EGU2020-20389
https://doi.org/10.5194/egusphere-egu2020-20389
EGU General Assembly 2020
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development of a drought monitor for South-Asia

Toma Rani Saha, Luis Samaniego, Pallav K Shrestha, Stephan Thober, and Oldrich Rakovec
Toma Rani Saha et al.
  • Department Computational Hydrosystems, Helmholtz Centre for Environmental Research GmbH - UFZ Permoserstraße 15 04318 Leipzig / Germany (saha.toma-rani@ufz.de)

South Asia (SA) is highly vulnerable to extreme climatic events and experiences a wide range of natural hazards such as floods, drought, storms, and sea-level rise.  Droughts are recurrent in SA and its impact on regional agriculture, food storage, and livelihood is enormous. Agricultural droughts have severe consequences on the economy, society, health and water resources sectors. In this work, a state-of-the-art monitoring system of soil moisture drought in SA is developed. This study aims at improving the agricultural drought monitoring system for SA and contributing towards better adaptation solutions in the region. The SA drought monitoring system is inspired by the German Drought Monitor (www.ufz.de/duerremonitor)[1]. First, we implement the mesoscale hydrologic model (mHM, https://git.ufz.de/mhm) to reconstruct daily soil moisture from 1981 to 2019 using a near-real-time precipitation product (CHIRPS version 2, 0.25-degree resolution). Second, the SMI is estimated with a non-parametric kernel-based cumulative distribution function [2] based on mHM’s historic soil moisture reconstruction. The generated SMI maps are classified into five classes based on severity: abnormally dry, moderate drought, severe drought, extreme drought and exceptional drought. Third, we develop the South Asia Drought Monitor (SADM) which is an interactive web-portal (http://southasiadroughtmonitor.pythonanywhere.com/) for the dissemination of the simulated near-real-time drought classes. To achieve maximum dissemination, the daily and monthly SMI fields will be uploaded and published on the SADM portal. The SADM will help to inform decision-makers, the general public, researchers, and stakeholders in the SA. The drought monitoring system will allow the scientific community to conduct micro-level in-depth research and to enable policymakers to formulate proper planning and to take mitigation measures in sectors encompassing energy, health, forestry, and agriculture at local to regional scales.

 

[1] Zink, M., Samaniego, L., Kumar, R., Thober, S., Mai, J., Schäfer, D., Marx, A., 2016: The German drought monitor, Environ. Res. Lett. 11 (7), art. 074002, DOI:10.1088/1748-9326/11/7/074002.

[2] Samaniego, L., Kumar, R. and Zink, M.,2013: Implications of Parameter Uncertainty on Soil Moisture Drought Analysis in Germany, Journal of Hydrometeorology, DOI: 10.1175/JHM-D-12-075.1.

 

How to cite: Saha, T. R., Samaniego, L., Shrestha, P. K., Thober, S., and Rakovec, O.: Development of a drought monitor for South-Asia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20389, https://doi.org/10.5194/egusphere-egu2020-20389, 2020

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  • CC1: Comment on EGU2020-20389, Carmelo Cammalleri, 04 May 2020

    Thanks for your presentation. I would argue that in the "what can be contributed" category it may be included a risk/impact analysis. Also, what are you planning for near-real time meteo forcing?

    • AC1: Reply to CC1, Toma Rani Saha, 04 May 2020

      Thank you for your comment! Doing a risk impact analysis especially, to examine crop sensitivity to water stress would be interesting. Currently, we are focusing on developing an agricultural drought monitoring tool and impact analysis could be an area to investigate in future. In this presentation, 'what can be contributed' mainly indicates 'what can be contributed through this research'. I will rephrase that. The tool is not final yet and we are comparing the outputs using CHIRPS_v2 and ERA5. The current abstract/presentation only presents the output with CHIRPS_v2. In the final tool, one of these two datasets will be used as both are updated in near-real-time.