Rain-Use-Efficiency: What it Tells us about the
Contents
1. ANPP yea a x iNDVI b 1 where a is the slope of the regression b the intercept and an error term The resulting linear regression coefficients a b are in turn used to provide satellite estimates of ANPP from iNDVI referred to as ANPP a Equation 2 ANPP at a x iNDVI b 2 The same method was applied over 2000 2010 using iNDVI data from both GIMMS 3g and MODIS Time series of ANPPfcia and ANPPsat are described in detail in Dardel et al 17 The focus Remote Sens 2014 6 3456 here is on the relationships between these series and the rainfall series using linear regression Ordinary Least Square regression and Principal Component Analysis of z scores of the three series As ANPPsa is simply obtained as a linear scaling of iNDVI most analyses can be equally performed using either INDVI or ANPPsat When calculating rain use efficiencies however only ANPP at should be used to avoid the methodological issues described before non zero intercept see Section 1 3 2 5 Calculation of Rain Use Efficiency and ANPP Residuals Two time series of Rain Use Efficiency are calculated as the ratio of spatially averaged ANPP ia and ANPP at respectively to annual rainfall over the Gourma window These time series are referred to as RUE ged and RUE sat The ANPP residuals method is also applied to both field observations and satellite estimates First a linear regression between ANPP estimates fie2. 77 of the variance Figure 4a b The overall consistency of the three datasets each of which could be impaired by quality issues therefore strongly supports the use of satellite NDVI archive for long term monitoring of dry lands Figure 3 a Time series of ANPP derived from field data and satellite data aggregated at the scale of the Gourma window Years without field data are not considered b Scatterplot and linear regression of field ANPP against integrated NDVI from GIMMS 3 g illustrates Equation 1 a Temporal profiles of ANPP seld and ANPP at b Linear regression AN PP seg iN DVI 1984 2010 T T r T r r 1500 r T r o ANPP r 0 65 e200 INRE 2204x 79 e 1500 ANPP y lt r 1999 pr 1000 2 1000 5 z a z z 2 Z 500 lt 500 4 1984 0 4 L L 1 4 4 0 L L L 1985 1990 1995 2000 2005 2010 0 0 2 0 4 0 6 0 8 Year GIMMS 3g iNDVI Figure 4 a Regression of field ANPP against JJASO rainfall b Same for satellite derived ANPP The year is indicated next to each point two digits a ANPP aig o b ANPP y a 120 77 O10 Fa anassoenesnasa r 0 76 99 Joss 10 88 QU pa Fd 1000 2 WS 1000 Kol E Ob 003 00 907 03 O91 n O89 Zo 5 Sys R a s a AER Pho a ra a e c 90 A O85 O08 gt Okee Z 500 O33 J 4 soot Y 08 POo4 87 j 7 oes OM 0 Pa 1 1 L 1 0 ad 1 L 1 1 0 100 200 300 400 0 100 200 300 400 Rainf
3. Correlation between satellite derived ANPP usually based on NDVI or field ANPP and rainfall has been reported in the past for semi arid regions The strongest relationships are usually established when average ANPP is estimated along large climatic gradients For example 7 as high as 0 89 was reported for climatic means ranging from 200 to 800 mm yr in North America in Muldavin et al 56 or 0 90 for a 200 1200 mm yr gradient in Sala et al 57 The correlation based on interannual variability is usually much lower Diouf and Lambin 36 7 0 41 Evans and Geerken 30 r 0 50 to 0 77 depending on the method used to estimate rainfall Prince et al 8 r 0 52 Muldavin et al 56 7 0 56 to 0 66 Spatial averaging tends to increase the correlation Nicholson et al 34 found a correlation of 0 85 7 between Sahel averaged NDVI and rainfall Remote Sens 2014 6 3461 for years 1982 1993 For our dataset the variance explained by rainfall for both ANPP 76 and ANPPiieia 77 is therefore in the upper values of the literature range The spatial averaging method although it is performed here over a much smaller area than Nicholson et al 34 may contribute to reduce the noise in the data but aggregation may also rise up scaling issues and require adequate sampling Holm et al 58 3 5 2 Interannual Variability Different ecological factors may cause variability in RUE and ANPP residuals 4 Field data sugges
4. while the residuals do not show any trend over time a GIMMS 3g iNDVI average sandy units b Satellite residuals average sandy units _ l 600 cama trend 0 3 units period pv 0 005 trend 7 2 units period pv 0 95 0 81 400 d 5 2 z 0 6 200 Z 0 4 E OF Z 3 Z 6 0 2 200 0 L 1 1 1 1 1 400 1 1 1 1 1 1 1985 1990 1995 2000 2005 2010 1985 1990 1995 2000 2005 2010 Year Year c ANPP sed average sandy sites 600 d Field residuals average sandy sites 2000 foo trend 782 5 units period pv 0 003 4 f seee trend 17 3 units period pv 0 9 D Ta 1500 oD 2 z Q A 1000 om a 7T lt 500 2 0 i i 400 i i 1985 1990 1995 2000 2005 2010 1985 1990 1995 2000 2005 2010 Year Year By contrast over the neighboring shallow soils Sh and Sho the spatially averaged iNDVI displays no trend over 1984 2010 Figure 9a whereas the satellite residuals display a weakly significant decreasing trend p 0 1 Figure 9b However field data for two specific shallow soil sites which are located into site n 16 or close to site n 8 the Sh and Sh areas reveal contrasted behaviors Figure 10 On site n 16 ANPPrieia progressively recovers after the driest years Figure 10a while field residuals are stable over time Figure 10b implying that this given site behaves like the sandy soil sites although its productivity is much lower Remote Sens 2014
5. years 2001 and 2003 cited above The correlation between the rainfall dataset ranges between 0 62 Remote Sens 2014 6 3472 and 0 72 Table A1 Yet the correlations between ANPP and rainfall are higher with rainfall from the in situ network As a result it seems that a simple dataset from either the SYNOP stations or the TAMSAT datasets could be used to infer the general patterns of RUE and residuals over the Sahel albeit with less precision than with dense network gauge data Figure A1 Comparison of various rainfall datasets field network average of rain gauges co located to each vegetation site average of the Hombori and Rharous rain gauges and the TAMSAT gridded satellite product All three products are averaged over the growing season JJASO period Comparison of rainfall datasets JJASO Field network 400 Hombori Rharous 2 J v TAMSAT 300 E E 200 z e4 v 100F 4 0 i 1 1 it 1 1 1985 1990 1995 2000 2005 2010 Year Figure A2 a RUEfeia b RUEsat field residuals and d satellite residuals estimated using the three different rainfall datasets field network in green Hombori and Gourma Rharous rain gauges in blue and TAMSAT gridded product in grey a RUE nag b RUE at l RUE DM kg ha mm RUE DM kg ha mm l i n 4 4 i 4 1 4 L 4 4 4 1985 1990 1995 2000 2005 2010 1985 1990 1995 2000 2005 2010 Year Year Remote Sens 2
6. 1050 3 Fensholt R Rasmussen K Nielsen T T Mbow C Evaluation of earth observation based long term vegetation trends Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS Terra MODIS and SPOT VGT data Remote Sens Environ 2009 113 1886 1898 4 Veron S R Paruelo J M Oesterheld M Assessing desertification J Arid Environ 2006 66 751 763 Remote Sens 2014 6 3468 10 11 12 13 14 15 16 17 18 19 20 21 Bai Y F Wu J G Xing Q Pan Q M Huang J H Yang D L Han X G Primary production and rain use efficiency across a precipitation gradient on the Mongolia plateau Ecology 2008 89 2140 2153 Le Houerou H N Rain use efficiency A unifying concept in arid land ecology J Arid Environ 1984 7 213 247 Wessels K J Comments on Proxy global assessment of land degradation by Bai et al 2008 Soil Use Manag 2009 25 91 92 Prince S D de Colstoun E B Kravitz L L Evidence from rain use efficiencies does not indicate extensive Sahelian desertification Glob Chang Biol 1998 4 359 374 Hein L de Ridder N Desertification in the Sahel A reinterpretation Glob Chang Biol 2006 12 751 758 Hein L de Ridder N Hiernaux P Leemans R de Wit A Schaepman M Desertification in the Sahel Towards better accounting for ecosystem dynamics in the interpretation of remote sensing images J Arid
7. Environ 2011 75 1164 1172 Miehe S Comment on Hein L 2006 The impacts of grazing and rainfall variability on the dynamics of a Sahelian rangeland J Arid Environ 2006 64 488 504 Prince S D Wessels K J Tucker C J Nicholson S E Desertification in the Sahel A reinterpretation of a reinterpretation Glob Chang Biol 2007 13 1308 1313 Eklundh L Olsson L Vegetation index trends for the African Sahel 1982 1999 Geophys Res Lett 2003 30 1430 1433 Anyamba A Tucker C J Analysis of Sahelian vegetation dynamics using NOAA AVHRR NDVI data from 1981 2003 J Arid Environ 2005 63 596 614 Herrmann S M Anyamba A Tucker C J Recent trends in vegetation dynamics in the African Sahel and their relationship to climate Glob Environ Chang Hum Policy Dimens 2005 15 394 404 Fensholt R Proud S R Evaluation of Earth Observation based global long term vegetation trends Comparing GIMMS and MODIS global NDVI time series Remote Sens Environ 2012 119 131 147 Dardel C Kergoat L Hiernaux P Mougin E Grippa M Tucker C J Re greening Sahel 30 years of remote sensing data and field observations Mali Niger Remote Sens Environ 2014 140 350 364 Descroix L Mahe G Lebel T Favreau G Galle S Gautier E Olivry J C Albergel J Amogu O Cappelaere B et al Spatio temporal variability of hydrological regimes around the boundaries between Sahelian and Sudanian a
8. around 250 mm Saharo Sahelian transition and RUE values in the range of 1 7 to 8 DM kg ha mm for regions with mean annual precipitation around 350 mm The ratio of field measurements of ANPP to annual rainfall typically ranges from 3 to 6 33 36 Figure 6 Plots of RUE against annual rainfall JJASO sum a RUE calculated with field ANPP b RUE calculated with satellite estimates of ANPP c Residuals obtained with field ANPP regressed against rainfall d Residuals obtained with satellite estimates of ANPP regressed against rainfall F a RUE geld _ _ E b RUE t _ _ 0 10 1 ff 0 pv 0 77 lt O88 D 10 L g4 Oss O89 E 4 ggo 09 99 amp 00 009 QY 95 i s 85 99 10 ad 3L 087 5 O01 09 a ae 2 sig 2 o 87 800 905 O08 O et 003 04 0s 03 a O04 93 a 34 fo 2 J m 2 D m e l OM i_ L L J l L L L i L 100 150 200 250 300 350 400 100 150 200 250 300 350 400 Rainfall JJASO mm Rainfall JJASO mm 400 c Field residuals l 400 d Satellite residuals __ _ O88 O10 999 E 200 f oss 089 J g 200 bc 89 400 J on 9 7 2 8 087 008 s as 10 A o 8990 ons O 0 ee 997 901 ae cos O02 O01 099 wv 87 p z 93 i i E Gos 03 6 200 93 4 o 200F 003 a 400 L 1 1 1 400 1 L 1 1 1 100 150 200 250 300 350 400 100 150 200 250 300 350 400 Rainfall JJASO mm Rainfall JJASO mm
9. channel 2 reflectances is performed using the atmospheric Rayleigh scattering over oceans method of Vermote and Kaufman 48 calibration of the NDVI itself is performed using the technique of Los 49 which relies on spectrally invariant Remote Sens 2014 6 3455 targets such as desert areas and finally artificial trends due to orbital drift are corrected using the empirical mode decomposition recomposition EMD technique of Pinzon et al 50 which separates the trends due to a varying solar zenith angle from the overall signal No atmospheric correction is applied except for the aerosol content due to the volcanic eruptions of El Chinchon 1982 and Mt Pinatubo 1991 In the GIMMS 3 g dataset another stage was implemented in the processing because of the dual gain introduced in the late 2000s with the first AVHRR 3 instrument SeaWIFS NDVI data from 1997 to 2010 are used to calibrate the GIMMS 3g NDVI data using Bayesian methods 47 Thus GIMMS 3g NDVI is the longest available dataset of vegetation cover available worldwide at a bi monthly frequency which provides a unique opportunity to perform trends and interannual variability analyses over more than 30 years 2 3 2 Temporal and Spatial Aggregation Seasonal NDVI integrals noted hereafter iNDVI 51 were calculated on a per pixel basis as follows i the base level for integration was estimated as the dry season NDVI average January to April and November to December ii a
10. cover thanks to Earth Observation satellites allowed estimating RUE over large areas and throughout long time periods Thus over the past decades the RUE index has been widely used to assess land degradation worldwide Although RUE is an appealing concept both its derivation from satellite data and its interpretation in ecological terms present substantial difficulties which fueled significant debates 7 The case of the Sahel drought has given rise to such a debate with opposing theories and interpretations and several revisits of published findings 8 12 Indeed in the 1970s and 1980s two successive and very severe droughts occurred in the Sahel which had dramatic impact on the population and their resources These extreme events embedded in a long period of below average rainfall contributed to revive the vision of a Sahel suffering from desertification Some of the most commonly cited causes of degradation comprise a climate driven degradation of ecosystems droughts and land use changes for instance changes in crop practices over exploitation of land shortening of fallow duration rangeland management over grazing or extensive wood cutting deforestation However this hypothesis of an ongoing desertification process has been challenged over the last decades by several studies based on satellite observations that reported a spectacular re greening of Remote Sens 2014 6 3448 the Sahel occurring since the beginning of
11. km line by classifying the 1 m x 1 m plots into the 4 strata previously defined Destructive measurements are performed over a few plots 3 for the low and high density strata 6 for the middle one which is also the more frequent which were randomly selected along the line For these plots herbaceous plants are cut dried and weighted Finally a weighted average relying on the stratification provides the aboveground herbaceous mass at the site level Remote Sens 2014 6 3452 Figure 1 Map of the central Gourma in Sahelian Mali portraying the different soil types and the network of long term ecological survey sites The map is derived from a supervised classification of soil types and water bodies from Landsat images The subset shown here corresponds to the area over which GIMMS 3g data are averaged referred to as the Gourma window in this article Sa and Sa refer to two specific sandy soil units for which further investigation was performed see Section 3 6 Similarly Sh and Shp refer to two specific shallow soil units 2 0 0 W 1 0 0 W Legend vegetation sites E water 2 shallow soils sandy soils clay soils 2 1 3 ANPP Estimation Dardel et al 17 analyzed the time series of ground ANPP averaged at the Gourma window scale in relation to the GIMMS 3g time series using peak mass as a proxy for ANPP In this study ANPP estimation is further refined First more severe criteria are used to select
12. normalized NDVI was calculated for each year by subtracting the average dry season value from the original data iii temporal integration of normalized NDVI was then applied over the growing season July to October Finally a spatial average was calculated over the Gourma window Indeed even though it is not technically correct to average values obtained from a ratio the difference with averaging the reflectances themselves is negligible not shown Besides it was proven that performing a spatial average of the 9 km wide AVHRR pixels further reduces uncertainties 52 For comparison MODIS MOD13C2 NDVI data were used as well during the 2000 2010 period the data being available on a monthly basis with a spatial resolution of 0 05 MODIS NDVI was integrated over the JIASO growing season and spatially averaged over the Gourma window just like the GIMMS 3g data The results obtained using MODIS are reported in Appendix B 2 4 Estimation of Satellite Derived ANPP Satellite derived ANPP is estimated by calibrating spatially averaged iNDVI on the multi site weighted average ANPP observations hereafter called ANPPwea as explained in Section 2 1 The same method was used in Dardel et al 17 except that the average over the growing season August September was used instead of iNDVI in the present study Therefore iNDVI is regressed against ANPP eq over 1984 2010 with iNDVI as the independent variable Equation 1
13. the GIMMS 3g data prevented strict comparisons of satellite data to specific sites since most sites are homogeneous at the 1 km scale but not at the 1 12 scale Yet several areas dominated by shallow soils or deep sandy soils are large enough to be observed with GIMMS 3g data A typical shallow soil signal is obtained by averaging GIMMS 3g data over areas labeled Sh and Sh on Figure 1 and compared to a sandy soil signal averaged over the neighboring Sa and Say areas also shown on Figure 1 Hereafter only the results obtained with the ANPP residuals method are shown since the results obtained using the RUE index lead to similar conclusions Remote Sens 2014 6 3463 Over the two units of deep sandy soils analyzed Sa and Sa the spatially averaged iNDVI is strongly increasing over the whole period Figure 8a while satellite residuals are stable Figure 8b as they are over the whole Gourma region The same signal is found with the field data available within the same zone Figure 8c d Thus the deep sandy soils exhibit the same resilience as the Gourma region when considered as a whole Figure 8 a ANPP and b satellite residuals from GIMMS 3g iNDVI averaged over the sandy units Sa and Saz displayed in Figure 1 c ANPPyeig and d field residuals obtained from averaged ANPP over the sandy sites in the Gourma window ANPP residuals are calculated using the mean rainfall over the Gourma region ANPP increases
14. 00 2005 2010 1985 1990 1995 2000 2005 2010 Year Year 400 c Field residuals _ 400 d Satellite residuals trend 24 5 units period pv 0 81 trend 90 7 units period pv 0 27 200 Ta a a ob ob ay ay Qa 0 Q Ss g yz 6 200 F b e4 400 400 1985 1990 1995 2000 2005 2010 1985 1990 1995 2000 2005 2010 Year Year When RUE and ANPP residuals are based on satellite derived ANPP instead of ANPP ei results also support a non significant trend over 1984 2010 Figure 5b d However the temporal profiles of RUE gat lack the succession of high and low RUE anomalies detected with RUEseia and the interannual variability does not correspond well to the one of RUEfeig except when the MODIS data are considered see Appendix B Remote Sens 2014 6 3459 These results are still valid when TAMSAT or the two stations rainfall datasets Hombori and Gourma Rharous are used instead of the Gourma rain gauge network co located with each vegetation site no significant trends over the whole 1984 2010 period are detected for field or satellite derived RUE or residuals and similar slight differences in the shape of field and satellite RUE or residuals temporal profiles are found see Appendix A Thus both methods present consistent results in terms of temporal trends over the Gourma region when data field or satellite are aggregated at the window scale 3 4 RUE and ANPP Residuals in Relation to Rainfall Amount By co
15. 014 6 3473 Figure A2 Cont c Field residuals d Satellite residuals 400 E 200 ob ob at Er ay a o0 3 200 S 5 o 400 400 1985 1990 1995 2000 2005 2010 1985 1990 1995 2000 2005 2010 Year Year Field network Hombori Rharous 2 v TAMSAT Table A1 Correlation between ANPP iNDVI and the 3 rainfall datasets Correlation between the Rainfall Datasets r field ANPP TAMSAT 0 66 7 iNDVI TAMSAT 0 61 r field network Homb Rha 0 76 7 field ANPP Homb Rha 0 63 17 iNDVI Homb Rha 0 75 r field network TAMSAT 0 62 r field ANPP field network 0 76 7 NDVI field network 0 76 7 TAMSAT Homb Rha 0 72 Correlation Field ANPP Rainfall Correlation iNDVI Rainfall Appendix B Estimations of ANPP RUE and ANPP residuals with the AVHRR GIMMS 3g data are compared to estimations with MODIS NDVI data over the 2000 2010 period Figures A3 and A4 Both NDVI datasets are well correlated to the ANPP e data aggregated over the Gourma window Figure A3 As a result the time series of ANPP from GIMMS 3g MODIS and field data are very close to one another Figure A4a and so are the RUE and residuals Figure A4b Note that for consistency regressions are derived for both datasets over the 2000 2010 period thus for GIMMS 3g the regression differs from the regression used over the whole period Figure A3 Figure A3 Linear regr
16. 3 PP Pa 500 c Local rainfall Site n 8 500 d Local rainfall Site n 16 HE local rainfall HE local rainfall 400 oo x field network average 4 400 me Me field network average oss x E amp amp 300 x x i q z z i Se x y d 200 ax 2 3 Z a 100 0 1985 1990 1995 2000 2005 2010 1985 1990 1995 2000 2005 2010 Year Year 3 6 3 Reconciling Re greening and the Sahelian Hydrological Paradox Field data suggest that some of the shallow soil areas are prone to degradation in the sense of decreasing ANPP while others are less affected or not affected at all Since ponds water originates from the shallow soils only 23 62 degradation of a fraction of these landscape units is sufficient to cause a strong increase in run off at the scale of the whole Gourma The satellite derived RUE and ANPP residuals are consistent with this different response of ANPP between shallow and sandy soils since they suggest decreasing ANPP residuals and RUE not shown over the shallow soils areas Sh and Sh Figure 9 Thus our results suggest that land degradation and increasing run off coefficient can happen over very small surfaces yet having tremendous hydrological impact at the regional scale The re greening Remote Sens 2014 6 3466 observed at a wider scale remains indisputable but our results highlight the fact that contrasted changes may happen at a smaller spatial scale i e smaller than the GIMMS 3 g pixel resolu
17. 6 3464 Figure 9 a GIMMS 3g iNDVI and b satellite residuals over the shallow soil unit Sh and Shz described on Figure 1 Satellite residuals are calculated using the mean rainfall over the Gourma region No trend is found for iNDVI while the residuals decrease a iNDVI GIMMS3g average shallow units 200 seen trend 0 units period pv 0 99 meen trend 106 3 units period pv 0 1 03 b Satellite residuals average shallow units gt 100 iNDVI NDVI units Residuals DM kg ha 0 i 200 J 1985 1990 1995 2000 2005 2010 1985 1990 1995 2000 2005 2010 Year Year Conversely for site n 8 ANPPreg at the end of the 1984 2010 period is much lower than in 1986 1990 Figure 10a ANPPrieia time series reveal very low ANPP values for every year after 2005 no observations being available between 1995 and 2005 Field trip reports mentioned soil erosion and run off concentration in the early 1990s which added limitations to plant growth for this site The field residuals decrease as well Figure 10b the trend being close to significance p 0 11 However in that case the correlation between ANPP and rainfall disappears 7 0 01 p 0 7 This occurs when the changes in vegetation function are large for instance in the case of intense degradation 32 Therefore the trend of the residuals comes close to the trend of ANPP limiting the efficiency of the residuals method to d
18. 980s drought should lead to a decrease in RUE without being attributable to any widespread desertification Relying on that hypothesis and on a few field observations Hein and de Ridder 9 argued that the lack of significant increase in Sahelian RUE over the 1984 2000 period had to be interpreted as an evidence of widespread desertification of the whole Sahelian region But Prince et al 12 and Miehe 11 in turn challenged their hypothesis and results They both advocated linearity of the ANPP rainfall relationship then found either stable or increasing RUE trends over most parts of the Sahel region and finally concluded that no widespread desertification was threatening the Sahel Therefore interpreting the RUE temporal changes requires prior investigation of the proportionality of the ANPP rainfall relationship or linearity for the ANPP residuals as well as verification of the hypothesis of no dependency of RUE to rainfall 1 5 Limitations Related to the Data Used As it is common for long term ecological studies other issues may arise because of inaccuracies related to the rainfall and ANPP data themselves In addition estimating ANPP over large regions requires up scaling of ground data using remote sensing methods The Normalized Difference Vegetation Index NDVI is particularly used to estimate ANPP and to perform RUE analyses 8 15 29 34 36 most often without being calibrated over ground measurements of ANPP In water lim
19. A Figure 2 JJASO rainfall anomalies over 1984 2010 derived from averaging daily records from gauges installed near each vegetation site of the Gourma region Anomalies are calculated from the 1984 2010 mean They clearly show the recovery of the precipitation after the extreme drought of 1984 Anomalies of annual rainfall JJASO Field network sedes trend 146 mm period P 0 001 150 100 mm 50 100 Mean 1984 2010 242 mm 1985 1990 1995 2000 2005 2010 Year 2 3 Normalized Difference Vegetation Index Data 2 3 1 The NDVI GIMMS 3g Dataset The NDVI 3g dataset is produced by the Global Inventory Modeling and Mapping Studies GIMMS group from data collected with the AVHRR Advanced Very High Resolution Radiometer sensors which have been carried out by polar orbiting meteorological satellites from the NOAA National Oceanic and Atmospheric Administration and are currently flying onboard the MetOps 1 and 2 satellites The GIMMS 3g dataset extends the widely used GIMMS dataset which is available over the 1981 2006 period 41 NDVI 3g data are provided globally from 1981 to 2012 with a 1 12 spatial resolution and 15 days maximum value composite images 47 The GIMMS 3g NDVI is produced using a similar processing scheme than for the GIMMS dataset NDVI data are calculated from the AVHRR channel 1 red 550 700 nm and channel 2 near infrared 730 1000 nm reflectances Calibration of the channel 1 and
20. Relationship between the variability of primary production and the variability of annual precipitation in world arid lands J Arid Environ 1988 15 1 18 56 Muldavin E H Moore D I Collins S L Wetherill K R Lightfoot D C Aboveground net primary production dynamics in a northern Chihuahuan Desert ecosystem Oecologia 2008 155 123 132 57 Sala O E Parton W J Joyce L A Lauenroth W K Primary production of the central grassland region of the United States Ecology 1988 69 40 45 58 Holm A M Cridland S W Roderick M L The use of time integrated NOAA NDVI data and rainfall to assess landscape degradation in the arid shrubland of Western Australia Remote Sens Environ 2003 85 145 158 59 Haas E M Bartholome E Lambin E F Vanacker V Remotely sensed surface water extent as an indicator of short term changes in ecohydrological processes in sub Saharan Western Africa Remote Sens Environ 2011 115 3436 3445 60 Hiernaux P Gerard B The influence of vegetation pattern on the productivity diversity and stability of vegetation The case of brousse tigree in the Sahel Acta Oecol 1999 20 147 158 61 Hiernaux P Diarra L Trichon V Mougin E Soumaguel N Baup F Woody plant population dynamics in response to climate changes from 1984 to 2006 in Sahel Gourma Mali J Hydrol 2009 375 103 113 62 Timouk F Kergoat L Mougin E Lloyd C R Ceschia E Cohard J M de R
21. Remote Sens 2014 6 3446 3474 doi 10 3390 rs6043446 remote sensing ISSN 2072 4292 www mdpi com journal remotesensing Article Rain Use Efficiency What it Tells us about the Conflicting Sahel Greening and Sahelian Paradox C cile Dardel Laurent Kergoat 1 Pierre Hiernaux Manuela Grippa Eric Mougin a Philippe Ciais and Cam Chi Nguyen Geosciences Environnement Toulouse GET Observatoire Midi Pyr n es UMR 5563 CNRS UPS IRD CNES 14 Avenue Edouard Belin F 31400 Toulouse France E Mails laurent kergoat get obs mip fr L K pierre hiernaux get obs mip fr P H manuela grippa get obs mip fr M G eric mougin get obs mip fr E M cam chi nguyen get obs mip fr C C N Laboratoire des Sciences du Climat et de l Environnement LSCE UMR 8212 CNRS CEA UVSQ F 91190 Gif Sur Yvette France E Mail philippe ciais Isce ipsl fr Author to whom correspondence should be addressed E Mail cecile dardel gmail com Tel 33 646 736 393 Received 16 January 2014 in revised form 3 April 2014 Accepted 8 April 2014 Published 22 April 2014 Abstract Rain Use Efficiency RUE defined as Aboveground Net Primary Production ANPP divided by rainfall is increasingly used to diagnose land degradation Yet the outcome of RUE monitoring has been much debated since opposite results were found about land degradation in the Sahel region The debate is fueled by methodological issues especially when usi
22. The influence of Climate Change and Climatic Variability on the Hydrologic Regime and Water Resources In S cheresse D sertification et Ressources en Eau de Surface Application Aux petits Bassins du BURKINA FASO IAHS Publisher Vancouver BC Canada 1987 pp 355 365 Savenije H H G The runoff coefficient as the key to moisture recycling J Hydrol 1996 176 219 225 Fensholt R Rasmussen K Kaspersen P Huber S Horion S Swinnen E Assessing land degradation recovery in the african sahel from long term earth observation based primary productivity and precipitation relationships Remote Sens 2013 5 664 686 Veron S R Oesterheld M Paruelo J M Production as a function of resource availability Slopes and efficiencies are different J Veg Sci 2005 16 351 354 Fensholt R Rasmussen K Analysis of trends in the Sahelian rain use efficiency using GIMMS NDVI RFE and GPCP rainfall data Remote Sens Environ 2011 115 438 451 Evans J Geerken R Discrimination between climate and human induced dryland degradation J Arid Environ 2004 57 535 554 Begue A Vintrou E Ruelland D Claden M Dessay N Can a 25 year trend in Soudano Sahelian vegetation dynamics be interpreted in terms of land use change A remote sensing approach Glob Environ Chang 2011 21 413 420 Wessels K J van den Bergh F Scholes R J Limits to detectability of land degradation by trend analysis of vegetation i
23. all JJASO mm Rainfall JJASO mm Remote Sens 2014 6 3458 3 3 RUE and Residuals Interannual Variability and Trends The relationship between field derived ANPP and rainfall is illustrated by the temporal evolution of RUE and ANPP residuals over 1984 2010 Figure 5a c RUEseig displays a low value in 1984 followed by a series of relatively high but variable RUE or positive residuals in 1985 1989 and then a rapid decrease from 1988 to 1992 followed by a slow recovery until 2010 Over the whole period neither RUEgei nor field ANPP residuals show significant trends p values of 0 29 and 0 81 respectively The trend in RUEgeig non significant is in fact due to one single very low value observed in 1984 Noticeably an analysis of RUEseia short term trends starting after 1992 may suggest an increase in RUE This again illustrates that trends have to be established over long periods of time and have to be robust over several time periods 17 33 Figure 5 Time series of RUE calculated with a field ANPP and b satellite estimates of ANPP Time series of ANPP residuals derived from c field ANPP and d satellite ANPP Trends are not significant at the 95 level apeerecceens trend 0 5 units period pv 0 29 trend 0 5 units period pv 0 2 am Sr am Sf E E zi 4t 4 TE Ta S 2 3h g 3 Q 2 Q 2 m f it yt 0 n ni 1 1 4 4 0 1 1 1 i 1 L 1985 1990 1995 20
24. ataset spanning a 27 year long period Similar simulations than the ones performed by Wessels et al 32 not detailed here suggest that a degradation of 20 to 30 may be detected with our time series provided they do not occur at the beginning or end of the series Keeping these limits in mind we can conclude that in situ observations and satellite based estimates of ANPP support the conclusions that the pastoral Gourma fully benefited from the rainfall increase over the last three decades showing a global pattern of vegetation regeneration after the droughts when the data are spatially aggregated 3 6 Reconciling Stable RUE and Increasing Run off Coefficient 3 6 1 The Hydrological Sahelian Paradox in the Gourma Region The results shown above make a strong case for stable RUE over 1984 2010 in the Gourma region suggesting ecosystem resilience to water utilization Yet there is also compelling evidence that the run off coefficient over the same area has dramatically increased over the same period see Section 1 2 In fact Gardelle et al 23 demonstrated that most ponds in the Gourma started to grow after the 1970s drought with an important acceleration of the phenomenon usually a few years after the Remote Sens 2014 6 3462 1983 1984 drought The case of the Agoufou pond located near sites 17 18 20 and 31 in Figure 1 illustrates this phenomenon see also Haas et al 59 The annual increase of pond s volume derived from La
25. ation being characterized by decreasing vegetation cover and increasing run off coefficient Such results show that contrasted changes may co exist within a region where a strong overall re greening pattern is observed highlighting that both the scale of observations and the scale of the processes have to be considered when performing assessments of vegetation changes and land degradation Keywords Sahel re greening degradation RUE RESTREND NDVI GIMMS 3g MODIS herbaceous vegetation Sahelian paradox run off 1 Introduction 1 1 RUE and the Desertification Re greening Debate Rain Use Efficiency is defined as the ratio between net primary production NPP or aboveground NPP ANPP and rainfall It has been increasingly used to analyze the variability of vegetation production in arid and semi arid biomes where rainfall is a major limiting factor for plant growth 1 5 Indeed RUE was designed to separate rainfall contribution to vegetation production from other factors such as plant life form nutrient status or anthropogenic effects like management and cropping practices 6 For a given ecosystem preserved from any kind of functional change through time the RUE values should be stable over time 6 Thus the underlying assumption is that a decline of RUE over time is a diagnosis for land degradation or even desertification 4 Since the 1980s the increasing availability of remote sensing observations of vegetation
26. by the fact that the small fraction of the landscape showing signs of degradation is dominated in terms of ANPP by the large fraction of resilient and productive deep sandy soils We suggest that similar processes may exist in many other dry lands as soon as some shallow soils are present and are not scrutinized separately Thus these Sahelian paradoxes seem more related to an observation shortcoming than to an actual paradox It reflects that global studies analyzing changes in vegetation cover at continental to regional scales may miss local scale changes such as the ones highlighted in this study Global patterns Remote Sens 2014 6 3467 of re greening may actually hide real changes in the eco hydrological processes yet concerning a very small fraction of the landscape but leading to tremendous modifications of the hydrological systems A more complete picture of the actual changes happening in the Sahel region could be achieved by taking into account these scaling issues As we hypothesize that the shallow soils present all over the Sahel may experience consequent changes such as the ones observed in the Gourma region we suggest to perform further investigation over similar soils continent wide High resolution imagery such as Landsat or airborne images could be used to assess whether these eco hydrological changes are affecting a larger extent of the region Acknowledgments We thank the anonymous reviewers for their in depth ana
27. e NDVI which is slightly positive for bare soils instead of ANPP is used in RUE calculation or if a rainfall threshold is needed for the vegetation to start growing 28 29 In both cases the non zero intercept will cause RUE to artificially tend to infinity values at very low rainfall Thus when calculating the trends of RUE over time significant trends will emerge if rainfall undergoes temporal changes within this range of values Remote Sens 2014 6 3449 Such trends cannot be interpreted as changes in the ecosystem s functioning since they are only caused by mathematical artifacts Different methods have been proposed to remove methodological difficulties see Fensholt et al 27 for a review For instance Fensholt et al 27 only retained pixels with a strong correlation between satellite NDVI and annual rainfall but with no significant correlation between RUE and rainfall Regressing ANPP against rainfall as suggested by Evans and Geerken 30 for instance alleviates the non zero intercept problems The slope of the linear regression is a first useful indicator of the ANPP rainfall relationship also referred to as the Precipitation Marginal Response PMR 28 31 A second indicator derived from this relationship is the difference between predicted ANPP from rainfall and observed ANPP In the following text this method will be referred to as the ANPP residuals method By construction if one assumes that the relatio
28. e able to capture sheet run off Nowadays some of the shallow soil sites show clear signs of degradation while others do not In fact all shallow soils Remote Sens 2014 6 3465 suffered from the 1984 s drought but this degradation was intensified over some sites only In addition to herbaceous productivity woody cover for these sites also tend to decay as they have been deprived of water because of run off concentration 61 Figure 10 a ANPPweiq and b field ANPP residuals over two specific shallow soil sites 1 km close to the Sh and Shy shallow units see Figure 1 and text for more details ANPP residuals are calculated using rainfall from the nearest gauges referred to as local rainfall for c site n 8 and d site n 16 and are compared to the window averaged rainfall referred to as field network average a ANPP feta lah 2 distinct shallow soil sites b Field residuals over 2 distinct shallow soil sites r r r r 150 O site n 8 O site n 8 trend 64 3 units period pv 0 15 trend 70 4 units period pv 0 11 200 site n 16 100 site n 16 trend 101 9 units period pv 0 01 Pee le ee hae ca trend 31 7 units period pv 0 23 lt Ta f i o E 2 50 A S H 100 E OF a Ce e 4 2 e4 50 J 4 4 4 L al 00 4 4 1 L L 4 1985 1990 1995 2000 2005 2010 1985 1990 1995 2000 2005 2010 Year Year
29. ed RUE or the residuals of ANPP versus rainfall are second order signals which do not display significant trends over 1984 2010 Indeed the first principal component explains 90 of the total variance and the coefficients are similar for the three variables 0 57 0 57 0 59 for ANPPricia ANPPsa and rainfall respectively The second principal component explains 6 of the total variance and the coefficients 0 71 0 70 0 01 indicate that it depicts the differences between ANPPricig and ANPPyat Therefore over the Gourma region we find no conflict between the RUE approach and the ANPP residuals method both in terms of temporal trends and when looking at their relationship with rainfall Both data sources i e field data and satellite observations give consistent results indicating that long term satellite datasets can be used to monitor RUE or residuals over long time periods 3 5 Ecological Interpretation 3 5 1 Comparison to Literature It is well known that rainfall controls plant productivity in many ecosystems and especially in semi arid ecosystems such as in the Sahel Herbaceous productivity in the Gourma is largely predicted by rain season rainfall amount The range of RUE values found in this study is comparable to the range Remote Sens 2014 6 3460 reported by studies in similar ecosystems 2 8 54 55 Le Houerou 54 for instance reported RUE between 1 and 4 DM kg ha mm for regions with mean annual precipitation
30. er decades Interannual variability of ANPP in pastoral Gourma north eastern Mali and temporal trends following the extreme drought of the early 1980s are both consistently portrayed by ground and remote sensing time series when aggregated over the 3 x 1 2 Gourma area Both series are remarkably correlated to rainfall over 1984 2010 in a linear way The residuals of ANPP regressed against rainfall as well as the Rain Use Efficiency RUE time series do not reveal any significant trend over time implying that vegetation is resilient over that period at the Gourma scale and that land degradation is not affecting the area at this level of aggregation We found no conflict between field derived and satellite derived results in terms of trends and no role of non linearity of the ANPP rainfall relationship impacting the interpretation two issues which are highly disputed At the same time an increase in run off coefficient has affected the same area over the period pointing towards some land degradation The apparent discrepancy of these two indicators of land degradation stable RUE and increasing run off coefficient is referred to as the second Sahelian paradox When shallow soils and deep soils were examined separately high resilience was diagnosed on the deep sandy soil sites but the shallow soils either showed signs of resilience or degradation We argue that the second Sahelian paradox observed in the Gourma region can be explained
31. erred to hereafter as the Gourma window Field observations of aboveground herbaceous vegetation mass are available from 1984 to 2010 The Gourma is a pastoral region land under cultivation representing less than 5 of the surface The vegetation is mainly constituted of annual herbaceous plants the woody plants covering less than 3 of the surface Twenty one sites of 1 km have been used in this study which were measured every year for 27 years at least when the field campaigns could be performed some temporal gaps in the dataset do exist but sites irregularly measured apart from these gaps have not been used These twenty one sites Figure 1 sample the bioclimatic gradient of the Gourma region annual rainfall ranging from 140 mm at the northern edge of the Gourma window to 470 mm at the southern edge Frappart et al 42 as well as the landscape heterogeneity soil type water regime topography land use grazing pressure intensity 43 2 1 2 Sampling Strategy Herbaceous ANPP is derived from the aboveground herbaceous mass measured for each site through a stratified random sampling method 17 44 The stratification stage was built to take into account the variability in aboveground herbaceous vegetation density at the site scale Four classes the strata are defined following the vegetation density bare soil low density middle density and high density The frequency of each stratum at the site scale is estimated along a 1
32. ession performed over 2000 2010 between ANPP eeiq and iNDVI derived from a GIMMS 3g data and b MODIS MOD13C2 data 1500 r r r 1500P e 0 67 eee 2 2010 t y 0 87 s y 2398x 114 y 4982x 149 3s lt Ss 1000 Z 2 1000 a A z 500 S 500 a a 2004 82 03 0 4 0 5 0 6 0 7 8 03 0 2 GIMMS 3g iNDVI MODIS iNDVI Remote Sens 2014 6 3474 Figure A4 Estimates of a ANPP b RUE and c ANPP residuals over 2000 2010 from field observations green GIMMS 3g iNDVI orange and MODIS iNDVI purple a ANPP 2000 2010 6 P RUE 2000 2010 l 400 ANPP residuals 2000 2010 _ 1500 E A 5 200 2 1000 4 Z 2 a 0 Qa Zz 3 4 A F lt amp 500 Tal 3 200 lt gt 6 0 1 400 2000 2005 2010 2000 2005 2010 2000 2005 2010 Year Year Year O from field data from GIMMS 3g iNDVI from MODIS iNDVI 2014 by the authors licensee MDPI Basel Switzerland This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license http creativecommons org licenses by 3 0
33. etect vegetation cover changes that are not related to precipitation It may still be possible to give an interpretation in terms of degradation but a careful examination of all series and especially precipitation has to be performed These conclusions are supported by the analysis of Veron et al 4 who recommended the use of the RUE index or the ANPP residuals in conjunction with the PMR Precipitation Marginal Response Veron ef al 28 and average ANPP to analyze land degradation Therefore the response of the shallow soils ecosystems to the drought is not uniform A similar analysis performed with the field data collected over site n 22 located on Figure 1 resulted in insignificant trends either in ANPPgeig or in field residuals thus showing an intermediate behavior between site n 8 and site n 16 Shallow soil sites in the Gourma region typically consist of bedrock schist or sandstone or iron pan outcrops interspersed with shallow patches of sand sandy loam or clay loam over which herbaceous vegetation and shrubs may grow 60 Run off is high from the rock or iron pan outcrops and was most often occurring as sheet run off that progressively evolved towards a more concentrated run off in a web of rills and gullies The sandy loam patches are classically long shaped set perpendicular to the slope separated by bare soil impluviums creating the patterned vegetation locally known as tiger bush where vegetated patches ar
34. ited regions the NDVI ANPP relationship has been firmly established from both empirical studies 37 38 and theoretical ones 39 The latter is generally built on the Monteith model which states that NDVI linearly correlates with the fraction of photosynthetically active radiation absorbed by plants fPAR which in turn correlates with photosynthesis and after integration in time to ANPP 40 These relationships are known to vary in space and time depending on vegetation type but this variability is significantly reduced when focusing of one single type of ecosystem which is the case in our study The slope of the APAR ANPP relationship is referred to as the Light Use Efficiency LUE APAR being the absorbed photosynthetically active radiation This approach has proven very successful over the years However it is recognized that uncertainty affects the different terms of the Monteith model and especially the LUE which may depend on plant type phenology water temperature or nutrient stresses In addition the derivation of long series of NDVI needs to solve a number of problems like orbital drift changing atmospheric conditions sensor degradation 41 As a result the accuracy of long term estimates of ANPP derived from NDVI is not well known especially for biomes where long term ground data are scarce Given these uncertainties it is certainly important to evaluate long term time series of RUE and ANPP residuals with corresponding
35. ld or satellite and annual rainfall is calculated providing the predicted ANPP field or satellite time series ANPP residuals are then calculated as the difference between observed ANPP and predicted ANPP and are referred to as field residuals and satellite residuals for field observations and satellite data respectively Temporal trends over the period 1984 2010 are calculated using Ordinary Least Square regressions for each of these datasets RUEfieia RUEsat field residuals satellite residuals Their relationship with rainfall is examined as well 3 Results and Discussion 3 1 Limitations Related to ANPP Estimation from iNDVI A number of issues may impair the derivation of ANPP from long time series of AVHRR data which did not aim originally at monitoring vegetation and thus require significant challenges to be overcome before vegetation cover trends can be successfully derived succession of 10 sensors calibration issues band width atmospheric long term variability orbital drift to name a few While these data are processed to minimize all these perturbations 47 the impact on the ANPP iNDVI relation is not easily quantified Many factors are known to influence this relationship soil background and color grazing pressure floristic composition land use and land cover efc but neither their direct effect nor their temporal changes are clearly known 17 The overall consistency of the precipitation time se
36. long term ground data 17 33 36 1 6 Mains Objectives of this Study The present study focuses on the temporal changes of the herbaceous vegetation cover in the Gourma region in Mali where 27 years of field observations of the aboveground herbaceous mass are available A strong re greening pattern is observed using both field data and remote sensing Remote Sens 2014 6 3451 measurements 17 a re greening that we aim to understand through the analysis of RUE and ANPP residuals This study addresses the three main following questions i The first methodological goal will be to evaluate the use of remote sensing NDVI data to estimate indicators of land degradation such as RUE and ANPP residuals The consistency within these two methods RUE and residuals will be examined as well ii The second objective is to understand whether the re greening trends observed over the Gourma region can be explained by rainfall iii Then this study investigates how re greening and increased run off coefficient can be observed in the same region an explanation of the second Sahelian paradox that reconciles increased run off coefficient and overall re greening trends will be proposed 2 Data and Methods 2 1 Field Observations of Vegetation 2 1 1 Study Area The Gourma region is located in north eastern Mali south of the Niger River The exact area considered in this study extends from 14 5 to 17 5 N and from 2 2 to 0 8 W and will be ref
37. lyses very valuable remarks and suggestions This work was funded by ANR ESCAPE ANR 10 CEPL 005 and used data from Service d Observation AMMA CATCH and from DNM Mali First author was funded by the CNES Centre National d Etudes Spatiales and by the Midi Pyr n es Region We thank Jim Tucker and Assaf Anyamba for making the GIMMS 3g data available and Marielle Gosset for helpful discussions on TARCAT Finally we thank all the people involved in the field measurements all over the years Authors Contributions C cile Dardel and Laurent Kergoat designed the sudy performed the data analysis and wrote the manuscript Pierre Hiernaux and Eric Mougin designed the observation network provided data and assisted in their analysis Manuela Grippa and Philippe Ciais contributed to the data analysis and interpretation Cam Chi Nguyen provided the Gourma soil type map Conflicts of Interest The authors declare no conflict of interest References 1 Huxman T E Smith M D Fay P A Knapp A K Shaw M R Loik M E Smith S D Tissue D T Zak J C Weltzin J F et al Convergence across biomes to a common rain use efficiency Nature 2004 429 651 654 2 Ruppert J C Holm A Miehe S Muldavin E Snyman H A Wesche K Linstadter A Meta analysis of ANPP and rain use efficiency confirms indicative value for degradation and supports non linear response along precipitation gradients in drylands J Veg Sci 2012 23 1035
38. ndex data Remote Sens Environ 2012 125 10 22 Miehe S Kluge J von Wehrden H Retzer V Long term degradation of Sahelian rangeland detected by 27 years of field study in Senegal J Appl Ecol 2010 47 692 700 Nicholson S E Tucker C J Ba M B Desertification drought and surface vegetation An example from the West African Sahel Bull Am Meteorol Soc 1998 79 815 829 Wessels K J Prince S D Malherbe J Small J Frost P E VanZyl D Can human induced land degradation be distinguished from the effects of rainfall variability A case study in South Africa J Arid Environ 2007 68 271 297 Diouf A Lambin E F Monitoring land cover changes in semi arid regions Remote sensing data and field observations in the Ferlo Senegal J Arid Environ 2001 48 129 148 Tucker C J Vanpraet C L Sharman M J Vanittersum G Satellite Remote sensing of total herbaceous biomass production in the senegalese sahel 1980 1984 Remote Sens Environ 1985 17 233 249 Remote Sens 2014 6 3470 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 Prince S D Satellite Remote Sensing of Primary Production Comparison of Results for Sahelian Grasslands 1981 1988 Int J Remote Sens 1991 12 1301 1311 Myneni R B Williams D L On the Relationship between fAPAR and NDVI Remote Sens Environ 1994 49 200 211 Monteith J L Solar radiati
39. ndsat and other high resolution imagery is a proxy of the water entering the pond and therefore of the run off collected over the catchment The ratio of this volume to the annual precipitation rapidly increased after 1984 as can be seen in Figure 7 100 increase was detected for all 91 ponds in the Gourma Local observations like the growth of the gully network support a significant trend towards run off concentration and acceleration on some shallow soils especially in the first years with normal or above average rainfall after a major drought like for example year 1991 Such an increase in run off coefficient is often associated with land degradation which is apparently conflicting with the stable RUE and residuals found over the same period Figure 7 Time series of the ratio between the Agoufou pond s annual volume increment and the annual rainfall collected over the catchment The surface of the Agoufou pond is estimated from high spatial resolution remote sensing data while the height data come from field measurements This ratio is a proxy for the run off coefficient of the pond s catchment It has dramatically increased after the extreme droughts early 1970s and early 1980s 30 Volume ratio Agoufou pond to rainfall 20 1960 1970 1980 1990 2000 2010 Year 3 6 2 Focus on the Shallow Soils Behavior A detailed examination of shallow soils sites however shed some light on this question The resolution of
40. ng satellite remote sensing data to estimate ANPP and by differences in the ecological interpretation An alternative method which solves part of these issues relies on the residuals of ANPP regressed against rainfall ANPP residuals In this paper we use long term field observations of herbaceous vegetation mass collected in the Gourma region in Mali together with remote sensing data GIMMS 3g Normalized Difference Vegetation Index to estimate ANPP RUE and the ANPP residuals over the period 1984 2010 The residuals as well as RUE do not reveal any trend over time over the Gourma region implying that vegetation is resilient over that period when data are aggregated at the Gourma scale We find no conflict between field derived and satellite derived results in terms of trends The nature linearity of the ANPP rainfall relationship is investigated and is found to have no impact on the RUE and residuals interpretation However at odds with a stable RUE an increased run off coefficient has Remote Sens 2014 6 3447 been observed in the area over the same period pointing towards land degradation The divergence of these two indicators of ecosystem resilience stable RUE and land degradation increasing run off coefficient is referred to as the second Sahelian paradox When shallow soils and deep soils are examined separately high resilience is diagnosed on the deep soil sites However some of the shallow soils show signs of degrad
41. nship between ANPP and rainfall is linear the ANPP residuals are independent from rainfall Therefore significant temporal trends in the residuals truly show changes in vegetation production which are not related to rainfall These trends in the residuals may be positive or negative and reflect changes in vegetation composition land cover to name a few and finally indicate land degradation 4 Moreover the ANPP residuals can be examined to look for a non random distribution and possible dependency of residuals on rainfall amount 2 7 27 30 Indeed if the hypothesis of a linear relationship between ANPP and rainfall does not hold it should be evidenced by the shape of the distribution of RUE plotted against rainfall The ANPP residuals approach is usually recommended rather than the RUE one In this study however both methods are investigated so that possible false interpretations found with the widely used RUE method can be identified However the residuals method is also subject to some limitations Wessels et al 32 analyzed the performance of ordinary least square regressions OLS to detect land degradation by simulating various scenarios of land degradation with a 16 year long time series Among other factors they investigated the influence of the timing of the degradation i e whether it happens at the beginning middle or end of the time series and its intensity by testing a range of degradation from 20 to 40 In addition to sh
42. nstruction RUE and rainfall are not independent see Section 1 3 As a result the correlation between these two variables is prone to spurious correlation For instance Brett 53 showed that a small negative correlation between X Y and Y is likely to occur if X and Y are unrelated depending on the variation coefficient of Y In the case of RUE a large coefficient of variation of rainfall may produce a slightly negative and spurious correlation For the Gourma region RUE fea is slightly and positively correlated to rainfall 7 0 1 Figure 6a The correlation is significant at the 90 level p 0 10 but is caused by year 1984 only On the other hand the field residuals do not display any particular pattern when plotted against rainfall Figure 6c showing no support for non linearity of the ANPP rainfall relationship over the range of rainfall considered here The highest rainfall values result in proportionally high ANPP with no evidence of other factors like nutrient status being increasingly limiting when the water constraint is relieved When looking at the results obtained with satellite derived ANPP RUEsat is not correlated to rainfall and the satellite residuals do not suggest either any non linear relationship between ANPP and rainfall Figure 6b d The principal component analysis among ANPPrieig ANPPsa and rainfall supports the following statement the three variables ANPP eg ANPPsat and rainfall are strongly correlat
43. ology TARCAT dataset part II Constructing a temporally homogeneous rainfall dataset 2014 unpublished work Pinzon J Tucker C J A non stationary 1981 2012 AVHRR NDVI3 g time series Remote Sens 2014 in press Vermote E Kaufman Y J Absolute calibration of AVHRR visible and near infrared channels using ocean and cloud views Int J Remote Sens 1995 16 2317 2340 Los S O Estimation of the ratio of sensor degradation between NOAA AVHRR channels and 2 from monthly NDVI composites IEEE Trans Geosci Remote Sens 1998 36 206 213 Pinzon J E Brown M E Tucker C J EMD Correction of Orbital Drift Artifacts in Satellite Data Stream In Hilbert Huang Transform and Its Applications Huang N E Shen S P S Eds World Scientific Publishing Co Pte Ltd Singapore 2005 Volume 5 pp 167 186 Mbow C Fensholt R Rasmussen K Diop D Can vegetation productivity be derived from greenness in a semi arid environment Evidence from ground based measurements J Arid Environ 2013 97 56 65 Nagol J R Quantification of Error in AVHRR NDVI Data University of Maryland College Park College Park MD USA 2011 Brett M T When is a correlation between non independent variables spurious Oikos 2004 105 647 656 Remote Sens 2014 6 3471 54 Le Houerou H N The Grazing Land Ecosystems of the African Sahel Springer Berlin Germany 1989 Volume 75 p 282 55 Le Houerou H N Bingham R L Skerbek W
44. on and productivity in tropical ecosystems J Appl Ecol 1972 9 747 766 Tucker C J Pinzon J E Brown M E Slayback D A Pak E W Mahoney R Vermote E F El Saleous N An extended AVHRR 8 km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data Int J Remote Sens 2005 26 4485 4498 Frappart F Hiernaux P Guichard F Mougin E Kergoat L Arjounin M Lavenu F Koite M Paturel J E Lebel T Rainfall regime across the Sahel band in the Gourma region Mali J Hydrol 2009 375 128 142 Mougin E Hiernaux P Kergoat L Grippa M de Rosnay P Timouk F le Dantec V Demarez V Lavenu F Arjounin M et al The AMMA CATCH Gourma observatory site in Mali Relating climatic variations to changes in vegetation surface hydrology fluxes and natural resources J Hydrol 2009 375 14 33 Hiernaux P Mougin E Diarra L Soumaguel N Lavenu F Tracol Y Diawara M Sahelian rangeland response to changes in rainfall over two decades in the Gourma region Mali J Hydrol 2009 375 114 127 Sala O E Austin A T Methods of Estimating Aboveground Net Primary Productivity In Methods in Ecosystem Science Sala O E Jackson R B Mooney H A Howarth R W Eds Springer New York NY USA 2000 pp 31 43 Maidment R Grimes D Tarnavsky E Allan R Stringer M Hewison T Roebeling R Development of the 30 year TAMSAT African Rainfall Time Series and Climat
45. osnay P Hiernaux P Demarez V Taylor C M Response of surface energy balance to water regime and vegetation development in a Sahelian landscape J Hydrol 2009 375 178 189 Appendix A Comparison of rainfall data from a network of local gauges from the mean of two long term meteorological stations Hombori and Gourma Rharous and estimated with satellite TAMSAT Figure Al The three datasets are consistent in terms of interannual variability and trends Some discrepancies are found for two years 2001 and 2003 between the in situ network and the two other datasets while TAMSAT shows a different interannual variability for 1988 1989 and 2007 and a less pronounced trend over 1984 2010 The agreement is interesting given that rainfall in the Sahel is notoriously spatially heterogeneous and given that the spatial sampling of the three datasets is different around 15 gauges are used for the field network depending on the year 2 for the Hombori Gourma Rharous dataset while a spatial average is calculated over the Gourma window for TAMSAT Consequently the RUE and residuals calculated with the different datasets are found to be fairly close Figure A2 More specifically the conclusions drawn with the in situ network data hold true when the other rainfall datasets are used we find no significant temporal trends for RUE or residuals and the same differences in the shape of the time series between ANPP eiq and ANPP at except for the
46. owing that iNDVI needs to be reduced by 30 to 40 before significant negative trends can be detected they showed that land degradation happening at the beginning or at the end of the time series are usually undetected Furthermore their results suggest that the residuals method called RESTREND in their paper becomes unsuitable for a simulated degradation intensity greater that 20 since beyond this threshold the relationship between ANPP and rainfall breaks down 1 4 Limitations Related to Ecological Interpretation On the ecological side of the controversy is land degradation diagnosed from the temporal changes in RUE in the Sahel the key question is the dependency of RUE on rainfall The situation occurs if the ANPP rainfall relationship departs from proportionality First this can happen if RUE is intrinsically lower for higher rainfall amounts which has been suggested several times based on field observations e g Miehe et al 33 Hein and de Ridder 9 Ruppert et al 2 In that case an increase in rainfall would be accompanied by a decrease in RUE without any land degradation A similar situation may affect the other edge of the rainfall range Remote Sens 2014 6 3450 if RUE increases with rainfall because for instance RUE is intrinsically low for the lowest rainfall a recovery of the rainfall should result in increasing RUE 2 9 Therefore in the specific context of the Sahel region the rainfall increase after the 1
47. raged to provide one annual value for the Gourma window Figure 2 Summing over the JJASO period allows catching most of the annual rainfall that is useful for herbaceous vegetation growth Most of the years a rain gauge could be associated with every vegetation site meaning that there was a gauge with good quality data close enough typically within 10 km of the vegetation site The Gourma average JJASO rainfall was compared to the simple average of the two long term stations of Gourma Rharous station from Direction Nationale de la M t orologie du Mali DNM and Hombori SYNOP station from DNM Mali Also to evaluate the use of satellite based rainfall datasets in estimating RUE or ANPP residuals we included the TARCAT v2 0 dataset from the TAMSAT Research Group Tropical Applications of Meteorology using SATellite data and ground based Remote Sens 2014 6 3454 observations Maidment et al 46 TARCAT provides rainfall estimates from 1983 up to today with a spatial resolution of 0 0375 based on Meteosat thermal infra red data and calibrated with ground based rain gauge data It aims at providing consistent estimations of rainfall over Africa for long term studies since the calibration is constant over time for a given grid cell In the rest of the manuscript the baseline rainfall dataset is the one based on the network of gauges in the Gourma region The sensitivity of the results to the different rainfall products is reported in Appendix
48. reas of West Africa A synthesis J Hydrol 2009 375 90 102 Mahe G Paturel J E 1896 2006 Sahelian annual rainfall variability and runoff increase of Sahelian Rivers Comptes Rendus Geosci 2009 341 538 546 Leduc C Favreau G Schroeter P Long term rise in a sahelian water table The Continental Terminal in South West Niger J Hydrol 2001 243 43 54 Amogu O Descroix L Yero K S le Breton E Mamadou I Ali A Vischel T Bader J C Moussa I B Gautier E et al Increasing river flows in the Sahel Water 2010 2 170 199 Remote Sens 2014 6 3469 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 Descroix L Laurent J P Vauclin M Amogu O Boubkraoui S Ibrahim B Galle S Cappelaere B Bousquet S Mamadou I et al Experimental evidence of deep infiltration under sandy flats and gullies in the Sahel J Hydrol 2012 424 1 15 Gardelle J Hiernaux P Kergoat L Grippa M Less rain more water in ponds A remote sensing study of the dynamics of surface waters from 1950 to present in pastoral Sahel Gourma region Mali Hydrol Earth Syst Sci 2010 14 309 324 Sighomnou D Descroix L Genthon P Mah G Bouzou Moussa I Gautier E Mamadou L Vandervaere J P Bachir T Coulibaly B et al La crue de 2012 Niamey Un paroxysme du paradoxe du Sahel S cheresse 2013 24 3 13 Albergel J
49. ries with both field ANPP and satellite ANPP can be seen as an evaluation of the whole data processing involved 3 2 ANPP and Rainfall Over the 1984 2010 period satellite derived ANPP and field derived ANPP vary in a very consistent way Figure 3a The temporal variations of both ANPPwieg and ANPPsa show a recovery of plant production after the 1984 s drought characterized by an increasing trend and a strong interannual variability Field ANPP explains 65 of the iNDVI variance Figure 3b which is slightly higher than the 56 reported in Dardel et al 17 for the correlation between peak biomass and NDVI over 1984 2011 Improvements in the estimation of ANPPwicia together with the use of a remote sensing proxy for ANPP which is closer to the Monteith production model iNDVI instead of the average Remote Sens 2014 6 3457 over August September are most probably causing this The agreement between the two datasets is better during the 2000 2010 period 7 0 71 p 0 0001 than during the 1984 1995 period 1 0 48 p 0 018 When Equations 1 and 2 are applied to estimate ANPP from MODIS and GIMMS 3g over 2000 2010 the temporal profiles of the three ANPP datasets are very similar and the explained variance reaches 87 for MODIS and 67 for GIMMS 3g see Appendix B Whether ANPP is estimated from field data or from satellite GIMMS 3g NDVI data it is highly correlated with the JJASO rainfall which in both cases explains 76
50. riod this is known as the Sahelian paradox 25 Over a few areas such as south western Niger the increase in run off coefficient observed is in accordance with a decrease in vegetation cover 17 18 thus logically suggesting that an increase in the bare surfaces induce increasing run off coefficient However in most regions of Sahel the increasing run off coefficient contradicts the general re greening trends reported As increasing run off coefficient is also considered a consequence of land degradation 26 another paradoxical situation emerges How can vegetation productivity increase while at the same time run off coefficient increases A good understanding of how net primary productivity relates to rainfall is required to solve this second Sahelian paradox 1 3 Limitations Related to Methodological Issues Several studies have explored the changes in Sahelian RUE over the last decades reaching contrasted conclusions While some point towards land degradation 9 others conclude to the absence of extensive land degradation 8 15 27 The differences in interpretation partly result from methodological issues and partly from different ecological theories or expectations As far as methods are concerned the RUE concept is based on proportionality between ANPP and rainfall 6 This approach is meaningful when ANPP reaches zero when rainfall also reaches zero i e zero intercept 28 This may not be the case when for instanc
51. satellite archive that is the early 1980s 13 17 Cited causes of the re greening ranged from climate variability with a recovery of the rainfall amount to improved farming techniques and land reclamation Evidence from long term ground data has shown that vegetation evolution is not uniform throughout the whole Sahel Most regions show strong signs of re greening trends which can mostly be attributed to an increase in herbaceous production 17 However in other regions a decrease of vegetation production is undoubtedly taking place even if it seems not to be widespread For instance decreasing vegetation production has been observed from both long term ground data and satellite archive in south western Niger 17 1 2 The Sahelian Hydrological Paradox Concurrently to the observation of a re greening Sahel over the last decades several studies showed that the run off coefficient defined as run off divided by the rainfall amount has dramatically increased from the 1950s up to now for a review see Descroix et al 18 also refer to Mahe and Paturel 19 and references therein In many places the increased water table height fed by run off for instance in endoreic south western Niger 20 increased river run off 21 22 increased pond surface 23 or increased flood of the Niger river 24 have been revealed after the strong droughts of the 1970s and 1980s These phenomena are observed although rainfall decreased over the same pe
52. sites without significant temporal gaps thus selecting 21 sites over the 38 available in the database For instance sites with only a few years of measurements available either because the site monitoring was abandoned or because it started lately are set aside Remote Sens 2014 6 3453 Second for some sites which are sampled more often than others mostly because of their ease of access several measurements per year are performed It is thus possible to detect when the mass goes through several maxima during the growing season This occurs although rarely when a strong dry spell interrupts plant growth In that case ANPP is best approximated by the sum of the successive mass increments rather than by the mass maximum 45 This correction was applied in this study even though the impact on ANPP estimate is small since the vegetation is dominated by annual plants growing rapidly during a short rain season Finally we also compensated for late measurements of mass assuming an average rate of mass decay of 17 per month based on field data and a maximum of standing mass in mid September This occurred principally for year 1995 Still differences between our estimates and real ANPP may be caused by mass losses caused by herbivory which is not taken into account 2 1 4 Spatial Average Since the field data are collected over areas significantly smaller than the GIMMS 3 g satellite pixels we chose to average both field data and satelli
53. t that ANPP residuals may be negative in dry years as in 1984 2004 2008 Figure 5c Such a decrease is consistent with the idea that below a certain rainfall threshold generally involving strong dry spells vegetation growth may be more severely impaired for instance if a large mortality event occurs during the rainy season or if the drought limits resource capture e g LAI or resource use Increased herbivory impact in poor production years may also lead to an underestimation of ANPP from mass data 33 Such a drop of RUE for the driest years has also been shown in the meta analysis of field data by Ruppert et al 2 However some other dry years like 1985 1987 or 1990 do not support this concept In line with Miehe et al 33 we do not find any clear delayed effect of the 1984 drought since high RUE and positive residuals are found as early as 1985 This is not in line with the lag effect of drought suggested by Prince et al 8 Prince et al 12 and Diouf and Lambin 36 3 5 3 Ecosystems Resilience The fact that no trend of RUE or ANPP residuals are found either from field data or satellite data demonstrates the resilience of ecosystems in the Gourma region at least at that aggregated spatial scale In other words no degradation could be detected using these indicators However Wessels et al 32 suggested that a 30 to 40 decrease in ANPP is needed to be detected with a 16 year long time series Here we used a 23 year long d
54. te data over a relatively large window 3 x 1 2 The field sites are grouped following the three main soil types characterizing the Gourma region deep sandy soils shallow soils and clayed depressions Figure 1 Indeed soil type is a main driver of vegetation production in the Gourma as shown in Dardel et al 17 Figure 10 and in Hiernaux et al 44 Figure 5 and Table 3 Therefore an average ANPP for the Gourma window is obtained by weighting ANPP by the respective proportion of these three main soil types coefficients derived from a supervised classification from Landsat images 65 1 of sandy surfaces 30 8 of shallow soils and 4 1 of clayed depressions We therefore focus on the temporal changes of ANPP and RUE of a relatively large area 2 2 Rainfall Data Studies of RUE preferably rely on rainfall data collected by ground networks but rain gauge data are scarce and of varying reliability in the Gourma region of Sahel At the regional scale gridded rainfall products combining rain gauge measurements with satellite data can be used 29 Here rainfall data come from a research network of rain gauges installed over the Gourma region 42 Daily data available from 1984 up to 2010 have been quality checked to discard gauges with spurious gaps which occasionally occurs for isolated visual reading gauges or pluviographs Sums for the June July August September October JJASO period were calculated for each gauge and spatially ave
55. tion meaning that both the scale of observations and the finer scale of the processes have to be scrutinized when performing assessments of vegetation changes and more importantly when assessing land degradation Besides shallow soils in the Gourma region are relatively large landscape units which may not be the case of iron pans or rocky outcrops in many other parts of the Sahel Therefore a slight decrease of productivity or RUE in areas of already low productivity or RUE would most probably be undetected in many places where shallow soils exist at a smaller spatial scale than the GIMMS 3g resolution 58 Consequently in situations where local degradation and erosion occur over a small fraction of a landscape on patches surrounded by otherwise resilient re greening ecosystems both a stable RUE and an increasing run off coefficient can be observed Since increasing run off coefficient is observed in many places in the Sahel and since the present study gives more confidence in RUE observed from space which is often stable 27 we propose that divergent indicators of degradation may co exist in the Sahel as soon as shallow soils are present especially if their detection is complicated by a typical patch size smaller than the resolution of the remote sensing dataset 4 Conclusions Long term ecological surveys provide strong evidence that the longest global satellite archive can be used to estimate aboveground net primary production ANPP ov
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