scholarly journals Linking drought indices to impacts to support drought risk assessment in Liaoning province, China

2019 ◽  
Author(s):  
Yaxu Wang ◽  
Juan Lv ◽  
Jamie Hannaford ◽  
Yicheng Wang ◽  
Hongquan Sun ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Random Forest ◽  
Liaoning Province ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Drought Risk ◽  
Drought Vulnerability ◽  
Random Forest Analysis ◽  
Drought Impact ◽  
Drought Impacts

Abstract. Drought is a ubiquitous and reoccurring hazard that has wide ranging impacts on society, agriculture and the environment. Drought indices are vital for characterizing the nature and severity of drought hazards, and there have been extensive efforts to identify the most suitable drought indices for drought monitoring and risk assessments. However, to date, little effort has been made to explore which index(s) best represents drought impacts for various sectors in China. This is a critical knowledge gap, as impacts provide important ‘ground truth’ information. They can be used to demonstrate whether drought indices (used for monitoring or risk assessment) are relevant for identifying impacts, thus highlighting if an area is vulnerable to drought of a given severity. The aim of this study is to explore the link between drought indices and drought impacts, using Liaoning province (northeast China) as a case study due to its history of drought occurrence. To achieve this we use independent, but complementary, methods (correlation and random forest analysis). Using multiple drought indices – Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Soil Moisture (SoilM) and the Normalized Difference Vegetation Index (NDVI) – and drought impact data (on crop yield, livestock, rural people and the economy) correlation and random forest analysis were used to identify which indices link best to the recorded drought impacts for cities in Liaoning. The results show that the relationship varies between different categories of drought impacts and between cities. SPEI with a 6-month accumulation (SPEI6) had a strong correlation with all categories of drought impacts, while SPI12 had a weak correlation with drought impacts. Of the impact datasets, drought suffering area and drought impact area had a slightly strong relationship with all drought indices in Liaoning province, while population and number of livestock with difficulty in accessing drinking water had weak correlations with the indices. Based on the linkage, drought vulnerability was analyzed using various vulnerability factors. Crop cultivated area was positively correlated to the drought vulnerability for five out of the eight categories of drought impacts, while the total population had a strong negative relationship with drought vulnerability for half the drought impact categories. This study can support drought planning efforts in the region, and provides a methodology for application for other regions of China (and other countries) in the future, as well as providing context for the indices used in drought monitoring applications, so enabling improved preparedness for drought impacts.

scholarly journals Linking drought indices to impacts to support drought risk assessment in Liaoning province, China

2020 ◽  
Vol 20 (3) ◽  
pp. 889-906
Author(s):  
Yaxu Wang ◽  
Juan Lv ◽  
Jamie Hannaford ◽  
Yicheng Wang ◽  
Hongquan Sun ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Strong Relationship ◽  
Liaoning Province ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Drought Risk ◽  
Drought Impact ◽  
Drought Impacts ◽  
Standardised Precipitation Index ◽  
The Impact

Abstract. Drought is a ubiquitous and recurring hazard that has wide-ranging impacts on society, agriculture and the environment. Drought indices are vital for characterising the nature and severity of drought hazards, and there have been extensive efforts to identify the most suitable drought indices for drought monitoring and risk assessment. However, to date, little effort has been made to explore which index (or indices) best represents drought impacts for various sectors in China. This is a critical knowledge gap, as impacts provide important ground truth information for indices used in monitoring activities. The aim of this study is to explore the link between drought indices and drought impacts, using Liaoning province (northeast China) as a case study due to its history of drought occurrence. To achieve this we use independent, but complementary, methods (correlation and random forest analysis) to identify which indices link best to drought impacts for prefectural-level cities in Liaoning province, using a comprehensive database of reported drought impacts in which impacts are classified into a range of categories. The results show that the standardised precipitation evapotranspiration index with a 6-month accumulation (SPEI6) had a strong correlation with all categories of drought impacts, while the standardised precipitation index with a 12-month accumulation (SPI12) had a weak correlation with drought impacts. Of the impact datasets, “drought-suffering area” and “drought impact area” had a strong relationship with all drought indices in Liaoning province, while “population and number of livestock with difficulty in accessing drinking water” had weak correlations with the indices. The results of this study can support drought planning efforts in the region and provide context for the indices used in drought-monitoring applications, so enabling improved preparedness for drought impacts. The study also demonstrates the potential benefits of routine collection of drought impact information on a local scale.

scholarly journals Linking drought indices to impacts in the Liaoning Province of China

2020 ◽  
Vol 383 ◽  
pp. 267-272
Author(s):  
Miaomiao Ma ◽  
Juan Lv ◽  
Zhicheng Su ◽  
Jamie Hannaford ◽  
Hongquan Sun ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Drinking Water ◽  
Flood Control ◽  
Liaoning Province ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Chinese Government ◽  
Drought Impacts ◽  
Drought Relief ◽  
The Impact

Abstract. Drought is an inherent meteorological characteristic of any given region, but is particularly important in China due to its monsoon climate and the “three ladder” landform system. The Chinese government has constructed large-scale water conservation projects since 1949, and developed drought and water scarcity relief frameworks. However, drought still causes huge impacts on water supply, environment and agriculture. China has, therefore, created specialized agencies for drought management called Flood Control and Drought Relief Headquarters, which include four different levels: state, provincial, municipal and county. The impact datasets they collect provide an effective resource for drought vulnerability assessment, and provide validation options for hydro-meteorological indices used in risk assessment and drought monitoring. In this study, we use the statistical drought impact data collected by the Liaoning province Drought Relief Headquarter and meteorological drought indices (Standardized Precipitation Index, SPI and Standard Precipitation Evaporation Index, SPEI) to explore a potential relationship between drought impacts and these indices. The results show that SPI-24 and SPEI-24 are highly correlated to the populations that have difficulties in obtaining drinking water in four out of the six cities studied. Three impacts related to reservoirs and the availability of drinking water for humans and livestock exhibit strong correlations with SPI and SPEI of different accumulated periods. Results reveal that meteorological indices used for drought monitoring and early warning in China can be effectively linked to drought impacts. Further work is exploring how this information can be used to optimize drought monitoring and risk assessment in the whole Liaoning province and elsewhere in China.

scholarly journals Developing drought impact functions for drought risk management

2017 ◽  
Vol 17 (11) ◽  
pp. 1947-1960 ◽  
Author(s):  
Sophie Bachmair ◽  
Cecilia Svensson ◽  
Ilaria Prosdocimi ◽  
Jamie Hannaford ◽  
Kerstin Stahl
Keyword(s):  
Risk Management ◽  
Logistic Regression ◽  
Random Forest ◽  
Binary Data ◽  
Hurdle Model ◽  
Drought Risk ◽  
Drought Impact ◽  
Drought Impacts ◽  
The Impact ◽  
Drought Risk Management

Abstract. Drought management frameworks are dependent on methods for monitoring and prediction, but quantifying the hazard alone is arguably not sufficient; the negative consequences that may arise from a lack of precipitation must also be predicted if droughts are to be better managed. However, the link between drought intensity, expressed by some hydrometeorological indicator, and the occurrence of drought impacts has only recently begun to be addressed. One challenge is the paucity of information on ecological and socioeconomic consequences of drought. This study tests the potential for developing empirical drought impact functions based on drought indicators (Standardized Precipitation and Standardized Precipitation Evaporation Index) as predictors and text-based reports on drought impacts as a surrogate variable for drought damage. While there have been studies exploiting textual evidence of drought impacts, a systematic assessment of the effect of impact quantification method and different functional relationships for modeling drought impacts is missing. Using Southeast England as a case study we tested the potential of three different data-driven models for predicting drought impacts quantified from text-based reports: logistic regression, zero-altered negative binomial regression (hurdle model), and an ensemble regression tree approach (random forest). The logistic regression model can only be applied to a binary impact/no impact time series, whereas the other two models can additionally predict the full counts of impact occurrence at each time point. While modeling binary data results in the lowest prediction uncertainty, modeling the full counts has the advantage of also providing a measure of impact severity, and the counts were found to be reasonably predictable. However, there were noticeable differences in skill between modeling methodologies. For binary data the logistic regression and the random forest model performed similarly well based on leave-one-out cross validation. For count data the random forest outperformed the hurdle model. The between-model differences occurred for total drought impacts and for two subsets of impact categories (water supply and freshwater ecosystem impacts). In addition, different ways of defining the impact counts were investigated and were found to have little influence on the prediction skill. For all models we found a positive effect of including impact information of the preceding month as a predictor in addition to the hydrometeorological indicators. We conclude that, although having some limitations, text-based reports on drought impacts can provide useful information for drought risk management, and our study showcases different methodological approaches to developing drought impact functions based on text-based data.

scholarly journals Developing drought impact functions for drought risk management

2017 ◽  
Author(s):  
Sophie Bachmair ◽  
Cecilia Svensson ◽  
Ilaria Prosdocimi ◽  
Jamie Hannaford ◽  
Kerstin Stahl
Keyword(s):  
Risk Management ◽  
Logistic Regression ◽  
Random Forest ◽  
Binary Data ◽  
Hurdle Model ◽  
Drought Risk ◽  
Drought Impact ◽  
Drought Impacts ◽  
The Impact ◽  
Drought Risk Management

Abstract. Drought management frameworks are dependent on methods for monitoring and prediction, but quantifying the hazard alone is arguably not sufficient; the negative consequences that may arise from a lack of precipitation must also be predicted if droughts are to be better managed. However, the link between drought intensity, expressed by some hydro-meteorological indicator, and the occurrence of drought impacts has only recently begun to be addressed. One challenge is the paucity of information on ecological and socio-economic consequences of drought. This study tests the potential for developing empirical drought impact functions based on drought indicators (Standardized Precipitation and Standardized Precipitation Evaporation Index) as predictors, and text-based reports on drought impacts as a surrogate variable for drought damage. While there have been studies exploiting textual evidence of drought impacts, a systematic assessment of the effect of impact quantification method and different functional relationships for modeling drought impacts is missing. Using South–East England as a case study we tested the potential of three different data-driven models for predicting drought impacts quantified from text-based reports; logistic regression, zero-altered negative binomial regression (hurdle model), and an ensemble regression tree approach (random forest). The logistic regression model can only be applied to a binary impact/no impact time series, whereas the other two models can additionally predict the full counts of impact occurrence at each time point. While modeling binary data results in the lowest prediction uncertainty, modeling the full counts has the advantage of also providing a measure of impact severity, and the counts were found to be predictable within reasonable limits. However, there were noticeable differences in skill between modeling methodologies. For binary data the logistic regression and the random forest model performed similarly well based on leave-one-out cross-validation. For count data the random forest outperformed the hurdle model. The between-model differences occurred for total drought impacts as well as for two subsets of impact categories (water supply and freshwater ecosystem impacts). In addition, different ways of defining the impact counts were investigated, and were found to have little influence on the prediction skill. For all models we found a positive effect of including impact information of the preceding month as a predictor in addition to the hydro-meteorological indicators. We conclude that, although having some limitations, text-based reports on drought impacts can provide useful information for drought risk management, and our study showcases different methodological approaches to developing drought impact functions based on text-based data.

scholarly journals Drought Risk Assessment by Using Drought Hazard and Vulnerability Indexes

2018 ◽  
Author(s):  
Ismail Dabanli
Keyword(s):  
Risk Assessment ◽  
Risk Index ◽  
Standardized Precipitation Index ◽  
Hazard Index ◽  
Vulnerability Index ◽  
Drought Risk ◽  
Drought Vulnerability ◽  
Drought Hazard ◽  
Hazard Vulnerability ◽  
Composite Risk

Abstract. Drought has multiple impacts on socioeconomic sectors and it is expected to increase in the coming years due to non-stationary nature of climate variability and change. Here, we investigated drought hazard, vulnerability, and risk based on hydro-meteorological and actual socio-economic data for provinces of Turkey. Although, drought vulnerability and risk assessment are essential parts of drought phenomenon, so far, lack of proper integrated drought risk assessment in Turkey (and elsewhere) has led to higher socio-economic impacts. Firstly, the Drought Hazard Index (DHI) is derived based on the probability occurrences of drought using Standardized Precipitation Index (SPI) to facilitate the understanding of drought phenomenon. Secondly, the Drought Vulnerability Index (DVI) is calculated by utilizing four socio-economic indicators to quantify drought impact on society. Finally, the Drought Risk Index (DRI) is obtained by multiplying DHI and DVI for provinces of Turkey to highlight the relative importance of hazard and vulnerability assessment for drought risk management. A set of drought hazard, vulnerability, and composite risk maps were then developed. The outputs of analysis reveal that among 81 administrative provinces in Turkey, 73 provinces are exposed to the low drought risk (0 

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