Multi-scale evaluation of ISIMIP biome models against NDVI and MODIS NPP data 27th October, Authors: Rashid Rafique, Fang Zhao, Ghassem Asrar, Ning Zeng, et al. Joint Global Change Research Institute-Pacific Northwest National Lab & Department of Atmospheric and Oceanic Sciences-University of Maryland College Park, USA

2 Objectives:  NPP is a critical component of terrestrial ecosystem and the processes controlling NPP operate on a range of temporal and spatial scales  Therefore, our objective is to evaluate the ISIMIP models at different scales using NDVI ( a proxy of photosynthetic activity) and MODIS NPP. Specific Objective are: 1.Examine the spatial correlation of modeled NPP with NDVI and MODIS NPP 2.Examine the temporal pattern of modeled NPP in relation to NDVI and MODIS NPP 3.Evaluate the seasonal dynamics in modeled NPP in relation to NDVI and MODIS NPP Note: The results in this presentation are preliminary and incomplete. They are still in progress and subject to change.

3 Data and Models  The analysis conducted covers the period of for NDVI and for MODIS NPP  Most recent GIMMS’ NDVI (version NDVI3g) data was obtained  Five ecosystem models from ISIMIP project were used: DLEM, VEGAS, VSITI, CARAIB and ORCHIDEE  Modeled NPP was driven by Princeton climate forcings  Observational data sets were aggregated to 0.5 o at which model simulations were performed  The analysis was conducted for Globe, USA, Amazon, Sahel, China, Europe and Russia regions  is NDVI a suitable proxy of photosynthetic activity and hence NPP?

4 Results: Modeled NPP and NDVI 1- Global NDVI DLEMVEGAS VISITCARAIBORCHIDEE  DLEM and VISIT generally agreed with NDVI spatially, shown in spatial map and scatterplot  Ensemble produced better results (R=0.78)  All models and NDVI showed an increase from 1991 to 2010; more prominent in CARAIB  Peak season of NPP was in Jul except ORCHIDEE  Temporally, there was not strong consistency; CARAIB showed higher trend than other models Kg C m-2 y-1 S1( ), S2( ), S3( ), S4( )

5  DLEM with R=0.81 showed the highest correlation  Ensemble produced better results (R=0.88)  NDVI, VEGAS and VISIT showed more increase from period 1 to period 4  Peak season of NPP was in Jul except VISIT and CARAIB.  Temporally, models reasonably corresponded with NDVI  Some of the highest NDVI peaks were not captured by all models 2- USA Kg C m-2 y-1

6 3- Amazon  VEGAS with R=0.80 showed the highest correlation  Ensemble also produced good results (R=0.75)  For the peak seasons of NPP and NDVI, models and NDVI don’t agree  NDVI and VISIT showed more increase from period 1 to period 4.  Temporally, models generally corresponded with NDVI  VISIT showed some elevated peaks compared to other models Kg C m-2 y-1

7 4- Sahel  All models showed high correlation, especially VEGAS (R=0.93)  Ensemble produced better results (R=0.95)  NDVI showed two peaks but models showed one peak  The peak season was largely different among models and NDVI  Temporally, models generally not corresponded with NDVI  Models corresponded among them; CARAIB showed higher trend than others Kg C m-2 y-1

8 5- China  ORCHIDEE, DLEM and VEGAS showed higher correlations  Ensemble produced better results (R=0.89)  Relatively small changes were observed in NPP and NDVI from period 1 to period 4  Peak season was Jul except for NDVI and ORCHIDEE  Temporally, models were not able to capture the highest NDVI peaks  Few NDVI peaks showed same trend as of models  Models corresponded among them; CARAIB showed higher trend than others Kg C m-2 y-1

9 6- Europe  All models were not able to show any significant correlation  Ensemble produced better results (R=0.61)  Relatively small changes were observed in NPP and NDVI from period 1 to period 4  Peak season was Jul except CARAIB  Temporally, models were able to capture the NDVI peaks  VISIT showed higher trend than all other models Kg C m-2 y-1

10 7- Russia  All models were not able to show any significant correlation  Some models showed negative correlation  A diverse pattern of peak season was observed in NDVI and NPP  Temporally, models were partially able to capture the NDVI peaks  CARAIB showed higher trend than all other models Kg C m-2 y-1

11 Overall,  The NDVI spatial pattern in Sahel and China regions were matched with most of models  No model was able to show any significant correlation with NDVI in EU and Russia regions  Most case, ensemble produced better results than individual models  The seasonal pattern of NPP in Amazon and Sahel was largely different than other regions  ORCHIDEE consistently showed lowest peak of NPP in all regions  Amazon and Sahel showed higher increase in both NDVI and NPP during the period of from 1991 to 2010

12 1- Global NDVI DLEMVEGAS VISITCARAIBORCHIDEE  Most of models generally agreed with MODIS NPP spatially, shown in spatial map  VEGAS, CARAIB and ORCHIDEE showed strong correlation with MODIS NPP  Ensemble produced better results (R=0.88)  Temporally, MODIS NPP was in range of most models  CARAIB and ensemble showed consistently higher trends than others Results: Modeled NPP and MODIS NPP MODIS NPP data is only available at annual scale. Monthly NPP? Or: generate NPP data from MODIS GPP by simply multiply by factor of 0.5 for global level. This factor can be different for different regions. We will also appreciate if you have any idea about this issue?

13  VEGAS (0.74) and CARAIB (0.73) showed strong correlation  Ensemble produced better results (R=0.85)  Temporally, MODIS NPP was in range of most models  CARAIB showed consistently higher trend than other models  On certain occasions VISIT showed opposite trend compared to MODIS NPP and other models 2- USA

14 3- Amazon  Most of the models didn’t not show strong correlation  Maximum correlation of 0.59 was observed in CARAIB  Ensemble produced better results (R=0.67)  Temporally, generally MODIS NPP was in range of most models  VISIT consistently showed lower trend than other models  DLEM and VEGAS showed consistent pattern with each other

15 4- Sahel  All models showed strong correlation  Highest correlation of 0.91 was observed in CARAIB  Ensemble also produced better results (R=0.91)  Temporally, MODIS NPP was at the lower end of modeled NPP for most of the time  VISIT and CARAIB showed higher trends over certain occasion compared to other models

16 5- China  ORCHIDEE, DLEM and VEGAS showed strong correlation  Highest correlation of 0.86 was observed in ORCHIDEE  Ensemble also produced good results (R=0.84)  Temporally, MODIS NPP was in the range of modeled NPP  CARAIB showed consistently higher NPP than MODIS NPP and other models  VEGAS showed lower values than other models over most of time

17 6- Europe  No model showed strong correlation  However, ORCHIDEE and CARAIB showed moderate correlation  Highest correlation of 0.77 was observed in ORCHIDEE  Ensemble also produced good results (R=0.76)  Temporally, MODIS NPP was in the range of modeled NPP  DLEM showed consistently lower NPP than MODIS NPP and other models

18 7- Russia  VEGAS and DLEM showed the strong correlation  Highest correlation of 0.92 was observed in VEGAS  Ensemble also produced good results (R=0.88)  Temporally, MODIS NPP was consistently lower than the modeled NPP  CARAIB showed higher NPP over certain occasions  Most of models and MODIS NPP showed opposite trend over certain period of times.

19 Analysis of seasonal patterns between modeled NPP and MODIS NPP Is still in progress We are generating monthly MODIS NPP from MODIS GPP, as the monthly MODIS NPP doesn’t exist. If anyone has used/generated monthly MODIS NPP, please share with us Rashid Rafique:

20 Overall,  Most models generally matched with MODIS NPP (both spatially and temporally) at global level  Sahel region showed highest consistency between MODIS NPP and modeled NPPs  EU and Amazon showed higher level inconstancy (spatially) between MODIS NPP and modeled NPP  Temporally, models matched well with MODIS NPP in EU and Amazon regions  Comparatively, models showed better agreement with MODIS NPP than NDVI