Quantifying the impact of climate change on drought regimes using the Standardised Precipitation Index

The study presents a methodology to characterise short- or long-term drought events, designed to aid understanding of how climate change may affect future risk. An indicator of drought magnitude, combining parameters of duration, spatial extent and intensity, is presented based on the Standardised Precipitation Index (SPI). The SPI is applied to observed (1955–2003) and projected (2003–2050) precipitation data from the Community Integrated Assessment System (CIAS). Potential consequences of climate change on drought regimes in Australia, Brazil, China, Ethiopia, India, Spain, Portugal and the USA are quantified. Uncertainty is assessed by emulating a range of global circulation models to project climate change. Further uncertainty is addressed through the use of a high-emission scenario and a low stabilisation scenario representing a stringent mitigation policy. Climate change was shown to have a larger effect on the duration and magnitude of long-term droughts, and Australia, Brazil, Spain, Portugal and the USA were highlighted as being particularly vulnerable to multi-year drought events, with the potential for drought magnitude to exceed historical experience. The study highlights the characteristics of drought which may be more sensitive under climate change. For example, on average, short-term droughts in the USA do not become more intense but are projected to increase in duration. Importantly, the stringent mitigation scenario had limited effect on drought regimes in the first half of the twenty-first century, showing that adaptation to drought risk will be vital in these regions

Avaliação da relação seca/produtividade agrícola em cenário de mudanças climáticas.

As mudanças climáticas alertam para um possível aumento de eventos meteorológicos extremos em todo o mundo, sendo crescente a preocupação de como o clima pode mudar o ambiente e afetar a produção das culturas agrícolas. Este estudo investiga a relação entre a produtividade agrícola e a seca em algumas mesorregiões do estado de Minas Gerais, em cenários de mudanças climáticas. Foram utilizados dados meteorológicos diários projetados pelo modelo ECHAM5/MPI-OM, para o período de 2008 a 2020 para o cenário A1B. Utilizou-se a metodologia da zona agroecológica (AEZ) para estimar a produtividade futura do milho. Empregou-se o índice de seca Z de Palmer em um modelo de regressão linear com a produtividade do milho estimada pela metodologia da AEZ. O desempenho dos modelos foi verificado por meio das estatísticas: coeficiente de determinação (r2), raiz do erro quadrático médio(RMSE), erro absoluto médio (MAE) e índice de concordância de Willmott (d). Os resultados do índice de concordância de Willmott variaram entre 0,48 e 0,90, e os valores de r2 foram pouco expressivos.Contudo, a produtividade estimada pela metodologia AEZ projetou maiores perdas na produtividade do milho devido a limitações por água para os anos agrícolas de 2008/2009, 2009/2010, 2014/2015,2018/2019 para as mesorregiões Triângulo/Alto Paranaíba, Central Mineira e Jequitinhonha

Integrated methodological framework fos assesing the risk of failure in water supply incorporating drought forecast. Case study: Andean regulated river basin

[EN] Hydroclimatic drought conditions can affect the hydrological services offered by mountain river basins causing severe impacts on the population, becoming a challenge for water resource managers in Andean river basins. This study proposes an integrated methodological framework for assessing the risk of failure in water supply, incorporating probabilistic drought forecasts, which assists in making decisions regarding the satisfaction of consumptive, non-consumptive and environmental requirements under water scarcity conditions. Monte Carlo simulation was used to assess the risk of failure in multiple stochastic scenarios, which incorporate probabilistic forecasts of drought events based on a Markov chains (MC) model using a recently developed drought index (DI). This methodology was tested in the Machángara river basin located in the south of Ecuador. Results were grouped in integrated satisfaction indexes of the system (DSIG). They demonstrated that the incorporation of probabilistic drought forecasts could better target the projections of simulation scenarios, with a view of obtaining realistic situations instead of optimistic projections that would lead to riskier decisions. Moreover, they contribute to more effective results in order to propose multiple alternatives for prevention and/or mitigation under drought conditions.This study was part of the doctoral thesis of Aviles A. at the Technical University of Valencia. This research was funded by the University of Cuenca through its Research Department (DIUC) and the Municipal public enterprise of telecommunications, drinking water, sewage and sanitation of Cuenca (ETAPA) through the projects: BIdentificacion de los procesos hidrometeorologicos que desencadenan inundaciones en la ciudad de Cuenca usando un radar de precipitacion" and "Ciclos meteorologicos y evapotranspiracion a lo largo de una gradiente altitudinal del Parque Nacional Cajas". The authors also thank INAMHI and the CBRM for providing the information for this study. The authors wish to thank the Spanish Ministry of Economy and Competitiveness for its financial support through the ERAS project (CTM2016-77804-P). We thank Angel Vazquez, who helped in the programming of the multiple simulations. Also we thank to the TropiSeca project.Avilés-Añazco, A.; Solera Solera, A.; Paredes Arquiola, J.; Pedro Monzonís, M. (2018). Integrated methodological framework fos assesing the risk of failure in water supply incorporating drought forecast. Case study: Andean regulated river basin. Water Resources Management. 32(4):1209-1223. https://doi.org/10.1007/s11269-017-1863-7S12091223324Andreu J, Capilla J, Sanchís E (1996) AQUATOOL, a generalized decision-support system for water-resources planning and operational management. 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Marcombo, Barcelona, España pp 51–61Avilés A, Célleri R, Paredes J, Solera A (2015) Evaluation of Markov chain based drought forecasts in an Andean Regulated River basin using the skill scores RPS and GMSS. Water Resour Manag 29(6):1949–1963. https://doi.org/10.1007/s11269-015-0921-2Avilés A, Célleri R, Solera A, Paredes J (2016) Probabilistic forecasting of drought events using Markov chain-and Bayesian network-based models: a case study of an Andean Regulated River Basin. Water 8:1–16Barua S, Ng A, Perera B (2012) Drought assessment and forecasting: a case study on the Yarra River catchment in Victoria, Australia. Aust J Water Resour 15(2):95–108. https://doi.org/10.7158/W10-848.2012.15.2Bazaraa MS, Jarvis JJ, Sherali HD (2011) Linear programming and network flows, fourth Edi. John Wiley & Sons, New JerseyBrown C, Baroang KM, Conrad E et al (2010) IRI technical report 10–15, managing climate risk in water supply systems. Palisades, NYCancelliere A, Di Mauro G, Bonaccorso B, Rossi G (2007) Drought forecasting using the standardized precipitation index. Water Resour Manag 21(5):801–819. https://doi.org/10.1007/s11269-006-9062-yCancelliere A, Nicolosi V, Rossi G (2009) Assessment of drought risk in water supply systems in coping with drought risk in agriculture and water supply systems. Advances in natural and technological hazards research 26. In: Coping with drought risk in agriculture. Springer, pp 93–109. https://doi.org/10.1007/978-1-4020-9045-5_8Chen YD, Zhang Q, Xiao M, Singh VP, Zhang S (2016) Probabilistic forecasting of seasonal droughts in the Pearl River basin, China. Stoch Environ Res Risk Assess 30(7):2031–2040. https://doi.org/10.1007/s00477-015-1174-6Gong G, Wang L, Condon L, Shearman A, Lall U (2010) A simple framework for incorporating seasonal Streamflow forecasts into existing water resource management practices. JAWRA J Am Water Resour Assoc 46(3):574–585. https://doi.org/10.1111/j.1752-1688.2010.00435.xHaro D, Solera A, Paredes J, Andreu J (2014) Methodology for drought risk assessment in within-year regulated reservoir systems. Application to the Orbigo River system (Spain). Water Resour Manag 28(11):3801–3814. https://doi.org/10.1007/s11269-014-0710-3Haro-Monteagudo D, Solera A, Andreu J (2017) Drought early warning based on optimal risk forecasts in regulated river systems: application to the Jucar River basin (Spain). J Hydrol 544:36–45. https://doi.org/10.1016/j.jhydrol.2016.11.022Hashimoto T, Loucks DP, Stedinger JR (1982) Reliability, resiliency, and vulnerability criteria. Water Resour Res 18(1):14–20. https://doi.org/10.1029/WR018i001p00014Hwang Y, Carbone GJ (2009) Ensemble forecasts of drought indices using a conditional residual resampling technique. J Appl Meteorol Climatol 48(7):1289–1301. https://doi.org/10.1175/2009JAMC2071.1Kao S-C, Govindaraju RS (2010) A copula-based joint deficit index for droughts. J Hydrol 380(1-2):121–134. https://doi.org/10.1016/j.jhydrol.2009.10.029Keyantash JA, Dracup JA (2004) An aggregate drought index: assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage. Water Resour Res 40(9):1–13. https://doi.org/10.1029/2003WR002610Khadr M (2016) Forecasting of meteorological drought using hidden Markov model (case study: the upper Blue Nile river basin, Ethiopia). Ain Shams Eng J 7(1):47–56. https://doi.org/10.1016/j.asej.2015.11.005Madadgar S, Moradkhani H (2013) A Bayesian framework for probabilistic seasonal drought forecasting. J Hydrometeorol 14(6):1685–1706. https://doi.org/10.1175/JHM-D-13-010.1Madadgar S, Moradkhani H (2014) Spatio-temporal drought forecasting within Bayesian networks. J Hydrol 512:134–146. https://doi.org/10.1016/j.jhydrol.2014.02.039Mahmoudzadeh H, Mahmoudzadeh H, Afshar M, Yousefi S (2016) Applying first-order Markov chains and SPI drought index to monitor and forecast drought in West Azerbaijan Province of Iran. Int J Geo Sci Environ Plan 1:44–53. 10.22034/ijgsep.2016.40669Mishra AK, Singh VP (2010) Review paper a review of drought concepts. J Hydrol 391(1-2):202–216. https://doi.org/10.1016/j.jhydrol.2010.07.012Nalbantis I, Tsakiris G (2009) Assessment of hydrological drought revisited. Water Resour Manag 23(5):881–897. https://doi.org/10.1007/s11269-008-9305-1Ochola WO, Kerkides P (2003) A Markov chain simulation model for predicting critical wet and dry spells in Kenya: Analysing rainfall events in the kano plains. Irrig Drain 52(4):327–342. https://doi.org/10.1002/ird.094Paulo AA, Pereira LS (2007) Prediction of SPI drought class transitions using Markov chains. Water Resour Manag 21(10):1813–1827. https://doi.org/10.1007/s11269-006-9129-9Phan TD, Smart JCR, Capon SJ, Hadwen WL, Sahin O (2016) Applications of Bayesian belief networks in water resource management: a systematic review. Environ Model Softw 85:98–111. https://doi.org/10.1016/j.envsoft.2016.08.006Pouget L, Roldán T, Gómez M et al (2015) Use of seasonal climate predictions in the water sector—preliminary results from the EUPORIAS project. In: Andreu J, Solera A, Paredes J et al (eds) Drought: research and science-policy interfacing. Taylor & Francis Group, London, UK, p 247Rossi G, Cancelliere A (2013) Managing drought risk in water supply systems in Europe: a review. Int J Water Resour Dev 29(2):272–289. https://doi.org/10.1080/07900627.2012.713848Rossi G, Caporali E, Garrote L (2012) Definition of risk indicators for reservoirs management optimization. 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Spatial patterns and temporal variability of drought in Western Iran

An analysis of drought in western Iran from 1966 to 2000 is presented using monthly precipitation data observed at 140 gauges uniformly distributed over the area. Drought conditions have been assessed by means of the Standardized Precipitation Index (SPI). To study the long-term drought variability the principal component analysis was applied to the SPI field computed on 12-month time scale. The analysis shows that applying an orthogonal rotation to the first two principal component patterns, two distinct sub-regions having different climatic variability may be identified. Results have been compared to those obtained for the largescale using re-analysis data suggesting a satisfactory agreement. Furthermore, the extension of the large-scale analysis to a longer period (1948–2007) shows that the spatial patterns and the associated time variability of drought are subjected to noticeable changes. Finally, the relationship between hydrological droughts in the two sub-regions and El Niño Southern Oscillation events has been investigated finding that there is not clear evidence for a link between the two phenomen

Characterising droughts in Central America with uncertain hydro-meteorological data

Central America is frequently affected by droughts that cause significant socio-economic and environmental problems. Drought characterisation, monitoring and forecasting are potentially useful to support water resource management. Drought indices are designed for these purposes, but their ability to characterise droughts depends on the characteristics of the regional climate and the quality of the available data. Local comprehensive and high-quality observational networks of meteorological and hydrological data are not available, which limits the choice of drought indices and makes it important to assess available datasets. This study evaluated which combinations of drought index and meteorological dataset were most suitable for characterising droughts in the region. We evaluated the standardised precipitation index (SPI), a modified version of the deciles index (DI), the standardised precipitation evapotranspiration index (SPEI) and the effective drought index (EDI). These were calculated using precipitation data from the Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS), the CRN073 dataset, the Climate Research Unit (CRU), ECMWF Reanalysis (ERA-Interim) and a regional station dataset, and temperature from the CRU and ERA-Interim datasets. The gridded meteorological precipitation datasets were compared to assess how well they captured key features of the regional climate. The performance of all the drought indices calculated with all the meteorological datasets was then evaluated against a drought index calculated using river discharge data. Results showed that the selection of database was more important than the selection of drought index and that the best combinations were the EDI and DI calculated with CHIRPS and CRN073. Results also highlighted the importance of including indices like SPEI for drought assessment in Central America.Universidad de Costa Rica/[805-B0-810]/UCR/Costa RicaUniversidad de Costa Rica/[805-A9-532]/UCR/Costa RicaUniversidad de Costa Rica/[805-B3-600]/UCR/Costa RicaUniversidad de Costa Rica/[805-B0-065]/UCR/Costa RicaUniversidad de Costa Rica/[805-B3-413]/UCR/Costa RicaUniversidad de Costa Rica/[805-B4-227]/UCR/Costa RicaUniversidad de Costa Rica/[805-B4-228]/UCR/Costa RicaUniversidad de Costa Rica/[805-B5-295]/UCR/Costa RicaUppsala University/[54100006]//SueciaMarie Curie Intra-European Fellowship/[No.329762]//EuropaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI)UCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de Físic

``Agro-meteorological indices and climate model uncertainty over the UK''

Five stakeholder-relevant indices of agro-meteorological change were analysed for the UK, over past (1961--1990) and future (2061--2090) periods. Accumulated Frosts, Dry Days, Growing Season Length, Plant Heat Stress and Start of Field Operations were calculated from the E-Obs (European Observational) and HadRM3 (Hadley Regional Climate Model) PPE (perturbed physics ensemble) data sets. Indices were compared directly and examined for current and future uncertainty. Biases are quantified in terms of ensemble member climate sensitivity and regional aggregation. Maps of spatial change then provide an appropriate metric for end-users both in terms of their requirements and statistical robustness. A future UK is described with fewer frosts, fewer years with a large number of frosts, an earlier start to field operations (e.g., tillage), fewer occurrences of sporadic rainfall, more instances of high temperatures (in both the mean and upper range), and a much longer growing season

A Monte Carlo Simulation-Based Approach to Evaluate the Performance of Three Meteorological Drought Indices in Northwest of Iran

Although meteorological drought indices are considered as important tools for drought monitoring, they are embedded with different theoretical and experimental structures. Regarding the different geographic and climatic conditions around the world, the most meteorological drought indices have been commonly applied for drought monitoring in different parts of the world. Interestingly, it is observed that such indices in the published studies on drought monitoring have usually yielded inconsistent performance. On the other hand, most studies on drought monitoring as well as the performance of drought indices has been based on short-term historical data (less than 50 years). Therefore, this study aimed to analyze and compare the performance of three common indices of SPI, RAI and PNPI to predict long-term drought events using the Monte Carlo procedure and historical data. To do this end, the 50-year recorded or historical rainfall data across 11 synoptic stations in the Northwest of Iran were employed to generate 1000 synthetic data series so that the characteristics of long-term drought might be determined and the performance of those three indices might be analyzed and compared. The results indicated a very high comparative advantage of the SPI in terms of yielding a satisfactory and detailed analysis to determine the characteristics of long-term drought. Also, the RAI indicated significant deviations from normalized natural processes. However, these results could not reasonably and sufficiently predict long-term drought. Finally, the PNPI was determined as the most uncertain and spatial index (depending on average or coefficient of variation of rainfall data) in drought monitoring