information for landslide ap- gies. Although such technologies have been improved signif- plications. However, to our knowledge, relevant research has icantly over the past decade, their retrieving accuracy is still never been carried out. largely affected by frozen soil conditions (Zhuo et al., 2015a) The aim of this study is hence to evaluate the usefulness of and dense vegetation coverages, particularly in mountain- NWP-modelled soil moisture for landslide monitoring. Here ous regions (Temimi et al., 2010); furthermore, the acquired the advanced WRF model (version 3.8) is adopted, because it data only cover the top few centimetres of soil. Although the offers numerous physics options such as micro-physics, sur- more recently launched satellites such as Sentinel-1 (1 km face physics, atmospheric radiation physics, and planetary and 3 d resolution) has shown some promising performance boundary layer physics (Srivastava et al., 2015), and it can of soil moisture estimation (Gao et al., 2017; Paloscia et al., be integrated with a number of LSM schemes, each vary- 2013), its availability only covers the recent years (Geudtner ing in physical parameterisation complexities. So far there et al., 2014). Those disadvantages restrict the full utilisation is limited literature comparing the soil moisture accuracy of satellite soil moisture products for landslide monitoring of different LSMs options in the WRF model. Therefore, applications as discussed in our previous study (Zhuo et al., in this study, we select three of the WRF’s most advanced 2019). In Zhuo et al. (2019), it is discussed that both the tem- LSM schemes (i.e. Noah; Noah-Multiparameterization, here poral and spatial resolutions of the ESA CCI satellite soil Noah-MP; and CLM4) to compare their soil moisture perfor- moisture product (Dorigo et al., 2017) is too coarse for land- mance for landslide hazard assessment. Furthermore, since slide applications, and its data are mostly only available after all three schemes can provide multi-layer soil moisture in- the year 2002. Moreover, the shallow depth soil moisture ob- formation, it is useful to include all those simulations for servation from the satellite hinders the accuracy of landslide the comparison so that the optimal depth of soil moisture predictions. Therefore, other alternative soil moisture estima- could be determined for the landslide monitoring applica- tion methods need to be explored. tion. In order to compare with the performance of our pre- One emerging area relies on modelling. Some studies have vious study on using the satellite soil moisture data (Zhuo used modelled soil moisture data for landslide applications et al., 2019), the same study area, Emilia Romagna, is used (Ponziani et al., 2012; Ciabatta et al., 2016; Zhao et al., here. The study period covers 10 years from 2006 to 2015 to 2019a, b). However, to our knowledge, there is a lack of ex- include a long-term record of landslide events. In addition, isting studies using modelled soil moisture from state-of-the- because slope angle is one of the major factors controlling art land surface models (LSMs) for landslide studies, such as the stability of the slope, it is hence used in this study to di- the Noah LSM (Ek et al., 2003) and the Community Land vide the study area into several slope groups, so that a more Model (CLM) (Oleson et al., 2010). LSMs describe the in- accurate landslide prediction model could be built. teractions between the atmosphere and the land surface by The description of the study area and the datasets used simulating exchanges of momentum, heat, and water within are included in Sect. 2. Methodologies regarding the WRF the Earth system (Maheu et al., 2018). They are capable of model, the related LSM schemes and the adopted landslide simulating the most important subsurface hydrological pro- threshold evaluation approach are provided in Sect. 3. Sec- cesses (e.g. soil moisture) and can be integrated with the tion 4 shows the WRF soil moisture evaluation results against advanced numerical weather prediction (NWP) system like the in situ observations, and the WRF rainfall evaluations WRF (Weather Research and Forecasting) (Skamarock et over the whole study area. Section 5 covers the compari- al., 2008) for comprehensive soil moisture estimations (i.e.
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