scholarly journals Variability of snow depth at the plot scale: implications for mean depth estimation and sampling strategies

2011 ◽  
Vol 5 (3) ◽  
pp. 617-629 ◽  
Author(s):  
J. I. López-Moreno ◽  
S. R. Fassnacht ◽  
S. Beguería ◽  
J. B. P. Latron
Keyword(s):  
Standard Deviation ◽  
Spatial Variability ◽  
Spatial Autocorrelation ◽  
Snow Depth ◽  
Spatial Configuration ◽  
Estimation Error ◽  
Sampling Strategy ◽  
Ground Truth ◽  
Depth Estimation ◽  
Depth Estimate

Abstract. Snow depth variability over small distances can affect the representativeness of depth samples taken at the local scale, which are often used to assess the spatial distribution of snow at regional and basin scales. To assess spatial variability at the plot scale, intensive snow depth sampling was conducted during January and April 2009 in 15 plots in the Rio Ésera Valley, central Spanish Pyrenees Mountains. Each plot (10 × 10 m; 100 m2) was subdivided into a grid of 1 m2 squares; sampling at the corners of each square yielded a set of 121 data points that provided an accurate measure of snow depth in the plot (considered as ground truth). The spatial variability of snow depth was then assessed using sampling locations randomly selected within each plot. The plots were highly variable, with coefficients of variation up to 0.25. This indicates that to improve the representativeness of snow depth sampling in a given plot the snow depth measurements should be increased in number and averaged when spatial heterogeneity is substantial. Snow depth distributions were simulated at the same plot scale under varying levels of standard deviation and spatial autocorrelation, to enable the effect of each factor on snowpack representativeness to be established. The results showed that the snow depth estimation error increased markedly as the standard deviation increased. The results indicated that in general at least five snow depth measurements should be taken in each plot to ensure that the estimation error is <10 %; this applied even under highly heterogeneous conditions. In terms of the spatial configuration of the measurements, the sampling strategy did not impact on the snow depth estimate under lack of spatial autocorrelation. However, with a high spatial autocorrelation a smaller error was obtained when the distance between measurements was greater.

scholarly journals Variability of snow depth at the plot scale: implications for mean depth estimation and sampling strategies

2011 ◽  
Vol 5 (3) ◽  
pp. 1627-1653
Author(s):  
J. I. López-Moreno ◽  
S. R. Fassnacht ◽  
S. Beguería ◽  
J. B. P. Latron
Keyword(s):  
Standard Deviation ◽  
Spatial Variability ◽  
Spatial Autocorrelation ◽  
Snow Depth ◽  
Spatial Configuration ◽  
Estimation Error ◽  
Sampling Strategy ◽  
Ground Truth ◽  
Depth Estimation ◽  
Data Points

Abstract. Snow depth variability over small distances can affect the representativeness of depth samples taken at the local scale, which are often used to assess the spatial distribution of snow at regional and basin scales. To assess spatial variability at the plot scale, intensive snow depth sampling was conducted during January and April 2009 in 15 plots in the Rio Ésera Valley, central Spanish Pyrenees Mountains. Each plot (10 × 10 m; 100 m2) was subdivided into a grid of 1 m2 squares; sampling at the corners of each square yielded a set of 121 data points that provided an accurate measure of snow depth in the plot (considered as ground truth). The spatial variability of snow depth was then assessed using sampling locations randomly selected within each plot. The plots were highly variable, with coefficients of variation up to 0.25. This indicates that to improve the representativeness of snow depth sampling in a given plot the snow depth measurements should be increased in number and averaged when spatial heterogeneity is substantial. The spatial autocorrelation of snowpack distribution can affect the local representativeness of snowpack. Snow depth distributions were simulated at the same plot scale under varying levels of standard deviation and spatial autocorrelation, to enable the effect of each factor on snowpack representativeness to be established. The results showed that the snow depth estimation error increased markedly as the standard deviation increased. The results indicated that in general at least 5 snow depth measurements should be taken in each plot to ensure that the estimation error is <10 %; this applied even under highly heterogeneous conditions. In terms of the spatial configuration of the measurements, no particular sampling strategy provided an improved estimate of snow depth, but using a greater distance between measurements within a plot improved the representativeness of the estimates.

scholarly journals Seasonal development of spatial snow-depth variability across different scales in the Swiss Alps

2011 ◽  
Vol 52 (58) ◽  
pp. 216-222 ◽  
Author(s):  
Luca Egli ◽  
Nena Griessinger ◽  
Tobias Jonas
Keyword(s):  
Spatial Variability ◽  
Snow Depth ◽  
Weather Prediction ◽  
Ground Truth ◽  
Numerical Weather Prediction Model ◽  
Swiss Alps ◽  
Seasonal Development ◽  
Precipitation Pattern ◽  
Scaling Analysis ◽  
Snow Hydrology

AbstarctThe depth of snow cover is temporally and spatially heterogeneous at different scales in Alpine regions. For snow hydrology/climatology the spatial variability of snow depths is a key parameter for capturing the total amount of snow in a given area. Here a scale analysis of the spatial variability of snow depths during the accumulation period is investigated. The development of the variability is characterized by a parameter, β, describing the relationship between the standard deviation and the mean of snow depths. The analysis includes two datasets: (1) 141 snow-depth point measurements representing flat-field observations, and (2) snow precipitation from the numerical weather prediction model COSMO-7. The results reveal that β is almost invariant at scales between 10 and 300 km. The COSMO-7 data exhibit the same scale invariance above 50 km, indicating that the spatial variability of snow depths is formed by the precipitation pattern at these scales. The scaling analysis of β allows determination of the absolute accuracy of estimating the total amount of snow in a given area and helps to validate different snow models or remote-sensing techniques by ground truth verification.

Uncertainties on the Extension of a Polluted Zone

2011 ◽  
Author(s):  
Chantal de Fouquet ◽  
Yves Benoit ◽  
Claire Carpentier ◽  
Bruno Fricaudet
Keyword(s):  
Standard Deviation ◽  
Spatial Variability ◽  
Concentration Level ◽  
Estimation Error ◽  
Local Concentration ◽  
Polluted Sites ◽  
Error Standard Deviation ◽  
Variographic Analysis ◽  
Proportional Effect ◽  
Polluted Zone

Data collected during the sampling of polluted sites are mainly used - through an exploratory and variographic analysis, to characterize to characterize the concentration level and the spatial variability; - at fixed support, to estimate the concentrations in order to map the pollution. Kriging gives also the standard deviation of the estimation error, making it possible to delimit the zones in which the estimation is considered to lack in precision. If a proportional effect is present the map of error standard deviation has to be corrected to take into account the increase of spatial variability with the local concentration mean.

scholarly journals Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach

2020 ◽  
Vol 14 (6) ◽  
pp. 1763-1778 ◽  
Author(s):  
Jianwei Yang ◽  
Lingmei Jiang ◽  
Kari Luojus ◽  
Jinmei Pan ◽  
Juha Lemmetyinen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Snow Depth ◽  
Spatial Scales ◽  
Ground Truth ◽  
Depth Estimation ◽  
Predictor Variables ◽  
Science Data ◽  
Increasing Trend ◽  
Mean Square Errors

Abstract. We investigated the potential capability of the random forest (RF) machine learning (ML) model to estimate snow depth in this work. Four combinations composed of critical predictor variables were used to train the RF model. Then, we utilized three validation datasets from out-of-bag (OOB) samples, a temporal subset, and a spatiotemporal subset to verify the fitted RF algorithms. The results indicated the following: (1) the accuracy of the RF model is greatly influenced by geographic location, elevation, and land cover fractions; (2) however, the redundant predictor variables (if highly correlated) slightly affect the RF model; and (3) the fitted RF algorithms perform better on temporal than spatial scales, with unbiased root-mean-square errors (RMSEs) of ∼4.4 and ∼7.3 cm, respectively. Finally, we used the fitted RF2 algorithm to retrieve a consistent 32-year daily snow depth dataset from 1987 to 2018. This product was evaluated against the independent station observations during the period 1987–2018. The mean unbiased RMSE and bias were 7.1 and −0.05 cm, respectively, indicating better performance than that of the former snow depth dataset (8.4 and −1.20 cm) from the Environmental and Ecological Science Data Center for West China (WESTDC). Although the RF product was superior to the WESTDC dataset, it still underestimated deep snow cover (>20 cm), with biases of −10.4, −8.9, and −34.1 cm for northeast China (NEC), northern Xinjiang (XJ), and the Qinghai–Tibetan Plateau (QTP), respectively. Additionally, the long-term snow depth datasets (station observations, RF estimates, and WESTDC product) were analyzed in terms of temporal and spatial variations over China. On a temporal scale, the ground truth snow depth presented a significant increasing trend from 1987 to 2018, especially in NEC. However, the RF and WESTDC products displayed no significant changing trends except on the QTP. The WESTDC product presented a significant decreasing trend on the QTP, with a correlation coefficient of −0.55, whereas there were no significant trends for ground truth observations and the RF product. For the spatial characteristics, similar trend patterns were observed for RF and WESTDC products over China. These characteristics presented significant decreasing trends in most areas and a significant increasing trend in central NEC.

Snow depth estimation based on GNSS-IR cluster analysis

2021 ◽  
Author(s):  
shuangcheng zhang ◽  
chenglong zhang ◽  
ying zhao ◽  
hao li ◽  
qi liu ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Snow Depth ◽  
Depth Estimation

scholarly journals Design of a scanning laser meter for monitoring the spatio-temporal evolution of snow depth and its application in the Alps and in Antarctica

2016 ◽  
Vol 10 (4) ◽  
pp. 1495-1511 ◽  
Author(s):  
Ghislain Picard ◽  
Laurent Arnaud ◽  
Jean-Michel Panel ◽  
Samuel Morin
Keyword(s):  
Spatial Variability ◽  
Snow Depth ◽  
Laser Scanning ◽  
Single Point ◽  
Measurement Techniques ◽  
Strong Wind ◽  
Slow Increase ◽  
Automatic Instrument ◽  
Spatio Temporal ◽  
Wind Events

Abstract. Although both the temporal and spatial variations of the snow depth are usually of interest for numerous applications, available measurement techniques are either space-oriented (e.g. terrestrial laser scans) or time-oriented (e.g. ultrasonic ranging probe). Because of snow heterogeneity, measuring depth in a single point is insufficient to provide accurate and representative estimates. We present a cost-effective automatic instrument to acquire spatio-temporal variations of snow depth. The device comprises a laser meter mounted on a 2-axis stage and can scan  ≈  200 000 points over an area of 100–200 m2 in 4 h. Two instruments, installed in Antarctica (Dome C) and the French Alps (Col de Porte), have been operating continuously and unattended over 2015 with a success rate of 65 and 90 % respectively. The precision of single point measurements and long-term stability were evaluated to be about 1 cm and the accuracy to be 5 cm or better. The spatial variability in the scanned area reached 7–10 cm (root mean square) at both sites, which means that the number of measurements is sufficient to average out the spatial variability and yield precise mean snow depth. With such high precision, it was possible for the first time at Dome C to (1) observe a 3-month period of regular and slow increase of snow depth without apparent link to snowfalls and (2) highlight that most of the annual accumulation stems from a single event although several snowfall and strong wind events were predicted by the ERA-Interim reanalysis. Finally the paper discusses the benefit of laser scanning compared to multiplying single-point sensors in the context of monitoring snow depth.

scholarly journals Spatial Pattern Oriented Multi-Criteria Sensitivity Analysis of a Distributed Hydrologic Model

2018 ◽  
Author(s):  
Mehmet Cüneyd Demirel ◽  
Julian Koch ◽  
Gorka Mendiguren ◽  
Simon Stisen
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Variability ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Performance Measures ◽  
Sampling Strategy ◽  
Behavioral Model ◽  
Hydrologic Model ◽  
Actual Evapotranspiration ◽  
Model Parameters

Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represents an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity are typically not reflecting other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definition based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

scholarly journals Spatial variability of Soil Nutrients Using Geospatial Techniques: A case study in soils of Sanwer Tehsil of Indore district of Madhya Pradesh

2014 ◽  
Vol XL-8 ◽  
pp. 1353-1363 ◽  
Author(s):  
G. S. Tagore ◽  
G. D. Bairagi ◽  
R. Sharma ◽  
P. K. Verma
Keyword(s):  
Spatial Variability ◽  
Soil Nutrients ◽  
Spatial Dependence ◽  
Chemical Properties ◽  
Sampling Strategy ◽  
Spatial Prediction ◽  
Accurate Estimation ◽  
Suitable Alternative ◽  
Available N ◽  
Physico Chemical

A study was conducted to explore the spatial variability of major soil nutrients in a soybean grown region of Malwa plateau. From the study area, one hundred sixty two surface soil samples were collected by a random sampling strategy using GPS. Then soil physico-chemical properties i.e., pH, EC, organic carbon, soil available nutrients (N, P, K, S and Zn) were measured in laboratory. After data normalization, classical and geo-statistical analyses were used to describe soil properties and spatial correlation of soil characteristics. Spatial variability of soil physico-chemical properties was quantified through semi-variogram analysis and the respective surface maps were prepared through ordinary Kriging. Exponential model fits well with experimental semi-variogram of pH, EC, OC, available N, P, K, S and Zn. pH, EC, OC, N, P, and K has displayed moderate spatial dependence whereas S and Zn showed weak spatial dependence. Cross validation of kriged map shows that spatial prediction of soil nutrients using semi-variogram parameters is better than assuming mean of observed value for any un-sampled location. Therefore it is a suitable alternative method for accurate estimation of chemical properties of soil in un-sampled positions as compared to direct measurement which has time and costs concerned.

scholarly journals Adaptive and High-Resolution Estimation of Specific Differential Phase for Polarimetric X-Band Weather Radars

2018 ◽  
Vol 35 (3) ◽  
pp. 555-573 ◽  
Author(s):  
Ricardo Reinoso-Rondinel ◽  
Christine Unal ◽  
Herman Russchenberg
Keyword(s):  
Standard Deviation ◽  
Spatial Variability ◽  
Heavy Rain ◽  
Storm Events ◽  
Band Frequency ◽  
Adaptive Approach ◽  
Range Resolution ◽  
Differential Phase ◽  
Weather Radars ◽  
X Band

ABSTRACTOne of the most beneficial polarimetric variables may be the specific differential phase KDP because of its independence from power attenuation and radar miscalibration. However, conventional KDP estimation requires a substantial amount of range smoothing as a result of the noisy characteristic of the measured differential phase ΨDP. In addition, the backscatter differential phase δhv component of ΨDP, significant at C- and X-band frequency, may lead to inaccurate KDP estimates. In this work, an adaptive approach is proposed to obtain accurate KDP estimates in rain from noisy ΨDP, whose δhv is of significance, at range resolution scales. This approach uses existing relations between polarimetric variables in rain to filter δhv from ΨDP while maintaining its spatial variability. In addition, the standard deviation of the proposed KDP estimator is mathematically formulated for quality control. The adaptive approach is assessed using four storm events, associated with light and heavy rain, observed by a polarimetric X-band weather radar in the Netherlands. It is shown that this approach is able to retain the spatial variability of the storms at scales of the range resolution. Moreover, the performance of the proposed approach is compared with two different methods. The results of this comparison show that the proposed approach outperforms the other two methods in terms of the correlation between KDP and reflectivity, and KDP standard deviation reduction.

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