Modeling crop land soil moisture and impacts of supplimental irrigaiton in a rainfed region of Bangladesh

doi:10.4236/jacen.2014.31B004

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Vol.3, No.1B, 16-19 (2014) Journal of Agricultural Chemistry and Environment

http://dx.doi.org/10.4236/jacen.2014.31B004

Modeling crop land soil moisture and impacts of

supplimental irrigaiton in a rainfed region of

Bangladesh

Pramod K. Pandey*, Sagor Biswas

Department of Population Health and Reproduction, University of Californian, Davis, USA;

*Corresponding Author: [email protected]

Received October 213

ABSTRACT

A robust water balance model has been tested

for predicting soil moisture levels and supple-

mental irrigation requirement of a rainfed region

of Bangladesh. The predictions were used for

improving the understanding of the impacts of

rainwater harvesting on rainfed agriculture. The

climate data (i.e., rainfall, temperature, evapora-

tion, and evapotranspiration) were used as in-

puts for predicting the variations in soil mois-

ture. Soil moisture levels under rainfed and sup-

plementary irrigation conditions were compared.

Results showed that rainwater harvesting i.e.,

rain water storage tanks during rainy seasons

can be potentially useful for storing rainwater,

which can be utilized for enhancing crop land

soil moisture during dry seasons for enhancing

crop yield. The study presented here will be use-

ful for improving and disseminating rainwater

harvesting approaches for enhancing water

availability in rainfed regions.

KEYWORDS

Rainwater Harvesting; Rainfed Crop Land;

Supplemental Irrigation; Crop Yiel d

1. INTRODUCTION

It is required to increase the agricultural water availa-

bility in rainfed regions to enhance the global food pro-

duction. Approximately more than 80% of the global

crop land is rainfed, which produces more than 70% of

global food productions currently [1-3]. For improving

food production further, additional water resources capa-

ble of providing the irrigation to crop lands is required

[4]. One option is increasing the facilities/structures for

rainwater harvesting in the crop land itself [5,6]. In many

rainfed regions, for instance, in Bangladesh, more than

76% of rainfall oc curs in rainy season (May to October);

however, a major portion of it losses as runoff. Due to

insufficient water storages, farmers often face irrigation

water shortages during dry seasons. Providing the facili-

ties capable of storing the rain water during rainy season

can potentially facilitate water availability for irrigation.

Previous studies have shown that harvested rainwater in

on-farm reservoirs during rainy season can enhance crop

yield considerably [7,8]. Here we have exploited a water

balance model [8] for calculating soil moisture and crop

yield under rainwater harvesting facilities and without

rainwater harvesting (i.e., rainfed) for improving the un-

derstanding of rainfed agriculture and rainwater harvest-

ing approaches.

2. METHODS

2.1. Study Area

The study area is show n in F i gure 1 . Jessore, a distr ict

situated in the southwestern part of Bangladesh (BD),

receives about 1741 mm of annual rainfall. Nearly 76%

of annual rainfall occurs from May to October. Out of

that about 28% of the total annual rainfall occurs in the

month of July. Temperature varies from 10˚C to 36˚C.

Relative humidity varies from 72% to 86%, and wind

speed varies from 0.76 to 4.6 m/s. The rainfall and tem-

perature variations of the study area are shown in Fig-

ures 2 and 3. Average monthly evapotranspiration varia-

tion is shown in Figure 4.

2.2. Model

The model used in the study has been described else-

where [8]. The model has two components: 1) water bal-

ance simulation for crop land; and 2) water balance si-

mulation for water storage tanks. Water storage tanks re-

ceive water from upland catchment area of 5 ha (as ru-

noff), and direct precipitation on tank’s surface. The

stored water in the tanks was applied as supplemental

irrigation (when needed) to the crop land for enhancing

P. K. Pandey, S. Biswas / Journal of Agricultural Chemistry and Environment 3 (2014) 16-19

Figure 1. Study area (Jessore District, Bangladesh).

Figure 2. Precipitation (Jessore District, Bangladesh).

Figure 3. Temperature (Jessore District, Bangladesh).

soil moisture. In simulation, we used crop land area of 1

ha, reservoir area of 15% of the catchment area (i.e., 0.8

ha).

Figure 4. Potential evapotranspiration (Jessore, BD).

The model uses curve number for estimating the ru-

noff from catchment to water storage tanks. Daily see-

page, evaporation, and spill from the tank were simulated,

and the simulation methods are described previously [5,

8]. Crop water requirement were predicted using readily

available soil moisture and non-readily available soil

moisture [8]. The crop coefficient of bean crop was used

for simulating the crop water requirement at various crop

growth stages as described previously [8].

The model requires multiple input parameters, which

are described in two previous studies [5,8]. Readers are

encouraged to preview the published studies for under-

standing the model’s details. In this study, we used rainfall,

temperature, and evapotranspiration data from the Jes-

sore District of Bangladesh. The average monthly rainfall

and min/max temperatures were obtained from Bangla-

desh Agricultural Research Council (BARC) [9]. Using

the monthly data, we estimated daily data using polyno-

mial equations (fitted on monthly data). Due to unavaila-

bility of evapotranspiration data of the Jessore District,

we used neighboring climate stations for estimating the

evaporation and evapotranspiration for the study area. The

data of the neighboring stations (i.e. West Bengal, India)

were obtained from two sources: 1) Indian Meteorologi-

cal Department (IMD) [10], and 2 ) Hydrology and Water

Resources Information System for India [11]. The climate

data (i.e., temperature and rainfall) of the location in India

were compared with the Jessore District, and the data

were comparable. For example, the annual monthly rain-

fall data had similarity of 78% and annual monthly tem-

perature had the similarity of 87%. After combining the

deviation of rainfall and temperature, we anticipate that

there was a possibility of 12.7% deviation in climates

between the study area and neighboring climate station.

3. CALCULATIO NS AND INPUT DATA

To estimate the daily precipitation from the average

monthly data shown in Figure 2, we did perform two

separate interpolations: 1) rising limb of precipitation;

and 2) falling limb of precipitation, which yielded daily

200

400

600

800

1000

1 3 5 7 911

Precipitaiton (mm)

Months

2005 2006 2007 2008

2009 2010 Average

1357911

Temperature (d eg C)

Months

2005 2006 2007 2008

2009 2010 Average

y = 0.2965x3-8.1653x2+ 59.066x + 7.1564

R² = 0.8126

120

160

200

0246810 12 14

Potential evapotranspiratoin monthly (mm)

Months

P. K. Pandey, S. Biswas / Journal of Agricultural Chemi s try and Environment 3 (2014) 16-19

precipitation data (Figure 5). Two separate interpolations

were needed because single interpolation was not able to

capture the peak rainfall, which occurred in the month of

July. The rising and falling limbs of precipitation are

shown in Figure 5, and interpolated precipitation values

(daily) are also shown in the figure. Similarly, daily eva-

potranspiration was estimated using the average monthly

shown in Figure 4. The daily evapotranspiration is

shown in Figure 6.

The model used in this study requires daily input data

(precipitation, temperature, evaporation, and evapotrans-

piration). Evaporation was estimated from evapotranspi-

ration. Previous studies reported that evaporation values

vary approximately 120% - 130% of evapotranspiration.

In this study, we used daily evaporation values as 130%

of the dail y ev a potranspira t ion values.

4. RESULTS AND DISCUSSION

The average annual interpolated daily precipitation

was slightly less than the observed data. The observed

average annual precipitation for the study area was about

1741 mm, while interpolation yielded average annual

precipitation of 1478 mm i.e., 81% of the observed val-

ues. The average annual evapotranspiration was approx-

imately 1189 mm, while interpolation yielded average

annual evapotranspiration of 1147 mm. Figure 7 shows

the soil moisture variations in rainfed and irrigated con-

ditions. In addition, daily precipitation and supplemental

irrigation is also shown in the figure. The simulation is

shown for starting from Julian Day 1 to Julian Day 150.

As shown in the figure, soil moisture was considerably

elevated when supplemental irrigation was applied (sup -

plemental irrigation is shown as vertical red bars in Fig-

ure 7). Soil moisture in the rainfed and irrigated condi-

tions were estimated for two seasons: (Season 1: Julian

Day 20 - 119; and Season 2: Juli a n Da y 16 5 - 264). Com-

pared to the first season, in the second season i.e., be-

yond Julian Day 165, soil moisture in rainfed and irrigat-

ed conditions were comparable because of excess rainfall.

The available water storages in ponds were not utilized

as supplemental irrigation because the soil moisture was

suffi cient without supplem ental irrigation ( data not shown).

At the end of cropping season, the soil moisture con-

tent in irrigated condition was almost three times greater

than the rainfed soil moisture. Although actual evapo-

transpiration (ET) was almost two times greater in irri-

gated condition compared to rainfed condition, the actual

yield increased about three times in irrigated condition

when compared to the rainfed condition. Addition of 128

mm of supplementary irrigation decreased the green wa-

ter use by 45% and increased the total water use by 55%

compared to rainfed condition. Subsequently the overall

water use efficiency showed a nearly 55% increase in

irrigated condition. As shown in the Table 1, water re-

Figure 5. Interpolated daily precipitation.

Figure 6. Daily evapotranspiration.

Figure 7. Soil moisture and supplemental irrigation.

charge values (R) of uncultivated land in irrigated condi-

tion was 85% of the recharge value of rainfed fed condi-

tion. ET value of uncultivated land (i.e., catchment) in

irrigated condition was 86% of the rainfed condition. In

cultivated land, R and ET values in irrigated condition

were higher than the rainfed condition. For example, R

y = 4E-06x

-0.0006x

+ 0.0377x -0.1228

R² = 0.9839y = -3E-06x

+ 0.0028x

-0.9952x + 126.7

R² = 0.9403

050100 150 200 250300 350 400

Interpolated precipitaiton (mm)

Precipitation (mm)

Julian days

Rising limb of rainfallFalling limb of rainfall

Interpolated precipitaitonPoly. (Rising limb of rainfall)

Poly. (Falling limb of rainfall)

050100 150 200 250300 350 400

Potential Evapotranspiration (mm)

Julian Days

100

140

180

020406080100 120 140 160

Precipitation (mm)

Soil moisture (mm)

Julian Days

Supplimental irrigationSoil moisture with irrigation

Soil moisture with rainfedPrecipitation

P. K. Pandey, S. Biswas / Journal of Agricultural Chemistry and Environment 3 (2014) 16-19

Table 1. Water balance parameters of catchment area, culti-

vated land, and crop yields.

Conditions

Parameters

AMe

(mm) ETa

(mm) Ya/Ym Ya

(kg/ha) TS

(mm) GW

(mm)

Irr. 90.2 268.7 1.0 6000 128 73.53

Rain. 33.2 130.5 0.36 2141 0.0 130.48

Parameters

OWUE

(kg/m3)

UA CA

R (m3/yr) ET (m3/yr)

Irr. 2.98 28101 21588 5907 7842

Rain. 1.64 33060 25397 5498 6298

**Note: Irr. = irrigated; rain. = rainfed; AMe = availab le moistur e at the end

of croppi ng seas on 1; Ya = actual cr op yiel d; Y m = maximum crop yield; TS

= total supplemental irrigation applied; GW = green water use; OWUE =

overall water use efficiency; R = recharge; ET = Evapotranspiration; UA =

Uncultivated land area (catchment area of 5 ha); CA = Catchment area

(cultivated land area of 1 ha).

value in rainfed condition was 93% of the irrigated con-

dition, and ET value in rainfed condition was approx-

imately 80% of the irrigated condition. In summary, the

results of the study showed that rainwater harvesting

approach can be an effective alternative for enhancing

agricultural water availability in the rainfed regions.

5. CONCLUSION

A water balance model was used to estimate the im-

pacts of rainwater harvesting appr o ach on enhancing

rainfed crop land soil moistures and crop yield for a

southwestern district of Bangladesh. The model esti-

mated rainwater storages in water storage tank (designed

in the farm land). The model uses algorithms to estimate

the water requirement of the crop land as well as water

availability in the tanks. This decision making allows

model to estimate the supplemental irrigation require-

ment in the crop land as well as supplemental irrigation

availability in the tanks. There sults showed th at the r ain-

water harvesting approach presented here increased crop

yield considerably in the studied rainfed region of the

Bangladesh. The model requires four major parameters:

precipitation, temperature, evaporation, and evapotrans-

piration. To run the model, daily input data are required.

In this study, daily data were estimated from the availa-

ble monthly data and used to feed the model. We antic-

ipate that the availability of daily observed data will im-

prove the model predictions significantly, therefore, fur-

ther studies utilizing the daily observed data for predict-

ing supplemental irriga tion, soil moisture, and crop yield

will be necessary. We suggest future studies utilizing the

climate data of multiple locations (rainfed) to enhance

the model as well as model predictions.

ACKNOWLEDGEMENTS

We than k to Dr. Pieter van der Zaag, Professor, UNESCO-IHE Insti-

tute for Water Education, Delft, Netherlands, Water Resources Section,

Delft University of Technology, Delft, Netherlands for his help in mod-

el development, which is already published elsewhere [8].

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