Carbon dynamics in inland and coastal ecosystems. Dragon 3 ID 10561

Transcription

1 Carbon dynamics in inland and coastal ecosystems Dragon 3 ID 10561

2 Project Team Ronghua Ma Hongtao Duan Yuchao Zhang Juhua Luo Lin Chen Steven Loiselle Alessandro Donati Claudio Rossi

3 Project Young Scientists ( ) Ph.D. studies with Dragon support Kun Xue - Vertical algal biomass algorithm development Jing Li Temporal dynamics of algal biomass analysis Master students Cosimo Montefrancesco Drivers of algal dynamics Zhigang Cao Underwater light conditions Zuochen Li CDOM and POC blooms

4 Young Scientists Training Algorithm development Analysis and modelling tool development Communication training Dissemination activities (ASLO-Granada, Dragon 3, NIGLAS conferences) Seminars/short courses on (in Nanjing, in Siena) CDOM, POC, Phytoplankton dynamics, Bloom algorithm development Carbon modelling Community science

5 Project Goal: To develop new methodologies to study and monitor carbon dynamics in aquatic ecosystems Particulate organic carbon Dissolved organic carbon Carbon cycling Carbon dynamics in inland and coastal ecosystems Radiative transfer C A G terrestrial uptake B D atmospheric carbon mixing depth sedimentation deposition POC DOC DIC F pelagic carbon pool H mineralization sediment pool release of CO 2 and CH 4 F outflow direct uptake E POC DOC DIC

6 Project schedule Bio-optical properties Algorithms for CDOM, POC and Chla-a dynamics in Case II waters June 2012 August 2014 Radiative transfer Optical conditions in optically complex waters October 2012 January 2015 Aquatic carbon dynamics Carbon models exploring spatial and temporal dynamics March 2014 June 2016

7 Main Results So far. Monitoring aquatic carbon dynamics by remote sensing algorithms development temporal and spatial analysis radiative transfer drivers analysis (ongoing) carbon sources and sinks (ongoing) carbon models (ongoing)

8 Dragon Publications (1 of 4) Jiang, G., R. Ma, S.A. Loiselle and H. Duan (2012) Optical approaches to examining the dynamics of dissolved organic carbon in optically complex inland waters. Environmental Research Letters 7(3), Duan, H., R. Ma, and C. Hu (2012) Evaluation of remote sensing algorithms for cyanobacterial pigment retrievals during spring bloom formation in several lakes of East China. Remote Sensing of Environment 126, Jiang, G., R. Ma, H. Duan, S. A. Loiselle, J. Xu, D. Liu (2013) Remote Determination of Chromophoric Dissolved Organic Matter in Lakes, China, International Journal of Digital Earth DOI: / Qi, L., R. Ma, W. Hu, S.A. Loiselle (2013) Assimilation of MODIS Chlorophyll-a Data Into a Coupled Hydrodynamic-Biological Model of Taihu Lake Selected Topics in Applied Earth IEEE Journal of Observations and Remote Sensing, DOI /JSTARS

9 Dragon Publications (2 of 4) Duan H., Feng L., Ma R., Zhang Y., S.A. Loiselle (2014) Variability of Particulate Organic Carbon in inland waters observed from MODIS Aqua imagery Environ. Res. Lett Duan, H., R. Ma, Y. Zhang, S. A. Loiselle (2014) Are algal blooms occurring later in Lake Taihu? Climate local effects outcompete mitigation prevention J. Plankton Res. 0(0): 1 6. doi: /plankt/fbt132 Duan H, R. Ma, S.A. Loiselle, Q. Shen, H. Yin, Y. Zhang (2014) Optical characterization of black water blooms in eutrophic waters. Science of The Total Environment 2014; : Zhang M, R. Ma, J. Li, B. Zhang, H. Duan (2014) A Validation Study of an Improved SWIR Iterative Atmospheric Correction Algorithm for MODIS- Aqua Measurements in Lake Taihu, China. Geoscience and Remote Sensing, IEEE Transactions Zhang Y., R. Ma, H. Duan, S. A. Loiselle, J. Xu, M. Ma (2014) A Novel Algorithm to Estimate Algal Bloom Coverage to Subpixel Resolution in Lake Taihu Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of

10 Dragon Publications (3 of 4) Zhang Y., R. Ma, H. Duan, S. A. Loiselle, J. Xu (2014) A Spectral Decomposition Algorithm for Estimating Chlorophyll-a concentrations in Lake Taihu, China. Remote Sensing, 6(6), Jiang, G., R. Ma, H. Duan, S.A Loiselle (2015) Remote sensing of particulate organic carbon dynamics in a eutrophic lake (Taihu Lake, China), accepted for publication in Science of The Total Environment Duan, H., S. A. Loiselle, L. Zhu, L. Feng, Y. Zhang, R. Ma (2015) Distribution and incidence of algal blooms in Lake Taihu. Aquatic Sciences, 1-8 ( /s ) Villa, P., H. Duan, S. A. Loiselle (2015). Using Remote Sensing to Assess the Impact of Human Activities on Water Quality: Case Study of Lake Taihu, China. In Advances in Watershed Science and Assessment (pp ). Springer International Publishing.

11 Dragon Publications (4 of 4) Duan H., X. Xu, R. Ma, L. Feng, S. A. Loiselle, M. Zhang, C. Hu (in review) Algal bloom dynamics in Lake Taihu: links to global and local drivers Loiselle S. A., H.T. Duan, Z.G. Cao (2015) Characteristics of underwater light. In: Field Photochemical processes taking place in surface waters, role of natural organic matter in photochemical reactions and to recently developed tools, analytical techniques (in review) Royal Society of Chemistry Zhang Y., R.Ma, M. Zhang, H. Duan, S.A. Loiselle, Ji. Xua (2015) Fourteen year record ( ) of the spatial and temporal dynamics of cyanobacterial blooms in Lake Chaohu observed from time-series MODIS images (in review) Xue K., Y. Zhang, H. Duan, R. Ma, S.A. Loiselle (2015) A novel remote sensing approach to estimate vertical distribution of phytoplankton in a eutrophic lake (in review)

12 Issues and Challenges Complex atmospheric conditions reduce temporal resolution Complex catchment and hydrological conditions reduce identification of drivers (direct and indirect impacts) Many optical conditions are short term ( blooms ) Hyperspectral data needed for optically complex waters Field work is ongoing (e.g. small lake survey), but time intensive and costly Experimenting with community data gathering

13 EO data planning 2015 and 2016 HY-1 CZI - new and archived (data quality limitations) HJ-1 CCD - new and archived (under study) MERIS archived (being used) MODIS archived (being used) SMOS L2 new and archived (under study) Sentinel 2 (fingers crossed) Additional data GOCI Geostationary Ocean Color Imager HICO (Hyperspectral Imager for the Coastal Ocean)

14 Project Planning 2015 and 2016 Biooptical properties Radiative transfer Aquatic carbon dynamics Laboratory / in situ comparison and model development (POC & DOC sources) POC /Chla profile studies Driver analysis, carbon modelling Land use effects on aquatic carbon characteristics and dynamics Junior scientist exchange

15 preliminary result presentations Variability of Particulate Organic Carbon in eutrophic lakes presented by Dr. Yuchao Zhang Vertical distribution of algal biomass presented by Kun Xue Algal inventory estimation approaches presented by Jing Li

16 A novel algorithm to estimate POC concentrations in eutrophic lakes Dr. Yuchao Zhang

17 Carbon cycle in inland lakes Inland lakes are: recipients of terrestrial carbon. reserves of stored carbon. emitters of greenhouse gases.

18 POC in inland lakes Greenho CO use gas 2 CH CO 4 2 Dissolved Organic Carbon (DOC) Through the 0.45μm filter Organic Carbon in inland lakes Particulate Organic Carbon POC

19 Sources: POC Sources and Sinks biological production during photosynthesis transformation from DOC upwelling of organic sediment Sinks: transformation to DOC export out of the surface waters biological removal mechanisms

20 Remote sensing of water color According to the optical properties of water Case-I water(ocean) Case-II water(inland lakes/coastal zone) Apparent optical properties Inherent optical properties Biological optical properties of the water body

21 Research Challenges 1 Water color remote sensing satellites such as CZCS, MODIS, MERIS, SeaWiFS and GOCI with algorithms to estimate the distribution of POC in case-i waters 2 Algorithms for case-i water do not apply for case-ii waters, where major POC transformations occur.

22 Research objectives Analyze the relationship between POC concentrations and particulate characteristics chlorophyll, suspended solids concentration, etc. Identify relationships between POC and inherent (or apparent)optical properties Developed a new biological optical models to estimate POC concentration

23 Study area Lake Chaohu Chaohu

24 Materials and Methods The field sampling Surface/Underwater spectral measurement and processing Water sampling Backscatter coefficient measurement Others:Wind speed/direction, transparency, water depth Data processing Inherent optical characteristics measurement absorption coefficient / backscattering coefficient Water quality parameters measured Chla SPM(SPIM SPOM) Carbon component measurement POC DOC C/N

25 POC vs. water parameters

26 Gons and Simis Models POC a ph (665) POC a ph (665)

27 Calibrate model parameters

28 Estimated a ph (665) p=2.232 γ=0.601

29 POC retrieval model

30 Accuracy assessment Absolute error α=y i -X i Relative error β=(y i -X i ) /X i RMSE :root mean square of α Evaluation indicators RRMSE: root mean square of β MNB: arithmetic mean value of β NRMS: standard deviation of β

31 Model validation

32 Model comparison

33 Model comparison

34 Conclusions POC was highly related to the particulate absorption at 665 nm and strongly correlated with chla. Gons algorithm (RMSE rel =21.90%) can provide a better result than Simis (RMSE rel =23.81%). Gons and Simis algorithms both achieve good results and can be combined with MERIS satellite for POC estimates in Chaohu Lake. This study can provide technical and data support for inland lake water carbon cycle research.

35 A novel remote sensing approach to estimate vertical distribution of phytoplankton Kun Xue PhD student Nanjing Institute of Geography and Limnology, CAS

36 Outline Background Study region Data Results Summary

37 Background Increasing occurrence and intensity of algal blooms Monitored using remote sensing technology

38 Most models assumed to be vertical homogeneous Blooms area change dramatically Vertical movement of algae Vertical distribution of phytoplankton

39 Study region Lake Chaohu

40 Methods Field measurements Chla, SPIM, DOC R rs Wind speed MODIS satellite data R rc data R rc (λ) = ρ t (λ) ρ r (λ) = ρ a (λ) + πt(λ)t 0 (λ)r rs (λ)

41 Vertical characteristics of optically active substances water surface value CV of vertical profile N mean SD min max N mean(%) SD(%) min(%) max(%) Chla SPIM DOC

42 Vertical distribution type of Chla average vertical Chla Type N fitted function R 2 CV type f (z) = C 1 Type % uniform -- h 1 = + σ 2π 2 2 (z) C exp[ ( ) ] 2 0 Type % Gaussian 0.85 f Type % exponential 3(z) = m1 exp( m2 z) Type % power f f (z) = n z n z σ

43 Vertical distribution type of Chla

44 R rs response to different algae vertical types

45 Relationship of Chla vertical type and wind speed NDVI = R R rs rs (748) Rrs (675) (748) + R (675) rs CSI = R R rs rs (700) Rrs (675) (700) + R (675) rs NDBI Rrs = R R rs rs (550) Rrs (675) (550) + R (675) rs

46 Chla vertical type decision tree

47 NDBI using MODIS

48 Chla vertical distribution type

49 Conclusions 1. Analysis of the vertical profiles of algal biomass, 2. Integrated remote sensing reflectance data and wind speed to identify the vertical distribution type, 3. Map vertical distribution using satellite reflectance data.

50 Satellite-based algal inventory estimation approaches Jing Li PhD student Nanjing Institute of Geography and Limnology, CAS 50

51 Outline Background Approach Results Long term trends 51

52 Cyanobacterial blooms frequently occur in lakes some are toxic Question: How to assess them remotely? 52

53 Previous Approach: Surface information Variation in Days( /day) One Day( /hour) (Sun et al, 2015) 53

54 Study area: Lake Chaohu 54

55 Algal inventory algorithm 7/7/

56 Surface Chl-a (Zhang et al, 2015) 56

57 Results 57

58 Results: water column validation before calibration after calibration 58

59 Monthly variation of algal inventory Highest: October (63.88t) Lowest: April (53.11t) 59

60 Spatial and temporal algal inventory patterns 60

61 Spatial and temporal algal inventory patterns 61

62 Annual variation in algal inventory Highest:2007 (61.50t) Lowest: 2004 (40.34t) 62

63 Spatial and temporal algal inventory patterns 63

64 Spatial and temporal algal inventory patterns 64

65 Conclusions A new algorithm was developed and tested for algal inventory under non-blooming conditions The remote-sensing estimates of algal inventories in both the point s water column and Lake Chaohu were consistent with the in-situ data Long-term ( ) algal inventory distributions were derived for Lake Chaohu for the first time Results led to basic understanding of evaluating bloom conditions and also eutrophic status in future. 65