Skip to main content

Advertisement

SpringerLink
Log in

Data assimilation for constructing long-term gridded daily rainfall time series over Southeast Asia

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

The data scarcity and poor availability of observed daily rainfalls over Southeast Asia has limited the possibility to a wider range of studies in light of impacts from climate change and extreme hydro-meteorological processes such as floods, droughts, and other watershed management practices. To fill such a gap, data assimilation was carried out in this study to construct a long-term gridded daily (0.50° × 0.50°) rainfall time series (1951–2014) over Southeast Asia. In rainfall data assimilation, the available and globally accepted high resolution gridded datasets viz. Southeast Asia observed (SA-OBS) (1981–2014), APHRODITE (1951–2007), TRMM (1998–2018), PRINCETON (1951–2008) along with limited rain gauges-based rainfalls were utilized. In this study, eight gap filling methods were employed and tested at 20 selected rainfall grids to fill the long gaps presented in the SA-OBS gridded dataset. The strength of each method and associated uncertainties were evaluated in the computed rainfalls utilizing multiple functions at missing grids. The accuracy of each method, in case of extreme rainfalls, was tested by quantile–quantile (Q–Q) plots at different quantile intervals. The distance power method based on the Pearson correlation coefficient and the multiple linear regression method performed satisfactorily and produced minimum uncertainties in filling rainfall gaps. To test the accuracy and compatibility of gap-filled SA-OBS gridded dataset with other sources of datasets, the seasonality analysis and rainfall indices comparison were carried out. Results showed that the gap-filled SA-OBS dataset was better comparable to other sources of rainfalls. For the construction of the long-term rainfall time series (1951–2014), quantile mapping was adopted for bias correction and the quality of the final merged dataset was evaluated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Abudu S, Bawazir AS, King JP (2009) Infilling missing daily evapotranspiration data using neural networks. J Irrig Drain Eng 136(5):317–325

    Article  Google Scholar 

  • Adler RF, Huffman GJ, Bolvin DT, Curtis S, Nelkin EJ (2000) Tropical rainfall distribution determined using TRMM combined with other satellite and rain gauge information. J Appl Meteorol 39:2007–2023

    Article  Google Scholar 

  • Barca E, Berardi L, Laucelli DB, Passarella G, Giustolisi O (2015) Evolutionary polynomial regression application for missing data handling in meteo-climatic gauging stations. In: Graspa working papers, pp 1–4

  • Besselaar Van den EJ, Van der Schrier G, Cornes RC, Iqbal AS, Klein Tank AM (2017) SA-OBS: a daily gridded surface temperature and precipitation dataset for Southeast Asia. J Clim 30(14):5151–5165

    Article  Google Scholar 

  • Burnham KP, Anderson DR (2004) Multimodel inference: understanding AIC and BIC in model selection. Sociol Methods Res 33(2):261–304

    Article  Google Scholar 

  • Caldera HPGM, Piyathisse VRPC, Nandalal KDW (2016) A comparison of methods of estimating missing daily rainfall data. Eng J Inst Eng Sri Lanka 49(4):1–8

    Google Scholar 

  • Cannon AJ, Sobie SR, Murdock TQ (2015) Bias correction of GCM precipitation by quantile mapping: how well do methods preserve changes in quantiles and extremes? J Clim 28(17):6938–6959

    Article  Google Scholar 

  • Dastorani MT, Moghadamnia A, Piri J, Rico-Ramirez M (2010) Application of ANN and ANFIS models for reconstructing missing flow data. Environ Monit Assess 166(1–4):421–434

    Article  Google Scholar 

  • Di Luzio M, Johnson GL, Daly C, Eischeid JK, Arnold JG (2008) Constructing retrospective gridded daily precipitation and temperature datasets for the conterminous United States. J Appl Meteorol Clim 47(2):475–497

    Article  Google Scholar 

  • Di Piazza A, Conti FL, Noto LV, Viola F, La Loggia G (2011) Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy. Int J Appl Earth Observ Geoinf 13(3):396–408

    Article  Google Scholar 

  • Dumedah G, Coulibaly P (2011) Evaluation of statistical methods for infilling missing values in high-resolution soil moisture data. J Hydrol 400(1–2):95–102

    Article  Google Scholar 

  • Eischeid JK, Bruce Baker C, Karl TR, Diaz HF (1995) The quality control of long-term climatological data using objective data analysis. J Appl Meteorol 34(12):2787–2795

    Article  Google Scholar 

  • El Kenawy AM, McCabe MF (2016) A multi-decadal assessment of the performance of gauge-and model-based rainfall products over Saudi Arabia: climatology, anomalies and trends. Int J Clim 36(2):656–674

    Article  Google Scholar 

  • Fang G, Yang J, Chen YN, Zammit C (2015) Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China. Hydrol Earth Syst Sci 19(6):2547–2559

    Article  Google Scholar 

  • Gad I, Manjunatha BR (2017) Performance evaluation of predictive models for missing data imputation in weather data. In: International conference on advances in computing, communications and informatics (ICACCI), IEEE, pp 1327–1334

  • Ge F, Peng T, Fraedrich K, Sielmann F, Zhu X, Zhi X, Liu X, Tang W, Zhao P (2018) Assessment of trends and variability in surface air temperature on multiple high-resolution datasets over the Indochina Peninsula. Theor Appl Clim. https://doi.org/10.1007/s00704-018-2457-x

    Article  Google Scholar 

  • Goyal MK, Gupta V (2014) Identification of homogeneous rainfall regimes in Northeast Region of India using fuzzy cluster analysis. Water Res Manag 28(13):4491–4511

    Article  Google Scholar 

  • Goyal MK, Sarma AK (2017) Analysis of the change in temperature trends in Subansiri River basin for RCP scenarios using CMIP5 datasets. Theor Appl Clim 129(3):1175–1187

    Google Scholar 

  • Hasan MM, Croke B (2013) Filling gaps in daily rainfall data: a statistical approach. In: 20th international congress on modelling and simulation, Adelaide, Australia, 1–6 December 2013. http://www.mssanz.org.au/modsim. Accessed 29 Nov 2018

  • Haylock MR, Hofstra N, Tank AK, Klok EJ, Jones PD, New M (2008) A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J Geophy Res Atmos 113(D20):1–12

    Article  Google Scholar 

  • Hoyer S, Hamman J (2017) xarray: ND labeled arrays and datasets in Python. J Open Res Soft 5(1):5

    Google Scholar 

  • Kashani MH, Dinpashoh Y (2012) Evaluation of efficiency of different estimation methods for missing climatological data. Stoch Environ Res Risk Assess 26(1):59–71

    Article  Google Scholar 

  • Levy MC, Cohn A, Lopes AV, Thompson SE (2017) Addressing rainfall data selection uncertainty using connections between rainfall and streamflow. Sci Rep 7(1):219

    Article  Google Scholar 

  • Libertino A, Allamano P, Laio F, Claps P (2018) Regional-scale analysis of extreme precipitation from short and fragmented records. Adv Water Res 112:147–159

    Article  Google Scholar 

  • Loo YY, Billa L, Singh A (2015) Effect of climate change on seasonal monsoon in Asia and its impact on the variability of monsoon rainfall in Southeast Asia. Geosci Front 6(6):817–823

    Article  Google Scholar 

  • Lu Y, Qin XS, Mandapaka PV (2015) A combined weather generator and K-nearest-neighbor approach for assessing climate change impact on regional rainfall extremes. Int J Clim 35(15):4493–4508

    Article  Google Scholar 

  • Mandapaka PV, Qin X, Lo EY (2017) Analysis of spatial patterns of daily precipitation and wet spell extremes in Southeast Asia. Int J Clim 37(S1):1161–1179

    Article  Google Scholar 

  • Miró JJ, Caselles V, Estrela MJ (2017) Multiple imputation of rainfall missing data in the Iberian Mediterranean context. Atmos Res 197:313–330

    Article  Google Scholar 

  • Mishra V, DiNapoli S (2014) The variability of the Southeast Asian summer monsoon. Int J Clim 34(3):893–901

    Article  Google Scholar 

  • Nadarajah S, Choi D (2007) Maximum daily rainfall in South Korea. J Earth Syst Sci 116(4):311–320

    Article  Google Scholar 

  • Nalder IA, Wein RW (1998) Spatial interpolation of climatic normals: test of a new method in the Canadian boreal forest. Agric For Meteorol 92(4):211–225

    Article  Google Scholar 

  • Ngo-Duc T, Tangang FT, Santisirisomboon J, Cruz F, Trinh-Tuan L, Nguyen-Xuan T, Phan-Van T, Juneng L, Narisma G, Singhruck P, Gunawan D (2017) Performance evaluation of RegCM4 in simulating extreme rainfall and temperature indices over the CORDEX-Southeast Asia region. Int J Clim 37(3):1634–1647

    Article  Google Scholar 

  • Onyutha C, Willems P (2017) Space-time variability of extreme rainfall in the River Nile basin. Int J Clim 37(14):4915–4924

    Article  Google Scholar 

  • Panofsky HA, Brier GW (1968) Some applications of statistics to meteorology. Mineral Industries Extension Services, College of Mineral Industries, Pennsylvania State University, Pennsylvania

    Google Scholar 

  • Piani C, Haerter JO, Coppola E (2010) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Clim 99(1–2):187–192

    Article  Google Scholar 

  • Qie X, Wu X, Yuan T, Bian J, Lu D (2014) Comprehensive pattern of deep convective systems over the Tibetan Plateau–South Asian monsoon region based on TRMM data. J Clim 27(17):6612–6626

    Article  Google Scholar 

  • Raktham C, Bruyère C, Kreasuwun J, Done J, Thongbai C, Promnopas W (2015) Simulation sensitivities of the major weather regimes of the Southeast Asia region. Clim Dyn 1(5–6):1403–1417 44(

    Article  Google Scholar 

  • Ramos-Calzado P, Gómez-Camacho J, Pérez-Bernal F, Pita-López MF (2008) A novel approach to precipitation series completion in climatological datasets: application to Andalusia. Int J Clim 28(11):525–1534

    Article  Google Scholar 

  • Sánchez E, Avilés A, Samaniego E (2014) Evaluation of infilling methods for time series of daily precipitation and temperature: the case of the Ecuadorian Andes. Maskana 5(1):99–115

    Article  Google Scholar 

  • Sattari MT, Rezazadeh-Joudi A, Kusiak A (2017) Assessment of different methods for estimation of missing data in precipitation studies. Hydrol Res 48(4):1032–1044

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Simolo C, Brunetti M, Maugeri M, Nanni T (2010) Improving estimation of missing values in daily precipitation series by a probability density function-preserving approach. Int J Clim 30(10):1564–1576

    Google Scholar 

  • Singh V, Goyal MK (2016) Analysis and trends of precipitation lapse rate and extreme indices over north Sikkim eastern Himalayas under CMIP5ESM2-M RCPs experiments. Atmos Res 167(1):34–60

    Article  Google Scholar 

  • Singh V, Sharma A, Goyal MK (2017) Projection of hydro-climatological changes over eastern Himalayan catchment by the evaluation of RegCM4 RCM and CMIP5 GCM models. Hydrol Res. https://doi.org/10.2166/nh.2017.193

    Article  Google Scholar 

  • Stibig H-J, Stolle F, Dennis R, Feldkötter C (2007) Forest cover change in Southeast Asia—the regional pattern. JRC Sci Tech Report. http://www.jrc.ec.europa.eu. Accessed 17 Nov 2018

  • Suepa T, Qi J, Lawawirojwong S, Messina JP (2016) Understanding spatio-temporal variation of vegetation phenology and rainfall seasonality in the monsoon Southeast Asia. Environ Res 147:621–629

    Article  Google Scholar 

  • Sugahara S, Da Rocha RP, Silveira R (2009) Non-stationary frequency analysis of extreme daily rainfall in Sao Paulo, Brazil. Int J Clim 29(9):1339–1349

    Article  Google Scholar 

  • Sun Q, Miao C, Duan Q, Ashouri H, Sorooshian S, Hsu KL (2018) A review of global precipitation data sets: data sources, estimation, and intercomparisons. Rev Geophys 56(1):79–107

    Article  Google Scholar 

  • Sushama L, Said SB, Khaliq MN, Kumar DN, Laprise R (2014) Dry spell characteristics over India based on IMD and APHRODITE datasets. Clim Dyn 43(12):3419–3437

    Article  Google Scholar 

  • Teegavarapu RS (2014) Statistical corrections of spatially interpolated missing precipitation data estimates. Hydrol Proc 28(11):3789–3808

    Article  Google Scholar 

  • Teegavarapu RS, Nayak A (2017) Evaluation of long-term trends in extreme precipitation: implications of in-filled historical data use for analysis. J Hydrol 550:616–634

    Article  Google Scholar 

  • Teegavarapu RS, Aly A, Pathak CS, Ahlquist J, Fuelberg H, Hood J (2018) Infilling missing precipitation records using variants of spatial interpolation and data-driven methods: use of optimal weighting parameters and nearest neighbour-based corrections. Int J Clim 38(2):776–793

    Article  Google Scholar 

  • Trenberth KE, Fasullo JT, Shepherd TG (2015) Attribution of climate extreme events. Nat Clim Change 5(8):725

    Article  Google Scholar 

  • Vallam P, Qin XS (2017) Projecting future precipitation and temperature at sites with diverse climate through multiple statistical downscaling schemes. Theor Appl Clim. https://doi.org/10.1007/s00704-017-2299-y

    Article  Google Scholar 

  • Vicente-Serrano SM, Beguería S, López-Moreno JI, García-Vera MA, Stepanek P (2010) A complete daily precipitation database for northeast Spain: reconstruction, quality control, and homogeneity. Int J Clim 30(8):1146–1163

    Article  Google Scholar 

  • Villafuerte MQ, Matsumoto J (2015) Significant influences of global mean temperature and ENSO on extreme rainfall in Southeast Asia. J Clim 28(5):1905–1919

    Article  Google Scholar 

  • Wagner PD, Kumar S, Fiener P, Schneider K (2011) Hydrological modeling with SWAT in a monsoon-driven environment: experience from the Western Ghats, India. Trans ASABE 54(5):1783–1790

    Article  Google Scholar 

  • Wang Z, Chang CP (2012) A numerical study of the interaction between the large-scale monsoon circulation and orographic precipitation over South and Southeast Asia. J Clim 25:2440–2445

    Article  Google Scholar 

  • Wang Z, Zeng Z, Lai C, Lin W, Wu X, Chen X (2017) A regional frequency analysis of precipitation extremes in Mainland China with fuzzy c-means and L-moments approaches. Int J Clim 37(S1):429–444

    Article  Google Scholar 

  • Woldesenbet TA, Elagib NA, Ribbe L, Heinrich J (2017) Gap filling and homogenization of climatological datasets in the headwater region of the Upper Blue Nile Basin, Ethiopia. Int J Clim 37(4):2122–2140

    Article  Google Scholar 

  • Wong CL, Yusop Z, Ismail T (2018) Trend of daily rainfall and temperature in peninsular Malaysia based on gridded data set. Int J Geom 14(44):65–72

    Google Scholar 

  • Xavier P, Rahmat R, Cheong WK, Wallace E (2014) Influence of Madden-Julian Oscillation on Southeast Asia rainfall extremes: observations and predictability. Geophys Res Lett 41(12):4406–4412

    Article  Google Scholar 

  • Yatagai A, Kamiguchi K, Arakawa O, Hamada A, Yasutomi N, Kitoh A (2012) APHRODITE: constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull Am Meteorol Soc 93(9):1401–1415

    Article  Google Scholar 

  • Zaw WT, Naing TT (2009) Modeling of rainfall prediction over Myanmar using polynomial regression. ICCET’09. IEEE Int Conf Comput Eng Technol 1:316–320

    Google Scholar 

  • Zhao H, Yang S, Wang Z, Zhou X, Luo Y, Wu L (2015) Evaluating the suitability of TRMM satellite rainfall data for hydrological simulation using a distributed hydrological model in the Weihe River catchment in China. J Geogr Sci 25(2):177–195

    Article  Google Scholar 

Download references

Acknowledgements

This project was supported by Start-Up Grant (M4081327.030) from School of Civil and Environmental Engineering, Nanyang Technological University, Singapore. We acknowledge the SA-OBS dataset and the data providers in the SACA&D project (http://saca-bmkg.knmi.nl). We are also thankful for the providers of PRINCETON (http://hydrology.princeton.edu/data.pgf.php) rainfall product, TRMM rainfall data products (https://pmm.nasa.gov/data-access/downloads/trmm) and APHRODITE (http://www.chikyu.ac.jp/precip/english/products.html) rainfalls dataset. Please note the data from this study will be made available upon request.

Author information

Authors and Affiliations

  1. School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Vishal Singh & Qin Xiaosheng

  2. National Institute of Hydrology, Roorkee, Uttarakhand, 247667, India

    Vishal Singh

Authors
  1. Vishal Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qin Xiaosheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qin Xiaosheng.

Ethics declarations

Conflict of interest

The authors have found no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 8092 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, V., Xiaosheng, Q. Data assimilation for constructing long-term gridded daily rainfall time series over Southeast Asia. Clim Dyn 53, 3289–3313 (2019). https://doi.org/10.1007/s00382-019-04703-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-019-04703-6

Keywords

Search

Navigation