Future work and application - Summary, discussion and future work

Chapter 4 Summary, discussion and future work

4.3 Future work and application

4.3.1 The limitation of experiment design

The sensitivity tests for the spatial scale of irrigation area should be investigated further because the size of land used changes may impact its climate response, which is referred in section 2.2 (Chagnon, 2005; Ho, 2017; Khanna et al., 2017; Lawrence and Vandecar, 2014). Different shape of irrigation area should be tested since the shape of the irrigation area might also affect the result and the realistic shape of irrigation field could be more complex than rectangle, which is used in our study. The strategy of adding water could be more realistic by adding water during the day time rather than each time step.

Also, despite three irrigation scenarios used in this study, more detailed sensitivity test of added water amount might contribute to a clearer explanation of non-linear precipitation

response between F_self and F_max. The unrealistic irrigation management of

“maximum/minimum of irrigation intensity seasonal cycle among five irrigation areas”

may overestimate or underestimate the result and require further concern. For example, Sacks et al. (2009) found that with extreme irrigation which is over 100 times of actual magnitude, the response showed larger magnitude although with similar changing tendency.

The sensitivity test of the depth of the soil moisture which represents the available water should also be investigated because usually deeper soil column displays stronger memory effect of moisture. The reaction of lifting condensation level (LCL) could be further analyzed to explain why the irrigation-induced moist static instability does not result in more convection. This was done by Lu et al. (2017) for the case of America irrigation through regional model and showed a larger reduction of the planetary boundary layer height than LCL, inhibiting the transport of water vapor to a higher level.

The irrigation-induced increase of water vapor at near surface is not transported to LCL and dose not condense. Consequently, this excessive water vapor does not release their latent heat to the atmosphere, which leads to an unchanged precipitation.

The results (e.g., the response of LCL) might be model dependent since irrigation’s impact might be distinct with different model resolution, domain and boundary layer and convection parameterizations (e.g., Hirsch et al., 2015). For instance, Taylor et al. (2012)

examined the coupling between soil moisture and precipitation and found opposite results between observation and six state-of-the-art global weather and climate models. These inconsistent results indicated that coarse grid resolution in such models might not capture the mesoscale structure, which is crucial for convection triggering. Hence, simulations with different grid size should be investigated. In addition, the adoption of cloud resolving model might contribute to the clarification of diverse results due to boundary layer and convection parameterizations (Cheng and Cotton, 2004).

Since ocean is prescribed by climatological sea surface temperature in coupled land-atmosphere simulations, the interaction between ocean and land/land-atmosphere is excluded in our study. However, the same land-used change process under different sea surface temperature pattern might show diverse result through the interaction with the ocean dynamics (Chen, 2017). Hence different prescribed sea surface temperature patterns (e.g., ENSO) and fully couple ocean experiment are worth doing to examine more realistic response of land-atmosphere coupling toward irrigation.

4.3.2 Other factors that result in the diversity of LAC responses

The mean state of atmospheric circulation should be further considered because from our conclusion, the mean state of atmospheric circulation is also an important factor which influences the atmospheric response of irrigation, such as the alteration of cloud

cover (Sacks et al., 2009). The dominative synoptic environments and the wind field could be the aspect of considering the different of the mean atmospheric circulation.

Several research stated that synoptic environments play a primal role in the formation of precipitation (Barnston and Schickedanz, 1984; Harding and Snyder, 2012a; Zhou et al., 2016). Ho (2017) revealed that different atmospheric vorticity corresponded to distinct response of irrigation, displaying the non-negligible influence of prevailing wind fiend.

The soil moisture spatial gradient between irrigation area and its surrounding could be further analyzed because such moisture gradient might also influence the response of the atmospheric circulation through large scale effects (Hsu et al., 2017). The consideration of soil moisture spatial gradient might contribute to the explanation of atmospheric response in our simulations.

4.3.3 Application

In summary, the results of this study show that the potential response of LAC after irrigation might be different based on the original local hydroclimatological characteristics although the hydroclimate could not explain all the response perfectly. The conclusion could be applied to the shift of LAC under ENSO and climate changes because the alteration of precipitation and/or energy may have similar effects as irrigation. For example, during strong El Niño events in 1997 and 1998, precipitation decreased and

surface net radiation increased obviously in central Africa (green box in Figure 4.5).

Hence the hydroclimate there shifted toward the water-limit condition. On the other hand, precipitation increased and surface net radiation decreased obviously in the southeast of South America (purple box in Figure 4.5), shifting the hydroclimate toward the energy-limit condition. How the LAC might shift is an important topic to explore.

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9), D05109.

Alter, R. E., Douglas, H. C., Winter, J. M., & Eltahir, E. A. (2018). Twentieth century regional climate change during the summer in the central United States attributed to agricultural intensification. Geophysical Research Letters, 45(3), 1586-1594.

Badger, A. M., & Dirmeyer, P. A. (2015). Climate response to Amazon forest replacement by heterogeneous crop cover. Hydrology and Earth System Sciences, 19(11), 4547-4557. doi:10.5194/hess-19-4547-2015

Barnston, A. G., & Schickedanz, P. T. (1984). The effect of irrigation on warm season precipitation in the southern Great Plains. Journal of Climate and Applied Meteorology, 23(6), 865-888.

Budyko, M. I. (1961). The Heat Balance of the Earth's Surface. Soviet Geography, 2(4), 3-13. doi:10.1080/00385417.1961.10770761

Chagnon, F. J. F. (2005). Contemporary climate change in the Amazon. Geophysical Research Letters, 32(13). doi:10.1029/2005gl022722

Chen, C.-C. (2017). The Relation between Climate and Deforestation in the Maritime Continent.

Cheng, W. Y., & Cotton, W. R. (2004). Sensitivity of a cloud-resolving simulation of the genesis of a mesoscale convective system to horizontal heterogeneities in soil moisture initialization. Journal of Hydrometeorology, 5(5), 934-958.

DeFries, R. S., Foley, J. A., & Asner, G. P. (2004). Land‐use choices: balancing human needs and ecosystem function. Frontiers in Ecology and the Environment, 2(5), 249-257.

Diffenbaugh, N. S. (2009). Influence of modern land cover on the climate of the United States. Climate Dynamics, 33(7-8), 945.

Dirmeyer, P. A. (2011). The terrestrial segment of soil moisture-climate coupling.

Geophysical Research Letters, 38(16), n/a-n/a. doi:10.1029/2011gl048268

Douglas, E. M., Niyogi, D., Frolking, S., Yeluripati, J. B., Pielke Sr, R. A., Niyogi, N., . . . Mohanty, U. (2006). Changes in moisture and energy fluxes due to agricultural land use and irrigation in the Indian Monsoon Belt. Geophysical Research Letters, 33(14).

Findell, K. L., & Eltahir, E. A. B. (1997). An analysis of the soil moisture-rainfall feedback, based on direct observations from Illinois. Water Resources Research, 33(4).

Green, R. E., Cornell, S. J., Scharlemann, J. P., & Balmford, A. (2005). Farming and the fate of wild nature. SCIENCE, 307(5709), 550-555.

Harding, K. J., & Snyder, P. K. (2012a). Modeling the Atmospheric Response to Irrigation in the Great Plains. Part I: General Impacts on Precipitation and the Energy Budget. Journal of Hydrometeorology, 13(6), 1667-1686.

doi:10.1175/jhm-d-11-098.1

Harding, K. J., & Snyder, P. K. (2012b). Modeling the Atmospheric Response to Irrigation in the Great Plains. Part II: The Precipitation of Irrigated Water and Changes in Precipitation Recycling. Journal of Hydrometeorology, 13(6), 1687-1703. doi:10.1175/jhm-d-11-099.1

Hirsch, A. L., Pitman, A., Kala, J., Lorenz, R., & Donat, M. (2015). Modulation of land-use change impacts on temperature extremes via land–atmosphere coupling over Australia. Earth Interactions, 19(12), 1-24.

Ho, A.-C. (2017). Responses of Land-Atmosphere Interactions to the Change in Irrigation Area Size. doi:10.6342/NTU201701733

Hsu, H., Lo, M. H., Guillod, B. P., Miralles, D. G., & Kumar, S. (2017). Relation between precipitation location and antecedent/subsequent soil moisture spatial patterns. Journal of Geophysical Research: Atmospheres.

Im, E.-S., Marcella, M. P., & Eltahir, E. A. (2014). Impact of potential large-scale irrigation on the West African monsoon and its dependence on location of

Jung, M., Reichstein, M., Ciais, P., Seneviratne, S. I., Sheffield, J., Goulden, M. L., . . . Zhang, K. (2010). Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 467(7318), 951-954.

doi:10.1038/nature09396

Khanna, J., Medvigy, D., Fueglistaler, S., & Walko, R. (2017). Regional dry-season climate changes due to three decades of Amazonian deforestation. Nature Climate Change, 7(3), 200-204. doi:10.1038/nclimate3226

Koster, R. D., Dirmeyer, P. A., Guo, Z., Bonan, G., Chan, E., Cox, P., . . . Yamada, T.

(2004). Regions of Strong Coupling Between Soil Moisture and Precipitation.

SCIENCE, 305, 3. doi:10.1126/science.1100217

Lawrence, D., & Vandecar, K. (2014). Effects of tropical deforestation on climate and agriculture. Nature Climate Change, 5(1), 27-36. doi:10.1038/nclimate2430 Lee, E., Chase, T. N., Rajagopalan, B., Barry, R. G., Biggs, T. W., & Lawrence, P. J.

(2009). Effects of irrigation and vegetation activity on early Indian summer monsoon variability. International Journal of Climatology, 29(4), 573-581.

Lo, M. H., & Famiglietti, J. S. (2013). Irrigation in California's Central Valley strengthens the southwestern US water cycle. Geophysical Research Letters, 40(2), 301-306.

Lo, M. H., Wu, C. M., Ma, H. Y., & Famiglietti, J. S. (2013). The response of coastal stratocumulus clouds to agricultural irrigation in California. Journal of Geophysical Research: Atmospheres, 118(12), 6044-6051.

Lu, Y., Harding, K., & Kueppers, L. (2017). Irrigation Effects on Land–Atmosphere Coupling Strength in the United States. Journal of Climate, 30(10), 3671-3685.

Mahmood, R., Pielke, R. A., Hubbard, K. G., Niyogi, D., Dirmeyer, P. A., McAlpine, C., . . . Beltrán‐Przekurat, A. (2014). Land cover changes and their

biogeophysical effects on climate. International Journal of Climatology, 34(4), 929-953.

Matson, P. A., Parton, W. J., Power, A., & Swift, M. (1997). Agricultural intensification

McDermid, S., Mearns, L., & Ruane, A. (2017). Representing agriculture in Earth System Models: Approaches and priorities for development. Journal of Advances in Modeling Earth Systems, 9(5), 2230-2265.

Mei, R., & Wang, G. (2011). Impact of Sea Surface Temperature and Soil Moisture on Summer Precipitation in the United States Based on Observational Data. Journal of Hydrometeorology, 12(5), 1086-1099. doi:10.1175/2011jhm1312.1

Miralles, D. G., van den Berg, M. J., Gash, J. H., Parinussa, R. M., de Jeu, R. A. M., Beck, H. E., . . . Johannes Dolman, A. (2013). El Niño–La Niña cycle and recent trends in continental evaporation. Nature Climate Change, 4(2), 122-126.

doi:10.1038/nclimate2068

Pielke Sr, R. A., Pitman, A., Niyogi, D., Mahmood, R., McAlpine, C., Hossain, F., . . . Fall, S. (2011). Land use/land cover changes and climate: modeling analysis and observational evidence. Wiley Interdisciplinary Reviews: Climate Change, 2(6), 828-850.

Puma, M. J., & Cook, B. I. (2010). Effects of irrigation on global climate during the 20th century. Journal of Geophysical Research, 115(D16).

doi:10.1029/2010jd014122

Quesada, B., Arneth, A., & Noblet‐Ducoudré, N. (2017). Atmospheric, radiative, and hydrologic effects of future land use and land cover changes: A global and multimodel climate picture. Journal of Geophysical Research: Atmospheres, 122(10), 5113-5131.

Ramankutty, N., Evan, A. T., Monfreda, C., & Foley, J. A. (2008). Farming the planet:

1. Geographic distribution of global agricultural lands in the year 2000. Global biogeochemical cycles, 22(1).

Sacks, W. J., Cook, B. I., Buenning, N., Levis, S., & Helkowski, J. H. (2009). Effects of global irrigation on the near-surface climate. Climate Dynamics, 33(2-3), 159-175.

Taylor, C. M., de Jeu, R. A., Guichard, F., Harris, P. P., & Dorigo, W. A. (2012).

Afternoon rain more likely over drier soils. Nature, 489(7416), 423.

Tuttle, S., & Salvucci, G. (2016). Empirical evidence of contrasting soil moisture–

precipitation feedbacks across the United States. SCIENCE, 352(6287).

doi:0.1126/science.aaa7185

Wada, Y., & Bierkens, M. F. (2014). Sustainability of global water use: past

reconstruction and future projections. Environmental Research Letters, 9(10), 104003.

Wey, H. W., Lo, M. H., Lee, S. Y., Yu, J. Y., & Hsu, H. H. (2015). Potential impacts of wintertime soil moisture anomalies from agricultural irrigation at low latitudes on regional and global climates. Geophysical Research Letters, 42(20), 8605-8614.

Xie, Z.-H., Zeng, Y.-J., Xia, J., Qin, P.-H., Jia, B.-H., Zou, J., & Liu, S. (2017). Coupled modeling of land hydrology–regional climate including human carbon emission and water exploitation. Advances in Climate Change Research, 8(2), 68-79.

Zeng, X., Barlage, M., Castro, C., & Fling, K. (2010). Comparison of Land–

Precipitation Coupling Strength Using Observations and Models. Journal of Hydrometeorology, 11(4), 979-994. doi:10.1175/2010jhm1226.1

Zhou, Y., Wu, D., K.-M. Lau, W., & Tao, W.-K. (2016). Scale Dependence of Land–

Atmosphere Interactions in Wet and Dry Regions as Simulated with NU-WRF over the Southwestern and South-Central United States. Journal of

Hydrometeorology, 17(8), 2121-2136.

Figures

Figure 1.1 The illustration of the land-atmosphere coupling (positive coupling as an example) adopted in this study. LE: surface latent heat flux; SH: surface sensible heat flux; 𝜃_𝑒: equivalent potential temperature; Δx: the change of x.

Figure 1.2 Global irrigation map of 2001~2010 average according to Wada and Bierkens (2014). Irrigation unit: log10 (mm/month).

Figure 2.1 Five intense irrigation areas targeted from a monthly irrigation intensity data set derived by Wada and Bierkens (2014): North India (location No 1), North China Plain (location No 3), America Great Plains (location No 4), Southwest Europe (location No 5) and Middle East (location No 6). Purple areas are the original region of significant agricultural irrigation; Red boxes are simulated irrigation region in the model.

Figure 2.2 Climatology of monthly irrigation intensity of five selected locations between 2001 to 2010.

Figure 2.3 Climatology of monthly irrigation intensity of three selected locations between 2001 to 2010. Curves of Central Asia (location No 4) and Europe(location No 5) are combined into “minimum of irrigation intensity seasonal cycle”. Also,

“maximum of irrigation intensity seasonal cycle” is defined.

Figure 2.4 Standard deviation of monthly area averaged precipitation among 25 years in F_ctl.

Figure 2.5 Scatter plot of surface net radiation and the FAO grass reference evapotranspiration (PET) in F_ctl. The climatological monthly data from five irrigation areas are shown. Different symbols dots represent different places. The black dashed line represents the same magnitude between net radiation and PET.

Figure 2.6 Seasonal cycle of (a) America Great Plains (location No 4) and (b) five irrigation areas in F_ctl as examples of hydroclimatological scatter chart. Green box stands for data under higher net radiation. X axis: top 10 cm soil moisture (kg/m^2) of F_ctl; Y axis: net radiation of F_ctl. Different symbols represent different places.

Figure 2.7.1 The differences of vertically integrated moisture (blue) and evapotranspiration minus precipitation (red) after irrigation: F_self – F_ctl. X axis:

month.

Figure 2.7.2 The differences of vertically integrated moisture (blue) and evapotranspiration minus precipitation (red) after irrigation: F_max – F_ctl. X axis:

month.

Figure 3.1.1 Hydroclimatological scatter chart of 𝐼_𝐿𝐸 from F_ctl. (a) 𝐼_𝐿𝐸 (W/m^2):

coupling strength between soil moisture and evapotranspiration, (b) 𝛽_𝐿𝐸 ((W/m^2)/(kg/m^2)): the slope between soil moisture and evapotranspiration, (c) 𝑆_𝑤 (kg/m^2): the standard deviation of daily soil moisture. Different symbols represent different places.

Figure 3.1.2 Hydroclimatological scatter chart of 𝐼_𝐿𝐸 from I_ctl. (a) 𝐼_𝐿𝐸 (W/m^2):

Figure 3.2 Hydroclimatological scatter chart of 𝐼_𝑃 , the coupling between evapotranspiration and precipitation, from F_ctl. Different symbols represent different places.

Figure 3.3 Hydroclimatological scatter chart of 𝐼_{𝑃𝐵𝐿𝐻} from F_ctl. (a) 𝐼_{𝑃𝐵𝐿𝐻} (m):

coupling strength between sensible heat flux and boundary layer height, (b) 𝛽_{𝑃𝐵𝐿𝐻} (m/(W/m^2)): the slope between sensible heat flux and boundary layer height, (c) 𝑆_𝑠 (W/m^2): the standard deviation of daily sensible heat flux. Different symbols represent different places.

Figure 3.4.1 The differences of surface net radiation (green, downward positive), evapotranspiration (orange, upward positive) and top 10 cm soil moisture (blue) after irrigation: I_max – I_ctl. X axis: month.

Figure 3.4.2 The differences of surface net radiation (green, downward positive), evapotranspiration (orange, upward positive) and top 10 cm soil moisture (blue) after irrigation: I_self – I_ctl. X axis: month.

Figure 3.4.3 The differences of surface net radiation (green, downward positive), evapotranspiration (orange, upward positive) and top 10 cm soil moisture (blue) after irrigation: I_min – I_ctl. X axis: month.

Figure 3.5 Hydroclimatological scatter chart of Δ evapotranspiration/Δ irrigation (unitless). (a) I_max - I_ctl, (b) I_self - I_ctl, (c) I_min - I_ctl. X axis: top 10 cm soil moisture (kg/m^2) of I_ctl; Y axis: net radiation of I_ctl.

Figure 3.6 Hydroclimatological scatter chart of Δ soil moisture of top 10 cm depth/Δ irrigation ((kg/m^2)/(W/m^2)). (a) I_max - I_ctl, (b) I_self - I_ctl, (c) I_min - I_ctl. X axis: top 10 cm soil moisture (kg/m^2) of I_ctl; Y axis: net radiation of I_ctl.

Figure 3.7.1 Hydroclimatological scatter chart of Δ𝐼_𝐿𝐸/Δsm10 ((W/m^2)/(kg/m^2)):

(a) I_max - I_ctl, (b) I_self - I_ctl, (c) I_min - I_ctl. Different symbols represent different places. Δsm10 represents change of top 10 cm depth soil moisture. X axis:

top 10 cm soil moisture (kg/m^2) of I_ctl; Y axis: net radiation of I_ctl.

Figure 3.7.2 Hydroclimatological scatter chart of Δ 𝛽_𝐿𝐸 /Δsm10 ((W/m^2)/(kg/m^2)^2) after irrigation: (a) I_max - I_ctl, (b) I_self - I_ctl, (c) I_min - I_ctl. Different symbols represent different places. Δsm10 represents change of top 10 cm depth soil moisture. X axis: top 10 cm soil moisture (kg/m^2) of I_ctl; Y axis: net radiation of I_ctl.

Figure 3.7.3 Hydroclimatological scatter chart of Δ 𝑆_𝑤 /Δsm10 (unitless) after irrigation: (a) I_max - I_ctl, (b) I_self - I_ctl, (c) I_min - I_ctl. Different symbols represent different places. Δsm10 represents change of top 10 cm depth soil moisture.

X axis: top 10 cm soil moisture (kg/m^2) of I_ctl; Y axis: net radiation of I_ctl.

Figure 3.8 The probability density function of daily soil moisture of South Europe (location No 5) over 25 years from November to February. Blue line: I_ctl; red lind:

I_max. Data corresponds to orange circle in Figure 3.7.3(a).

Figure 3.9.1 Differences after irrigation: F_self - F_ctl. (a) precipitation, (b) net radiation, (c) 𝐼_𝐿𝐸 , (d) 𝐼_𝑃 , (e) 𝐼_{𝑃𝐵𝐿𝐻} . The color of dots represent month. At each location, data approaching to left X axis represent earlier month, corresponding to the shaded color.

Figure 3.9.2 Differences after irrigation: F_max - F_ctl. (a) precipitation, (b) net radiation, (c) 𝐼_𝐿𝐸 , (d) 𝐼_𝑃 , (e) 𝐼_{𝑃𝐵𝐿𝐻} . The color of dots represent month. At each location, data approaching to left X axis represent earlier month, corresponding to the shaded color.

Figure 3.10 Hydroclimatological scatter chart of seasonal mean from F_ctl. Red dots:

JJA; blue dots: DJF. Numbers represent the magnitude of surface cooling effect (green), moistening effect (purple) and total effect (pink) of F_max – F_ctl. Note that during the same season, irrigated water is same among five areas; also, the irrigation in JJA is stronger than DJF (Figure 2.3).

Figure 3.11.1 The differences of MSE vertical structure in JJA after irrigation: F_max – F_ctl.

Figure 3.11.2 The differences of MSE vertical structure in DJF after irrigation: F_max – F_ctl.

Figure 3.12 Atmospheric column water budget analysis of F_ctl: evapotranspiration (upward positive) minus precipitation (downward positive). Negative value indicates non-local water vapor sources for precipitation. X axis: month. LE: evapotranspiration;

prec.: precipitation.

Figure 3.13.1 Atmospheric column water budget differences after irrigation: F_self – F_ctl. Black and blue dots represent that the difference pass the unpaired t-test under 95% significant level. X axis: month. LE: evapotranspiration; prec.: precipitation.

Figure 3.13.2 Atmospheric column water budget differences after irrigation: F_max – F_ctl. Black and blue dots represent that the differences pass the unpaired t-test under 95% significant level. X axis: month. LE: evapotranspiration; prec.: precipitation.

Figure 3.14 Annual mean omega differences after irrigation. (a) F_self - F_ctl, (b) F_max - F_ctl.

Figure 3.15 Omega differences after irrigation in North China Plain (location No 3).

(a) May to August of F_self - F_ctl, (b) May and June of F_max - F_ctl. Corresponding to blue box in Figure 3.13.

Figure 3.16.1 The differences of annual mean evapotranspiration (positive upward) after irrigation: F_self - F_ctl. Black crosses represent annual data which pass the unpaired two-tail t-test under 95% significant level. Green box represents irrigated area in simulations.

Figure 3.16.2 The differences of annual mean evapotranspiration (positive upward) after irrigation: F_max - F_ctl. Black crosses represent annual data which pass the unpaired two-tail t-test under 95% significant level. Green box represents irrigated area in simulations.

Figure 3.17 The differences of water vapor flux at 850mb from F_max - F_ctl in (a) May and (b)June. Vector: water vapor flux (m/s) anomaly; shaded: the divergence of water vapor flux (10^-9 1/s) anomaly. Green box represents the irrigation area.

Figure 3.18 The differences of precipitation (W/m^2) from F_max - F_ctl in (a) May and (b) June. Black crosses represent annual data which pass the unpaired two-tail t-test under 95% significant level. Green box represents the irrigation area.

Figure 3.19 Hydroclimatological scatter chart of 𝐼_𝐿𝐸 from F_ctl (black frame), F_self (blue frame) and F_max (pink frame). (a) 𝐼_𝐿𝐸: coupling strength between soil moisture and evapotranspiration, (b) 𝛽_𝐿𝐸 : the slope between soil moisture and evapotranspiration, (c) 𝑆_𝑤 : the standard deviation of daily soil moisture. Different symbols represent different places. Color of frame represent different simulations.

Figure 3.20 Hydroclimatological scatter chart of 𝐼_{𝑃𝐵𝐿𝐻} from F_ctl (black frame), F_self (blue frame) and F_max (pink frame). (a) 𝐼_{𝑃𝐵𝐿𝐻}: coupling strength between sensible heat flux and boundary layer height, (b) 𝛽_{𝑃𝐵𝐿𝐻}: the slope between sensible heat flux and boundary layer height, (c) 𝑆_𝑠: the standard deviation of daily sensible heat flux. Different symbols represent different places. Color of frame represent different simulations.

Figure 3.21 Hydroclimatological scatter chart of 𝐼_𝑃 from F_ctl (black frame), F_self (blue frame) and F_max (pink frame). Different symbols represent different places.

Color of frame represent different simulations.

Figure 4.1 The illustration of the strength of three land-atmosphere coupling indices (𝐼_𝐿𝐸, 𝐼_{𝑃𝐵𝐿𝐻} and 𝐼_𝑃) and their controlling factors: (a) 𝐼_𝐿𝐸, (b) 𝐼𝑃𝐵𝐿𝐻, (c) 𝐼𝑃. The shift of red dot represents the response of coupling strength due to the change of controlling factors. Blue line represents the changing tendency (i.e., increase or decrease) of coupling index among the shift of the controlling factors, but it does not

Figure 4.2 The illustration of the responses of land surface fluxes and atmosphere structure with more available water under different hydroclimatological characteristics: (a) approaching to the water-limit condition, (b) the transition zone and (c) approaching to the energy-limit condition. LE: surface latent heat flux; SH: surface sensible heat flux.

Figure 4.3 Seasonal cycle of 𝐼_𝐿𝐸 in America Great Plains (location No 4) in F_ctl (blue line) and F_Self (red line). (a) 𝐼_𝐿𝐸 : coupling strength between soil moisture and evapotranspiration, (b) 𝛽_𝐿𝐸: the slope between soil moisture and evapotranspiration, (c) 𝑆_𝑤: the standard deviation of daily soil moisture.

Figure 4.4 Seasonal cycle of 𝐼_{𝑃𝐵𝐿𝐻} in America Great Plains (location No 4) in F_ctl (blue line) and F_Self (red line). (a) 𝐼_{𝑃𝐵𝐿𝐻}: coupling strength between sensible heat flux and boundary layer height, (b) 𝛽_{𝑃𝐵𝐿𝐻} : the slope between sensible heat flux and boundary layer height, (c) 𝑆_𝑠: the standard deviation of daily sensible heat flux.

Figure 4.5 Anomaly of (a) precipitation and (b) surface net radiation during El Niño in 1997 and 1998. Reference climatology is calculated from 1984 to 2010.

Precipitation comes from GPCC reanalysis data; net radiation comes from ECMWF reanalysis data.

Tables

Table 2.1 The abbreviation of each simulation settings.

在文檔中在不同水文氣候特徵下農業灌溉對陸地-大氣耦合強度之影響 (頁 49-104)