Noah-MP Land Surface Model Research Articles

Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme

Abstract The widely-used community Noah-MP land surface model currently adopts snow albedo parameterizations that are semi-physical in nature and have systematic biases which impact the accuracy of weather and climate modeling systems that use Noah-MP as the land component. We hypothesized that integrating the snowpack radiative transfer scheme from the latest version of the Snow, Ice, and Aerosol Radiative (SNICAR) model can improve the physical representation of snow albedo processes and reduce corresponding land model uncertainties. Therefore, we evaluate Noah-MP simulations employing the SNICAR scheme and compare model accuracy to a Noah-MP simulation using the default semi-physical Biosphere-Atmosphere Transfer Scheme (BATS) scheme using in-situ spectral snow albedo observations at three Rocky Mountain field stations. The agreement between simulated and in-situ observed ground snow albedo is significantly enhanced in NoahMP-SNICAR simulations relative to NoahMP-BATS simulations (root mean square error reductions from 0.116 to 0.103). Especially, NoahMP-SNICAR improves modeled snow albedo variability for fresh snow and aged snowpack (correlation increase from 0.42 to 0.67). The underestimated variability of snow albedo in NoahMP-BATS is a result of inadequate representation of physical linkages between snow albedo evolution and environmental/snowpack conditions (temperature, snow density, snow water equivalent, and light-absorbing particles), which is substantially improved by the NoahMP-SNICAR scheme. This new development of NoahMP-SNICAR physics provides a means to improve snow albedo accuracy and reduce corresponding uncertainties while providing new modeling capabilities such as hyperspectral snow albedo, and effects of snow grain size, snow grain shape, and light-absorbing particles in future studies.

Modeling water balance components of conifer species using the Noah-MP model in an eastern Mediterranean ecosystem

Abstract. Few studies have investigated the performance of land surface models for semiarid Mediterranean forests. This study aims to parameterize and test the performance of the Noah-MP land surface model for an eastern Mediterranean ecosystem. To this end, we calibrated the model for root zone soil moisture and transpiration of two conifer species, Pinus brutia, and Cupressus sempervirens, in a plantation forest on the Mediterranean island of Cyprus. The study area has a long-term average annual rainfall of 315 mm. Observations from 4 sap flow and 48 soil moisture sensors, for the period from December 2020 to June 2022, were used for model parameterization. A local sensitivity analysis found that the surface infiltration (REFKDT), hydraulic conductivity (SATDK), and stomatal resistance (RSMIN) parameters had the highest impacts on the water balance components (soil evaporation, tree transpiration, surface runoff, and drainage). The model performed better during the wetter 9-month validation period (379 mm rain) than during the drier 10-month calibration period (175 mm rain). Average soil moisture in the top 60 cm of the soil profile was reasonably well captured for both species (daily Nash–Sutcliffe efficiency > 0.80 for validation). Among the three soil layers, the second layer (20–40 cm) showed better simulation performance during both periods and for both species. The model exhibited limitations with respect to simulating transpiration, particularly during the drier calibration period. The inclusion of a root distribution function in the model, along with the monitoring of soil moisture below the 60 cm soil depth in the field, could improve the accuracy of model simulations in such water-limited ecosystems.

Refining water and carbon fluxes modeling in terrestrial ecosystems via plant hydraulics integration

Plant hydraulics substantially affects terrestrial water and carbon cycles by modulating water transport and carbon assimilation. Despite improved drought simulations in certain ecosystems through their integration into land surface models (LSMs), the broader application of plant hydraulics in diverse ecosystems and hydroclimates is still underexplored. In this study, we implemented the recently developed Noah-Multiparameterization Land Surface Model (Noah-MP LSM) equipped with a plant hydraulics scheme (Noah-MP-PHS) across 40 FLUXNET sites globally. Employing the Shuffled Complex Evolution-University of Arizona (SCE-UA) auto-calibration algorithm, we optimized key plant hydraulics parameters for these sites spanning eight vegetation types in both arid and humid climates. Noah-MP-PHS significantly improves the simulation of evapotranspiration (ET) and gross primary production (GPP) by better representing atmospheric and soil water stress compared to traditional soil hydraulic schemes (SHSs, such as Noah and CLM). The augmented Noah-MP-PHS models reduce surface flux overestimation and underestimation, exhibiting an average increase of 0.14 and 0.15 in Kling-Gupta Efficiency (KGE) compared to Noah and CLM, respectively. The explicit consideration of plant capacitance in PHS reveals substantial deep-layer and nocturnal root water uptake especially under dry conditions. We employed eXplainable Machine learning (XML) to quantify the model's relative sensitivity to newly introduced leaf-, stem- and root-related parameters in PHS. The sensitivity analysis reveals a rise in root parameter importance and a decline in leaf and stem parameters as conditions shift from humid to arid. These findings indicate that as aridity states vary, the most influential parameters affecting surface fluxes variation may change in parameter calibration for PHS applications. Our findings underscore the importance of incorporating plant hydraulics into LSMs to enhance simulations of terrestrial water and carbon dynamics. These findings are crucial for understanding ecosystem responses to global climate changes and guide the broader application of PHS at larger scales.

Optimization of snow-related processes in Noah-MP land surface model over the mid-latitudes of Asian region

Snow plays a critical role in modulating surface energy, water cycles, and climate prediction. Optimizing snow-related parameterizations can enhance the model behaviors in simulating snow-related physical processes and reduce the cold bias observed in winter climate simulations in the Northern Hemisphere. In this study, the topographic complexity and wind speed were incorporated into the parameterization of the snow cover fraction (SCF) and newly fallen snow density (SFD) respectively within the Noah with multi-parameterization (Noah-MP) LSM to optimize the simulation of snow-related processes in the mid-latitude regions of East Asia. A control simulation and three sensitivity experiments were conducted to investigate and quantify the effects of topographic complexity and wind speed on the simulation of snow-related processes and land surface temperature (LST) against MODIS products. The results showed that modifications to the two schemes effectively mitigated the overestimation of snow cover and albedo, and alleviated cold biases in the most of study area. The influence of SFD scheme considering wind speed was more pronounced in regions with more snowfall and higher wind speed, while the SCF scheme considering topographic complexity showed a more widespread effect. The combination of these two modified schemes yielded the best performance. The mean biases of SCF, albedo, and LST over the entire study region with both modified schemes were reduced by 0.126 (∼63 %), 0.044 (∼41 %), and 0.584 °C (∼18 %), respectively. Their RMSEs were reduced by 0.119 (∼36 %), 0.036 (∼22 %), and 0.489 °C (∼10 %), respectively. This study highlights the importance of wind conditions and topographic complexity in the simulations of snow-related characteristics over the mid-latitudes of Asian region.

Sensitivity of horizontal resolution and land surface model in operational WRF forecast for Online Nuclear Emergency Response System (ONERS)

Accurate Meteorological forecasts are crucial for the assessment of plume dispersion and dose prediction in nuclear power plant (NPP) sites. In this work the forecast sensitivity of the Weather Research and Forecasting (WRF) model is tested by running a series of forecast simulations for horizontal resolution, and land surface models (LSM) in the context of Online Nuclear Emergency Response System (ONERS) for Indian NPP sites. 72 h forecast simulations are made for three seasons viz. summer, southeast and northeast monsoon using the Global Forecast data. Three simulation experiments, namely 2 km-NOAH, 3 km-NOAH and 3 km-NOAHMP are conducted using two different nested domain configurations (18–6–2 km and 9–3 km) and two LSM schemes (NOAH and NOAH-MP) and tested at four different NPP sites. Forecast comparison of surface winds, relative humidity, temperature, heat fluxes and planetary boundary layer heights with data from meteorological tower, radiosonde and the Modern-Era Retrospective analysis for Research and Applications 2 (MERRA-2) shows 3 km-NOAH is equally capable in predicting surface parameters as well as vertical profiles compared to 2 km-NOAH with marginal differences. 3 km-NOAHMP shows less mean bias and better correlation for boundary layer height and heat fluxes. Comparison of spatial flow-field with 5th generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) data shows synoptic scale seasonal winds, sea level pressure systems and temperature hot-spots are better captured by 3 km-NOAHMP compared to 6 km coarse domain in the 18–6–2 km configuration. The daily accumulated rainfall by all simulations is overestimated compared to ERA5 data. The predictions by 3 km-NOAHMP better agree with Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM-IMERGE) data whereas 2 km-NOAH predicts delayed rainfall occurrence. Dispersion simulations of hypothetical plume release from a coastal NPP site with all three forecasts properly show the influence of local scale diurnal land-sea breeze and seasonal winds on the plume movement. Therefore the 9–3 km domain with NOAHMP LSM is found to be a suitable choice for operational weather forecast in ONERS for Indian NPP sites.

Influence of SMAP soil moisture retrieval assimilation on runoff estimation across South Asia

This study was designed to characterize and quantify the influence of surface soil moisture assimilation on estimated runoff (surface flow and baseflow) and hydraulically-routed streamflow across three large river basins in South Asia that are at risk of impending water stress. Soil Moisture Active Passive (SMAP) surface soil moisture retrievals were assimilated into the Noah-MP land surface model. The gridded runoff was hydraulically routed to obtain volumetric streamflow values for a river network using a runoff routing module (Hydrological Modeling and Analysis Platform, HyMAP). The open loop (OL, model-only) and data assimilated (DA, includes soil moisture retrieval assimilation) Noah-MP runoff estimates highlighted the improvements in estimated total runoff across irrigated areas. Soil moisture assimilation impacted baseflow more relative to surface runoff. The HyMAP-based OL and DA streamflow generally underestimated the streamflow at upstream stations and overestimated the streamflow at downstream stations within the Indus basin due to missing physics related to reservoir operations. For stations located in the Ganges–Brahmaputra basins, the OL and DA estimation performance varied. The OL and DA results showed that the assimilation of soil moisture retrievals improves gridded runoff and volumetric streamflow across irrigated areas. Considerable relative change in streamflow (>70% increase in magnitude relative to OL) is noted after assimilation across the highly irrigated lower Indus basin and high precipitation regions in Bangladesh. Improving the modeling system’s representativeness of ground conditions via inclusion of water management information could improve large-scale streamflow modeling across South Asia.

Improved soil moisture estimation and detection of irrigation signal by incorporating SMAP soil moisture into the Indian Land Data Assimilation System (ILDAS)

Land surface models have facilitated the estimation of soil moisture over a range of spatiotemporal scales. However, limitations in model parameterization and under-representation of anthropogenic processes restrict their ability to estimate local-scale soil moisture variability, especially over irrigated areas. Assimilation of satellite-based soil moisture retrievals into land surface models can be a viable approach to overcome these constraints, specially over highly irrigated countries such as India, where such applications are rare. Additionally, large-scale validation of modeled soil moisture has been limited over India till now due to lack of a representative station network. By assimilating Soil Moisture Active Passive (SMAP)-based estimates into the state-of-the-art Indian Land Data Assimilation System (ILDAS) and combining with a new soil moisture station network of more than 200 stations, this study demonstrates improved soil moisture estimations and capture of irrigation signals over the region. The Noah-MP land surface model is forced by multiple local and global meteorological datasets and Ensemble Kalman Filter (EnKF) is used for assimilation of soil moisture. Comparison of open-loop and data assimilated soil moisture against station soil moisture data shows relative spatial mean improvement of 0.0178 in correlation and 0.0029 m3/m3 in RMSE. Further statistical comparison with in-situ data has also shown better results over most of the stations, as evident from improved correlations and reduced unbiased RMSE after assimilation. Finally, the climatology of soil moisture over the different irrigation fractions reveals that data assimilated outputs over irrigated grid cells tend to have higher soil moisture during dry winter season, demonstrating the ability to capture irrigation signals. These findings quantify the value of data assimilation in improving soil moisture estimates and the ability to capture unmodeled processes such as irrigation, which lays the science groundwork for upcoming space missions such as NASA ISRO Synthetic Aperture Radar (NISAR).

Enhancing water-carbon fluxes and yield predictions of winter wheat using irrigation and data assimilation techniques in a land surface model

Croplands are essential to land–atmosphere interactions and have a great impact on water balance, carbon fluxes, regional weather and climate, and food supply. This study utilized the Noah-MP land surface model, enhanced by a dynamic crop growth process known as Noah-MP-Crop, and employed a multi-pass data assimilation scheme (MLDAS) to assimilate leaf area index, surface soil moisture, and solar-induced chlorophyll fluorescence into the model for refining model’s simulations of land state variables (soil moisture and leaf biomass) and optimize the model vegetation parameters [specific leaf area (SLA) and maximum carboxylation rate normalized to 25 °C (Vcmax25)]. In conjunction with the irrigation module in this model, four experiments were conducted at the local observation-site scale to estimate and validate water-carbon fluxes [actual evapotranspiration (ETa) and gross primary productivity (GPP)] and crop yield within the winter wheat growing phase, which included open-loop simulation with and without irrigation (OLirr and OL), data assimilation with and without irrigation (DAirr and DA), separately. In-situ observations from the Yucheng Station in 2006 were used to evaluate these four experiments. Results indicated that the combined experiment (DAirr) provided the best agreement with the observed ETa, GPP, and yield. This superior performance is mainly attributed to the accurate vegetation information and soil moisture profile provided by the combined experiments with the model. Results indicated that the DAirr experiment performed better than the OL experiment in ETa estimation, with a higher correlation coefficient (R) of 0.85 compared to 0.71, a reduced root mean square error (RMSE) from 1.51 to 1.06 mm day−1, a lower normalized RMSE (NRMSE) from 43.21 % to 23.39 %, and a decreased mean absolute error (MAE) from 1.16 to 0.78 mm day−1. Similarly, DAirr increased R from 0.71 to 0.87 and cut down RMSE (MAE) from 5.81 (4.32) to 3.48 (2.26) g m−2 day−1 and NRMSE from 70.39 % to 18.20 % for GPP estimation. Moreover, winter wheat yield from DAirr (770.9 g m−2) agreed better with ground measurements (819.7 g m−2) than that from OL (400.6 g m−2). Additionally, results revealed that assimilating all three variables simultaneously is superior to single-variable assimilation and significantly outperforms the OL approach. All in all, this study has provided initial insights into the feasibility of assimilating three variables at the site scale into the model for improving the estimation accuracy of water-carbon fluxes and winter wheat yield, which laid a foundation for subsequent regional simulations.

Sensitivity of joint atmospheric-terrestrial water balance simulations to soil representation: Convection-permitting coupled WRF-Hydro simulations for southern Africa

Regional weather and climate models play a crucial role in understanding and representing the regional water cycle, yet the accuracy of soil data significantly affects their reliability. In this study, we employ the fully coupled Weather Research and Forecasting Hydrological Modeling system (WRF-Hydro) to assess how soil hydrophysical properties influence regional land-atmosphere coupling and the water cycle over the southern Africa region. We utilize four widely-used global soil datasets, including default soil data for model from the Food and Agriculture Organization, and alternative datasets from the Harmonized World Soil Database, Global Soil Dataset for Earth System Model, and global gridded soil information system SoilGrids. By conducting convection-permitting coupled WRF-Hydro simulations with the Noah-MP land surface model using each of the aforementioned soil datasets, our benchmark analysis reveals substantial differences in soil hydrophysical properties and their significant impact on the simulated regional water cycle during the austral summer. Alterations in soil datasets lead to both spatial and temporal variations in surface water and energy fluxes, which in turn profoundly influence the atmospheric thermodynamic structure. Reduced soil water-holding capacity leads to subsequent reduction in soil moisture and latent heat, resulting in significant decreases in convective available potential energy and convective inhibition, signaling potential effects on precipitation distributions. In arid interior regions of southern Africa, shifts towards drier and warmer surface conditions due to soil data discrepancies are found to enhance atmospheric moisture convergence, suggesting a possible localized negative feedback of soil moisture on precipitation. Overall, the results for southern Africa indicate that soil data discrepancies exert more pronounced impact on terrestrial fields in dry subregions and on atmospheric fields in temperate subregions, highlighting the broad uncertainties in the regional water cycle reproduced within the model.

Joint assimilation of satellite-based surface soil moisture and vegetation conditions into the Noah-MP land surface model

This study explores the potential of integrating satellite retrievals of surface soil moisture (SSM) and vegetation conditions into the Noah-MP land surface model. In total, five data assimilation (DA) experiments were carried out. One of the experiments only assimilates SSM retrievals from the Soil Moisture Active Passive mission, two experiments only assimilate retrievals of vegetation conditions: either optical retrievals of leaf area index (LAI) from the Copernicus Global Land Service, or X-band microwave-based retrievals of vegetation optical depth (VOD) from the Advanced Microwave Scanning Radiometer 2. Additionally, two joint DA experiments are performed, each incorporating SSM and one of the vegetation products. The DA experiments are compared with a model-only run, and all experiments are evaluated using independent ground reference data of soil moisture, evapotranspiration, net ecosystem exchange and gross primary production (GPP). Assimilating only SSM improves estimates of the soil moisture profile (median SSM anomaly correlation improves with 0.02 compared to a model-only run), whereas assimilating LAI predominantly improves GPP estimates (reduction in median RMSD of 0.024 gC m−2 day−1 compared to a model-only run). The joint assimilation of SSM and vegetation conditions captures both of these improvements in a single, physically consistent analysis product. The DA increments show that this combined setup allows one satellite product to compensate for potential degradations introduced into the system by the other product. Furthermore, the joint SSM and VOD DA experiment has the smallest ensemble spread in its estimates (21% reduction in SSM spread compared to a model-only run). Overall, our results underline the potential of multi-sensor and multivariate DA, in which information from different sources is combined to improve the estimates of several land surface states and fluxes simultaneously.

Are the Noah and Noah-MP land surface models accurate for frozen soil conditions?

The accurate prediction of frozen soil conditions is a critical component of dynamic terrain characterization for engineering operations in cold regions. Currently the trusted resource for dynamic terrain within the US military uses gridded global soil conditions (i.e., temperature and moisture) generated by the Land Information System (LIS) architecture. LIS combines static terrain data with trusted surface weather information into the Noah land surface model to generate a continuous representation of the global surface conditions. Both the input weather data and the model output land surface state are adjusted to match the observed state using an advanced ensemble Kalman filter data assimilation technique to incorporate in situ and remote sensing observations. While this product has been robustly evaluated under unfrozen conditions only sporadic studies have investigated its ability to accurately simulate freeze/thaw cycles, and soil temperature and moisture under frozen conditions. Here, we present a broad comparison and evaluation of the simulated soil state against an in situ network of soil temperature and moisture distributed across the Continental United States that focuses on the winter season. This comparison includes both the Noah and the newer Noah with multi-parameterization (Noah-MP) land surface models. A comparison of the two models reveals substantial differences in simulated soil moisture, frozen content, and frozen soil strength. In general, Noah predicts colder soils with deeper frost depths, and frozen soils that are slightly stronger than Noah-MP. Initial results evaluating Noah and Noah-MP against in situ observations show that both models have significantly degraded model performance for frozen soil conditions compared to their performance for unfrozen soil. In particular, Noah has a significant and persistent cold bias of approximately −3 K when the soil temperature is below freezing. This cold bias is not present in Noah-MP and further analysis shows that this improvement was almost entirely caused by a better representation of thermal conduction through snow in Noah-MP. Specifically, we find that the bulk effective thermal conductivity of a snowpack is 2× greater in Noah, which allows for greater surface heat loss during the winter and, consequently, lower soil temperatures. This analysis reveals that the more advanced multi-layer snow model in Noah-MP substantially improves the representation of soil temperature during the cold season.

Simulation Evaluation of Transpiration Ratio (T/ET) in the Yellow River Basin

It is of great significance to improve the simulation accuracy of vegetation dynamics in land surface models for the study of dynamic changes of vegetation on land surface water and heat cycling. Parameter optimization is an effective means to improve the simulation accuracy of land surface model. Aiming at the application of Noah-MP land surface model in the Yellow River Basin, this paper improves the simulation performance of regional evAPOtranspiration and transpiration ratio by parameter optimization.

Assessment of snow simulation using Noah-MP land surface model forced by various precipitation sources in the Central Tianshan Mountains, Central Asia

Accurate mountainous snow estimation is paramount for hydrological processes and water resources estimation in arid regions. Using Land surface models (LSMs) is a practical numerical approach for snow estimation in a complex orography region such as the Tianshan Mountains. However, the bias of precipitation forcing is a major source of uncertainty for snow simulation over scarce-data regions. This study evaluated the performance of 4 km snow simulations using the Noah-MP LSM driven by eight precipitation sources. They are retrieved from non-model products and climate model simulations during the 2018-2019 cold season in the Central Tianshan Mountains (CTS) region. Multi-source validation datasets (in-situ observation, field snow pits, and remote sensing products) have been used for evaluation and uncertainty estimation. The results showed that the difference in precipitation amount significantly affected the snowpack simulation performance. Compared with non-model products (Global Land Data Assimilation System (GLDAS), Global Precipitation Measurement (GPM), and Gauge-adjusted Global Satellite Mapping of Precipitation (GSMaP)), the cold season precipitation from climate model simulations exhibited a better performance overall in the high-elevation regions (elevation > 1000 m) evaluated by in-situ observations. In addition, the 4 km convection-permitting modeling (CPM) precipitation (WRF-Morrison and WRF-WSM6) showed higher accuracy (RMSE: 23.48 mm/season and 18.94 mm/season, respectively) than gray-zone resolution simulations and ERA5-land driven runs in the high-elevation regions. The snow depth simulation driven by WRF-Morrison precipitation had the second smallest RMSE (4.67 cm/day) and lowest bias (-0.74 cm/day) value in the high-elevation regions compared with in-situ observation. Meanwhile, CPM precipitation-driven runs exhibited the smallest RMSE value based on the assessment of snow field pits. In addition, the CPM-driven simulation achieved the closest match to the elevation-based distribution of snow cover days in regions with elevation over 1000 m and duration over 60 days compared to the MODIS product. The findings of this study highlight that CPM with proper parameterization configurations has added values in producing realistic topographic precipitation for snow modeling using LSMs over data-scarcity mountainous areas.

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.

Benefit of incorporating GLASS remote sensing vegetation products in improving Noah-MP land surface temperature simulations on the Tibetan Plateau

Land Surface Temperature (LST) is important for diagnosing surface energy balance in land surface models (LSMs). However, LST simulation in current LSMs tends to show large cold biases, partially due to the reason that the model's prescribed vegetation parameters (e.g., Leaf Area Index (LAI) and Fraction of Vegetation Cover (FVC)) are misrepresented, especially in regions with complex topography and climate such as Tibetan Plateau. Recent advancements in remote sensing technologies provide a unique opportunity to improve the model's vegetation parameters at large scales. In this study, we practice two experiments to improve LST simulations in Noah-MP LSM by (1) incorporating LAI and FVC from the Global Land Surface Satellite (GLASS) remote sensing product (exp_RS); and (2) incorporating an empirical LAI and FVC parameterization scheme based on the soil temperature stress factor (exp_RL02). Results show that the effect of vegetation on simulated LST is the most significant in summer season when the model-satellite LAI and FVC differences are the largest. Compared to the default experiment that uses static LAI and FVC values from the model's look-up table (exp_CTL), the results in exp_RS and exp_RL02 show domain-wide improvement of the simulated LST. The LAI and FVC effect on LST are also well reflected in model's energy budget components (i.e., longwave emissivity, sensible and latent heat fluxes, etc). Validation of the model simulated soil temperature with in-situ observations further demonstrate the model improvements. Our study underscores the important role of vegetation in regulating surface energy transfer processes. Our study also highlights the feasibility and benefit of incorporating remote sensing data in improving land surface model simulations.

Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments

Abstract Accurate streamflow simulations rely on good estimates of the catchment-scale soil moisture distribution. Here, we evaluated the potential of Sentinel-1 backscatter data assimilation (DA) to improve soil moisture and streamflow estimates. Our DA system consisted of the Noah-MP land surface model coupled to the HyMAP river routing model and the water cloud model as a backscatter observation operator. The DA system was set up at 0.01° resolution for two contrasting catchments in Belgium: (i) the Demer catchment dominated by agriculture and (ii) the Ourthe catchment dominated by mixed forests. We present the results of two experiments with an ensemble Kalman filter updating either soil moisture only or soil moisture and leaf area index (LAI). The DA experiments covered the period from January 2015 through August 2021 and were evaluated with independent rainfall error estimates based on station data, LAI from optical remote sensing, soil moisture retrievals from passive microwave observations, and streamflow measurements. Our results indicate that the assimilation of Sentinel-1 backscatter observations can partly correct errors in surface soil moisture due to rainfall errors and overall improve surface soil moisture estimates. However, updating soil moisture and LAI simultaneously did not bring any benefit over updating soil moisture only. Our results further indicate that streamflow estimates can be improved through Sentinel-1 DA in a catchment with strong soil moisture–runoff coupling, as observed for the Ourthe catchment, suggesting that there is potential for Sentinel-1 DA even for forested catchments. Significance Statement The purpose of this study is to improve streamflow estimation by integrating soil moisture information from satellite observations into a hydrological modeling framework. This is important preparatory work for operational centers that are responsible for producing the most accurate flood forecasts for the society. Our results provide new insights into how and where streamflow forecasting could benefit from high-spatial-resolution Sentinel-1 radar backscatter observations.

Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over Europe

Abstract. Data assimilation (DA) of remotely sensed leaf area index (LAI) can help to improve land surface model estimates of energy, water, and carbon variables. So far, most studies have used bias-blind LAI DA approaches, i.e. without correcting for biases between model forecasts and observations. This might hamper the performance of the DA algorithms in the case of large biases in observations or simulations or both. We perform bias-blind and bias-aware DA of Copernicus Global Land Service LAI into the Noah-MP land surface model forced by the ERA5 reanalysis over Europe in the 2002–2019 period, and we evaluate how the choice of bias correction affects estimates of gross primary productivity (GPP), evapotranspiration (ET), runoff, and soil moisture. In areas with a large LAI bias, the bias-blind LAI DA leads to a reduced bias between observed and modelled LAI, an improved agreement of GPP, ET, and runoff estimates with independent products, but a worse agreement of soil moisture estimates with the European Space Agency Climate Change Initiative (ESA CCI) soil moisture product. While comparisons to in situ soil moisture in areas with weak bias indicate an improvement of the representation of soil moisture climatology, bias-blind LAI DA can lead to unrealistic shifts in soil moisture climatology in areas with strong bias. For example, when the assimilated LAI data in irrigated areas are much higher than those simulated without any irrigation activated, LAI will be increased and soil moisture will be depleted. Furthermore, the bias-blind LAI DA produces a pronounced sawtooth pattern due to model drift between DA updates, because each update pushes the Noah-MP leaf model to an unstable state. This model drift also propagates to short-term estimates of GPP and ET and to internal DA diagnostics that indicate a suboptimal DA system performance. The bias-aware approaches based on a priori rescaling of LAI observations to the model climatology avoid the negative effects of the bias-blind assimilation. They retain the improvements in GPP anomalies from the bias-blind DA but forego improvements in the root mean square deviations (RMSDs) of GPP, ET, and runoff. As an alternative to rescaling, we discuss the implications of our results for model calibration or joint parameter and state update DA, which has the potential to combine bias reduction with optimal DA system performance.

Performance evaluation of the WRF model under different physical schemes for air quality purposes in Buenos Aires, Argentina

This work presents the performance evaluation of the Weather Research and Forecasting (WRF) model to estimate surface wind speed and direction, air temperature, and water vapor mixing ratio considering 22 configurations at high spatial resolution (1 km) during one week in winter and one week in spring, in order to determine the best-performing schemes for air quality purposes in the Metropolitan Area of Buenos Aires, Argentina. Results show that the use of urban schemes mostly affects wind speed and temperature. The single-layer urban canopy model (UCM) coupled with the Boulac planetary boundary layer (PBL) scheme exhibits the best results for wind speed. Wind direction and water vapor mixing ratio are more sensitive to the land surface model scheme, with results slightly improving with the Noah-MP land surface model. Wind speed and direction errors are larger when the former is lower. When removing from the analysis wind speed values below 2.6 m s–1 for the winter week and 3.1 m s–1 for the spring week, the root mean square errors for wind direction decreased between 50 and 72% of the original value, depending on the configuration and week. Overall, under the studied conditions, configurations including Noah-Mp land surface model or the combination of a simple UCM with BouLac PBL are suitable for air quality applications, as they reproduce both temperature and water vapor mixing ratio relatively well, with errors below 10% and Correlation values above 0.7, and are the best performing configurations for wind direction and speed, respectively.

Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability

Abstract. The widely used open-source community Noah with multi-parameterization options (Noah-MP) land surface model (LSM) is designed for applications ranging from uncoupled land surface hydrometeorological and ecohydrological process studies to coupled numerical weather prediction and decadal global or regional climate simulations. It has been used in many coupled community weather, climate, and hydrology models. In this study, we modernize and refactor the Noah-MP LSM by adopting modern Fortran code standards and data structures, which substantially enhance the model modularity, interoperability, and applicability. The modernized Noah-MP is released as the version 5.0 (v5.0), which has five key features: (1) enhanced modularization as a result of re-organizing model physics into individual process-level Fortran module files, (2) an enhanced data structure with new hierarchical data types and optimized variable declaration and initialization structures, (3) an enhanced code structure and calling workflow as a result of leveraging the new data structure and modularization, (4) enhanced (descriptive and self-explanatory) model variable naming standards, and (5) enhanced driver and interface structures to be coupled with the host weather, climate, and hydrology models. In addition, we create a comprehensive technical documentation of the Noah-MP v5.0 and a set of model benchmark and reference datasets. The Noah-MP v5.0 will be coupled to various weather, climate, and hydrology models in the future. Overall, the modernized Noah-MP allows a more efficient and convenient process for future model developments and applications.

Effect of permafrost degradation on carbon sequestration of alpine ecosystems

Permafrost degradation profoundly affects carbon storage in alpine ecosystems, and the response characteristics of carbon sequestration are likely to differ at the different stages of permafrost degradation. Furthermore, the sensitivity of different stages of permafrost degradation to climate change is likely to vary. However, related research is lacking so far on the Qinghai-Tibetan Plateau (QTP). To investigate these issues, the Shule River headwaters on the northeastern margin of the QTP was selected. We applied InVEST and Noah-MP land surface models in combination with remote sensing and field survey data to reveal the dynamics of different carbon (vegetation carbon, soil organic carbon (SOC), and ecosystem carbon) pools from 2001 to 2020. A space-for-time analysis was used to explore the response characteristics of carbon sequestration along a gradient of permafrost degradation, ranging from lightly degraded permafrost (H-SP) to severely degraded permafrost (U-EUP), and to analyze the sensitivity of the permafrost degradation gradient to climate change. Our results showed that: (1) the sensitivity of mean annual ground temperature (MAGT) to climatic variables in the U-EUP was stronger than that in the H-SP and S-TP, respectively; (2) rising MAGT led to permafrost degradation, but increasing annual precipitation promoted permafrost conservation; (3) vegetation carbon, SOC, and ecosystem carbon had similar spatial distribution patterns, with their storage decreasing from the mountain area to the valley; (4) alpine ecosystems acted as carbon sinks with the rate of 0.34 Mg‧ha−1‧a−1 during 2001–2020, of which vegetation carbon and SOC accumulations accounted for 10.65 % and 89.35 %, respectively; and (5) the effects of permafrost degradation from H-SP to U-EUP on carbon density changed from promotion to inhibition.