Combined use of meteorological drought indices at multi-time scales for improving hydrological drought detection

2016 ◽  
Vol 571 ◽  
pp. 1058-1068 ◽  
Author(s):  
Ye Zhu ◽  
Wen Wang ◽  
Vijay P. Singh ◽  
Yi Liu
2018 ◽  
Vol 22 (9) ◽  
pp. 5041-5056 ◽  
Author(s):  
José Miguel Delgado ◽  
Sebastian Voss ◽  
Gerd Bürger ◽  
Klaus Vormoor ◽  
Aline Murawski ◽  
...  

Abstract. A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará's research foundation for meteorology) and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation, (b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil.


2015 ◽  
Vol 16 (3) ◽  
pp. 1397-1408 ◽  
Author(s):  
Hongshuo Wang ◽  
Jeffrey C. Rogers ◽  
Darla K. Munroe

Abstract Soil moisture shortages adversely affecting agriculture are significantly associated with meteorological drought. Because of limited soil moisture observations with which to monitor agricultural drought, characterizing soil moisture using drought indices is of great significance. The relationship between commonly used drought indices and soil moisture is examined here using Chinese surface weather data and calculated station-based drought indices. Outside of northeastern China, surface soil moisture is more affected by drought indices having shorter time scales while deep-layer soil moisture is more related on longer index time scales. Multiscalar drought indices work better than drought indices from two-layer bucket models. The standardized precipitation evapotranspiration index (SPEI) works similarly or better than the standardized precipitation index (SPI) in characterizing soil moisture at different soil layers. In most stations in China, the Z index has a higher correlation with soil moisture at 0–5 cm than the Palmer drought severity index (PDSI), which in turn has a higher correlation with soil moisture at 90–100-cm depth than the Z index. Soil bulk density and soil organic carbon density are the two main soil properties affecting the spatial variations of the soil moisture–drought indices relationship. The study may facilitate agriculture drought monitoring with commonly used drought indices calculated from weather station data.


2020 ◽  
Author(s):  
Ruja Mansorian ◽  
Mohammad Zare ◽  
Guy Schumann

<p>In this study, long-term time series of precipitation data were used for determining the drought condition using the standard precipitation index (SPI) for 3, 6 and 12 month time scales. The indicators were calculated with two methods: a) using a gamma distribution and transforming the probability of occurrence to standard normal distribution, b) using the non-parametric plotting position method. Then, the SPI values for two consequent years 2013-14 and 2014-15 were extracted from data to study on meteorological drought. The SPI index calculations showed that the first year had near normal, whereas the second year had extreme drought condition. In parallel, 34 Landsat 8 satellite images were downloaded during the indicated time period to determine normalized difference vegetation index (NDVI) and vegetation condition index (VCI) as agricultural drought indices. The mean values of VCI for each month were considered as representative value for drought condition of the area. When the agricultural and meteorological drought indices were determined, the correlation coefficient (r) were calculated for finding the relation between these types of droughts. the results show that the highest correlation between SPI-3,6 and 12-month time scales and VCI occurred in 4, 2 and 4 months lag time respectively, with corresponding r value of 0.67, 0.65 and 0.69. The best agreement between these indices with calculated lag time proves the hypothesis that agricultural drought occurs after meteorological drought. Therefore, the results could be applied by farmers to plan an appropriate irrigation scheduling for upcoming droughts, specially, in arid and semi-arid areas. It could be concluded that for having suitable planning in water scarcity condition, understanding the situation helps water planners have better insight about management polices to minimize the effects of this natural hazard on human. To sum up, finding a relation between different types of droughts is helpful for monitoring, predicting and detecting droughts to better prepare for drought phenomena and to minimize losses</p>


Precipitation over the Upper Blue Nile Basin in Ethiopia contributes with 85% of the Nile river which provides 93% of Egypt’s conventional water resources. This study aims at assessing the meteorological drought in different locations in the Upper Blue Nile Basin and their relationship with the hydrological drought of Nile river in Egypt. The metrological drought was calculated by the Standard Precipitation Index (SPI) at five stations inside and close to the Upper Blue Nile Basin in Ethiopia, whereas the hydrological drought was calculated by the Streamflow Drought Index (SDI) at Dongola station at Nasser lake entrance. Both indices were calculated using the Drought Indices Calculator (DrinC) software. The selected study period was from 1973 to 2017 based on the availability of recorded data for meteorological stations in Ethiopia, and the streamflow for Dongola station. The data was categorized for each station by considering time periods of 1, 3, 6, 9, and 12 months based on their homogeneity. The correlation between SPI and SDI was evaluated using the Pearson correlation coefficient. The results showed a correlation between SPI for the five stations in the Upper Blue Nile Basin and SDI for Dongola station, where Gore station represented the highest frequency of significance at different time scales especially at the 3-months’ scale. The results confirm the relationship between SPI at Gore Station and SDI at Dongola Station, which means that the hydrological drought in Egypt is highly affected by the meteorological drought in the area surrounding Gore station. The paper recommends improving techniques for monitoring and overseeing drought hazards and assessing more meteorological stations to accurately predict climate change variations in Upper Blue Nile Basin and its effect on Egypt’s water resources.


2017 ◽  
Author(s):  
José Miguel Delgado ◽  
Sebastian Voss ◽  
Gerd Bürger ◽  
Klaus Vormoor ◽  
Aline Murawski ◽  
...  

Abstract. A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeast Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará's research foundation for meteorology) and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a generalized linear model to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation, (b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE) and the relative operating characteristic (ROC) skill score. Forecasts of monthly precipitation had little or no skill considering RMSE. Still, the forecast of extreme events of low monthly precipitation showed skill for the rainy season (ROC skill score of 0.24 to 0.33). A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and significant skill when forecasting drought events of e.g. SPEI01 (ROC skill score of 0.53 to 0.61). Similar results were obtained for low regional reservoir storage forecasts. Regarding the skill in the forecasted months, it was greater for April, when compared to February and March (the remaining months of the rainy season). This work showed that a multimodel ensemble can forecast drought events of time scales relevant to water managers in northeast Brazil with skill. But no or little skill could be found in the forecasts of the whole range of monthly precipitation or drought indices (e.g. forecasting average years). Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeast Brazil.


2021 ◽  
Vol 13 (4) ◽  
pp. 2066
Author(s):  
Jin Hyuck Kim ◽  
Jang Hyun Sung ◽  
Eun-Sung Chung ◽  
Sang Ug Kim ◽  
Minwoo Son ◽  
...  

Due to the recent appearance of shares socioeconomic pathway (SSP) scenarios, there have been many studies that compare the results between Coupled Model Intercomparison Project (CMIP)5 and CMIP6 general circulation models (GCMs). This study attempted to project future drought characteristics in the Cheongmicheon watershed using SSP2-4.5 of Australian Community Climate and Earth System Simulator-coupled model (ACCESS-CM2) in addition to Representative Concentration Pathway (RCP) 4.5 of ACCESS 1-3 of the same institute. The historical precipitation and temperature data of ACCESS-CM2 were generated better than those of ACCESS 1-3. Two meteorological drought indices, namely, Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were used to project meteorological drought while a hydrological drought index, Standardized Streamflow Index (SDI), was used to project the hydrological drought characteristics. The metrological data of GCMs were bias-corrected using quantile mapping method and the streamflow was obtained using Soil and Water Assessment Tool (SWAT) and bias-corrected meteorological data. As a result, there were large differences of drought occurrences and severities between RCP4.5 and SSP2-4.5 for the values of SPI, SPEI, and SDI. The differences in the minimum values of drought index between near (2021–2060) and far futures (2061–2100) were very small in SSP2-4.5, while those in RCP4.5 were very large. In addition, the longest drought period from SDI was the largest because the variation in precipitation usually affects the streamflow with a lag. Therefore, it was concluded that it is important to consider both CMIP5 and CMIP6 GCMs in establishing the drought countermeasures for the future period.


2016 ◽  
Author(s):  
Gregor Laaha ◽  
Tobias Gauster ◽  
Lena M. Tallaksen ◽  
Jean-Philippe Vidal ◽  
Kerstin Stahl ◽  
...  

Abstract. In 2015 large parts of Europe were affected by a drought. In two companion papers we summarize a collaborative initiative of members of UNESCO’s EURO FRIEND-Water program to perform a timely pan-European assessment of the event. In this second paper, we analyse the event of 2015 relative to the event of 2003 based on streamflow observations. Analyses are based on range of low flow and hydrological drought indices for about 800 records across Europe that were collected in a community effort based on a common protocol. We compare the hydrological footprints of both events with the meteorological footprints presented by Ionita et al. (2016), in order to learn from similarities and differences of both perspectives and to draw conclusions for drought management. Overall, the hydrological drought of 2015 is characterised by a different spatial extent than the drought of 2003. In terms of low flow magnitude, a region around the Czech Republic was most affected with annual low flows that exhibited return intervals of 100 years and more. In terms of deficit volumes, the geographical centre of the event was in the area of Southern Germany where the drought lasted particularly long. A detailed assessment at various spatial and temporal scales showed that the different behaviour in these regions was also a result of diverging wetness preconditions in the catchments. Extreme droughts emerged where antecedent conditions were particularly dry. In regions with wet preconditions, low flow events developed later, and were mostly less severe. The space-time patterns of monthly low flow characteristics show that meteorological and hydrological events spread differently across Europe, and they evolved differently in regard to extent and severity. The results underline that drought is a hazard that leaves different footprints on the various components of the water cycle, on different spatial and temporal scales. The different dynamic development of major hydrometeorological characteristics, temperature and precipitation anomalies versus the streamflow magnitude, duration and deficit volume also determine differences in the impacts of hydrological drought on various water use sectors and on river ecology. For an assessment of drought impacts on water resources, therefore, hydrological data is required in addition to the hydro-meteorological drought indices. Additional efforts with a pan-European dimension need to be undertaken to make timely hydrological assessments more operational in the future.


2019 ◽  
Vol 43 (1) ◽  
pp. 64-75
Author(s):  
Abebe Kebede ◽  
Jaya Prakash Raju ◽  
Diriba Korecha ◽  
Samuel Takele ◽  
Melessew Nigussie

AbstractDrought is an extreme event that causes great economic and environmental damage. The main objective of this study is to evaluate sensitivity, characterization and propagation of drought in the Upper Blue Nile. Drought indices: standardized precipitation index (SPI) and the recently developed standardized reconnaissance drought index (RDIst) are applied for five weather stations from 1980 to 2015 to evaluate RDIst applicability in the Upper Blue Nile. From our analysis both SPI and RDIst applied for 3-, 6-, 12 month of time scales follow the same trend, but in some time steps the RDIst varies with smaller amplitude than SPI. The severity and longer duration of drought compared with others periods of meteorological drought is found in the years 1984, 2002, 2009, 2015 including five weather stations and entire Upper Blue Nile. For drought relationships the correlation analysis is made across the time scales to evaluate the relationship between meteorological drought (SPI), soil moisture drought (SMI), and hydrological drought (SRI). We found that the correlation between three indices (SPI, SMI and SRI) at different time scales the 24-month time scale is dominant and are given by 0.82, 0.63 and 0.56.


2020 ◽  
Vol 80 (1) ◽  
Author(s):  
Kee An Hong ◽  
Jer Lang Hong ◽  
Izihan Ibrahim

In this study, drought occurrence in the Melaka basin has been assessed using the meteorological and hydrological drought indices. A continuous rainfall and streamflow data of 40 years were used for drought analysis. Results show that in terms of meteorological drought index, the severe drought occurred in 1986-1988. The streamflow drought index indicates that the extreme drought occurred in 1982-1984. Further analysis based on seasonal precipitation and streamflow data shows that there is no drought for 79% of the time for the period 1960-2000 where there are hydrological records. For most of the dry and wet seasons, it is more likely that the frequency of occurrence of hydrological droughts only is higher than the frequency of occurrence of meteorological and hydrological droughts simultaneously or only meteorological droughts.


Hydrology ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 49 ◽  
Author(s):  
Doan Quang Tri ◽  
Tran Tho Dat ◽  
Dinh Duc Truong

The objective of this study was to establish drought classification maps to simulate and calculate the lack of discharge in the Ba River basin in Vietnam. The maps were established using three meteorological drought indices (the Standardized Precipitation Index (SPI), the Drought Index (J), and the Ped Index (Ped)), the Soil and Water Assessment Tool (SWAT) model, and the hydrological drought index (KDrought). The results from the calculation of the SPI, Aridity Index (AI), and Ped at three stations (An Khe, Ayunpa, and MDrak) showed that the J index was suitable for the study area. Based on the J index, an extreme drought was predicted to occur at the Ayunpa, An Khe, and MDrak stations. During the calibration process, the SWAT Calibration Uncertainties Program (SWAT-CUP) model, with automatic algorithms, was used to select the parameters to optimize the SWAT model. For the calibration and validation, the observed discharge at two hydrology stations, An Khe and Cung Son, from the periods 1981–1991 and 1992–2002, respectively, were used. The simulated discharge was found to be acceptable, with the Nash–Sutcliffe efficiency (NSE), Percent bias (PBIAS), and R2 reaching good levels in both calibration and validation. The results from the calculation of the drought index (KDrought), and the established drought classification maps in 2016, showed that the most affected areas were the communes of the Gia Lai and Dak Lak provinces. The results from the simulation and calculations were found to be consistent with the situation that occurred in practice. The application of meteorological and hydrological drought indices, as well as the hydrological model, to support impact assessments of drought classification in space and time, as well as the establishment of forecasting and warning maps, will help managers to effectively plan policy responses to drought.


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