Soil Moisture Assimilation Improves Terrestrial Biosphere Model GPP Responses to Sub-Annual Drought at Continental Scale
Abstract
:1. Introduction
2. Materials and Methods
2.1. Carbon Cycle Data Assimilation System (CCDAS) and Simulation Experiments
2.2. Data
2.2.1. Meteorological Data
2.2.2. Atmospheric CO2 Concentrations
2.2.3. ESA-CCI Soil Moisture
2.2.4. GRACE-REC TWS Data
2.2.5. Top-Down Net Ecosystem CO2 Flux
2.2.6. FLUXCOM Gross Primary Productivity
2.2.7. Standardized Precipitation-Evapotranspiration Index (SPEI)
2.2.8. TRENDY GPP
2.2.9. GIMMS NDVI
2.2.10. LT_SIF
2.2.11. ENSO Indices
2.3. Statistical Analysis
2.4. Evaluation Metrics
3. Results
3.1. Soil Moisture Assimilation Performance
3.2. The Inter-Annual Variability of the Carbon and Water Fluxes
3.3. Drought Impacts on Continental GPP
4. Discussion
4.1. The Mechanism of Soil Moisture Controls on Carbon Fluxes
4.2. Legacy in the Regional GPP Variability
4.3. Drought Impacts on Continental GPP
4.4. Caveats and Implications
5. Conclusions
- 1.
- The combination of soil moisture and atmospheric CO2 concentrations in CCDAS results in a much better simulation of the terrestrial biosphere model in simulating coupled water–carbon processes in ecosystems. For NEP and GPP, the RMSE between the optimized model and Jena CarboScope as well as FLUXCOM is reduced by 6.88% and 14.93% compared to the prior experiment, and by 4.44% and 15.90% compared to only assimilating CO2. The reason why adding soil moisture observations can better represent the carbon and water cycle is that the soil moisture constrains the model’s phenology and photosynthesis through affecting the water uptake and surface energy balance-related parameters ( Tϕ, Cw0, srdepth, Sb, emis0, and Offset) and additionally controls (through these parameters) plant-available soil moisture.
- 2.
- The assimilation of soil moisture has shown the ability of improving the model’s representation of inter-annual variability of terrestrial carbon–water cycles and the atmospheric CO2 growth rate, resulting in high correlations with ENSO indices (soil moisture: >0.70, NEP: <−0.60, GPP: <−0.20, and CGR: >0.40). Soil moisture is the main factor controlling GPP at the time of ENSO events. For continents mainly distributed north of 30°N (North America, Europe, and Asia), the ENSO results in lower GPP but higher soil moisture, while for continents mainly south of 30° (South America, Africa, and Oceania), ENSO results in the same trend of GPP and soil moisture. We also found that the ENSO shows lagged effects on GPP and varies continently. North America, Asia, and Europe (mainly distributed in the Northern Hemisphere) show a >3-month time lag compared to South America, Africa, and Oceania (mainly distributed in the Southern Hemisphere), which show just a <3-month time lag.
- 3.
- We show that the optimized GPP is capable of reasonably depicting the response of GPP to sub-annual drought (≤9 months) by contrasting the continentally aggregated GPP with the comprehensive drought index SPEI. For the GPP optimized by CCDAS with both soil moisture and CO2 concentration, GPP responded to 68% of droughts detected globally with less than 9 months’ duration, while for TRENDY GPP, GIMMS NDVI, and LT_SIF, these percentages are 20%, 34%, and 23%, respectively. Based on this 9-month time scale, we analyzed the regional-scale GPP response to drought and showed that for regions at high latitude in the Northern Hemisphere, where temperature is the primary control of drought, an increase in GPP occurs with the occurrence of drought events. Similarly, for the Amazon rainforest region, where radiation is the main controlling factor, GPP is slightly enhanced when drought occurs. This indicates that using satellite-based soil moisture observations with a data assimilation approach can effectively constrain the water–carbon processes of the model and the simulation results based on this constraint can effectively reveal the response of GPP to sub-annual (≤9 months) drought at the regional scale.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Station Name | Station Code | Latitude | Longitude | Elevation |
---|---|---|---|---|
Alert, NWT, Canada | ALT | 82.3°N | 62.3°W | 210 |
Baring Head, New Zealand | BHD | 41.4°S | 174.9°E | 85 |
Point Barrow, Alaska | BRW | 71.3°N | 156.6°W | 11 |
Christmas Island | CHR | 2.0°N | 157.3°W | 2 |
Cape Kumukahi, Hawaii | KUM | 19.5°N | 154.8°W | 3 |
Mauna Loa Observatory, Hawaii | MLO | 19.5°N | 155.6°W | 3397 |
American Samoa | SMO | 14.2°S | 170.6°W | 30 |
South Pole | SPO | 90.0°S | 2810 |
No. | Symbol | Description | Units | Prior | Prior unc. | Posterior |
---|---|---|---|---|---|---|
1 | Maximum carboxylation rate (C3/C4) for PFT 1-13: TrEv (1; Tropical broadleaf evergreen tree), TrDec (2; Tropical broadleaf deciduous tree), TmpEv (3; Temperate broadleaf evergreen tree), TmpDec (4; Temperate deciduous tree), EvCn (5; Evergreen coniferous tree), DecCn (6; Deciduous coniferous tree), EvShr (7; Evergreen shrub), DecShr (8; Deciduous) | μmol(CO2)m-2s-1 | 6.00 × 10−5 | 1.20 × 10−5 | 7.84 × 10−6 | |
2 | μmol(CO2)m-2s-1 | 9.00 × 10−5 | 1.80 × 10−5 | 2.15 × 10−4 | ||
3 | μmol(CO2)m-2s-1 | 4.10 × 10−5 | 8.20 × 10−6 | 3.81 × 10−5 | ||
4 | μmol(CO2)m-2s-1 | 3.50 × 10−5 | 7.00 × 10−6 | 3.46 × 10−5 | ||
5 | μmol(CO2)m-2s-1 | 2.90 × 10−5 | 5.80 × 10−6 | 4.87 × 10−5 | ||
6 | μmol(CO2)m-2s-1 | 5.30 × 10−5 | 1.06 × 10−5 | 6.72 × 10−5 | ||
7 | μmol(CO2)m-2s-1 | 5.20 × 10−5 | 1.04 × 10−5 | 3.49 × 10−5 | ||
8 | μmol(CO2)m-2s-1 | 1.60 × 10−4 | 3.20 × 10−5 | 9.16 × 10−5 | ||
9 | μmol(CO2)m-2s-1 | 4.20 × 10−5 | 8.40 × 10−6 | 4.45 × 10−5 | ||
10 | μmol(CO2)m-2s-1 | 8.00 × 10−6 | 1.60 × 10−6 | 6.54 × 10−6 | ||
11 | μmol(CO2)m-2s-1 | 2.00 × 10−5 | 4.00 × 10−6 | 2.97 × 10−7 | ||
12 | μmol(CO2)m-2s-1 | 2.00 × 10−5 | 4.00 × 10−6 | 2.06 × 10−5 | ||
13 | μmol(CO2)m-2s-1 | 1.17 × 10−4 | 2.34 × 10−5 | 7.07 × 10−5 | ||
14 | aJ,V | Ratio Vmax to Jmax (max. electron transport rate) for PFT 1–13 | - | 1.96 | 9.80 × 10−2 | 1.81 |
15 | aJ,V | - | 1.99 | 9.95 × 10−2 | 1.75 | |
16 | aJ,V | - | 2.00 | 1.00 × 10−1 | 1.99 | |
17 | aJ,V | - | 2.00 | 1.00 × 10−1 | 2.07 | |
18 | aJ,V | - | 1.79 | 8.95 × 10−2 | 1.87 | |
19 | aJ,V | - | 1.79 | 8.95 × 10−2 | 1.81 | |
20 | aJ,V | - | 1.96 | 9.80 × 10−2 | 1.95 | |
21 | aJ,V | - | 1.66 | 8.30 × 10−2 | 1.66 | |
22 | aJ,V | - | 1.90 | 9.50 × 10−2 | 1.81 | |
23 | aJ,V | - | 1.40 × 10−4 | 2.80 × 10−5 | 4.47 × 10−5 | |
24 | aJ,V | - | 1.85 | 9.25 × 10−2 | 1.80 | |
25 | aJ,V | - | 1.85 | 9.25 × 10−2 | 1.84 | |
26 | aJ,V | - | 1.88 | 9.40 × 10−2 | 2.13 | |
27 | Leaf respiration ratio | - | 4.00 × 10−1 | 1.00 × 10−1 | 4.40 × 10−1 | |
28 | Growth respiration ratio | - | 2.00 × 10−1 | 1.00 × 10−2 | 2.48 × 10−1 | |
29 | Soil respiration temperature factor, fast pool | - | 1.50 | 7.50 × 10−1 | 1.67 | |
30 | Soil respiration temperature factor, slow pool | - | 1.50 | 7.50 × 10−1 | 1.35 | |
31 | Fast pool soil carbon turnover time | year | 6.60 × 10−1 | 4.00 × 10−1 | 4.25 × 10−2 | |
32 | Soil moisture exponential for soil respiration | - | 1.00 | 1.00 | 2.87 | |
33 | Fraction of fast soil decomposition | - | 2.00 × 10−1 | 2.00 × 10−1 | 2.32 × 10−1 | |
34 | Activation energy, dark respiration | J mol−1 | 4.50 × 104 | 2.25 × 103 | 2.56 × 104 | |
35 | Activation energy, carboxylation rate (C3) | J mol−1 | 5.85 × 104 | 2.93 × 103 | 6.76 × 104 | |
36 | Activation energy, O2 | J mol−1 | 3.59 × 104 | 1.80 × 103 | 3.58 × 104 | |
37 | Activation energy, CO2 | J mol−1 | 5.94 × 104 | 2.97 × 103 | 5.61 × 104 | |
38 | Activation energy, carboxylation rate (C4) | J mol−1 | 5.10 × 104 | 2.55 × 103 | 5.16 × 104 | |
39 | Photon capture efficiency (C3) | - | 2.80 × 10−1 | 1.40 × 10−2 | 2.46 × 10−1 | |
40 | Quantum efficiency (C4) | - | 4.00 × 10−2 | 2.00 × 10−3 | 3.45 × 10−2 | |
41 | Michaelis–Menten constant CO2 | μmol(CO2)mol(air)-1 °C−1 | 4.60 × 10−4 | 2.30 × 10−5 | 4.04 × 10−4 | |
42 | Michaelis–Menten constant O2 | μmol(CO2)mol(air)-1 °C−1 | 3.30 × 10−1 | 1.65 × 10−2 | 3.51 × 10−1 | |
43 | a | Temperature slope CO2 compensation point | μmol(CO2)mol(air)-1 °C−1 | 1.70 × 10−6 | 8.50 × 10−8 | 1.42 × 10−6 |
44 | β | Net CO2 sink/soil factor for PFT 1–13, used for adjusting soil carbon pool3 | - | 1.00 | 2.50 × 10−1 | 4.79 × 10−1 |
45 | β | - | 1.00 | 2.50 × 10−1 | 1.99 | |
46 | β | - | 1.00 | 2.50 × 10−1 | 1.01 | |
47 | β | - | 1.00 | 2.50 × 10−1 | 3.61 × 10−1 | |
48 | β | - | 1.00 | 2.50 × 10−1 | 2.28 × 10−1 | |
49 | β | - | 1.00 | 2.50 × 10−1 | 7.39 × 10−1 | |
50 | β | - | 1.00 | 2.50 × 10−1 | 1.63 | |
51 | β | - | 1.00 | 2.50 × 10−1 | 1.28 | |
52 | β | - | 1.00 | 2.50 × 10−1 | 1.59 | |
53 | β | - | 1.00 | 2.50 × 10−1 | 1.67 | |
54 | β | - | 1.00 | 2.50 × 10−1 | 6.32 × 10−1 | |
55 | β | - | 1.00 | 2.50 × 10−1 | 9.36 × 10−1 | |
56 | β | - | 1.00 | 2.50 × 10−1 | 6.55 × 10−2 | |
57 | Maximum leaf area index | - | 4.00 | 1.00 × 10−1 | 3.41 | |
58 | Phenology temperature trigger for PFT 4–6 | °C | 1.00 × 101 | 2.00 | 6.99 | |
59 | Phenology temperature trigger for PFT 8 | °C | 8.00 | 2.00 | 8.26 | |
60 | Phenology temperature trigger for PFT 9–12 | °C | 2.00 | 2.00 | -2.20 | |
61 | Phenology temperature trigger for PFT 13 | °C | 1.50 × 101 | 2.00 | 2.32 × 101 | |
62 | Spatial range of phenology temperature trigger for PFT 4–6, 8, 13 | °C | 2.00 | 1.00 | 2.06 | |
63 | Spatial range of phenology temperature trigger for PFT 9–12 | °C | 2.00 | 1.00 | 1.74 × 10−1 | |
64 | Day length at leaf shedding for PFT 4–6, 8, 11 | hour | 1.05 × 101 | 1.00 | 5.40 | |
65 | Spatial range of day length at leaf shedding for PFT 4–6, 8, 11 | hour | 5.00 × 10−1 | 2.50 × 10−1 | 7.78 × 10−1 | |
66 | Initial linear leaf growth | d−1 | 5.00 × 10−1 | 5.00 × 10−1 | 8.44 × 10−1 | |
67 | Inverse of leaf longevity for PFT 2, 4, 6, 8–10, 12, 13 | hour | 1.00 × 10−1 | 1.00 × 10−1 | 4.72 × 10−3 | |
68 | Inverse of leaf longevity for PFT 5, 11 | hour | 5.00 × 10−3 | 5.00 × 10−3 | 1.11 × 10−2 | |
69 | Length of dry spell before leaf shedding for PFT 1, 3, 7 | d−1 | 3.00 × 101 | 3.00 × 101 | 4.46 × 101 | |
70 | Length of dry spell before leaf shedding for PFT 2 | d−1 | 3.00 × 101 | 3.00 × 101 | 9.66 | |
71 | Length of dry spell before leaf shedding for PFT 9–10, 12, 13 | day | 3.00 × 101 | 3.00 × 101 | 8.10 | |
72 | Stomata internal to atmospheric CO2 (C3) | - | 6.50 × 10−1 | 6.50 × 10−2 | 5.55 × 10−1 | |
73 | Stomata internal to atmospheric CO2 (C4) | - | 3.70 × 10−1 | 3.70 × 10−2 | 4.47 × 10−1 | |
74 | Ratio of maximum water supply rate | mm d−1 | 5.00 × 10−1 | 1.50 × 10−1 | 9.56 × 10−1 | |
75 | emis0 | Emissivity of the atmosphere | - | 6.40 × 10−1 | 3.20 × 10−2 | 7.51 × 10−1 |
76 | srdepth | Rooting depth scalar for PFT 1–13 | - | 1.00 | 1.00 × 10−1 | 9.47 × 10−1 |
77 | srdepth | - | 1.00 | 1.00 × 10−1 | 4.59 × 10−1 | |
78 | srdepth | - | 1.00 | 1.00 × 10−1 | 9.91 × 10−1 | |
79 | srdepth | - | 1.00 | 1.00 × 10−1 | 9.77 × 10−1 | |
80 | srdepth | - | 1.00 | 1.00 × 10−1 | 1.12 | |
81 | srdepth | - | 1.00 | 1.00 × 10−1 | 9.02 × 10−1 | |
82 | srdepth | - | 1.00 | 1.00 × 10−1 | 1.07 | |
83 | srdepth | - | 1.00 | 1.00 × 10−1 | 1.20 | |
84 | srdepth | - | 1.00 | 1.00 × 10−1 | 6.14 × 10−1 | |
85 | srdepth | - | 1.00 | 1.00 × 10−1 | 4.40 × 10−1 | |
86 | srdepth | - | 1.00 | 1.00 × 10−1 | 1.02 | |
87 | srdepth | - | 1.00 | 1.00 × 10−1 | 9.90 × 10−1 | |
88 | srdepth | - | 1.00 | 1.00 × 10−1 | 1.47 | |
89 | Sb | Shape parameter retention curve scalar for soil texture 1-6: coarse (1; loam sand), medium/coarse (2; sandy loam), medium (3; loam), fine/medium (4; sandy clay loam), fine (5; clay loam), organic (6; loam) | - | 1.00 | 1.00 × 10−1 | 8.18 × 10−1 |
90 | Sb | - | 1.00 | 1.00 × 10−1 | 8.89 × 10−1 | |
91 | Sb | - | 1.00 | 1.00 × 10−1 | 1.20 | |
92 | Sb | - | 1.00 | 1.00 × 10−1 | 1.16 | |
93 | Sb | - | 1.00 | 1.00 × 10−1 | 7.19 × 10−1 | |
94 | Sb | - | 1.00 | 1.00 × 10−1 | 9.91 × 10−1 | |
95 | Sn | Soil porosity scalar for soil texture 1-6 | - | 1.00 | 1.00 × 10−1 | 1.33 |
96 | Sn | - | 1.00 | 1.00 × 10−1 | 3.15 × 10−1 | |
97 | Sn | - | 1.00 | 1.00 × 10−1 | 1.04 | |
98 | Sn | - | 1.00 | 1.00 × 10−1 | 6.26 × 10−1 | |
99 | Sn | - | 1.00 | 1.00 × 10−1 | 1.30 | |
100 | Sn | - | 1.00 | 1.00 × 10−1 | 9.91 × 10−1 | |
101 | Offset | Initial atmospheric CO2 concentration | ppm | 3.36 × 102 | 1.00 | 3.37 × 102 |
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Station Name | Location | “prior” | “co2” | “sm + co2” |
---|---|---|---|---|
ALT | 82.3°N, 62.3°W | 17.5 ppm | 1.54 ppm | 2.07 ppm |
BHD | 41.4°S, 174.9°E | 15.56 ppm | 1.13 ppm | 0.89 ppm |
BRW | 71.3°N, 156.6°W | 17.72 ppm | 1.71 ppm | 2.54 ppm |
CHR | 2.0°N, 157.3°W | 15.9 ppm | 1.12 ppm | 1.12 ppm |
KUM | 19.5°N, 154.8°W | 18.03 ppm | 1.28 ppm | 1.77 ppm |
MLO | 19.5°N, 155.6°W | 17.76 ppm | 0.97 ppm | 1.42 ppm |
SMO | 14.2°S, 170.6°W | 15.77 ppm | 0.89 ppm | 0.31 ppm |
SPO | 90.0°S | 15.39 ppm | 0.80 ppm | 1.29 ppm |
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Xing, X.; Wu, M.; Scholze, M.; Kaminski, T.; Vossbeck, M.; Lu, Z.; Wang, S.; He, W.; Ju, W.; Jiang, F. Soil Moisture Assimilation Improves Terrestrial Biosphere Model GPP Responses to Sub-Annual Drought at Continental Scale. Remote Sens. 2023, 15, 676. https://doi.org/10.3390/rs15030676
Xing X, Wu M, Scholze M, Kaminski T, Vossbeck M, Lu Z, Wang S, He W, Ju W, Jiang F. Soil Moisture Assimilation Improves Terrestrial Biosphere Model GPP Responses to Sub-Annual Drought at Continental Scale. Remote Sensing. 2023; 15(3):676. https://doi.org/10.3390/rs15030676
Chicago/Turabian StyleXing, Xiuli, Mousong Wu, Marko Scholze, Thomas Kaminski, Michael Vossbeck, Zhengyao Lu, Songhan Wang, Wei He, Weimin Ju, and Fei Jiang. 2023. "Soil Moisture Assimilation Improves Terrestrial Biosphere Model GPP Responses to Sub-Annual Drought at Continental Scale" Remote Sensing 15, no. 3: 676. https://doi.org/10.3390/rs15030676
APA StyleXing, X., Wu, M., Scholze, M., Kaminski, T., Vossbeck, M., Lu, Z., Wang, S., He, W., Ju, W., & Jiang, F. (2023). Soil Moisture Assimilation Improves Terrestrial Biosphere Model GPP Responses to Sub-Annual Drought at Continental Scale. Remote Sensing, 15(3), 676. https://doi.org/10.3390/rs15030676