skip to main content

DROUGHT HAZARD CHARACTERISTIC USING SOIL MOISTURE DEFICIT INDEX MODELLING

*Lulu Mari Fitria  -  Sekolah Tinggi Teknologi Nasional Yogyakarta, Indonesia
Septiana Fathurrohmah  -  Sekolah Tinggi Teknologi Nasional Yogyakarta, Indonesia

Citation Format:
Abstract
Drought happen when the rainfall decreases in the extreme condition for long period of  time (above normal). Drought hazard mapping can be analyzed by various approaches, like environmental approach, ecological approach, hydrological approach, meteorological approach, geological approach, agricultural approach, and many other. Badan Meteoroligi dan Geofisika (BMKG) measures the drought hazard by utilizing Standardized Precipitation Index (SPI)The comparison of rainfall rate through SPI has positive correlation with drought type, for example SPI 3 indicates agricultural drought; while SPI 6, SPI 9 and SPI 12 indicate hydrological drought. The analysis of drought hazard level also can be done using soil moisture level measurement. Soil moisture is the result of water shortages in the hydroclimatological concept. Soil moisture analysis utilizes several influenced variables, such as soil water, precipitation, evapotranspiration, and percolation. Each of variables was analyzed using GIS Software as a method of soil moisture modeling. Drought index level analysis is using soil moisture deficit index, which indicates that drought occurs if the index score less than (-0,5). Some assumptions used in this modeling are both SMDI modeling using WHC (Water Holding Capacity) and  without using WHC. This modeling used medium term analysis during 2007-2012 to prove the occurrence of extreme drought on 2009 and 2012 for measurement of drought level in agriculture area. Based on SMDI, it is known that the dangers of SMDI drought have positive correlation to SPI 3, SPI 6, SPI 9, and SPI 12, where SPI is in accordance with the interpretation of meteorolgy, agriculture, and hydrological drought indices. 

Note: This article has supplementary file(s).

Fulltext View|Download |  Research Instrument
DROUGHT HAZARD CHARACTERISTIC USING SOIL MOISTURE DEFICIT INDEX MODELLING
Subject Drought, hazard, modelling, soil moisture
Type Research Instrument
  Download (11MB)    Indexing metadata
Keywords: Drought; hazard; modeling; soil moisture
Funding: Urban And Regional Planning Department, Sekolah Tinggi Teknologi Nasional Yogyakarta

Article Metrics:

  1. Agutu, N. O. et al. (2017). Assessing multi-satellite remote sensing, reanalysis, and land surface models products in characterizing agricultural drought in East Africa. Remote Sensing of Environment, 194, pp. 287–302. https://doi.org/10.1016/j.rse.2017.03.041.">[Crossref]

  2. Hao, Z. et al. (2017). An integrated package for drought monitoring, prediction and analysis to aid drought modeling and assessment. Environmental Modelling & Software, 91, pp. 199–209. https://doi.org/10.1016/j.envsoft.2017.02.008.">[Crossref]

  3. Huang, S. et al. (2017). The propagation from meteorological to hydrological drought and its potential influence factors. Journal of Hydrology, 547, pp. 184–195. https://doi.org/10.1016/j.jhydrol.2017.01.041">[Crossref]

  4. Livada, I. and Assimakopoulos, V. D. (2006). Spatial and temporal analysis of drought in greece using the Standardized Precipitation Index (SPI). Theoretical and Applied Climatology. Springer Nature, 89(3–4), pp. 143–153. https://doi.org/10.1007/s00704-005-0227-z.">[Crossref]

  5. Lu, H. et al. (2017). Effects of meteorological droughts on agricultural water resources in southern China. Journal of Hydrology, 548, pp. 419–435. https://doi.org/10.1016/j.jhydrol.2017.03.021">[Crossref]

  6. Ma, M. et al. (2015). Hydrologic model-based Palmer indices for drought characterization in the Yellow River basin, China. Stochastic Environmental Research and Risk Assessment. Springer Nature, 30(5), pp. 1401–1420. https://doi.org/10.1007/s00477-015-1136-z">[Crossref]

  7. Manatsa, D. et al. (2010). Analysis of multidimensional aspects of agricultural droughts in Zimbabwe using the Standardized Precipitation Index (SPI). Theoretical and Applied Climatology. Springer Nature, 102(3–4), pp. 287–305. https://doi.org/10.1007/s00704-010-0262-2">[Crossref]

  8. Miller, R. B. and Fox, G. A. (2017). A tool for drought planning in Oklahoma: Estimating and using drought-influenced flow exceedance curves. Journal of Hydrology: Regional Studies, 10, pp. 35–46. https://doi.org/10.1016/j.ejrh.2017.01.001.">[Crossref]

  9. Moeletsi, M. E. and Walker, S. (2012). Assessment of agricultural drought using a simple water balance model in the Free State Province of South Africa. Theoretical and applied climatology. Springer, 108(3–4), pp. 425–450.

  10. Nagarajan, R. (2009). Drought Indices, in Drought Assessment. Springer Netherlands, pp. 160–204. https://doi.org/10.1007/978-90-481-2500-5_5.">[Crossref]

  11. Narasimhan, B. and Srinivasan, R. (2003). Developing an Agricultural Drought Assessment System Using Hydrologic Model SWAT and GIS in 2003, Las Vegas, American Society of Agricultural and Biological Engineers, NV July 27-30, 2003. https://doi.org/10.13031/2013.14951">[Crossref]

  12. Nasir, M. (no date). Deteksi Usia Tanaman Padi Berdasarkan Indeks Warna..

  13. Sivakumar, M. V. K. (2011). Agricultural drought—WMO perspectives, in Agricultural drought indices proceedings of an expert meeting, p. 24.

  14. Tessema, R. S. (2007). Agricultural Drought Assessment for Upper Blue Nile Basin, Ethiopia using SWAT. Unesco-IHE.

  15. Thomas, T. et al. (2014). Comprehensive evaluation of the changing drought characteristics in Bundelkhand region of Central India. Meteorology and Atmospheric Physics. Springer Nature, 127(2), pp. 163–182. https://doi.org/10.1007/s00703-014-0361-1.">[Crossref]

  16. Thornthwaite, C. W. and Mather, J. R. (1957). New Jersey Water Supply Development. Journal - American Water Works Association. Wiley, 49(8), pp. 969–985. https://doi.org/10.1002/j.1551-8833.1957.tb16891.x.">[Crossref]

  17. Tsakiris, G., Pangalou, D. and Vangelis, H. (2006). Regional Drought Assessment Based on the Reconnaissance Drought Index (RDI). Water Resources Management. Springer Nature, 21(5), pp. 821–833. https://doi.org/10.1007/s11269-006-9105-4">[Cossref]

  18. Wahyunto, W. and Heryanto, B. (2006). Pendugaan produktivitas tanaman padi sawah melalui analisis citra satelit. Informatika pertanian, 15, pp. 853–869.

  19. Wilhelmi, O. V and Wilhite, D. A. (2002). Assessing Vulnerability to Agricultural Drought: A Nebraska Case Study. Natural Hazards. Springer Nature, 25(1), pp. 37–58. https://doi.org/10.1023/a:1013388814894">[Crossref]

  20. Zin, W. Z. W., Jemain, A. A. and Ibrahim, K. (2012). Analysis of drought condition and risk in Peninsular Malaysia using Standardised Precipitation Index. Theoretical and Applied Climatology. Springer Nature, 111(3–4), pp. 559–568. https://doi.org/10.1007/s00704-012-0682-2">[Crossref]

  21. Zolotokrylin, A. N. (2010). DROUGHTS: CAUSES, DISTRIBUTION AND CONSEQUENCES. Natural Disasters-Volume I. EOLSS Publications, p. 239.


Last update:

No citation recorded.

Last update: 2024-03-29 12:02:36

No citation recorded.