Impact of Soil Moisture on Winter 2-m Temperature Forecast in Northern China

&#160;The causes of the underestimated diurnal 2-m temperature range and the overestimated 2-m specific humidity in Northern China&#8217;s winter in the Rapid-refresh Multi-scale Analysis and Prediction System - Short Term (RMAPS-ST) system are investigated. Three simulations based on RMAPS-ST are conducted from Nov. 1st, 2016 to Feb. 28th, 2017. Further analyses show that the partitioning of surface upward sensible heat fluxes and downward ground heat fluxes might be the main contributing factor in 2-m temperature forecast biases. In this study, two simulations are conducted to examine the effect of soil moisture initialization and soil hydraulic property on the 2-m temperature and 2-m specific humidity forecast biases. Firstly, the High-Resolution Land Data Assimilation System (HRLDAS) is used to provide an alternative soil moisture initialization, and the result shows that the drier soil moisture leads to noticeable change in energy partition at the land surface, which in turn results in improved prediction of the diurnal 2-m temperature range, although it also enlarges the 2-m specific humidity bias in some parts of the domain. Secondly, a soil texture dataset developed by Beijing Normal University (BNU) and a revised hydraulic parameters are applied to provide a more detailed description of soil properties, which could further improve the 2-m specific humidity biases. In summary, the combination of using optimized soil moisture initialization, updated soil map and revised soil hydraulic parameters can help improve the 2-m temperature and 2-m specific humidity prediction in RMAPS-ST.

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Impact of Soil Moisture on Winter 2-m Temperature Forecasts in Northern China

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0060.1 ◽

2020 ◽

Vol 21 (4) ◽

pp. 597-614 ◽

Cited By ~ 3

Author(s):

Ji-Qin Zhong ◽

Bing Lu ◽

Wei Wang ◽

Cheng-Cheng Huang ◽

Yang Yang

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Northern China ◽

Heat Fluxes ◽

Specific Humidity ◽

Hydraulic Parameters ◽

Temperature Range ◽

Temperature Forecast ◽

Soil Hydraulic ◽

Soil Moisture Initialization

AbstractIn this study, the causes of the underestimated diurnal 2-m temperature range and the overestimated 2-m specific humidity in the winter of northern China in the Rapid-Refresh Multiscale Analysis and Prediction System–Short Term (RMAPS-ST) are investigated. Three simulations based on RMAPS-ST are conducted from 1 November 2016 to 28 February 2017. Further analyses show that the partitioning of surface upward sensible heat fluxes and downward ground heat fluxes might be the main contributing factor to the 2-m temperature forecast bias. In this study, two simulations are conducted to examine the effect of soil moisture initialization and soil hydraulic property on the 2-m temperature and 2-m specific humidity forecasts. First, the High-Resolution Land Data Assimilation System (HRLDAS) is used to provide an alternative soil moisture initialization. The results show that the drier soil moisture could lead to noticeable change in energy partitioning at the land surface, which in turn results in improved prediction of the diurnal 2-m temperature range, although it also enlarges the 2-m specific humidity bias in some parts of the domain. Second, a soil texture dataset developed by Beijing Normal University and the revised hydraulic parameters are applied to provide a more detailed description of soil properties, which could further improve the 2-m specific humidity bias. In summary, the combination of using optimized soil moisture initialization, an updated soil map, and revised soil hydraulic parameters can help improve the 2-m temperature and 2-m specific humidity prediction in RMAPS-ST.

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The Impacts of Soil Moisture Initialization on the Forecasts of Weather Research and Forecasting Model: A Case Study in Xinjiang, China

Water ◽

10.3390/w12071892 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1892

Author(s):

Hailiang Zhang ◽

Junjian Liu ◽

Huoqing Li ◽

Xianyong Meng ◽

Ablimitijan Ablikim

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Weather Prediction ◽

Potential Temperature ◽

Wrf Model ◽

Specific Humidity ◽

Water Evaporation ◽

Weather Research And Forecasting ◽

Soil Moisture Initialization ◽

Weather Research

Soil moisture is a critical parameter in numerical weather prediction (NWP) models because it plays a fundamental role in the exchange of water and energy cycles between the atmosphere and the land surface through evaporation. To improve the forecast skills of the Weather Research and Forecasting (WRF) model in Xinjiang, China, this study investigated the impacts of soil moisture initialization on the WRF forecasts by performing a series of simulations. A group of simulations was conducted using the single-column model (SCM) from 1200 UTC on 15 to 18 August 2019, at Urumchi, Xinjiang (43.78° N, 87.6° E); another was performed using the WRF model for a real weather case in Xinjiang from 0000 UTC 15 August to 1200 UTC 18 August 2019, which included an episode of heavy precipitation and gales. Our most notable findings are as follows. Specific humidity increases and potential temperature decreases persistently when soil moisture increases because of soil water evaporation. Soil moisture initialization could impact the energy budget and modulate the partition of the total available energy at the land surface significantly through evaporation and the greenhouse effect. Replacing the soil moisture with a proper multiple of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) soil moisture data could significantly improve the critical success index (CSI) and frequency bias (FBIAS) of precipitation and the root-mean-squared errors (RMSEs) of 2-m specific humidity and 2-m temperature. These findings indicate the prospect of a new way to improve the forecast skills of WRF in Xinjiang or other similar regions.

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Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction

Hydrology and Earth System Sciences ◽

10.5194/hess-21-2509-2017 ◽

2017 ◽

Vol 21 (5) ◽

pp. 2509-2530 ◽

Cited By ~ 13

Author(s):

Roland Baatz ◽

Harrie-Jan Hendricks Franssen ◽

Xujun Han ◽

Tim Hoar ◽

Heye Reemt Bogena ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Land Surface Model ◽

Cosmic Ray ◽

Surface Model ◽

Hydraulic Parameters ◽

Soil Map ◽

Soil Hydraulic Parameters ◽

Soil Hydraulic

Abstract. In situ soil moisture sensors provide highly accurate but very local soil moisture measurements, while remotely sensed soil moisture is strongly affected by vegetation and surface roughness. In contrast, cosmic-ray neutron sensors (CRNSs) allow highly accurate soil moisture estimation on the field scale which could be valuable to improve land surface model predictions. In this study, the potential of a network of CRNSs installed in the 2354 km2 Rur catchment (Germany) for estimating soil hydraulic parameters and improving soil moisture states was tested. Data measured by the CRNSs were assimilated with the local ensemble transform Kalman filter in the Community Land Model version 4.5. Data of four, eight and nine CRNSs were assimilated for the years 2011 and 2012 (with and without soil hydraulic parameter estimation), followed by a verification year 2013 without data assimilation. This was done using (i) a regional high-resolution soil map, (ii) the FAO soil map and (iii) an erroneous, biased soil map as input information for the simulations. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the FAO soil map and the biased soil map, soil moisture predictions improved strongly to a root mean square error of 0.03 cm3 cm−3 for the assimilation period and 0.05 cm3 cm−3 for the evaluation period. Improvements were limited by the measurement error of CRNSs (0.03 cm3 cm−3). The positive results obtained with data assimilation of nine CRNSs were confirmed by the jackknife experiments with four and eight CRNSs used for assimilation. The results demonstrate that assimilated data of a CRNS network can improve the characterization of soil moisture content on the catchment scale by updating spatially distributed soil hydraulic parameters of a land surface model.

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Stone Content Influence on Land Surface Model Simulation of Soil Moisture and Evapotranspiration at Reynolds Creek Watershed

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0075.1 ◽

2020 ◽

Vol 21 (8) ◽

pp. 1889-1904

Author(s):

Kshitij Parajuli ◽

Scott B. Jones ◽

David G. Tarboton ◽

Lawrence E. Hipps ◽

Lin Zhao ◽

...

Keyword(s):

Soil Moisture ◽

Soil Profile ◽

Land Surface ◽

Model Simulation ◽

Land Surface Model ◽

Surface Model ◽

Hydraulic Parameters ◽

Reynolds Creek ◽

Soil Hydraulic Parameters ◽

Soil Hydraulic

AbstractConsiderable advancement in spatiotemporal resolution of remote sensing and ground-based measurements has enabled refinement of parameters used in land surface models for simulating surface water fluxes. However, land surface modeling capabilities are still inadequate for accurate representation of subsurface properties and processes, which continue to limit the accuracy of land surface model simulation. Our objective in this study was to examine the performance of the variously parameterized Noah land surface model with multiphysics option (Noah-MP) in simulating evapotranspiration (ET) and soil moisture dynamics in stony soils using verification from eddy covariance ET and in situ soil moisture data during the growing season of year 2015, obtained from the Lower Sheep subcatchment within the Reynolds Creek Experimental Watershed in southwestern Idaho. We evaluated the performance of Noah-MP considering four different scenarios with 1) a one-layer soil profile with Noah-MP default soil hydraulic parameters and three more five-layer soil profiles using 2) Noah-MP default soil hydraulic parameters; 3) soil hydraulic parameters derived from a pedotransfer function using field observations; and 4) hydraulic parameters from scenario 3, which also accounted for stone content in each layer. Each modeling experiment was forced with the same set of initial conditions, atmospheric input, and vegetation parameters. Our results indicate that enhanced representation of soil profile properties and stone content information noticeably improve the Noah-MP land surface model simulation of soil moisture content and evapotranspiration.

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Evaluation and improvement of the default soil hydraulic parameters for the Noah Land Surface Model

Geoderma ◽

10.1016/j.geoderma.2016.09.022 ◽

2017 ◽

Vol 285 ◽

pp. 247-259 ◽

Cited By ~ 20

Author(s):

Andrea Sz. Kishné ◽

Yohannes Tadesse Yimam ◽

Cristine L.S. Morgan ◽

Bright C. Dornblaser

Keyword(s):

Land Surface ◽

Land Surface Model ◽

Surface Model ◽

Hydraulic Parameters ◽

Soil Hydraulic Parameters ◽

Noah Land Surface Model ◽

Soil Hydraulic

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Evaluation and Comparison of Noah and Pleim–Xiu Land Surface Models in MM5 Using GÖTE2001 Data: Spatial and Temporal Variations in Near-Surface Air Temperature

Journal of Applied Meteorology and Climatology ◽

10.1175/jam2561.1 ◽

2007 ◽

Vol 46 (10) ◽

pp. 1587-1605 ◽

Cited By ~ 37

Author(s):

J-F. Miao ◽

D. Chen ◽

K. Borne

Keyword(s):

Soil Moisture ◽

Air Temperature ◽

Land Surface ◽

Model Performance ◽

Surface Air Temperature ◽

Near Surface ◽

Surface Models ◽

Urban Heat ◽

Soil Moisture Initialization ◽

Evident Difference

Abstract In this study, the performance of two advanced land surface models (LSMs; Noah LSM and Pleim–Xiu LSM) coupled with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3.7.2, in simulating the near-surface air temperature in the greater Göteborg area in Sweden is evaluated and compared using the GÖTE2001 field campaign data. Further, the effects of different planetary boundary layer schemes [Eta and Medium-Range Forecast (MRF) PBLs] for Noah LSM and soil moisture initialization approaches for Pleim–Xiu LSM are investigated. The investigation focuses on the evaluation and comparison of diurnal cycle intensity and maximum and minimum temperatures, as well as the urban heat island during the daytime and nighttime under the clear-sky and cloudy/rainy weather conditions for different experimental schemes. The results indicate that 1) there is an evident difference between Noah LSM and Pleim–Xiu LSM in simulating the near-surface air temperature, especially in the modeled urban heat island; 2) there is no evident difference in the model performance between the Eta PBL and MRF PBL coupled with the Noah LSM; and 3) soil moisture initialization is of crucial importance for model performance in the Pleim–Xiu LSM. In addition, owing to the recent release of MM5, version 3.7.3, some experiments done with version 3.7.2 were repeated to reveal the effects of the modifications in the Noah LSM and Pleim–Xiu LSM. The modification to longwave radiation parameterizations in Noah LSM significantly improves model performance while the adjustment of emissivity, one of the vegetation properties, affects Pleim–Xiu LSM performance to a larger extent. The study suggests that improvements both in Noah LSM physics and in Pleim–Xiu LSM initialization of soil moisture and parameterization of vegetation properties are important.

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Influence of Land Cover and Soil Moisture on the Horizontal Distribution of Sensible and Latent Heat Fluxes in Southeast Kansas during IHOP_2002 and CASES-97

Journal of Hydrometeorology ◽

10.1175/jhm554.1 ◽

2007 ◽

Vol 8 (1) ◽

pp. 68-87 ◽

Cited By ~ 71

Author(s):

Margaret A. LeMone ◽

Fei Chen ◽

Joseph G. Alfieri ◽

Mukul Tewari ◽

Bart Geerts ◽

...

Keyword(s):

Soil Moisture ◽

Latent Heat ◽

Land Surface ◽

Field Experiments ◽

Time Of Day ◽

Horizontal Distribution ◽

Heat Fluxes ◽

Surface Model ◽

Green Vegetation ◽

The Impact

Abstract Analyses of daytime fair-weather aircraft and surface-flux tower data from the May–June 2002 International H2O Project (IHOP_2002) and the April–May 1997 Cooperative Atmosphere Surface Exchange Study (CASES-97) are used to document the role of vegetation, soil moisture, and terrain in determining the horizontal variability of latent heat LE and sensible heat H along a 46-km flight track in southeast Kansas. Combining the two field experiments clearly reveals the strong influence of vegetation cover, with H maxima over sparse/dormant vegetation, and H minima over green vegetation; and, to a lesser extent, LE maxima over green vegetation, and LE minima over sparse/dormant vegetation. If the small number of cases is producing the correct trend, other effects of vegetation and the impact of soil moisture emerge through examining the slope ΔxyLE/ΔxyH for the best-fit straight line for plots of time-averaged LE as a function of time-averaged H over the area. Based on the surface energy balance, H + LE = Rnet − Gsfc, where Rnet is the net radiation and Gsfc is the flux into the soil; Rnet − Gsfc ∼ constant over the area implies an approximately −1 slope. Right after rainfall, H and LE vary too little horizontally to define a slope. After sufficient drying to produce enough horizontal variation to define a slope, a steep (∼−2) slope emerges. The slope becomes shallower and better defined with time as H and LE horizontal variability increases. Similarly, the slope becomes more negative with moister soils. In addition, the slope can change with time of day due to phase differences in H and LE. These trends are based on land surface model (LSM) runs and observations collected under nearly clear skies; the vegetation is unstressed for the days examined. LSM runs suggest terrain may also play a role, but observational support is weak.

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Exploring hydrological processes at a small agricultural catchment in the Czech Republic

10.5194/egusphere-egu21-4952 ◽

2021 ◽

Author(s):

Tailin Li ◽

Nina Noreika ◽

Jakub Jeřábek ◽

Tomáš Dostál ◽

David Zumr

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Hydrological Processes ◽

Hydraulic Parameters ◽

Rainfall Runoff ◽

The Czech Republic ◽

Mike She ◽

Stream Discharge ◽

Soil Hydraulic Parameters ◽

Soil Hydraulic

A better understanding of hydrological processes in agricultural catchments is not only crucial to hydrologists but also helpful for local farmers. Therefore, we have built the freely-available web-based WALNUD dataset (Water in Agricultural Landscape &#8211; NU&#269;ice Database) for our experimental catchment Nu&#269;ice (0.53 km2), the Czech Republic. We have included observed precipitation, air temperature, stream discharge, and soil moisture in the dataset. Furthermore, we have applied numerical modelling techniques to investigate the hydrological processes (e.g. soil moisture variability, water balance) at the experimental catchment using the dataset.The Nu&#269;ice catchment, established in 2011, serves for the observation of rainfall-runoff processes, soil erosion and water balance of the cultivated landscape. The average altitude is 401 m a.s.l., the mean land slope is 3.9 %, and the climate is humid continental (mean annual temperature 7.9 &#176;C, average annual precipitation 630 mm). The catchment consists of three fields covering over 95 % of the area. There is a narrow stream which begins as a subsurface drainage pipe in the uppermost field draining the water at catchment. The typical crops are winter wheat, rapeseed, mustard and alfalfa. The installed equipment includes a standard meteorological station, several rain gauges distributed in the area of the basin, and an H flume to monitor the stream discharge, water turbidity and basic water quality indicators. The soil water content (at point scale) and groundwater level are also recorded. Recently, we have installed two cosmic-ray soil moisture sensors (StyX Neutronica) to estimate large-scale topsoil water content at the catchment.Even though the soil management and soil properties in the fields of Nu&#269;ice seem to be nearly homogeneous, we have observed variability in the topsoil moisture pattern. The method for the explanation of the soil water regime was the combination of the connectivity indices and numerical modelling. The soil moisture profiles from the point-scale sensors were processed in a 1-D physically-based soil water model (HYDRUS-1D) to optimize the soil hydraulic parameters. Further, the soil hydraulic parameters were used as input into a 3D spatially-distributed model, MIKE-SHE. The MIKE-SHE simulation has been mainly calibrated with rainfall-runoff observations. Meanwhile, the spatial patterns of the soil moisture were assessed from the simulation for both dry and wet catchment conditions. From the MIKE-SHE simulation, the optimized soil hydraulic parameters have improved the estimation of soil moisture dynamics and runoff generation. Also, the correlation between the observed and simulated soil moisture spatial patterns showed different behaviors during the dry and wet catchment conditions.This study has been supported by the Grant Agency of the Czech Technical University in Prague, grant No. SGS20/156/OHK1/3T/11 and the Project SHui which is co-funded by the European Union Project: 773903 and the Chinese MOST.

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Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments

Hydrology and Earth System Sciences ◽

10.5194/hess-15-2437-2011 ◽

2011 ◽

Vol 15 (8) ◽

pp. 2437-2457 ◽

Cited By ~ 25

Author(s):

S. Nie ◽

J. Zhu ◽

Y. Luo

Keyword(s):

Parameter Estimation ◽

Soil Moisture ◽

Kalman Filter ◽

Ensemble Kalman Filter ◽

Land Surface ◽

Estimation Method ◽

State Parameter ◽

Hydraulic Parameters ◽

Constrained Estimation ◽

Estimated Parameters

Abstract. The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, soil moisture initial condition and atmospheric forcing. A physically based land surface model is used for this purpose. Using a series of identical twin experiments in two kinds of initial parameter distribution (IPD) scenarios, the narrow IPD (NIPD) scenario and the wide IPD (WIPD) scenario, model-generated near surface soil moisture observations are assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil moisture suction and a soil texture empirical parameter) in the model. The estimation of single imperfect parameter is successful with the ensemble mean value of all three estimated parameters converging to their true values respectively in both NIPD and WIPD scenarios. Increasing the number of imperfect parameters leads to a decline in the estimation performance. A wide initial distribution of estimated parameters can produce improved simultaneous multi-parameter estimation performances compared to that of the NIPD scenario. However, when the number of estimated parameters increased to three, not all parameters were estimated successfully for both NIPD and WIPD scenarios. By introducing constraints between estimated hydraulic parameters, the performance of the constrained three-parameter estimation was successful, even if temporally sparse observations were available for assimilation. The constrained estimation method can reduce RMSE much more in soil moisture forecasting compared to the non-constrained estimation method and traditional non-parameter-estimation assimilation method. The benefit of this method in estimating all imperfect parameters simultaneously can be fully demonstrated when the corresponding non-constrained estimation method displays a relatively poor parameter estimation performance. Because all these constraints between parameters were obtained in a statistical sense, this constrained state-parameter estimation scheme is likely suitable for other land surface models even with more imperfect parameters estimated in soil moisture assimilation applications.

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Evaluating a land surface model at a water-limited site: implications for land surface contributions to droughts and heatwaves

10.5194/egusphere-egu21-4083 ◽

2021 ◽

Author(s):

Mengyuan Mu ◽

Martin De Kauwe ◽

Anna Ukkola ◽

Andy Pitman ◽

Teresa Gimeno ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Heat Fluxes ◽

Surface Model ◽

Strong Impact ◽

Water Fluxes ◽

Land Surface Models ◽

Groundwater Aquifer ◽

Surface Models

Land surface models underpin coupled climate model projections of droughts and heatwaves. However, the lack of simultaneous observations of individual components of evapotranspiration, concurrent with root-zone soil moisture, has limited previous model evaluations. Here, we use a comprehensive set of observations from a water-limited site in southeastern Australia including both evapotranspiration and soil moisture to a depth of 4.5 m to evaluate the Community Atmosphere-Biosphere Land Exchange (CABLE) land surface model. We demonstrate that alternative process representations within CABLE had the capacity to improve simulated evapotranspiration, but not necessarily soil moisture dynamics - highlighting problems of model evaluations against water fluxes alone. Our best simulation was achieved by resolving a soil evaporation bias; a more realistic initialisation of the groundwater aquifer state; higher vertical soil resolution informed by observed soil properties; and further calibrating soil hydraulic conductivity. Despite these improvements, the role of the empirical soil moisture stress function in influencing the simulated water fluxes remained important: using a site calibrated function reduced the soil water stress on plants by 36 % during drought and 23 % at other times. These changes in CABLE not only improve the seasonal cycle of evapotranspiration, but also affect the latent and sensible heat fluxes during droughts and heatwaves. The range of parameterisations tested led to differences of ~150 W m-2 in the simulated latent heat flux during a heatwave, implying a strong impact of parameterisations on the capacity for evaporative cooling and feedbacks to the boundary layer (when coupled). Overall, our results highlight the opportunity to advance the capability of land surface models to capture water cycle processes, particularly during meteorological extremes, when sufficient observations of both evapotranspiration fluxes and soil moisture profiles are available.

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