Logistic Regression Analysis for Spatial Patterns of Drought Persistence

Abstract

Drought is one of the natural hazards with potentially significant impacts on society, economy, and other natural resources over the globe. However, the understanding of drought characteristics and its persistence can significantly help to reduce the potential impacts of drought. Moreover, the knowledge about the spatiotemporal pattern of seasonal drought frequency and drought persistence is important for water resource management, agricultural development, energy consumption, and crop yields. Therefore, the present study is employed to examine the seasonal drought frequency and drought persistence in the region. In this regard, the standardized precipitation index (SPI) at the three-month time scale was used to determine meteorological drought. Furthermore, the logistic regression model is used to calculate the odds and probability of drought persistence from one season to the next for the selected stations by identifying the spatial pattern of seasonal drought frequency and persistence. The potential of the current analysis is validated on six selected stations of the northern area of Pakistan. The outcomes related to the current analysis provide the basis for taking more considerations on early warning systems and help to make the valuable decision for water resource management and agriculture sectors in Pakistan.

Trends of Rainfall Variability and Drought Monitoring Using Standardized Precipitation Index in a Scarcely Gauged Basin of Northern Pakistan

“…Extreme drought events were found in the year of 1982 and 2000, while severe droughts were mostly found in 1982,[1983][1984][1985][1990][1991], and 2000 for the 12-month time scale; moreover, moderate drought events were also found and classified into the groups of 1982-1983, 1985-1989, 1990-1991, 1997-1998, 2000-2002, and 2004. Some of the drought events found in this study are in line with previous studies [43,71,[74][75][76]. Jamro et al (2019) [71] also revealed three drought periods (i.e., 1946-1953, 1984-1989, and 1998-2002) in GB.…”

A New Comprehensive Framework for Identifying and Monitoring the Interseasonal Characteristics of Meteorological Drought

“…Several authors have added numerous frameworks in the literature aimed at assessing drought conditions across various climate conditions and environmental regions [57,78,79]. Specifcally, some researchers monitored and modeled drought characteristics in their frameworks to enhance forecasting accuracy, which is useful for updated decisions and early warning policies [44,[80][81][82][83]. Terefore, modeling and monitoring of drought characteristics are crucial for early warning and decision-making [12,63,84,85].…”

“…Drought indices are particularly useful tools and play an important role in monitoring droughts and providing a comprehensive overview of drought conditions [33][34][35][36][37][38][39]. Te Standardized Precipitation Index (SPI) has prevailed to assess meteorological drought due to simplicity in calculation and ability to assess drought for diferent time scales [24,30,[40][41][42][43][44][45][46]. Based on the importance of SPI for monitoring meteorological drought, the current research employs SPI for monitoring and modeling the spatiotemporal interseasonal characteristics of meteorological drought in selected stations.…”

The Influence of Demographic Factors on Urban Medical Insurance Expenditure under Big Data

“…First, the regression analysis [27][28][29][30][31][32][33][34][35] was performed on lny, lnx 1 , x 2 , x 3 , x 4 , the residual sequence of the equation, named E, was tested for unit root stationarity for E, and the results are shown in Figure 2.…”