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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2017-183
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
28 Apr 2017
Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).
Regression-based season-ahead drought prediction for southern Peru conditioned on large-scale climate variables
Eric Mortensen1, Shu Wu2, Michael Notaro2, Steven Vavrus2, Rob Montgomery3, José De Piérola4, Carlos Sánchez4, and Paul Block1 1Department of Civil and Environmental Engineering, University of Wisconsin–Madison, Madison, 53706, USA
2Nelson Institute Center for Climatic Research, University of Wisconsin–Madison, Madison, 53706, USA
3Montgomery Associates Resource Solutions LLC, Cottage Grove, 53527, USA
4Southern Peru Copper Corporation, Santiago de Surco, Lima 33, Peru
Abstract. Located at a complex topographic, climatic, and hydrologic crossroads, southern Peru is a semi-arid region that exhibits high spatiotemporal variability in precipitation. The economic viability of the region hinges on this water, yet southern Peru is prone to water scarcity caused by seasonal drought. Droughts here are often triggered during El Niño episodes; however, other large-scale climate mechanisms also play a noteworthy role in controlling the region’s hydrologic cycle. An extensive season-ahead drought prediction model is developed to help bolster existing capacity of stakeholders to plan for and mitigate the deleterious impacts of this hydrologic extreme. In addition to existing climate indices, large-scale climatic variables, such as sea surface temperature, are investigated to identify potential drought predictors. A principal component regression framework is applied to eleven potential predictors to produce an ensemble forecast of January-March precipitation. Model hindcasts of 51 years, compared to climatology and another model conditioned solely on an El Niño-Southern Oscillation index, achieve notable skill and perform better for several metrics, including ranked probability skill score and a hit-miss statistic. Extending the lead time and spatially disaggregating precipitation predictions to the local level may further assist regional stakeholders and policymakers preparing for drought.

Citation: Mortensen, E., Wu, S., Notaro, M., Vavrus, S., Montgomery, R., De Piérola, J., Sánchez, C., and Block, P.: Regression-based season-ahead drought prediction for southern Peru conditioned on large-scale climate variables, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-183, in review, 2017.
Eric Mortensen et al.
Eric Mortensen et al.
Eric Mortensen et al.

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Short summary
Some don't realize how much they need water until there isn't any. This is the reality faced by people in southern Peru, a dry area of the world where several economic activities and cities vie for water. With a precipitation prediction model, I hope that stakeholders and decision makers in this region will have another tool in their belt to respond to the negative impacts of drought in advance. I did this research because I believe that the issues that the world faces must be tackled together.
Some don't realize how much they need water until there isn't any. This is the reality faced by...
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