Towards Global Drought Early Warning Capability: Expanding international cooperation for the development of a framework for global drought monitoring and forecasting

Pozzi, W.; Sheffield, Justin; Stefanski, Robert; Cripe, Douglas; Pulwarty, Roger S.; Vogt, J.; Heim, Richard R.; Brewer, Michael J.; Svoboda, Mark D.; Westerhoff, Rogier; Dijk, Albert I. J. M. van; Lloyd‐Hughes, Benjamin; Pappenberger, Florian; Werner, Micha; Dutra, Emanuel; Wetterhall, F.; Wagner, W.; Schubert, Siegfried D.; Mo, Kingtse C.; Nicholson, Margaret; Bettio, Lynette; Núñez, Liliana; Beek, Rens van; Bierkens, M. F.; Goncalves, L.; Mattos, João Gerd Zell de; Lawford, R. G.

doi:10.1175/bams-d-11-00176

Cited by 17 publications

(17 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, policy may evolve radically differently due to tipping points of vulnerability thresholds derived from changes in climate and policy. In addition, flexible adaptation may be guided in the near term by improvements in monitoring and early warning systems (Pozzi et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Strategies to reduce water stress in Euro-Mediterranean river basins

Garrote

Granados

Iglesias

2016

Science of The Total Environment

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Strategies to reduce water stress in Euro-Mediterranean river basins

Garrote

Granados

Iglesias

2016

Science of The Total Environment

View full text Add to dashboard Cite

“…Hydrological drought is generally related to the deficit of surface runoff, streamflow, reservoir, or groundwater level. Since it is directly linked to drought impacts, it is argued that more attention is needed to study the hydrological drought (Cloke & Hannah, ; Mishra & Singh, ; Pozzi et al, ). The commonly used hydrologic drought indicators include Palmer Hydrologic Drought Index (PHDI) (Palmer, ), runoff or streamflow percentile, Standardized Runoff Index (SRI) (Shukla & Wood, ), or reservoir level (Hayes et al, ).…”

Section: Drought Types and Indicesmentioning

confidence: 99%

“…Due to its complexity with diverse origins and occurrence at different temporal and spatial scales, drought prediction has presented a major challenge to climatologists and hydrologists as well as decision and policy makers. Generally, three types of methods have been used for drought prediction: statistical, dynamical, and hybrid methods (Mariotti et al, ; Mishra & Singh, ; Pozzi et al, ). The statistical prediction method uses empirical relationships of historical records, taking different influencing factors as predictors.…”

Section: Introductionmentioning

confidence: 99%

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Hao

Singh

Xia

2018

Reviews of Geophysics

389

232

View full text Add to dashboard Cite

Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large‐scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high‐quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

show abstract

“…Development of a Global Drought Information System was a key recommendation of a World Climate Research Program workshop "Drought Predictability and Prediction in a Changing Climate" held in 2011 (Heim and Brewer, 2012;Pozzi et al, 2013). Though thus far, the implementation of a global seasonal hydrologic prediction system has largely been elusive notwithstanding major strides in the last two decades in the development of large scale hydrologic models (Liang et al, 1994;Mitchell et al, 2004;Wang et al, 2009) and improvement in seasonal climate forecast skill (Goddard et al, 2001(Goddard et al, , 2003Palmer et al, 2004;Saha et al, 2006;Barnston et al, 2010;Yuan et al, 2011).…”

Section: Published By Copernicus Publications On Behalf Of the Europementioning

confidence: 99%

On the sources of global land surface hydrologic predictability

Shukla

Sheffield

Wood

et al. 2013

Hydrol. Earth Syst. Sci.

105

View full text Add to dashboard Cite

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1-6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs -soil moisture and snow water content -and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two modelbased experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period . We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year.Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

show abstract

Towards Global Drought Early Warning Capability: Expanding international cooperation for the development of a framework for global drought monitoring and forecasting

Cited by 17 publications

References 0 publications

Strategies to reduce water stress in Euro-Mediterranean river basins

Strategies to reduce water stress in Euro-Mediterranean river basins

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

On the sources of global land surface hydrologic predictability

Contact Info

Product

Resources

About