Modulation of tropical cyclone genesis by Madden–Julian Oscillation in the Southern Hemisphere

This chapter advances intelligent methodologies to study the modulation of cyclone genesis. Tropical cyclones (TCs) are hazardous weather elements with detrimental impacts on populations, wildlife, ecosystems, infrastructure, and the economy of developed as well as developing nations. Understanding the climatological behavior of TCs in relation to onsets, origin, and causal factors conductive to cyclogenesis can aid in the risk-management of cyclone vulnerability. This chapter studies the observed modulation of TC genesis in two study regions, namely the South Indian Ocean (SIO: 0–30° S, 30° E–130° E) and the South Pacific Ocean (SPO: 0–30° S, 130° E–130° W) was examined for the period 1980–2012. We define regional Madden–Julian Oscillation (MJO) indices based on the convective anomalies of large OLR variability centers, which exhibit a stronger modulation of the TC genesis than previously identified. Overall, an increase in the number of TC formations was recorded for the enhanced convective phase of the MJO compared to the dry phase. The modulation of TC genesis by MJO appeared to be pronounced with a ratio of 2:1 to the east of 70° E (for the SIO) and 7:1 to the west of 170° W (for the SPO). Stronger modulation in the latter region is attributable to (1) MJO-induced wind field impacts that were notably larger than the background mean flow, (2) TC genesis locations being consistent with MJO action centers, i.e., the TCs occur over the region of the MJO-induced low-level circulation with enhanced convection, and (3) TC genesis occurs in the South Pacific Convergence Zone (SPCZ), a region where MJO has a strong modulating effect. An analysis of large-scale dynamic and thermodynamic conditions demonstrated that low-level relative vorticity was strongly related to TC genesis modulation in both the SIO and SPO regions. However, the MJO appears to show little effect on TC genesis in the western SIO due to the existence of climatological conditions less conducive to TC formation throughout the cyclonic season. Finally, the chapter ascertains that TCs are generally produced further from the equatorial region in the southwest Indian zone where the MJO signal appears to be very weak

Spatio-temporal drought risk mapping approach and its application in the drought-prone region of south-east Queensland, Australia

Strategic management of water resources in drought-vulnerable regions can be greatly hampered by frequent, severe and long-lasting droughts. To enable better drought relief policy and amicable solutions and proactive actions for preparedness and mitigation of drought impacts, this study adopts a spatio-temporal methodology for the assessment of drought risk of drought-prone areas in south-east Queensland, Australia. In this study, the spatially representative depiction of the drought risk in a drought-prone region with multiple vulnerability, exposure and drought hazard indicators is considered in order to develop a geographic information systems-based drought risk mapping tool. Spatial indicators of drought are categorised into various subclasses, and the conditional joint probability of each indicator is the determined in accordance with the Bayes theorem. The fuzzy logic approach is then embraced as a new approach in this study to standardise the different drought factors on a range of 0–1 followed by an aggregation of drought vulnerability, exposure and hazard indices using the fuzzy GAMMA overlay operation in ArcGIS 10.5 to produce the optimal drought risk map for the case study region. The analysis of drought’s different phases shows varying vulnerability levels in different austral seasons (summer, autumn and spring of 2007) and annually (2007, 2009 and 2013) that is well represented by drought hazard index, i.e. rainfall departure. The application of the fuzzy set to incorporate and classify drought factors reveals its useful implications for handling of spatial drought-related data and the development of the drought risk index. The validation of the method performed with upper and lower layer soil moisture data reveals significant correlation with the drought risk index. The study has implications for drought risk mapping, particularly in utilising the ability of the fuzzy logic-based analytical technique integrated with GIS-based mapping tools for spatio-temporal drought risk studies. The approach in this paper can be considered as a practical mapping tool for drought studies, to better enable drought management, drought mitigation and relief-planning actions that need to be implemented by different decision-makers in water resources, agriculture and other socio-economic areas

Application of hybrid artificial neural network algorithm for the prediction of Standardized Precipitation Index

The application of wavelet transformation has become a popular area of interest in hydrological modeling as it enables the use of spectral and temporal information contained in input data. Drought modeling is one such area that is still far from complete, considering the stochastic nature of drought characteristics per every drought events. This study therefore aims to predict a drought index, i.e. the Standardized Precipitation Index (SPI), using artificial neural network (ANN) and a hybrid ANN with wavelet analysis (WA-ANN) using four main inputs: precipitation, potential evapotranspiration, Southern Oscillation Index, and Nino 4 index for Brisbane, Australia. For WA-ANN, the four inputs were decomposed into three detail and one approximation levels using Daubechies-4 (db4) orthogonal mother wavelet. The evaluation of prediction performance showed that WA-ANN outperformed ANN model with an increased accuracy by 49.89% based on Root Mean Squared Error values

Investigating drought duration-severity-intensity characteristics using the Standardized Precipitation-Evapotranspiration Index: case studies in drought-prone Southeast Queensland

Drought characterization is crucial for identifying impacts on irrigation, agriculture, hydrologic engineering, and water resources management. This case study demonstrates the scientific relevance of the standardized precipitation-evapotranspiration index (SPEI) as a robust drought metric that incorporates influence of supply-demand balance. Using long-term data, the SPEI was calculated at multiple timescales to identify historical water deficit periods in selected drought-prone case study regions in southeast Queensland, Australia. The drought duration (D; number of months with continuously negative SPEI representing below average water resources), severity (S; accumulated negative SPEI in a drought-identified period), intensity (I; minimum SPEI), and return periods were enumerated for iconic dry events over multiple (1-, 3-, 6-, 9-, 12-, and 24-month) timescales. The SPEI was evaluated with corresponding drought indicators (precipitation and soil moisture) and climatological Rainfall Anomaly Index to yield drought severity information from a meteorological perspective. The results showed disparities in duration, severity, and intensity (D–S–I) of different droughts among the case study regions; reaffirming the significance of SPEI for regional drought impact assessment. Accordingly, this case study advocates SPEI as a convenient metric for detecting drought onsets and terminations, including drought ranking and recurrence evaluations that are vital statistics in hydrologic engineering

Development of copula statistical drought prediction model using the Standardized Precipitation-Evapotranspiration Index

Modeling of drought properties is paramount for real-life decision-making in hydrologic engineering, agriculture, water management, and drought-risk relief. This study models joint behavior of the Standardized Precipitation-Evapotranspiration Index and drought properties (severity, S; duration, D; intensity, I), conditional upon pertinent climate mode indices. The El Niño–Southern Oscillation indicators were selected for conditional prediction of drought events, and the D-S-I properties were used to investigate the drought-risk. Vine copula algorithm was used to establish bivariate and trivariate joint distributions of drought behavior for conditional probability–based simulations, where the predictions were made for accurately modeling the drought. Results yielded considerably small differences between the observed and predicted drought properties, elucidating the effectiveness of copula-statistical models in future drought-risk modeling. The findings have implications for drought and aridity management in other agricultural regions where complex relationships between climate anomalies and drought properties are likely to exacerbate the net risk of a future drought event

Quantifying flood events in Bangladesh with a daily-step flood monitoring index based on the concept of daily effective precipitation

Bangladesh, located in the Bay of Bengal, is a developing nation that is prone to devastating flood events that cause loss of lives and several other forms of humanatarian disasters. Identifying, developing and validating new scientific techniques that can be used for flood-risk warning and regular monitoring, including flood risk mitigation and adaptation, can help reduce the catastrophic effect of floods in Bangladesh and other developing countries. In this study, a daily Flood Index (IF) based on daily effective precipitation (PE) is utilized for quantifying floods for two cites in Bangladesh: Dhaka (23.7° N, 90.38° E) and Bogra (24.51° N; 89.22° E), where flooding is a common phenomenon. Based on total daily precipitation (P) data, in this study the IFis determined by calculating the PE using an exponentially decaying time-reduction function, which considers the gradual depletion of water resources over time. PE per day is normalized and compared with the average and standard deviation of yearly maximums, within the considered hydrological period. The start of a flood event is identified for IF ≥ 0. Subsequently, the IF severity (Iacc F ) as consecutively positive IF, duration (DF) as number of days with positive IF, and peak danger (Imax F ) asmaximumIF are estimated for identified flood events, using the run-sum theory. The analysis carried out has accurately identified historical flood events at Dhaka station in 1984 and 2007 as having the largest Iacc F value (i.e., greatest severity). Flood severity (Iacc F ) and peak danger (Imax F ) parameters have been verified by accumulated precipitation corresponding to the same period. For Bogra station, the 1998 and 1979 events were found to be the most intense. Seasonality analysis of the flood index shows most floods happened during the summer monsoon, although for the Dhaka station, flood events occurred between early June and November, and in Bogra, from late June to October. A rational study of effective precipitation provides a useful severity and danger indicator. The results indicate the practicality of daily IF for flood-risk assessments where the severity, peak danger, and duration need to be considered as a unified index for flood-risk monitoring. The proposed daily flood index can be useful for hydrologists for the purpose of daily operational flood monitoring in high flood-risk nations

Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia

Widespread detrimental and long-lasting droughts are having catastrophic impacts around the globe. Researchers, organizations, and policy makers need to work together to obtain precise information, enabling timely and accurate decision making to mitigate drought impacts. In this study, a spatial modeling approach based on an adaptive neuro-fuzzy inference system (ANFIS) and several metaheuristic optimizations (ANFIS-BA, ANFIS-GA, ANFIS-ICA, ANFIS-PSO) was developed to predict the spatial occurrence of drought in a region in southeastern Queensland, Australia. In this approach, data describing the distribution of eight drought-contributing factors were prepared for input into the models to serve as independent variables. Relative departures of rainfall (RDR) and relative departures of soil moisture (RDSM) were analyzed to identify locations where drought conditions have occurred. The set of locations in the study area identified as having experienced drought conditions was randomly divided into two groups, 70% were used for training and 30% for validation. The models employed these data to generate maps that predict the locations that would be expected to experience drought. The prediction accuracy of the model-produced drought maps was scrutinized with two evaluation metrics: area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). The results demonstrate that the hybridized models (ANFIS-BA (AUCmean = 83.7%, RMSEmean = 0.236), ANFIS-GA (AUCmean = 81.62%, RMSEmean = 0.247), ANFIS-ICA (AUCmean = 82.12%, RMSEmean = 0.247), and ANFIS-PSO (AUCmean = 81.42%, RMSEmean = 0.255)) yield better predictive performance than the standalone ANFIS model (AUCmean = 71.8%, RMSEmean = 0.344). Furthermore, sensitivity analyses indicated that plant-available water capacity, the percentage of soil comprised of sand, and mean annual precipitation were the most important predictors of drought hazard. The versatility of the new approach for spatial drought modeling and the capacity of ANFIS model hybridization to improve model performance suggests great potential to assist decision makers in their formulations of drought risk, recovery, and response management, and in the development of contingency plans