J. Mt. Sci. (2012) 9: 256–261 DOI: 10.1007/s11629-012-2186-z

Water Losses in Arid and Semi-Arid Zone: Evaporation, Evapotranspiration and Seepage MUPENZI Jean de la Paix 1, 2, 3, 4*, LI Lanhai 1, GE Jiwen 2, NGAMIJE Jean 3, ACHAL Verenyam 1, HABIYAREMYE Gabriel 3; HABUMUGISHA Jean de Dieu 4

1 State Key laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, 818 Beijing road south, Urumqi, Xinjiang, 830011, China 2 China University of Geosciences (Wuhan), 388 Lumo Road, Wuhan 430074, Hubei, China 3 Independent Institute of Lay Adventists of Kigali (INILAK) P O Box 6392, Kigali-Rwanda 4 Institut Polytechnique de Byumba (IPB), PO Box 25 Byumba, Rwanda *Corresponding author, e-mail: [email protected]

© Science Press and Institute of Mountain Hazards and Environment, CAS and Springer-Verlag Berlin Heidelberg 2012

Abstract: The primary purpose of this study was to assess water losses by evapotranspiration, evaporation and seepage in arid zone. Normally, evaporation and seepage are the main causes of water losses. For modeling water losses, a combination of Genetic Programming (GP), Penman-Monteith (PM) and Penman combination model for measurement of evapotranspiration, evaporation and seepage has been developed. The results were found to be varying depending on how the evaporation and seepage phenomena are modeled. These results show that that there is an improvement in reducing evapotranspiration, evaporation and seepage losses in arid and semi-arid region. Keywords: Arid zone; Evaporation; Evapotranspiration; Seepage; Water losses; China

Introduction Water losses is a big problem throughout the world. Irrigation and industries are the main consumers of water. In the arid and semi-arid regions of the world, attention is being focused on Received: 20 May 2011 Accepted: 8 December 2011

256

the search for new methods to conserve existing water supplies. Studies have revealed that evaporation ponds for the disposal of saline drainage water are used most extensively in the Aral Sea Basin; most ponds are large and located outside irrigation perimeters (Shiau and Lee 2005; FAO 1997). Water stored on the surface, in lakes or reservoirs is subject to loss by seepage and evaporation. Evaporation is an essential part of the water cycle and it is the process by which liquid water moves from soil, lake or river in form of vapor to the atmosphere. It has been shown that evaporation from the oceans accounts for more than 75% of the water delivered as precipitation, with the balance occurring on land, inland waters and plant surfaces (Armienta and Rodriguez 1995). (Armienta and Rodriguez 1995; Shiff 1961). Various studies have discussed the problem of evaporation with suggestions that the rate of evaporation depends on the temperature difference between the evaporating surface and the air (Armienta and Rodriguez 1995), the relative humidity, and wind (Armienta and Rodriguez 1995; Shiff 1961). It also revealed that the amount of water lost to evaporation from storages depends on many factors including atmospheric evaporative demand

J. Mt. Sci. (2012) 9: 256–261

(Deutscher 1960), the size of the water storage and storage method (Schulman 1957). The water is not lost only by evaporation but also by seepage (Woolley 1962). Studies revealed that “during the rainstorm water soaked into the ground in the hill above the driveway (Yang et al. 2006). In Agriculture, water like other main factors affects crop growth and it is eventually reflected by biomass collection and yields (Yang et al. 2003; Yang et al. 2006). Water is used differently according to the purpose and technique that is adopted. Several studies showed that crop root and shoot biomass collection are dependent on the nutrient absorption (Liu and Tian 2007; Liu et al. 2008; Yang et al. 2008; Chai et al. 2005). Several studies have tried to describe the seepage showing that the flow rate of a seeping liquid or gases is usually given by the relation Q = kShw/L, where k is the empirical coefficient of infiltration, S is the total cross-sectional area of the seepage that includes the cross sections of both pores and solid particles, and hw is the head loss over the length L of the seepage path (Shchelkachev and Lapuk 1949; Harboe et al. 1994). The infiltration velocity is given by Darcy’s law: W = kl, where hw/L = I is the pressure gradient. In many cases, water losses by seepage from reservoirs also contribute significantly (Aravin and Numerov 1953; Shchelkachev and Lapuk 1949; Harboe et al. 1994). In the water balance of reservoir system, evaporation plays a crucial role (Wegner 1999). To assess water losses, many studies used mean monthly evaporation rate and established relationship of seepage as a function of reservoir storage level in the reservoir water balance equation where they included the spill tank (Shiau and Lee 2005; Ganji et al. 2008; Harboe et al. 1994; Sun et al. 1996). Many other methods have been used trying to resolve this problem; among them are for example; water budget methods, energy budget methods (Makdessi et al. 2005; Fritschen 1966), mass transfer methods. These studies have combined different methods (Penman 1948; Mosner and Aulenbach 2003; Jensen et al. 1990) but the problem of water loss persists. The main goal of this study was to combine the GP, PME (Penman–Monteith evapotranspiration) and Penman Combination model for measurement of evaporation-seepage in arid zone where climate change influences the increase of the evaporation

and seepage phenomena. It was done with specific goal to suggest a possible method for using calibrated data for the variety of land covers of interest to water managers especially in arid zones which sometimes are abandoned by the scientists and researchers because of their inaccessibility.

1

Study Area

The Datong basin is one of the Cenozoic basins of the Shanxi Rift System (Wang et al. 2001); it is located in an arid/semi-arid region of Northwestern China, in the Northeastern part of Shanxi Province. Several pollutant and materials leading to serious endemic waterborne diseases have been diagnosed in patients in Datong basin due to long-term intake of high concentration of materials in groundwater from seepage in early 1990s (Xie et al., 2008). Datong basin is edged by mountains in the southeast and the northwest. The bedrocks in the east are mostly metamorphic, volcanic and sedimentary rocks, including gneiss and basalt of the Hengshan Segment of Wutai Group of Archean. In the west, there are mostly Cambrian and Ordovician limestone and Carboniferous, and Permian sandstone and shale. Evolution of Datong Basin began in the early Pleistocene, and the aquifers that comprise sediments deposited are mainly alluvial–lacustrine silty sands and silts rich in organic material (containing up to 1.0% organic carbon) (Guo et al. 2003; Wang et al. 2004; Xie et al. 2009). All these elements are the sources of water losses in this region. Moreover, the irrigation is the most consumer of water where the used water is estimated to 20,000 m3 by month in dry season.

2

Method

This research work used Genetic programming (GP) and Penman model for measurement of evaporation-seepage (Discipulus 1998; Khu et al. 2001; Koza 1992; Babovic and Keijzer 2000; Sivapragasam et al. 2006). The following formula was used for calculation:

Et = f (ht-1 , SAt-1 , Tt-24 , RHt-24 , Nt-24 , Vt-24 ) (1) where, Et = evaporation losses at time t (mm3),

257

J. Mt. Sci. (2012) 9: 256–261

h(t−1)= reservoir storage depth one fortnight before (m), SA(t−1) =surface area of reservoir one fortnight before (m2), T(t−24)= temperature at the same fortnight 1 year before (◦C), RH(t−24)= relative humidity at the same fortnight 1 year before t (%), N(t−24)= sunshine hour at the same fortnight 1 year before (hours/day), V(t−24) =wind velocity at the same fortnight 1 year before (kmph). Penman Combination Model was used for evaporation loss measurements according the following Formula:

E=

AHn+ Eay A+ y

(2)

where, E = daily evapotraspiration in mm per day, A =slope of the saturation vapour pressure Vs temperature at the mean air temperature, Hn = net radiation in mm of evaporable water per day, Ea = parameter including wind velocity and saturation deficit, γ = psychrometric constant = 0.49 mm of Hg/◦C. Here Ea is estimated and calculated as:

Ea = 0.35(1 +

u2 ) - (ew − ea) 160

(3)

where, u2 =mean wind speed at 2 m above ground in km/day, ew =saturation vapour pressure at mean air temperature in mm of Hg, ea =actual vapour pressure, as already defined. For estimating evaporation from a water surface, it used the Evaporation-Seepage Model for Datong Basin where evaporation-seepage loss model is found by comparison of actual and predicted evaporation-seepage loss using the following formula:

Et = (0.081h3t -24 , T 4 t -24 , Vt-24 )/ ( RHt-24 ) 4 ( Nt-24 ) 2

(4)

Calculation of Evaporation-Seepage Model was based on the following formula (Jensen et al. 1990; Walter et al. 2000):

Et = 0.03[SAt -1 (SAt -1 - 2h t -1 ) + RHt -24 (RHt-24 - 2h t -1 )] + h t -1 Nt-24 × [0.2 − h 2

t −1

+ SA2 t −1

(5)

+ 0.5RH 2 t −24 ) + 0.1ht −1 RHt −24 − 0.5ht −1 + 0.18SAt −1 + 0.07]Vt −24 For the estimated net radiation, the following equation was applied:

258

Hn = Ha(1 − r 0(a − b

n )− N|

σT 4 a (0.56 − 0.092 ea )(0.10 + 0.90

(6) n ) N

where, Ha =incident solar radiation outside the atmosphere on a horizontal surface, a= constant depending upon the latitude φ and is given by a = 0.29 cos φ, r =albedo, b =a constant, n =actual duration of bright sunshine in hours, N= maximum possible hours of bright sunshine (it’s a function of latitude), Ta =mean air temperature in degrees Kelvin, σ= Stefan–Boltzman constant, ea =actual mean vapor pressure in the air in mm of Hg Penman–Monteith evapotranspiration (PM) This study used the PM equation which, is a generalization of the Penman equations that allows for a composited plant stomata resistance to vapor transport that is specified through a bulk surface resistance , it was used according various studies which tried to investigate its applicability (Penman 1948; Monteith 1965; Ritchie 1972; Arnold et al. 1998; Neitsch 2005; Jensen et al. 1990; Walter et al. 2000; Allen et al. 1998) using the following formula: < (Rn − G ) ET 0 = + N < r ( 1 + Cd μ 2 ) | (7) Cn r Ta + 273 μ 2 ) < + r ( 1 + Cd μ 2 where, ETo = standardized grass reference evapotranspiration (mm h-1), Δ = slope of saturation vapor pressure curve (kPa °C-1) at mean air temperature (T) , Rn = net radiation (MJ m-2 h1) , G = soil heat flux density (MJ m-2 h-1), γ = psychrometric constant ( kPa°C-1), Ta = mean hourly air temperature ( °C), U2 = wind speed at 2 meters (m s-1), es = saturation vapor pressure (kPa) at the mean hourly air temperature (T) in °C, ea = actual vapor pressure (kPa) at the mean hourly air temperature (T) in °C , lamda = latent heat of vaporization in (MJ kg-1), Cd = bulk surface resistance and aerodynamic resistance coefficient. When Rn>0 (day time) and When Rn <= 0 (night time) Soil heat flux ( MJm

−2

h −1 ) : G = 0 .1R n

(8)

Soil heat flux ( MJm

−2

h −1 ) : G = 0 .5 R n

(9)

Cn = numerator constant that changes with

J. Mt. Sci. (2012) 9: 256–261

Table 1 Analysis of actual and predicted EvaporationSeepage Loss (ESL) in Datong Basin (mm3) No.

Months

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Jan-1999 Feb-1999 Mar-1999 Apr-1999 May-1999 Jun-1999 Jul-1999 Aug-1999 Sep-1999 Oct-1999 Nov-1999 Dec-1999 Jan-2000 Feb-2000

Actual ESL 0.011 0.041 0.128 0.211 0.301 0.286 0.242 0.235 0.181 0.111 0.043 0.019 0.012 0.044

Predicted ESL GP Penman 0.015 0.018 0.045 0.017 0.075 0.016 0.076 0.009 0.061 0.012 0.042 0.008 0.052 0.007 0.046 0.012 0.0032 0.018 0.052 0.019 0.009 0.11 0.011 0.009 0.014 0.017 0.047 0.019

reference surface and calculation time step (For daily time steps in this case, the constants Cn and Cd are 900 and 0.96, respectively) mm s3 Mg−1 d−1 for 24 h time steps, and 37°C mm s3 Mg−1 h−1 for hourly time steps for the grass-reference surface)

3

Results and Discussions

With references to various studies carried out in many countries, the analysis of precipitation in this region as indicated in Table 1, Figure 1 and Figure 2 showed that the high amount are identified from May to September where August

Figure 1 Distribution of actual and predicted evaporation-seepage losses in Datong Basin

and July count 380.0 mm and 356.0 respectively in 1999. Precipitation is very low from November every year to the end of March of the following year. Air temperature, wind speed, vapor-pressure deficit, LAI, incoming solar radiation, and soil moisture were considered as explanatory variables for this relation where Potential evapotranspiration (PET) was estimated by applying PenmanMonteith Method recommended by FAO with climatic data from 2 stations during considering period from January 1999 to February 2000 in Northwest China. The spatial and temporal variations of the potential evapotranspiration in Shanxi Province are analyzed. It shows that the whole potential evapotranspiration has decreased. The average monthly evapotranspiration rate was irregular as indicated in Table 2 and Figure 3. High decreases of PET rates are more pronounced during spring and summer comparatively to winter and autumn. This may be due to the vegetation coefficient in arid and semi-arid zones which decreased progressively causing the poor evapotranspiration. Since the solar radiation is very poor during winter, evaporation-seepage may be higher than evapotranspiration and that is why the application of constant pan coefficient produced a very poor correlation (R2=0.36) using 14 measured values considering only the average monthly as indicated in all tables. The base determinant of measured ET0 is incoming solar radiation, which is unaffected by the local surface cover. Note that the precipitation recorded at Datong Basin of Shanxi

Figure 2 Cumulative monthly precipitation from January 1999 to February 2000

259

J. Mt. Sci. (2012) 9: 256–261

Table 2 Average monthly values of energy fluxes and ration (January 1999 to February 2000) Month

ISR

NR

Jan-1999 Feb-1999 Mar-1999 Apr-1999 May-1999 Jun-1999 Jul-1999 Aug-999 Sep-1999 Oct-1999 Nov1999 Dec-1999 Jan-2000 Feb-2000

112 116 138 152 156 272 298 299 240 198 136 120 114 116

88 91 96 101 140 180 186 188 140 120 87 85 72 61

Latent HF 52 46 36 34 78 79 101 104 102 69 52 46 31 35

Sensible HF 48 39 56 52 48 61 56 38 52 46 57 43 42 51

Soil HF -4 -8 -7 -3 -1 3 4 4 2 -3 -4 -2 -3 -6

Bowen Ratio 0.73 1.26 1.12 1.15 1.08 1.17 0.86 0.52 0.46 1.01 1.12 1.52 1.21 1.13

Sunshine hrs 108.3 196.3 217.3 223.1 307.2 317.6 259 317.1 283.5 279.6 163.7 177.5 145 213.8

Sunshine hrs/day 202.6 204.7 246.6 268 299 245.1 243 274.8 238.6 262.6 193 161 182.4 225.4

EF 0.011 0.041 0.128 0.211 0.301 0.286 0.242 0.235 0.181 0.111 0.043 0.019 0.012 0.044

Note: ISR=Incoming solar radiation; NR=Net radiation; HF=Heat flux; EF=Evaporative fraction

programming (GP) plays role in modeling the evaporation process. The performance of the GP model is compared with Penman combination model and the traditional Penman-Monteith (PM) method. This study has proved that a combination of GP, PM method and the Penman combination model performed better than individual models. Therefore, this combination appears to be a promising tool for modeling the evapotranspiration and evaporation-seepage process to help in arid zone.

Acknowledgements Figure 3 Analysed monthly evapotranspiration rate in Datong Basin from January 1999 to February 2000

Province in Northwest China was irregular through different seasons with precipitation in mm below the long-term average of about 1807.3 mm in 1999; but an annual average precipitation for long term in this region is estimated to be 2343.8 mm. Briefly the analysis of these different method reveals that evaporation-seepage losses are very high than evapotranspiration losses in arid zone.

4

Conclusions

This study was intended to assess the statement of water losses in arid zone. Evapotranspiration is one of the major components of the hydrological cycle. It showed that the genetic 260

This research was funded by the State Key Development Program for Basic Research of China (973 program, Grant No. 2010CB951002), National Natural Science Funds of China (Grant No. 40972218), the Natural Sciences Foundation of China (Grant No. 40871027) and the Knowledge Innovation project of Chinese Academy of science (Grant No. KZCX2–YW–334).

References Allen RG, Periera LS, Raes D, Smith M (1998) Crop Evapotranspiration: Guidelines for Computing Crop Requirements, Irrigation and Drainage Paper No. 56. FAO, Rome, Italy. p 300. Armienta H M A, Rodriguez C R (1995) Environmental exposure to chromium compounds in the Valley of Leon, Mexico. Environmental Health Perspectives103: 47-51. Aravin VI, Numerov SN (1953) Teoriia dvizheniia zhidkostei i gazov v nedeformiruemoiporistoisrede; Moscow. p 132 (Rusian). Arnold JG, Sirinivasan R, Muttiah RS, Williams JR (1998) Large

J. Mt. Sci. (2012) 9: 256–261

area hydrologic modeling and assessment, Part 1: Model development, Journal of The American Water Resources Assocociation, 34(1):73-89 Babovic V, Keijzer M (2000) Rainfall runoff modeling based on genetic programming. Nord Hydrology. 33:331-346. Chai FJ, Li GY, Zhang Q (2005) Study on effects of irrigation frequency on moisture and salt regime and growth of cotton under mulch-film drip irrigation. Journal of Irrigation and Drainage 24(3): 12-15. Deutschearu Sschusfsu R, Grenzflachenaktsitovfef E (1960) Original lectures 3rd. int.congress of surface activity. Mainz, Universitätsdruckerei, vol 4. Discipulus owner’s manual (1998). RML Technologies: Littleton Co. http://www.aimlearning.com [accessed on May 2010 ] FAO (1997) Management of Agricultural Drainage Water Quality. Water Report 13. FAO, Rome Fritschen LJ (1966B) Energy balance method. Paper presented at the american society of agricultural engineers conference on evapo-transpiration and its role in water resources management, Chicago, IL. St. Joseph, MI, pp 34-37 Ganji A, Khalili D, Karamouz M, Ponnambalam K, Javan M (2008) A fuzzy stochastic dynamic nash game analysis of policies for managing water allocation in a reservoir system. Water Research and Management 22:51-66. DOI:10.1007/s11269-006-9143-y. Guo HM, Wang YX, Shpeizer GM , Yan SL (2003) Natural occurrence of arsenic in shallow groundwater, Shanyin, Datong basin, China. Journal of Environmental Science and Health, Part A: Environmental Science and Engieneering & Toxic and Hazardous Substance Control 38: 2565-2580. Harboe R, Gautam TR, Onta PR (1994) Conjunctive operation of hydroelectric and thermal power plants. J Water Resour Plan Manage 120 (6):778–793. DOI: 10.1061/(ASCE)0733-9496 (1994)120:6 (778). International Associatioofn Scientifihcy Drology (1963) General Assembly of Berkeiey, Pt. I,. Gentbrugge, Belgium (Publ. no. 62.), pp 7-71. ISGS (2010) Water pictures: Groundwater seepage. U.S. Department of the Interior | U.S. Geological Survey. URL: http://ga.water.usgs.gov/edu/gwdriveway.html [accessed on 22-Dec-2011] Jensen ME, Burman RD, Allen RG (1990) Evapotranspiration and Irrigation Water Requirements. ASCE Manuals and Reports on Engineering Practices No. 70, p 35. Khu ST, Liong SY, Babovic V, Madsen H, Muttil N (2001) Genetic programming and its application in real-time runoff forecasting. Journal ofthe American Water Resources Association. 37:439–451. DOI: 10.1111/ j.1752-1688.2001. tb00980.x Koza JR (1992) Genetic programming: on the programming of computers by natural selection. MIT Press, Cambridge, M Liu XY , Tian CY (2007) Coupling effect of water and nitrogen of cotton under plastic mulching by drip irrigation. Plant Nutrition and Fertilizer Science 13(2): 286-291 Liu RX, Guo WQ , Chen BL (2008) Physiological responses of cotton plant to fertilizer nitrogen at flowering and bollforming stages under soil drought. Chinese Journal of Applied Ecology 19(7): 1475-1482 Makdessi M, Rahman A, Kibria G (2005) Investigation of the thermodynamic component of penman’s method for estimating evaporation. In: Zerger A, Argent RM (eds) MODSIM.. Monteith JL (1965) Evaporation and environment, In: Proceedings of the 19th Symposium of the Society for Experimental Biology. Cambridge University Press, New York, NY. pp 205-233. Mosner MS, Aulenbach BT (2003) Comparisons of methods used to estimate lake evaporation for a water budget of Lake Seminole, southwestern Georgia and northwestern Florida.. In K. J. Hatcher (ed.) Proceedings of the 2003 Georgia Water Resources Conference, Institute of Ecology, The University of Georgia, Athens, GA, USA. pp 591- 594

Neitsch SL, Amold JG, Kiniry JR, Srinivasan R, Williams JR (2005) Soil and Water Assessment Tool SWAT Theory, Version 2005, USDA Agricultural Research Service and Texas A&M Blackland Research Center Temple, Texas. Penman HL (1948) Natural evaporation from open water, bare soil and grass. Proc R Soc Lond A Math Phys Sci 193(1032):120–145. DOI:10.1098/rspa.1948.0037 Penman HL (1948) Natural evaporation from open water, bare soil, and grass. Proceedings of the Royal Society, London A193. pp 120–146. Ritchie JT (1972) Model for predicting evaporation from a row crop with incomplete cover. Water Resources Research., 1204-1213 Schulman NJH (1957) Proc. 2nd Inc. Congr. of Surface Activity, London, Butterworths Scientific Publications, vol. 1, p 259280. Shchelkachev VN, Lapuk BB (1949) Podzemnaiagidravlika. Moscow-Leningrad, p 142 Shiau JT, Lee HC (2005) Derivation of optimal hedging rules for a water-supply reservoir through compromise programming. Water Resources Management 19:111–132. DOI: 10.1007/ s11269-005-1502-6 Sivapragasam C, Maheswaran R, Veena V (2006) Genetic programming approach for flood routing in natural channels. Hydrological Process 22(5):623–628. DOI:10.1002/hyp.6628 Sun YH, Yang SL, Yeh WWG, Louie PWF (1996). Modeling reservoir evaporation losses by generalized networks. Journal of Water Resoucres and Planning and Management. 122(3):222–226. DOI: 10.1061/(ASCE)0733-9496(1996)122: 3(222) Walter IA, Allen RG, Elliott R, Mecham B, Jensen ME, Itenfisu D, Howell TA, Snyder R, Brown P, Echings S, Spofford T, Hattendorf M, Cuenca RH, Right JL, Martin D (2000)ASCE standardized reference evapotranspiration equation, in: Evans, R.G., Benham, B.L., Trooien, T.P. (Eds.), Proceedings of the National Irrigation Symposium, ASAE, Nov. 14–16, Phoenix, AZ. pp 209–215. Wang R, Wang Y, Guo H (2001) Hydrogeochemistry of shallow groundwaters from the northern part of the Datong basin. In Proc. 9th Internat. Symp. Water-Rock interaction, ed. Cidu R. A.A.Balkema, Rotterdam. pp 605–608. Wang YX, Guo HM, Yan SL, Wang RF, Li YL (2004) Geochemical evolution of shallow groundwater systems and their vulnerability to contaminants: A case study at Datong Basin, Shanxi province, China. Science Press Beijing. pp 123130 Wegner D (1999) A report on evaporation and ground water seepage. In: Columbia EARTHSCAPE (An online resource on the global environment). http://www.earthscape.org /p2/hp/hp_win99/ hp_win99c.html [accessed on July 2010 ] Woolley T (1962) Soil-applied hexadecanol as an evapotranspiration suppressant. ' Journal of Soil and Water Conservation., vol. 17, no. 3, p. 130. Xie X, Wang YX, Andre DE (2008) Sources and mobilization processes of arsenic in the shallow aquifers of the Datong Basin. PhD dissertation, China Univesrity of Geosciences, Wuhan, P.R. China. p78 Xie X, Wang YX, Duan M, Liu H (2009) Sediment geochemistry and arsenic mobilization in shallow aquifers of the Datong basin, northern China. Environmental Geochemistry and Health 31: 493–502. DOI: 10.1007/s10653-008-9204-7. Yang GY, Luo YP, Li BG (2003) Effect of different soil water condition growth of root and shoot of winter wheat. Agricultural Research in the Arid Areas 21(3): 104-109 Yang GY, Luo YP , Li BG (2006) The effects of sustaining time of watering stress on root and shoot weight accumulation in winter wheat. Agricultural Research in the Arid Areas 24(4): 94-98 Yang ZB, Chen BL, Zhou ZG (2008) Effects of nitrogen application rate on spatiotemporal variability of biomass accumulation of cotton’s fruiting branch at flower and boll stage. Chinese Journal of Applied Ecology 19(10): 2215-2220.

261