scholarly journals Development of the Pan-Arctic Snowfall Reconstruction: New Land-Based Solid Precipitation Estimates for 1940–99

2007 ◽  
Vol 8 (6) ◽  
pp. 1243-1263 ◽  
Author(s):  
J. E. Cherry ◽  
L-B. Tremblay ◽  
M. Stieglitz ◽  
G. Gong ◽  
S. J. Déry
Keyword(s):  
Land Surface ◽  
Snow Depth ◽  
Large Scale ◽  
Land Surface Model ◽  
Seasonal Prediction ◽  
The United States ◽  
Cold Season ◽  
Surface Model ◽  
Blowing Snow ◽  
Solid Precipitation

Abstract A new product, the Pan-Arctic Snowfall Reconstruction (PASR), is developed to address the problem of cold season precipitation gauge biases for the 1940–99 period. The method used to create the PASR is different from methods used in other large-scale precipitation data products and has not previously been employed for estimating pan-arctic snowfall. The NASA Interannual-to-Seasonal Prediction Project Catchment Land Surface Model is used to reconstruct solid precipitation from observed snow depth and surface air temperatures. The method is tested at four stations in the United States and Canada where results are examined in depth. Reconstructed snowfall at Dease Lake, British Columbia, and Barrow, Alaska, is higher than gauge observations. Reconstructed snowfall at Regina, Saskatchewan, and Minot, North Dakota, is lower than gauge observations, probably because snow is transported by wind out of the Prairie region and enters the hydrometeorological cycle elsewhere. These results are similar to gauge biases estimated by a water budget approach. Reconstructed snowfall is consistently higher than snowfall from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) but does not have a consistent relationship with snowfall derived from the WMO Solid Precipitation Intercomparison Project correction algorithms. Advantages of the PASR approach include that 1) the assimilation of snow depth observations captures blowing snow where it is deposited and 2) the modeling approach takes into account physical snowpack evolution. These advantages suggest that the PASR product could be a valuable alternative to statistical gauge corrections and that arctic ground-based solid precipitation observing networks might emphasize snow depth measurements over gauges.

scholarly journals Simulation of Northern Eurasian Local Snow Depth, Mass, and Density Using a Detailed Snowpack Model and Meteorological Reanalyses

2013 ◽  
Vol 14 (1) ◽  
pp. 203-219 ◽  
Author(s):  
Eric Brun ◽  
Vincent Vionnet ◽  
Aaron Boone ◽  
Bertrand Decharme ◽  
Yannick Peings ◽  
...  
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Land Surface Model ◽  
Northern Eurasia ◽  
Surface Model ◽  
Monthly Precipitation ◽  
Blowing Snow ◽  
Snow Water

Abstract The Crocus snowpack model within the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface model was run over northern Eurasia from 1979 to 1993, using forcing data extracted from hydrometeorological datasets and meteorological reanalyses. Simulated snow depth, snow water equivalent, and density over open fields were compared with local observations from over 1000 monitoring sites, available either once a day or three times per month. The best performance is obtained with European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim). Provided blowing snow sublimation is taken into account, the simulations show a small bias and high correlations in terms of snow depth, snow water equivalent, and density. Local snow cover durations as well as the onset and vanishing dates of continuous snow cover are also well reproduced. A major result is that the overall performance of the simulations is very similar to the performance of existing gridded snow products, which, in contrast, assimilate local snow depth observations. Soil temperature at 20-cm depth is reasonably well simulated. The methodology developed in this study is an efficient way to evaluate different meteorological datasets, especially in terms of snow precipitation. It reveals that the temporal disaggregation of monthly precipitation in the hydrometeorological dataset from Princeton University significantly impacts the rain–snow partitioning, deteriorating the simulation of the onset of snow cover as well as snow depth throughout the cold season.

scholarly journals Reconstructing solid precipitation from snow depth measurements and a land surface model

2005 ◽  
Vol 41 (9) ◽  
Author(s):  
Jessie Ellen Cherry ◽  
L. Bruno Tremblay ◽  
Stephen J. Déry ◽  
Marc Stieglitz
Keyword(s):  
Land Surface ◽  
Snow Depth ◽  
Land Surface Model ◽  
Surface Model ◽  
Solid Precipitation

scholarly journals Large-scale groundwater modeling using global datasets: a test case for the Rhine-Meuse basin

2011 ◽  
Vol 8 (2) ◽  
pp. 2555-2608 ◽  
Author(s):  
E. H. Sutanudjaja ◽  
L. P. H. van Beek ◽  
S. M. de Jong ◽  
F. C. van Geer ◽  
M. F. P. Bierkens
Keyword(s):  
Sensitivity Analysis ◽  
Land Surface ◽  
Large Scale ◽  
Groundwater Modeling ◽  
Land Surface Model ◽  
Water Levels ◽  
Surface Model ◽  
Groundwater Model ◽  
Model Output ◽  
Global Datasets

Abstract. Large-scale groundwater models involving aquifers and basins of multiple countries are still rare due to a lack of hydrogeological data which are usually only available in developed countries. In this study, we propose a novel approach to construct large-scale groundwater models by using global datasets that are readily available. As the test-bed, we use the combined Rhine-Meuse basin that contains groundwater head data used to verify the model output. We start by building a distributed land surface model (30 arc-second resolution) to estimate groundwater recharge and river discharge. Subsequently, a MODFLOW transient groundwater model is built and forced by the recharge and surface water levels calculated by the land surface model. Although the method that we used to couple the land surface and MODFLOW groundwater model is considered as an offline-coupling procedure (i.e. the simulations of both models were performed separately), results are promising. The simulated river discharges compare well to the observations. Moreover, based on our sensitivity analysis, in which we run several groundwater model scenarios with various hydrogeological parameter settings, we observe that the model can reproduce the observed groundwater head time series reasonably well. However, we note that there are still some limitations in the current approach, specifically because the current offline-coupling technique simplifies dynamic feedbacks between surface water levels and groundwater heads, and between soil moisture states and groundwater heads. Also the current sensitivity analysis ignores the uncertainty of the land surface model output. Despite these limitations, we argue that the results of the current model show a promise for large-scale groundwater modeling practices, including for data-poor environments and at the global scale.

Global evaluation of the nutrient enabled version of land surface model ORCHIDEE-CNP (v1.2)

2020 ◽  
Author(s):  
Yan Sun ◽  
Daniel S Goll ◽  
Jinfeng Chang ◽  
Philippe Ciais ◽  
Betrand Guenet ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Comprehensive Evaluation ◽  
Large Fraction ◽  
Land Surface Model ◽  
P Uptake ◽  
Surface Model ◽  
Global Evaluation ◽  
Total N ◽  
P Fluxes

<p>Future land carbon (C) uptake under climate changes and rising atmospheric CO<sub>2</sub> is influenced by nitrogen (N) and phosphorus (P) constraints. A few existing land surface models (LSMs) account for both N and P dynamics, but lack comprehensive evaluation. This will lead to large uncertainty in estimating the P effect on terrestrial C cycles. With the increasing number of measurements and data-driven products for N- and P- related variables, comprehensive model evaluations on large scale is becoming feasible.</p><p>In this study, we evaluated the performance of ORCHIDEE-CNP (v1.2) which explicitly simulates N and P cycles in plant and soil, in four aspects: 1) terrestrial C fluxes, 2) N and P fluxes and budget, 3) leaf and soil stoichiometry and 4) resource use efficiencies. We found that ORCHIDEE-CNP improves the simulation of the magnitude of gross primary productivity (GPP) due to more realistic strength of the CO<sub>2</sub> fertilization effect of GPP than the without-nutrient-version ORCHIDEE. However, ORCHIDEE-CNP cannot capture the positive and increasing C sink in North Hemisphere over past decades, which is mainly due to that a large fraction of N and P ‘locked’ in soil organic matter cannot be re-allocated into vegetation and leads to a strong N and P limitation on plant growth. ORCHIDEE-CNP generally simulates comparable global total N and P fluxes (e.g. N biofixation, P weathering, N and P uptake etc.) for both natural and agricultural biomes. Overall, ORCHIDEE-CNP doesn’t performance worse in C fluxes than ORCHIDEE, and gives reasonable N and P cycles, which is acceptable in simulating the coupling relationships between C, N and P cycles can be used to explore the nutrient limitations on land C sink from present to the future. </p>

scholarly journals Impact of Urbanization on Heavy Convective Precipitation under Strong Large-Scale Forcing: A Case Study over the Milwaukee–Lake Michigan Region

2014 ◽  
Vol 15 (1) ◽  
pp. 261-278 ◽  
Author(s):  
Long Yang ◽  
James A. Smith ◽  
Mary Lynn Baeck ◽  
Elie Bou-Zeid ◽  
Stephen M. Jessup ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Lake Michigan ◽  
Land Surface Model ◽  
Urban Land ◽  
Surface Model ◽  
Urban Representations ◽  
Simulation Results ◽  
Impact Of Urbanization ◽  
The City

Abstract In this study, observational and numerical modeling analyses based on the Weather Research and Forecasting Model (WRF) are used to investigate the impact of urbanization on heavy rainfall over the Milwaukee–Lake Michigan region. The authors examine urban modification of rainfall for a storm system with continental-scale moisture transport, strong large-scale forcing, and extreme rainfall over a large area of the upper Midwest of the United States. WRF simulations were carried out to examine the sensitivity of the rainfall distribution in and around the urban area to different urban land surface model representations and urban land-use scenarios. Simulation results suggest that urbanization plays an important role in precipitation distribution, even in settings characterized by strong large-scale forcing. For the Milwaukee–Lake Michigan region, the thermodynamic perturbations produced by urbanization on the temperature and surface pressure fields enhance the intrusion of the lake breeze and facilitate the formation of a convergence zone, which create favorable conditions for deep convection over the city. Analyses of model and observed vertical profiles of reflectivity using contoured frequency by altitude displays (CFADs) suggest that cloud dynamics over the city do not change significantly with urbanization. Simulation results also suggest that the large-scale rainfall pattern is not sensitive to different urban representations in the model. Both urban representations, the Noah land surface model with urban land categories and the single-layer urban canopy model, adequately capture the dominant features of this storm over the urban region.

scholarly journals Modelling river runoff and estimating its weather-related uncertainty for 11 large-scale rivers located in different regions of the globe

2017 ◽  
Vol 49 (4) ◽  
pp. 1072-1087 ◽  
Author(s):  
Yeugeniy M. Gusev ◽  
Olga N. Nasonova ◽  
Evgeny E. Kovalev ◽  
Georgii V. Aizel
Keyword(s):  
River Runoff ◽  
Land Surface ◽  
Input Data ◽  
Large Scale ◽  
Land Surface Model ◽  
Grid Cell ◽  
Surface Model ◽  
Model Parameters ◽  
Data Sets ◽  
Natural Conditions

Abstract In order to study the possibility of reproducing river runoff with making use of the land surface model Soil Water–Atmosphere–Plants (SWAP) and information based on global data sets 11 river basins suggested within the framework of the Inter-Sectoral Impact Model Intercomparison Project and located in various regions of the globe under a wide variety of natural conditions were used. Schematization of each basin as a set of 0.5° × 0.5° computational grid cells connected by a river network was carried out. Input data including atmospheric forcing data and land surface parameters based, respectively, on the global WATCH and ECOCLIMAP data sets were prepared for each grid cell. Simulations of river runoff performed by SWAP with a priori input data showed poor agreement with observations. Optimization of a number of model parameters substantially improved the results. The obtained results confirm the universal character of SWAP. Natural uncertainty of river runoff caused by weather noise was estimated and analysed. It can be treated as the lowest limit of predictability of river runoff. It was shown that differences in runoff uncertainties obtained for different rivers depend greatly on natural conditions of a river basin, in particular, on the ratio of deterministic and random components of the river runoff.

scholarly journals Toward a Surface Soil Moisture Product at High Spatiotemporal Resolution: Temporally Interpolated, Spatially Disaggregated SMOS Data

2018 ◽  
Vol 19 (1) ◽  
pp. 183-200 ◽  
Author(s):  
Y. Malbéteau ◽  
O. Merlin ◽  
G. Balsamo ◽  
S. Er-Raki ◽  
S. Khabba ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Temporal Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Surface Soil ◽  
Land Surface Model ◽  
Open Loop ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Spatiotemporal Resolution

Abstract High spatial and temporal resolution surface soil moisture is required for most hydrological and agricultural applications. The recently developed Disaggregation based on Physical and Theoretical Scale Change (DisPATCh) algorithm provides 1-km-resolution surface soil moisture by downscaling the 40-km Soil Moisture Ocean Salinity (SMOS) soil moisture using Moderate Resolution Imaging Spectroradiometer (MODIS) data. However, the temporal resolution of DisPATCh data is constrained by the temporal resolution of SMOS (a global coverage every 3 days) and further limited by gaps in MODIS images due to cloud cover. This paper proposes an approach to overcome these limitations based on the assimilation of the 1-km-resolution DisPATCh data into a simple dynamic soil model forced by (inaccurate) precipitation data. The performance of the approach was assessed using ground measurements of surface soil moisture in the Yanco area in Australia and the Tensift-Haouz region in Morocco during 2014. It was found that the analyzed daily 1-km-resolution surface soil moisture compared slightly better to in situ data for all sites than the original disaggregated soil moisture products. Over the entire year, assimilation increased the correlation coefficient between estimated soil moisture and ground measurements from 0.53 to 0.70, whereas the mean unbiased RMSE (ubRMSE) slightly decreased from 0.07 to 0.06 m3 m−3 compared to the open-loop force–restore model. The proposed assimilation scheme has significant potential for large-scale applications over semiarid areas, since the method is based on data available at the global scale together with a parsimonious land surface model.

scholarly journals Near-Surface and Land Surface Forecast System of the Vancouver 2010 Winter Olympic and Paralympic Games

2011 ◽  
Vol 12 (4) ◽  
pp. 508-530 ◽  
Author(s):  
Natacha B. Bernier ◽  
Stéphane Bélair ◽  
Bernard Bilodeau ◽  
Linying Tong
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Snow Depth ◽  
Land Surface Model ◽  
Surface Model ◽  
Near Surface ◽  
Elevation Changes ◽  
Forecast Models ◽  
Operational Models ◽  
Paralympic Games

Abstract A high-resolution 2D near-surface and land surface model was developed to produce snow and temperature forecasts over the complex alpine region of the Vancouver 2010 Winter Olympic and Paralympic Games. The model is driven by downscaled operational outputs from the Meteorological Service of Canada’s regional and global forecast models. Downscaling is applied to correct forcings for elevation differences between the operational forecast models and the high-resolution surface model. The high-resolution near-surface and land surface model is then used to further refine the forecasts. The model was validated against temperature and snow depth observations. The largest improvements were found in regions where low-resolution (i.e., on the order of 10 km or more) operational models typically lack the spatial resolution to capture rapid elevation changes. The model was found to better reproduce the intermittent snow cover at low-lying stations and to reduce snow depth error by as much as 3 m at alpine stations.

scholarly journals Impacts of snow and organic soils parameterization on North-Eurasian soil temperature profiles simulated by the ISBA land surface model

2015 ◽  
Vol 9 (6) ◽  
pp. 6733-6790
Author(s):  
B. Decharme ◽  
E. Brun ◽  
A. Boone ◽  
C. Delire ◽  
P. Le Moigne ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Temperature ◽  
Soil Properties ◽  
Temperature Profile ◽  
Land Surface ◽  
Snow Depth ◽  
Land Surface Model ◽  
Surface Model ◽  
Temperature Profiles

Abstract. In this study we analysed how an improved representation of snowpack processes and soil properties in the multi-layer snow and soil schemes of the ISBA land surface model impacts the simulation of soil temperature profiles over North-Eurasian regions. For this purpose, we refine ISBA's snow layering algorithm and propose a parameterization of snow albedo and snow compaction/densification adapted from the detailed Crocus snowpack model. We also include a dependency on soil organic carbon content for ISBA's hydraulic and thermal soil properties. First, changes in the snowpack parameterization are evaluated against snow depth, snow water equivalent, surface albedo, and soil temperature at a 10 cm depth observed at the Col de Porte field site in the French Alps. Next, the new model version including all of the changes is used over Northern-Eurasia to evaluate the model's ability to simulate the snow depth, the soil temperature profile and the permafrost characteristics. The results confirm that an adequate simulation of snow layering and snow compaction/densification significantly impacts the snowpack characteristics and the soil temperature profile during winter, while the impact of the more accurate snow albedo computation is dominant during the spring. In summer, the accounting for the effect of soil organic carbon on hydraulic and thermal soil properties improves the simulation of the soil temperature profile. Finally, the results confirm that this last process strongly influences the simulation of the permafrost active layer thickness and its spatial distribution.

scholarly journals Assimilation of SMOS brightness temperature into a large-scale distributed conceptual hydrological model

2019 ◽  
Author(s):  
Renaud Hostache ◽  
Dominik Rains ◽  
Kaniska Mallick ◽  
Marco Chini ◽  
Ramona Pelich ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Land Surface ◽  
Large Scale ◽  
Hydrological Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Meteorological Model ◽  
Temperature Observations

Abstract. The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help in reducing errors and uncertainties in soil moisture simulations with a large-scale conceptual hydro-meteorological model. In particular, we use as forcings the ERA-Interim public dataset and we couple the CMEM radiative transfer model with a hydro-meteorological model enabling therefore soil moisture and SMOS-like brightness temperature simulations. The hydro-meteorological model is configured using recent developments of the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application as well as to data availability and computational requirements. In this case, the model spatial resolution is adapted to the spatial grid of the satellite data, and the soil stratification is tailored to the satellite datasets to be assimilated and the forcing data. The hydrological model is first calibrated using a sample of SMOS brightness temperature observations (period 2010–2011). Next, SMOS-derived brightness temperature observations are sequentially assimilated into the coupled SUPERFLEX-CMEM model (period 2010–2015). For this experiment, a Local Ensemble Transform Kalman Filter is used and the meteorological forcings (ERA interim-based rainfall, air and soil temperature) are perturbed to generate a background ensemble. Each time a SMOS observation is available, the SUPERFLEX state variables related to the water content in the various soil layers are updated and the model simulations are resumed until the next SMOS observation becomes available. Our empirical results show that the SUPERFLEX-CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set up using the CLM land surface model. This shows that a simple model, when carefully calibrated, can yield performance level similar to that of a much more complex model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72. The assimilation of SMOS brightness temperature observation into the SUPERFLEX-CMEM modelling chain improves the correlation between predicted and in situ observed soil moisture by 0.03 on average showing improvements similar to those obtained using the CLM land surface model.

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