Suchergebnisse

Abstract
Drought is widely written about as a complex, multifaceted phenomenon, with complexity arising not just from biophysical drivers, but also human understanding and experiences of drought and its impacts. This has led to a proliferation of different drought definitions and indicators, creating a challenge for the design of drought monitoring and early warning (MEW) systems, which are a key component of drought preparedness. Here, we report on social learning workshops conducted in the United Kingdom aimed at improving the design and operation of drought MEW systems as part of a wider international project including parallel events in the United States and Australia. We highlight key themes for MEW design and use: "types" of droughts, indicators and impacts, uncertainty, capacity and decision-making, communications, and governance. We shed light on the complexity of drought through the multiple framings of the problem by different actors, and how this influences their needs for MEW. Our findings suggest that MEW systems need to embrace this complexity and strive for consistent messaging while also tailoring information for a wide range of audiences in terms of the drought characteristics, temporal and spatial scales, and impacts that are important for their particular decision-making processes. We end with recommendations to facilitate this approach.

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.

The flooding in winter 2015/2016 was one of the most extraordinary hydrological episodes witnessed in the UK in recent decades. The winter was defined by a succession of severe storms, bringing extreme rainfall and associated widespread flooding across northern and western regions. For the UK, December 2015 was the wettest month (for any calendar month) and it was the second wettest winter (December–February) in a series from 1910, while a number of new records were established for rainfall accumulations over shorter timescales. The hydrological response was remarkable; period of record maximum peak flows were established across northern areas of the UK, and the high flow response was notable in terms of its spatial extent, duration and frequency. The flooding had severe impacts on properties, infrastructure and livelihoods. Inevitably, the flooding was at the forefront of public, political and media attention through much of the winter and, occurring only two years after the major flooding of 2013/2014, led to a significant debate around flood management in a warming world.

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.

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.

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.

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.

15 Pags.- 9 Figs.- 2 Tabls. ; We analyzed the relationships between meteorological drought and hydrological drought using very dense and diverse network of gauged natural drainage basins across the conterminous U.S. Specifically, this work utilized a dataset of 289 gauging stations, covering the period 1940–2013. Drainage basins were obtained for each gauging station using a digital terrain model. In addition to meteorological data (e.g., precipitation, air temperature and the atmospheric evaporative demand), we obtained a number of topographic, soil and remote sensing variables for each defined drainage basin. A hydrological drought index (the Standardized Streamflow Index; SSI) was computed for each basin and linked to the Standardized Precipitation Evapotranspiration Index (SPEI), which was used as a metric of climatic drought severity. The relationships between different SPEI time-scales and their corresponding SSI were assessed by means of a Pearson correlation coefficient. Also, the general patterns of response of hydrological droughts to climatic droughts were identified using a principal component analysis. Overall, results demonstrate a positive response of SSI to SPEI at shorter time-scales, with strong seasonality and clear spatial differences. We also assessed the role of some climatic and environmental factors in explaining these different responses using a predictive discriminant analysis. Results indicate that elevation and vegetation coverage are the main drivers of the diverse response of SSI to SPEI time-scales. Similar analyses were made for three sub-periods (1940–1964, 1965–1989 and 1989–2013), whose results confirm considerable differences in the response of SSI to SPEI over the past eighty years. ; This work was supported by the research project I-Link1001 (Validation of climate drought indices for multi-sectorial applications in North America and Europe under a global warming scenario) financed by CSIC, PCIN-2015-220 and CGL2014-52135-C03-01 financed by the Spanish Commission of Science and Technology and FEDER, IMDROFLOOD financed by the Water Works 2014 co-funded call of the European Commission and INDECIS, which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462). Marina Peña-Gallardo was granted by the Spanish Ministry of Economy and Competitiveness. Miquel Tomas-Burguera was supported by a doctoral grant by the Spanish Ministry of Education, Culture and Sport. Jamie Hannaford was supported by the Belmont Forum project 'DrIVER', NERC Grant Number (grant NE/L010038/1). Marco Maneta acknowledges support from the USDA NIFA grant 2016-67026-25067. ; Peer reviewed

Abstract. Droughts in Thailand are becoming more severe due to
climate change. Developing a reliable drought monitoring and early warning
system (DMEWS) is essential to strengthen a country's resilience to
droughts. However, for a DMEWS to be valuable, the drought indicators
provided to stakeholders must have relevance to tangible impacts on the
ground. Here, we analyse drought indicator-to-impact relationships in
Thailand, using a combination of correlation analysis and machine learning
techniques (random forest). In the correlation analysis, we study the link
between meteorological drought indicators and high-resolution remote sensing vegetation indices used as proxies for crop yield and forest growth impacts. Our analysis shows that this link varies depending on land use, season and region. The random forest models built to estimate regional crop productivity allow a more in-depth analysis of the crop- and region-specific importance of different drought indicators. The results highlight seasonal patterns of drought vulnerability for individual crops, usually linked to their growing season, although the effects are somewhat attenuated in irrigated regions. Integration of the approaches provides new, detailed knowledge of crop- and region-specific indicator-to-impact links, which can form
the basis of targeted mitigation actions in an improved DMEWS in Thailand
and could be applied to other parts of Southeast Asia and beyond.