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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Earth Sci.</journal-id>
<journal-title>Frontiers in Earth Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Earth Sci.</abbrev-journal-title>
<issn pub-type="epub">2296-6463</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">1093632</article-id>
<article-id pub-id-type="doi">10.3389/feart.2023.1093632</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Earth Science</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Impact of ENSO events on meteorological drought in the Weihe River basin, China</article-title>
<alt-title alt-title-type="left-running-head">Fan et al.</alt-title>
<alt-title alt-title-type="right-running-head">
<ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/feart.2023.1093632">10.3389/feart.2023.1093632</ext-link>
</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Fan</surname>
<given-names>Jingjing</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2076616/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wei</surname>
<given-names>Shibo</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Liu</surname>
<given-names>Dengfeng</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<uri xlink:href="https://loop.frontiersin.org/people/922618/overview"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Qin</surname>
<given-names>Tianling</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Xu</surname>
<given-names>Fanfan</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wu</surname>
<given-names>Chenyu</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Liu</surname>
<given-names>Guanpeng</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Cheng</surname>
<given-names>Yao</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>College of Water Resources and Hydropower</institution>, <institution>Hebei University of Engineering</institution>, <addr-line>Handan</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Hebei Key Laboratory of Intelligent Water Conservancy</institution>, <institution>Hebei University of Engineering</institution>, <addr-line>Handan</addr-line>, <country>China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>State Key Laboratory of Eco-hydraulics in Northwest Arid Region</institution>, <institution>School of Water Resources and Hydropower</institution>, <institution>Xi&#x2019;an University of Technology</institution>, <addr-line>Xi&#x2019;an</addr-line>, <country>China</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>China Institute of Water Resources and Hydropower Research</institution>, <addr-line>Beijing</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/90326/overview">Xander Wang</ext-link>, University of Prince Edward Island, Canada</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1739876/overview">Zhiyong Liu</ext-link>, Sun Yat-sen University, China</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/170507/overview">Ahmed Kenawy</ext-link>, Mansoura University, Egypt</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Dengfeng Liu, <email>liudf@xaut.edu.cn</email>; Tianling Qin, <email>qintl@iwhr.com</email>
</corresp>
</author-notes>
<pub-date pub-type="epub">
<day>24</day>
<month>04</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2023</year>
</pub-date>
<volume>11</volume>
<elocation-id>1093632</elocation-id>
<history>
<date date-type="received">
<day>14</day>
<month>11</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>03</day>
<month>04</month>
<year>2023</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2023 Fan, Wei, Liu, Qin, Xu, Wu, Liu and Cheng.</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Fan, Wei, Liu, Qin, Xu, Wu, Liu and Cheng</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>El Ni&#xf1;o&#x2013;Southern Oscillation (ENSO) events influence elements of the terrestrial water cycle such as precipitation and temperature, which in turn have a significant impact on drought. This work assessed the impact of El Ni&#xf1;o and La Ni&#xf1;a on droughts from 1970 to 2020 in the Weihe River basin (WRB) in China. This study used a standardized precipitation index (SPI) to characterize meteorological drought. The regional drought response to extreme events in El Ni&#xf1;o/La Ni&#xf1;a was analyzed using principal component analysis (PCA), Wilcoxon and Mann&#x2013;Whitney tests, and other methods. The results showed that, based on PCA, the WRB is divided into two regions, with the northwest region (67%) comprising more area than the southeast region (33%). El Ni&#xf1;o/La Ni&#xf1;a significantly impacted drought in the WRB. Droughts mainly occurred in the El Ni&#xf1;o year and the year following La Ni&#xf1;a. El Ni&#xf1;o had the highest number of drought years (44%), followed by the year following La Ni&#xf1;a (43%). The number of droughts was lowest in the year following El Ni&#xf1;o (22%). At 1-, 3-, and 6-month timescales, significant droughts mainly occurred from July to December in El Ni&#xf1;o years and the summer following La Ni&#xf1;a. On a 12-month timescale, significant droughts mainly occurred from January to April in El Ni&#xf1;o years, while no droughts occurred in La Ni&#xf1;a years. The longer the timescale of the SPI, the more months of significant drought in El Ni&#xf1;o years; however, the intensity of drought in the basin was reduced. In the year following La Ni&#xf1;a, summer droughts intensified on a 6-month timescale compared to a 3-month timescale. El Ni&#xf1;o and La Ni&#xf1;a had greater impacts on the drought index in the northwest region of the WRB. In the northwest region, 60% of the months showed significant drought, compared to only 2% of the months in the southeast region. The drought intensity was higher in the northwest region. The results of this study provide a reference for drought management and early warning systems in the WRB and support solutions to water shortage.</p>
</abstract>
<kwd-group>
<kwd>El Ni&#xf1;o</kwd>
<kwd>La Ni&#xf1;a</kwd>
<kwd>drought analysis</kwd>
<kwd>standardized precipitation index</kwd>
<kwd>arid and semi-arid region</kwd>
</kwd-group>
<contract-num rid="cn001">52209013 52009053</contract-num>
<contract-num rid="cn002">ZD2022085</contract-num>
<contract-sponsor id="cn001">National Natural Science Foundation of China<named-content content-type="fundref-id">10.13039/501100001809</named-content>
</contract-sponsor>
<contract-sponsor id="cn002">Department of Education of Hebei Province<named-content content-type="fundref-id">10.13039/501100003482</named-content>
</contract-sponsor>
</article-meta>
</front>
<body>
<sec id="s1">
<title>1 Introduction</title>
<p>In the context of global warming, droughts are likely to become more severe and frequent (<xref ref-type="bibr" rid="B8">Dai, 2013</xref>; <xref ref-type="bibr" rid="B49">Singh et al., 2022</xref>). Drought is a recurrent natural phenomenon caused by chronic water deficits compared to normal conditions (<xref ref-type="bibr" rid="B23">Jim&#xb4;enez-donaire et al., 2019</xref>; <xref ref-type="bibr" rid="B46">Sen et al., 2020</xref>). Although droughts have devastating effects on agriculture, ecology, and society, they are the least understood and costliest natural disasters worldwide (<xref ref-type="bibr" rid="B37">Markonis et al., 2021</xref>; <xref ref-type="bibr" rid="B61">Xu et al., 2021</xref>; <xref ref-type="bibr" rid="B39">Peng et al., 2020</xref>). Since the 1970s, frequent drought events have seriously impacted the quality of life of people, as well as industry, agriculture, and national economic development (<xref ref-type="bibr" rid="B36">Ma and Ren, 2007</xref>; <xref ref-type="bibr" rid="B54">Trenberth et al., 2014</xref>; <xref ref-type="bibr" rid="B76">Zhang Q. et al., 2018</xref>). China is located in the East Asian monsoon region, which is densely populated, economically developed, and a sensitive and high-risk area for climate change. Approximately 20 million hectares/year of farmland is affected by drought, accounting for 60% of the area affected by all types of meteorological disasters, resulting in a reduction in food production from millions of tons to over 30 million tons (<xref ref-type="bibr" rid="B77">Zhang et al., 2008</xref>; <xref ref-type="bibr" rid="B84">Zhou et al., 2018</xref>). However, drought is a serious threat to food security and ecological safety, as well as to human survival and development (<xref ref-type="bibr" rid="B50">Su et al., 2018</xref>; <xref ref-type="bibr" rid="B9">Dai et al., 2022</xref>).</p>
<p>The El Ni&#xf1;o&#x2013;Southern Oscillation (ENSO) is the strongest interannual variability in the global climate system produced by sea&#x2013;air interactions (<xref ref-type="bibr" rid="B4">Bjerknes, 1969</xref>). ENSO warm (El Ni&#xf1;o) and cold (La Ni&#xf1;a) phase events occur approximately every 2&#x2013;7&#xa0;years, usually starting to develop during the Northern Hemisphere summer and peaking at the end of the year or early in the following year (<xref ref-type="bibr" rid="B63">Yan, 2022</xref>). El Ni&#xf1;o refers to the warmer surface water temperature in the equatorial eastern Pacific, while La Ni&#xf1;a is the colder surface water temperature in the eastern equatorial Pacific (<xref ref-type="bibr" rid="B33">Luo, 2020</xref>). ENSO-driven precipitation anomalies have important implications for SST anomalies, surface wind fields, and oceanic thermocline changes (<xref ref-type="bibr" rid="B28">Kug et al., 2009</xref>; <xref ref-type="bibr" rid="B38">Okumura, 2019</xref>). Meanwhile, ENSO-driven tropical precipitation anomalies significantly affect global climate (<xref ref-type="bibr" rid="B25">Kim and Kug, 2018</xref>; <xref ref-type="bibr" rid="B67">Yeh et al., 2018</xref>). In the context of global warming, the average global surface temperature is approximately 1.0&#xb0;C warmer than in the past, seriously affecting the global climate system (<xref ref-type="bibr" rid="B19">IPCC, 2018</xref>; <xref ref-type="bibr" rid="B20">IPCC, 2021</xref>; <xref ref-type="bibr" rid="B21">IPCC, 2022</xref>). The ENSO-driven positive precipitation anomalies in the equatorial Pacific both show a significant strengthening and eastward shift, leading to an eastward shift of the main variability modes of the Walker circulation and the ENSO-driven atmospheric teleconnections in the North Pacific and North America (<xref ref-type="bibr" rid="B85">Zhou et al., 2014</xref>; <xref ref-type="bibr" rid="B27">Kug et al., 2010</xref>; <xref ref-type="bibr" rid="B3">Bayr et al., 2014</xref>; <xref ref-type="bibr" rid="B40">Perry et al., 2017</xref>).</p>
<p>ENSO significantly affects human social activities through its tropical precipitation anomalies, which impact agriculture, the economy, ecosystems, and extreme events. These phenomena include wildfires in Southern California due to La Ni&#xf1;a in 2017 and drought in SA due to El Ni&#xf1;o in 2018&#x2013;2019 (<xref ref-type="bibr" rid="B67">Yeh et al., 2018</xref>). The La Ni&#xf1;a event is a key influence on the occurrence of prolonged droughts in northern China during spring and summer (<xref ref-type="bibr" rid="B75">Zhang L. et al., 2018</xref>). The persistent drought in northern China in 2010 is also closely related to the La Ni&#xf1;a event (<xref ref-type="bibr" rid="B48">Shen et al., 2012</xref>). The La Ni&#xf1;a event is one of the main drivers of the frequent cold weather in the eastern part of the country during the first winter of 2021 (<xref ref-type="bibr" rid="B83">Zheng et al., 2021</xref>; <xref ref-type="bibr" rid="B82">Zheng et al., 2022</xref>). The El Ni&#xf1;o event was an important reason for the intensification of autumn drought in southern China after 1990 (<xref ref-type="bibr" rid="B79">Zhang et al., 2014</xref>). The severe meteorological drought in southern China in the autumn of 2019 is inextricably linked to the El Ni&#xf1;o event (<xref ref-type="bibr" rid="B35">Ma et al., 2020</xref>).</p>
<p>Many studies have evaluated ENSO&#x2019;s impact on the climate. However, as the complex diversity of ENSO increases, the climate impact of tropical sea&#x2013;air variability becomes unstable and its impact on drought in China becomes increasingly complex. The Weihe River basin (WRB) is located in the Loess Plateau, which has one of the largest rates of soil erosion on Earth (<xref ref-type="bibr" rid="B86">Zhou et al., 2022</xref>). The annual sediment discharge in the Loess Plateau is about 1.28 billion tons (<xref ref-type="bibr" rid="B71">Zhang et al., 2010</xref>). In addition, the WRB is an arid and semi-arid region, with precipitation primarily in summer, resulting in an uneven spatiotemporal distribution of water resources (<xref ref-type="bibr" rid="B10">Feng et al., 2022</xref>). More recently, the streamflow of the WRB has decreased significantly to 60% below the climate change (<xref ref-type="bibr" rid="B14">Guo et al., 2014</xref>). This decrease has led to increased shortages of water resources, which restrict the water ecological environment protection and economic and social development of the WRB (<xref ref-type="bibr" rid="B69">Yue et al., 2021</xref>). No studies have reported on the impact of El Ni&#xf1;o/La Ni&#xf1;a on drought in the WRB based on the classification of meteorological stations. Therefore, this study applied classification methods (e.g., principal component analysis) to cluster meteorological stations to better study the impacts of El Ni&#xf1;o/La Ni&#xf1;a on drought in the WRB.</p>
<p>The objective of this study was to reveal the impact of El Ni&#xf1;o/La Ni&#xf1;a on meteorological droughts in the WRB, an arid and semi-arid area in the northwest of China. This study classified 16 meteorological observation stations based on principal component analysis (PCA) to determine the partition. The characteristics of drought variation in El Ni&#xf1;o/La Ni&#xf1;a years and the following years were then analyzed and the influence of El Ni&#xf1;o and La Ni&#xf1;a on meteorological drought was investigated. The results revealed the mechanism of the influence of ENSO events on meteorological droughts to enhance understanding of the formation of meteorological droughts, which has important implications for the climate prediction of future meteorological droughts.</p>
</sec>
<sec id="s2">
<title>2 Study area</title>
<p>The WRB (104&#xb0;00&#x2032;E&#x2013;110&#xb0;20&#x2032;E, 33&#xb0;50&#x2032;N&#x2013;37&#xb0;18&#x2032;N) is the largest tributary of the Yellow River and covers a drainage area of 1.35 &#xd7; 10<sup>5</sup>&#xa0;km<sup>2</sup>. The river originates from Gansu Province and flows into the Yellow River at Tongguan Port, with a length of approximately 818&#xa0;km (<xref ref-type="bibr" rid="B16">Hu et al., 2022</xref>) (<xref ref-type="fig" rid="F1">Figure 1</xref>). The elevation of the WRB gradually decreases from west to east from 3926 to 325&#xa0;m. The annual precipitation and air temperature of the WRB range from 500 to 800&#xa0;mm and 7.8&#x2013;13.5&#xb0;C, respectively (<xref ref-type="bibr" rid="B6">Chang et al., 2015</xref>; <xref ref-type="bibr" rid="B78">Zhang T. et al., 2022</xref>). The precipitation within the basin is spatially uneven and exhibits a declining trend from southeast to northwest. The WRB is the main grain-yielding area and an important industry and commerce area in Northwestern China. The establishment of the Guanzhong&#x2013;Tianshui economic zone will greatly promote the economic development of the whole western region (<xref ref-type="bibr" rid="B6">Chang et al., 2015</xref>). The geographical location is unique. The upstream area is located in the hilly area, while the middle reaches span the Loess Plateau. Soil erosion is serious, and the ecological environment is fragile (<xref ref-type="bibr" rid="B29">Li C. et al., 2019</xref>). Frequent droughts in the basin have increasingly serious economic, social, and ecological impacts (<xref ref-type="bibr" rid="B34">Ma et al., 2022</xref>).</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Geographical location of the Weihe River basin and distribution of each meteorological station.</p>
</caption>
<graphic xlink:href="feart-11-1093632-g001.tif"/>
</fig>
</sec>
<sec id="s3">
<title>3 Data and methodology</title>
<sec id="s3-1">
<title>3.1 Data</title>
<p>This study used monthly precipitation and temperature data from 16 meteorological observing stations in the WRB from 1970 to 2020. These observed meteorological data were downloaded from the National Meteorological Information Center of China (<ext-link ext-link-type="uri" xlink:href="https://data.cma.cn/">https://data.cma.cn/</ext-link>). The monthly precipitation data were used to calculate the standardized precipitation index (SPI). According to the El Ni&#xf1;o/La Ni&#xf1;a Event Discriminatory Method developed by the Chinese Meteorological Administration, an El Ni&#xf1;o (La Ni&#xf1;a) event was defined as a 3-month sliding NINO3.4 index average of &#x2265;0.5&#xb0;C (&#x2264;&#x2212;0.5&#xb0;C) lasting for at least 5&#xa0;months (<xref ref-type="bibr" rid="B72">Zhang et al., 2021</xref>). El Ni&#xf1;o and La Ni&#xf1;a historical events were obtained from the National Climate Center of China Meteorological Administration (<ext-link ext-link-type="uri" xlink:href="http://cmdp.ncccma.net/download/ENSO/Monitor/ENSO_history_events.pdf">http://cmdp.ncccma.net/download/ENSO/Monitor/ENSO_history_events.pdf</ext-link>). Nine peak years of El Ni&#xf1;o events of moderate intensity or higher (1972, 1983, 1987, 1992, 1994, 1997, 2002, 2009, and 2015) and eight peak years of La Ni&#xf1;a events of moderate intensity or higher (1971, 1973, 1975,1988, 2000, 2008, 2010, and 2020) and the following year since 1970 were selected for analysis (El Ni&#xf1;o/La Ni&#xf1;a years) to explore the impact of El Ni&#xf1;o/La Ni&#xf1;a on drought in the WRB.</p>
</sec>
<sec id="s3-2">
<title>3.2 Principal component analysis</title>
<p>Differences in the spatial distribution of precipitation in the WRB are influenced by the location of the sea and land, the western Pacific subtropical high-pressure system, and the Mongolian high-pressure system (<xref ref-type="bibr" rid="B15">He et al., 2012</xref>). The complexity of the monthly precipitation series in the WRB showed significant spatial differences, with the eastern part higher than the western part and the southern part higher than the northern part (<xref ref-type="bibr" rid="B66">Yang et al.</xref>; <xref ref-type="bibr" rid="B32">Liu et al., 2022</xref>). Therefore, before analyzing the impact of El Ni&#xf1;o and La Ni&#xf1;a on drought, the 16 meteorological stations were divided into two typical principal component site categories through PCA of the monthly precipitation series. PCA was first developed by Karl Pearson in 1901 (<xref ref-type="bibr" rid="B55">Vicente-Serrano, 2005</xref>; <xref ref-type="bibr" rid="B2">Badakhshan et al., 2021</xref>).</p>
<p>PCA is a multivariate statistical method used to reduce statistical indicators (<xref ref-type="bibr" rid="B45">Rutledge, 2018</xref>; <xref ref-type="bibr" rid="B11">Gao et al., 2020</xref>). Generally, the comprehensive indicators generated by the transformation are called principal components, and each principal component is a linear combination of the original variables (<xref ref-type="bibr" rid="B1">Arslan, 2009</xref>). The number of components was selected according to the criteria of an eigenvalue &#x3e;1, and the components were rotated (varimax) to redistribute the final explained variance and to obtain more physically stable and robust patterns (<xref ref-type="bibr" rid="B44">Richman, 1986</xref>). The spatial classification of monthly precipitation patterns in the WRB was performed using the factorial loading values of each component obtained to group the observatories by the maximum loading rule. Each observatory was assigned to the component with the highest loading value.</p>
</sec>
<sec id="s3-3">
<title>3.3 Standardized precipitation index</title>
<p>The standardized precipitation index (SPI) was used as a meteorological drought index. The value was determined by normalizing the long-term precipitation record for the desired period for a given station after it was fitted to a probability density function, as described by McKee (<xref ref-type="bibr" rid="B41">Prajapati et al., 2021</xref>). The SPI only considers the deficit of precipitation. This study divided drought into four categories according to the SPI value: mild (&#x2212;1 &#x3c; SPI &#x2264; &#x2212;0.5), moderate (&#x2212;1.5 &#x3c; SPI &#x2264; &#x2212;1), severe (&#x2212;2 &#x3c; SPI &#x2264; &#x2212;1.5), and extreme (SPI &#x2264; &#x2212;2). This also calculated the SPI at different timescales (1, 3, 6, and 12&#xa0;months). An SPI of &#x2264; &#x2212;0.5 was defined as a dry event, and the number and intensity of droughts are calculated. The number of droughts was defined as the number of months and years of drought disasters. The drought intensity was the SPI monthly scale value for mild drought and above. The intensity of decadal drought in the study area was calculated as the average of the aforementioned SPI values. Finally, the drought index was calculated using the observed precipitation series from the 16 meteorological stations, the regional series of homogeneous regions obtained by PCA, and the series for the entire WRB.</p>
</sec>
<sec id="s3-4">
<title>3.4 Standardized precipitation evapotranspiration index</title>
<p>The standardized precipitation evapotranspiration index (SPEI) proposed by <xref ref-type="bibr" rid="B56">Vicente-Serrano et al. (2010)</xref> is a drought index that considers both precipitation and evapotranspiration. Potential evapotranspiration (PET) is calculated according to Thornthwaite&#x2019;s method (<xref ref-type="bibr" rid="B53">Thornthwaite, 1948</xref>). By calculating the difference between monthly precipitation and potential evapotranspiration, the water deficit accumulation sequence is established. Then, the log-logistic probability distribution function is used to standardize the cumulative probability density, and the SPEI index is obtained. The SPEI dry events and drought categories are consistent with the SPI.</p>
</sec>
</sec>
<sec sec-type="results" id="s4">
<title>4 Results</title>
<sec id="s4-1">
<title>4.1 Classification in the Weihe River basin based on monthly series</title>
<p>
<xref ref-type="fig" rid="F2">Figure 2</xref> shows the PCA results of the monthly precipitation series based on 16 meteorological stations. We obtained two components with eigenvalues &#x3e;1. <xref ref-type="fig" rid="F3">Figure 3</xref> indicates the spatial representativeness of these components. The spatial distribution pattern of factor loads was consistent, and the principal components do not overlap. Components 1 and 2 represent the northwestern and southeast regions, respectively.</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>Results of principal component analysis.</p>
</caption>
<graphic xlink:href="feart-11-1093632-g002.tif"/>
</fig>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>Spatial distribution of the principal components.</p>
</caption>
<graphic xlink:href="feart-11-1093632-g003.tif"/>
</fig>
<p>
<xref ref-type="fig" rid="F4">Figure 4</xref> shows the monthly precipitation classification of the WRB when applying the maximum loading rule. The northwestern region is large, including 10 meteorological stations (Minxian, Linyao, Huajialing, Huanxian, Pingliang, Xifengzhen, Guyuan, Xiji, Luochuan, and Wuqi). The southeast region is small, including six meteorological stations (Changwu, Wugong, Huashan, Foping, Shangzhou, and Zhen&#x2019;an).</p>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption>
<p>Spatial distribution of the meteorological stations.</p>
</caption>
<graphic xlink:href="feart-11-1093632-g004.tif"/>
</fig>
<p>Once the homogeneous areas were identified, a regional precipitation series for each area was formed using the weighted averages of monthly precipitation records at each observatory. Similarly, a regional series for the WRB overall was created using the same procedure. The weight factor was the ratio of the surface area represented by each observatory to the total area of that region based on Thiessen&#x2019;s polygon method (<xref ref-type="bibr" rid="B17">Huang and Li, 1996</xref>; <xref ref-type="bibr" rid="B24">Jones and Hulme, 1996</xref>). The spatial representativeness of the regional precipitation series was consistent with the classified patterns. The correlation analysis between the two regional series and the precipitation series of the different observatories indicated that the regional series represented the evolution of the monthly precipitation of the homogeneous areas obtained from the PCA classification (<xref ref-type="fig" rid="F5">Figure 5</xref>).</p>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption>
<p>Correlation between the two regional series and monthly precipitation series in the different observatories.</p>
</caption>
<graphic xlink:href="feart-11-1093632-g005.tif"/>
</fig>
</sec>
<sec id="s4-2">
<title>4.2 Drought evolution in different regions</title>
<p>
<xref ref-type="fig" rid="F6">Figure 6</xref> shows the evolution of the SPI at the 6-month timescale in the two regions from 1970 to 2020. The number of occurrences of different drought classes was spatially inconsistent and decreased with higher classes. As shown in <xref ref-type="table" rid="T1">Table 1</xref>, more droughts were recorded in the northwest (193) and southeast (186) regions. In the northwest region, the drought was the most severe in the 1990s and the number of extreme droughts was the highest during this time, with no extreme droughts occurring in the 2000s and 2020s. In the southeast region, the drought intensity was highest in the 1990s, with the highest number of extreme droughts. No extreme droughts occurred in the 1970s, 1980s, and 2020s. In 2020, there was no drought in the northwest region, but moderate and severe drought occurred in the southeast region. The response time to drought in different regions was inconsistent because there was little or no rain for some time, and the precipitation is significantly less than in other periods, resulting in droughts not occurring at the same time (<xref ref-type="bibr" rid="B74">Zhang L. et al., 2022</xref>). In the 1990s, the WRB was most severely drought-stricken, and the frequency of drought in the northwest region was higher than in the southeast region.</p>
<fig id="F6" position="float">
<label>FIGURE 6</label>
<caption>
<p>Evolution of SPI series at a 6-month timescale obtained from the two regional precipitation series. <bold>(A)</bold> Northwest Region. <bold>(B)</bold> Southwest Region.</p>
</caption>
<graphic xlink:href="feart-11-1093632-g006.tif"/>
</fig>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>Numbers of drought occurrences in different regions.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center"/>
<th align="center">Mild drought</th>
<th align="center">Moderate drought</th>
<th align="center">Severe drought</th>
<th align="center">Extreme drought</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">Northwest Region</td>
<td align="center">85</td>
<td align="center">62</td>
<td align="center">34</td>
<td align="center">12</td>
</tr>
<tr>
<td align="center">Southeast Region</td>
<td align="center">89</td>
<td align="center">55</td>
<td align="center">29</td>
<td align="center">13</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s4-3">
<title>4.3 Response of drought to El Ni&#xf1;o/La Ni&#xf1;a in the Weihe River basin</title>
<p>This study defined an El Ni&#xf1;o/La Ni&#xf1;a peak year of moderate intensity or higher as the event year; i.e., an El Ni&#xf1;o/La Ni&#xf1;a year, to explore the impact of ENSO on drought in the WRB. <xref ref-type="fig" rid="F7">Figure 7</xref> shows the multi-year mean SPI values at different timescales of El Ni&#xf1;o/La Ni&#xf1;a and the year following these events in the WRB. The SPI values were obtained from the regional precipitation series for the WRB. <xref ref-type="fig" rid="F7">Figure 7</xref> indicates the general drought response and significant differences (<italic>p</italic>&#x3c;0.05) at different timescales when El Ni&#xf1;o/La Ni&#xf1;a occurred. As shown in <xref ref-type="fig" rid="F7">Figure 7A</xref>, at a timescale of 1&#xa0;month, a negative average SPI value was observed in July during El Ni&#xf1;o years. The July of El Ni&#xf1;o years showed significant differences compared to the rest of the years, the normal years, and the La Ni&#xf1;a years. During El Ni&#xf1;o years, negative average SPI values were recorded for July and November at the 3-month timescale; moreover, &#x3e;78% of SPI values from August to November were negative. Significant drought (SPI/SPEI &#x2264; &#x2212;0.5 and <italic>p</italic> &#x3c; 0.05) only occurred in September compared to other years and normal years. At the 6-month timescale, negative average SPI values occurred from July in the El Ni&#xf1;o year to February of the following year. Moreover, &#x3e;78% of SPI values from August to December and February of the following year were negative. These figures were significant in November and December between El Ni&#xf1;o and the rest of the years. The multi-year average SPI values from March to December of the following year were positive, and there were significant humid conditions from August to October and December compared to La Ni&#xf1;a. However, at longer timescales (12&#xa0;months), the number of significant drought months increased, and El Ni&#xf1;o years showed significant droughts in January, April, and September and June of the following year compared to normal years and other years. The average SPI values from August to December in the year following El Ni&#xf1;o were positive, with no significant differences in December compared to La Ni&#xf1;a years. The longer the timescale of SPI, the more months of significant drought in El Ni&#xf1;o years and the year following El Ni&#xf1;o, while the intensity of drought weakened.</p>
<fig id="F7" position="float">
<label>FIGURE 7</label>
<caption>
<p>Mean SPI values at different timescales from the SPI series in the Weihe River basin. <bold>(A)</bold> El Ni&#xf1;o and <bold>(B)</bold> La Ni&#xf1;a years (boxed) and the year after these events. Green columns, significant differences in SPI values between El Ni&#xf1;o/La Ni&#xf1;a and the remaining years; blue columns, no significant difference; square, significant differences in SPI values between El Ni&#xf1;o and La Ni&#xf1;a years; circles, significant differences in SPI values between El Ni&#xf1;o/La Ni&#xf1;a years and normal years; arrows, months with consistent anomalies among events [&#x3e;70% of events have the same sign as the mean SPI (<xref ref-type="bibr" rid="B55">Vicente-Serrano, 2005</xref>)].</p>
</caption>
<graphic xlink:href="feart-11-1093632-g007.tif"/>
</fig>
<p>On different timescales in the WRB, El Ni&#xf1;o and La Ni&#xf1;a showed different effects on the SPI index. As shown in <xref ref-type="fig" rid="F7">Figure 7B</xref>, at a timescale of 1&#xa0;month, the average SPI values in February, November, and December of the following year were positive. These months differed significantly from other years, normal years, and El Ni&#xf1;o years and were significantly humid. Significant droughts were observed in June and July of the following year. At the 3-month timescale, the multi-year average SPI values of La Ni&#xf1;a from May to September of the following year were negative, and 71% of the SPI values from May to August were negative, indicating significant droughts in July and August compared to normal years and El Ni&#xf1;o years. At the 6-month timescale, &#x3e;71% of the SPI values from July to November in El Ni&#xf1;o years were negative. Significant differences were observed between La Ni&#xf1;a and other years and between normal years and El Ni&#xf1;o years in July and August. At the 12-month timescale, no significant difference was observed between La Ni&#xf1;a and other years and normal years. Significant differences were observed between La Ni&#xf1;a and El Ni&#xf1;o years only in August and October. On different timescales, La Ni&#xf1;a had a stronger drought the following year than El Ni&#xf1;o the following year. At the 3-, 6-, and 12-month timescales, as the timescale increased, La Ni&#xf1;a intensified the drought in the following year, with the worst summer droughts. At the 12-month timescale, La Ni&#xf1;a had less impact on drought than El Ni&#xf1;o. The response of SPI12 (SPI at the 12-month timescale) to El Ni&#xf1;o in the WRB was significant, with 25% of months experiencing significant drought. The remaining timescales show fewer months of significant drought. The responses of SPI3 (SPI at the 3-month timescale) and SPI6 (SPI at the 6-month timescale) to La Ni&#xf1;a were significant, and both showed significant droughts in July and August of the next year. Short-term drought events were strongly associated with La Ni&#xf1;a.</p>
<p>At a timescale of 1&#xa0;month, the WRB experienced significant drought (SPI) in July of El Ni&#xf1;o years, November of the year following El Ni&#xf1;o, and June of the year following La Ni&#xf1;a. Significant droughts (SPEI) occurred in June of the year following La Ni&#xf1;a. At the 3-month timescale, significant droughts (SPI and SPEI) occurred in September of El Ni&#xf1;o years and July of the year following La Ni&#xf1;a. The SPEI showed significant droughts in September of El Ni&#xf1;o years and July of the year following La Ni&#xf1;a. At the 6-month timescale, significant droughts (SPI) occurred in November and December of El Ni&#xf1;o years and July and August of the year following La Ni&#xf1;a. No significant droughts occurred by SPEI. At the 12-month timescale, the trends of SPI and SPEI values were the same, with no significant droughts in La Ni&#xf1;a years and the year following La Ni&#xf1;a (<xref ref-type="fig" rid="F7">Figure 7</xref>; <xref ref-type="sec" rid="s12">Supplementary Figure S1</xref>).</p>
<p>As shown in <xref ref-type="fig" rid="F8">Figure 8</xref>, the highest percentages of years with drought were 44% in El Ni&#xf1;o years, 43% in the year following La Ni&#xf1;a, and 38% in the year following La Ni&#xf1;a years. The smallest percentages were observed in the year following El Ni&#xf1;o (22%). Different categories of drought occurred differently according to the event. Extreme and severe droughts occurred only in El Ni&#xf1;o years and the following year, while mild droughts occur most frequently in different events.</p>
<fig id="F8" position="float">
<label>FIGURE 8</label>
<caption>
<p>Percentages of drought years in El Ni&#xf1;o/La Ni&#xf1;a and the following year.</p>
</caption>
<graphic xlink:href="feart-11-1093632-g008.tif"/>
</fig>
<p>
<xref ref-type="fig" rid="F9">Figure 9</xref> shows the percentage of the WRB that were significantly dry during El Ni&#xf1;o and La Ni&#xf1;a years. During El Ni&#xf1;o years, analysis of the differences between El Ni&#xf1;o years and the rest of the years (normal and La Ni&#xf1;a years) (<xref ref-type="fig" rid="F9">Figure 9A</xref>) showed that parts of the WRB were affected by significant drought. More than 13% of the total surface was affected between September of the El Ni&#xf1;o year and February of the following year. Moreover, during December of the El Ni&#xf1;o year, significant dry conditions occurred in a high percentage of the area (45%). Analysis of the significant dry conditions between El Ni&#xf1;o and the normal years (<xref ref-type="fig" rid="F9">Figure 9B</xref>) showed that the area of drought decreased in December and increased in January and February of the following year. The consistent surface areas between July of the El Ni&#xf1;o year and February of the following year were 13%&#x2013;51% (<xref ref-type="fig" rid="F9">Figure 9C</xref>).</p>
<fig id="F9" position="float">
<label>FIGURE 9</label>
<caption>
<p>Percentage of the Weihe River basin, during El Ni&#xf1;o/La Ni&#xf1;a (boxed), and the following year, with significant drought areas in the 6-month SPI between <bold>(A)</bold> El Ni&#xf1;o/La Ni&#xf1;a and the remaining years and <bold>(B)</bold> El Ni&#xf1;o/La Ni&#xf1;a and normal years. <bold>(C)</bold> Percentage of the Weihe River basin with consistently negative anomalies among events during El Ni&#xf1;o and La Ni&#xf1;a years.</p>
</caption>
<graphic xlink:href="feart-11-1093632-g009.tif"/>
</fig>
<p>The proportions of significant drought areas in the year following La Ni&#xf1;a were higher than those during La Ni&#xf1;a years, which was related to decreased precipitation in the following year. During La Ni&#xf1;a years, significant dry conditions were recorded in 5% to 32% of the WRB between July and February of the following year, based on differences between La Ni&#xf1;a and the rest of the years. The analysis of the differences between La Ni&#xf1;a and normal years in these months showed obvious increases in significant drought areas. However, the La Ni&#xf1;a signal was very consistent, with &#x3e;54% of the area having a negative mean SPI value. Droughts mainly occurred at the end of El Ni&#xf1;o years and the beginning of the following year, and in the year following La Ni&#xf1;a.</p>
</sec>
<sec id="s4-4">
<title>4.4 Spatial differences in the influence of El Ni&#xf1;o/La Ni&#xf1;a on drought conditions in the Weihe River basin</title>
<p>Here, the spatial differences presented by the SPI averages at the various timescales during the El Ni&#xf1;o and La Ni&#xf1;a years are described from the regional series of the two homogeneous regions obtained from PCA. <xref ref-type="fig" rid="F10">Figure 10</xref> shows the average SPI values according to the different timescales during El Ni&#xf1;o and La Ni&#xf1;a for the northwestern region. <xref ref-type="fig" rid="F10">Figure 10A</xref> demonstrates that &#x3e;78% of SPI values from July to September were negative on a 1-month timescale in El Ni&#xf1;o years. El Ni&#xf1;o years showed significant differences only in July compared to the rest of the years, normal years, and La Ni&#xf1;a years. At the 3-month timescale, &#x3e;78% of SPI values from August to November were negative. El Ni&#xf1;o years showed significant droughts from August to October compared to the rest of the years, with significant differences only in September compared to normal years. The drought pattern intensified on a 6-month timescale, with September to December showing significant droughts compared to the rest of the years. The year following El Ni&#xf1;o showed significant humidity from August to October and December. Significant droughts were recorded from January to April of El Ni&#xf1;o years and from January to April of the following year at the timescale of 12&#xa0;months but only compared to the normal years.</p>
<fig id="F10" position="float">
<label>FIGURE 10</label>
<caption>
<p>Mean SPI values at different timescales from SPI series in the northwest region. <bold>(A)</bold> El Ni&#xf1;o and <bold>(B)</bold> La Ni&#xf1;a years (boxed) and the year after these events. Same as <xref ref-type="fig" rid="F7">Figure 7</xref>.</p>
</caption>
<graphic xlink:href="feart-11-1093632-g010.tif"/>
</fig>
<p>The temporal patterns were reversed during La Ni&#xf1;a, with fewer months showing significant differences. As shown in <xref ref-type="fig" rid="F10">Figure 10B</xref>, at a timescale of 1&#xa0;month, February and June of the following year in La Ni&#xf1;a differed significantly from the rest of the years, the normal years, and the El Ni&#xf1;o years, with February significantly wetter and June significantly drier. At the 3-month timescale, &#x3e;71% of SPI values for the year following La Ni&#xf1;a were negative from May to August, with June to August (summer) showing significant droughts. At the 6-month timescale, between August of La Ni&#xf1;a years and April of the following year, the average SPI values were positive. The following year, the mean SPI values were negative from May to December, with significant differences from July to October compared to El Ni&#xf1;o and only in July and August compared to the rest of the years and the normal years. On a 12-month timescale, La Ni&#xf1;a year shows significant droughts in May compared to normal years and significant differences from August to October of the following year compared to El Ni&#xf1;o years.</p>
<p>
<xref ref-type="fig" rid="F11">Figure 11</xref> shows the mean SPI values at different timescales during El Ni&#xf1;o and La Ni&#xf1;a in the southeast region. The mean SPI values were higher than those in the northwest region at different timescales and the number of months with significant differences was less than that in the northwest region. As shown in <xref ref-type="fig" rid="F11">Figure 11A</xref>, at a timescale of 1&#xa0;month, only July in El Ni&#xf1;o showed significant droughts, and &#x3e;78% of the SPI values were negative. At the 3-month timescale, &#x3e;78% of the SPI values were positive from March to June in El Ni&#xf1;o years, with the following August to September showing significant dampness compared to La Ni&#xf1;a years. At timescales of 6 and 12&#xa0;months, no significant anomalies were recorded in relation to El Ni&#xf1;o and La Ni&#xf1;a years, although significant differences were observed between the year following El Ni&#xf1;o and the La Ni&#xf1;a year only for a few months of the following year.</p>
<fig id="F11" position="float">
<label>FIGURE 11</label>
<caption>
<p>Mean SPI values at different timescales from SPI series in the southeast region. <bold>(A)</bold> El Ni&#xf1;o and <bold>(B)</bold> La Ni&#xf1;a years (boxed) and the year after these events. Same as <xref ref-type="fig" rid="F7">Figure 7</xref>.</p>
</caption>
<graphic xlink:href="feart-11-1093632-g011.tif"/>
</fig>
<p>As shown in <xref ref-type="fig" rid="F11">Figure 11B</xref>, at the 1-month timescale, February, November, and December of the following year in La Ni&#xf1;a differed significantly from the normal years and El Ni&#xf1;o years. At the 3-month timescale, February of the following year differed significantly from the rest of the years and the normal years, showing significant wetness. No significant anomalies were recorded in relation to El Ni&#xf1;o and La Ni&#xf1;a events at the 6- and 12-month timescales.</p>
<p>El Ni&#xf1;o and La Ni&#xf1;a had greater impacts on the drought index in the northwest of the WRB, with more months of significant drought and greater intensity levels in the northwest compared to the southeast. The negative anomalies were stronger in El Ni&#xf1;o years than in La Ni&#xf1;a years, with different months of significant drought in different regions. The northwest SPI12 showed significant responses to El Ni&#xf1;o, with 38% of months experiencing significant drought, while the SPI6 responded significantly to La Ni&#xf1;a, with 17% of months experiencing significant drought. The southeast experienced significant droughts in July of El Ni&#xf1;o years only on the 1-month timescale.</p>
<p>At the 1-month timescale, the northwest region experienced significant droughts (SPI and SPEI) in July of El Ni&#xf1;o years and June of the year following La Ni&#xf1;a. At the 3-month timescale, significant droughts (SPI) occurred from August to October in El Ni&#xf1;o years and from June to August in the year following La Ni&#xf1;a. The SPEI showed significant droughts in September of the El Ni&#xf1;o year and July and August of the year following La Ni&#xf1;a. At the 6-month timescale, significant droughts (SPI and SPEI) occurred in September and November of El Ni&#xf1;o years and October of the year following La Ni&#xf1;a. At the 1-, 3-, and 6-month timescales, the longer the SPI and SPEI timescale, the later the significant droughts occurred. At the 12-month timescale, SPI and SPEI showed significant droughts in April in El Ni&#xf1;o years (<xref ref-type="fig" rid="F10">Figure 10</xref>; <xref ref-type="sec" rid="s12">Supplementary Figure S2</xref>). At the 1-month timescale, the southeast region experienced significant drought (SPI) in July of El Ni&#xf1;o years and November of the year following El Ni&#xf1;o. No significant droughts occurred by SPEI. At the 3-, 6-, and 12-month timescales, no significant droughts (SPI and SPEI) occurred in the southeastern region (<xref ref-type="fig" rid="F11">Figure 11</xref>; <xref ref-type="sec" rid="s12">Supplementary Figure S3</xref>).</p>
<p>The areas with significant changes in negative averages differed during the various months of El Ni&#xf1;o and La Ni&#xf1;a. The mean SPI values obtained from SPI series from different meteorological stations on a 6-month timescale showed that most of the WRB was affected by drought between July of El Ni&#xf1;o years and February of the following year and from May to December in the year following La Ni&#xf1;a. <xref ref-type="fig" rid="F12">Figure 12</xref> and <xref ref-type="fig" rid="F13">Figure 13</xref> show areas with significant differences between the corresponding El Ni&#xf1;o/La Ni&#xf1;a and the rest of the years (inside the black line), areas with significant differences between El Ni&#xf1;o/La Ni&#xf1;a and normal years (inside the black dashed line), and the meteorological stations with consistency among events.</p>
<fig id="F12" position="float">
<label>FIGURE 12</label>
<caption>
<p>Spatial distribution of SPI6 averages from July of El Ni&#xf1;o years (open circles) to March of the following year (pluses). Area within the black solid line, significant difference between the El Ni&#xf1;o years and the remaining years, showing significant drought; area within the black dashed line, significant difference between El Ni&#xf1;o and normal years, showing significant drought; solid circles, observatories with consistent anomalies among El Ni&#xf1;o events.</p>
</caption>
<graphic xlink:href="feart-11-1093632-g012.tif"/>
</fig>
<fig id="F13" position="float">
<label>FIGURE 13</label>
<caption>
<p>Spatial distribution of SPI6 averages for different months from May to December for the year after La Ni&#xf1;a (pluses). Same as <xref ref-type="fig" rid="F12">Figure 12</xref>.</p>
</caption>
<graphic xlink:href="feart-11-1093632-g013.tif"/>
</fig>
<p>As shown in <xref ref-type="fig" rid="F12">Figure 12</xref>, the areas of significant drought were higher in the northwest than in the southeast region from August of El Ni&#xf1;o years to February of the following year. From September to November in El Ni&#xf1;o years, a high percentage of the northwest region showed significant drought between El Ni&#xf1;o and the rest of the years and the normal years. From December of El Ni&#xf1;o to February of the following year, the areas of significant drought decreased. December of El Ni&#xf1;o years showed larger areas of significant drought compared to the rest of the years, but a smaller area compared to El Ni&#xf1;o and normal years. January and February of the year following showed opposite trends of those for December. Event consistency was recorded at 50% of the meteorological stations in December and at a few meteorological stations in the remaining months.</p>
<p>The southeast region was not affected by significant droughts during the year following La Ni&#xf1;a (between La Ni&#xf1;a and the rest of the years and between La Ni&#xf1;a and normal years). As shown in <xref ref-type="fig" rid="F13">Figure 13</xref>, no droughts occurred in the southeast region in May and June. Significant drought conditions were recorded in the northwest region from July to November of the year following La Ni&#xf1;a, indicating significant drought conditions between La Ni&#xf1;a and normal years. La Ni&#xf1;a resulted in smaller areas of significant drought compared to the rest of the years. Many meteorological stations showed consistency in recorded events compared to El Ni&#xf1;o years.</p>
</sec>
</sec>
<sec sec-type="discussion" id="s5">
<title>5 Discussion</title>
<p>The main objective of this study was to study the effects of ENSO on meteorological drought in the WRB through the classification of meteorological stations. We compared the effects of El Ni&#xf1;o and La Ni&#xf1;a on drought. <xref ref-type="bibr" rid="B80">Zhao et al. (2018)</xref> reported that ENSO is significantly related to meteorological and hydrological droughts in the WRB. <xref ref-type="bibr" rid="B12">Gore et al. (2020)</xref> found that El Ni&#xf1;o (La Ni&#xf1;a) conditions weaken (strengthen) the Walker circulation, causing drier (wetter) conditions over parts of southern Africa. These studies on the impact of ENSO on drought mostly focused on the whole region. <xref ref-type="bibr" rid="B52">Sun et al. (2015)</xref> analyzed the drought zoning, drought characteristics, and drought frequency in northeast China based on PCA. <xref ref-type="bibr" rid="B57">Wang et al. (2017)</xref> studied the drought trends and characteristics of periodic variation at multiple timescales in different regions. The present study refined the analysis of the influence of El Ni&#xf1;o/La Ni&#xf1;a on drought by dividing the WRB. The results showed that the northwest and southeast regions respond differently to ENSO. These results improve the understanding of the mechanism by which ENSO affects droughts and inform the development of a drought warning system.</p>
<p>As shown in <xref ref-type="sec" rid="s12">Supplementary Figure S4A</xref>, in El Ni&#xf1;o years, the average annual precipitation in the northwest region of has 66.4 mm and 51.2&#xa0;mm less than the following year and the normal year. La Ni&#xf1;a year received more precipitation (519.8&#xa0;mm) than in the following year (466.7&#xa0;mm) and in the normal year (512.0&#xa0;mm), but more precipitation in the normal year than in the following year. The El Ni&#xf1;o annual precipitation (616.0&#xa0;mm) in the southeast region is less than that in the following year and normal year, but the precipitation in the following year (696.6&#xa0;mm) is more than that in the normal year (637.9&#xa0;mm), which is consistent with the situation in the northwest region. The precipitation in La Ni&#xf1;a year is 41.7&#xa0;mm and 52.9&#xa0;mm more than that in the following year and normal year. The normal annual precipitation was 5.8&#xa0;mm lower than the year following La Ni&#xf1;a.</p>
<p>As shown in <xref ref-type="sec" rid="s12">Supplementary Figure S4B</xref>, the highest temperature in the northwestern region was 7.7&#xb0;C in El Ni&#xf1;o years, followed by 7.6&#xb0;C in the following year, and the lowest in normal years. Temperatures in La Ni&#xf1;a years (7.6&#xb0;C) were lower than in El Ni&#xf1;o years. The temperatures in La Ni&#xf1;a years were higher than the following year and normal years, while the temperatures in the following year (7.3&#xb0;C) were lower than normal (7.6&#xb0;C). Temperature was generally higher in the southeast region than in the northwest region. El Ni&#xf1;o and La Ni&#xf1;a years were both warmer than the following year. Temperatures in El Ni&#xf1;o and the following year were higher than those in normal years, while temperatures in La Ni&#xf1;a year and the following year were lower than those in normal years.</p>
<p>Comparison of El Ni&#xf1;o years with normal years revealed that 91% of regions in the WRB showed decreasing trends in precipitation and 95% of the regions showed increasing trends in temperature (<xref ref-type="sec" rid="s12">Supplementary Figure S4C</xref>). Compared with the next year, the precipitation in the year following El Ni&#xf1;o was higher than that in El Ni&#xf1;o, and 94% of the regions showed decreasing trends in temperatures. Comparing La Ni&#xf1;a years with normal years, 55% of the regions showed an increasing trend in precipitation, while 72% of the regions showed a decreasing trend in temperature. Comparing La Ni&#xf1;a year with the following year, 98% of the regions showed a decreasing trend in precipitation in the following year. The temperature in the year following in La Ni&#xf1;a was lower than that in La Ni&#xf1;a. El Ni&#xf1;o had a decreasing effect on precipitation while La Ni&#xf1;a had an increasing effect on precipitation, consistent with the results of the precipitation in the WRB under the influence of ENSO reported by <xref ref-type="bibr" rid="B80">Zhao et al. (2018)</xref>. The temperatures rose in El Ni&#xf1;o years, consistent with Su&#x2018;s analysis of high-temperature weather in the north under the influence of ENSO (<xref ref-type="bibr" rid="B51">Su et al., 2022</xref>).</p>
<p>
<xref ref-type="bibr" rid="B59">Wang et al. (2022)</xref> observed a northerly wind anomaly during El Ni&#xf1;o, resulting in a weak northward transport of water vapor from the South China Sea and the Bay of Bengal, and less precipitation in northern China. <xref ref-type="bibr" rid="B60">Wu et al. (2017)</xref> reported unusually high precipitation due to an anticyclonic circulation in the northwest Pacific Ocean and an anomalous double-blocking circulation in the year following El Ni&#xf1;o. This finding can be explained by the drought in the WRB mainly occurring in El Ni&#xf1;o years, in which the next year was generally wet. The influence of El Ni&#xf1;o on precipitation in northern China may be related to the strength of summer monsoons. When El Ni&#xf1;o is strong, the monsoons affecting northern China are weak in summer and autumn, resulting in low annual precipitation in El Ni&#xf1;o years and high precipitation in summer and autumn in La Ni&#xf1;a years (<xref ref-type="bibr" rid="B22">Jia and Zhang, 2020</xref>). Therefore, SPI12 responded significantly to El Ni&#xf1;o, and droughts in short timescales were closely related to La Ni&#xf1;a (<xref ref-type="bibr" rid="B58">Wang et al., 2021</xref>).</p>
<p>We calculated SPEI at different timescales (1, 3, 6, and 12&#xa0;months) in the WRB and the northwest and southeast regions. The mean SPEI values at different timescales in El Ni&#xf1;o/La Ni&#xf1;a years and the next year are shown in <xref ref-type="sec" rid="s12">Supplementary Figures S1&#x2013;S3</xref>. At 1-, 3-, and 6-month timescales, the impact of El Ni&#xf1;o/La Ni&#xf1;a years on SPEI was consistent with the following year. Few months differed significantly between El Ni&#xf1;o/La Ni&#xf1;a and normal years and the rest of the year. SPEI cannot show differences between the effects of El Ni&#xf1;o and La Ni&#xf1;a on drought, or the differences in the El Ni&#xf1;o/La Ni&#xf1;a effects on drought from the following year. On the 12-month timescale, the effects of El Ni&#xf1;o/La Ni&#xf1;a and its next year on SPEI differed. Drought mainly occurred in El Ni&#xf1;o years, while no drought occurred in La Ni&#xf1;a years and the following year. The response of SPEI to El Ni&#xf1;o and La Ni&#xf1;a is not as strong as for SPI, which is consistent with the findings of <xref ref-type="bibr" rid="B58">Wang et al. (2021)</xref>. According to <xref ref-type="bibr" rid="B81">Zhao et al. (2017)</xref>, Walker circulation significantly impacts precipitation in China. Weakened circulation leads to decreased precipitation in northern China and may cause drought. <xref ref-type="bibr" rid="B3">Bayr et al. (2014)</xref> found that ENSO affects precipitation in northern China by influencing the intensity of Walker circulation. In summary, ENSO affects the SPI index of the WRB by influencing precipitation and, thus, drought in the WRB, consistent with the findings of <xref ref-type="bibr" rid="B30">Li et al (2019)</xref>.</p>
<p>During the La Ni&#xf1;a period, due to the cold water anomaly in the equatorial eastern Pacific, the equatorial easterly wind increased and the Hadley circulation weakened (<xref ref-type="bibr" rid="B26">Kong et al., 2021</xref>). The western Pacific subtropical high weakened and moved northward (<xref ref-type="bibr" rid="B62">Xue and Zhao, 2017</xref>). The rain belt also moved northward (<xref ref-type="bibr" rid="B73">Zhang et al., 2023</xref>). El Ni&#xf1;o can cause atmospheric Gill-type responses through anomalous Walker circulation, making the western Pacific subtropical high stronger and move westward and southward (<xref ref-type="bibr" rid="B42">Qian et al., 2018</xref>). The northwest area of the WRB is far from the ocean and the transfer of water vapor gradually decreases from the southeast to the northwest. The precipitation in the northwest is lower than that in the southeast (Yang et al., 2023). In general, the northwest region had a significantly higher number of months with significant drought compared to the southeast region. This finding is consistent with the report by <xref ref-type="bibr" rid="B18">Huang et al. (2015)</xref> of a higher probability of drought in the northwest compared to the southeast.</p>
</sec>
<sec sec-type="conclusion" id="s6">
<title>6 Conclusion</title>
<p>We assessed the impact of El Ni&#xf1;o and La Ni&#xf1;a on drought from 1970 to 2020 in the WRB in China using standardized precipitation indices at different timescales. The conclusions are as follows.<list list-type="simple">
<list-item>
<p>(1) In the 1990s, the most severe drought occurred in the WRB, with more droughts occurring in the northwest region than in the southeast. El Ni&#xf1;o/La Ni&#xf1;a have different levels of impact on drought in the WRB, with the highest number of drought years occurring in El Ni&#xf1;o years, followed by the following year of La Ni&#xf1;a.</p>
</list-item>
<list-item>
<p>(2) The number of months with significant drought increased but the drought intensity weakened in the WRB as the timescale of SPI increases. At the timescales of 3 and 6&#xa0;months, the summer drought in the following year of La Ni&#xf1;a was the most severe. At the 12-month timescale, La Ni&#xf1;a had a lower impact on drought than El Ni&#xf1;o.</p>
</list-item>
<list-item>
<p>(3) The effects of drought in El Ni&#xf1;o and La Ni&#xf1;a were spatially inconsistent. On different timescales, significant high-intensity droughts were recorded in the northwest region. Significant droughts existed in southeast region in July of El Ni&#xf1;o years at the 1-month timescale (SPI).</p>
</list-item>
</list>
</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s7">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="sec" rid="s12">Supplementary Material</xref>; further inquiries can be directed to the corresponding authors.</p>
</sec>
<sec id="s8">
<title>Author contributions</title>
<p>JF, SW, and DL contributed to the study conception and design. DL, TQ, and YC organized the database. SW performed the statistical analysis. SW wrote the first draft of the manuscript. FX, CW, and GL wrote sections of the manuscript. All authors contributed to manuscript revision and read and approved the submitted version. All authors contributed to the article and approved the submitted version.</p>
</sec>
<sec id="s9">
<title>Funding</title>
<p>This research was funded by the National Natural Science Foundation of China (grant number 52209013), Department of Education of Hebei Province (grant number ZD2022085), State Key Laboratory of Eco-hydraulics in Northwest Arid Region (grant number 2019KFKT-4), and National Natural Science Foundation of China (grant number 52009053).</p>
</sec>
<sec sec-type="COI-statement" id="s10">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s11">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="s12">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/feart.2023.1093632/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/feart.2023.1093632/full&#x23;supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="DataSheet1.pdf" id="SM1" mimetype="application/pdf" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
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