Download presentation

Presentation is loading. Please wait.

Published byYvette Rundell Modified about 1 year ago

1
ES 470 SAMPLING AND ANALYSIS OF HYDROLOGICAL DATA Manoj K. Shukla, Ph.D. Assistant Professor Environmental Soil Physics FEBRUARY 09, 2006, (W147, 3 - 5 PM)

2
J. H. Dane G.C. Topp (Editors) Methods of Soil Analysis- Part 4, Physical Methods ES-470

3
Scales of Variability Regional Molecules Particles or Pore Aggregate Column or Horizon Field or Watershed Pedosphere ES-470

4
Variability Spatial: variability with increasing distance (space) from a location Temporal: variability with increasing duration/time We will limit our discussion to field scale ES-470

5
Agriculture Field ??? In situ soil exhibits large degree of variability or heterogeneity Changes in soil types need to be accounted for in the composite sampling The composite sample must maintain the heterogeneity of the insitu soil ES-470

6
Sources Intrinsic Factors: Soil forming factors, time, soil texture, mineralogy, pedogenesis (geological, hydrological, biological factors) The intrinsic variables have a distinct component that can be called regionalized, i.e., it varies in space, with nearby areas tending to be alike Extrinsic Factors: Land use and management, fertilizer application, other amendments, drainage, tillage ES-470

7
Structure of Variability Random sampling is done to ensure that estimates are unbiased Meet the criterion of independent sampling under identical conditions Y i = + i where Y i is the realization of a soil attribute at location i, m is the mean value for the spatial domain, and i is a random error term ES-470

8
An attribute (i.e., bulk density, nitrate concentration, etc.) is described through two statistical parameters E [Y i ] = First moment or Mean E [(Y i - Second moment or Variance ES-470

9
Mean and variance or first and second moment are often assumed to be the parameters of a normal (Gaussian) probability distribution function; and Allow for a series of sophisticated statistical analysis E [Y i ] = E [(Y i - Arithmetic mean = m = (x 1 + x 2 + x 3 ) / 3 Geometric mean = m = (x 1 * x 2 * x 3 ) 1/3 Harmonic mean = m = (1/x 1 + 1/x 2 + 1/x 3 )* (1/n) Variance (s 2 ) = (1/n) * ∑(x i – x m ) 2 ES-470

10
1.351.351.351.351.35 1.351.351.351.351.35 1.351.351.351.351.35 1.351.351.351.351.35 Mean = 1.35 g kg -1 Variance = 01.351.341.351.351.341.351.301.351.351.35 1.351.301.351.321.32 1.341.351.351.341.35 Mean = 1.339 g kg -1 Variance = 0.0003 Soil N content data E [Y i ] = E [Y i ] = ± ES-470

11
Normal (Gaussian) Distribution Mean The function is symmetric about the mean, it gains its maximum value at the mean, the minimum value is at plus and minus infinity ES-470

12
Histogram for Sand Content Sigma Plot 8.0 Normal distribution ES-470

13
Histogram for Saturated Hydraulic Conductivity Skewed distribution- Positive ES-470

14
Skewed distribution- negative Skewed distribution- Positive One of the tail is longer than other- Distribution is skewed ES-470

15
Different Data Structures ES-470

16
So in place of E [Y i ] = An Appropriate model E [Y i ] = x i i Where (x i ) can be a constant or a function, both dependent on a spatial or temporal scale Therefore, simple randomization may not be sufficient Stratified sampling will be better Stratified sampling- the area is divided into sub areas called strata ES-470

17
1.Formulate objectives 2.Formulate hypotheses 3.Design a sampling scheme 4.Collect data 5.Data Interpretation Objective: Determine the relative magnitude of statistical and spatial variability at Field scale Case Study ES-470

18
Sampling Design? 1.Simple random 2.Stratified 3.Two-stage 4.Cluster 5.Systematic 1 2 -3 4******** **** **** 5 ES-470

19
How many samples? Sample size for simple random sampling Relative error should be smaller than a chosen limit (r) Where - /2 = (1- /2) quartile of the standard normal distribution; S- standard deviation of y in the area; is mean Standard deviation or coefficient of variation is known Absolute error to be smaller than a chosen limit d Time and Resources ???? ES-470

20
df\p0.400.250.100.050.0250.010.0050.0005 10.3249201.0000003.0776846.31375212.7062031.8205263.65674636.6192 20.2886750.8164971.8856182.9199864.302656.964569.9248431.5991 30.2766710.7648921.6377442.3533633.182454.540705.8409112.9240 40.2707220.7406971.5332062.1318472.776453.746954.604098.6103 50.2671810.7266871.4758842.0150482.570583.364934.032146.8688 60.2648350.7175581.4397561.9431802.446913.142673.707435.9588 70.2631670.7111421.4149241.8945792.364622.997953.499485.4079 80.2619210.7063871.3968151.8595482.306002.896463.355395.0413 90.2609550.7027221.3830291.8331132.262162.821443.249844.7809 100.2601850.6998121.3721841.8124612.228142.763773.169274.5869 110.2595560.6974451.3634301.7958852.200992.718083.105814.4370 120.2590330.6954831.3562171.7822882.178812.681003.054544.3178 130.2585910.6938291.3501711.7709332.160372.650313.012284.2208 140.2582130.6924171.3450301.7613102.144792.624492.976844.1405 150.2578850.6911971.3406061.7530502.131452.602482.946714.0728 Students t-table df = degree of freedom; p is probability level ES-470

21
Relative error = 0.01 g kg -1 Mean of Y = 1.17 g kg -1 Standard deviation = 0.05 Example data of N concentration: 1.10, 1.11, 1.12, 1.13, 1.13, 1.14, 1.16, 1.17, 1.19, 1.20, 1.23, 1.24, 1.25 ES-470 Alpha = 0.05 Degree of freedom = 13-1 = 12 t Students (table) = 1.782

22
Relative error (r) = 0.02 g kg -1 Alpha = 0.10 Degree of freedom = 13-1 = 12 T Students (table) = 1.782 r = 0.01 r = 0.02 ES-470

23
Variation in properties Deterministic parameters Stochastic parameters Mean value and an uncertainty statistics VarianceSemi variogram function It is always implied: Domain is first- or second- order stationary Process is adequately characterized by a mean value and an uncertainty statistics E(Y i ) s = Y m Var(Y i ) =0 Var(Y i ) s = 2 s Var[(Y i ) s -(Y i+h ) s ]= 2 h ES-470

24
We will use a data collected on a grid of 20 x 20 cm in a field seeded to grass for last 20 years ES-470

25
Variability can be expressed by coefficient of variation Where: x = an individual value n = the number of test values = the mean of n values Standard deviation of two independent sets where: n 1 = number of values in the first set; s 1 = standard deviation of the first set of values; n 2 = number of values in second set; s 2 = standard deviation of second set of values ES-470

26
Coefficient of variation (CV) Statistical variability of soil properties at local scale i c - Steady state infiltration rate (cm/min) K s - Sat. hydraulic conductivity (cm/min) I - Cumulative infiltration (cm) I 5 - Infiltration rate at 5 min (cm/min) Textural Water Transmission AWC- Available water content (cm) VTP - Volume of transport pores ( s - 6 ) (%) VSP - Volume of storage pores (%) Shukla et al. 2004 ES-470

27
Descriptive statistics (or CV) cannot discriminate between intrinsic (natural variations) and extrinsic (imposed) sources of variability Geostatistical analysis- grid based or spatial sampling For example-20 m x 20 m ES-470

28
Nugget (C 0 ) Partial Sill (C 1 ) Range (a) Lag (h; m) Pannatier, 1996 ArC View Variowin ES-470

29
Note: increases with increasing lag or separation distance A small non-zero value may exist at = 0 This limiting value is known as nugget variance It results from various sources of unexplained errors, such as measurement error or variability occurring at scales too small to characterize given the available data At large h, many variograms have another limiting value This limiting value is known as sill Theoretically, it is equal to the variance of data The value for h where sill occurs is known as range ES-470

30
Variogram The most common function used in geostatistical studies to characterize spatial correlation is the variogram The variogram, (h), is defined as one-half the variance of the difference between the sample values for all points separated by the distance h where var [ ] indicate variance and E { } expected value ES-470

31
Estimator for the variogram is calculated from data using where N(h) is total number of pairs of observations separated by a distance h. Caution- variograms can be strongly affected by outliers in the data ES-470

32
Variogram Model Variogram model is a mathematical description of the relationship between the variance and the separation distance (or lag), h There are four widely used equations ES-470

33
Isotropic Models Spherical Model Linear Model Exponential Model Gaussian Model ES-470

34
C 0, Nuggeta, Range Sill Linear ModelSpherical Model Does not have a sill or range and the variance is undefined Precisely defined sill or range ES-470

35
b ~ a/3b ~ a/3 0.5 Exponential ModelGaussian Model Range is 1/3 of the range for spherical model Range is 1/sqrt(3) of the range for spherical model ES-470

36
Variogram is constructed by 1.Calculating the squared differences for each pair of observations (x j - x k ) 2.Determining the distance between each pair of observation 3.Averaging the squared differences for those pairs of observations with the same separation distance If observations are evenly spaced on a transect, separation distances are multiple of the smallest distance h1 = 2 m; h2 = 4m; h3 = 6 m …… ES-470

37
When observations are placed on an irregular pattern, variograms are : constructed by assigning appropriate lag interval Binning procedure B ins are created with interval centers at distances h1 = (1-2) m; h2 = (2-4) m; h3 =(4-6) m ………………….. ES-470

38
Important considerations when calculating a variogram: As separation distance becomes too large, spurious results occur because fewer pairs of observation exist for large separations due to finite boundary Width of lag interval can affect the sample variogram due to number of samples and variation in the separation distances that fall into a particular lag interval Uncorrelated and correlated data show different nugget effects Number of datasets used influence on variogram ES-470

39
Before you start spatial analysis: Check for normal distribution WSA- water stability of aggregates (%) sand- sand content (%) Ic- saturated hydraulic conductivity (cm/h) ES-470

40
Use of descriptive statistics Mean, median (most middle), skewness, etc. PropertySandSiltClayAWCIc Mean20.260.319.50.10.2 Median21.85919.20.10.2 Std Error0.7>0.010.01 Std Dev3.70.010.04 Skewness-0.91.0-0.1 Minimum1.180.1 Maximum25.80.20.3 ES-470

41
Plot the data to see the structure Saturated Hydraulic Conductivity Y X ES-470

42
Estimator variance Variance = 13.7Variance = 15.5Variance = 16.1Z(x)Z(x+h)20.5620.58 20.5821.84 21.8421.84 21.8419.84 19.8421.84 21.8413.84 13.84Z(x)Z(x+3h)20.5621.84 20.5819.84 21.8421.84 21.8413.84Z(x)Z(x+2h)20.5621.84 20.5821.84 21.8419.84 21.8421.84 19.8413.84 ES-470 Example

43
Sand Content Saturated Hydraulic Conductivity ES-470

44
Sand Content Spherical Model SS = 0.04598 Nugget = 0 Range = 37.92 m Sill = 16.0 Spherical Model SS = 0.00994 Nugget = 3.04 Range = 49.77 m Sill = 16.0 Modeling of Variogram ES-470

45
Spherical Model SS = 0.0494 Nugget = 0 Range = 19.8 m Sill = 0.0384 Saturated Hydraulic Conductivity Spherical Model SS = 0.04938 Nugget = 0.004 Range = 19.8 m Sill = 0.0384 ES-470

46
ParametersNugget Range (m) Sill Sand content 3.0449.7716.0 Silt content (%) 7.50 7.5058.927.6 Clay content (%) 1.56 1.5644.7 3.08 3.08 Cumulative infiltration (cm) 50.079.0175.0 Steady state infiltration rate (cm/min) 0.00170.40.003 Available water content (cm) 0.00122.4 0.0004 0.0004 Parameters for spherical variogram model for soil properties ES-470

47
Spatial variability: nugget – total sill ratio (NSR) Lower NSR – higher spatial dependence Nugget to total sill ratio Textural Water Transmission Shukla et al. 2004 NSR < 0.25 highly spatial variable NSR > 0.75 less spatial variable Cambardella et al., 1994 ES-470

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google