Development of statistical and geospatial-based framework for drought-risk assessment

Drought is an insidious, complex and one of the least understood natural phenomena resulting from a deficiency of water resources. While droughts cannot be prevented, its impacts, however, can be mitigated through proper design of water storage infrastructure and management strategies. A comprehensive drought management plan necessitates the development of a framework that can help reduce the drought-related risk. In Australia, there are limited drought vulnerability and risk assessment models that must (1) include the drought monitoring index that measures the supply-demand balance of water resources, (2) incorporate large-scale climate drivers influencing amplitude of drought events in the statistical prediction models, and (3) objectively quantify the drought-risk on both temporal and spatial scales. The goal of this study is to apply statistical and geospatial tools in developing a framework for assessing drought-related risks in light of improving the drought mitigation strategies. A new, temporal and spatial-explicit analytical framework for drought-risk assessment is developed based on three objectives focussed in the drought-prone southeast Queensland (SEQ) region. (1) Evaluating and affirming the suitability of the Standardised Precipitation-Evapotranspiration Index (SPEI) for the characterisation of drought events. (2) Developing a copula-based statistical, probabilistic model for predicting the SPEI and the jointly distributed drought properties (i.e., durations, severities and intensities) conditional on the large-scale climate mode indices. (3) Developing a spatially descriptive drought-risk index by combining the drought hazard, exposure and vulnerability factors using a fuzzy logic algorithm. The first objective of this study demonstrates the scientific relevance of the SPEI as a robust drought assessment metric that incorporates the influence of water supply-demand balance on drought events. Subsequently, the severity (S; accumulated negative SPEI in a drought-identified period), intensity (I; minimum SPEI) and the duration (D; number of months with continuously negative SPEI representing the below average water resources) based on run-sum approach are enumerated to identify historical water deficit periods. Significant disparities in the identified D-S-I affirms the significance of SPEI for regional drought impact assessments. Accordingly, this study advocates the SPEI as a convenient metric for detecting drought onsets and terminations, including its ability for drought ranking and drought recurrence evaluations that are considered vital for water resource management. The second objective models the joint behaviour of SPEI and D-S-I properties using copula model, conditional upon the pertinent climate mode indices (i.e., El-Niño Southern Oscillation indicators). The vine copula algorithm is employed to derive the bivariate and trivariate joint-distributions of drought variables for conditional probability-based predictions. The results yield marginal differences between the observed and the predicted drought properties, elucidating the effectiveness of copula functions in drought-risk modelling. The results have implications for drought and aridity management in agricultural regions where complex relationships between climate drivers and drought properties are likely to exacerbate the risk of a future event. The third objective develops a methodology using vulnerability, exposure and hazard indicators to provide a spatio-temporal framework for drought-risk assessment. The conditional joint probability of each drought indicator is estimated using the Bayes theorem. Various fuzzy membership functions are then applied to standardise and aggregate the indicators to derive drought vulnerability, exposure and hazard indices. The resulting indices are integrated with fuzzy GAMMA overlay operation to generate optimal drought-risk maps. The maps reveal varying levels of drought risk in different austral seasons and annually that is well represented by the drought hazard index, i.e., rainfall departure. The validation of the method with respect to the upper and lower layer soil moisture reveal significant correlations with the spatial drought-risk index. It is therefore prudent to state that the fuzzy logic-based analytical technique applied for spatio-temporal drought-risk mapping can be considered as a practical tool that can enable better drought management, drought mitigation and relief-planning decisions. The statistically and spatially relevant drought-risk assessments frameworks formulated in this study provides promising outcomes that are valuable for the mitigation of drought impacts, and therefore, sets a pathway to construct strategic planning procedures and management of water resources in drought-prone, arid or semi-arid regions

Climate variations of genesis and rapid intensification of tropical cyclones in the southern hemisphere

M.S. University of Hawaii at Manoa 2014.Includes bibliographical references.This thesis focuses on short-term climate variation of tropical cyclones in the Southern Hemispheric Ocean. Two major research areas are explored: (1) the modulation of tropical cyclone genesis by the Madden-Julian Oscillation (MJO), and (2) seasonal and intraseasonal variability of rapid intensification (RI). The observed modulation of tropical cyclone (TC) genesis is examined using 32-years of outgoing longwave radiation (OLR), reanalysis winds and TC best track data. A newly introduced MJO index based on the convective anomalies of large OLR variability centers shows a much stronger modulation of TC genesis by MJO than previously detected. An increased number of TC formations are observed during the enhanced convective phase of MJO than during a dry phase. The modulation is more pronounced to the east of 70°E in South-Indian Ocean (SIO) with a modulation ratio of 2:1 and to the west of 170°W in the SPO with a modulation ratio of 7:1. The stronger modulation in SPO is mainly due to: (1) MJO-induced wind fields are larger than the background mean flow, (2) TC genesis location being consistent with MJO basic state, i.e., TCs co-occur over the region of MJO-induced low-level circulation and enhanced convection, and (3) TC genesis occurs in the South-Pacific convergence zone upon which MJO has a strong modulation. Analysis of large-scale dynamic and thermodynamic environmental conditions reveal low-level relative vorticity is strongly attributable to TC genesis modulation in both ocean basins where SPO has an additional contribution from mid-tropospheric relative humidity that is also modulated by MJO-induced perturbations. The MJO has little effect on TC genesis in SW Indian Ocean because of the existence of favorable climatological environmental conditions throughout the TC season, and TCs form further away from the equator where the MJO signal is very weak or non-existent

Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia

A quantitative understanding of the hydro-environmental factors that influence the occurrence of agricultural drought events would enable more strategic climate change adaptation and drought management plans. Practical drought hazard mapping remains challenging due to possible exclusion of the most pertinent drought drivers, and to the use of inadequate predictive models that cannot describe drought adequately. This research aims to develop new approaches to map agricultural drought hazard with state-of-the-art machine learning models, including classification and regression trees (CART), boosted regression trees (BRT), random forests (RF), multivariate adaptive regression splines (MARS), flexible discriminant analysis (FDA) and support vector machines (SVM). Hydro-environmental datasets were used to calculate the relative departure of soil moisture (RDSM) for eight severe droughts for drought-prone southeast Queensland, Australia, over the period 1994–2013. RDSM was then used to generate an agricultural drought inventory map. Eight hydro-environmental factors were used as potential predictors of drought. The goodness-of-fit and predictive performance of all models were evaluated using different threshold-dependent and threshold-independent methods, including the true skill statistic (TSS), Efficiency (E), F-score, and the area under the receiver operating characteristic curve (AUC-ROC). The RF model (AUC-ROC = 97.7%, TSS = 0.873, E = 0.929, F-score = 0.898) yielded the highest accuracy, while the FDA model (with AUC-ROC = 73.9%, TSS = 0.424, E = 0.719, F-score = 0.512) showed the worst performance. The plant available water holding capacity (PAWC), mean annual precipitation, and clay content were the most important variables to be used for predicting the agricultural drought. About 21.2% of the area is in high or very high drought risk classes, and therefore, warrant drought and environmental protection policies. Importantly, the models do not require data on the precipitation anomaly for any given drought year; the spatial patterns in AGH were consistent for all drought events, despite very different spatial patterns in precipitation anomaly among events. Such machine-learning approaches are able to construct an overall risk map, thus assisting in the adoption of a robust drought contingency planning measure not only for this area, but also, in other regions where drought presents a pressing challenge, including its influence on key practical dimensions of social, environmental and economic sustainability