{"title":"一个量化干旱预测稳健性和不确定性的框架","authors":"Shaobo Zhang, Zuhao Zhou, Yuqing Zhang, Peiyi Peng, Chongyu Xu","doi":"10.1002/joc.70019","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Drought may be exacerbated by global warming, but drought projections are largely inconsistent. The existing frameworks cannot adequately quantify the robustness and uncertainty of drought projections. Therefore, this study proposes a framework to solve this problem and verifies it in the Chinese Mainland. This framework consists of three main components: (1) the meteorological drought in the 21st century is projected using an impact propagation modelling chain; (2) the robustness of drought projections is quantified using Identical Trend Percentage (ITP) and Signal-to-Noise Ratio (SNR); (3) the uncertainty of drought projections is investigated using improved multi-way analysis of variance. The study reveals that this framework can include more uncertainty sources, investigate the propagation patterns of uncertainty components and quantify the robustness of drought projections. The results show that drought projections are not robust. Specifically, the mean ITP ranges from 49% to 69%, indicating that nearly half of the projections display trends opposite to those of the mean values. In addition, the mean SNR of drought projections ranges between −0.36 and 0.15, with an absolute value far from 1.0. The dominant uncertainty source is the choice of drought index, of which the mean relative contribution ranges between 47% and 61%. When propagating along with the impact propagation modelling chain, the relative importance among existing uncertainty sources usually remains stable if no new physical quantities are joining in. If the relative importance among the existing uncertainty sources for one particular quantity is different from that for the other quantity, the relative importance among the existing uncertainty sources may be adjusted when the two quantities are pooled together by the newly joined processes. Excluding unreasonable drought indices generally reduces the uncertainty and improves the robustness of drought projections; however, it is insufficient to derive robust drought projections.</p>\n </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"45 12","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Framework for Quantifying the Robustness and Uncertainty of Drought Projections\",\"authors\":\"Shaobo Zhang, Zuhao Zhou, Yuqing Zhang, Peiyi Peng, Chongyu Xu\",\"doi\":\"10.1002/joc.70019\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>Drought may be exacerbated by global warming, but drought projections are largely inconsistent. The existing frameworks cannot adequately quantify the robustness and uncertainty of drought projections. Therefore, this study proposes a framework to solve this problem and verifies it in the Chinese Mainland. This framework consists of three main components: (1) the meteorological drought in the 21st century is projected using an impact propagation modelling chain; (2) the robustness of drought projections is quantified using Identical Trend Percentage (ITP) and Signal-to-Noise Ratio (SNR); (3) the uncertainty of drought projections is investigated using improved multi-way analysis of variance. The study reveals that this framework can include more uncertainty sources, investigate the propagation patterns of uncertainty components and quantify the robustness of drought projections. The results show that drought projections are not robust. Specifically, the mean ITP ranges from 49% to 69%, indicating that nearly half of the projections display trends opposite to those of the mean values. In addition, the mean SNR of drought projections ranges between −0.36 and 0.15, with an absolute value far from 1.0. The dominant uncertainty source is the choice of drought index, of which the mean relative contribution ranges between 47% and 61%. When propagating along with the impact propagation modelling chain, the relative importance among existing uncertainty sources usually remains stable if no new physical quantities are joining in. If the relative importance among the existing uncertainty sources for one particular quantity is different from that for the other quantity, the relative importance among the existing uncertainty sources may be adjusted when the two quantities are pooled together by the newly joined processes. Excluding unreasonable drought indices generally reduces the uncertainty and improves the robustness of drought projections; however, it is insufficient to derive robust drought projections.</p>\\n </div>\",\"PeriodicalId\":13779,\"journal\":{\"name\":\"International Journal of Climatology\",\"volume\":\"45 12\",\"pages\":\"\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2025-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Climatology\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.70019\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Climatology","FirstCategoryId":"89","ListUrlMain":"https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.70019","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
A Framework for Quantifying the Robustness and Uncertainty of Drought Projections
Drought may be exacerbated by global warming, but drought projections are largely inconsistent. The existing frameworks cannot adequately quantify the robustness and uncertainty of drought projections. Therefore, this study proposes a framework to solve this problem and verifies it in the Chinese Mainland. This framework consists of three main components: (1) the meteorological drought in the 21st century is projected using an impact propagation modelling chain; (2) the robustness of drought projections is quantified using Identical Trend Percentage (ITP) and Signal-to-Noise Ratio (SNR); (3) the uncertainty of drought projections is investigated using improved multi-way analysis of variance. The study reveals that this framework can include more uncertainty sources, investigate the propagation patterns of uncertainty components and quantify the robustness of drought projections. The results show that drought projections are not robust. Specifically, the mean ITP ranges from 49% to 69%, indicating that nearly half of the projections display trends opposite to those of the mean values. In addition, the mean SNR of drought projections ranges between −0.36 and 0.15, with an absolute value far from 1.0. The dominant uncertainty source is the choice of drought index, of which the mean relative contribution ranges between 47% and 61%. When propagating along with the impact propagation modelling chain, the relative importance among existing uncertainty sources usually remains stable if no new physical quantities are joining in. If the relative importance among the existing uncertainty sources for one particular quantity is different from that for the other quantity, the relative importance among the existing uncertainty sources may be adjusted when the two quantities are pooled together by the newly joined processes. Excluding unreasonable drought indices generally reduces the uncertainty and improves the robustness of drought projections; however, it is insufficient to derive robust drought projections.
期刊介绍:
The International Journal of Climatology aims to span the well established but rapidly growing field of climatology, through the publication of research papers, short communications, major reviews of progress and reviews of new books and reports in the area of climate science. The Journal’s main role is to stimulate and report research in climatology, from the expansive fields of the atmospheric, biophysical, engineering and social sciences. Coverage includes: Climate system science; Local to global scale climate observations and modelling; Seasonal to interannual climate prediction; Climatic variability and climate change; Synoptic, dynamic and urban climatology, hydroclimatology, human bioclimatology, ecoclimatology, dendroclimatology, palaeoclimatology, marine climatology and atmosphere-ocean interactions; Application of climatological knowledge to environmental assessment and management and economic production; Climate and society interactions