Integrated meteorological and hydrological drought model: A management tool for proactive water resources planning of semi-arid regions

Rad, Arash Modaresi; Ghahraman, B; Khalili, Davar; Ghahremani, Zahra; Ardakani, Samira Ahmadi

doi:10.1016/j.advwatres.2017.07.007

Cited by 59 publications

(22 citation statements)

References 74 publications

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“…For natural systems, we expect the 12-month SPI and SSI values to strongly correlate, particularly in areas with relatively strong seasonality and extensive storage capacity (Barker et al 2016). Moreover, two recent studies also confirm that a 12-month time scale is suitable to relate SPI to SSI, in a semi-arid environment (Marcos-Garcia et al 2017, Rad et al 2017).…”

Section: Drought Detection Using Standardized Indicesmentioning

confidence: 85%

Diagnosing drought using the downstreamness concept: the effect of reservoir networks on drought evolution

Oel

Martins

Costa

et al. 2018

Hydrological Sciences Journal

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To effectively manage hydrological drought, there is an urgent need to better understand and evaluate its human drivers. Using the "downstreamness" concept, we assess the role of a reservoir network in the emergence and evolution of droughts in a river basin in Brazil. In our case study, the downstreamness concept shows the effect of a network of reservoirs on the spatial distribution of stored surface water volumes over time. We demonstrate that, as a consequence of meteorological drought and recovery, the distribution of stored volumes became spatially skewed towards upstream locations, which affected the duration and magnitude of hydrological drought both upstream (where drought was alleviated) and downstream (where drought was aggravated). The downstreamness concept thus appears to be a useful entry point for spatiotemporally explicit assessments of hydrological drought and for determining the likelihood of progression from meteorological drought to a human-modified hydrological drought in a basin.

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Section: Drought Detection Using Standardized Indicesmentioning

confidence: 85%

Diagnosing drought using the downstreamness concept: the effect of reservoir networks on drought evolution

Oel

Martins

Costa

et al. 2018

Hydrological Sciences Journal

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“…In hydrological and climatological studies, the copula has been widely used to model the dependence between hydroclimatic variables. In recent years, copulas have also been used to develop multivariate drought indices by constructing the joint distribution of two or more hydroclimatic variables related to droughts, such as multivariate standardized drought index (MSDI) and standardized precipitation‐streamflow index (Hao & AghaKouchak, ; Rad et al, ). In this study, a multivariate drought index (MDI) is introduced using copulas to examine the joint effect of soil moisture and runoff on drought characteristics.…”

Section: Models Algorithms and Data Sourcesmentioning

confidence: 99%

Copula‐Based Convection‐Permitting Projections of Future Changes in Multivariate Drought Characteristics

Zhang

Wang

2019

JGR Atmospheres

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Probabilistic projections of future drought characteristics play a crucial role in climate change adaptation and disaster risk reduction. This study presents a copula‐based probabilistic framework for projecting future changes in multivariable drought characteristics through convection‐permitting Weather Research and Forecasting simulations with 4‐km horizontal grid spacing. A probabilistic multivariate drought index is introduced to examine the joint effects of drought indicators with uncertainty intervals for four major river basins located in South Central Texas of the United States. Markov chain Monte Carlo is used to address uncertainties in assessing copula parameters and in predicting climate‐induced changes in hydrological regimes. Our findings reveal that the severity and intensity of drought episodes can be amplified when considering the compound effects of soil moisture and runoff regimes by using the probabilistic multivariate drought index. The South Central Texas region is projected to experience more drought events with shorter duration and higher intensity in a changing climate. The drought severity will not necessarily increase due to the decreasing drought duration. In addition, our findings indicate that the intensity of future droughts is expected to increase as a result of the deficiency of soil moisture even though precipitation extremes are projected to become more frequent. Moreover, climate change impacts on multivariate drought characteristics will intensify with the increasing temporal scales (i.e., short‐, medium‐, and long‐term droughts) although the number of future drought events may decrease by the end of this century.

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“…In other words, it is a shortage of water associated with normal conditions (Sheffield and Wood 2011). Typically, drought is categorised into four types (Wilhite and Glantz 1985;Rad et al 2017):…”

Section: Introductionmentioning

confidence: 99%

Quantile Regression Based Methods for Investigating Rainfall Trends Associated with Flooding and Drought Conditions

Abbas

Xuan

Song

2019

Water Resour Manage

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Conducting trend analysis of climatic variables is one of the key steps in many climate change impact studies where trend is often checked against aggregated variables. However, there is also a strong need to investigate the trend of the data in different regimesexamples include high flow versus low flow, and heavy precipitation versus prolonged dry period. For this matter, quantile regression (QR) based methods are preferred as they can reveal the temporal dependencies of the variable in question for not only the mean value, but also its quantiles. As such, the tendencies revealed by the QR methods are more informative and helpful in studies where different mitigation methods need to be considered at different severity levels.In this paper, we demonstrate the use of several quantile regressions methods to analyse the long-term trend of rainfall records in two climatically different regions: The Dee River catchment in the United Kingdom, for which daily rainfall data of 1970-2004 are available; and the Beijing Metropolitan Area in China for which monthly rainfall data from 1950 to 2012 are available. Two quantiles are used to represent heavy rainfall condition (0.98 quantile) and severe dry condition (0.02 quantile). The trends of these two quantiles are then estimated using linear quantile regression before being spatially interpolated to demonstrate their spatial distribution (for Dee river only). The method is also compared with traditional indices such as SPI. The results show that the quantile regression method can reveal patterns for both extremely wet and dry conditions of the areas. The clear difference between trends at the chosen quantiles manifests the utility of QR in this context.

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Integrated meteorological and hydrological drought model: A management tool for proactive water resources planning of semi-arid regions

Cited by 59 publications

References 74 publications

Diagnosing drought using the downstreamness concept: the effect of reservoir networks on drought evolution

Diagnosing drought using the downstreamness concept: the effect of reservoir networks on drought evolution

Copula‐Based Convection‐Permitting Projections of Future Changes in Multivariate Drought Characteristics

Quantile Regression Based Methods for Investigating Rainfall Trends Associated with Flooding and Drought Conditions

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