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Global Water Balance Distribution PDF Cetak E-mail
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Senin, 12 Februari 2007 17:49

Tahun Buletin 2005

 

by: Eleonora Runtunuwu

 

ABSTRACT

This paper attempts to study the global distribution of water balance components, i.e. potential evapotranspiration, soil moisture, actual evapotranspiration, and water balance.  Long term grid climatic data from the Climate Research Unit (CRU) are used, including a global, monthly mean data set of temperature, precipitation, diurnal temperature range, relative humidity, wind speed, at 10’x10’Latitude/Longitude resolution, for the period of 1961-1990. The climatic water balance approach of Thornthwaite and Mather was used to produce, the global annual distribution of potential evapotranspiration, soil moisture, actual evapotranspiration, and water surplus-deficit to all grids. The results showed that these water balance components have proven to be good indicators for the distribution of water resouerces in the global and regional scale. The study provides insight to apply water balance method for irrigation proposes. KEY WORDS: water balance, global, climatologically data, Thornthwaite and Mather method. 

MATERIALS AND METHODS

 

Thornthwaite and Mather (1957) introduced the climatic water balance computation by bookkeeping techniques by  which the water supply (in this case, precipitation) and the climatic demand for water (or potential evapotranspiration, E0). Is compared. The amount of water stored under variable soil-moisture conditions is determined. It will be a deficit, when the difference between the climatic water demand and the actual water loss is negative. In contrast, it will be surplus, when the excess of moisture exists beyond the plant water needs when the soil capillaries are recharged by water. Water deficit is the amount of water that must be supplied by irrigation to keep vegetation growing at an optimum rate; while water surplus is the moisture that will ultimately percolate to the water table and will be generated as stream flow.

 

 

There are numerous methods for computing E0 which have been developed using various climatic input data. Kondoh (1994) examined four methods, i.e. Thornthwaite, Penman, Modified Brutsaert-Stricker, and Morton by using FAOCLIM Agroclimatic Database to calculate E0 for more than 800 points of climatic station in Monsoon Asia. Jensen et al. (1990) compared twenty different methods of estimating E0 and suggested that the Penman-Monteith equation provides the most accurate estimation of monthly E0 from well-watered grass under varied climatic conditions. Salazar and Poveda (2006) validated the diverse evapotranspiration estimation methods (Morton, modified Penman - with Priestley and Taylor approximation, Thornthwaite and Turc) using the long-term water balance in the Amazon River Basin. However, for a wide area of E0 estimation, data availability is a subject of concern in choosing a suitable method.

 

 

The present study used spatial image data sets. However, since there was limited available spatial image datasets, the monthly potential evapotranspiration, E0, was calculated by using Priestly-Taylor method (1972). The resulted of monthly E0 analysis combined with monthly precipitation and soil water holding capacity (Wc) were used to calculate Ea as shown in Figure A flow chart for the water balance calculations (After Runtunuwu, 2006). The detailed steps of analysis can be referred to Thornthwaite and Mather (1957), Kondoh et.al (2004), and Delgado and Guenni (2006).

 

 

{mosimage}

Figure 1. A flow chart for the water balance calculations (After Runtunuwu, 2006)

 

As shown in Figure 1 the input data for Priestly and Taylor method is the albedo, air temperature, cloudiness, and elevation. The global vegetation types is obtained from the potential vegetation distribution (Figure Distribution of potential natural vegetation) (After Runtunuwu, 2005) as refered to Runtunuwu (2005). The monthly albedo of each vegetation type was selected from Mathew (1983) and Kotoda (1986) as summarized in Table  Monthly albedo values (%) of the 13 main groups of vegetation types.

{mosimage}

Figure 2. Distribution of potential natural vegetation (After Runtunuwu, 2005) 

The data used for analysis were: (a) Climatic data: Long term climatic data from the Climate Research Unit (CRU) are used. This is a global, monthly mean data set of temperature, precipitation, diurnal temperature range, relative humidity, wind speed, at 10’x10’Latitude/Longitude resolution, for the period 1961- 1990 (New et al., 1999; 2000; 2002) and it may be found at http://www.cru.uea.ac.uk/cru/data/tmc.htm. (b) Albedo: Global Ecosystem Database, NOAA/NGDC and EPA, Disc A, 1992, (c) Elevation: Global Land One-Kilometer Base Elevation (GLOBE) Digital Elevation Data, NOAA/NGDC, Ver. 1.0 1998, and (d) Soil water holding capacity: UNEP/GRID-Geneva.

 

 

Table 1. Monthly albedo values (%) of the 13 main groups of vegetation types.

NoVegetation typeMonth
JFMAMJJASOND
1Tropical rain forest11

11

11

11

11

11

11

11

11

11

11

11

2Tropical seasonal forest11

11

11

11

11

11

11

11

11

11

11

11

3Sub tropical rain forest11

11

11

11

11

11

11

11

11

11

11

11

4Sub tropical seasonal forest11

11

11

11

11

11

11

11

11

11

11

11

5Temperate forest121212121212121212121212
6Boreal coniferous forest111111121212151515121212
7Savanna, grassland202019181818202020181818
8Cropland161616181818202020181818
9Rice paddy16151081013221815151515
10Grass, crops202019181717171717171920
11Tundra121212121212171717151515
12Semi desert and desert323230302826283030303032
13Glacier ice75

75

75

75

75

75

75

75

75

75

75

75

RESULTS AND DISCUSSION 

Water balance computation

Figure The seasonal fluctuation of precipitation, soil moisture, and evapotranspiration  in tropical seasonal forest in Chittagong, Bangladesh. and Table Example of monthly Ea (mm) computation for tropical seasonal forest at Chittagong, Bangladesh. show examples of the Ea computation for tropical seasonal forest at Chittagong, Bangladesh. The input data for E0 estimation based on Priestly and Taylor method were mentioned. The parameters used for Ea calculation were annual precipitation (P), water holding capacity (Wc), and soil moisture (Sm). The monthly input data for E0 estimation, P and Wc were derived from available global datasets, while the Sm, P, Ea were estimated by using equations in Figure A flow chart for the water balance calculations (After Runtunuwu, 2006). 

From Table Example of monthly Ea (mm) computation for tropical seasonal forest at Chittagong, Bangladesh. the initial month for calculation was September, since the August was the last month of the period with precipitation more than potential evapotranspiration. Based on Thornthwaite and Mather (1957) considering the sum of all the P - Ea value is positive (221 mm) the value of AWL to start accumulating the negative values of P - Ea  is 0 (after August). From September to November the precipitation was less than potential evapotranspiration and soil moisture was utilized in evapotranspiration. The runoff assumed to be not occur in this period. From December to August, precipitation is greater than potential evapotranspiration. At the time, soil moisture was recharged and runoff begins to occur.

{mosimage} 

Figure 3. The seasonal fluctuation of precipitation, soil moisture, and evapotranspiration  in tropical seasonal forest in Chittagong, Bangladesh. 

By applying this approach to all classes, the twelve monthly datasets of potential evapotranspiration, soil moisture, actual evapotranspiration, and water surplus-deficit were produced to obtain the annual value, as shown in Figures  Distribution of potential evapotranspiration (mm/year), Distribution of soil moisture (mm/year), Distribution of actual evapotranspiration (mm/year) and Distribution of water surplus-deficit (mm/year) , respectively.  

 

Table 2. Example of monthly Ea (mm) computation for tropical seasonal forest at Chittagong, Bangladesh.

  
VariableJanFebMarAprMayJunJulAugSepOctNovDecAnnual
T (0C)20.521.826.826.827.527.227.426.927.426.824.720.625.4
n/N (%)36273132405159677166553947.8
E (m)104104104104104104104104104104104104 
r (%)11111111111111111111111111
Ea (mm)807459124147156170170147127127651445
              
P (mm)61226851864324833911991113751975
P – E0(mm)-74-62-33-394027731422153-16-91-60(+) 529
AWL (mm)         -16-107-167 
Sm (mm)63214080808080643416487
dsm (mm)        0-16-30-18 
Ea (mm)1515278514715617017015412667231153
 
T is air temperature; n/M is cloudiness; E is elevation;  is albedo;  is annual
Potential evapotranspiration distribution

 

 

The distribution of the potential evapotranspiration (Figure 4) may be related with the temperature trend as it strongly depends on the latitudinal pattern. The highest value of more than 750 mm/year is distributed in the tropics region, and will be decreased up to the highest latitude. The change by less than 750 mm/year is found in the sub tropical and the temperate regions for all continentals. The lowest value could be found in the boreal region especially in the northern part of Asia, Europe and North America.

precipitation; Wc is water holding capacity; AWL is water accumulation potential water loss; Sm is soil moisture; E0 is potential evapotranspiration, and Ea is actual evapotranspiration.

{mosimage} 

Figure 4. Distribution of potential evapotranspiration (mm/year)

 

Soil moisture distribution

The distribution of soil moisture is shown in Figure Distribution of soil moisture (mm/year). The lower value is distributed in the desert region of Sahara, and semi arid of southern Africa, Australia, western part of South America, and central Asia. The middle value (20-50 mm/year) is distributed in the tropical forest of the middle of Africa, India, and the northern part of South America. The high value of more than 60 mm) is distributed in tropical rain forest of Indonesia, North America, and some parts in northern Asia. The highest value of more than 140 mm/year is distributed in Greenland of North America.

 

{mosimage} 

Figure 5. Distribution of soil moisture (mm/year)

 

 

Actual evapotranspiration distribution

The pattern of actual evapotranspiration is almost the same with the potential evapotranspiration distribution. However, the actual evapotranspiration values are lower compared to the potential one (Figure Distribution of actual evapotranspiration (mm/year). It is high in the tropics and low in the sub tropical and temperate regions. The negative value is distributed in the some places of boreal region. The values of more than 750mm/year are distributed in the tropics, such as in southern America, middle Africa, and Indonesia. The 300-750 mm/year could be found in south and Southeast Asia, northern and eastern Australia. The same value ranges also appear in the some parts of Central America and the southern part of Sahara region. The value of lower than 300 mm/year are widely distributed in the temperate regions. The negative value vast covers the dry region such as Sahara, central Australia, and Saudi Arabia.

{mosimage}

Figure 6. Distribution of actual evapotranspiration (mm/year)

Water surplus-deficit distribution  

The distribution of water surplus-deficit is depicted in Figure Distribution of water surplus-deficit (mm/year). The high value is dominantly distributed in the middle and high latitude of the northern hemisphere, such as in the northern America, Europe, and northern Asia. It also distributed in some places in the tropics such as Indonesia, and a little part of southern America. The lower positive value is distributed in the middle Africa, dry area of China and the West Asia. The highest negative value is found in the Australia, Africa, and the southern part of Southern America.

 

{mosimage}

Figure 7. Distribution of water surplus-deficit (mm/year)

 

 

CONCLUSIONS

 

This study has provided the global distribution of the four main water balance components: potential evapotranspiration, soil moisture, actual evapotranspiration, and water surplus-deficit by using the global climatology datasets. These water balance components have a close relation to the humid and arid conditions, implying that it is possible to make the distribution of water resources at the global or regional scales. The study provides insight the, we could apply the water balance method to the micro scale with the day-to-day application of irrigation. The water balance method is a very powerful tool for irrigation scheduling.

 

 

REFERENCES

 

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