Quantifying both climate and land use/cover changes on runoff variation in Han River basin, China

Abstract

Climate change and land use/cover change (LUCC) can both exert great impacts on the generation processes of precipitation and runoff. However, previous studies usually neglected considering the contribution component of future LUCC in evaluating changes in hydrological cycles. In this study, an integrated framework is developed to quantify and partition the impact of climate change and LUCC on future runoff evolution. First, a daily bias correction (DBC) method and the Cellular Automaton-Markov (CA-Markov) model are used to project future climate and LUCC scenarios, and then future runoff is simulated by the calibrated Soil and Water Assessment Tool (SWAT) model with different climate and LUCC scenarios. Finally, the uncertainty of future runoff and the contribution rate of the two driving factors are systematically quantified. The Han River basin in China was selected as a case study. Results indicate that: 1) both climate change and LUCC will contribute to future runoff intensification, the variation of future runoff under combined climate and LUCC is larger than these under climate change or LUCC alone; 2) the projected uncertainty of median value of multi-models under RCP4.5 (RCP8.5) will reach 18.14% (20.34%), 12.18% (14.71%), 11.01% (13.95%), and 11.41% (14.34%) at Baihe, Ankang, Danjiangkou, and Huangzhuang stations, respectively; 3) the contribution rate of climate change to runoff at Baihe, Ankang, Danjiangkou, and Huangzhuang stations under RCP4.5 (RCP8.5) are 91%–98% (84%–94%), while LUCC to runoff under RCP4.5 (RCP8.5) only accounts for 2%–9% (6%–16%) in the annual scale. This study may provide useful adaptive strategies for policymakers on future water resources planning and management.

Graphical abstract

Keywords

climate change / LUCC / runoff response / uncertainty analysis / contribution rate

References

[1] Abbaspour K C, Yang J, Maximov I, Siber R, Bogner K, Mieleitner J, Zobrist J, Srinivasan R (2007). Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. J Hydrol (Amst), 333(2–4): 413–430

[2] Ahmadalipour A, Moradkhani H, Rana A (2018). Accounting for downscaling and model uncertainty in fine-resolution seasonal climate projections over the Columbia River Basin. Clim Dyn, 50(1–2): 717–733

[3] Berihun M L, Tsunekawa A, Haregeweyn N, Meshesha D T, Adgo E, Tsubo M, Masunaga T, Fenta A A, Sultan D, Yibeltal M, Ebabu K (2019). Hydrological responses to land use/land cover change and climate variability in contrasting agro-ecological environments of the Upper Blue Nile basin, Ethiopia. Sci Total Environ, 689: 347–365

[4] Block K, Mauritsen T (2013). Forcing and feedback in the MPI-ESM-LR coupled model under abruptly quadrupled CO₂. J Adv Model Earth Syst, 5(4): 676–691

[5] Changnon D, Gensini V A (2019). Changing spatiotemporal patterns of 5- and 10-Day Illinois heavy precipitation amounts, 1900–2018. J Appl Meteorol Climatol, 58(7): 1523–1533

[6] Chauvin F, Douville H, Ribes A (2017). Atlantic tropical cyclones water budget in observations and CNRM-CM5 model. Clim Dyn, 49(11–12): 4009–4021

[7] Chawla I, Mujumdar P P (2015). Isolating the impacts of land use and climate change on streamflow. Hydrol Earth Syst Sci, 19(8): 3633–3651

[8] Chen H, Guo S L, Xu C Y, Singh V P (2007). Historical temporal trends of hydro-climatic variables and runoff response to climate variability and their relevance in water resource management in the Hanjiang basin. J Hydrol (Amst), 344(3–4): 171–184

[9] Chen J, Brissette F P, Chaumont D, Braun M (2013). Performance and uncertainty evaluation of empirical downscaling methods in quantifying the climate change impacts on hydrology over two North American river basins. J Hydrol (Amst), 479: 200–214

[10] Clerici N, Cote-Navarro F, Escobedo F J, Rubiano K, Villegas J C (2019). Spatio-temporal and cumulative effects of land use-land cover and climate change on two ecosystem services in the Colombian Andes. Sci Total Environ, 685: 1181–1192

[11] Costa M H, Botta A, Cardille J A (2003). Effects of large-scale changes in land cover on the discharge of the Tocantins River, Southeastern Amazonia. J Hydrol (Amst), 283(1–4): 206–217

[12] da Silveira I, Zuidema P, Kirtman B P (2019). Fast SST error growth in the southeast Pacific Ocean: comparison between high and low-resolution CCSM4 retrospective forecasts. Clim Dyn, 53(9–10): 5237–5251

[13] Frey L, Bender A M, Svensson G (2021). Processes controlling the vertical aerosol distribution in marine stratocumulus regions- a sensitivity study using the climate model NorESM1-M. Atmos Chem Phys, 21(1): 577–595

[14] Gassman P W, Reyes M R, Green C H, Arnold J G (2007). The soil and water assessment tool: historical development, applications, and future research directions. Trans ASABE, 50(4): 1211–1250

[15] Gu L, Chen J, Yin J B, Xu C-Y (2020). Responses of precipitation and runoff to climate warming and implications for future drought changes in China. Earth’s Future, 8(10): e2020EF001718.

[16] Guan D J, Li H F, Inohae T, Su W C, Nagaie T, Hokao K (2011). Modeling urban land use change by the integration of cellular automaton and Markov model. Ecol Modell, 222(20–22): 3761–3772

[17] Guo Y, Fang G, Xu Y P, Tian X, Xie J (2020). Identifying how future climate and land use/cover changes impact streamflow in Xinanjiang Basin, East China. Sci Total Environ, 710: 136275

[18] Halmy M W A, Gessler P E, Hicke J A, Salem B B (2015). Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Appl Geogr, 63: 101–112

[19] Huang N E, Shen Z, Long S R, Wu M C, Shih H H, Zheng Q, Yen N C, Tung C C, Liu H H (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings Mathematical Physical & Engineering Conferences, 454: 903–995

[20] Huang N E, Shen Z, Long S R (1999). A new view of nonlinear water waves: the Hilbert Spectrum 1. Annu Rev Fluid Mech, 31(1): 417–457

[21] Hyandye C, Martz L W (2017). A Markovian and cellular automata land-use change predictive model of the Usangu Catchment. Int J Remote Sens, 38(1): 64–81

[22] Ji D, Wang L, Feng J, Wu Q Z, Zhou M Z (2014). Description and basic evaluation of BNU-ESM version 1. Geoscientific Model Devel Discuss, 7(2): 1601–1647

[23] Khazaei M R, Zahabiyoun B, Saghafian B (2012). Assessment of climate change impact on floods using weather generator and continuous rainfall-runoff model. Int J Climatol, 32(13): 1997–2006

[24] Kundu S, Khare D, Mondal A (2017). Individual and combined impacts of future climate and land use changes on the water balance. Ecol Eng, 105: 42–57

[25] Kusangaya S, Warburton M L, van Garderen E A, Jewitt G P W (2014). Impacts of climate change on water resources in southern Africa: a review. Phys Chem Earth, 67–69: 47–54

[26] Li S S, Zhang L, Du Y, Zhang Y H, Yan C C (2020). Anthropogenic impacts on streamflow-compensated climate change effect in the Hanjiang River basin, China. J Hydrol Eng, 25(1): 04019058

[27] Li Y, Chang J, Wang Y, Guo A, Luo L, Ma F, Fan J(2019). Spatiotemporal impacts of land use land cover changes on hydrology from the mechanism perspective using SWAT model with time-varying parameters. Hydrol Res, 50(1): 244–261

[28] Liang Z, Tang T, Li B, Liu T, Wang J, Hu Y (2018). Long-term streamflow forecasting using SWAT through the integration of the random forests precipitation generator: case study of Danjiangkou Reservoir. Hydrol Res, 49(5): 1513–1527

[29] Lin B, Chen X, Yao H, Chen Y, Liu M, Gao L, James A (2015). Analyses of landuse change impacts on catchment runoff using different time indicators based on SWAT model. Ecol Indic, 58: 55–63

[30] Luo G, Yin C, Chen X, Xu W, Lu L (2010). Combining system dynamic model and CLUE-S model to improve land use scenario analyses at regional scale: a case study of Sangong watershed in Xinjiang, China. Ecol Complex, 7(2): 198–207

[31] Memarian H, Kumar Balasundram S, Bin Talib J, Teh Boon Sung C, Mohd Sood A, Abbaspour K (2012). Validation of CA-Markov for simulation of land use and cover change in the Langat Basin, Malaysia. J Geogr Inf Syst, 04(06): 542–554

[32] Mishra S K, Pandey A, Singh V P (2012). Special issue on soil conservation service curve number (SCS-CN) methodology. J Hydrol Eng, 17(11): 1157

[33] Mpelasoka F S, Chiew F H S (2009). Influence of rainfall scenario construction methods on runoff projections. J Hydrometeorol, 10(5): 1168–1183

[34] Paymard P, Yaghoubi F, Nouri M, Bannayan M (2019). Projecting climate change impacts on rainfed wheat yield, water demand, and water use efficiency in northeast Iran. Theor Appl Climatol, 138(3–4): 1361–1373

[35] Nouri J, Gharagozlou A, Arjmandi R, Faryadi S, Adl M (2014). Predicting urban land use changes using a CA-Markov Model. Arab J Sci Eng, 39(7): 5565–5573

[36] Pan S H, Liu D D, Wang Z L, Zhao Q, Zou H, Hou Y K, Liu P, Xiong L H (2017). Runoff responses to climate and land use/cover changes under future scenarios. Water, 9(7): 475

[37] Pontius R G Jr, Neeti N (2010). Uncertainty in the difference between maps of future land change scenarios. Sustain Sci, 5(1): 39–50

[38] Poska A, Sepp E, Veski S, Koppel K (2008). Using quantitative pollen-based land-cover estimations and a spatial CA-Markov model to reconstruct the development of cultural landscape at Ruge, South Estonia. Veg Hist Archaeobot, 17(5): 527–541

[39] Richey A S, Thomas B F, Lo M H, Reager J T, Famiglietti J S, Voss K, Swenson S, Rodell M (2015). Quantifying renewable groundwater stress with GRACE. Water Resour Res, 51(7): 5217–5238

[40] Schmidli J, Frei C, Vidale P L (2006). Downscaling from GC precipitation: a benchmark for dynamical and statistical downscaling methods. Int J Climatol, 26(5): 679–689

[41] Schuol J, Abbaspour K C, Srinivasan R, Yang H (2008). Estimation of freshwater availability in the West African sub-continent using the SWAT hydrologic model. J Hydrol (Amst), 352(1–2): 30–49

[42] Senroy N, Suryanarayanan S, Ribeiro P F (2007). An improved Hilbert–Huang method for analysis of time-varying waveforms in power quality. IEEE Trans Power Syst, 22(4): 1843–1850

[43] Shen M X, Chen J, Zhuan M J, Chen H, Xu C Y, Xiong L H (2018). Estimating uncertainty and its temporal variation related to global climate models in quantifying climate change impacts on hydrology. J Hydrol (Amst), 556: 10–24

[44] Shen J J, Yen W P, O’Fallon J (2003). Interpretation and application of Hilbert-Huang transformation for seismic performance analyses. In: 6th US Conference & Workshop on Lifeline Earthquake Engineering (TCLEE)

[45] Tao F, Yokozawa M, Hayashi Y, Lin E (2003). Future climate change, the agricultural water cycle, and agricultural production in China. Agric Ecosyst Environ, 95(1): 203-215 2

[46] Teng J, Vaze J, Chiew F H S, Wang B, Perraud J M (2012). Estimating the relative uncertainties sourced from GCMs and hydrological models in modeling climate change impact on runoff. J Hydrometeorol, 13(1): 122–139

[47] Trolle D, Nielsen A, Andersen H E, Thodsen H, Olesen J E, Børgesen C D, Refsgaard J C, Sonnenborg T O, Karlsson I B, Christensen J P, Markager S, Jeppesen E (2019). Effects of changes in land use and climate on aquatic ecosystems: coupling of models and decomposition of uncertainties. Sci Total Environ, 657: 627–633

[48] Umair M, Kim D, Choi M (2019). Impacts of land use/land cover on runoff and energy budgets in an East Asia ecosystem from remotely sensed data in a community land model. Sci Total Environ, 684: 641–656

[49] Wagner P D, Kumar S, Schneider K (2013). An assessment of land use change impacts on the water resources of the Mula and Mutha Rivers catchment upstream of Pune, India. Hydrol Earth Syst Sci, 17(6): 2233–2246

[50] Wang L, Guo S L, Hong X J, Liu D D, Xiong L H (2017). Projected hydrologic regime changes in the Poyang Lake Basin due to climate change. Front Earth Sci, 11(1): 95–113

[51] Woldesenbet T A, Elagib N A, Ribbe L, Heinrich J (2018). Catchment response to climate and land use changes in the Upper Blue Nile sub-basins, Ethiopia. Sci Total Environ, 644: 193–206

[52] Wu C H, Huang G R (2015). Changes in heavy precipitation and floods in the upstream of the Beijiang River basin, south China. Int J Climatol, 35(10): 2978–2992

[53] Wu T (2012). A mass-flux cumulus parameterization scheme for large-scale models: description and test with observations. Clim Dyn, 38(3–4): 725–744

[54] Wu Z H, Huang N E (2004). A study of the characteristics of white noise using the empirical mode decomposition method. Proc Royal Soc, Math Phys Eng Sci, 460(2046): 1597–1611

[55] Yan L, Xiong L H, Liu D D, Hu T S, Xu C Y (2017). Frequency analysis of nonstationary annual maximum flood series using the time-varying two-component mixture distributions. Hydrol Processes, 31(1): 69–89

[56] Yin J, He F, Xiong Y J, Qiu G Y (2017). Effects of land use/land cover and climate changes on surface runoff in a semi-humid and semi-arid transition zone in northwest China. Hydrol Earth Syst Sci, 21(1): 183–196

[57] Yin J, Gentine P, Zhou S, Sullivan S C, Wang R, Zhang Y, Guo S (2018). Large increase in global storm runoff extremes driven by climate and anthropogenic changes. Nat Commun, 9(1): 4389

[58] Yu B, Li G, Chen S, Lin H (2020). The role of internal variability in climate change projections of North American surface air temperature and temperature extremes in CanESM2 large ensemble simulations. Clim Dyn, 55(3–4): 869–885

[59] Zhang D F, Han Z Y, Shi Y (2017). Comparison of climate projections between driving CSIRO-Mk3.6.0 and downscaling simulation of RegCM4.4 over China. Adv Clim Change Res, 8(4): 245–255

[60] Zhang H, Huang G H, Wang D, Zhang X (2011). Uncertainty assessment of climate change impacts on the hydrology of small prairie wetlands. J Hydrol (Amst), 396(1–2): 94–103

[61] Zhang L, Nan Z, Yu W, Ge Y (2016a). Hydrological responses to land-use change scenarios under constant and changed climatic conditions. Environ Manage, 57(2): 412–431

[62] Zhang L, Chen X L, Lu J Z, Fu X K, Zhang Y F, Liang D, Xu Q Q (2019). Precipitation projections using a spatiotemporally distributed method: a case study in the Poyang Lake watershed based on the MRI-CGCM3. Hydrol Earth Syst Sci, 23(3): 1649–1666

[63] Zhang Q, Liu J Y, Singh V P, Gu X H, Chen X H (2016b). Evaluation of impacts of climate change and human activities on streamflow in the Poyang Lake basin, China. Hydrol Processes, 30(14): 2562–2576

[64] Zhao M M, He Z B, Du J, Chen L F, Lin P F, Fang S (2019). Assessing the effects of ecological engineering on carbon storage by linking the CA-Markov and InVEST models. Ecol Indic, 98: 29–38

[65] Zhou F, Xu Y, Chen Y, Xu C Y, Gao Y, Du J (2013). Hydrological response to urbanization at different spatio-temporal scales simulated by coupling of CLUE-S and the SWAT model in the Yangtze River Delta region. J Hydrol (Amst), 485: 113–125

[66] Zuo D, Xu Z, Yao W, Jin S, Xiao P, Ran D (2016). Assessing the effects of changes in land use and climate on runoff and sediment yields from a watershed in the Loess Plateau of China. Sci Total Environ, 544: 238–250

RIGHTS & PERMISSIONS

Higher Education Press