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Journal of Arid Land  2016, Vol. 8 Issue (4): 506-520    DOI: 10.1007/s40333-016-0126-4
Research Articles     
Runoff of arid and semi-arid regions simulated and projected by CLM-DTVGM and its multi-scale fluctuations as revealed by EEMD analysis
NING Like1,2, XIA Jun3,1*, ZHAN Chesheng1, ZHANG Yongyong1
1 Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
2 University of Chinese Academy of Sciences, Beijing 100049, China;
3 State Key Laboratory of Water Resources & Hydropower Engineering Science, Wuhan University, Wuhan 430000, China
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Abstract  Runoff is a major component of the water cycle, and its multi-scale fluctuations are important to water resources management across arid and semi-arid regions. This paper coupled the Distributed Time Variant Gain Model (DTVGM) into the Community Land Model (CLM 3.5), replacing the TOPMODEL-based method to simulate runoff in the arid and semi-arid regions of China. The coupled model was calibrated at five gauging stations for the period 1980–2005 and validated for the period 2006–2010. Then, future runoff (2010–2100) was simulated for different Representative Concentration Pathways (RCP) emission scenarios. After that, the spatial distributions of the future runoff for these scenarios were discussed, and the multi-scale fluctuation characteristics of the future annual runoff for the RCP scenarios were explored using the Ensemble Empirical Mode Decomposition (EEMD) analysis method. Finally, the decadal variabilities of the future annual runoff for the entire study area and the five catchments in it were investigated. The results showed that the future annual runoff had slowly decreasing trends for scenarios RCP 2.6 and RCP 8.5 during the period 2010–2100, whereas it had a non-monotonic trend for the RCP 4.5 scenario, with a slow increase after the 2050s. Additionally, the future annual runoff clearly varied over a decadal time scale, indicating that it had clear divisions between dry and wet periods. The longest dry period was approximately 15 years (2040–2055) for the RCP 2.6 scenario and 25 years (2045–2070) for the RCP 4.5 scenario. However, the RCP 8.5 scenario was predicted to have a long dry period starting from 2045. Under these scenarios, the water resources situation of the study area will be extremely severe. Therefore, adaptive water management measures addressing climate change should be adopted to proactively confront the risks of water resources.

Key wordssand and dust storm      weight allocation criterion      Kriging interpolation      score map      Al-Howizeh/Al-Azim marshes      Sistan Basin     
Received: 14 August 2015      Published: 10 August 2016

The National Basic Research Program of China (2012CB956204).

Corresponding Authors: XIA Jun     E-mail:
Cite this article:

NING Like, XIA Jun, ZHAN Chesheng, ZHANG Yongyong. Runoff of arid and semi-arid regions simulated and projected by CLM-DTVGM and its multi-scale fluctuations as revealed by EEMD analysis. Journal of Arid Land, 2016, 8(4): 506-520.

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Beven K J. 2011. Rainfall-Runoff Modelling: The Primer. Hoboken: John Wiley & Sons.

Cammalleri C, Micale F, Vogt J. 2015. On the value of combining different modelled soil moisture products for European drought monitoring. Journal of Hydrology, 525: 547–558.

Choi H I, Liang X Z. 2010. Improved terrestrial hydrologic representation in mesoscale land surface models. Journal of Hydrometeorology, 11(3): 797–809.

Cleveland W S. 1979. Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368): 829–836.

Collins W D, Bitz C M, Blackmon M L, et al. 2006. The community climate system model version 3 (CCSM3). Journal of Climate, 19(11): 2122–2143.

Dai Y J, Zeng X B, Dickinson R E. 2001. The Common Land Model (CLM): Technical Documentation and User’s Guide. Georgia: Georgia Institute of Technology.

Gudmundsson L, Tallaksen L M, Stahl K, et al. 2012. Comparing large-scale hydrological model simulations to observed runoff percentiles in Europe. Journal of Hydrometeorology, 13(2): 604–620.

Gupta H V, Sorooshian S, Yapo P O. 1999. Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. Journal of Hydrologic Engineering, 4(2): 135–143.

Huang N E, Shen Z, Long S R, et al. 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of Mathematical, Physical and Engineering Sciences. London: The Royal Society, 903–995.

Huang N E, Wu Z H. 2008. A review on Hilbert-Huang transform: Method and its applications to geophysical studies. Reviews of Geophysics, 46(2), doi: 10.1029/2007RG000228.

Kallache M, Rust H W, Kropp J. 2005. Trend assessment: applications for hydrology and climate research. Nonlinear Processes in Geophysics, 12(2): 201–210.

Katsavrias C, Preka-Papadema P, Moussas X. 2012. Wavelet analysis on solar wind parameters and geomagnetic indices. Solar Physics, 280(2): 623–640.

Lai X, Wen J, Cen S X, et al. 2014. Numerical simulation and evaluation study of soil moisture over China by using CLM 4.0 model. Chinese Journal of Atmospheric Sciences, 38(3): 499–512. (in Chinese)

Li M X, Ma Z G, Niu G Y. 2011. Modeling spatial and temporal variations in soil moisture in China. Chinese Science Bulletin, 56(17): 1809–1820.

Moss R H, Edmonds J A, Hibbard K A, et al. 2010. The next generation of scenarios for climate change research and assessment. Nature, 463(7282): 747–756.

Nash J E, Sutcliffe J V. 1970. River flow forecasting through conceptual models part I-A discussion of principles. Journal of Hydrology, 10(3): 282–290.

Naujokat B. 1986. An update of the observed quasi-biennial oscillation of the stratospheric winds over the tropics. Journal of the Atmospheric Sciences, 43(17): 1873–1877.

Niu G Y, Yang Z L, Dickinson R E, et al. 2005. A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models. Journal of Geophysical Research: Atmospheres, 110(D21): D07103.

Oleson K W, Dai Y G, Bonan G, et al. 2004. Technical description of the community land model (CLM). In: NCAR Technical Note NCAR/TN–461+STR. National Center for Atmospheric Research. Colorado, USA.

Oleson K W, Niu G Y, Yang Z L, et al. 2008. Improvements to the Community Land Model and their impact on the hydrological cycle. Journal of Geophysical Research: Biogeosciences, 113(G1): G01021.

Qian S, Fu Y, Pan F F. 2010. Climate change tendency and grassland vegetation response during the growth season in Three-River Source Region. Science China Earth Sciences, 53(10): 1506–1512.

Shangguan W, Dai Y J, Liu B Y, et al. 2013. A China data set of soil properties for land surface modeling. Journal of Advances in Modeling Earth Systems, 5(20): 212–224.

Sheffield J, Goteti G, Wood E F. 2006. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. Journal of Climate, 19(13): 3088–3111.

Song X M, Zhan C S, Xia J. 2012. Integration of a statistical emulator approach with the SCE-UA method for parameter optimization of a hydrological model. Chinese Science Bulletin, 57(26): 3397–3403.

Tank A M G K, Zwiers F W, Zhang X B. 2009. Guidelines on: Analysis of extremes in a changing climate in support of informed decisions for adaptation. Switzerland: World Meteorological Organization.

Thompson S A. 1999. Hydrology for Water Management. Rotterdam: Balkema.

Tian Q H, Zhou X J, Gou X H, et al. 2012. Analysis of reconstructed annual precipitation from tree-rings for the past 500 years in the middle Qilian Mountain. Science China Earth Sciences, 55(5): 770–778.

Wang A H, Lettenmaier D P, Sheffield J. 2011. Soil moisture drought in China, 1950-2006. Journal of Climate, 24(13): 3257–3271.

Wu Z H, Huang N E, Long S R, et al. 2007. On the trend, detrending, and variability of nonlinear and nonstationary time series. Proceedings of the National Academy of Sciences of the United States of America, 104(38): 14889–14894.

Wu Z H, Huang N E. 2009. Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1(1): 1–41.

Xia J. 2002. Hydrological Nonlinear Theories and Approaches. Wuhan: Wuhan University Press. (in Chinese)

Xia J, Wang G S, Tan G, et al. 2005. Development of distributed time-variant gain model for nonlinear hydrological systems. Science in China Series D: Earth Sciences, 48(6): 713–723.

Xu W T, Wu B F, Yan C Z, et al. 2005. China land cover 2000 using SPOT VGT S10 data. Journal of Remote Sensing, 9(2): 204–214. (in Chinese)

Yang J P, Ding Y J, Chen R S, et al. 2002. Spatial change of dry and wet climate boundary in China in the recent 50 years. Journal of Glaciology and Geocryology, 24(6): 731–736. (in Chinese)
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