Search

Drought is a function of both natural and human influences, but fully characterizing the interactions between human and natural influences on drought remains challenging. To better characterize parts of the drought feedback loop, this study combines hydrological and societal perspectives to characterize and quantify the potential for drought action. For the hydrological perspective, we examine historical groundwater data, from which we determine the decadal likelihoods of exceeding hydrologic thresholds relevant to different water uses. Stakeholder interviews yield data about how people rate the importance of water for different water uses. We combine these to quantify the Potential Drought Action Index (PDAI). The PDAI is demonstrated for a study site in south-central Oklahoma, where water availability is highly influenced by drought and management of water resources is contested by local stakeholders. For the hydrological perspective, we find that the historical decadal likelihood of exceedance for a moderate threshold associated with municipal supply has ranged widely: from 23 % to 75 %, which corresponds well with natural drought variability in the region. For the societal perspective, stakeholder interviews reveal that people value water differently for various uses. Combining this information into the PDAI illustrates that potential drought action increases as the hydrologic threshold is exceeded more often; this occurs as conditions get drier and when water use thresholds are more moderate. The PDAI also shows that for water uses where stakeholders have diverse views of importance, the PDAI will be diverse as well, and this is exacerbated under drier conditions. The variability in stakeholder views of importance is partially explained by stakeholders' cultural worldviews, pointing to some implications for managing water when drought risks threaten. We discuss how the results can be used to reduce potential disagreement among stakeholders and promote sustainable water management, which is particularly important for planning under increasing drought. [ABSTRACT FROM AUTHOR]

Global Climate Model (GCM) projections suggest that drought will increase across large areas of the globe, but lack skill at simulating climate variations at local-scales where adaptation decisions are made. As such, GCMs are often downscaled using statistical methods. This study develops a 3-step framework to assess the use of large-scale environmental patterns to assess local precipitation in statistically downscaling to local drought. In Step 1, two statistical downscaling models are developed: one based on temperature and precipitation and another based on temperature and a large-scale predictor that serves as a proxy for precipitation. A key component is identifying the large-scale predictor, which is customized for the location of interest. In Step 2, the statistical models are evaluated using NCEP/NCAR Reanalysis data. In Step 3, we apply a large ensemble of future GCM projections to the statistical models. The technique is demonstrated for predicting drought, as measured by the Palmer Drought Severity Index, in South-central Oklahoma, but the framework is general and applicable to other locations. Case study results using the Reanalysis show that the large-scale predictor explains slightly more variance than precipitation when predicting local drought. Applying future GCM projections to both statistical models indicates similar drying trends, but demonstrates notable internal variability. The case study demonstrates: (1) where a large-scale predictor performs comparably (or better) than precipitation directly, then it is an appealing predictor choice to use with future projections, (2) when statistically downscaling to local scales, it is critical to consider internal variability, as it may be more important than predictor selection. [ABSTRACT FROM AUTHOR]

The suitability of dynamical downscaling in producing high-resolution climate scenarios for impact assessments is limited by the quality of the driving data and regional climate model (RCM) error. Multiple RCMs driven by a single global climate model simulation of current climate show a reduction in bias compared to the driving data, and the remaining bias motivates exploration of bias correction and higher RCM resolution. The merits of bias correcting the mean climate of the driving data (boundary bias correction) versus bias correcting the mean of the RCM output data are explored and compared to model resolution sensitivity. This analysis focuses on the simulation of summer temperature and precipitation extremes using a single RCM, the Nested Regional Climate Model (NRCM). The NRCM has a general cool bias for hot and cold extremes, a wet bias for wet extremes and a dry bias for dry extremes. Both bias corrections generally reduced the bias and overall error with some indication that boundary bias correction provided greater benefits than bias correcting the mean of the RCM output data, particularly for precipitation. High resolution tended not to lead to further improvements, though further work is needed using multiple resolution evaluation datasets and convection permitting resolution simulations to comprehensively assess the value of high resolution. [ABSTRACT FROM AUTHOR]

The Canadian Prairies have experienced severe and extended droughts that have had significant impacts on agriculture, energy and other socio-economic sectors; it is therefore desirable to assess future changes to drought characteristics in this drought prone region, in the context of a changing climate. This study addresses validation and projected changes to short- and long-term drought characteristics, i.e. severity, frequency and duration, over the Canadian Prairies, using an ensemble of ten Canadian RCM (CRCM) simulations, of which five correspond to the current 1971-2000 period and the other five are the matching simulations for the future 2041-2070 period. These five pairs of current and future CRCM simulations were driven by five different members of a Canadian Global Climate Model ensemble. Validation of CRCM simulated precipitation suggests that the model reproduces the observed precipitation distribution for all seasons, except summer, across a large portion of the Canadian Prairies. However, comparison of CRCM simulated drought characteristics with those observed suggests that the model has difficulties in reproducing observed severity, frequency and duration of drought events, particularly those associated with longer events, possibly due to the overestimation of summer precipitation by the model. Analysis of projected changes to precipitation and drought characteristics between the 1971-2000 and 2041-2070 periods suggests a decrease in mean precipitation in summer and an increase for the other seasons, while the severity, frequency and maximum duration of both short- and long-term droughts are projected to increase over the southern Prairies, with the largest projected changes associated with longer drought events. Classification of the watersheds spanning the southern Prairies based on changes to both severity and frequency further reveal the vulnerability of this region in a changing climate. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]