Snow plays an important role in catastrophic weather, climate change, and water recycling. In order to analyze the ability of different land surface models to simulate snow depth in China, we used atmospheric forcing data from the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) to drive the CLM3.5 (the Community Land Model version 3.5), Noah (NCEP, OSU, Air Force and Office of Hydrology Land Surface Model), and Noah-MP (the community Noah land surface model with multi-parameterization options) land surface models. We also used 2380 daily snow-depth site observations of CMA to analyze the simulation effects of different models on the snow depth in China and different regions during the periods of snow accumulation and snowmelt from 2015 to 2019. The results show that CLM3.5, Noah, and Noah-MP can simulate the spatial distribution of the snow depth in China, but there are some differences between the models. In particular, the snow depth and snow cover simulated by CLM3.5 are lower than those simulated by Noah and Noah-MP in Northwest China and the Tibetan Plateau. From the overall quantitative assessment results for China, the snow depth simulated by CLM3.5 is underestimated, while that simulated by Noah is overestimated. Noah-MP has the best overall performance; for example, the biases of the three models during the snow-accumulation periods are -0.22 cm, 0.27 cm, and 0.15 cm, respectively. Furthermore, the three models perform differently in the three snowpack regions of Northeast China, Northwest China, and the Tibetan Plateau; Noah-MP has the best snow-depth performance in Northeast China, while CLM3.5 has the best snow-depth performance in the Tibetan Plateau region. Noah-MP performs best in the snow-accumulation period, and Noah performs best in the snowmelt period for Northwest China. In conclusion, no single model can perform optimally for snow simulations in different regions of China and at different times of the year, and the multi-model integration of snow may be an effective way to obtain high-quality snow simulation results. So this study provides some scientific references for the spatiotemporal evolution of snow in the context of climate change, monitoring and analysis of snow, the study of land surface models for snow, and the sustainable development and utilization of snow resources in China and other regions.

Multivariate data assimilation (DA), a novel way to couple big data with land surface models, was extensively employed in forecasting-reanalyzing systems (FRSs), for example, ECMWF and GLDAS. Meanwhile, most (distributed) hydrological models, like soil and water assessment tool (SWAT), have not been equipped with straightforward ways to link to DA algorithms. Therefore, it is one of the main barriers to utilizing such hydrological models in FRSs. This paper deals with multivariate DA into SWAT (DA-SWAT), which is complicated since the original model does not provide full access to the models' initial conditions (ICs) at the hydrologic response unit (HRU) scale. The preceding DA-SWAT works commonly used an integrated approach in which the DA and SWAT codes were implemented in the same programming environment. We discuss how this approach complicates and prevents the application of DA-SWAT in multivariate, multimodel, and multisensor systems. Accordingly, we proposed a new approach for DA-SWAT by which SWAT can be perfectly linked with any DA algorithm of interest coded in any desired programming environment. Our framework utilizes input/output text files to access ICs and to link DA with SWAT. Moreover, we designed some univariate and multivariate scenarios for assimilating in situ streamflow measurement and MODIS's snow cover fraction (SCF) data, which has not yet been focused on in the SWAT calibration context. Results show that compared to the univariate assimilation of streamflow (SCF), the multivariate assimilation mitigates the equifinality problem and more accurately estimates SCF (streamflow) by improving NS and PBIAS measures with the differences of 0.4 (0.86), 12% (64%), respectively.

Central Asia is vulnerable to climate change due to its scarce water resources and fragile ecosystems. However, the limited number of meteorological observations in the region restrict the study of its climate, hydrology and ecology. In order to improve the downscaled springtime temperature in Central Asia, this study explored the impact of atmospheric and snow data assimilation on climate simulations in Central Asia based on the Weather Research and Forecast (WRF) model and the WRF Data Assimilation (DA) system. The results based on climate simulations in Central Asia during the spring of 2017 show that the WRF model surface temperature simulation has a significant cold bias in Central Asia due to underestimation of snow melt. By assimilating conventional meteorological observations, the cold bias in Central Asia was reduced. This improvement is the result of both the direct effect of the analysis increment, and feedback effects from snow and atmosphere. In addition to the assimilation of atmospheric data, snow melt in Central Asia was better simulated through the assimilation of Japan Aerospace Exploration Agency (JAXA) Satellite Monitoring for Environmental Studies (JASMES) snow cover data. This further reduced the cold bias of the springtime temperature in Central Asia. Compared with an experiment that only assimilated atmospheric observations, the experiment that assimilated both snow and atmospheric data reduced the increase in temperature from the analysis and simulated a warmer land surface. This resulted in more sensible heat flux from the surface to the atmosphere and stronger sublimation and evaporation and thus improved the simulation of soil moisture.

Accurate estimation of snow mass or snow water equivalent (SWE) over space and time is required for global and regional predictions of the effects of climate change. This work investigates whether integration of remotely sensed terrestrial water storage (TWS) information, which is derived from the Gravity Recovery and Climate Experiment (GRACE), can improve SWE and streamflow simulations within a semi-distributed hydrology land surface model. A data assimilation (DA) framework was developed to combine TWS observations with the MESH (Modelisation Environnementale Communautaire - Surface Hydrology) model using an ensemble Kalman smoother (EnKS). The snow-dominated Liard Basin was selected as a case study. The proposed assimilation methodology reduced bias of monthly SWE simulations at the basin scale by 17.5% and improved unbiased root-mean-square difference (ubRMSD) by 23%. At the grid scale, the DA method improved ubRMSD values and correlation coefficients for 85% and 97% of the grid cells, respectively. Effects of GRACE DA on streamflow simulations were evaluated against observations from three river gauges, where it effectively improved the simulation of high flows during snowmelt season from April to June. The influence of GRACE DA on the total flow volume and low flows was found to be variable. In general, the use of GRACE observations in the assimilation framework not only improved the simulation of SWE, but also effectively influenced streamflow simulations.

As the Earth warms, the spatial and temporal response of seasonal snow remains uncertain. The global snow science community estimates snow cover and mass with information from land surface models, numerical weather prediction, satellite observations, surface measurements, and combinations thereof. Accurate estimation of snow at the spatial and temporal scales over which snow varies has historically been challenged by the complexity of land cover and terrain and the large global extent of snow-covered regions. Like many Earth science disciplines, snow science is in an era of rapid advances as remote sensing products and models continue to gain granularity and physical fidelity. Despite clear progress, the snow science community continues to face challenges related to the accuracy of seasonal snow estimation. Namely, advances in snow modeling remain limited by uncertainties in modeling parameterization schemes and input forcings, and advances in remote sensing techniques remain limited by temporal, spatial, and technical constraints on the variables that can be observed. Accurate monitoring and modeling of snow improves our ability to assess Earth system conditions, trends, and future projections while serving highly valued global interests in water supply and weather forecasts. Thus, there is a fundamental need to understand and improve the errors and uncertainties associated with estimates of snow. A potential method to overcome model and observational shortcomings is data assimilation, which leverages the information content in both observations and models while minimizing their limitations due to uncertainty. This article proposes data assimilation as a way to reduce uncertainties in the characterization of seasonal snow changes and reviews current modeling, remote sensing, and data assimilation techniques applied to the estimation of seasonal snow. Finally, remaining challenges for seasonal snow estimation are discussed.

Permafrost, a key component of Arctic ecosystems, is currently affected by climate warming and anticipated to undergo further significant changes in this century. The most pronounced changes are expected to occur in the transition zone between the discontinuous and continuous types of permafrost. We apply a transient temperature dynamic model to investigate the spatiotemporal evolution of permafrost conditions on the Seward Peninsula, Alaska-a region currently characterized by continuous permafrost in its northern part and discontinuous permafrost in the south. We calibrate model parameters using a variational data assimilation technique exploiting historical ground temperature measurements collected across the study area. The model is then evaluated with a separate control set of the ground temperature data. Calibrated model parameters are distributed across the domain according to ecosystem types. The forcing applied to our model consists of historic monthly temperature and precipitation data and climate projections based on the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. Simulated near-surface permafrost extent for the 2000-2010 decade agrees well with existing permafrost maps and previous Alaska-wide modeling studies. Future projections suggest a significant increase (3.0 degrees C under RCP 4.5 and 4.4 degrees C under RCP 8.5 at the 2 m depth) in mean decadal ground temperature on average for the peninsula for the 2090-2100 decade when compared to the period of 2000-2010. Widespread degradation of the near-surface permafrost is projected to reduce its extent at the end of the 21st century to only 43% of the peninsula's area under RCP 4.5 and 8% under RCP 8.5.

Terrestrial hydrologic trends over the conterminous United States are estimated for 1980-2015 using the National Climate Assessment Land Data Assimilation System (NCA-LDAS) reanalysis. NCA-LDAS employs the uncoupled Noah version 3.3 land surface model at 0.125 degrees x 0.125 degrees forced with NLDAS-2 meteorology, rescaled Climate Prediction Center precipitation, and assimilated satellite-based soil moisture, snow depth, and irrigation products. Mean annual trends are reported using the nonparametric Mann-Kendall test at p < 0.1 significance. Results illustrate the interrelationship between regional gradients in forcing trends and trends in other land energy and water stores and fluxes. Mean precipitation trends range from +3 to +9 mm yr(-1) in the upper Great Plains and Northeast to -1 to -9 mm yr(-1) in the West and South, net radiation flux trends range from +0.05 to +0.20 W m(-2) yr(-1) in the East to -0.05 to -0.20 W m(-2) yr(-1) in the West, and U.S.-wide temperature trends average about +0.03 K yr(-1). Trends in soil moisture, snow cover, latent and sensible heat fluxes, and runoff are consistent with forcings, contributing to increasing evaporative fraction trends from west to east. Evaluation of NCA-LDAS trends compared to independent data indicates mixed results. The RMSE of U.S.-wide trends in number of snow cover days improved from 3.13 to 2.89 days yr(-1) while trend detection increased 11%. Trends in latent heat flux were hardly affected, with RMSE decreasing only from 0.17 to 0.16 W m(-2) yr(-1), while trend detection increased 2%. NCA-LDAS runoff trends degraded significantly from 2.6 to 16.1 mm yr(-1) while trend detection was unaffected. Analysis also indicated that NCA-LDAS exhibits relatively more skill in low precipitation station density areas, suggesting there are limits to the effectiveness of satellite data assimilation in densely gauged regions. Overall, NCA-LDAS demonstrates capability for quantifying physically consistent, U.S. hydrologic climate trends over the satellite era.

This paper presents the development and application of a physically based hydrological data assimilation system (HDAS) using the gridded and parallelized Soil and Water Assessment Tool (SWATGP) distributed hydrological model. This SWAT-HDAS software integrates remotely sensed data, including the leaf area index (LAI), snow cover fraction, snow water equivalent, soil moisture, and ground-based observational data (e.g., from discharge and ground sensor networks), with SWATGP and the Parallel Data Assimilation Framework (PDAF) to accurately characterize watershed hydrological states and fluxes. SWAT-HDAS employs high-performance computational technologies to address the computational challenges of high-resolution and/or large-area modeling. Multiple observational system simulation experiments (OSSEs), including soil moisture assimilation experiments, snow water equivalent assimilation experiments, and streamflow assimilation experiments, were designed to validate the assimilation efficiency of various types of observations within SWAT-HDAS using an ensemble Kalman filter (EnKF) algorithm. Both the temporal and spatial correlations in the trend/pattern and the magnitudes of improvement between the simulated and true states (i.e., for soil moisture, snow water equivalent, and discharge) were satisfactory using the integrated assimilation, which suggests the reliability of SWAT-HDAS for regional hydrology studies. The streamflow assimilation experiment also showed that the observation location dramatically influences the assimilation efficiency. The quantity and quality of observations have effects of varying degrees on the streamflow predictions. SWAT-HDAS is a promising tool for hydrological studies and applications under climate and environmental change scenarios.

Fire is an endemic process at high latitudes, connected to a range of other land surface properties, such as land cover, biomass, and permafrost, and intimately linked to the carbon balance of the high-latitude land surface. Much of our current understanding of these links and their climate consequences is through land surface models, so it is important to ensure that for their credibility, these models should be consistent with available data. Over the vast panboreal region, a key source of information on fire is satellite data. Comparisons between satellite-based burned area data from the Global Fire Emissions Database and three dynamic vegetation models (LPJ-WM, CLM4CN, and SDGVM) indicate that all models fail to represent the observed spatial and temporal properties of the fire regime. Although the three dynamic vegetation models give comparable values of the boreal net biome production (NBP), fire emissions are found to differ by a factor 4 between the models, because of widely different estimates of burned area and because of different parameterizations of the fuel load and combustion process. Including a more realistic representation of the fire regime in the models shows that for northern high latitudes, (i) severe fire years do not coincide with source years or vice versa, (ii) the interannual variability of fire emissions does not significantly affect the interannual variability of NBP, and (iii) overall biomass values alter only slightly, but the spatial distribution of biomass exhibits changes. We also demonstrate that it is crucial to alter the current representations of fire occurrence and severity in land surface models if the links between permafrost and fire are to be captured, in particular, the dynamics of permafrost properties, such as active layer depth. This is especially important if models are to be used to predict the effects of a changing climate, because of the consequences of permafrost changes for greenhouse gas emissions, hydrology, and land cover.