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Biomass production, evapotranspiration and water use efficiency of arid rangelands in the Northern Cape, South Africa
Journal of Arid Environments 75 (2011) 1223e1227
Contents lists available at ScienceDirect
Journal of Arid Environments journal homepage: www.elsevier.com/locate/jaridenv
Short Communication
Biomass production, evapotranspiration and water use efficiency of arid rangelands in the Northern Cape, South Africa A.R. Palmer a,1, *, I.A.M. Yunusa b a b
Agricultural Research Council, Animal Production Institute, PO Box 101, Grahamstown 6140, South Africa School of Environmental and Rural Sciences, University of New England, Armidale, NSW 2351, Australia
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 July 2010 Received in revised form 4 March 2011 Accepted 18 May 2011 Available online 15 June 2011
Annual above-ground net primary production (ANPP), evapotranspiration (ET) and water use efficiency (WUE) of rangeland have the potential to provide an objective basis for establishing pricing for ecosystem services. To provide estimates of ANPP, we surveyed the biomass, estimated ET and prepared a water use efficiency for dwarf shrublands and arid savanna in the Riemvasmaak Rural Area, Northern Cape, South Africa. The annual production fraction was surveyed in 33 MODIS 1 km2 pixels and the results regressed against the MODIS fPAR product. This regression model was used to predict the standing green biomass (kg DM ha1) for 2009 (dry year). Using an approach which combines potential evapotranspiration (ET0) and the MODIS fPAR product, we estimated actual evapotranspiration (ETa). These two models (greening standing biomass and ETa) were used to calculate the annual WUE for 2009. WUE was 1.6 kg DM mm1 ha1 yr1. This value may be used to provide an estimate of ANPP in the absence of direct measurements of biomass and to provide a comparison of the water use efficiency of this rangeland with other rangeland types. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Dwarf shrubland ET0 fPAR MODIS Savanna Succulent WUE
1. Introduction Healthy rangeland ecosystems provide the basis for sustaining rural agricultural economies, and the necessity to price the value of these ecosystems and the services they provide is now widely agreed (Richmond et al., 2007; Swinton et al., 2007). Rangelands primarily provide forage to sustain domestic livestock, and the quantity and quality of forage are viewed by range managers as the most important variables that define the service provided by the rangeland. A good understanding of above-ground net primary production (ANPP) is necessary if we are to attribute a monetary value to the biomass production of an ecosystem, as the market cannot recognize the consequences of environmental degradation if the ecosystem service does not have a price (Richmond et al., 2007). This will require the development of new robust techniques for modeling and validating net primary production. The necessity to predict biomass production is an essential part of agricultural planning and models of livestock carrying capacity
* Corresponding author. Tel.: þ27 46 6222638; fax: þ27 46 6362623. E-mail address: [email protected] (A.R. Palmer). 1 Centre for African Conservation Ecology, Nelson Mandela Metropolitan University, PO Box 77000, Port Elizabeth, South Africa. 0140-1963/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.jaridenv.2011.05.009
(e.g. SPUR) estimate ANPP which may be consumed by herbivores (Wight and Skiles, 1987). These estimates make use of plant growth models which are used to predict annual increments in biomass production. Some of these models have integrated the affect of animal type, daily nutritional requirements and rangeland condition into their predictive frameworks (Richardson et al., 2007; Wight and Skiles, 1987) to provide realistic scenarios for rangeland managers. Micro-climatic variables that are necessary to estimate potential evapotranspiration (ET0) are now regularly measured at more than 550 sites across South Africa. However, ET0 on its own does not provide actual evapotranspiration (ETa), and without direct measurement of the water fluxes using eddy covariance system or scintillometry, we are obliged to derive ETa from remotely sensed products. By combining remote sensing technology, micrometeorological measurements and ground-based measurements of green biomass, we show that it is possible to achieve a more realistic estimate of WUE than one based solely on univariate models (e.g. mean annual rainfall). With the availability of data from the MODIS sensors it has also become possible to develop testable estimates of ANPP and evapotranspiration (Myneni et al., 1997). In this study we combine these three data sources from one study site to estimate ANPP. The estimates are based upon calculating the potential evapotranspiration of the vegetation during the growing
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season (Allen et al., 1998), adjusting these values to ETa using remote sensing products from the MODIS programme, and combining these with ground-based surveys of green biomass, to predict the WUE which may then be used to estimate ANPP in years without detailed biomass measurements. 2. Methods 2.1. Study areas The Riemvasmaak Rural Area (RiRA) is located in north western South Africa (28.215 S, 20 Ee28.588 S, 20.53 E), where the mean annual rainfall is low (w130 mm per annum), dry season climate is extreme (mean monthly maximum temperature >30 C), and precipitation occurs mainly in autumn. The RiRA occupies 1797 km2, and supports domestic livestock and a small wildlife population. The RiRA is situated predominantly in the Nama-karoo and savanna biomes, and receives autumn (MarcheMay) rainfall, with high inter-annual rainfall variability (MAP ¼ 132 mm; inter-annual CV ¼ 65%) (Schulze and Lynch, 2007). Mean daily maximum temperatures varies from 26 C in January to 15 C in June, but get as high as 44 C in summer and <0 C in winter. The vegetation comprises four vegetation types, namely Lower Gariep Broken Veld (Fig. 1), Bushmanland Arid Grassland, Kalahari Karroid Shrubland and Gordonia Duneveld (Rutherford and Mucina, 2006). 2.2. Modeling of green biomass Canopy cover of the vegetation was sampled using the line intercept method (Canfield, 1941). A single 100 m transect was sampled at each of 33 pre-defined sites. The location of each line transect was determined after stratifying the entire study area on the basis of MODIS fPAR values (peak of 2009 growing season only). As irrigation agriculture is practiced along the Orange River, we avoided MODIS pixels adjacent to the river which may have been affected by signal from irrigated lands. Along each 100 m transect, five plots (0.2 m 1 m) were placed immediately adjacent to the
line transect at 20 m intervals and the annual biomass fraction was collected from each 0.2 m2 plot following Flombaum and Sala (2007). Plant species in each plot were recorded, the canopy cover measured and the current years green leaves and twigs were harvested. Samples were separated and packed on the basis of growth form (grass, stemesucculent, dwarf shrubs and trees). On returning to the laboratory, the harvested plant material was ovendried at 70 C for 90 h and weighed. The length of the canopy cover (in cm) for growth forms in each plot was summed to provide a total canopy cover per plot. The technique proposed by Flombaum and Sala (2007) was un-successful in defining regression equations for cover versus biomass for each functional class. As many plots had 100% canopy cover, the mean dry mass of the harvested plants in these plots were used to estimate the annual biomass production fraction of all the intercepted species encountered along the line transects. The dry mass of all the perennial shrubs, trees and grasses were summed to get the total above-ground biomass of 1 km2 plot. This represents an estimate of the annual production fraction of the standing green biomass of the four functional groups of perennials along the entire primary production/rainfall gradient. These were related to values from the 33 MODIS fPAR pixels from the 2009 growing season, to prepare a regression equation for predicting the annual production fraction directly from fPAR. 2.3. MODIS fPAR data An algorithm is applied to surface relectance data from the MODIS sensors on board the Terra and Aqua satellites to estimate leaf area index and fPAR. This 8 day 1 km product has been available since March 2000 (Myneni et al., 2002) and provides the opportunity to use the fPAR data to model above-ground green biomass. Pre-processed MODIS imagery was acquired from the Land Processes Distributed Data Archive (Land Process DAAC National Center, EROS, Sioux Falls, South Dakota, USA) for the period from 26 March 2000 to December 2010. The MODIS LAI/fPAR algorithm uses up to 7 atm-corrected surface spectral bi-directional reflectance factors and their uncertainties, and outputs the most probable values for LAI, fPAR and their respective dispersions (Myneni et al., 2002). The fPAR values were extracted for pixels that matched the ground co-ordinates for the dates when the ground measurements of biomass production were collected. The MODIS fPAR product includes a quality flag which indicates when the primary algorithm was rejected and a back-up algorithm was used. On the sampling dates and within the 33 pixels selected for the generation of the fPAR-biomass regression model, only data from the primary algorithm were encountered. These fPAR values were used for further all estimates of biomass and actual evapotranspiration. 2.4. Evapotranspiration (ET) estimates
Fig. 1. The lower Gariep Broken Veld in the Riemvasmaak rural area.
As direct measurement of ETa was not carried out in this study, we derived ETa from the weather station data and satellite imagery. The weather station at RiemvasmaakeWitklip (28.64321 S 20.3532 E) provided hourly records of rainfall, temperature (maximum and minimum), radiation, wind speed and direction, and relative humidity. These data were used to calculate hourly potential evapotranspiration (ET0) using the modified PenmaneMonteith equation (Allen et al., 1998). Using the MODIS LAI product as a surrogate for stomatal conductance, Leuning et al. (2009) have successfully adjusted the output from the PenmaneMonteith equation to accurately reflect ETa recorded by an eddy covariance system. This approach was not possible in the Richtersveld where the primary MODIS LAI algorithm is often rejected, and we applied an alternative scaling method for ETa from ET0 and MODIS fPAR, where
A.R. Palmer, I.A.M. Yunusa / Journal of Arid Environments 75 (2011) 1223e1227
-1
Biomass (kg ha )
Green biomass v MODIS fPAR (2009) 1000 y = 20.878x - 85.036 R2 = 0.5986
800 600 400 200 0 0
10
20
30
40
MODIS fPAR Fig. 2. Relationship between above-ground annual production fraction of green biomass and MODIS fPAR for sampling sites in the Riemvasmaak rural area.
ETa ¼ ET0 ðfPAR fPAR min =fPAR max Þ
(1)
Using a maximum value compositing routine, we identified the date of maximum active greenness, as reflected by fPAR, during the period March 2000 to December 2010. This was DOY 81 in 2009. Since this is also the day of the highest leaf area index (the MODIS LAI algorithm did not fail at these high greenness levels), we
1225
assumed that this would be the highest ETa achieved by this vegetation type. This is based on functional convergence theory (Field, 1991) which hypothesizes that plants scale leaf area and light harvesting ability by the availability of resources, driven by evolutionary processes, in order to optimize their carbon fixation. The ET0 for this day, derived using the PenmaneMonteith equation, was 5.3 mm day1. This we defined as the maximum potential ETa for the vegetation type as it occurred at the time of maximum green biomass. We extracted the LAI from the MODIS LAI database for all the sample sites at which LAI could be computed during the peak of the growing season (DOY 81) in 2009 (x ¼ 0.3) and further adjusted the ETa by this value for the vegetation on that day. This gives a maximum ETa for arid savanna of 1.9 mm day1. As further independent validation, we estimated ET directly from PAR and MODIS fPAR where
ET ¼ PAR fPAR 1:55
(2)
By deriving PAR from total radiation using a co-efficient of 0.45 for clear summer sunny days (Blackburn and Proctor, 1983) (PAR ¼ 1783 mmol m2 s1), and using a mean MODIS fPAR value of 0.204 on DOY 81 in 2009, equation (2) also predicts a daily ETa of 1.9 mm day1.
Fig. 3. (a) Daily ET0 and ETa (mm day1) derived from the MODIS fPAR product during 2009 and (b) trend in standing green biomass (kg ha1) predicted by the MODIS fPAR regression model for 2009 and 2010.
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The method for calculating daily ETa from ET0 using fPAR (Equation (1)) was used in all subsequent calculations of ETa. 2.5. Biomass production model e WUE The WUE may be used to compare different landscape condition classes (le Houerou et al., 1988; Snyman, 1999) and has the potential to be used to model NPP when only the ETa is available. To calculate WUE, the annual increment in biomass (kg DM) is divided by total ETa for that year. If this approach is to be used to determine the carrying capacity of the region for livestock production, only an annual estimate of ETa is required. Using the regression relationship that predicts standing green biomass (kg DM) from MODIS fPAR, we prepared biomass trends for the period 1 January 2009e31 December 2010 for the RiRA as a proof of concept. The difference between maximum and minimum annual standing green biomass was used to determine the annual positive increment in dry matter and is termed the annual production fraction (APf). We acknowledge that this is not total ANPP, as un-quantified herbivory by domestic livestock continued to take place during the short growing season. Using ETa for 2009, we calculated annual WUE (kg DM mm1 ha1 yr1) for the period when the full meteorological record was available.
4. Discussion The prediction of ANPP using the regression equation appears to be a promising approach to biomass production modeling for arid rangelands, with ANPP being predicted to be 87% of the modeled GPP (Running et al., 2004). Deriving ETa from the MODIS fPAR and PenmaneMonteith equation has also proved moderately successful, although independent validation using another measure would improve the validity of these estimates. The WUE calculated for this rangeland (1.6 kg DM mm1 ha1 yr1) is lower than most known WUE estimates for natural vegetation. Water use efficiency defines how efficiently the individual plant or landscape uses precipitation to produce biomass (le Houerou, 1984) and it has the potential to play an important role in setting pricing for ecosystem services (Richmond et al., 2007). WUE has been equated with rangeland condition at both the landscape (Holm et al., 2003) and the paddock scale (Snyman, 1999). Implementation of payment for ecosystem services will necessitate a comprehensive understanding of the WUE of various land condition classes. We have demonstrated in this study that it is possible to approximate landscape and catchment-scale WUE using MODIS fPAR. The technique outlined in this and other papers (Palmer et al., 2010) provides us with the ability to predict annual biomass production and evapotranspiration in arid and semi-arid shrublands with the MODIS fPAR product, and to derive WUE.
3. Results Acknowledgments 3.1. MODIS fPAR v green biomass The projected green biomass per MODIS pixel for the RiRA produced the regression (Fig. 2)
APf kg DM ha1 ¼ 20:87 fPAR 85:04
r 2 ¼ 0:6
The Agricultural Research Council’s Institute for Soil Climate and Water provided the meteorological data from the SA Weather Network. Funding for the project was provided by the National Research Foundation and Red Meat Research and Development SA.
(3)
for the relationship between MODIS fPAR and the measured standing green biomass, and provides the basis for biomass growth curves (Fig. 3) for the entire area of the RiRA from January 2009 to December 2010. The maximum green biomass in 2009 was 341 kg DM ha1, and in 2010 a maximum of 290 kg DM ha1 yr1 was predicted by the model. ANPP during 2009 was 197 kg DM ha1 yr1 and in 2010 was 146 kg DM ha1 yr1. We have compared these estimates against the predictions of the MODIS GPP product (MODIS 17A2) (Running et al., 2004), and in 2009 mean GPP for all 33 sites was 106 kg C ha1 yr1 (w225 kg DM ha1 yr1 using the aboveground biomass to C conversion of 0.47) (Janssens et al., 1999). 3.2. Evapotranspiration The study area displays a very high evaporative demand in the dry season, when there is usually no precipitation, ET0 is high (maximum 9.6 mm day1), and there is very little plant growth. The maximum daily ETa in the wet season was calculated to be 1.9 mm day1after adjustment of the ET0 using scaled fPAR values, and this declines to approximately 0.2 mm day1 in the hot dry season. The model predicts total annual ETa for 2009 of 119 mm. This is 23 mm higher than the annual rainfall during 2009 (96 mm). 3.3. Water use efficiency WUE was 1.65 kg DM mm1 ha1 yr1 in 2009. This is lower than published WUE values for “sub-climax” rangeland (WUE ¼ 2.3) (Snyman, 1999) and lower than most published WUE estimates for vegetation and crops types (Palmer et al., 2010). This result may reflect the degraded state of this rangeland.
References Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Evapotranspiration e Guidelines for Computing Crop Water Requirements. FAO e Food and Agriculture Organization of the United Nations, Rome. Blackburn, W.J., Proctor, J.T.A., 1983. Estimating photosynthetically active radiation from measured solar irradiance. Solar Energy 31, 233e234. Canfield, R., 1941. Application of the line interception method in sampling range vegetation. Journal of Forestry 39, 388e394. Field, C.B., 1991. Ecological Scaling of Carbon Gain to Stress and Resource Availability. In: Mooney, H., Winner, S., Pell, E. (Eds.), Integrated Responses of Plants to Stress. Academic Press, San Diego, CA, pp. 35e65. Flombaum, P., Sala, O.E., 2007. A non-destructive and rapid method to estimate biomass and aboveground net primary production in arid environments. Journal of Arid Environments 69, 352e358. Holm, A.M., Watson, I.W., Loneragan, W.A., Adams, M.A., 2003. Loss of patch-scale heterogeneity on primary productivity and rainfall-use efficiency in Western Australia. Basic and Applied Ecology 4, 569e578. Janssens, I.A., Sampson, D.A., Cermak, J., Meiresonne, L., Riguzzi, F., Overloop, S., Ceulemans, R., 1999. Above- and belowground phytomass and carbon storage in a Belgian scots pine stand. Annals of Forest Science 56, 81e90. le Houerou, H.N., 1984. Rain use efficiency: a unifying concept in arid-land ecology. Journal of Arid Environments 7, 213e247. le Houerou, H.N., Bingham, R.L., Skerbek, W., 1988. Relationship between the variability of primary production and the variability of annual precipitation in world arid lands. Journal of Arid Environments 15, 1e18. Leuning, R., Zhang, Y.Q., Rajaud, A., Cleugh, H., Tu, K., 2009. A simple surface conductance model to estimate regional evaporation using MODIS leaf area index and the PenmaneMonteith equation. Water Resources Research 45, W10419. 2008. Myneni, R.B., Hoffman, S., Knyazikhin, Y., Privette, J.L., Glassy, J., Tian, Y., Wang, Y., Song, X., Zhang, Y., Smith, G.R., Lotsch, A., Friedl, M., Morisette, J.T., Votava, P., Nemani, R.R., Running, S.W., 2002. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment 83, 214e231. Myneni, R.B., Nemani, R.R., Running, S.W., 1997. Estimation of global leaf area index and absorbed par using radiative transfer models. IEEE Transactions on Geoscience and Remote Sensing 35, 1380e1393. Palmer, A.R., Short, A., Yunusa, I., 2010. Biomass production and water use efficiency of grassland in KwaZulu-Natal, South Africa. African Journal of Range & Forage Science 27, 163e169.
A.R. Palmer, I.A.M. Yunusa / Journal of Arid Environments 75 (2011) 1223e1227 Richardson, F.D., Hahn, B.D., Hoffman, M.T., 2007. Modelling the sustainability and productivity of pastoral systems in the communal areas of Namaqualand. Journal of Arid Environments 70, 701e717. Richmond, A., Kaufmann, R.K., Myneni, R.B., 2007. Valuing ecosystem services: a shadow price for net primary production. Ecological Economics 64, 454e462. Running, S.W., Nemani, R.R., Heinsch, F.A., Zhao, M.S., Reeves, M., Hashimoto, H., 2004. A continuous satellite-derived measure of global terrestrial primary production. BioScience 54, 547e560. Rutherford, M.C., Mucina, L. (Eds.), 2006. Vegetation of South Africa, Lesotho and Swaziland. Strelitzia. South African National Biodiversity Institute, Pretoria.
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Schulze, R., Lynch, S., 2007. Annual Precipitation. Water Research Commission, Pretoria. Snyman, H.A.,1999. Quantification of the soilewater balance under different veld condition classes in a semi-arid climate. African Journal of Range and Forage Science 16,108e117. Swinton, S.M., Lupi, F., Robertson, G.P., Hamilton, S.K., 2007. Ecosystem services and agriculture: cultivating agricultural ecosystems for diverse benefits. Ecological Economics 64, 245e252. Wight, J.R., Skiles, J.W., 1987. SPUR: Simulation of Production and Utilization of Rangelands. Documentation and User Guide. US Department of Agriculture, Agricultural Research Service.
Contents lists available at ScienceDirect
Journal of Arid Environments journal homepage: www.elsevier.com/locate/jaridenv
Short Communication
Biomass production, evapotranspiration and water use efficiency of arid rangelands in the Northern Cape, South Africa A.R. Palmer a,1, *, I.A.M. Yunusa b a b
Agricultural Research Council, Animal Production Institute, PO Box 101, Grahamstown 6140, South Africa School of Environmental and Rural Sciences, University of New England, Armidale, NSW 2351, Australia
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 July 2010 Received in revised form 4 March 2011 Accepted 18 May 2011 Available online 15 June 2011
Annual above-ground net primary production (ANPP), evapotranspiration (ET) and water use efficiency (WUE) of rangeland have the potential to provide an objective basis for establishing pricing for ecosystem services. To provide estimates of ANPP, we surveyed the biomass, estimated ET and prepared a water use efficiency for dwarf shrublands and arid savanna in the Riemvasmaak Rural Area, Northern Cape, South Africa. The annual production fraction was surveyed in 33 MODIS 1 km2 pixels and the results regressed against the MODIS fPAR product. This regression model was used to predict the standing green biomass (kg DM ha1) for 2009 (dry year). Using an approach which combines potential evapotranspiration (ET0) and the MODIS fPAR product, we estimated actual evapotranspiration (ETa). These two models (greening standing biomass and ETa) were used to calculate the annual WUE for 2009. WUE was 1.6 kg DM mm1 ha1 yr1. This value may be used to provide an estimate of ANPP in the absence of direct measurements of biomass and to provide a comparison of the water use efficiency of this rangeland with other rangeland types. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Dwarf shrubland ET0 fPAR MODIS Savanna Succulent WUE
1. Introduction Healthy rangeland ecosystems provide the basis for sustaining rural agricultural economies, and the necessity to price the value of these ecosystems and the services they provide is now widely agreed (Richmond et al., 2007; Swinton et al., 2007). Rangelands primarily provide forage to sustain domestic livestock, and the quantity and quality of forage are viewed by range managers as the most important variables that define the service provided by the rangeland. A good understanding of above-ground net primary production (ANPP) is necessary if we are to attribute a monetary value to the biomass production of an ecosystem, as the market cannot recognize the consequences of environmental degradation if the ecosystem service does not have a price (Richmond et al., 2007). This will require the development of new robust techniques for modeling and validating net primary production. The necessity to predict biomass production is an essential part of agricultural planning and models of livestock carrying capacity
* Corresponding author. Tel.: þ27 46 6222638; fax: þ27 46 6362623. E-mail address: [email protected] (A.R. Palmer). 1 Centre for African Conservation Ecology, Nelson Mandela Metropolitan University, PO Box 77000, Port Elizabeth, South Africa. 0140-1963/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.jaridenv.2011.05.009
(e.g. SPUR) estimate ANPP which may be consumed by herbivores (Wight and Skiles, 1987). These estimates make use of plant growth models which are used to predict annual increments in biomass production. Some of these models have integrated the affect of animal type, daily nutritional requirements and rangeland condition into their predictive frameworks (Richardson et al., 2007; Wight and Skiles, 1987) to provide realistic scenarios for rangeland managers. Micro-climatic variables that are necessary to estimate potential evapotranspiration (ET0) are now regularly measured at more than 550 sites across South Africa. However, ET0 on its own does not provide actual evapotranspiration (ETa), and without direct measurement of the water fluxes using eddy covariance system or scintillometry, we are obliged to derive ETa from remotely sensed products. By combining remote sensing technology, micrometeorological measurements and ground-based measurements of green biomass, we show that it is possible to achieve a more realistic estimate of WUE than one based solely on univariate models (e.g. mean annual rainfall). With the availability of data from the MODIS sensors it has also become possible to develop testable estimates of ANPP and evapotranspiration (Myneni et al., 1997). In this study we combine these three data sources from one study site to estimate ANPP. The estimates are based upon calculating the potential evapotranspiration of the vegetation during the growing
1224
A.R. Palmer, I.A.M. Yunusa / Journal of Arid Environments 75 (2011) 1223e1227
season (Allen et al., 1998), adjusting these values to ETa using remote sensing products from the MODIS programme, and combining these with ground-based surveys of green biomass, to predict the WUE which may then be used to estimate ANPP in years without detailed biomass measurements. 2. Methods 2.1. Study areas The Riemvasmaak Rural Area (RiRA) is located in north western South Africa (28.215 S, 20 Ee28.588 S, 20.53 E), where the mean annual rainfall is low (w130 mm per annum), dry season climate is extreme (mean monthly maximum temperature >30 C), and precipitation occurs mainly in autumn. The RiRA occupies 1797 km2, and supports domestic livestock and a small wildlife population. The RiRA is situated predominantly in the Nama-karoo and savanna biomes, and receives autumn (MarcheMay) rainfall, with high inter-annual rainfall variability (MAP ¼ 132 mm; inter-annual CV ¼ 65%) (Schulze and Lynch, 2007). Mean daily maximum temperatures varies from 26 C in January to 15 C in June, but get as high as 44 C in summer and <0 C in winter. The vegetation comprises four vegetation types, namely Lower Gariep Broken Veld (Fig. 1), Bushmanland Arid Grassland, Kalahari Karroid Shrubland and Gordonia Duneveld (Rutherford and Mucina, 2006). 2.2. Modeling of green biomass Canopy cover of the vegetation was sampled using the line intercept method (Canfield, 1941). A single 100 m transect was sampled at each of 33 pre-defined sites. The location of each line transect was determined after stratifying the entire study area on the basis of MODIS fPAR values (peak of 2009 growing season only). As irrigation agriculture is practiced along the Orange River, we avoided MODIS pixels adjacent to the river which may have been affected by signal from irrigated lands. Along each 100 m transect, five plots (0.2 m 1 m) were placed immediately adjacent to the
line transect at 20 m intervals and the annual biomass fraction was collected from each 0.2 m2 plot following Flombaum and Sala (2007). Plant species in each plot were recorded, the canopy cover measured and the current years green leaves and twigs were harvested. Samples were separated and packed on the basis of growth form (grass, stemesucculent, dwarf shrubs and trees). On returning to the laboratory, the harvested plant material was ovendried at 70 C for 90 h and weighed. The length of the canopy cover (in cm) for growth forms in each plot was summed to provide a total canopy cover per plot. The technique proposed by Flombaum and Sala (2007) was un-successful in defining regression equations for cover versus biomass for each functional class. As many plots had 100% canopy cover, the mean dry mass of the harvested plants in these plots were used to estimate the annual biomass production fraction of all the intercepted species encountered along the line transects. The dry mass of all the perennial shrubs, trees and grasses were summed to get the total above-ground biomass of 1 km2 plot. This represents an estimate of the annual production fraction of the standing green biomass of the four functional groups of perennials along the entire primary production/rainfall gradient. These were related to values from the 33 MODIS fPAR pixels from the 2009 growing season, to prepare a regression equation for predicting the annual production fraction directly from fPAR. 2.3. MODIS fPAR data An algorithm is applied to surface relectance data from the MODIS sensors on board the Terra and Aqua satellites to estimate leaf area index and fPAR. This 8 day 1 km product has been available since March 2000 (Myneni et al., 2002) and provides the opportunity to use the fPAR data to model above-ground green biomass. Pre-processed MODIS imagery was acquired from the Land Processes Distributed Data Archive (Land Process DAAC National Center, EROS, Sioux Falls, South Dakota, USA) for the period from 26 March 2000 to December 2010. The MODIS LAI/fPAR algorithm uses up to 7 atm-corrected surface spectral bi-directional reflectance factors and their uncertainties, and outputs the most probable values for LAI, fPAR and their respective dispersions (Myneni et al., 2002). The fPAR values were extracted for pixels that matched the ground co-ordinates for the dates when the ground measurements of biomass production were collected. The MODIS fPAR product includes a quality flag which indicates when the primary algorithm was rejected and a back-up algorithm was used. On the sampling dates and within the 33 pixels selected for the generation of the fPAR-biomass regression model, only data from the primary algorithm were encountered. These fPAR values were used for further all estimates of biomass and actual evapotranspiration. 2.4. Evapotranspiration (ET) estimates
Fig. 1. The lower Gariep Broken Veld in the Riemvasmaak rural area.
As direct measurement of ETa was not carried out in this study, we derived ETa from the weather station data and satellite imagery. The weather station at RiemvasmaakeWitklip (28.64321 S 20.3532 E) provided hourly records of rainfall, temperature (maximum and minimum), radiation, wind speed and direction, and relative humidity. These data were used to calculate hourly potential evapotranspiration (ET0) using the modified PenmaneMonteith equation (Allen et al., 1998). Using the MODIS LAI product as a surrogate for stomatal conductance, Leuning et al. (2009) have successfully adjusted the output from the PenmaneMonteith equation to accurately reflect ETa recorded by an eddy covariance system. This approach was not possible in the Richtersveld where the primary MODIS LAI algorithm is often rejected, and we applied an alternative scaling method for ETa from ET0 and MODIS fPAR, where
A.R. Palmer, I.A.M. Yunusa / Journal of Arid Environments 75 (2011) 1223e1227
-1
Biomass (kg ha )
Green biomass v MODIS fPAR (2009) 1000 y = 20.878x - 85.036 R2 = 0.5986
800 600 400 200 0 0
10
20
30
40
MODIS fPAR Fig. 2. Relationship between above-ground annual production fraction of green biomass and MODIS fPAR for sampling sites in the Riemvasmaak rural area.
ETa ¼ ET0 ðfPAR fPAR min =fPAR max Þ
(1)
Using a maximum value compositing routine, we identified the date of maximum active greenness, as reflected by fPAR, during the period March 2000 to December 2010. This was DOY 81 in 2009. Since this is also the day of the highest leaf area index (the MODIS LAI algorithm did not fail at these high greenness levels), we
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assumed that this would be the highest ETa achieved by this vegetation type. This is based on functional convergence theory (Field, 1991) which hypothesizes that plants scale leaf area and light harvesting ability by the availability of resources, driven by evolutionary processes, in order to optimize their carbon fixation. The ET0 for this day, derived using the PenmaneMonteith equation, was 5.3 mm day1. This we defined as the maximum potential ETa for the vegetation type as it occurred at the time of maximum green biomass. We extracted the LAI from the MODIS LAI database for all the sample sites at which LAI could be computed during the peak of the growing season (DOY 81) in 2009 (x ¼ 0.3) and further adjusted the ETa by this value for the vegetation on that day. This gives a maximum ETa for arid savanna of 1.9 mm day1. As further independent validation, we estimated ET directly from PAR and MODIS fPAR where
ET ¼ PAR fPAR 1:55
(2)
By deriving PAR from total radiation using a co-efficient of 0.45 for clear summer sunny days (Blackburn and Proctor, 1983) (PAR ¼ 1783 mmol m2 s1), and using a mean MODIS fPAR value of 0.204 on DOY 81 in 2009, equation (2) also predicts a daily ETa of 1.9 mm day1.
Fig. 3. (a) Daily ET0 and ETa (mm day1) derived from the MODIS fPAR product during 2009 and (b) trend in standing green biomass (kg ha1) predicted by the MODIS fPAR regression model for 2009 and 2010.
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The method for calculating daily ETa from ET0 using fPAR (Equation (1)) was used in all subsequent calculations of ETa. 2.5. Biomass production model e WUE The WUE may be used to compare different landscape condition classes (le Houerou et al., 1988; Snyman, 1999) and has the potential to be used to model NPP when only the ETa is available. To calculate WUE, the annual increment in biomass (kg DM) is divided by total ETa for that year. If this approach is to be used to determine the carrying capacity of the region for livestock production, only an annual estimate of ETa is required. Using the regression relationship that predicts standing green biomass (kg DM) from MODIS fPAR, we prepared biomass trends for the period 1 January 2009e31 December 2010 for the RiRA as a proof of concept. The difference between maximum and minimum annual standing green biomass was used to determine the annual positive increment in dry matter and is termed the annual production fraction (APf). We acknowledge that this is not total ANPP, as un-quantified herbivory by domestic livestock continued to take place during the short growing season. Using ETa for 2009, we calculated annual WUE (kg DM mm1 ha1 yr1) for the period when the full meteorological record was available.
4. Discussion The prediction of ANPP using the regression equation appears to be a promising approach to biomass production modeling for arid rangelands, with ANPP being predicted to be 87% of the modeled GPP (Running et al., 2004). Deriving ETa from the MODIS fPAR and PenmaneMonteith equation has also proved moderately successful, although independent validation using another measure would improve the validity of these estimates. The WUE calculated for this rangeland (1.6 kg DM mm1 ha1 yr1) is lower than most known WUE estimates for natural vegetation. Water use efficiency defines how efficiently the individual plant or landscape uses precipitation to produce biomass (le Houerou, 1984) and it has the potential to play an important role in setting pricing for ecosystem services (Richmond et al., 2007). WUE has been equated with rangeland condition at both the landscape (Holm et al., 2003) and the paddock scale (Snyman, 1999). Implementation of payment for ecosystem services will necessitate a comprehensive understanding of the WUE of various land condition classes. We have demonstrated in this study that it is possible to approximate landscape and catchment-scale WUE using MODIS fPAR. The technique outlined in this and other papers (Palmer et al., 2010) provides us with the ability to predict annual biomass production and evapotranspiration in arid and semi-arid shrublands with the MODIS fPAR product, and to derive WUE.
3. Results Acknowledgments 3.1. MODIS fPAR v green biomass The projected green biomass per MODIS pixel for the RiRA produced the regression (Fig. 2)
APf kg DM ha1 ¼ 20:87 fPAR 85:04
r 2 ¼ 0:6
The Agricultural Research Council’s Institute for Soil Climate and Water provided the meteorological data from the SA Weather Network. Funding for the project was provided by the National Research Foundation and Red Meat Research and Development SA.
(3)
for the relationship between MODIS fPAR and the measured standing green biomass, and provides the basis for biomass growth curves (Fig. 3) for the entire area of the RiRA from January 2009 to December 2010. The maximum green biomass in 2009 was 341 kg DM ha1, and in 2010 a maximum of 290 kg DM ha1 yr1 was predicted by the model. ANPP during 2009 was 197 kg DM ha1 yr1 and in 2010 was 146 kg DM ha1 yr1. We have compared these estimates against the predictions of the MODIS GPP product (MODIS 17A2) (Running et al., 2004), and in 2009 mean GPP for all 33 sites was 106 kg C ha1 yr1 (w225 kg DM ha1 yr1 using the aboveground biomass to C conversion of 0.47) (Janssens et al., 1999). 3.2. Evapotranspiration The study area displays a very high evaporative demand in the dry season, when there is usually no precipitation, ET0 is high (maximum 9.6 mm day1), and there is very little plant growth. The maximum daily ETa in the wet season was calculated to be 1.9 mm day1after adjustment of the ET0 using scaled fPAR values, and this declines to approximately 0.2 mm day1 in the hot dry season. The model predicts total annual ETa for 2009 of 119 mm. This is 23 mm higher than the annual rainfall during 2009 (96 mm). 3.3. Water use efficiency WUE was 1.65 kg DM mm1 ha1 yr1 in 2009. This is lower than published WUE values for “sub-climax” rangeland (WUE ¼ 2.3) (Snyman, 1999) and lower than most published WUE estimates for vegetation and crops types (Palmer et al., 2010). This result may reflect the degraded state of this rangeland.
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