Numerical modeling of permafrost dynamics requires adequate representation of atmospheric and surface processes, a reasonable parameter estimation strategy, and site-specific model development. The three main research objectives of the study are: (i) to propose a novel methodology that determines the required level of surface process complexity of permafrost models by conducting parameter sensitivity and calibration, (ii) to design and compare three numerical models of increasing surface process complexity, and (iii) to calibrate and validate the numerical models at the Yakou catchment on the Qinghai-Tibet Plateau as an exemplary study site. The calibration was carried out by coupling the Advanced Terrestrial Simulator (numerical model) and PEST (calibration tool). Simulation results showed that (i) A simple numerical model that considers only subsurface processes can simulate active layer development with the same accuracy as other more complex models that include surface processes. (ii) Peat and mineral soil layer permeability, Van Genuchten alpha, and porosity are highly sensitive. (iii) Liquid precipitation aids in increasing the rate of permafrost degradation. (iv) Deposition of snow insulated the subsurface during the thaw initiation period. We have developed and released an integrated code that couples the numerical software ATS to the calibration software PEST. The numerical model can be further used to determine the impacts of climate change on permafrost degradation.

Permafrost degradation is a growing direct impact of climate change. Detecting permafrost shrinkage, in terms of extension, depth reduction and active layer shift is fundamental to capture the magnitude of trends and address actions and warnings. Temperature profiles in permafrost allow direct understanding of the status of the frozen ground layer and its evolution in time. The Sommeiller Pass permafrost monitoring station, at about 3000 m of elevation, is the key site of the regional network installed in 2009 during the European Project PermaNET in the Piedmont Alps (NW Italy). The station consists of three vertical boreholes with different characteristics, equipped with a total of 36 thermistors distributed in three different chains. The collected raw data shows a degradation of the permafrost base at approximately 60 m of depth since 2014, corresponding to about 0.03 degrees C/yr. In order to verify and better quantify this potential degradation, three on-site sensor calibration campaigns were carried out to understand the reliability of these measurements. By repeating calibrations in different years, two key results have been achieved: the profiles have been corrected for errors and the re-calibration allowed to distinguish the effective change of permafrost temperatures during the years, from possible drifts of the sensors, which can be of the same order of magnitude of the investigated thermal change. The warming of permafrost base at a depth of similar to 60 m has been confirmed, with a rate of (4.2 +/- 0.5)center dot 10(-2) degrees C/yr. This paper reports the implementation and installation of the on-site metrology laboratory, the dedicated calibration procedure adopted, the calibration results and the resulting adjusted data, profiles and their evolution with time. It is intended as a further contribution to the ongoing studies and definition of best practices, to improve data traceability and comparability, as prescribed by the World Meteorological Organization Global Cryosphere Watch programme.

Understanding and simulating the hydrological cycle, especially in a context of climate change, is crucial for quantitative water risk assessment and basin management. The hydrological cycle is complex as it is a combination of non-linear natural processes and anthropogenic influences that alter landforms and water flows. Human-induced changes of relevance, including changes in land uses, construction of dams and artificial reservoirs, and diversion of the river course, lead to changes in water flows throughout the basin. These should be explicitly accounted for a realistic representation of the anthropogenically altered hydrological cycle. Such a realistic representation of the hydrological cycle is a necessary input for the water risk assessment in a particular region. In this paper, we present a hydrological digital twin (HDT) model of a large anthropized alpine basin: the Adige basin located in the northeast of Italy.Most catchments model often overlook land-uses changes over time and forget to model reservoir operation and their influence over time on water flow. Yet, for example, the Adige basin has>30 reservoirs affecting the water flow. We therefore use the GEOframe modeling framework to demonstrate the ability to create a hydrological twin model accounting for these anthropogenic changes.Specifically, we model each component of the water cycle over 39 years (1980-2018) at daily timescale through calibration of the Adige HDT with a multi-site approach using discharge data of 33 stations, based on a high-resolution (1 km) temperature and precipitation dataset and a calculated crop potential evapotranspiration (PETc) dataset, which accounts for human-induced change of the land cover over time. The modeling system also includes the simulation of artificial reservoirs and dams by the dynamically zoned target release (DZTR) reservoir model.The Adige HDT is assessed/validated/compared through a variety of hydrological processes (i.e., river and reservoir discharges, PETc and actual evapotranspiration, snow, and soil moisture) and data sources (i.e., observations and remote sensing data).Overall, the HDT reproduces well the measured discharge in space and time with a Kling Gupta Efficiency (KGE) above 0.7 (0.8) for 30 (23) of the 33 gauge-stations. For 7 artificial reservoirs with available data, the reservoir turbinated discharges are successfully reproduced with an average KGE of 0.92. A comparison between modeled and MODIS remote sensing snow data showed an average error of < 10% across the entire basin; the model also presented a good spatio-temporal agreement both with GLEAMS potential (and actual evapotranspiration) with an average KGE of 0.63 (0.60) and a high-level of correlation (0.5 on average) with the ASCAT satellite retrieved soil moisture.The findings of this paper demonstrate the potential of the open-source, component-based, GEOframe system to build a HDT, to provide a reliable and long term (39 years) estimation of all the water cycle components in a complex anthropized river basin at high spatial resolution. Spatially detailed HDT models results of this type can be used to inform basin-wise adaptation policy decisions and better water management practices in a time of changing climate.

The objective of the study was to configure the Hydrological Modeling System (HEC-HMS) in such a way that it could simulate all-important hydrological components (e.g., streamflow, soil moisture, snowmelt water, terrestrial water storage, baseflow, surface flow, and evapotranspiration) in the Three-River Headwater Region. However, the problem we faced was unsatisfactory simulations of these hydrological components, except streamflow. The main reason we found was the auto-calibration method of HEC-HMS because it generated irrational parameters, especially with the inclusion of Temperature Index Method and Soil Moisture Accounting (an advanced and complex loss method). Similar problems have been reported by different previous studies. To overcome these problems, we designed a comprehensive approach to estimate initial parameters and to calibrate the model manually in such a way that the model could simulate all the important hydrological components satisfactorily.

Soil moisture is an important driver of growth in boreal Alaska, but estimating soil hydraulic parameters can be challenging in this data-sparse region. Parameter estimation is further complicated in regions with rapidly warming climate, where there is a need to minimize model error dependence on interannual climate variations. To better identify soil hydraulic parameters and quantify energy and water balance and soil moisture dynamics, we applied the physically based, one-dimensional ecohydrological Simultaneous Heat and Water (SHAW) model, loosely coupled with the Geophysical Institute of Permafrost Laboratory (GIPL) model, to an upland deciduous forest stand in interior Alaska over a 13-year period. Using a Generalized Likelihood Uncertainty Estimation parameterisation, SHAW reproduced interannual and vertical spatial variability of soil moisture during a five-year validation period quite well, with root mean squared error (RMSE) of volumetric water content at 0.5 m as low as 0.020 cm(3)/cm(3). Many parameter sets reproduced reasonable soil moisture dynamics, suggesting considerable equifinality. Model performance generally declined in the eight-year validation period, indicating some overfitting and demonstrating the importance of interannual variability in model evaluation. We compared the performance of parameter sets selected based on traditional performance measures such as the RMSE that minimize error in soil moisture simulation, with one that is designed to minimize the dependence of model error on interannual climate variability using a new diagnostic approach we call CSMP, which stands for Climate Sensitivity of Model Performance. Use of the CSMP approach moderately decreases traditional model performance but may be more suitable for climate change applications, for which it is important that model error is independent from climate variability. These findings illustrate (1) that the SHAW model, coupled with GIPL, can adequately simulate soil moisture dynamics in this boreal deciduous region, (2) the importance of interannual variability in model parameterisation, and (3) a novel objective function for parameter selection to improve applicability in non-stationary climates.

Snow on sea ice is a sensitive indicator of climate change because it plays an important role regulating surface and near surface air temperatures. Given its high albedo and low thermal conductivity, snow cover is considered a key reason for amplified warming in polar regions. This study focuses on retrieving snow depth on sea ice from brightness temperatures recorded by the Microwave Radiation Imager (MWRI) on board the FengYun (FY)-3B satellite. After cross calibration with the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) Level 2A data from January 1 to May 31, 2011, MWRI brightness temperatures were used to calculate sea ice concentrations based on the Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) algorithm. Snow depths were derived according to the proportional relationship between snow depth and surface scattering at 18.7 and 36.5 GHz. To eliminate the influence of uncertainties in snow grain sizes and sporadic weather effects, seven-day averaged snow depths were calculated. These results were compared with snow depths from two external data sets, the IceBridge ICDIS4 and AMSR-E Level 3 Sea Ice products. The bias and standard deviation of the differences between the MWRI snow depth and IceBridge data were respectively 1.6 and 3.2 cm for a total of 52 comparisons. Differences between MWRI snow depths and AMSR-E Level 3 products showed biases ranging between -1.01 and -0.58 cm, standard deviations from 3.63 to 4.23 cm, and correlation coefficients from 0.61 to 0.79 for the different months.