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The effects of annual precipitation and mean air temperature on annual runoff in global forest regions

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Abstract

Changing trends in runoff and water balance under a warming atmosphere are a major subject of interest in recent climatic and hydrological research. Forest basins represent the most complex systems including critical hydrological processes. In this study, we investigate the relationship between annual total runoff (Q), precipitation (P), and mean temperature (T) using observed data collected from 829 (forest) site years around the world. It is shown that the strong linear relationship between annual P and Q is a function of mean T. By empirically perturbing observed annual Q and P with T, a set of ΔQ-zero lines are derived for different mean T. To evaluate the extent to which the future changes in annual P and T alter Q, the future projections of ΔP and ΔT under a warming scenario (A1B) from five coupled AOGCMs (Atmosphere-Ocean General Circulation Models) are compared with the empirical ΔQ-zero lines derived in this study. It is found that five AOGCMs show different distributions with respect to the ΔQ-zero lines, which can be attributed to the contrasting dominant sensitivities of various influencing factors to water balance partitioning among models. The knowledge gained in this empirical study is helpful to predict water resources changes under changing climate as well as to interpret hydrologic simulations in AOGCM future projections.

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Acknowledgements

We would like to thank anonymous reviewers, whose comments were useful for revising this manuscript. We also acknowledge the international modeling groups for providing their data for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the model data, the JSC/CLIVAR Working Group on Coupled Modelling (WGCM) and their Coupled Model Intercomparison Project (CMIP) and Climate Simulation Panel for organizing the model data analysis activity, and the IPCC WG1 TSU for technical support. This work was supported by JSPS KAKENHI, Grants-in-Aid for Scientific Research on Innovative Areas (22119009) and (S)(23226012), and Innovative program of climate change projection for the 21st Century from The Ministry of Education, Culture, Sports, Science and Technology (MEXT).

Author information

Authors and Affiliations

  1. Kasuya Research Forest, Kyushu University, Sasaguri, Fukuoka, 811-2415, Japan

    Jaeil Cho & Hikaru Komatsu

  2. School of Forestry and Resource Conservation, National Taiwan University, 1, Sce. 4, Roosevelt Road, Taipei, Taiwan

    Hikaru Komatsu

  3. Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan

    Yadu Pokhrel, Pat J.-F. Yeh & Taikan Oki

  4. Department of Mechanical and Environmental Informatics, Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo, 152-8552, Japan

    Shinjiro Kanae

Authors
  1. Jaeil Cho
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  2. Hikaru Komatsu
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  3. Yadu Pokhrel
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  4. Pat J.-F. Yeh
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  5. Taikan Oki
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Corresponding author

Correspondence to Jaeil Cho.

Electronic supplementary materials

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Figure S1

Scatter plots of annual mean temperature (T) versus annual total evapotranspiration (E) with the colors indicates annual total precipitation (P). (DOC 87.0 kb)

Figure S2

The ΔQ-zero lines as a function of ΔP and ΔT (Eq. 2). See the text for explanation. The colors of lines indicate mean annual temperature T before change. Scattering plots show the data ΔP and ΔT from 2001 to 2100 simulated by five AOGCMs using the projected A1B scenario. The color bar indicates latitude (degree) in grids in model simulation (also see Fig. 4). (DOC 397  kb)

Table S1

A summary of the complied data from previous literature used in this study according to the location and forest type. (DOC 35.5 kb)

Table S2

List of AOGCM simulations based on the IPCC SRES (special report on emission scenarios) A1B. (DOC 24.5 kb)

Appendix A

Appendix A

The zero-lines of ΔQ (= Q 1 – Q 0 ) between ΔP (= P 1 – P 0 ) and ΔT (= T 1 – T 0 ) in Fig. 4 are calculated from Eq. (2).

$$ \begin{array}{*{20}{c}} {{Q_0} = 0.{87}{P_0} + \left( { - 0.{4855}T_0^{{2}} - {18}.{968}{T_0} - {253}.{57}} \right)} \\ {{Q_1} = 0.{87}{P_1} + \left( { - 0.{4855}T_1^2 - {18}.{968}{T_1} - {253}.{57}} \right)} \\ \end{array}, $$
(A1)

When ΔQ is zero, the following equation can be derived:

$$ \Delta T = \frac{{0.0459}}{{(1 - 0.0256({T_1} + {T_0}))}}\Delta P $$
(A2)

Thus, the ΔQ-zero line has a positive relationship between ΔP and ΔT, and it depends on T 0 , but not on P 0 .

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Cho, J., Komatsu, H., Pokhrel, Y. et al. The effects of annual precipitation and mean air temperature on annual runoff in global forest regions. Climatic Change 108, 401–410 (2011). https://doi.org/10.1007/s10584-011-0197-3

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