Historical and future Palmer Drought Severity Index with improved hydrological modeling

Wang, Zhengrong; Yang, Yuting; Zhang, Cicheng; Guo, Hui; Hou, Ying

doi:10.1016/j.jhydrol.2022.127941

Cited by 30 publications

(8 citation statements)

References 72 publications

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“…Where the LST i is the smoothed 8-day land surface temperature, LST max and LST min defined as maximum and minimum LST i value in multiyear, and i is based on the monthly scales. The TCI i for each pixel and period is calculated using (18) [33], [43], [44], [45]…”

Section: Methodsmentioning

confidence: 99%

“…For more information, see https://creativecommons.org/licenses/by/4.0/ Traditional procedures for monitoring drought conditions are based on ground measurements of hydro-climatic variables, including rainfall, temperature, relative humidity, and soil water content. For instance, the palmer drought severity index (PDSI) utilizes rainfall and temperature, the standardized precipitation index (SPI) uses rainfall regimes, crop moisture index integrates SM, rainfall, and temperature, standardized anomaly index uses rainfall variability, and soil moisture drought index incorporates weekly SM and evapotranspiration values [17], [18], [19], [20], [21]. However, the number and uneven spatial distribution of available stations across the landscape impact the reliability of such interpolation.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Spatiotemporal Characteristic of Droughts Using In Situ and Remote Sensing-Based Drought Indices

Jalayer

Sharifi

Abbasi‐Moghadam

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Drought has been identified as one of the significant complicated natural disasters exacerbated by land degradation and climate change. Hence, monitoring drought and evaluating its spatiotemporal dynamics are essential to manage regional drought conditions and protecting the natural environment. In this study, various single remote sensing-based drought indices including soil moisture condition index (SMCI), precipitation condition index (PCI), temperature condition index (TCI), and vegetation condition index (VCI) and combined RS-based drought Indices including optimized meteorological drought index (OMDI) and synthesized drought index (SDI) have been used to investigate the spatiotemporal variations of meteorological and agricultural droughts between 2000 and 2021 in Iran. The in situ drought indices, including the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) series of 1, 3, 6, 12, and 24 months were utilized to verify remote sensing-based drought indices and evaluate their applicability for analyzing drought conditions. The results indicated that the correlation coefficients of the in situ drought indices with the combined drought indices are higher than the RS-based single drought indexes. Generally, single-factor drought indexes, including VCI, TCI, PCI, and SMCI, have specific characteristics. The PCI and SMCI have an acceptable correlation with the short-term SPI and SPEI and are more applicable to monitoring short-term drought conditions. Further, the TCI has better performance in monitoring long-term drought conditions in Iran. This research concluded that the central, eastern, and southeastern parts of Iran mainly were experiencing exceptional and extreme drought conditions as the worst agricultural and meteorological drought conditions observed in the years 2008 and 2021 in the region during the last 20 years. The results also showed that, in 2019 and 2020, most areas of Iran had higher OMDI and

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of Spatiotemporal Characteristic of Droughts Using In Situ and Remote Sensing-Based Drought Indices

Jalayer

Sharifi

Abbasi‐Moghadam

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Currently, the most commonly used meteorological drought indicators include the Palmer drought severity index (PDSI), which is a drought index that is calculated based on the relationship between water supply and demand; the standardized precipitation index (SPI), which is capable of objectively comparing precipitation levels between different geographical regions and time periods; and the standardized evapotranspiration index (SPEI), which characterizes wet and dry conditions calculated using precipitation and temperature data. Although the PDSI is commonly used to monitor and predict drought conditions in certain regions by factoring in various water balance components, there are certain regional limitations in its application (Faiz et al, 2022;Wang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Prediction and assessment of meteorological drought characteristics in China based on a future climate model

Huang,

Liu,

Jia

et al. 2023

Preprint

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Under the background of a warm and humid climate, predicting and evaluating the future pattern of wet and dry change characteristics in watersheds is of great research significance. Based on the Geophysical Fluid Dynamics Laboratory earth system coupling model and GFDL‒ESM2M climate model, which is highly applicable in China, the characteristics of standardized precipitation evapotranspiration index (SPEI) variations in China at annual and monthly scales was assessed based on downscaled climate data under four typical representative concentration pathway scenarios. The results are as follows: (1) In the future, the annual scale meteorological drought trend in China will become more "aridity", and the monthly scale dry and wet changes will be non-uniform. With the increase of carbon dioxide emission concentration, the proportion of regional aridity in China will be increased in the future. (2) The proportion of wetting area will decrease, and extreme weather events are more likely to occur. In the future, the drought duration in China will mainly be 1–4 months. (3) The return period of drought in large areas of the country will be 1–50 years, and the return period in some western regions even exceed 500 years.

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“…Drought indices are calculated using databases of precipitation, soil moisture, crop yield, flows, and surface water and groundwater levels. Previous studies used Standardized Precipitation Evapotranspiration Index SPEI [ 10 ], Palmer Drought Severity Index PDSI [ 11 ], Modified Palmer Drought Severity Index MPDSI [ 12 ], Standardized Precipitation Requirement Index SPRI [ 13 ], Surface Water Supply Index SWSI [ 14 ], Standardized Soil Moisture Index SSMI [ 15 ], Standardized Runoff Index SRI [ 16 ], Standardized Water Supply and Demand Index SWSDI [ 17 ], Normalized Difference Vegetation Index NDVI [ 18 ] and Streamflow Drought Index SDI [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty in Determination of Meteorological Drought Zones Based on Standardized Precipitation Index in the Territory of Poland

Wicher-Dysarz

Dysarz

Jaskuła

2022

IJERPH

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The primary aim of this work is to assess the accuracy of the methods for spatial interpolation applied for the reconstruction of the spatial distribution of the Standardized Precipitation Index (SPI). The one-month version called SPI-1 is chosen for this purpose due to the known greatest variability of this index in comparison with its other versions. The analysis has been made for the territory of the entire country of Poland. At the same time the uncertainty related to the application of such computational procedures is determined based on qualitative and quantitative measures. The public data of two kinds are applied: (1) measurements of precipitation and (2) the locations of the meteorological stations in Poland. The analysis has been made for the period 1990–2020. However, all available observations since 1950 have been implemented. The number of available meteorological stations has decreased over the analyzed period. In January 1990 there were over one thousand stations making observations. In the end of the period of the study, the number of stations was below six hundred. Obviously, the temporal scarcity of data had an impact on the obtained results. The main tools applied were ArcGIS supported with Python scripting, including generally used modules and procedures dedicated to geoprocessing. Such an approach appeared crucial for the effective processing of the large number of data available. It also guaranteed the accuracy of the produced results and brought about drought maps based on SPI-1. The methods tested included: Inverse Distance Weighted, Natural Neighbor, Linear, Kriging, and Spline. The presented results prove that all the procedures are inaccurate and uncertain, but some of them provide satisfactory results. The worst method seems to be the interpolation based on Spline functions. The practical aspects related to the implementation of the methods led to removal of the Linear and Kriging interpolations from further use. Hence, Inverse Distance Weighted, as well as Natural Neighbor, seem to be well suited for this problem.

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Historical and future Palmer Drought Severity Index with improved hydrological modeling

Cited by 30 publications

References 72 publications

Assessment of Spatiotemporal Characteristic of Droughts Using In Situ and Remote Sensing-Based Drought Indices

Assessment of Spatiotemporal Characteristic of Droughts Using In Situ and Remote Sensing-Based Drought Indices

Prediction and assessment of meteorological drought characteristics in China based on a future climate model

Uncertainty in Determination of Meteorological Drought Zones Based on Standardized Precipitation Index in the Territory of Poland

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