MATHEMATICAL RELATIONSHIP BETWEEN SOIL MOISTURE AND GROUNDWATER LEVEL IN A LOAMY SAND SOIL IN THE NIGER DELTA REGION OF NIGERIA

Transcription

1 INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 ISSN (Print) ISSN (Online) Volume 5, Issue 12, December (2014), pp IAEME: IJARET.asp Journal Impact Factor (2014): (Calculated by GISI) IJARET I A E M E MATHEMATICAL RELATIONSHIP BETWEEN SOIL MOISTURE AND GROUNDWATER LEVEL IN A LOAMY SAND SOIL IN THE NIGER DELTA REGION OF NIGERIA Fubara-Manuel, I 1, Otoko, R.G 2 1 Department of Agricultural and Environmental Engineering 2 Department of Civil Engineering, Rivers State University of Science and Technology, Port Harcourt, Nigeria ABSTRACT Agriculture, which used to be the mainstay of the economy of the Niger Delta region of Nigeria, was abandoned because of the discovery of oil in the 1950s. Oil exploration has, however, left a trail of sorrow as a result of environmental pollution. There is now a new awakening in the realization that oil is not a renewable resource and, hence, effort needs to be diverted to revitalize the agricultural sector. Farmers in this region rely entirely on rain-fed cropping. Since this area is characterized by distinct dry and wet seasons, cropping activities are scewed towards the wet season, thus resulting in subsistence level of production. Supplemental irrigation has been advocated as a way forward, but the soil moisture status dictates when to irrigate, and how much irrigation water to apply. This study therefore investigated the mathematical relationship between soil moisture and groundwater level in the Niger Delta region of Nigeria. Soil moisture contents and groundwater levels from shallow wells were measured on a bi-monthly basis in The data were then subjected to regression analysis. The result showed a simple linear relationship between soil moisture and groundwater level. With this regression equation, it is now possible for farmers in this region to determine the moisture status of the soil just by measuring the level of water in their shallow wells. This is an invaluable tool in irrigation scheduling. Keywords: Agriculture, Oil Exploration, Environmental Pollution, Soil Moisture, Groundwater, Regression Analysis. 1

2 INTRODUCTION Crop production is largely dependent on soil, availability of soil moisture, and other inputs that include the materials for cultivation. The soil is the repository of the moisture which the plant requires to grow. However, it is the rainfall that replenishes the soil. When rain falls, part of it infiltrates into the soil and can also percolate through the root zone to join the groundwater. This process, of course, depends partly on the moisture status of the soil which can be depleted by evaporation, transpiration and other plant processes that require water. In general, therefore, the variation of soil moisture is the integrated result of many factors such as precipitation, plant transpiration, soil evaporation, surface runoff, and underground percolation, etc. (Ma et al; 2011). Plants take up water from the soil through their roots even though the rooting characteristics vary genetically. Plants may however be limited in their rooting by factors other than genetics. High water table, shallow soils, and impermeable formation near the ground surface resulting from compaction by agricultural machines can restrain the extent of plant root formation. Apart from its importance in agriculture, soil moisture also plays a vital role in climatic studies. According to Chen and Hu (2004), temporal and spatial variations of soil moisture are receiving increasing attention in climate studies because soil moisture is an essential element in processes that drive land surface water and energy fluxes, which affect ecosystem dynamics and biogeochemical cycles in the land-atmosphere system. Chen and Hu (2004) also aver that soil moisture variation in shallow ground water areas behave very differently from those in areas with deep groundwater table. These conditions are similar to humid tropical climates that have distinct wet and dry seasons. During the wet season, the moisture content is high, with shallow groundwater table. When rain stops during this period, the high moisture content is largely attributable to the influence of groundwater. However, in the dry season, the moisture content is low and the deep groundwater contributes little or nothing to the moisture content depending, of course, on the depth of soil from the surface. For different soils, the influence of water table on soil moisture is also different. (Miguez-Macho et al., 2008). Lo and Famiglietti (2011) also pointed out that several studies have established that soil moisture increases after adding a groundwater component in land surface models, owing to the additional supply of subsurface water. Farmers in the Niger Delta region of Nigeria, which is in the humid tropic, are confronted with myriads of problems which include lack of genuine government support, soil pollution by oil companies, fragmented farm land holdings, high illiteracy level, and total dependence on rain-fed agriculture. The transition from rain-fed to irrigated agriculture has been advocated as one panacea for evolving from the present subsistence level of crop production (Fubara-Manuel, 2005). However, one major variable that dictates when to irrigate, and how much water to apply, is the soil moisture status. In the less developed countries of the tropics, labour is abundant and cheap. Farmers can therefore provide hand dug wells or shallow wells (< 10m) in their farms. It is however more difficult and costly to access soil-testing laboratories that will enable them determine the soil moisture status. This assertion is supported by Saxton and Rawls (2006). The main objective of this study, therefore, was to use linear regression analysis to relate soil moisture with groundwater level with a view to enabling a farmer determine the moisture content of a soil by measuring the water in his well. MATERIALS AND METHODS Theory When a soil is below field capacity and surplus rainfall collects on the surface, the water crosses the interface into the ground at an initial rate dependent on the existing soil moisture content. 2

3 As the rainfall supply continues, the rate of infiltration decreases as the soil becomes wetter and less able to take up water (Shaw, 1983). Thus, the capacity of any soil to absorb rain water, falling continuously at an excessive rater, goes on decreasing with time until infiltration is reached at the minimum time, since infiltration is a function of time (Surech, 2006) Groundwater occurrence is chiefly the result of infiltration from the soil and from streams and lakes, all of which receive their supply of water from precipitation. However, Kumar (1995) posits that of all the factors controlling groundwater recharge, the antecedent soil moisture regime probably is the most important. Soil moisture and groundwater variations can therefore be effectively analyzed by water balance. The water balance equation is generally derived from the conservation of mass principle which can be stated as Where Inflow outflow = S (1) Inflow, outflow = total flow into and out of the area during the time interval being considered S = change in soil moisture within the area during the time being considered. de Ridder and Boonstra (1994) derived three water balance equations, one each for the unsaturated zone, land surface, and saturated zone (groundwater), and then integrated these equations to arrive at an overall water balance for the area, given as P - E o - E = + + (2) Where P = Precipitation for the time interval, t E o = evaporation from the land surface E = rate of evaporation from the unsaturated zone Q si = Lateral inflow of surface water into the water balance area Q so = Lateral outflow of surface water from the water balance area A = Water balance area Q gi = Total rate of groundwater inflow into the shallow unconfined aquifer. Q go = Total rate of groundwater outflow from the shallow unconfined aquifer W u = Change in soil water storage in the unsaturated zone during the computation interval of an equivalent layer of water. W s = Change in surface water storage µ = Specific yield or effective porosity, as a fraction of the volume of soil h = Rise or fall of the water table during the computation interval. Rennolls et al. (1980) formulated a first-order autoregressive model to describe the response of water level in a borehole to a series of rainfall events. The mathematical model is of the form: = λ (3) 3

4 and = + (4) where = actual water-table level λ = a constant (drainage factor) = a factor depending on drainage pore space (constant) x t = rainfall y t = measured water-table level in the borehole. (subscript t indicates on day t ) e t = the difference between the actual water-table level and measured level in the borehole. Viswanathan (1983) observed that two implied assumptions in the model by Rennolls et al. (1980) were too restrictive. The assumptions were that (i) λ and, which represent the state of the aquifer (including soil conditions) are constant, and (2) there is no time lag between the rainfall and water-table level. The model was therefore modified by assuming that (1) the aquifer parameters are time dependant, and (2) the water-table level rise on day t is a function of rainfall on days t, t-1, t- 2, etc Viswanathan s model is of the form: h = λ t h t-1 + o, t R t + 1,t R t-1 + 2,t R t-2 + 8,t R t-8 + β t (5) and h t = h + t (6) where h = estimated water level in the borehole above certain datum level (m) h t = measured water level in the borehole (m) R t = rainfall (m) = error between estimated and actual water-table levels (m) t Subscript t indicates day t, while t-1, t-2 indicate days t-1, t-2, etc. λ t, o,t, 1,t 8,t, β t are model parameters considered as time-dependent variables. is assumed to have the following properties t (i) [ ] = 0 (ii),., =., Where, = 1 (m = n), = 0 (m n) (iii) is dependent of h t-1, R t, R t-1, R t-8 (7) Where E [ ] is the expected value From the data obtained, the model was able to assert that most of the recharge due to rainfall usually takes places within the first two days of the event. After two days, there is very little 4

5 variation in the water-table level due to rainfall although there is a slight drop on the third, fourth and fifth day, presumably due to the escape of entrapped air beneath the water-table level. Description of the Study Area The study was carried out at the Research Farm of the Rivers State University of Science and Technology, Port Harcourt, in the Niger Delta Region of Nigeria. Port Harcourt is characterized by a humid tropical climate with a mean annual rainfall of about 2100mm, and temperature that varies between 24 and 30 0 C. The soil type is ultisol (USDA Classification) and its texture is loamy sand. The experiment was conducted in a field, 50m x 50m that had been kept fallow for about six months. It was divided into grids, 5m x 5m with two shallow wells dug 25m apart at the centre of each 25m x 25m area. The upper part of each well was cemented, and 0.3m above ground level to prevent entry of surface runoff. Data Collection The moisture content of the soil was determined by the gravimetric method. Soil samples from each grid square was collected at random at a depth of 0.5m in air-tight polythene bags and taken to the laboratory for analysis. In order to avoid depletion of the soil to an intolerable level, samples were collected on a bi-monthly basis. This was done every second and last week of each month, between January and December, Groundwater level measurements were made with water tapes, while rainfall data were obtained from the Nigerian Meteorological station. RESULTS AND DISCUSSION Figure 1 presents the rainfall trend in the last ten years from 2004 to Fig.1: Annual rainfall trend ( ) 5

6 The figure indicates that rainfall increased from 2004 to 2007, but drastically decreased in There was another increase in 2009 but this was followed by a continuous decline from 2010 to Although the rainfall increased again in 2012, there was a further decrease in This trend does not however conform with the findings of Mbajiorgu (2013). According to this scientist, the effects of global warming phenomenon on the terrestrial water balance include (i) higher rainfall intensities i.e heavier rainfalls, and (ii) higher frequency of great floods with increasing trends in runoff in humid areas. It is therefore obvious that these effects are yet to fully manifest in our study area. Mbajiorgu (2013) further posits that climate change is expected to impact water resources in Nigeria and Africa through a decrease in both quality and quantity, threatening rain-fed agriculture. This is particularly evident in the Niger Delta region of Nigeria, where farmers rely entirely on rainfed cropping. Hitherto, farmers used to commence planting in late February or early March. However, in recent times, farmers have been compelled to shift the planting period to April or May because of insufficient soil moisture in February and March. Figure 2 corroborates this assertion. The figure shows that the minimum rainfall in 2013 occurred Fig. 2: Monthly Average Rainfall, 2013 between August and March, although there was heavy rainfall in October. The most critical periods were December and February, usually the driest period. Cline (2008) observes that beyond a certain range of temperatures, warming tends to reduce yields because crops speed through their 6

7 development, producing less grain in the process. Furthermore, high temperatures also interfere with the ability of plants to get and use the water. On the whole, Figure 2 suggests that without irrigation, no effective crop growth could have taken place between November and March under rain-fed condition, taking into consideration the high surface runoff usually associated with rainfall in this region. Fubara-Manuel (2005) also observed that in July 2003 and 2004, supplemental irrigation was needed by maize for effective growth, even though July always witnessed one of the heaviest rainfall. Superimposed on Figure 3, the regression curve, is the regression equation which shows that the mathematical relationship between soil moisture and groundwater level is of the form: Where Y = X (7) Y = soil moisture content (%) X = groundwater level from the surface. Fig. 3: Groundwater vs Soil moisture 7

8 The general trend is that soil moisture content is inversely proportional to groundwater level measured from the ground surface. The coefficient of retardance R 2 is This indicates that about 86% of the variation in soil moisture is explained or accounted for by variation in groundwater level. This high value of R 2 is desirable for forecasting purposes because the higher the value of R 2, the smaller the value of the standard error of estimate. REFERENCES [1] Chen, X., and Hu, Q, Groundwater influences on soil moisture and surface evaporation. Journal of Hydrology 297, [2] Cline, R.W., Global warming and agriculture. Journal of Economic literature 46(2), [3] de Ridder, A.N., and Boonstra, J., Analysis of water balances. In: Ritzema, H.P (Ed.), Drainage principles and applications, Publication 16, p International Institute for Land Reclamation and Improvement (ILIRI), Wageningen, the Netherlands. [4] Fubara- Manuel, I., Scheduling irrigation in an area transiting from rain-fed to irrigated agriculture using water balance approach. Proceedings of the 6 th International Conference of the Nigeria Institution of Agriculture Engineers, Yenagoa, Vol.27, [5] Kumar, P.U., Estimation of groundwater recharge using soil moisture balance approach. Journal of Indian Water Resources Society, 1 (2), [6] Lo, M.-H., and Famiglietti, S.J., Precipitation response to land subsurface hydrologic processes in atmospheric general circulation model simulations, J. Geophys. Res., 116, D05107, doi: /2010jd (htt://dx.doi.org/ /2010jd [7] Mbajiorgu, C.C., Effects of climate change on water resources. Proceedings of the 5 th National Conference of the Nigerian Association of Hydrological Sciences, Nsukka, Vol. 5, [8] Rennolls, K., Carnell, R., and Tee, V., A descriptive model of the relationship between rainfall and soil water table. Journal of Hydrology 47 (1), [9] Saxton, K.E., and Rawls, J.W Soil water characteristics estimates by texture and organic matter for hydrologic solutions. Soil Sci. Soc. Am. J. 70, [10] Shaw, M.E., Hydrology in practice. Van Nostrand Reinhold (UK) Co. Ltd. England. [11] Suresh, R., Soil and water conservation engineering. Standard Publishers Distributors, Delhi [12] Viswanathan, N.M., The rainfall/water-table level relationship of an unconfined acquifer. Ground water 21(1), [13] Ma, X., Chen, Y., Zhu, C., and Li, W., The variation in soil moisture and the appropriate groundwater table for desert riparian forest along the lower Tarim River. Journal of Geographical Sciences 21 (1), [14] Sunil Ajmera and Dr. Rakesh Kumar Shrivastava, Water use Management Considering Single and Dual Crop Coefficient Concept Under an Irrigation Project: A Case Study, International Journal of Civil Engineering & Technology (IJCIET), Volume 4, Issue 4, 2013, pp , ISSN Print: , ISSN Online: [15] Safayat Ali Shaikh, Optimal Cropping Pattern in an Irrigation Project, International Journal of Civil Engineering & Technology (IJCIET), Volume 4, Issue 5, 2013, pp , ISSN Print: , ISSN Online: