Snow Depth Data Research Articles

Effects of radar side-lobes on snow depth retrievals from Operation IceBridge

AbstractArctic snow depth data products from four years (2009–12) of Operation IceBridge (OIB) surveys are examined. In our analysis, we found spurious spikes in the snow depth distributions of both the multi-year and seasonal ice covers. These spikes are artifacts that stem from the incorrect identification of side lobes and main lobes of the impulse response of the snow radar as returns from the air–snow interface. The current OIB snow depth retrieval algorithm does not explicitly account for the presence of these side lobes and main lobes. As a result, overall accuracy of snow depth returns and related statistics is negatively affected. Although the range locations of these side lobes are predictable for each radar installation, they vary with individual airborne campaigns. Comparisons with limited in situ snow surveys show significant differences of >20 cm between OIB and in situ snow surveys. These artifacts affect OIB ice thickness estimates because they rely on estimates of sea-ice freeboard, which are calculated as the differences between coincident snow freeboard from lidar elevations and the retrieved snow depth estimates discussed here. Since these products are widely distributed to the scientific community, our results suggest that earlier geophysical studies based on these products may need to be re-examined.

Estimation of Continental-Basin-Scale Sublimation in the Lena River Basin, Siberia

The Lena River basin in Siberia produces one of the largest river inflows into the Arctic Ocean. One of the most important sources of runoff to the river is spring snowmelt and therefore snow ablation processes have great importance for this basin. In this study, we simulated these processes with fine resolution at basin scale using MicroMet/SnowModel and SnowAssim. To assimilate snow water equivalent (SWE) data in SnowAssim, we used routine daily snow depth data and Sturm’s method. Following the verification of this method for SWE estimation in the basin, we evaluated the impact of snow data assimilation on basin-scale snow ablation. Through validation against MODIS snow coverage data andin situsnow survey observations, we found that SnowAssim could not improve on the original simulation by MicroMet/SnowModel because of estimation errors within the SWE data. Vegetation and accumulated snowfall control the spatial distribution of sublimation and we established that sublimation has an important effect on snow ablation. We found that the ratio of sublimation to snowfall in forests was around 26% and that interannual variation of sublimation modulated spring river runoff.

Elevation dependency of mountain snow depth

Abstract. Elevation strongly affects quantity and distribution patterns of precipitation and snow. Positive elevation gradients were identified by many studies, usually based on data from sparse precipitation stations or snow depth measurements. We present a systematic evaluation of the elevation–snow depth relationship. We analyse areal snow depth data obtained by remote sensing for seven mountain sites near to the time of the maximum seasonal snow accumulation. Snow depths were averaged to 100 m elevation bands and then related to their respective elevation level. The assessment was performed at three scales: (i) the complete data sets (10 km scale), (ii) sub-catchments (km scale) and (iii) slope transects (100 m scale). We show that most elevation–snow depth curves at all scales are characterised through a single shape. Mean snow depths increase with elevation up to a certain level where they have a distinct peak followed by a decrease at the highest elevations. We explain this typical shape with a generally positive elevation gradient of snow fall that is modified by the interaction of snow cover and topography. These processes are preferential deposition of precipitation and redistribution of snow by wind, sloughing and avalanching. Furthermore, we show that the elevation level of the peak of mean snow depth correlates with the dominant elevation level of rocks (if present).

Estimating degree-day factors from MODIS for snowmelt runoff modeling

Abstract. Degree-day factors are widely used to estimate snowmelt runoff in operational hydrological models. Usually, they are calibrated on observed runoff, and sometimes on satellite snow cover data. In this paper, we propose a new method for estimating the snowmelt degree-day factor (DDFS) directly from MODIS snow covered area (SCA) and ground-based snow depth data without calibration. Subcatchment snow volume is estimated by combining SCA and snow depths. Snow density is estimated to be the ratio between observed precipitation and changes in the snow volume for days with snow accumulation. Finally, DDFS values are estimated to be the ratio between changes in the snow water equivalent and difference between the daily temperature and the melt threshold value for days with snow melt. We compare simulations of basin runoff and snow cover patterns using spatially variable DDFS estimated from snow data with those using spatially uniform DDFS calibrated on runoff. The runoff performances using estimated DDFS are slightly improved, and the simulated snow cover patterns are significantly more plausible. The new method may help reduce some of the runoff model parameter uncertainty by reducing the total number of calibration parameters. This method is applied to the Lienz catchment in East Tyrol, Austria, which covers an area of 1198 km2. Approximately 70% of the basin is covered by snow in the early spring season.

Mapping snow cover variations using a MODIS daily cloud-free snow cover product in northeast China

Cloud contamination is one of the major barriers for wider applications of MODIS snow cover products. This study presents a cloud-removal approach, through multiday backward replacements based on Terra and Aqua daily MODIS snow cover products (MOD10A1 and MYD10A1), to generate a series of daily cloud-free snow cover products for advanced applications (MODMYD_MC). The products are evaluated using in situ snow depth data measured during 2000 to 2010 at 53 weather stations in the Heilongjiang Province, northeast China. The results show that the annual mean cloud covers of MOD10A1, MYD10A1, MODMYD_DC (the daily combination of MOD10A1 and MYD10A1), and MODMYD_MC are 50%, 54%, 35%, and 0%, mean snow covers are 6%, 6%, 10%, and 19%, and their mean agreements of snow cover mapping are 42%, 40%, 51%, and 91%, respectively. The snow-covered days (SCDs) derived from MODMYD_MC are also in good agreement (91%) with those obtained from in situ observations. The MODMYD_MC snow cover images are then used to investigate the detailed variation of snow cover in the XiaoXing’AnLing watershed. The snow-covered area in the watershed has an increasing trend in the recent decade, with the minimum present in the 2002 (hydrologic year) and the maximum present in 2010. The plains with lower elevation show shorter SCD but larger interannual variations than in the mountainous areas. This study indicates that MODMYD_MC can be applied to monitor the spatiotemporal variations of snow cover in northeast China and elsewhere in the world.

Using daily air temperature thresholds to evaluate snow melting occurrence and amount on Alpine glaciers by <i>T</i>-index models: the case study of the Forni Glacier (Italy)

Abstract. Glacier melt conditions (i.e., null surface temperature and positive energy budget) can be assessed by analyzing data acquired by a supraglacial automatic weather station (AWS), such as the station installed on the surface of Forni Glacier (Italian Alps). When an AWS is not present, the assessment of actual melt conditions and the evaluation of the melt amount is more difficult and simple methods based on T-index (or degree days) models are generally applied. These models require the choice of a correct temperature threshold. In fact, melt does not necessarily occur at daily air temperatures higher than 0 °C. In this paper, we applied both energy budget and T-index approaches with the aim of solving this issue. We start by distinguishing between the occurrence of snowmelt and the reduction in snow depth due to actual ablation (from snow depth data recorded by a sonic ranger). Then we find the daily average temperature thresholds (by analyzing temperature data acquired by an AWS on Forni Glacier) which, on the one hand, best capture the occurrence of significant snowmelt conditions and, on the other, make it possible, using the T-index, to quantify the actual snow ablation amount. Finally we investigated the applicability of the mean tropospheric lapse rate to reproduce air temperature conditions at the glacier surface starting from data acquired by weather stations located outside the glacier area. We found that the mean tropospheric lapse rate allows for a good and reliable reconstruction of glacier air temperatures and that the choice of an appropriate temperature threshold in T-index models is a very important issue. From our study, the application of the +0.5 °C temperature threshold allows for a consistent quantification of snow ablation while, instead, for detecting the beginning of the snow melting processes a suitable threshold has proven to be at least −4.6 °C.

Snow depth, density, and SWE estimates derived from GPS reflection data: Validation in the western U. S.

AbstractGeodetic‐quality GPS systems can be used to measure average snow depth in the ∼1000 m2 area around the GPS antenna, a sensing footprint size intermediate between in situ and satellite observations. SWE can be calculated from density estimates modeled on the GPS‐based snow depth time series. We assess the accuracy of GPS‐based snow depth, density, and SWE data at 18 GPS sites via comparison to manual observations. The manual validation survey was completed around the time of peak accumulation at each site. Daily snow depth derived from GPS reflection data is very similar to the mean snow depth measured manually in the ∼1000 m2 scale area around each antenna. This comparison spans site‐averaged depths from 0 to 150 cm. The GPS depth data exhibit a small negative bias (−6 cm) across this range of snow depths. Errors tend to be smaller at sites with more usable GPS ground tracks. Snow bulk density is modeled using the GPS snow depth time series and model parameters are estimated from nearby SNOTEL sites. Modeled density is within 0.02 g cm−3 of the density measured in a single snow pit at the validation sites, for 12 of 18 comparisons. GPS‐based depth and modeled density are multiplied to estimate SWE. SWE estimates are very accurate over the range observed at the validation sites, from 0 to 60 cm (R2 = 0.97 and bias = −2 cm). These results show that the near real‐time GPS snow products have errors small enough for monitoring water resources in snow‐dominated basins.

A processing–modeling routine to use SNOTEL hourly data in snowpack dynamic models

SNOTEL hourly and daily data are a strategic information about snowpack dynamics in western United States. Hourly data are highly noisy due to, e.g., non-physical temperature-based fluctuations of the signal or gauge under-catch. Noise may hinder, among other factors, the correct evaluation of precipitation events or the measurement of SWE, hence the reconstruction of accumulation and melt run-off timing. This makes hourly data practically useless without a denoising procedure. As this time resolution is widely used in hydrologic applications, here we test SNOTEL hourly data in modeling snowpack dynamics. A one-dimensional model of snow depth, snow water equivalent and bulk snow density has been adopted to this aim. We define an automated processing routine to denoise data-series of snow depth, snow water equivalent, bulk snow density, liquid and solid precipitation. Special attention is paid into distinguishing the different types of precipitation, and processing snow depth data. Since sub-daily physical oscillations in snow depth data are difficult to be separated from instrument noise, a joint processing–modeling procedure has been designed. Forty SNOTEL sites throughout the western United States with multi-year data are considered for testing the procedure. The analysis shows that the model performance, expressed in terms of median values of the Nash–Sutcliffe coefficient, are higher than 0.8 for all the three variables, provided the first year of each dataset is used in the calibration phase.

Analysis of a changing hydrologic flood regime using the Variable Infiltration Capacity model

Summary The Pecatonica River and several other streams in the Wisconsin Driftless area show a decreasing trend in annual peak flows. Previous studies of the Pecatonica River detected a significant decreasing historical trend in late winter snowmelt-driven floods, while the rainfall-driven spring and summer flood peaks exhibited no significant trend during the period of record. Unlike several previous studies which attribute the decline in flood peaks mainly to changes in land management, we hypothesize that climate change had a significant contribution to the overall decrease in flood peaks. In particular, we hypothesize that the increase in winter temperatures caused the decrease in snow depth, which in turn resulted in a decreasing trend in flood peaks. In an attempt to validate this hypothesis, we used long-term daily precipitation, temperature, and river flow data observed in the watershed as inputs to the Variable Infiltration Capacity (VIC) model to generate other non-monitored climatic variables. Trends in these climatic variables were then related to the trend in flood peaks in the Pecatonica River. Due to the complexity of the hydrologic system and numerous data and modeling-related uncertainties, the above hypothesis cannot be validated with certainty. Nonetheless, the results in two different modes (event and continuous simulation) provide support to the speculation that the decreasing trend in flood peaks was a result of decreasing snow depth. The model runs resulted in a decrease in snow depths for the period of record (1915–2009), increase in sublimation and evaporation, no change in base flow, and mixed results in infiltration. These analyses also suggest that VIC can be used in other similar regions in snowmelt-driven flood peak studies. It should be recognized, however, that the success of these applications can be severely constrained by various uncertainties, including but not limited to, the poor quality or absence of snow depth data.

An observation-based assessment of the influences of air temperature and snow depth on soil temperature in Russia

This study assessed trends in the variability of soil temperature (TSOIL) using spatially averaged observation records from Russian meteorological land stations. The contributions of surface air temperature (SAT) and snow depth (SND) to TSOIL variation were quantitatively evaluated. Composite time series of these data revealed positive trends during the period of 1921–2011, with accelerated increases since the 1970s. The TSOIL warming rate over the entire period was faster than the SAT warming rate in both permafrost and non-permafrost regions, suggesting that SND contributes to TSOIL warming. Statistical analysis revealed that the highest correlation between SND and TSOIL was in eastern Siberia, which is underlain by permafrost. SND in this region accounted for 50% or more of the observed variation in TSOIL. TSOIL in the non-permafrost region of western Siberia was significantly correlated with changes in SAT. Thus, the main factors associated with TSOIL variation differed between permafrost and non-permafrost regions. This finding underscores the importance of including SND data when assessing historical and future variations and trends of permafrost in the Northern Hemisphere.

Soil, snow, weather, and sub-surface storage data from a mountain catchment in the rain–snow transition zone

Abstract. A comprehensive hydroclimatic data set is presented for the 2011 water year to improve understanding of hydrologic processes in the rain–snow transition zone. This type of data set is extremely rare in scientific literature because of the quality and quantity of soil depth, soil texture, soil moisture, and soil temperature data. Standard meteorological and snow cover data for the entire 2011 water year are included, which include several rain-on-snow (ROS) events. Surface soil textures and soil depths from 57 points are presented as well as soil texture profiles from 14 points. Meteorological data include continuous hourly shielded, unshielded, and wind-corrected precipitation, wind speed and direction, air temperature, relative humidity, dew point temperature, and incoming solar and thermal radiation data. This data is often viewed as "forcing data", and is gap filled and serially complete. Sub-surface data included are hourly soil moisture data from multiple depths from seven soil profiles within the catchment, and soil temperatures from multiple depths from two soil profiles. Hydrologic response data include hourly stream discharge from the catchment outlet weir, continuous snow depths from one location, intermittent snow depths from 5 locations, and snow depth and density data from ten weekly snow surveys. Snow and hydrologic response data are meant to provide data on the catchment hydrologic response to the weather data. This data is mostly presented "as measured" although snow depths from one sensor and streamflow at the catchment outlet have been gap filled and are serially complete. Though the weather, snow, and hydrologic response data only covers one water year, the presentation of the additional subsurface data (soil depth, texture, moisture, and temperature) makes it one of the most detailed and complete hydro-climatic data sets from the climatically sensitive rain–snow transition zone. The data presented are appropriate for a wide range of modeling (energy balance snow modeling, soil capacitance parametric modeling, etc.) and descriptive studies. Data is available at doi:10.1594/PANGAEA.819837.

Altitudinal gradients, midwinter melt, and wind effects on snow accumulation in semiarid midlatitude Andes under La Niña conditions

AbstractThe Andes Cordillera remains a sparsely monitored and studied snow hydrology environment in comparison to similar mountain ranges in the Northern Hemisphere. In order to uncover some of the key processes driving snow water equivalent (SWE) spatial variability, we present and analyze a distributed SWE data set, sampled at the end of accumulation season 2011. Three representative catchments across the region were monitored, obtaining measurements in an elevation range spanning 2000 to 3900 m asl and from 32.4° to 34.0°S in latitude. Climatic conditions during this season corresponded to a moderate La Niña phenomenon, which is generally correlated with lower‐than normal accumulation. Collected measurements can be described at the regional and watershed extents by altitudinal gradients that imply an increase by a factor of two in snow depth between 2200 and 3000 m asl, though with significant variability at the upper sites. In these upper sites, we found north‐facing, wind‐sheltered slopes showing 25% less average SWE values than south‐facing, wind‐exposed ones. This suggests that under these conditions, solar radiation dominated wind transport effects in controlling end‐of‐winter variability. Nevertheless, we found clusters of snow depth measurements above 3000 m asl that can be explained by wind exposure differences. This is the first documented snow depth data set of this spatial extent for this region, and it is framed within an ongoing research effort aimed at improving understanding and modeling of snow hydrology in the extratropical Andes Cordillera.

LiDAR‐derived snowpack data sets from mixed conifer forests across the Western United States

AbstractAirborne‐based Light Detection and Ranging (LiDAR) offers the potential to measure snow depth and vegetation structure at high spatial resolution over large extents and thereby increase our ability to quantify snow water resources. Here we present airborne LiDAR data products at four Critical Zone Observatories (CZO) in the Western United States: Jemez River Basin, NM, Boulder Creek Watershed, CO, Kings River Experimental Watershed, CA, and Wolverton Basin, CA. We make publicly available snow depth data products (1 m2 resolution) derived from LiDAR with an estimated accuracy of <30 cm compared to limited in situ snow depth observations.

Analysis of extreme ground snow loads for Canada using snow depth records

Snow depth records from daily measurements at climatological stations were obtained from Environment Canada and were processed and analyzed. It was identified that there are 549 stations, each with at least 20 years of useable annual maximum snow depth data. Both the Gumbel distribution and generalized extreme value distribution were used to fit the annual maximum snow depth, considering several distribution fitting methods. Statistical analysis results indicated that, according to the Akaike information criterion, the Gumbel distribution is preferred for 72 % stations. The estimated return period value of annual maximum snow depth at stations was used to calculate their corresponding ground snow load. The at-site analysis results were used as the basis to spatially interpolate the ground snow loads for locations tabulated in the National Building Code of Canada (NBCC) since a code location and a climatological site are usually not co-located. For the interpolation, the ordinary co-kriging method with elevation as co-variate was used because a cross-validation analysis by using several deterministic and probabilistic spatial interpolation techniques indicated that the ordinary co-kriging method is preferred. A comparison of the newly estimated ground snow loads to those locations tabulated in the 1995 edition and 2010 edition of the NBCC was also presented.

Research on Exploitation Technology of Snow Disaster Forecast Warning System on Roads

Through using the technology of laser ranging and establishing the stations which can monitor snow depth automatically, a real-time, continuous and automatic monitoring for the status of snow-covered roads was achieved. In this paper, forecast warning system was constructed. Firstly, the data of snow depth was transmitted to database using GPRS technology. Then the snow disaster was evaluated quantitatively according to the existing criteria, and the evaluation results were stored back to the database. Finally, the evaluation results about snow disaster were published through the publishing platform. The results indicated that the forecast warning system was stable and fast, which could estimate the road snow disaster levels accurately.

Spatial-temporal variation of snow depth in Tibet and its response to climatic change in the past 30 years

Snow depth is an important parameter to characterize snow features,and is also one of the sensitive factors of regional response to climate change. Based on snow depth daily data and monthly temperature, precipitation,wind and sunshine hours data from meteorological stations during 1979 to 2010,the authors analyzed the spatial and temporal variation of snow depth in Tibetan Plateau and its response to climatic change by using methods of anomaly analysis,mutation analysis,spatial analysis and power spectral analysis. The results showed that,in the period from 1979 to 2010,the snow depth increased obviously and significantly with linear trend rate 0. 26 cm /10a,but there was a pronounced decrease phase from 1999 to 2010,thus forming the situation that the snow depth increased first and then decreased in general in Tibetan Plateau. In the four seasons,winter mean snow depth contributed most significantly to the annual situation,with the correlation coefficient between them up to 0. 88. The snow depth was extremely excessive in the 1990s but with no climate mutation. An analysis of power spectrum showed that the snow depth had quasi- periodic oscillation of 6- 7 years. The results indicated that there were significant spatial differences in the snow depth of Tibetan Plateau. In the peripheral high mountains,snow depth was distributed extensively and had a long duration,but in the vast interior it was rare or even thin. The snow depth was significantly affected by the altitude with a steep step effect. And most of linear trend rates of snow depth in Tibetan Plateau were between- 0. 08 and 0. 08 cm / a,with the percentage reaching 74. 6%. The results of regression analysis indicated that the increased area of snow depth accounted for 76. 9%,while the decreased area accounted for 23. 1%. There was obvious statistical and spatial correlation between snow depth and temperature, precipitation,wind speed and sunshine duration in general; there was negative correlation between snow depth and temperature,wind speed and sunshine duration,but positive correlation between snow depth and precipitation. The results of multiple regression analysis showed that,in spring and autumn,the correlation coefficients between simulated snow depth and observation data were both above 0. 6 and passed 0. 01 significance test,while they were only between 0. 4 and 0. 5 in summer and winter and didn't pass the 0. 05 significance test.

Helicopter-borne radar imaging of snow cover on and around glaciers in Alaska

AbstractDuring spring 2013, we performed 500 MHz, helicopter-borne impulsive ground-penetrating radar surveys of several glaciers and glacier forelands in south-central Alaska, USA. These surveys were designed to obtain spatially distributed measurements of snow accumulation spanning a broad range of continental and maritime climatic zones. Visual assessment of radar images shows that data quality varied with the terrains and was optimal for snow that covered smooth glacier ice and firn, smooth debris-covered areas and moraines, freshwater lake and river ice, tundra, and taiga. Conversely, returns from the base of the snowpack were unrecognizable over rough debris-covered glacier termini, icefalls and some high-altitude accumulation basins. Optimal flying speed was 15-20ms–1 (30–40kt). At these speeds, which are two to three times faster than previously reported for such surveys, we could still identify snow-depth data with confidence, at a point spacing of ~1.5-2.0m. Data quality on glaciers decreased with increased air speed, though useful echoes from the base of the snowpack were still obtained at 40-45 ms–1 (87 kt; data point spacing of 6-8 m). Similar high-speed surveys over non-glacial terrains were unsuccessful, as basal reflections were no longer recognizable.

Reliability of long-term snow depth data sets from remote sensing over the western arid zone of China

Due to the coarse spatial resolution of imagery and heterogeneity of snow physical parameters, the currently available snow depth data sets derived from passive microwave sensors have shown significant accuracy variations in different parts of the world. This study aims to analyse the reliability of existing remote sensing snow depth products for the arid zone of China. Two long-term products were compared and evaluated including the GlobSnow Snow Water Equivalent product (‘the GlobSnow product’) and the Long-term Snow Depth Dataset of China (‘the WestDC product’). Nine-year ground measurements from 35 sampling sites with diverse topographical conditions were used as ground references for the accuracy assessment. Statistical analysis methods such as intra-class correlation coefficient and root mean square error (RMSE) were adopted for the consistency test and accuracy assessment. Analysis of variance (ANOVA) was employed to examine whether the data reliability varied by seasonal or locational factors. The results show that the two products in general do not agree well in the study region. However, the generalized data with different temporal resolutions tend to yield better results in terms of data consistency and accuracy. Compared to the GlobSnow product, the WestDC product has shown a better accuracy with the RMSE of 8.23, 7.43, and 6.56 cm for the original daily, and generalized weekly, and monthly data, respectively. According to the ANOVA test, season and latitude show statistically significant impacts on data reliability, while altitude and terrain complexity do not. Data reliability declines with higher latitude and it rapidly falls down to an unacceptable level in snow melting periods. This study provides a quantitative assessment on the quality of the snow depth products and raises the awareness of data consistency issues of these products for regional applications.

Reply to Shen et al.: No evidence to show nongrowing season NDVI affects spring phenology trend in the Tibetan Plateau over the last decade

We appreciate the comments on our article (1) from Shen et al. (2). They raise the issue of whether the nongrowing season (NGS) Normalized Difference Vegetation Index (NDVI) could affect spring phenology as interpreted from time series NDVI data. To address it, they compared the NGS NDVIs of two separate periods (1998–2000 and 2009–2011) and found a higher 2009–2011 NGS NDVI than that of 1998–2000, which was considered to negatively (e.g., advanced) bias retrieved spring phenology results [figure 1 A and B in the letter (2)]. We found that the NGS NDVI increase from 1998–2000 to 2009–2011 was overused to represent the trend from 1998 to 2011 because of the effects of outliers in 1998 and 2000 (Fig. 1A). We used snow depth data derived from passive microwave remote sensing for the studied area (3) and found that snow depth from January to April was significantly higher in 1998 and 2000 (Fig. 1B). The El Nino Southern Oscillation (ENSO) event resulted in the most severe snow storm in the 1997/1998 winter across the Tibetan Plateau (4), which led to lower NGS NDVI from January to April in 1998. The La Nina in year 1999/2000 also resulted in big snow and lower NGS NDVI on the plateau in 2000 (Fig. 1B). Actually, the trends of the NDVIs in February, March, and April were not statistically significant when we removed the outliers in 1998 and 2000 (Fig. 1C). January NDVI increasing was significant. However, it will not affect start of the vegetation growing season (SOS) retrieval because all of the green-up dates happened after March on the plateau.

Predicting deadwood densities of Cryptomeria japonica and Chamaecyparis obtusa forests using a generalized linear mixed model with a national-scale dataset

Predicting the stocks of deadwood in planted stands is important for controlling biodiversity, carbon content, and nutrient cycling in forest ecosystems and for managing deadwood accumulation because of conservation activities for biodiversity and economic reasons. To calculate the carbon stock in deadwood, the volume, carbon content, and appropriate wood density for the decomposition state must be known. To develop a predictive model for estimating deadwood density, we used a generalized linear mixed model and a national-scale dataset with large variations in different deadwood factors related to wood properties and climatic conditions. Our samples of fallen logs were obtained by noncommercial thinning of Cryptomeria japonica D. Don (C. japonica) and Chamaecyparis obtusa (Sieb. et Zucc.) Endl. (C. obtusa) planted forests throughout Japan, covering various climatic conditions. The wood density model for C. japonica was Dw=0.342−0.010y−0.001d+0.001Sa+0.012C−0.028Fc1−0.006 Fc2+re and that for C. obtusa was Dw=0.431−0.015y−0.001d+re, where d is the diameter, Sa is the stand age, C is contact with the forest floor, Fc1 and Fc2 are the climatic factors, and y is the years since death with random site effect re. Fc1 and Fc2 are the principal component scores showing Japanese climatic characteristics that were calculated through principal component analysis using temperature, precipitation, and snow depth data. The negligible variance in the random site effect for both tree species suggested that the models could be applied to other sites. In this study, our models showed accurate predictions of deadwood densities comparable to single exponential decay models, as shown by values for root-mean-square error and root-mean-square relative error.