1. Decisional Tool Based on the Pedagogical, Climatical and Phonological Indicators Obtained from Satellite and Observational Data for the Efficient Management of Irrigations in Main Agricultural Areas from Romania Monica Matei, Nicu Ciobotaru, Lucian Laslo, Madalina Boboc, Marius Raischi, Gyorgy Deak, and Ana-Maria Velcea National Institute for Research and Development in Environmental Protection, Romania,
Phone: +40 (0)
Fax: +40 (0)
Results
In the following tables (Table 3 and 4) are presented the descriptive statistics (number of values, minimum, maximum, mean, standard deviation and variance) for the indexes and parameters studied in two of the six case studies (Arad and Craiova), for each month in the interval :
Table 3 - Descriptive Statistics (Arad) N
Min.
Max.
Mean
Std. Dev.
Variance
Pp[mm]
204
0.00
174.80
50.46
33.70
scPDSI
-5.12
3.68
-0.10
1.80
3.23
SPI1
-2.50
2.64
0.06
1.04
1.09
SPI3
-3.43
2.43
0.09
1.19
LAI
203
0.29
32.70
9.94
7.16
51.22
Fpar
1.69
76.90
41.90
18.91
357.73
NDVI
-0.03
0.75
0.47
0.14
0.02
EVI
-0.04
0.59
0.28
0.12
0.01
Table 4 - Descriptive Statistics (Craiova) N
Min.
Max.
Mean
Std. Dev.
Variance
Pp [mm]
204
0.00
215.80
57.90
41.62
scPDSI
-3.70
6.77
0.38
2.09
4.35
SPI-1
-2.74
2.62
0.15
1.12
1.25
SPI-3
-2.63
3.16
0.28
1.13
1.28
LAI
203
25.96
7.82
6.14
37.71
Fpar
70.42
34.26
18.06
326.02
NDVI
-0.03
0.71
0.42
0.02
EVI
-0.05
0.46
0.23
0.10
0.01
Background The technical availability of water resource in Romania is modest in comparison with other European countries. Thus, Romania is in 22nd place by the total water resource rank, respectively in 16th place by the internal formed water resource (Eurostat, 2017) Water demand in Romania for 2015 was 17,5% from the technical available resource, shared between the main sectors as follows: industry and energy 66%, agriculture 18% and population 16% (ANAR, 2015). As the water consumption from industry and energy is at low level, almost entire abstracted quantity for energy being returned to the source, the agriculture remain the main consumptive sector. Even if the annual demand for water is in reduced proportion at country level (under a fifth), water shortage can occur due to the time and spatial variability of water resource The agricultural sector use irrigations for water deficit compensation. In Romania irrigated lands decreased from 3 millions ha in 1990 (figure 2 with irrigation facilities at that time) to the varied surface in last 16 years of ha in to ha in 2003 (INS, 2017). In a previous publication the relation between observational indexes and irrigated areas was analyzed at comparative level (Laslo et al, 2017). For the efficient management of water resource in irrigations, especially when hydric stress occur, correlations between irrigated quantities, remote sensing indicators and pedo-climatic indicators calculated from meteorological observational data were studied Case studies: the applicability of the proposed decision-making tool for efficient management of water resource in irrigations is studied for five main agricultural areas in Romania (Dobrogea Plateau, Moldavia Plain, Romanian Plain, Transylvania Plateau and Western Plain), in the period (Figure 1).
In tables 5, 6 and 7 are presented the factorial analysis results, computed with SPSS software for the studied indexes and parameters in the period of vegetation (April to September), covering data from all case studies.
Correlation Matrix
Pp [mm]
scPDSI
SPI-1
SPI-3
LAI
Fpar
NDVI
EVI
1.000
.621
.870
.578
.209
.195
.320
.288
.647
.818
.140
.138
.224
.186
.629
.071
.055
.182
.212
.185
.300
.266
.861
.909
.947
.826
.850
.975
Rotated Component Matrixa
Component 1 2
Pp [mm]
.164
.864
scPDSI
.079
.873
SPI-1
.006
.905
SPI-3
.158
.845
LAI
.968
.067
Fpar
.920
.053
NDVI
.951
.187
EVI
.973
.141
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization a. Rotation converged in 3 iterations.
Table 5 - Correlation Matrix between each index/parameter: with green- high values of correlation coefficient(> 0.8); with red: correlation without statistic significance (sig> 0.05)
Table 6 - Rotated matrix of the two principal components
Total Variance Explained
Component
Initial Eigenvalues
Extraction Sums of Squared Loadings
Rotation Sums of Squared Loadings
Total
% of Variance
Cumulative % 1
4.229
52.865
3.693
46.159 2
2.567
32.091
84.957
3.104
38.797
Study areas were selected as cultivated agricultural lands for satellite data, placed in vicinity of cities with meteorological stations, used for pedo-climatic indicators. For the irrigated lands the analyzed areas are represented by correspondent counties.
Table 7- Explained variance of the two principal components
The following tests consisted in hierarchical multiple regression applied for the studied indexes/parameters, grouped by the two components resulted in previous factorial analysis (Table 8 and 9). As dependent variable was introduced the irrigation norm per county, the others variables being used as predictors, introduced consecutively in two groups.
Model Summary- Data from all case studies
Model R
R Square
Adjusted R Square
Std. Error of the Estimate
Change Statistics
R Square Change
F Change
df1
df2
Sig. F Change 1
,372a
.139
.093
8,806.14
3.056 4 76
.022 2
,397b
.158
.064
8,947.17
.019
.406 72
.804
a. Predictors: (Constant), EVI, LAI, Fpar, NDVI
b. Predictors: (Constant), EVI, LAI, Fpar, NDVI, scPDSI, SPI-1, SPI-3, Pp [mm]
Pearson Correlation
Irrigation norm/county [mc/ha]
Constanta
Arad
Craiova
ALL
LAI
-.521
-.648
-,118
-,247
Fpar
-.503
-.654
-,029
-,190
NDVI
-.517
-.863
,040
-,173
EVI
-.536
-.815
-,062
-,224
Pp [mm]
-.398
-.629
-,157
-,138
scPDSI
-.589
-.711
-,008
-,017
SPI-1
-.321
-.560
-,092
-,087
SPI-3
-.472
-.369
,065
-,052
Figure 1- Location of study area and meteorological stations in Romania’s main agricultural areas
Figure 2- Location of study areas within the density of irrigation facilities (km/area of local administrative unit),
Reference year 1990
Table 8 – Multiple hierarchical regression with irrigation norm as dependent variable
Discussions and conclusions
Statistical analyses applied to studied variables revealed the following aspects:
- Descriptive statistics shows the amplitude, mean, standard deviation and variance of each parameter/index.
Correlation Matrix and Factorial Analysis had as results the identification of correlation coefficients between each variable (Table 5), respectively the separation in two principal components (Table 6): one characterized by the satellite data (EVI, LAI, Fpar and NDVI) and other by the observational data variables (scPDSI,SPI-1, SPI-3 and PP); the good correlation between variables of each group can be observed from the resulted coefficients; the variance proportion is explained % by the first group , the second group representing 32% from variance.
The multiple hierarchical regression was the final analyses used to test the prediction of irrigation norm by the predictors variables, grouped in two models (satellite data and observational data); in Table 8 are presented the results of the analysis for all case studies, first group with satellite data variables explaining 13,9% of variance; the second group explain supplementary 1,9 % of variance; a weak correlation between irrigation norm and the rest of the variables was observed at Craiova case study (table 9), this affecting the correlation for all cases combined (Table 8).
The statistical analysis of the satellite data and observational data indexes were tested as a tool to predict the irrigation norm in the presented case studies. The results reveal the possibility to use satellite data and pedo-climatic indexes to improve the efficiency of the irrigated quantities on agricultural lands, by testing and improving the correlation of variables. Researches are in progress for better satellite data resolution (Sentinel 2) and in-situ measurements References
Eurostat, Water Statistics, 2017
Laslo,L., Ciobotaru, N., Lupei, T., Matei, M. , Velcea, A.M., Boboc, M., Badea, G., Deak, Gy., Drought and Irrigations of Romanian Agricultural Areas, RevCAD, vol 23, 2017
McKee, T. B., Doesken, N. J. & Kleist, J., The relationship of drought frequency and duration of time scales. Anaheim, American Meteorological Society, pp
National Institute of Statistics - tempo-online database, 2017:
National Meteorological Administration, Climate Change - from physical bases to risks and adaptation, Editura PRINTECH, București, 2015
Palmer, W., "Meteorological Drought". Research paper no.45, U.S. Department of Commerce Weather Bureau, February 1965 (58 pgs)
Raspisaniye Pogodi Ltd. Sankt Petersburg, Reliable Prognosis. [Online] Available at:
World Meteorological Organisation, Standardized Precipitation Index - User Guide, Geneva: WMO
Methods Indexes calculated using data from meteorological observational stations: scPDSI -self calibrated Palmer Drought Severity Index: an complex index calculated using readily available temperature and precipitation data to estimate relative dryness, quantifying drought and wetness periods by classifying in classes (Table 1) between (extreme drought) and (extremely wet) SPI -Standardized Precipitation Index: calculated at 1 and 3 months: is an index used for the detection of drought and of the excess of moisture to a certain area (McKee et al., 1993) representing synthetic deviations from the average monthly rainfall of the specific measuring interval (Table 2) PP -monthly precipitations: calculated from meteorological stations data Sources of data: European Climate Assessment & Dataset project (ECAD- ROCADA database, Reliable Prognosis – Sankt Petersburs Rusia, MODIS Indicators (Moderate Resolution Imaging Spectroradiometer) satellite data: NDVI - Normalized Difference Vegetation Index: the quantitative indicator of green vegetation presence EVI- Enhanced Vegetation Index: derived from NDVI to improve the perception of vegetation in areas where this is very dense FPAR- Fraction of Photosynthetically Active Radiation Index: the report between incident solar radiation and its fraction used by the exposed parts of the plants in photosynthesis process LAI-Leaf Vegetation Index: is the biomass equivalent of FPAR, representing the leaf area covering a unit of ground area Irrigation statistics: monthly irrigation norm (m3/ha) at county level, calculated from ANIF (National Agency for Land Improvements) statistics SPSS software: statistical analysis of the previous described variables
Table 9- Pearson correlation between irrigation norm and predictors variables for different case studies
Table 1 - PDSI
Class
<= -4.00
Extreme drought
-3.00 to –3.99
Severe drought
-2.00 to –2.99
Moderate drought
-1.00 to –1.99
Mild drought
-0.50 to –0.99
Incipient drought
-0.49 to +0.49
Near normal
+0.50 to +0.99
Incipient wet spell
+1.00 to +1.99
Slightly wet
+2.00 to +2.99
Moderate wet
+3.00 to +3.99
Very wet
>= +4.00
Extremely wet
Source: Palmer, 1965
Table 2 - SPI
Class
<=-2.0
Extremely dry
-1.5 to -1.99
Severely dry
-1.0 to -1.49
Moderately dry
-0.99 to +0.99
Near normal
+1.0 to +1.49
Moderately wet
1.5 to 1.99
Severely wet
>=2.0
Source: World Meteorological Organisation, 2012
”This work was supported by a grant of the Ministry of National Education and Scientific Research, RDI Programe for Space Technology and Advanced Research - STAR, project number 166/ , and thank to the partners: National Institute Of Research - Development For Machines And Installations Designed To Agriculture And Food Industry – INMA and SPASTO ”