Assessing Agricultural Drought in the Anthropocene: A Modified Palmer Drought Severity Index

Abstract

In the current human-influenced era, drought is initiated by natural and human drivers, and human activities are as integral to drought as meteorological factors. In large irrigated agricultural regions with high levels of human intervention, where the natural farmland soil moisture has usually been changed significantly by high-frequency irrigation, the actual severity of agricultural drought is distorted in traditional drought indices. In this work, an agricultural drought index that considering irrigation processes based on the Palmer drought severity index (IrrPDSI) was developed to interpret the real agricultural drought conditions in irrigated regions, with a case study in the Haihe River Basin in northeast China. The water balance model in the original PDSI was revised by an auto-irrigation threshold method combined with a local irrigation schedule. The auto-irrigation setting of the index was used by taking irrigation quotas during specific growth stages of specific crops (wheat-corn) into consideration. A series of weekly comparative analyses are as follows: (1) The soil moisture analyses showed that soil moisture values calculated by the modified water balance model were close to the real values; (2) The statistical analyses indicated that most of the stations in the study area based on IrrPDSI had nearly normal distributed values; (3) The time series and spatial analyses showed that the results of the IrrPDSI-reported dry-wet evaluation were more consistent with documented real conditions. All the results revealed that IrrPDSI performed well when used to assess agricultural drought. This work has direct significance for agricultural drought management in large irrigated areas heavily disturbed by human activity.

Drought Analysis in the Yellow River Basin Based on a Short-Scalar Palmer Drought Severity Index

“…The ambiguous description on time scale is an intrinsic problem of the Palmer index family. According to the correlation analysis with SPI, it is generally accepted that PDSI is more inclined to reflect water deficiencies over a prolonged time period [35]. In an implicit manner, the time scale of scPDSI conceals in its standardization process.…”

Drought and society: Scientific progress, blind spots, and future prospects

“…To quantify the influence that agriculture and other human activities have on the frequency, distribution, and/or propagation of drought hazard, physical and engineering scientists have mostly employed numerical indexes (Al‐Faraj & Tigkas, 2016; Deo et al, 2009; Edalat & Stephen, 2019; Ghale et al, 2019; Guo, Huang, Huang, Wang, Fang, et al, 2019; Guo, Huang, Huang, Wang, Wang, & Fang, 2019; Huang et al, 2016; Jehanzaib, Shah, Kwon, & Kim, 2020; Li et al, 2019; Liu, Shi, & Sivakumar, 2020; Liu, Zhang, et al, 2020; Margariti et al, 2019; Mehran et al, 2015; Muhammad et al, 2020; Satgé et al, 2017; Shi et al, 2018; Tijdeman et al, 2018; Wan et al, 2017; Wang, Duan, et al, 2020; Wu et al, 2019; Xu et al, 2019; Yang et al, 2017; Zhao et al, 2019; Zou et al, 2018), statistical or geospatial analyses (Deo et al, 2009; Ghale et al, 2019; Jehanzaib, Shah, Kwon, & Kim, 2020; Kandakji et al, 2021; Liu et al, 2016; Liu, Zhang, et al, 2020; Mehran et al, 2015; Panda et al, 2007; Satgé et al, 2017; Wang, Duan, et al, 2020; Wang, Jiang, et al, 2020; Yang et al, 2017; Zou et al, 2018), and hydro‐meteorological models (Cook et al, 2009; Firoz et al, 2018; Ghale et al, 2019; He et al, 2017; Jehanzaib, Shah, Yoo, & Kim, 2020; Jiang et al, 2019; Kakaei et al, 2019; Kingston et al, 2021; Margariti et al, 2019; Mehran et al, 2015; Muhammad et al, 2020; Taufik et al, 2020; Tu et al, 2018; Wada et al, 2013; Wan et al, 2017; Wanders & Wada, 2015; Wang, Duan, et a...…”

“…Most of the recent contributions have focused on exposing the social processes that intersect with the transformation of a drought from a meteorological event, into soil moisture drought (Cook et al, 2009 ; Deo et al, 2009 ; Kandakji et al, 2021 ; Satgé et al, 2017 ; Taufik et al, 2020 ; Yang et al, 2017 ; Yu et al, 2018 ), hydrological drought (Al‐Faraj & Tigkas, 2016 ; Jehanzaib, Shah, Kwon, & Kim, 2020 ; Jehanzaib, Shah, Yoo, & Kim, 2020 ; Liu et al, 2016 ; Somorowska & Łaszewski, 2017 ; Wada et al, 2013 ; Wan et al, 2017 ; Wanders & Wada, 2015 ; Wang, Duan, et al, 2020 ; Wang, Jiang, et al, 2020 ; Yang et al, 2020 ; Yuan et al, 2017 ), and ultimately, socioeconomic drought (Edalat & Stephen, 2019 ; Guo, Huang, Huang, Wang, Fang, et al, 2019 ; Guo, Huang, Huang, Wang, Wang, & Fang, 2019 ; Heidari et al, 2020 ; Huang et al, 2016 ; Liu, Shi, & Sivakumar, 2020 ; Liu, Zhang, et al, 2020 ; Mehran et al, 2015 ; Shi et al, 2018 ; Zhao et al, 2019 ). Across these studies, the social processes that engender or exacerbate drought propagation are usually proxied by the expansion of agriculture activities, population growth, urbanization, and industrialization, with agriculture seen as the most significant social driver across the various phases of drought propagation.…”

“…Literature on soil moisture drought, for instance, argues that crops cultivation together with the grazing of pastures, constitute the prevailing reasons behind the reduction, conversion, as well as degradation of native soil and vegetation. In the long run, these agricultural transformations increase the amount of energy available on the surface of the land, alter local hydro‐climatological processes and in turn, induce temperature anomalies alongside changes in evapotranspiration and precipitation rates even beyond the region of disturbance (Cook et al, 2009 ; Deo et al, 2009 ; Kandakji et al, 2021 ; Satgé et al, 2017 ; Taufik et al, 2020 ; Yang et al, 2017 ; Yu et al, 2018 ).…”

A Micro-Scale Study of Flood Risk Assessment in Urban Fluvial Areas Using the Flood Potential Index

“…Another method is by using the water index in order to describe or analyze water-related problems (Tallar and Dhian, 2021;Nayak et al, 2020;Wang et al, 2020;Williams et al, 2019;Tallar and Suen, 2015;Tallar and Suen, 2016). Therefore, many studies have also combined the water index with a GIS-based model (Kawo and Karuppannan, 2018;Taloor et al, 2020), and further studies developed a flood index by using a GIS based-model (Yang et al, 2017;. Although improved tools and models have been used to analyze data concerning flood events, there is a relative lack of micro-scale studies concerning flood risk assessment combined with the flood potential index (FPI) to forecast future flood events in DKI Jakarta.…”