Skip to main content

Detecting hydrological droughts in ungauged areas from remotely sensed hydro-meteorological variables using rule-based models

Abstract

As a method of detecting hydrological droughts in ungauged areas, we propose rule-based models using percentiles from remotely sensed key hydro-meteorological variables. Four rule-based models of the Decision Trees, Adaptive Boosting of Decision Trees (Adaboost), Random Forest, and Extremely Randomized Trees are used for their capabilities of modeling nonlinear relationships, and their results are compared to the multiple linear regression. The temporal information of month and the percentiles of key variables of water and energy balance including precipitation, actual evapotranspiration, Normalized Difference Vegetation Index (NDVI), land surface temperature, and soil moisture are used as input variables. Drought severity values are calculated from streamflow percentiles for 3-, 6-, 9-, and 12-month time scales as an indicator for hydrological droughts. Data from six basins of the case study area are used for tuning model parameters and training, and the remaining two basins are used for final evaluation. Models with an ensemble of trees successfully detect hydrological droughts despite the limited input variables (for Adaboost, correlation coefficients ≥ 0.85, mean absolute error ≤ 0.12, root-mean-square error–observations standard deviation ratio ≤ 0.53, and larger Nash–Sutcliffe efficiency of drought severity ≥ 0.72 for the test data set). The most important variable is precipitation, followed by soil moisture (3-month time scale) or NDVI (longer time scales). Hydrological droughts in various time scales are detected in ungauged areas of the case study area. Serious droughts in early 2002, from late 2006 to mid-2007, from early 2008 to 2009, and from mid-2013 to 2017 are detected.

This is a preview of subscription content, access via your institution.

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

Availability of data and materials

All relevant data are within the manuscript and its supporting information files.

Abbreviations

7Q10:

The annual minimum 7-day mean streamflow with an annual exceedance probability of 90%

ANNs:

Artificial neural networks

BoM:

Bureau of meteorology

CCA:

Canonical correlation analysis

EANN:

Ensemble of ANNs

ELM:

Extreme learning machine

EMI:

El Nino-Southern Oscillation Modoki Index

Full-tobit:

Type I tobit regression

GAM:

Gaussian generalized additive model

GBM:

Gradient boosting machine

GEFS:

NOAA global ensemble forecasting system

GEP:

Gene expression programming

GLM:

Gaussian linear regression model

GRNN:

Generalized regression neural networks

IOD:

Indian Ocean Dipole Index

KKNN:

Kernel-k-nearest neighbors

LR:

Linear regression

LSM:

Land surface model

MARS:

Multivariate adaptive regression splines

NLCCA:

Nonlinear ANN-based canonical correlation analysis

OK:

Ordinary kriging

OSELM:

Online sequential extreme learning machines

OSMLR:

Online sequential multiple linear regression

PDO:

Pacific Decadal Oscillation Index

RBF:

Radial basis function

ROI-tobit:

Region-of-influence type I tobit

SOI:

Southern Oscillation Index

SST:

Sea surface temperature

SVMG:

Support vector machine with a Gaussian kernel

SVMP:

Support vector machine with a polynomial kernel

SVR:

Support vector regression

WMDP:

Water Monitoring Data Portal

WSC:

Water Survey of Canada

References

  1. Aonashi K, Awaka J, Hirose M, Kozu T, Kubota T, Liu G, Shige S, Kida S, Seto S, Takahashi N, Takayabu YN (2009) GSMaP passive, microwave precipitation retrieval algorithm: algorithm description and validation. J Meteorol Soc Jpn 87A:119–136

    Article  Google Scholar 

  2. Atieh M, Taylor G, Sattar AMA, Gharabaghi B (2017) Prediction of flow duration curves for ungauged basins. J Hydrol 545:383–394

    Article  Google Scholar 

  3. Beaudoing H, Rodell M (2016) NASA/GSFC/HSL, GLDAS Noah Land Surface Model L4 monthly 0.25 x 0.25 degree V2.1, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC). https://doi.org/10.5067/SXAVCZFAQLNO

  4. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  5. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Chapman & Hall, Boca Raton

    Google Scholar 

  6. Cammalleri C, Vogt J, Salamon P (2017) Development of an operational low-flow index for hydrological drought monitoring over Europe. Hydrol Sci J 62:346–358. https://doi.org/10.1080/02626667.2016.1240869

    Article  Google Scholar 

  7. Deo R, Sahin M (2016) An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland. Environ Monit Assess 188:90. https://doi.org/10.1007/s10661-016-5094-9

    Article  Google Scholar 

  8. Didan K (2015) MOD13A3 MODIS/Terra Vegetation Indices Monthly L3 Global 1 km SIN Grid V006, NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD13A3.006

  9. Elfahir EAB, Yeh PJF (1999) On the asymmetric response of aquifer water level to floods and droughts in Illinois. Water Resour Res 35(4):1199–1217

    Article  Google Scholar 

  10. Fisher RA (1915) Frequency distribution of the values of the correlation coefficient in samples of an indefinitely large population. Biometrika 10(4):507–521

    Google Scholar 

  11. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55:119–139

    Article  Google Scholar 

  12. Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63:3–42

    Article  Google Scholar 

  13. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning, 2nd edn. Springer, Berlin

    Book  Google Scholar 

  14. Huang S, Li P, Huang Q, Leng G, Hou B, Ma L (2017) The propagation from meteorological to hydrological drought and its potential influence factors. J Hydrol 547:184–195. https://doi.org/10.1016/j.jhydrol.2017.01.041

    Article  Google Scholar 

  15. Im J, Lu Z, Rhee J, Quackenbush LJ (2012) Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data. Remote Sens Environ 117:102–113

    Article  Google Scholar 

  16. Iorgulescu I, Beven KJ (2004) Nonparametric direct mapping of rainfall-runoff relationships: an alternative approach to data analysis and modeling? Water Resour Res 40:W08403

    Article  Google Scholar 

  17. Kogan F (2002) World droughts in the new millennium from AVHRR-based Vegetation Health Indices. EOS Trans Am Geophys Union 83(48):557–572

    Article  Google Scholar 

  18. Kubota T, Shige S, Hashizume H, Aonashi K, Takahashi N, Seto S, Hirose M, Takayabu YN, Nakagawa K, Iwanami K, Ushio T, Kachi M, Okamoto K (2007) Global precipitation map using satellite borne microwave radiometers by the GSMaP project: production and validation. IEEE Trans Geosci Remote Sens 45:2259–2275

    Article  Google Scholar 

  19. Li M, Im J, Beier C (2013) Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest. GISci Remote Sens 50(4):361–384. https://doi.org/10.1080/15481603.2013.819161

    Article  Google Scholar 

  20. Lima AR, Cannon AJ, Hsieh WW (2016) Forecasting daily streamflow using online sequential extreme learning machines. J Hydrol 537:431–443

    Article  Google Scholar 

  21. Lu Z, Im J, Rhee J, Hodgson M (2014) Building type classification using spatial and landscape attributes derived from LiDAR remote sensing data. Landsc Urban Plan 130:134–148

    Article  Google Scholar 

  22. Mishra AK, Singh VP (2011) Drought modeling—a review. J Hydrol 403:157–175

    Article  Google Scholar 

  23. Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans Am Soc Agric Biol Eng 50:885–900

    Google Scholar 

  24. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10:282–290. https://doi.org/10.1016/0022-1694(70)90255-6

    Article  Google Scholar 

  25. Newell A, Simon HA (1972) Human problem solving. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  26. Okamoto K, Iguchi T, Takahashi N, Iwanami K, Ushio T (2005) The Global Satellite Mapping of Precipitation (GSMaP) project. In: 25th IGARSS proceedings, pp 3414–3416

  27. Ouali D, Chebana F, Ouarda TBMJ (2017) Fully nonlinear statistical and machine-learning approaches for hydrological frequency estimation at ungauged sites. J Adv Model Earth Syst 9:1292–1306

    Article  Google Scholar 

  28. Park S, Im J, Jang E, Rhee J (2016) Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions. Agric For Meteorol 216:157–169

    Article  Google Scholar 

  29. Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106. https://doi.org/10.1007/BF00116251

    Article  Google Scholar 

  30. Reichle R, De Lannoy G, Koster RD, Crow WT, Kimball JS, Liu Q (2018) SMAP L4 global 3-hourly 9 km EASE-grid surface and root zone soil moisture geophysical data, version 4. NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder. https://doi.org/10.5067/KPJNN2GI1DQR

    Book  Google Scholar 

  31. Rhee J, Im J (2017) Meteorological drought forecasting for ungauged areas based on machine learning: using long-range climate forecast and remote sensing data. Agric For Meteorol 237:105–122

    Article  Google Scholar 

  32. Rhee J, Yang H (2018) Drought prediction for areas with sparse monitoring networks: a case study for Fiji. Water 10:788. https://doi.org/10.3390/w10060788

    Article  Google Scholar 

  33. Rhee J, Im J, Carbone GJ, Jensen JR (2008) Delineation of climate regions using in situ and remotely-sensed data for the Carolinas. Remote Sens Environ 112:3099–3111

    Article  Google Scholar 

  34. Rodell M, Houser PR, Jambor U, Gottschalck J, Mitchell K, Meng C, Arsenault K, Cosgrove B, Radakovich J, Bosilovich M, Entin JK, Walker JP, Lohmann D, Toll D (2004) The global land data assimilation system. Bull Am Meteorol Soc 85:381–394. https://doi.org/10.1175/BAMS-85-3-381

    Article  Google Scholar 

  35. Rouse JW, Haas RH, Schell JA, Deering DW (1974) Monitoring vegetation systems in the Great Plains with ERTS, In: Freden SC, Mercanti EP, Becker M (eds) Third earth resources technology satellite-1 symposium: technical presentations, NASA SP-351, NASA, Washington DC, USA, vol I, pp 309–317

  36. Running S, Mu Q, Zhao M (2017) MOD16A2 MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500 m SIN Grid V006, NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD16A2.006

  37. Sadri S, Burn DH (2012) Nonparametric methods for drought severity estimation at ungauged sites. Water Resour Res 48:W12505. https://doi.org/10.1029/2011WR01132

    Article  Google Scholar 

  38. Sellers PJ, Dickinson RE, Randall DA, Betts AK, Hall FG, Berry JA, Collatz GJ, Denning AS, Mooney HA, Nobre CA, Sato N, Field CB, Henderson-Sellers A (1997) Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science 275:502–509

    Article  Google Scholar 

  39. Shortridge JE, Guikema SD, Zaitchik BF (2016) Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds. Hydrol Earth Syst Sci 20:2611–2628. https://doi.org/10.5194/hess-20-2611-2016

    Article  Google Scholar 

  40. Shukla S, Wood AW (2008) Use of a standardized runoff index for characterizing hydrologic drought. Geophys Res Lett 35:L02405. https://doi.org/10.1029/2007GL032487

    Article  Google Scholar 

  41. Singh J, Knapp HV, Arnold JG, Demissie M (2005) Hydrologic modeling of the Iroquois River watershed using HSPF and SWAT. J Am Water Resour As 41(2):343–360

    Article  Google Scholar 

  42. Sinha D, Syed TH, Famiglietti JS, Reager JT, Thomas RC (2017) Characterizing drought in India using GRACE observations of terrestrial water storage deficit. J Hydrometeorol 18:381–396. https://doi.org/10.1175/JHM-D-16-0047.1

    Article  Google Scholar 

  43. Sun P, Liu S, Jiang H, Lu Y, Liu J, Lin Y, Liu X (2008) Hydrologic effects of NDVI time series in a context of climatic variability in an upstream catchment of the Minjiang River. J Am Water Resour As 44(5):1132–1143

    Article  Google Scholar 

  44. Tabari H, Nikbakht J, Talaee PH (2013) Hydrological drought assessment in Northwestern Iran based on Streamflow Drought Index (SDI). Water Resour Manag 27:137–151. https://doi.org/10.1007/s11269-012-0173-3

    Article  Google Scholar 

  45. Tannehill IR (1947) Drought: its causes and effects. Oxford University Press, Oxford

    Google Scholar 

  46. Thomas AC, Reager JT, Famiglietti JS, Rodell M (2014) A GRACE-based water storage deficit approach for hydrological drought characterization. Geophys Res Lett 41:1537–1545. https://doi.org/10.1002/2014GL059323

    Article  Google Scholar 

  47. Tongal H, Booij MJ (2018) Simulation and forecasting of streamflows using machine learning models coupled with base flow separation. J Hydrol 564:266–282

    Article  Google Scholar 

  48. Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150

    Article  Google Scholar 

  49. Ushio T, Kubota T, Shige S, Okamoto K, Aonashi K, Inoue T, Takahashi N, Iguchi T, Kachi M, Oki R, Morimoto T, Kawasaki Z (2009) A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data. J Meteorol Soc Jpn 87A:137–151

    Article  Google Scholar 

  50. Van Loon AF (2015) Hydrological drought explained. WIREs. Water 2:359–392. https://doi.org/10.1002/wat2.1085

    Article  Google Scholar 

  51. Vicente-Serrano SM, Beguería S, Lorenzo-Lacruz J, Camarero JJ, Lopez-Moreno JI, Azorin-Molina C, Revuelto J, Moran-Tejeda E, Sanchez-Lorenzo A (2012) Performance of drought indices for ecological, agricultural, and hydrological applications. Earth Interact 16:1–27. https://doi.org/10.1175/2012EI000434.1

    Article  Google Scholar 

  52. Wan Z, Hook S, Hulley G (2015) MOD11A2 MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1 km SIN Grid V006. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD11A2.006

  53. Welch BL (1947) The generalization of “Student’s” problem when several different population variances are involved. Biometrika 34(1–2):28–35

    Google Scholar 

  54. Wilhite DA (2000) Drought as a natural hazard: concepts and definitions. In: Wilhite DA (ed) Drought: a global assessment, vol I. Routledge, London, pp 3–18

    Google Scholar 

  55. Wilhite DA, Glantz MH (1985) Understanding the drought phenomenon: the role of definitions. Water Int 10(3):111–120. https://doi.org/10.1080/02508068508686328

    Article  Google Scholar 

  56. Worland SC, Farmer WH, Kiang JE (2018) Improving predictions of hydrological low-flow indices in ungauged basins using machine learning. Environ Modell Softw 101:169–182

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the APEC Climate Center.

Funding

Not applicable.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jinyoung Rhee.

Ethics declarations

Conflict of interest

The authors have no conflict of interest.

Code availability

Not applicable.

Additional information

Publisher's Note

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

Electronic supplementary material

Appendix

Appendix

Relevant literature on hydrological drought monitoring and/or forecasting using machine learning models (Table 3).

Table 3 Summary of relevant literature on hydrological drought monitoring and/or forecasting using machine learning models

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rhee, J., Park, K., Lee, S. et al. Detecting hydrological droughts in ungauged areas from remotely sensed hydro-meteorological variables using rule-based models. Nat Hazards 103, 2961–2988 (2020). https://doi.org/10.1007/s11069-020-04114-5

Download citation

Keywords

  • Hydrological droughts
  • Rule-based models
  • Remote sensing
  • Ungauged areas