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

Abstract. This study assesses the usability of Weather Research and Forecasting (WRF) model simulated soil moisture for landslide monitoring in the Emilia Romagna region, northern Italy during the 10-year period between 2006 and 2015. Particularly three advanced Land Surface Model (LSM) schemes (i.e., Noah, Noah-MP and CLM4) integrated with the WRF are used to provide comprehensive multi-layer soil moisture information. Through the temporal evaluation with the in-situ soil moisture observations, 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. Each simulated soil moisture product from the three LSM schemes is then used to build a landslide threshold model, and within each model, 17 different exceedance probably levels from 1 % to 50 % are adopted to determine the optimal threshold scenario (in total there are 612 scenarios). Slope degree information is also used to separate the study region into different groups. The threshold evaluation performance is based on the landslide forecasting accuracy using 45 selected rainfall events between 2014–2015. Contingency tables, statistical indicators, and Receiver Operating Characteristic analysis for different threshold scenarios are explored. The results have shown that the slope information is very useful in identifying threshold differences, with the threshold becoming smaller for the steeper area. For landslide monitoring, Noah-MP at the surface soil layer with 30 % exceedance probability provides the best landslide monitoring performance, with its hitting rate at 0.769, and its false alarm rate at 0.289.

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Assessment of simulated soil moisture from WRF Noah, Noah-MP, and CLM land surface schemes for landslide hazard application

Hydrology and Earth System Sciences ◽

10.5194/hess-23-4199-2019 ◽

2019 ◽

Vol 23 (10) ◽

pp. 4199-4218 ◽

Cited By ~ 3

Author(s):

Lu Zhuo ◽

Qiang Dai ◽

Dawei Han ◽

Ningsheng Chen ◽

Binru Zhao

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Single Point ◽

Significant Proportion ◽

Soil Layer ◽

Exceedance Probability ◽

Landslide Monitoring ◽

Surface Model ◽

Characteristic Analysis ◽

Study Region

Abstract. This study assesses the usability of Weather Research and Forecasting (WRF) model simulated soil moisture for landslide monitoring in the Emilia Romagna region, northern Italy, during the 10-year period between 2006 and 2015. In particular, three advanced land surface model (LSM) schemes (i.e. Noah, Noah-MP, and CLM4) integrated with the WRF are used to provide detailed multi-layer soil moisture information. Through the temporal evaluation with the single-point in situ soil moisture observations, 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 demonstrated that a single soil moisture sensor located in a plain area has a high correlation with a significant proportion of the study area (even in the mountainous region 141 km away, based on the WRF-simulated spatial soil moisture information). The evaluation of the WRF rainfall estimation shows there is no distinct difference among the three LSMs, and their performances are in line with a published study for the central USA. Each simulated soil moisture product from the three LSM schemes is then used to build a landslide prediction model, and within each model, 17 different exceedance probability levels from 1 % to 50 % are adopted to determine the optimal threshold scenario (in total there are 612 scenarios). Slope degree information is also used to separate the study region into different groups. The threshold evaluation performance is based on the landslide forecasting accuracy using 45 selected rainfall events between 2014 and 2015. Contingency tables, statistical indicators, and receiver operating characteristic analysis for different threshold scenarios are explored. The results have shown that, for landslide monitoring, Noah-MP at the surface soil layer with 30 % exceedance probability provides the best landslide monitoring performance, with its hit rate at 0.769 and its false alarm rate at 0.289.

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Towards an operationally capable satellite-based 0-100 cm soil moisture dataset from C3S

10.5194/egusphere-egu21-12268 ◽

2021 ◽

Author(s):

Adam Pasik ◽

Wolfgang Preimesberger ◽

Bernhard Bauer-Marschallinger ◽

Wouter Dorigo

Keyword(s):

Soil Moisture ◽

Soil Profile ◽

Land Surface ◽

Root Zone ◽

Soil Layer ◽

Land Surface Model ◽

In Situ Measurements ◽

Surface Model ◽

T Values

Multiple satellite-based global surface soil moisture (SSM) datasets are presently available, these however, address exclusively the top layer of the soil (0-5cm). Meanwhile, root-zone soil moisture cannot be directly quantified with remote sensing but can be estimated from SSM using a land surface model. Alternatively, soil water index (SWI; calculated from SSM as a function of time needed for infiltration) can be used as a simple approximation of root-zone conditions. SWI is a proxy for deeper layers of the soil profile which control evapotranspiration, and is hence especially important for studying hydrological processes over vegetation-covered areas and meteorological modelling.Here we introduce the advances in our work on the first operationally capable SWI-based root-zone soil moisture dataset from C3S Soil Moisture v201912 COMBINED product, spanning the period 2002-2020. The uniqueness of this dataset lies in the fact that T-values (temporal lengths ruling the infiltration) characteristic of SWI were translated into particular soil depths making it much more intuitive, user-friendly and easily applicable. Available are volumetric soil moisture values for the top 1 m of the soil profile at 10 cm intervals, where the optimal T-value (T-best) for each soil layer is selected based on a range of correlation metrics with in situ measurements from the International Soil Moisture Network (ISMN) and the relevant soil and climatic parameters. Additionally we present the results of an extensive global validation against in situ measurements (ISMN) as well as the results of investigations into the relationship between a range of soil and climate characteristics and the optimal T-values for particular soil depths.

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The influence of the land surface on hydrometeorology and ecology: new advances from modeling and satellite remote sensing

Hydrology Research ◽

10.2166/nh.2011.071 ◽

2011 ◽

Vol 42 (2-3) ◽

pp. 95-112 ◽

Cited By ~ 31

Author(s):

Venkat Lakshmi ◽

Seungbum Hong ◽

Eric E. Small ◽

Fei Chen

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Wrf Model ◽

Land Surface Model ◽

Surface Processes ◽

Surface Model ◽

Land Surface Processes ◽

Area Index ◽

Vegetation Fraction ◽

Moisture Variation

The importance of land surface processes has long been recognized in hydrometeorology and ecology for they play a key role in climate and weather modeling. However, their quantification has been challenging due to the complex nature of the land surface amongst other reasons. One of the difficult parts in the quantification is the effect of vegetation that are related to land surface processes such as soil moisture variation and to atmospheric conditions such as radiation. This study addresses various relational investigations among vegetation properties such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), surface temperature (TSK), and vegetation water content (VegWC) derived from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and EOS Advanced Microwave Scanning Radiometer (AMSR-E). The study provides general information about a physiological behavior of vegetation for various environmental conditions. Second, using a coupled mesoscale/land surface model, we examine the effects of vegetation and its relationship with soil moisture on the simulated land–atmospheric interactions through the model sensitivity tests. The Weather Research and Forecasting (WRF) model was selected for this study, and the Noah land surface model (Noah LSM) implemented in the WRF model was used for the model coupled system. This coupled model was tested through two parameterization methods for vegetation fraction using MODIS data and through model initialization of soil moisture from High Resolution Land Data Assimilation System (HRLDAS). Finally, this study evaluates the model improvements for each simulation method.

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Incorporating Hydraulic Lift into a Land Surface Model and Its Effects on Surface Soil Moisture Prediction

Journal of Hydrometeorology ◽

10.1175/jhm-385.1 ◽

2004 ◽

Vol 5 (6) ◽

pp. 1181-1191 ◽

Cited By ~ 18

Author(s):

Diandong Ren ◽

Ming Xue ◽

Ann Henderson-Sellers

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Energy Exchange ◽

Atmospheric Surface Layer ◽

Soil Layer ◽

Land Surface Model ◽

Hydraulic Lift ◽

Surface Model ◽

Daily Cycle ◽

Dry Conditions

Abstract In comparison with the Oklahoma Atmospheric Surface-layer Instrumentation System (OASIS) measurements, the Simulator for Hydrology and Energy Exchange at the Land Surface (SHEELS), a multilayer soil hydrological model, simulates a much faster drying of the superficial soil layer (5 cm) for a densely vegetated area at the OASIS site in Norman, Oklahoma, under dry conditions. Further, the measured superficial soil moisture contents also show a counterintuitive daily cycle that moistens the soil during daytime and dries the soil at night. The original SHEELS model fails to simulate this behavior. This work proposes a treatment of hydraulic lift processes associated with stressed vegetation and shows via numerical experiments that both problems reported above can be much alleviated by including the hydraulic lift effect associated with stressed vegetation.

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Response of the surface climate to different land surface models: WRF sensitivity to surface model options

10.5194/egusphere-egu21-15663 ◽

2021 ◽

Author(s):

Daniela C.A. Lima ◽

Rita M. Cardoso ◽

Pedro M.M. Soares

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Wrf Model ◽

Land Surface Model ◽

Heat Fluxes ◽

Surface Processes ◽

Surface Model ◽

Land Surface Models ◽

Land Surface Processes ◽

Surface Models

The Weather Research and Forecasting (WRF) model version 4.2 includes different land surface schemes, allowing a better representation of the land surface processes. Four simulations with the WRF model differing in land surface models and options were investigated as a sensitivity study over the European domain. These experiments span from 2004-2006 with a one-month spin-up and were performed at 0.11o horizontal resolution with 50 vertical levels, following the CORDEX guidelines. The lateral boundary conditions were driven by ERA5 reanalysis from European Centre for Medium-Range Weather Forecasts. For the first experiment, the Noah land surface model was used. For the remaining simulations, the Noah-MP (multi-physics) land surface model was used with different runoff and groundwater options: (1) original surface and subsurface runoff (free drainage), (2) TOPMODEL with groundwater and (3) Miguez-Macho & Fan groundwater scheme. The physical parameterizations options are the same for all simulations. These experiments allow the analysis of the sensitivity of different land surface options and to understand how the representation of land surface processes impacts on the atmosphere properties. This study focusses on the investigation of land-atmosphere feedbacks trough the analysis of the soil moisture &#8211; temperature and soil moisture &#8211; precipitation interactions, latent and sensible heat fluxes, and moisture fluxes. The influence of different surface model options on atmospheric boundary layer is also explored.Acknowledgements. The authors wish to acknowledge the LEADING (PTDC/CTA-MET/28914/2017) project funded by FCT. The authors would like to acknowledge the financial support FCT through project UIDB/50019/2020 &#8211; Instituto Dom Luiz.

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Effect of Land–Atmosphere Interactions on the IHOP 24–25 May 2002 Convection Case

Monthly Weather Review ◽

10.1175/mwr3057.1 ◽

2006 ◽

Vol 134 (1) ◽

pp. 113-133 ◽

Cited By ~ 118

Author(s):

Teddy R. Holt ◽

Dev Niyogi ◽

Fei Chen ◽

Kevin Manning ◽

Margaret A. LeMone ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Air Temperature ◽

Land Surface ◽

Weather Forecasting ◽

Wrf Model ◽

Land Surface Model ◽

Atmospheric Model ◽

Surface Model ◽

The Impact

Abstract Numerical simulations are conducted using the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) to investigate the impact of land–vegetation processes on the prediction of mesoscale convection observed on 24–25 May 2002 during the International H2O Project (IHOP_2002). The control COAMPS configuration uses the Weather Research and Forecasting (WRF) model version of the Noah land surface model (LSM) initialized using a high-resolution land surface data assimilation system (HRLDAS). Physically consistent surface fields are ensured by an 18-month spinup time for HRLDAS, and physically consistent mesoscale fields are ensured by a 2-day data assimilation spinup for COAMPS. Sensitivity simulations are performed to assess the impact of land–vegetative processes by 1) replacing the Noah LSM with a simple slab soil model (SLAB), 2) adding a photosynthesis, canopy resistance/transpiration scheme [the gas exchange/photosynthesis-based evapotranspiration model (GEM)] to the Noah LSM, and 3) replacing the HRLDAS soil moisture with the National Centers for Environmental Prediction (NCEP) 40-km Eta Data Assimilation (EDAS) operational soil fields. CONTROL, EDAS, and GEM develop convection along the dryline and frontal boundaries 2–3 h after observed, with synoptic-scale forcing determining the location and timing. SLAB convection along the boundaries is further delayed, indicating that detailed surface parameterization is necessary for a realistic model forecast. EDAS soils are generally drier and warmer than HRLDAS, resulting in more extensive development of convection along the dryline than for CONTROL. The inclusion of photosynthesis-based evapotranspiration (GEM) improves predictive skill for both air temperature and moisture. Biases in soil moisture and temperature (as well as air temperature and moisture during the prefrontal period) are larger for EDAS than HRLDAS, indicating land–vegetative processes in EDAS are forced by anomalously warmer and drier conditions than observed. Of the four simulations, the errors in SLAB predictions of these quantities are generally the largest. By adding a sophisticated transpiration model, the atmospheric model is able to better respond to the more detailed representation of soil moisture and temperature. The sensitivity of the synoptically forced convection to soil and vegetative processes including transpiration indicates that detailed representation of land surface processes should be included in weather forecasting models, particularly for severe storm forecasting where local-scale information is important.

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Development of observation-based global multilayer soil moisture products for 1970 to 2016

Earth System Science Data ◽

10.5194/essd-13-4385-2021 ◽

2021 ◽

Vol 13 (9) ◽

pp. 4385-4405

Author(s):

Yaoping Wang ◽

Jiafu Mao ◽

Mingzhou Jin ◽

Forrest M. Hoffman ◽

Xiaoying Shi ◽

...

Keyword(s):

Soil Moisture ◽

Root Mean Square ◽

Land Surface ◽

Soil Layer ◽

Added Value ◽

Surface Model ◽

Mean Square ◽

Wide Range ◽

Mean Square Errors

Abstract. Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model – ESM – simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling and retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multilayer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5∘ resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1; Wang and Mao, 2021) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations (mean bias from −0.044 to 0.033 m3 m−3, root mean square errors from 0.076 to 0.104 m3 m−3, Pearson correlations from 0.35 to 0.67) and multiple gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Three of the new SM products, which were produced by applying any of the three merging methods to the source datasets excluding the ESMs, had lower bias and root mean square errors and higher correlations than the ESM-dependent merged products. The ESM-independent products also showed a better ability to capture historical large-scale drought events than the ESM-dependent products. The merged products generally showed reasonable temporal homogeneity and physically plausible global sensitivities to observed meteorological factors, except that the ESM-dependent products underestimated the low-frequency temporal variability in SM and overestimated the high-frequency variability for the 50–100 cm depth. Based on these evaluation results, the three ESM-independent products were finally recommended for future applications because of their better performances than the ESM-dependent ones. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

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Evaluation of Reanalysis Soil Moisture Simulations Using Updated Chinese Soil Moisture Observations

Journal of Hydrometeorology ◽

10.1175/jhm416.1 ◽

2005 ◽

Vol 6 (2) ◽

pp. 180-193 ◽

Cited By ~ 84

Author(s):

Haibin Li ◽

Alan Robock ◽

Suxia Liu ◽

Xingguo Mo ◽

Pedro Viterbo

Keyword(s):

Soil Moisture ◽

Interannual Variability ◽

Land Surface ◽

Soil Layer ◽

Land Surface Model ◽

Cold Season ◽

Temporal Scale ◽

Department Of Energy ◽

Surface Model ◽

Deep Soil

Abstract Using 19 yr of Chinese soil moisture data from 1981 to 1999, the authors evaluate soil moisture in three reanalysis outputs: the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40); the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis 1 (R-1); and the NCEP–Department of Energy (DOE) reanalysis 2 (R-2) over China. R-2 shows improved interannual variability and better seasonal patterns of soil moisture than R-1 as the result of the incorporation of observed precipitation, but not for all stations. ERA-40 produces a better mean value of soil moisture for most Chinese stations and good interannual variability. Limited observations in the spring indicate a spring soil moisture peak for most of the stations. ERA-40 generally reproduced this event, while R-1 or R-2 generally did not capture this feature, either because the soil was already saturated or the deep soil layer was too thick and damped such a response. ERA-40 and R-1 have a temporal time scale comparable to observations, but R-2 has a memory of nearly 5 months for the growing season, about twice the temporal scale of the observations. The cold season tends to prolong soil moisture memory by about 3 months for R-2 and 1 month for ERA-40. The unrealistic long temporal scale of R-2 can be attributed to the deep layer of the land surface model, which is too thick and dominates the soil moisture variability. R-1 has the same land surface scheme as R-2, but shows a temporal scale close to observations, which is actually because of soil moisture nudging to a fixed climatology. This new long time series of observed soil moisture will prove valuable for other studies of climate change, remote sensing, and model evaluation.

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Daily Precipitation Frequency Distributions Impacts on Land-Surface Simulations of CONUS

Frontiers in Water ◽

10.3389/frwa.2021.640736 ◽

2021 ◽

Vol 3 ◽

Author(s):

Daniel P. Sarmiento ◽

Kimberly Slinski ◽

Amy McNally ◽

Jossy P. Jacob ◽

Chris Funk ◽

...

Keyword(s):

Soil Moisture ◽

Frequency Distribution ◽

Land Surface ◽

Surface Soil ◽

Daily Precipitation ◽

Soil Layer ◽

Surface Model ◽

Precipitation Frequency ◽

Frequency Distributions ◽

The Impact

Many precipitation-driven data products from land data assimilation systems support assessments of droughts, floods, and other societally-relevant land-surface processes. The accumulated precipitation used as input to these products has a significant impact on water budgets; however, the effects of daily distribution of precipitation on these products are not well known. A comparison of the Integrated Multi-satellite Retrievals for GPM (IMERG) and Climate Hazards Group InfraRed Precipitation with Stations version 2 (CHIRPS2) rainfall products over the continental United States (CONUS) was performed to quantify the impacts of the daily distribution of precipitation on biases and errors in soil moisture, runoff, and evapotranspiration (ET). Since the total accumulated precipitation between the IMERG and CHIRPS product differed, a third precipitation product, CHIRPS-to-IMERG (CHtoIM), was produced that used CHIRPS2 accumulated precipitation totals and the daily precipitation frequency distribution of IMERG. This new product supported a controlled analysis of the impact of precipitation frequency distribution on simulated hydrological fields. The CHtoIM had higher occurrences of precipitation in the 0–5 mm day−1 range, with a lower occurrence of dry days, which decreased soil moisture and surface runoff in the land-surface model. The surface soil layer had a tendency to reach saturation more often in the CHIRPS2 simulations, where the number of moderate to heavy precipitation days (>5 mm day−1) was increased. Using the blended CHtoIM product as input reduced errors in surface soil moisture by 5–15% when compared to Soil Moisture Active/Passive (SMAP) data. Similarly, ET errors were also slightly decreased (~2%) when compared to SSEBop data. Moderate changes in daily precipitation distributions had a quantifiable impact on soil moisture, runoff, and ET. These changes usually improved the model when compared to other modeled and observational datasets, but the magnitude of the improvements varied by region and time of year.

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Impact of Soil Moisture Initialization and Soil Texture on Simulated Land–Atmosphere Interaction in Taiwan

Journal of Hydrometeorology ◽

10.1175/jhm-d-15-0024.1 ◽

2016 ◽

Vol 17 (5) ◽

pp. 1337-1355 ◽

Cited By ~ 21

Author(s):

Tzu-Shun Lin ◽

Fang-Yi Cheng

Keyword(s):

Soil Moisture ◽

Soil Texture ◽

Land Surface ◽

Global Analysis ◽

Soil Column ◽

Wrf Model ◽

Base Case ◽

Soil Parameters ◽

Surface Model ◽

Soil Moisture Initialization

Abstract This study investigates the effect of soil moisture initializations and soil texture on the land surface hydrologic processes and its feedback on atmospheric fields in Taiwan. The simulations using the Weather Research and Forecasting (WRF) Model with the Noah land surface model were conducted for a 1-month period from 10 August to 12 September 2013 that included two typhoon-induced precipitation episodes and a series of clear-sky days. Soil moisture from the Global Land Data Assimilation System (GLDAS) was utilized to provide the soil moisture initialization process. In addition, updated soil textures based on field surveys in Taiwan were adopted for the WRF Model. Three WRF sensitivity runs were performed. The first simulation is the base case without any update (WRF-base), the second simulation utilizes GLDAS products to initialize the soil moisture (WRF-GLDAS), and the third simulation includes GLDAS products plus the updated soil textures and soil parameters (WRF-GSOIL). In WRF-base, the soil moisture initialization process is provided from National Centers for Environmental Prediction (NCEP) Final (FNL) Operational Global Analysis data, which are higher than the data from GLDAS products. The WRF-GLDAS and WRF-GSOIL with use of GLDAS data show lower soil moisture than WRF-base and agree better with observed data, while WRF-base shows a systematic wet bias of soil moisture throughout the simulation periods. In WRF-GSOIL, the soil textures with large-sized soil particles reveal higher soil conductivity; as a result, water drains through the soil column in a faster manner than the WRF-GLDAS, which leads to reduced soil moisture in western Taiwan. Among the three simulations, the variation of soil moisture is best simulated in WRF-GSOIL.

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