Noah-MP Model Research Articles

Effect of permafrost degradation on grassland net primary productivity in Qinghai–Tibet Plateau

Abstract Climate warming poses complex challenges for alpine ecosystems on the Qinghai–Tibetan Plateau (QTP), further exacerbated by permafrost degradation. Quantifying the specific ecological impacts of permafrost thaw remains elusive, as ecological variations are also influenced by external climate factors. This study tackles this gap by employing the Noah-MP model to simultaneously simulate permafrost thermal–hydrological dynamics and net primary production (NPP) across the Three River Headwaters Region from 1989 to 2018. Model results were validated against observations. To isolate the ecological effects of permafrost thaw, we implemented a novel relative time transformation on the simulation results. Our analysis reveals a 7.5 × 104 km2 reduction in permafrost coverage during the study period, coinciding with a 1.09 g C m−2 yr−2 increase in NPP. While precipitation is the primary driver of NPP changes in most years, soil moisture emerges as a crucial factor during permafrost disappearance, when the ground transitions to seasonally frozen ground. Surprisingly, the NPP response to permafrost disappearance exhibited a transient effect, diminishing to negligible levels within five years post-thaw. These findings enhance our understanding of the intricate and dynamic responses of the QTP ecosystem to permafrost degradation under a warming climate.

Improving runoff simulation in the Western United States with Noah-MP and variable infiltration capacity

Abstract. Streamflow predictions are critical for managing water resources and for environmental conservation, especially in the water-short Western United States. Land surface models (LSMs), such as the variable infiltration capacity (VIC) model and the Noah LSM with multiparameterization options (Noah-MP), play an essential role in providing comprehensive runoff predictions across the region. Virtually all LSMs require parameter estimation (calibration) to optimize their predictive capabilities. Here, we focus on the calibration of VIC and Noah-MP models at a 1/16° latitude–longitude resolution across the Western United States. We first performed global optimal calibration of parameters for both models for 263 river basins in the region. We find that the calibration significantly improves the models' performance, with the median daily streamflow Kling–Gupta efficiency (KGE) increasing from 0.37 to 0.70 for VIC, and from 0.22 to 0.54 for Noah-MP. In general, post-calibration model performance is higher for watersheds with relatively high precipitation and runoff ratios, and at lower elevations. At a second stage, we regionalize the river basin calibrations using the donor-basin method, which establishes transfer relationships for hydrologically similar basins, via which we extend our calibration parameters to 4816 hydrologic unit code (HUC)-10 basins across the region. Using the regionalized parameters, we show that the models' capabilities to simulate high and low flow conditions are substantially improved following calibration and regionalization. The refined parameter sets we developed are intended to support regional hydrological studies and hydrological assessments of climate change impacts.

Assessing the Sensitivity of Snow Depth Simulations to Land Surface Parameterizations within Noah-MP in Northern Xinjiang, China

Snow cover plays a crucial role in the surface energy balance and hydrology and serves as a key indicator of climate change. In this study, we conducted an ensemble simulation comprising 48 members generated by randomly combining the parameterizations of five physical processes within the Noah-MP model. Utilizing the variance-based Sobol total sensitivity index, we quantified the sensitivity of regional-scale snow depth simulations to parameterization schemes. Additionally, we analyzed the spatial patterns of the parameterization sensitivities and assessed the uncertainty of the multi-parameterization scheme ensemble simulation. The results demonstrated that the differences in snow depth simulation results among the 48 scheme combinations were more pronounced in mountain regions, with melting mechanisms being the primary factor contributing to uncertainty in ensemble simulation. Contrasting mountain regions, the sensitivity index for the physical process of partitioning precipitation into rainfall and snowfall was notably higher in basin areas. Unexpectedly, the sensitivity index of the lower boundary condition of the physical process of soil temperature was negligible across the entire region. Surface layer drag coefficient and snow surface albedo parameterization schemes demonstrated meaningful sensitivity in localized areas, while the sensitivity index of the first snow layer or soil temperature time scheme exhibited a high level of sensitivity throughout the entire region. The uncertainty of snow depth ensemble simulation in mountainous areas is predominantly concentrated between 0.2 and 0.3 m, which is significantly higher than that in basin areas. This study aims to provide valuable insights into the judicious selection of parameterization schemes for modeling snow processes.

Estimation of water balance components by Noah-MP land surface model for the Neyshaboor watershed, Khorasan Razavi, Iran

The numerical modeling of the land surface can make up for the insufficient station data in terms of number, dispersion, and temporal continuity. In this research, to evaluate the Noah-MP land surface model, the water balance components were estimated in the Neyshaboor watershed in the monthly time step during 2000–2009. Model input data were obtained from the global land data assimilation system version 1 (GLDAS-1), and the SWAT (soil and water assessment tool, a semi-distributed for small watershed to river basin-scale model) model output was used for the evaluation of the Noah-MP model. In this study, the ability of the Noah-MP model in simulating vegetation dynamically was studied. The precipitation was corrected before running the model for a more reliable evaluation. The time between 2000 and 2001 was considered a spin-up period and 2002–2009 for calibration and validation. The model has the best simulation in the mountainous areas; the runoff simulated by the Noah-MP model is in good agreement with the modeled runoff by SWAT in these areas. (R2 = 0.78, NSE = 0.62, RMSE = 1.98 m3/s). The R2 for simulated soil moisture for soil layers (0–10, 10–40 cm) was 0.62 and 0.57, and RMSE was 0.059 (m3/m3) and 0.052 (m3/m3), respectively, in Motamedieh field. The annual amount of evapotranspiration estimated by the two models is comparable to the average annual evapotranspiration in the watershed (about 300 mm). Based on the results from the research, the model has well simulated: the runoff in the mountainous areas, the moisture in the upper layer of the soil, and the average annual evapotranspiration in the study area.

Sensitivity Analysis of the Land Surface Characteristic Parameters in Different Climatic Regions of the Loess Plateau

Land surface parameters are crucial in land surface process model simulations. Considering the complex land surface characteristics of the Loess Plateau, a parametric sensitivity analysis was conducted to determine the key parameters of its Noah Multi-Parameterization (Noah-MP) land surface model. Sensitivity analysis can better elucidate the influence of different parameters on the model simulation results and evaluate the rationality of each model parameter. The extended Fourier amplitude sensitivity test (EFAST) method is a classical global sensitivity analysis method, whose theory is derived from the analysis of variance and Fourier transform. In this study, the EFAST method was used to perform sensitivity analyses on the land surface characteristic parameters in different climatic regions of the Loess Plateau. The results showed that the Noah-MP model can represent the land surface characteristics of the Loess Plateau well. With sensible and latent heat fluxes as criteria, the main sensitivity parameters were the vegetation roughness length (Z0), the soil quartz content (QUARTZ), the maximum volumetric soil moisture (MAXSMC), and the soil parameter “b”. The coupling effect between parameters has a greater impact on the sensitivity analysis. The probability densities of the three most sensitive parameters were evenly distributed in each interval, whereas those of the other parameters were distributed within 0–0.2 of the standardized value. Moreover, almost half of the land surface parameters accounted for 80% of the total sensitivity. Based on the seasonal sensitivity distribution of the land surface parameters, Z0 dominated throughout all four seasons, QUARTZ sensitivity was high in spring, and both MAXSMC and QUARTZ showed high sensitivities in winter.

Improving ecological indicators of arid zone deserts through simulation

Ecological indicators, such as soil moisture content and gross primary production (GPP), play important roles in the management of ecosystems and water resources and within climate change research. However, studies on monitoring and simulation of ecological indicators of arid zones remain limited. This study simulated soil moisture (SM), latent heat (LH), and gross primary productivity (GPP) for the arid Yarkant River Basin in China using the High-Resolution Land Data Assimilation System and Deep learning. Simulation of hydrology over a large spatial scale is difficult due to a lack of observed data and the difficulty in parameterizing models to represent complicated ecological mechanisms. A comparison of Noah Multi-Parameterization (Noah-MP) simulations to multiple datasets at an annual scale obtained correlation coefficients exceeding 0.8. The model was able to replicate the broad temporal dynamics of GPP and LH over the Yarkant River Basin. Point- and regional-scale assessments across the Yarkant River Basin obtained the optimal model performance. The temporal trends in simulated SM anomalies were consistent with observations across the different sub-basins. The simulations of the Noah-MP model driven by Global Land Data Assimilation System (GLDAS) and ERA5 data provided accurate representations of most hydrological variables, except for in the upper reaches of Yarkant River Basin, likely due to the extensive freeze-thaw activity in this transitional region. The results of this study can fill hydrological data gaps in arid zones areas.

A genetic particle filter scheme for univariate snow cover assimilation into Noah-MP model across snow climates

Abstract. Accurate snowpack simulations are critical for regional hydrological predictions, snow avalanche prevention, water resource management, and agricultural production, particularly during the snow ablation period. Data assimilation methodologies are increasingly being applied for operational purposes to reduce the uncertainty in snowpack simulations and to enhance their predictive capabilities. This study aims to investigate the feasibility of using a genetic particle filter (GPF) as a snow data assimilation scheme designed to assimilate ground-based snow depth (SD) measurements across different snow climates. We employed the default parameterization scheme combination within the Noah-MP (with multi-parameterization) model as the model operator in the snow data assimilation system to evolve snow variables and evaluated the assimilation performance of the GPF using observational data from sites with different snow climates. We also explored the impact of measurement frequency and particle number on the filter updating of the snowpack state at different sites and the results of generic resampling methods compared to the genetic algorithm used in the resampling process. Our results demonstrate that a GPF can be used as a snow data assimilation scheme to assimilate ground-based measurements and obtain satisfactory assimilation performance across different snow climates. We found that particle number is not crucial for the filter's performance, and 100 particles are sufficient to represent the high dimensionality of the point-scale system. The frequency of measurements can significantly affect the filter-updating performance, and dense ground-based snow observational data always dominate the accuracy of assimilation results. Compared to generic resampling methods, the genetic algorithm used to resample particles can significantly enhance the diversity of particles and prevent particle degeneration and impoverishment. Finally, we concluded that the GPF is a suitable candidate approach for snow data assimilation and is appropriate for different snow climates.

Assimilation of Blended Satellite Soil Moisture Data Products to Further Improve Noah-MP Model Skills

Microwave satellite remote sensing has enabled observations of soil moisture (SM) at the global scale, and multiple SM data products have been developed in the past decades. However, single-sensor-based measurements are insufficient for continuous spatiotemporal coverage. In the context of its climate program, the Climate Change Initiative, the European Space Agency (ESA) has developed robust, long term, global scale, multi instrument satellite derived time series of climate data record for key component of the climate system, including soil moisture (CCI), while the Soil Moisture Operational Product System (SMOPS) was specifically developed by National Oceanic and Atmospheric Administration (NOAA) to offer the real time blended SM datasets through merging all available individual products. Before combining, all individual SM data ingested into both SMOPS and CCI blended products are scaled to Global Land Data Assimilation System (GLDAS) 0-10 cm SM climatology. Benefiting from land surface model evolution and the availability of high-quality forcing data, GLDAS has become more comprehensive to track SM changes and dynamic trends. The development of GLDAS and the scaling procedure in CCI and SMOPS leave an open scientific and operational question: do the blended satellite SM data products have added value comparing to the GLDAS product? This study clearly reveals that both CCI and SMOPS can provide the reliable SM observations with independent information, although their climatology matches well with GLDAS. Relative to assimilation of GLDAS 0-10 cm SM data, Noah-MP model can be further improved by assimilating the blended satellite SM observations with respect to the quality-controlled in situ measurements. The strong consistency of results presented in this paper proves that the blended satellite SM data products are more useful than the GLDAS product in terms of improving Noah-MP model performance.

Grid-based calibration of the WRF-Hydro with Noah-MP model with improved groundwater and transpiration process equations

The physically-based distributed WRF-Hydro modelling system, including the Noah land surface model with multiple parameterization options (Noah-MP) and the hydrological extension of the WRF atmospheric model (Weather Research and Forecasting model), has recently been widely used for water balance investigations, streamflow and coupled land–atmosphere simulations. Despite the multiple available physical parameterizations in the model, equations for simulating particular losses from the water balance are missing, and a grid-based calibration of distributed parameters across multiple watersheds has not been studied. To fill these gaps, this study aims: (i) to analyze the impact of soil, runoff, groundwater and vegetation parameters on water balance components; (ii) to improve baseflow and transpiration equations; and (iii) to test a grid-based calibration approach for distributed model parameters, using streamflow observations. The WRF-Hydro groundwater model was improved through the introduction of a groundwater loss factor and the Jarvis stomatal conductance model was modified to account for nocturnal transpiration. The grid-based calibration was performed for three parameters (infiltration, hydraulic conductivity and percolation) for 19 spatially-distributed classes, with the Parameter Estimation (PEST) software. The study area includes 31 small mountainous watersheds (5–115 km2) in Cyprus, in the Eastern Mediterranean. A two-year period (2011–2013) was used for calibration and a five-year period (2013–2018) for the evaluation. The baseline model set-up overestimated streamflow, on average, by 50 % in 2011–2012 and more than 100 % in 2012–2013. Overall, streamflow and evapotranspiration (ET) could vary by about ±30 % from the baseline simulation, using different model parameters and model options. The simulation of groundwater losses as a function of groundwater level reduced total streamflow, on average, by 30 %. The use of the proposed Jarvis equation for nocturnal transpiration increased the total ET, on average, by 25 %. The grid-based approach facilitated the calibration of the distributed parameters over the area of the 31 watersheds. The median Nash-Sutcliff Efficiency (NSE) was 0.49 during calibration, but 0.02 in the drier evaluation period. The calibrated WRF-Hydro model reproduced the annual variability of ET and the improved groundwater and transpiration equations reduced the substantial streamflow overestimation of WRF-Hydro. The model performance during dry years demonstrated the need for representation of more processes that occur in semi-arid environments with ephemeral streams and are not included in WRF-Hydro and Noah-MP. The grid-based WRF-Hydro parameterization can be applied to the full study area for fully-coupled atmospheric-hydrologic simulations.

Improving the estimation of snow depth in the Noah-MP model by combining particle filter and Bayesian model averaging

Noah with multiparameterization (Noah-MP) is a new-generation land surface model that is enhanced by Noah and has multiparameterization as a major feature. Although various schemes have improved the applicability of this model, the ability to reduce the uncertainties of the simulation results remains a challenge. To improve the accuracy of snow-depth prediction based on the multiparameterization schemes within the Noah-MP model, we proposed a novel snow data assimilation framework configured using Bayesian model averaging (BMA) and genetic particle filter (GPF). Within this framework, we employed a kernel density smoother to create a probability density function of the model ensembles that are created using the GPF. We selected representative parameterization scheme combinations within the Noah-MP model to conduct the simulation experiments. We examined the feasibility of BMA applied in snow-depth prediction. On this basis, we investigated the performance of the BMA-GPF at eight seasonally snow-covered study sites with different snow climates. The results showed that BMA can be used to obtain a deterministic snow-depth prediction with a determination coefficient of 93.51% and a containing ration of prediction interval of 94.68%. These results were significantly higher than the four scheme combinations within the training period; however, the bias of deterministic prediction was well below expectations. The containing ration of uncertainty prediction interval was just 70% and was even lower than the four scheme combinations within the test period. The BMA-GPF snow data assimilation framework helped to obtain snow-depth prediction with a containing ration that ranged from 95% to 98% at eight sites and could be used in different snow climates. Further comparative results indicated that the BMA-GPF snow data assimilation framework no longer depended on the correction coefficients of the model prediction, and the assumption that the posterior probability of the state variable obeys the Gaussian distribution was not required. Finally, we concluded that the BMA-GPF is a suitable candidate approach to snow-depth multimodel ensemble simulation.

Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?

Data assimilation methods have been used to improve the performances of land surface models by integrating remote sensing and in situ measurements. However, the impact of data assimilation on improving the forecast of land surface variables has not been well studied, which is essential for weather and hydrology forecasting. In this study, a multi-pass land data assimilation scheme (MLDAS) based on the Noah-MP model was used to predict short-term land surface variables (e.g., sensible heat fluxes (H), latent heat fluxes (LE), and surface soil moisture (SM)) by jointly assimilating soil moisture, leaf area index (LAI) and solar-induced chlorophyll fluorescence (SIF). The test was conducted at the Mead site during the growing season (1 May to 30 September) in 2003, 2004, and 2005. Four assimilation-prediction scenarios (assimilating for 15 days, 45 days, 75 days, and 105 days from 1 May, then predicting one future month) are adapted to evaluate the influence of assimilation on subsequent prediction against Noah-MP open-loop simulation (OL). On average, MLDAS produces 28.65%, 27.79%, and 19.15% lower root square deviations (RMSD) for daily H, LE, and SM prediction compared to open-loop run, respectively. The influence of assimilation on prediction can reach around 60 days and 100 days for H (LE) and SM, respectively. Our findings indicate that data assimilation can improve the accuracy of land surface variables in a short-term prediction period.

Understanding the impact of vegetation dynamics on the water cycle in the Noah-MP model

The impact of extreme climate events, especially prolonged drought, on ecosystem response, can influence the land-atmosphere interactions and modify local and regional weather and climate. To investigate the impact of vegetation dynamics on the simulation of energy, water, and carbon exchange at the land surface and streamflow, especially during drought conditions, we compared the performance of multiple versions of the Noah- multiparameterization (MP) land surface model (both Noah-MP LSM, version 3.6 and 4.0.1) with default configurations as well as various vegetation physics options, including the dynamic or input leaf area index (LAI) and the fractional vegetated area (FVEG). At the site level, simulated water and energy fluxes from each version were compared to eddy covariance (EC) flux tower measurements and remote sensing data from Moderate-Resolution Imaging Spectroradiometer (MODIS) at well-characterized natural grassland sites in Kansas from 2008 to 2018. The ability of each version to reproduce annual mean river flows was compared to gauged observations at United States Geological Survey (USGS) stations over 11 years (2008–2018). Model performance in replicating spatial patterns during extreme events was assessed by comparing simulated soil moisture (SM) percentiles over the state of Kansas to the U.S. Drought Monitor (USDM). Results from these comparisons indicate that (a) even though there were differences in the latent heat (LE) components (i.e., transpiration, canopy evaporation, and soil evaporation), the total LE is mostly insensitive to variations in LAI across all model versions. This indicates that the incoming net radiation limits the total evaporation, as the presence of adequate soil moisture allows for higher soil evaporation when LAI limits transpiration; (b) regardless of the model version, the force of the precipitation largely dictates the accuracy of evapotranspiration (ET) simulation; (c) Overestimation of LE resulted in underestimation of streamflow, particularly over the land surface type dominated by a combination of grasses and cropland in the western and central part of the state; (d) all of the tested Noah-MP 4.0.1 vegetation physics produced spatial patterns of drought that more closely matched the USDM as compared to version 3.6. These findings have important relevance for applications of large-scale ecosystem-atmosphere feedbacks in water, carbon, and energy exchange.

Improving predictions of evapotranspiration by integrating multi-source observations and land surface model

Accurate estimation of evapotranspiration (ET) is essential for understanding terrestrial energy, water, and carbon cycles. This study proposes a hybrid model integrating in-situ and remote sensing-derived soil moisture (SM) observations and remote sensing leaf area index (LAI) with the Noah-MP model. The ensemble Kalman filter (EnKF) approach updates the leaf biomass and specific leaf area (SLA) by assimilating the remotely sensed LAI. A machine learning (ML) surrogate model is used to integrate multi-site SM profile observations and remote sensing SM products to estimate the three-layer SM. An iterative coupling of two parts implements the hybrid model: optimization of leaf biomass and SLA by assimilation of LAI in the Noah-MP model and simulation of three-layer SM in the ML surrogate model. The performance of the hybrid model is evaluated in the Heihe River Basin (HRB) in northwest China. The estimated ET from the hybrid model is compared with observations from the large aperture scintillometer (LAS) at the Arou, Daman, and Sidaoqiao sites and up-scaled watershed ET over the HRB. The findings indicate that the hybrid model performs well in vegetated areas but underestimates ET in extreme arid deserts. The three-site unbiased root mean squared errors (ubRMSEs) of ET estimates from the hybrid model are 29.06%, 42.76%, and 50.00% lower than Noah-MP at the Arou, Daman, and Sidaoqiao sites, respectively. The coupling of data assimilation (DA) and ML methods can improve vegetation dynamics and SM transport estimation in the Noah-MP model. The hybrid model can take advantage of DA and ML methods and integrate multi-source observations to improve the accuracy of ET estimation. The results also indicate that the ET predictions are more sensitive to root zone SM (0–40 cm) over croplands, grasslands, and shrublands, while the ET simulations are more affected by deeper rooting depths SM (0–100 cm) and groundwater over forests.

UAV-LiDAR Measurement of Vegetation Canopy Structure Parameters and Their Impact on Land–Air Exchange Simulation Based on Noah-MP Model

Land surface processes play a vital role in the exchange of momentum, energy, and mass between the land and the atmosphere. However, the current model simplifies the canopy structure using approximately three to six parameters, which makes the representation of canopy radiation and energy distribution uncertain to a large extent. To improve the simulation performance, more specific canopy structure parameters were retrieved by a UAV-LiDAR observation system and updated into the multiparameterization version of the Noah land surface model (Noah-MP) for a typical forest area. Compared with visible-light photogrammetry, LiDAR retrieved a more accurate vertical canopy structure, which had a significant impact on land–air exchange simulations. The LiDAR solution resulted in a 35.0∼48.0% reduction in the range of perturbations for temperature and another 27.8% reduction in the range of perturbations for moisture. This was due to the canopy structure affecting the radiation and heat fluxes of the forest, reducing their perturbation range by 7.5% to 30.1%. To reduce the bias of the land surface interaction simulation, it will be necessary to improve the method of retrieving the canopy morphological parameterization through UAV-LiDAR on a continued basis in the future.

Simulation of sensible and latent heat fluxes on the Tibetan Plateau from 1981 to 2018

The Noah-MP land surface model with modified thermal roughness length parameterization scheme was used for the simulation of land surface process on the Tibetan Plateau (TP) during 1981–2018, using the high-resolution China Meteorological Forcing Data. Characteristics of distributions and changes of surface sensible and latent heat (SH and LH) fluxes on the TP were analyzed using the simulation results after verification against the in situ observations. It is found that the Noah-MP model can well simulate the SH and LH fluxes. The inter-annual variations of SH fluxes on the central and western TP were larger than those on the eastern TP. All the regions showed a decreasing trend in SH flux from mid-1980s, but an increasing trend since about 2002. The LH fluxes on the eastern TP were larger than those on the central and western TP. But the inter-annual variations were smaller than those of central and western TP. The western and eastern TP got an increasing trend in LH flux during the 38 yrs. On the central TP, LH fluxes exhibited an increasing trend from mid-1980s to the beginning of the 21st century, and then decreased. For the whole TP, LH fluxes increased from mid-1980s to the beginning of the 21st century, and showed a decreasing trend from about 2003. The asynchronous changes of wind speed, Ts-Ta (land-air temperature difference) and water vapor condensation may increase the uncertainties of the trends in SH and LH fluxes.

Assimilating satellite-based soil moisture observations in a land surface model: The effect of spatial resolution

This article focuses on developing a data assimilation system that combines the modeled surface moisture estimates and satellite observations. Specifically, model states simulated by the Noah-MP land surface model are updated using an Ensemble Kalman Filter with products from the NASA SMAP (Soil Moisture Active Passive) satellite mission. The land surface model is run on two different regular grids, one at 12.5 km and the other at 500 m to produce surface and root zone soil moisture estimates across Oklahoma during April-July 2015. In the first case, the model was forced with the NLDAS-2 (North America Land Data Assimilation System) dataset and in the second with a downscaled version of the same dataset. Ground observations from the Oklahoma Mesonet network are compared to surface and root zone soil moisture output simulated by three different Noah-MP model runs i) an open loop simulation (in which no satellite data are assimilated); ii) assimilation of the 36 km SMAP radiometer-only product, and iii) assimilation of the 9 km SMAP radiometer-radar combined product. Results show that SMAP soil moisture retrievals improve the model performance (i.e., with respect to the open loop run) and that forcing the land surface model with higher resolution atmospheric forcings yields higher correlations and smaller errors in soil moisture simulations with respect to the original NLDAS-2 dataset. Although root zone soil moisture is not directly assimilated (since satellite observations are limited to the top 5 cm of the soil column), the assimilation of SMAP products at the surface is transferred to lower layers by the modeled physical processes and is shown to improve root zone soil moisture estimates as well.

Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China

Accurate simulation of snow cover process is of great significance to the study of climate change and the water cycle. In our study, the China Meteorological Forcing Dataset (CMFD) and ERA-Interim were used as driving data to simulate the dynamic changes in snow depth and snow water equivalent (SWE) in the Irtysh River Basin from 2000 to 2018 using the Noah-MP land surface model, and the simulation results were compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD), the long-term series of daily snow depth dataset in China (LSD), and China’s daily snow depth and snow water equivalent products (CSS). Before the simulation, we compared the combinations of four parameterizations schemes of Noah-MP model at the Kuwei site. The results show that the rainfall and snowfall (SNF) scheme mainly affects the snow accumulation process, while the surface layer drag coefficient (SFC), snow/soil temperature time (STC), and snow surface albedo (ALB) schemes mainly affect the melting process. The effect of STC on the simulation results was much higher than the other three schemes; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is much greater than that of a semi-implicit scheme. At the basin scale, the accuracy of snow depth modeled by using CMFD and ERA-Interim is higher than LSD and CSS snow depth based on microwave remote sensing. In years with high snow cover, LSD and CSS snow depth data are seriously underestimated. According to the results of model simulation, it is concluded that the snow depth and snow water equivalent in the north of the basin are higher than those in the south. The average snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly during the study period, but the end time of snow melting was significantly advanced.

Variations in surface roughness of heterogeneous surfaces in the Nagqu area of the Tibetan Plateau

Abstract. Temporal and spatial variations of the surface aerodynamic roughness lengths (Z0 m) in the Nagqu area of the northern Tibetan Plateau were analysed in 2008, 2010 and 2012 using MODIS satellite data and in situ atmospheric turbulence observations. Surface aerodynamic roughness lengths were calculated from turbulent observations by a single-height ultrasonic anemometer and retrieved by the Massman model. The results showed that Z0 m has an apparent characteristic of seasonal variation. From February to August, Z0 m increased with snow ablation and vegetation growth, and the maximum value reached 4–5 cm at the BJ site. From September to February, Z0 m gradually decreased and reached its minimum values of about 1–2 cm. Snowfall in abnormal years was the main reason for the significantly lower Z0 m compared with that in normal conditions. The underlying surface can be divided into four categories according to the different values of Z0 m: snow and ice, sparse grassland, lush grassland and town. Among them, lush grassland and sparse grassland accounted for 62.49 % and 33.74 %, and they have an annual variation of Z0 m between 1–4 and 2–6 cm, respectively. The two methods were positively correlated, and the retrieved values were lower than the measured results due to the heterogeneity of the underlying surface. These results are substituted into the Noah-MP (multi-parameterisation) model to replace the original parameter design numerical simulation experiment. After replacing the model surface roughness, the sensible heat flux and latent heat flux were simulated with a better diurnal dynamics.

Near-global-scale high-resolution seasonal simulations with WRF-Noah-MP v.3.8.1

Abstract. The added value of global simulations on the convection-permitting (CP) scale is a subject of extensive research in the earth system science community. An increase in predictive skill can be expected due to advanced representations of feedbacks and teleconnections in the ocean–land–atmosphere system. However, the proof of this hypothesis by corresponding simulations is computationally and scientifically extremely demanding. We present a novel latitude-belt simulation from 57∘ S to 65∘ N using the Weather Research and Forecasting (WRF)-Noah-MP model system with a grid increment of 0.03∘ over a period of 5 months forced by sea surface temperature observations. In comparison to a latitude-belt simulation with 45 km resolution, at CP resolution the representation of the spatial-temporal scales and the organization of tropical convection are improved considerably. The teleconnection pattern is very close to that of the operational European Centre for Medium Range Weather Forecasting (ECMWF) analyses. The CP simulation is associated with an improvement of the precipitation forecast over South America, Africa, and the Indian Ocean and considerably improves the representation of cloud coverage along the tropics. Our results demonstrate a significant added value of future simulations on the CP scale up to the seasonal forecast range.

Scale Impact of Soil Moisture Observations to Noah-MP Land Surface Model Simulations

Due to the limitations of satellite antenna technology, current operational microwave soil moisture (SM) data products are typically at tens of kilometers spatial resolutions. Many approaches have thus been proposed to generate finer resolution SM data using ancillary information, but it is still unknown if assimilation of the finer spatial resolution SM data has beneficial impacts on model skills. In this paper, a synthetic experiment is thus conducted to identify the benefits of SM observations at a finer spatial resolution on the Noah-MP land surface model. Results of this study show that the performance of the Noah-MP model is significantly improved with the benefits of assimilating 1 km SM observations in comparison with the assimilation of SM data at coarser resolutions. Downscaling satellite microwave SM observations from coarse spatial resolution to 1 km resolution is recommended, and the assimilation of 1 km remotely sensed SM retrievals is suggested for NOAA National Weather Service and National Water Center.