Selection of appropriate time scale with Boruta algorithm for regional drought monitoring using multi-scaler drought index

Spatial distribution of drought plays key role specifically in hydrological research. Drought is a complex stochastic natural hazard caused by prolonging shortage of rainfall and available water resources. The multi-scalar drought indices (based on probability distribution) are commonly used for making effective drought mitigation policies. In addition, the multi-scalar drought indices have great extent of the inaccurate determination of drought classes due to its probabilistic nature. However, the interpretation and applicability of various time scales are cumbersome for multi-scalar drought in various meteorological stations at a particular region. In this regards, accurate estimation and continuous monitoring of future drought at regional level requires a more appropriate and important time scale with respect to regions under study. In this study, we aimed to investigate the appropriate time scale for multi-scalar drought indices by using geo-reference points of meteorological stations. We used Boruta algorithm with two random forest adapters of machine learning algorithms for regionalized optimization of drought monitoring time scale. We explored the appropriate time scale for the Standardized Precipitation Temperature Index (SPTI) of 52 meteorological stations that are located across Pakistan. Results show that the significant importance of SPTI-1 (1-month time scale) for further spatial and temporal studies. That is, being high ranked and prominence, SPTI-1 has the ability to capture the characteristics of all other time scales that are in some circumstances applicable for drought characterization and classification

Measuring the ecological footprint of inbound and outbound tourists:evidence from a panel of 35 countries

The ecological footprint of tourism is imperative to assess for United Nation’s environmental sustainable agenda that is provoked for healthy visitation of tourists without damaging natural environment. This would ultimately reap economic and environmental benefts to sustained international tourism. This study examined the relationship between international tourism indicators, air pollutants, and ecological biodiversity underlying the premises of environmental Kuznets curve in the panel of 35 tourists-induced countries for the period of 1995–2016. The study used panel fxed efect and panel twostage least square regression technique for robust inferences. The results confrmed the following key points, i.e., (1) the U-shaped relationship found between inbound tourists and mono-nitrogen oxide (NOx), where inbound tourists initially do not emanate the NOx emissions, while at the later stages, the level of NOx emissions substantially raises the required strong policy intervention to reduce emissions and provide tourists safe and healthy destinations, (2) inbound tourists linked with the biodiversity loss, and it increases carbon dioxide (CO2) emissions and greenhouse gas (GHG) emissions in a panel of potential habitat area, while it decreases NOx and SO2 emissions, (4) international tourists’ departure exercised the ‘rebound efect’ on the ecosystem and air pollutants across countries, (5) there is a monotonic increasing relationship between outbound tourists and ecological footprint, while there is a fat/no relationship between outbound tourists, NOx, CO2, SO2, and GHG emissions, and (6) the food management practices supported the ecological diversity, and it reduces the carbon ‘foodprint,’ while it substantially increases SO2 emissions in outbound tourists’ model. The study emphasized the need for sustainable tourism infrastructure that conserves our natural environment and reduces climatic variability across the globe

A new spatiotemporal two-stage standardized weighted procedure for regional drought analysis

Drought is a complex phenomenon that occurs due to insufficient precipitation. It does not have immediate effects, but sustained drought can affect the hydrological, agriculture, economic sectors of the country. Therefore, there is a need for efficient methods and techniques that properly determine drought and its effects. Considering the significance and importance of drought monitoring methodologies, a new drought assessment procedure is proposed in the current study, known as the Maximum Spatio-Temporal Two-Stage Standardized Weighted Index (MSTTSSWI). The proposed MSTTSSWI is based on the weighting scheme, known as the Spatio-Temporal Two-Stage Standardized Weighting Scheme (STTSSWS). The potential of the weighting scheme is based on the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and the steady-state probabilities. Further, the STTSSWS computes spatiotemporal weights in two stages for various drought categories and stations. In the first stage of the STTSSWS, the SPI, SPEI, and the steady-state probabilities are calculated for each station at a 1-month time scale to assign weights for varying drought categories. However, in the second stage, these weights are further propagated based on spatiotemporal characteristics to obtain new weights for the various drought categories in the selected region. The STTSSWS is applied to the six meteorological stations of the Northern area, Pakistan. Moreover, the spatiotemporal weights obtained from STTSSWS are used to calculate MSTTSSWI for regional drought characterization. The MSTTSSWI may accurately provide regional spatiotemporal characteristics for the drought in the selected region and motivates researchers and policymakers to use the more comprehensive and accurate spatiotemporal characterization of drought in the selected region.Validerad;2022;Nivå 2;2022-05-02 (joosat);Funder: Deanship of Scientific Research at King Saud University (1435-075)</p