Spatio-temporal analysis of precipitation-based drought indices in Kucuk Menderes River Basin, Turkey

2021 ◽  
Author(s):  
Yonca Cavus ◽  
Ebru Eris ◽  
Hafzullah Aksoy ◽  
Halil Ibrahim Burgan ◽  
Hakan Aksu ◽  
...  
Keyword(s):  
Time Series ◽  
River Basin ◽  
Meteorological Data ◽  
Temperature Data ◽  
Drought Indices ◽  
Drought Severity ◽  
Drought Risk ◽  
Precipitation Data ◽  
Spatio Temporal ◽  
Precipitation And Temperature

<div><span>Drought is one of the extreme hydrological events which may seriously affect the majority of the population in many ways such as economically, socially and environmentally. Researches on the drought analysis may prevent these adverse consequences to a significant extent. Droughts are characterized by using various meteorological and hydrological indicators (i.e. precipitation, temperature, streamflow etc.). These indicators are used to derive drought indices. Spatio-temporal drought is analysed both in time and space by using drought indices based on site-specific precipitation and temperature data. In this study, Standardized Precipitation Index (SPI) using only precipitation data and Standardized Precipitation Evapotranspiration Index (SPEI) using precipitation and temperature data are considered at various time scales changing from 1 to 24 months for a more detailed drought characterization. On the other hand, so-called Dimensionless Precipitation Anomaly Index (DPAI) is introduced at annual scale in this study. The DPAI is used to determine dry periods from the recorded precipitation data. Cases are studied in Kucuk Menderes River Basin located in the Aegean region of Turkey. Precipitation and temperature data obtained from five meteorological stations over the river basin are used to determine drought index time series. Drought risk graphs and drought severity maps are obtained from time series of the drought indices. Drought risk is the likelihood of the drought occurrence that is quantified with the frequency calculated from the SPI and SPEI time series. As for the drought severity maps, they are created to understand its basin-scale variation by using the severities calculated from the dry periods of SPI and SPEI time series. Results show that the prolonged severe historical dry periods of the river basin are correctly identified by the drought indices. These indices used in this study based on easily available meteorological data are simple tools to explain temporal variability at a site or spatial variability over the basin. Also, the spatial distribution of drought severity over the river basin does not show a significant variability though more severe droughts are observed in the inner part of the river basin. Mild drought dominates at each time scale, this stems from the tendency of precipitation fluctuating around the average. Results in the study have considerable importance both in science and practice of drought. Although the methodology established from basic tools using meteorological data, the outcomes of the study are expected to become beneficial for drought management plans.</span></div>

Flash Drought Characteristics by Different Severities in Humid Subtropical Basins: A Case Study in the Gan River Basin, China

2021 ◽  
pp. 1-44
Author(s):  
Yuqing Zhang ◽  
Qinglong You ◽  
Guangxiong Mao ◽  
Changchun Chen ◽  
Xin Li ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Vapour Pressure Deficit ◽  
Severity Index ◽  
Drought Severity ◽  
Rapid Decline ◽  
Drought Risk ◽  
Drought Event ◽  
Drought Period ◽  
Drought Intensity

AbstractIt is essential to assess flash drought risk based on a reliable flash drought intensity (severity) index incorporating comprehensive information of the rapid decline (“flash”) in soil moisture towards drought conditions and soil moisture thresholds belonging to the “drought” category. In this study, we used the Gan River Basin as an example to define a flash drought intensity index that can be calculated for individual time steps (pentads) during a flash drought period over a given grid (or station). The severity of a complete flash drought event is the sum of the intensity values during the flash drought. We explored the spatial and temporal characteristics of flash droughts with different grades based on their respective severities. The results show that decreases in total cloud cover, precipitation, and relative humidity, as well as increases in 500 hPa geopotential height, convective inhibition, temperature, vapour pressure deficit, and wind speed can create favorable conditions for the occurrence of flash droughts. Although flash droughts are relatively frequent in the central and southern parts of the basin, the severity is relatively high in the northern part of the basin due to longer duration. Flash drought severity shows a slightly downward trend due to decreases in frequency, duration, and intensity from 1961 to 2018. Extreme and exceptional flash droughts decrease significantly while moderate and severe flash droughts trend slightly upward. Flash drought severity appears to be more affected by the interaction between duration and intensity as the grade increases from mild to severe. The frequency and duration of flash droughts are higher in July to October. The southern part of the basin is more prone to moderate and severe flash droughts, while the northern parts of the basin are more vulnerable to extreme and exceptional flash droughts due to longer durations and greater severities than other parts. Moderate, severe, extreme, and exceptional flash droughts occurred approximately every 3-6, 5-15, 10-50, and 30-200 year intervals, respectively, based on the copula analysis.

Drought Estimation-and-Projection Using Standardized Supply-Demand-Water Index and Artificial Neural Networks for Upper Tana River Basin in Kenya

2019 ◽  
Vol 10 (4) ◽  
pp. 11-27
Author(s):  
Raphael Muli Wambua
Keyword(s):  
Neural Networks ◽  
River Basin ◽  
Meteorological Data ◽  
Early Warning Systems ◽  
Kriging Interpolation ◽  
Drought Severity ◽  
Water Index ◽  
Tana River ◽  
Artificial Neural ◽  
Effects Of Drought

Drought occurrence, frequency and severity in the Upper Tana River basin (UTaRB) have critically affected water resource systems. To minimize the undesirable effects of drought, there is a need to quantify and project the drought trend. In this research, the drought was estimated and projected using Standardized Supply-Demand-Water Index (SSDI) and an Artificial Neural Network (ANN). Field meteorological data was used in which interpolated was conducted using kriging interpolation technique within ArcGIS environment. The results indicate those moderate, severe and extreme droughts at varying magnitudes as detected by the SSDI during 1972-2010 at different meteorological stations, with SSDI values equal or less than -2.0. In a spatial domain, the areas in south-eastern parts of the UTaRB exhibit the highest drought severity. Time-series forecasts and projection show that the best networks for SSDI exhibit respective ANNs architecture. The projected extreme droughts (values less than -2.00) and abundant water availability (SSDI values ≥ 2.00) were estimated using Recursive Multi-Step Neural Networks (RMSNN). The findings can be integrated into planning the drought-mitigation-adaptation and early-warning systems in the UTaRB.

scholarly journals Attribution Analysis of Hydrological Drought Risk Under Climate Change and Human Activities: A Case Study on Kuye River Basin in China

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1958 ◽  
Author(s):  
Zhang ◽  
Wang ◽  
Zhou
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Human Activities ◽  
Large Scale ◽  
Research Area ◽  
Drought Severity ◽  
Drought Risk ◽  
Drought Duration ◽  
Recurrence Period ◽  
The Impact

This study conducted quantitative diagnosis on the impact of climate change and human activities on drought risk. Taking the Kuye river basin (KRB) in China as the research area, we used variation point diagnosis, simulation of precipitation and runoff, drought risk assessment, and attribution quantification. The results show that: (1) the annual runoff sequence of KRB changed significantly after 1979, which was consistent with the introduction of large-scale coal mining; (2) under the same drought recurrence period, the drought duration and severity in the human activity stage were significantly worse than in the natural and simulation stages, indicating that human activities changed the drought risk in this area; and (3) human activities had little impact on drought severity in the short duration and low recurrence period, but had a greater impact in the long duration and high recurrence period. These results provide scientific guidance for the management, prevention, and resistance of drought; and guarantee sustainable economic and social development in the KRB.

Construction of Drought Indices from Passive Microwave Remote Sensing AMSR-E Data in Huaihe River Basin

2013 ◽  
Vol 397-400 ◽  
pp. 2503-2506
Author(s):  
Rui Wang ◽  
Jing Wen Xu ◽  
Dan Wang ◽  
Xing Mei Xie ◽  
Peng Wang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
River Basin ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Drought Indices ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Passive Microwave Remote Sensing ◽  
Spatio Temporal

On the basis of previous work, this paper aims to build several proper drought indices based on passive microwave remote sensing AMSR-E data in Huaihe River Basin. Compared with measured soil moisture, optimal drought indices have been selected to explore the spatio-temporal variation of drought conditions. The results indicate that there are satisfactory negative correlations between MPDIs (Microwave Polarization Index) and observed soil moisture. Moreover, MPDIs calculated by bands of 69GHz and 187GHz are much closer to variation trend of soil moisture than those obtained by other bands.

Drought Estimation-and-Projection Using Standardized Supply-Demand-Water Index and Artificial Neural Networks for Upper Tana River Basin in Kenya

2022 ◽  
pp. 1098-1117
Author(s):  
Raphael Muli Wambua
Keyword(s):  
Neural Networks ◽  
River Basin ◽  
Meteorological Data ◽  
Kriging Interpolation ◽  
Drought Severity ◽  
Water Index ◽  
Tana River ◽  
Artificial Neural ◽  
Effects Of Drought ◽  
Artificial Neural Network Ann

Drought occurrence, frequency and severity in the Upper Tana River basin (UTaRB) have critically affected water resource systems. To minimize the undesirable effects of drought, there is a need to quantify and project the drought trend. In this research, the drought was estimated and projected using Standardized Supply-Demand-Water Index (SSDI) and an Artificial Neural Network (ANN). Field meteorological data was used in which interpolated was conducted using kriging interpolation technique within ArcGIS environment. The results indicate those moderate, severe and extreme droughts at varying magnitudes as detected by the SSDI during 1972-2010 at different meteorological stations, with SSDI values equal or less than -2.0. In a spatial domain, the areas in south-eastern parts of the UTaRB exhibit the highest drought severity. Time-series forecasts and projection show that the best networks for SSDI exhibit respective ANNs architecture. The projected extreme droughts (values less than -2.00) and abundant water availability (SSDI values ³ 2.00) were estimated using Recursive Multi-Step Neural Networks (RMSNN). The findings can be integrated into planning the drought-mitigation-adaptation and early-warning systems in the UTaRB.

scholarly journals CMIP5 Models’ Ability to Capture Observed Trends under the Influence of Shifts and Persistence: An In-Depth Study on the Colorado River Basin

2019 ◽  
Vol 58 (8) ◽  
pp. 1677-1688 ◽  
Author(s):  
Kazi Ali Tamaddun ◽  
Ajay Kalra ◽  
Sanjiv Kumar ◽  
Sajjad Ahmad
Keyword(s):  
Time Series ◽  
River Basin ◽  
Colorado River ◽  
Simulation Models ◽  
Colorado River Basin ◽  
Cmip5 Models ◽  
Significant Shift ◽  
Precipitation Data ◽  
Grid Cells ◽  
Before And After

AbstractThis study evaluated the ability of phase 5 of the Coupled Model Intercomparison Project (CMIP5) to capture observed trends under the influence of shifts and persistence in their data distributions. A total of 41 temperature and 25 precipitation CMIP5 simulation models across 22 grid cells (2.5° × 2.5° squares) within the Colorado River basin were analyzed and compared with the Climate Research Unit Time Series (CRU-TS) observed datasets over a study period of 104 years (from 1901 to 2004). Both the modeled simulations and observations were tested for shifts, and the time series before and after the shifts were analyzed separately for trend detection and quantification. Effects of several types of persistence were accounted for prior to both the trend and shift detection tests. The mean significant shift points (SPs) of the CMIP5 temperature models across the grid cells were found to be within a narrower range (between 1957 and 1968) relative to the CRU-TS observed SPs (between 1924 and 1985). Precipitation time series, especially the CRU-TS dataset, had a lack of significant SPs, which led to an inconsistency between the models and observations since the number of grid cells with a significant SP was not comparable. The CMIP5 temperature trends, under the influence of shifts and persistence, were able to match the observed trends very satisfactorily (within the same order of magnitude and consistent direction). Unlike the temperature models, the CMIP5 precipitation models detected SPs that were earlier than the observed SPs found in the CRU-TS data. The direction (as well as the magnitude) of trends, before and after significant shifts, was found to be inconsistent between the modeled simulations and observed precipitation data. Shifts, based on their direction, were found either to strengthen or to neutralize the preexisting trends in both the model simulations and the observations. The results also suggest that the temperature and precipitation data distributions were sensitive to different types of persistence—such sensitivity was found to be consistent between the modeled and observed datasets. The study detected certain biases in the CMIP5 models in detecting the SPs (tendency of detecting shifts earlier for precipitation and later for temperature than the observed shifts) and also in quantifying the trends (overestimating the trend slopes)—such insights may be helpful in evaluating the efficacy of the simulation models in capturing observed trends under uncertainties and natural variabilities.

scholarly journals Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

2019 ◽  
Vol 11 (23) ◽  
pp. 2742 ◽  
Author(s):  
Tran ◽  
Tran ◽  
Myint ◽  
Latorre-Carmona ◽  
Ho ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Drought Index ◽  
Negative Impact ◽  
Statistical Significance ◽  
Standardized Precipitation Index ◽  
Agricultural Drought ◽  
Series Data ◽  
Drought Indices ◽  
Drought Severity

Drought is a major natural disaster that creates a negative impact on socio-economic development and environment. Drought indices are typically applied to characterize drought events in a meaningful way. This study aims at examining variations in agricultural drought severity based on the relationship between standardized ratio of actual and potential evapotranspiration (ET and PET), enhanced vegetation index (EVI), and land surface temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A new drought index, called the enhanced drought severity index (EDSI), was developed by applying spatiotemporal regression methods and time-series biophysical data derived from remote sensing. In addition, time-series trend analysis in the 2001–2018 period, along with the Mann–Kendal (MK) significance test and the Theil Sen (TS) slope, were used to examine the spatiotemporal dynamics of environmental parameters (i.e., LST, EVI, ET, and PET), and geographically weighted regression (GWR) was subsequently applied in order to analyze the local correlations among them. Results showed that a significant correlation was discovered among LST, EVI, ET, and PET, as well as their standardized ratios (|r| > 0.8, p < 0.01). Additionally, a high performance of the new developed drought index, showing a strong correlation between EDSI and meteorological drought indices (i.e., standardized precipitation index (SPI) or the reconnaissance drought index (RDI)), measured at meteorological stations, giving r > 0.7 and a statistical significance p < 0.01. Besides, it was found that the temporal tendency of this phenomenon was the increase in intensity of drought, and that coastal areas in the study area were more vulnerable to this phenomenon. This study demonstrates the effectiveness of EDSI and the potential application of integrating spatial regression and time-series data for assessing regional drought conditions.

Fingerprint-Based Attribution Study of Climatic Changes in the Hai River Basin of China

2012 ◽  
Vol 518-523 ◽  
pp. 5798-5804
Author(s):  
Xiang Yi Ding ◽  
Yang Wen Jia
Keyword(s):  
River Basin ◽  
Meteorological Data ◽  
Moving Average ◽  
Sudden Change ◽  
Temporal Variations ◽  
Climatic Changes ◽  
Climatic Conditions ◽  
Anthropogenic Forcing ◽  
The Past ◽  
Precipitation And Temperature

Many observational facts and studies have shown that the climatic conditions in the Hai River Basin, which is the political and cultural centre of China, changed significantly over last half of the 20th century. This study attempts to evaluate the variability of climatic elements such as precipitation and temperature in the basin based on observed meteorological data, and the temporal variations and sudden changes of precipitation and temperature during past 40 years (1961-2000) are analyzed combining moving-average and linear regression with Mann-Kendall method. In addition, the observed climatic changes are attributed to different factors including natural variability and anthropogenic forcing using the fingerprint-based attribution method. The results indicate that: 1) during 1961-2000, the precipitation slightly decreased and the estimated sudden change time was 1965, the temperature significantly increased and the estimated sudden change time was 1964; 2) natural climate variability may be the factors responsible for the observed precipitation changes during the past 40 years in the basin, while anthropogenic forcing may be the main factors responsible for the observed temperature changes during the past 40 years in the basin.

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