Journal metrics

Journal metrics

  • IF value: 4.256 IF 4.256
  • IF 5-year value: 4.819 IF 5-year 4.819
  • CiteScore value: 4.10 CiteScore 4.10
  • SNIP value: 1.412 SNIP 1.412
  • SJR value: 2.023 SJR 2.023
  • IPP value: 3.97 IPP 3.97
  • h5-index value: 58 h5-index 58
  • Scimago H index value: 99 Scimago H index 99
Volume 22, issue 10 | Copyright
Hydrol. Earth Syst. Sci., 22, 5125-5141, 2018
https://doi.org/10.5194/hess-22-5125-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 04 Oct 2018

Research article | 04 Oct 2018

Season-ahead forecasting of water storage and irrigation requirements – an application to the southwest monsoon in India

Arun Ravindranath1, Naresh Devineni1, Upmanu Lall2, and Paulina Concha Larrauri3 Arun Ravindranath et al.
  • 1Department of Civil Engineering, Center for Water Resources and Environmental Research (City Water Center), NOAA Center for Earth System Sciences and Remote Sensing Technologies, City University of New York (City College), New York, NY 10031, USA
  • 2Department of Earth and Environmental Engineering, Columbia Water Center, The Earth Institute, Columbia University, New York, NY 10027, USA
  • 3Columbia Water Center, The Earth Institute, Columbia University, New York, NY 10027, USA

Abstract. Water risk management is a ubiquitous challenge faced by stakeholders in the water or agricultural sector. We present a methodological framework for forecasting water storage requirements and present an application of this methodology to risk assessment in India. The application focused on forecasting crop water stress for potatoes grown during the monsoon season in the Satara district of Maharashtra. Pre-season large-scale climate predictors used to forecast water stress were selected based on an exhaustive search method that evaluates for highest ranked probability skill score and lowest root-mean-squared error in a leave-one-out cross-validation mode. Adaptive forecasts were made in the years 2001 to 2013 using the identified predictors and a non-parametric k-nearest neighbors approach. The accuracy of the adaptive forecasts (2001–2013) was judged based on directional concordance and contingency metrics such as hit/miss rate and false alarms. Based on these criteria, our forecasts were correct 9 out of 13 times, with two misses and two false alarms. The results of these drought forecasts were compared with precipitation forecasts from the Indian Meteorological Department (IMD). We assert that it is necessary to couple informative water stress indices with an effective forecasting methodology to maximize the utility of such indices, thereby optimizing water management decisions.

Download & links
Publications Copernicus
Download
Short summary
We present a framework for forecasting water storage requirements in the agricultural sector and an application of this framework to water risk assessment in India. Our framework involves defining a crop-specific water stress index and applying a particular statistical forecasting model to predict seasonal water stress for the crop of interest. The application focused on forecasting crop water stress for potatoes grown during the monsoon season in the Satara district of Maharashtra.
We present a framework for forecasting water storage requirements in the agricultural sector and...
Citation
Share