Changes in Meteorological Dry Conditions across Water Management Zones in Uganda
Abstract This study analysed trends and variability in extreme precipitation and potential evapotranspiration (PET) indices across Uganda. We made use of daily precipitation and temperature datasets from 1979 to 2013 obtained from the Climate Forecast System Reanalysis (CFSR) products of the Nationa...
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| Autor*in: |
Onyutha, Charles [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© Korean Society of Civil Engineers 2022 |
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| Übergeordnetes Werk: |
Enthalten in: KSCE journal of civil engineering - Seoul : Korean Soc. of Civil Engineers, 1997, 26(2022), 12 vom: 07. Okt., Seite 5384-5403 |
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| Übergeordnetes Werk: |
volume:26 ; year:2022 ; number:12 ; day:07 ; month:10 ; pages:5384-5403 |
| Links: |
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| DOI / URN: |
10.1007/s12205-022-0122-5 |
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SPR051137879 |
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| 520 | |a Abstract This study analysed trends and variability in extreme precipitation and potential evapotranspiration (PET) indices across Uganda. We made use of daily precipitation and temperature datasets from 1979 to 2013 obtained from the Climate Forecast System Reanalysis (CFSR) products of the National Centres for Environmental Prediction (NCEP). The PET was estimated using the Hargreaves method based on the minimum and maximum temperature. Analysis was focused on the level of Water Management Zones (WMZs). Examples of the extracted extreme climatic indices included the annual maximum number of consecutive days with precipitation intensity < 1 mm/day (CDD1) or < 5 mm/day (CDD5), annual sum of PET for days with PET rate > 5 mm/day (SPETD5), and spell of high evaporative demand (or annual maximum number of consecutive days with PET > 5 mm/day) (CDPET5). Attributes of the variability in meteorological dry conditions were investigated. Trend and variability were investigated using an approach based on the cumulative sum of difference (CSD) between exceedance and non-exceedance counts of data points. The magnitudes of the linear decrease or increase in the precipitation and PET indices were determined using Sen’s method. To apply these methods, the CSD-based Sub (Trend) and Variability Analysis Tool (CSD-VAT) was used. The long-term mean of the extracted indices showed Kyoga and Victoria to be the driest and wettest WMZs, respectively. An increase in the number of dry days (indicating increasing severity of meteorological dry conditions) was confined to the Karamoja region or the eastern parts of the Upper Nile and Kyoga WMZs. However, the rest of the country generally experienced negative trends in the precipitation and PET indices. Extreme precipitation indices of Kyoga and the Upper Nile WMZs exhibited consecutive positive and negative sub-trends over the early 1980s and from 1985 to 2013, respectively. Victoria and Albert WMZs mainly exhibited negative sub-trends for both precipitation and PET indices especially from the mid-1980s to the end of the data. Across Victoria and Albert WMZs, we found that the variability in extreme precipitation indices such as CDD1 and CDD5 was significant (p<0.05). However, these WMZs were characterized by insignificant (p>0.05) variability of the PET indices such as SPETD5 and CDPET5. Changes in the precipitation and PET indices were significantly (p<0.05) found to be positively linked to the quasi-biennial oscillation. The meteorological drought indicators were significantly (p<0.05) negatively correlated with the Indian Ocean dipole and Atlantic multidecadal oscillation. Our findings based on NCEP’s CFSR data show that variation in sub-trends of the meteorological dry conditions across Uganda resonates well with the changes in some indicators of large-scale ocean-atmosphere conditions. This is important for planning predictive adaptation to the impacts of climate variability on water resources applications which are sensitive to the severity of meteorological dry conditions. Apart from our findings, we introduced a number of extreme PET indices which can be used for analysis of meteorological dry conditions or drought. | ||
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10.1007/s12205-022-0122-5 doi (DE-627)SPR051137879 (SPR)s12205-022-0122-5-e DE-627 ger DE-627 rakwb eng Onyutha, Charles verfasserin (orcid)0000-0002-0652-3828 aut Changes in Meteorological Dry Conditions across Water Management Zones in Uganda 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2022 Abstract This study analysed trends and variability in extreme precipitation and potential evapotranspiration (PET) indices across Uganda. We made use of daily precipitation and temperature datasets from 1979 to 2013 obtained from the Climate Forecast System Reanalysis (CFSR) products of the National Centres for Environmental Prediction (NCEP). The PET was estimated using the Hargreaves method based on the minimum and maximum temperature. Analysis was focused on the level of Water Management Zones (WMZs). Examples of the extracted extreme climatic indices included the annual maximum number of consecutive days with precipitation intensity < 1 mm/day (CDD1) or < 5 mm/day (CDD5), annual sum of PET for days with PET rate > 5 mm/day (SPETD5), and spell of high evaporative demand (or annual maximum number of consecutive days with PET > 5 mm/day) (CDPET5). Attributes of the variability in meteorological dry conditions were investigated. Trend and variability were investigated using an approach based on the cumulative sum of difference (CSD) between exceedance and non-exceedance counts of data points. The magnitudes of the linear decrease or increase in the precipitation and PET indices were determined using Sen’s method. To apply these methods, the CSD-based Sub (Trend) and Variability Analysis Tool (CSD-VAT) was used. The long-term mean of the extracted indices showed Kyoga and Victoria to be the driest and wettest WMZs, respectively. An increase in the number of dry days (indicating increasing severity of meteorological dry conditions) was confined to the Karamoja region or the eastern parts of the Upper Nile and Kyoga WMZs. However, the rest of the country generally experienced negative trends in the precipitation and PET indices. Extreme precipitation indices of Kyoga and the Upper Nile WMZs exhibited consecutive positive and negative sub-trends over the early 1980s and from 1985 to 2013, respectively. Victoria and Albert WMZs mainly exhibited negative sub-trends for both precipitation and PET indices especially from the mid-1980s to the end of the data. Across Victoria and Albert WMZs, we found that the variability in extreme precipitation indices such as CDD1 and CDD5 was significant (p<0.05). However, these WMZs were characterized by insignificant (p>0.05) variability of the PET indices such as SPETD5 and CDPET5. Changes in the precipitation and PET indices were significantly (p<0.05) found to be positively linked to the quasi-biennial oscillation. The meteorological drought indicators were significantly (p<0.05) negatively correlated with the Indian Ocean dipole and Atlantic multidecadal oscillation. Our findings based on NCEP’s CFSR data show that variation in sub-trends of the meteorological dry conditions across Uganda resonates well with the changes in some indicators of large-scale ocean-atmosphere conditions. This is important for planning predictive adaptation to the impacts of climate variability on water resources applications which are sensitive to the severity of meteorological dry conditions. Apart from our findings, we introduced a number of extreme PET indices which can be used for analysis of meteorological dry conditions or drought. Trend analyses (dpeaa)DE-He213 Meteorological drought (dpeaa)DE-He213 Extreme precipitation indices (dpeaa)DE-He213 Potential evapotranspiration indices (dpeaa)DE-He213 Water scarcity (dpeaa)DE-He213 Uganda (dpeaa)DE-He213 Kerudong, Paskwale Acayerach aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 26(2022), 12 vom: 07. Okt., Seite 5384-5403 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:26 year:2022 number:12 day:07 month:10 pages:5384-5403 https://dx.doi.org/10.1007/s12205-022-0122-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2022 12 07 10 5384-5403 |
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10.1007/s12205-022-0122-5 doi (DE-627)SPR051137879 (SPR)s12205-022-0122-5-e DE-627 ger DE-627 rakwb eng Onyutha, Charles verfasserin (orcid)0000-0002-0652-3828 aut Changes in Meteorological Dry Conditions across Water Management Zones in Uganda 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2022 Abstract This study analysed trends and variability in extreme precipitation and potential evapotranspiration (PET) indices across Uganda. We made use of daily precipitation and temperature datasets from 1979 to 2013 obtained from the Climate Forecast System Reanalysis (CFSR) products of the National Centres for Environmental Prediction (NCEP). The PET was estimated using the Hargreaves method based on the minimum and maximum temperature. Analysis was focused on the level of Water Management Zones (WMZs). Examples of the extracted extreme climatic indices included the annual maximum number of consecutive days with precipitation intensity < 1 mm/day (CDD1) or < 5 mm/day (CDD5), annual sum of PET for days with PET rate > 5 mm/day (SPETD5), and spell of high evaporative demand (or annual maximum number of consecutive days with PET > 5 mm/day) (CDPET5). Attributes of the variability in meteorological dry conditions were investigated. Trend and variability were investigated using an approach based on the cumulative sum of difference (CSD) between exceedance and non-exceedance counts of data points. The magnitudes of the linear decrease or increase in the precipitation and PET indices were determined using Sen’s method. To apply these methods, the CSD-based Sub (Trend) and Variability Analysis Tool (CSD-VAT) was used. The long-term mean of the extracted indices showed Kyoga and Victoria to be the driest and wettest WMZs, respectively. An increase in the number of dry days (indicating increasing severity of meteorological dry conditions) was confined to the Karamoja region or the eastern parts of the Upper Nile and Kyoga WMZs. However, the rest of the country generally experienced negative trends in the precipitation and PET indices. Extreme precipitation indices of Kyoga and the Upper Nile WMZs exhibited consecutive positive and negative sub-trends over the early 1980s and from 1985 to 2013, respectively. Victoria and Albert WMZs mainly exhibited negative sub-trends for both precipitation and PET indices especially from the mid-1980s to the end of the data. Across Victoria and Albert WMZs, we found that the variability in extreme precipitation indices such as CDD1 and CDD5 was significant (p<0.05). However, these WMZs were characterized by insignificant (p>0.05) variability of the PET indices such as SPETD5 and CDPET5. Changes in the precipitation and PET indices were significantly (p<0.05) found to be positively linked to the quasi-biennial oscillation. The meteorological drought indicators were significantly (p<0.05) negatively correlated with the Indian Ocean dipole and Atlantic multidecadal oscillation. Our findings based on NCEP’s CFSR data show that variation in sub-trends of the meteorological dry conditions across Uganda resonates well with the changes in some indicators of large-scale ocean-atmosphere conditions. This is important for planning predictive adaptation to the impacts of climate variability on water resources applications which are sensitive to the severity of meteorological dry conditions. Apart from our findings, we introduced a number of extreme PET indices which can be used for analysis of meteorological dry conditions or drought. Trend analyses (dpeaa)DE-He213 Meteorological drought (dpeaa)DE-He213 Extreme precipitation indices (dpeaa)DE-He213 Potential evapotranspiration indices (dpeaa)DE-He213 Water scarcity (dpeaa)DE-He213 Uganda (dpeaa)DE-He213 Kerudong, Paskwale Acayerach aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 26(2022), 12 vom: 07. Okt., Seite 5384-5403 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:26 year:2022 number:12 day:07 month:10 pages:5384-5403 https://dx.doi.org/10.1007/s12205-022-0122-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2022 12 07 10 5384-5403 |
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10.1007/s12205-022-0122-5 doi (DE-627)SPR051137879 (SPR)s12205-022-0122-5-e DE-627 ger DE-627 rakwb eng Onyutha, Charles verfasserin (orcid)0000-0002-0652-3828 aut Changes in Meteorological Dry Conditions across Water Management Zones in Uganda 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2022 Abstract This study analysed trends and variability in extreme precipitation and potential evapotranspiration (PET) indices across Uganda. We made use of daily precipitation and temperature datasets from 1979 to 2013 obtained from the Climate Forecast System Reanalysis (CFSR) products of the National Centres for Environmental Prediction (NCEP). The PET was estimated using the Hargreaves method based on the minimum and maximum temperature. Analysis was focused on the level of Water Management Zones (WMZs). Examples of the extracted extreme climatic indices included the annual maximum number of consecutive days with precipitation intensity < 1 mm/day (CDD1) or < 5 mm/day (CDD5), annual sum of PET for days with PET rate > 5 mm/day (SPETD5), and spell of high evaporative demand (or annual maximum number of consecutive days with PET > 5 mm/day) (CDPET5). Attributes of the variability in meteorological dry conditions were investigated. Trend and variability were investigated using an approach based on the cumulative sum of difference (CSD) between exceedance and non-exceedance counts of data points. The magnitudes of the linear decrease or increase in the precipitation and PET indices were determined using Sen’s method. To apply these methods, the CSD-based Sub (Trend) and Variability Analysis Tool (CSD-VAT) was used. The long-term mean of the extracted indices showed Kyoga and Victoria to be the driest and wettest WMZs, respectively. An increase in the number of dry days (indicating increasing severity of meteorological dry conditions) was confined to the Karamoja region or the eastern parts of the Upper Nile and Kyoga WMZs. However, the rest of the country generally experienced negative trends in the precipitation and PET indices. Extreme precipitation indices of Kyoga and the Upper Nile WMZs exhibited consecutive positive and negative sub-trends over the early 1980s and from 1985 to 2013, respectively. Victoria and Albert WMZs mainly exhibited negative sub-trends for both precipitation and PET indices especially from the mid-1980s to the end of the data. Across Victoria and Albert WMZs, we found that the variability in extreme precipitation indices such as CDD1 and CDD5 was significant (p<0.05). However, these WMZs were characterized by insignificant (p>0.05) variability of the PET indices such as SPETD5 and CDPET5. Changes in the precipitation and PET indices were significantly (p<0.05) found to be positively linked to the quasi-biennial oscillation. The meteorological drought indicators were significantly (p<0.05) negatively correlated with the Indian Ocean dipole and Atlantic multidecadal oscillation. Our findings based on NCEP’s CFSR data show that variation in sub-trends of the meteorological dry conditions across Uganda resonates well with the changes in some indicators of large-scale ocean-atmosphere conditions. This is important for planning predictive adaptation to the impacts of climate variability on water resources applications which are sensitive to the severity of meteorological dry conditions. Apart from our findings, we introduced a number of extreme PET indices which can be used for analysis of meteorological dry conditions or drought. Trend analyses (dpeaa)DE-He213 Meteorological drought (dpeaa)DE-He213 Extreme precipitation indices (dpeaa)DE-He213 Potential evapotranspiration indices (dpeaa)DE-He213 Water scarcity (dpeaa)DE-He213 Uganda (dpeaa)DE-He213 Kerudong, Paskwale Acayerach aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 26(2022), 12 vom: 07. Okt., Seite 5384-5403 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:26 year:2022 number:12 day:07 month:10 pages:5384-5403 https://dx.doi.org/10.1007/s12205-022-0122-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2022 12 07 10 5384-5403 |
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10.1007/s12205-022-0122-5 doi (DE-627)SPR051137879 (SPR)s12205-022-0122-5-e DE-627 ger DE-627 rakwb eng Onyutha, Charles verfasserin (orcid)0000-0002-0652-3828 aut Changes in Meteorological Dry Conditions across Water Management Zones in Uganda 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2022 Abstract This study analysed trends and variability in extreme precipitation and potential evapotranspiration (PET) indices across Uganda. We made use of daily precipitation and temperature datasets from 1979 to 2013 obtained from the Climate Forecast System Reanalysis (CFSR) products of the National Centres for Environmental Prediction (NCEP). The PET was estimated using the Hargreaves method based on the minimum and maximum temperature. Analysis was focused on the level of Water Management Zones (WMZs). Examples of the extracted extreme climatic indices included the annual maximum number of consecutive days with precipitation intensity < 1 mm/day (CDD1) or < 5 mm/day (CDD5), annual sum of PET for days with PET rate > 5 mm/day (SPETD5), and spell of high evaporative demand (or annual maximum number of consecutive days with PET > 5 mm/day) (CDPET5). Attributes of the variability in meteorological dry conditions were investigated. Trend and variability were investigated using an approach based on the cumulative sum of difference (CSD) between exceedance and non-exceedance counts of data points. The magnitudes of the linear decrease or increase in the precipitation and PET indices were determined using Sen’s method. To apply these methods, the CSD-based Sub (Trend) and Variability Analysis Tool (CSD-VAT) was used. The long-term mean of the extracted indices showed Kyoga and Victoria to be the driest and wettest WMZs, respectively. An increase in the number of dry days (indicating increasing severity of meteorological dry conditions) was confined to the Karamoja region or the eastern parts of the Upper Nile and Kyoga WMZs. However, the rest of the country generally experienced negative trends in the precipitation and PET indices. Extreme precipitation indices of Kyoga and the Upper Nile WMZs exhibited consecutive positive and negative sub-trends over the early 1980s and from 1985 to 2013, respectively. Victoria and Albert WMZs mainly exhibited negative sub-trends for both precipitation and PET indices especially from the mid-1980s to the end of the data. Across Victoria and Albert WMZs, we found that the variability in extreme precipitation indices such as CDD1 and CDD5 was significant (p<0.05). However, these WMZs were characterized by insignificant (p>0.05) variability of the PET indices such as SPETD5 and CDPET5. Changes in the precipitation and PET indices were significantly (p<0.05) found to be positively linked to the quasi-biennial oscillation. The meteorological drought indicators were significantly (p<0.05) negatively correlated with the Indian Ocean dipole and Atlantic multidecadal oscillation. Our findings based on NCEP’s CFSR data show that variation in sub-trends of the meteorological dry conditions across Uganda resonates well with the changes in some indicators of large-scale ocean-atmosphere conditions. This is important for planning predictive adaptation to the impacts of climate variability on water resources applications which are sensitive to the severity of meteorological dry conditions. Apart from our findings, we introduced a number of extreme PET indices which can be used for analysis of meteorological dry conditions or drought. Trend analyses (dpeaa)DE-He213 Meteorological drought (dpeaa)DE-He213 Extreme precipitation indices (dpeaa)DE-He213 Potential evapotranspiration indices (dpeaa)DE-He213 Water scarcity (dpeaa)DE-He213 Uganda (dpeaa)DE-He213 Kerudong, Paskwale Acayerach aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 26(2022), 12 vom: 07. Okt., Seite 5384-5403 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:26 year:2022 number:12 day:07 month:10 pages:5384-5403 https://dx.doi.org/10.1007/s12205-022-0122-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2022 12 07 10 5384-5403 |
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10.1007/s12205-022-0122-5 doi (DE-627)SPR051137879 (SPR)s12205-022-0122-5-e DE-627 ger DE-627 rakwb eng Onyutha, Charles verfasserin (orcid)0000-0002-0652-3828 aut Changes in Meteorological Dry Conditions across Water Management Zones in Uganda 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2022 Abstract This study analysed trends and variability in extreme precipitation and potential evapotranspiration (PET) indices across Uganda. We made use of daily precipitation and temperature datasets from 1979 to 2013 obtained from the Climate Forecast System Reanalysis (CFSR) products of the National Centres for Environmental Prediction (NCEP). The PET was estimated using the Hargreaves method based on the minimum and maximum temperature. Analysis was focused on the level of Water Management Zones (WMZs). Examples of the extracted extreme climatic indices included the annual maximum number of consecutive days with precipitation intensity < 1 mm/day (CDD1) or < 5 mm/day (CDD5), annual sum of PET for days with PET rate > 5 mm/day (SPETD5), and spell of high evaporative demand (or annual maximum number of consecutive days with PET > 5 mm/day) (CDPET5). Attributes of the variability in meteorological dry conditions were investigated. Trend and variability were investigated using an approach based on the cumulative sum of difference (CSD) between exceedance and non-exceedance counts of data points. The magnitudes of the linear decrease or increase in the precipitation and PET indices were determined using Sen’s method. To apply these methods, the CSD-based Sub (Trend) and Variability Analysis Tool (CSD-VAT) was used. The long-term mean of the extracted indices showed Kyoga and Victoria to be the driest and wettest WMZs, respectively. An increase in the number of dry days (indicating increasing severity of meteorological dry conditions) was confined to the Karamoja region or the eastern parts of the Upper Nile and Kyoga WMZs. However, the rest of the country generally experienced negative trends in the precipitation and PET indices. Extreme precipitation indices of Kyoga and the Upper Nile WMZs exhibited consecutive positive and negative sub-trends over the early 1980s and from 1985 to 2013, respectively. Victoria and Albert WMZs mainly exhibited negative sub-trends for both precipitation and PET indices especially from the mid-1980s to the end of the data. Across Victoria and Albert WMZs, we found that the variability in extreme precipitation indices such as CDD1 and CDD5 was significant (p<0.05). However, these WMZs were characterized by insignificant (p>0.05) variability of the PET indices such as SPETD5 and CDPET5. Changes in the precipitation and PET indices were significantly (p<0.05) found to be positively linked to the quasi-biennial oscillation. The meteorological drought indicators were significantly (p<0.05) negatively correlated with the Indian Ocean dipole and Atlantic multidecadal oscillation. Our findings based on NCEP’s CFSR data show that variation in sub-trends of the meteorological dry conditions across Uganda resonates well with the changes in some indicators of large-scale ocean-atmosphere conditions. This is important for planning predictive adaptation to the impacts of climate variability on water resources applications which are sensitive to the severity of meteorological dry conditions. Apart from our findings, we introduced a number of extreme PET indices which can be used for analysis of meteorological dry conditions or drought. Trend analyses (dpeaa)DE-He213 Meteorological drought (dpeaa)DE-He213 Extreme precipitation indices (dpeaa)DE-He213 Potential evapotranspiration indices (dpeaa)DE-He213 Water scarcity (dpeaa)DE-He213 Uganda (dpeaa)DE-He213 Kerudong, Paskwale Acayerach aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 26(2022), 12 vom: 07. Okt., Seite 5384-5403 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:26 year:2022 number:12 day:07 month:10 pages:5384-5403 https://dx.doi.org/10.1007/s12205-022-0122-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2022 12 07 10 5384-5403 |
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Enthalten in KSCE journal of civil engineering 26(2022), 12 vom: 07. Okt., Seite 5384-5403 volume:26 year:2022 number:12 day:07 month:10 pages:5384-5403 |
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We made use of daily precipitation and temperature datasets from 1979 to 2013 obtained from the Climate Forecast System Reanalysis (CFSR) products of the National Centres for Environmental Prediction (NCEP). The PET was estimated using the Hargreaves method based on the minimum and maximum temperature. Analysis was focused on the level of Water Management Zones (WMZs). Examples of the extracted extreme climatic indices included the annual maximum number of consecutive days with precipitation intensity < 1 mm/day (CDD1) or < 5 mm/day (CDD5), annual sum of PET for days with PET rate > 5 mm/day (SPETD5), and spell of high evaporative demand (or annual maximum number of consecutive days with PET > 5 mm/day) (CDPET5). Attributes of the variability in meteorological dry conditions were investigated. Trend and variability were investigated using an approach based on the cumulative sum of difference (CSD) between exceedance and non-exceedance counts of data points. The magnitudes of the linear decrease or increase in the precipitation and PET indices were determined using Sen’s method. To apply these methods, the CSD-based Sub (Trend) and Variability Analysis Tool (CSD-VAT) was used. The long-term mean of the extracted indices showed Kyoga and Victoria to be the driest and wettest WMZs, respectively. An increase in the number of dry days (indicating increasing severity of meteorological dry conditions) was confined to the Karamoja region or the eastern parts of the Upper Nile and Kyoga WMZs. However, the rest of the country generally experienced negative trends in the precipitation and PET indices. Extreme precipitation indices of Kyoga and the Upper Nile WMZs exhibited consecutive positive and negative sub-trends over the early 1980s and from 1985 to 2013, respectively. Victoria and Albert WMZs mainly exhibited negative sub-trends for both precipitation and PET indices especially from the mid-1980s to the end of the data. Across Victoria and Albert WMZs, we found that the variability in extreme precipitation indices such as CDD1 and CDD5 was significant (p<0.05). However, these WMZs were characterized by insignificant (p>0.05) variability of the PET indices such as SPETD5 and CDPET5. Changes in the precipitation and PET indices were significantly (p<0.05) found to be positively linked to the quasi-biennial oscillation. The meteorological drought indicators were significantly (p<0.05) negatively correlated with the Indian Ocean dipole and Atlantic multidecadal oscillation. Our findings based on NCEP’s CFSR data show that variation in sub-trends of the meteorological dry conditions across Uganda resonates well with the changes in some indicators of large-scale ocean-atmosphere conditions. This is important for planning predictive adaptation to the impacts of climate variability on water resources applications which are sensitive to the severity of meteorological dry conditions. Apart from our findings, we introduced a number of extreme PET indices which can be used for analysis of meteorological dry conditions or drought.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Trend analyses</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Meteorological drought</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Extreme precipitation indices</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Potential evapotranspiration indices</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Water scarcity</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Uganda</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kerudong, Paskwale Acayerach</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">KSCE journal of civil engineering</subfield><subfield code="d">Seoul : Korean Soc. of Civil Engineers, 1997</subfield><subfield code="g">26(2022), 12 vom: 07. 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|
| author |
Onyutha, Charles |
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Onyutha, Charles misc Trend analyses misc Meteorological drought misc Extreme precipitation indices misc Potential evapotranspiration indices misc Water scarcity misc Uganda Changes in Meteorological Dry Conditions across Water Management Zones in Uganda |
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1976-3808 |
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Changes in Meteorological Dry Conditions across Water Management Zones in Uganda Trend analyses (dpeaa)DE-He213 Meteorological drought (dpeaa)DE-He213 Extreme precipitation indices (dpeaa)DE-He213 Potential evapotranspiration indices (dpeaa)DE-He213 Water scarcity (dpeaa)DE-He213 Uganda (dpeaa)DE-He213 |
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misc Trend analyses misc Meteorological drought misc Extreme precipitation indices misc Potential evapotranspiration indices misc Water scarcity misc Uganda |
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misc Trend analyses misc Meteorological drought misc Extreme precipitation indices misc Potential evapotranspiration indices misc Water scarcity misc Uganda |
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misc Trend analyses misc Meteorological drought misc Extreme precipitation indices misc Potential evapotranspiration indices misc Water scarcity misc Uganda |
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Changes in Meteorological Dry Conditions across Water Management Zones in Uganda |
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Changes in Meteorological Dry Conditions across Water Management Zones in Uganda |
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Onyutha, Charles |
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Onyutha, Charles Kerudong, Paskwale Acayerach |
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changes in meteorological dry conditions across water management zones in uganda |
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Changes in Meteorological Dry Conditions across Water Management Zones in Uganda |
| abstract |
Abstract This study analysed trends and variability in extreme precipitation and potential evapotranspiration (PET) indices across Uganda. We made use of daily precipitation and temperature datasets from 1979 to 2013 obtained from the Climate Forecast System Reanalysis (CFSR) products of the National Centres for Environmental Prediction (NCEP). The PET was estimated using the Hargreaves method based on the minimum and maximum temperature. Analysis was focused on the level of Water Management Zones (WMZs). Examples of the extracted extreme climatic indices included the annual maximum number of consecutive days with precipitation intensity < 1 mm/day (CDD1) or < 5 mm/day (CDD5), annual sum of PET for days with PET rate > 5 mm/day (SPETD5), and spell of high evaporative demand (or annual maximum number of consecutive days with PET > 5 mm/day) (CDPET5). Attributes of the variability in meteorological dry conditions were investigated. Trend and variability were investigated using an approach based on the cumulative sum of difference (CSD) between exceedance and non-exceedance counts of data points. The magnitudes of the linear decrease or increase in the precipitation and PET indices were determined using Sen’s method. To apply these methods, the CSD-based Sub (Trend) and Variability Analysis Tool (CSD-VAT) was used. The long-term mean of the extracted indices showed Kyoga and Victoria to be the driest and wettest WMZs, respectively. An increase in the number of dry days (indicating increasing severity of meteorological dry conditions) was confined to the Karamoja region or the eastern parts of the Upper Nile and Kyoga WMZs. However, the rest of the country generally experienced negative trends in the precipitation and PET indices. Extreme precipitation indices of Kyoga and the Upper Nile WMZs exhibited consecutive positive and negative sub-trends over the early 1980s and from 1985 to 2013, respectively. Victoria and Albert WMZs mainly exhibited negative sub-trends for both precipitation and PET indices especially from the mid-1980s to the end of the data. Across Victoria and Albert WMZs, we found that the variability in extreme precipitation indices such as CDD1 and CDD5 was significant (p<0.05). However, these WMZs were characterized by insignificant (p>0.05) variability of the PET indices such as SPETD5 and CDPET5. Changes in the precipitation and PET indices were significantly (p<0.05) found to be positively linked to the quasi-biennial oscillation. The meteorological drought indicators were significantly (p<0.05) negatively correlated with the Indian Ocean dipole and Atlantic multidecadal oscillation. Our findings based on NCEP’s CFSR data show that variation in sub-trends of the meteorological dry conditions across Uganda resonates well with the changes in some indicators of large-scale ocean-atmosphere conditions. This is important for planning predictive adaptation to the impacts of climate variability on water resources applications which are sensitive to the severity of meteorological dry conditions. Apart from our findings, we introduced a number of extreme PET indices which can be used for analysis of meteorological dry conditions or drought. © Korean Society of Civil Engineers 2022 |
| abstractGer |
Abstract This study analysed trends and variability in extreme precipitation and potential evapotranspiration (PET) indices across Uganda. We made use of daily precipitation and temperature datasets from 1979 to 2013 obtained from the Climate Forecast System Reanalysis (CFSR) products of the National Centres for Environmental Prediction (NCEP). The PET was estimated using the Hargreaves method based on the minimum and maximum temperature. Analysis was focused on the level of Water Management Zones (WMZs). Examples of the extracted extreme climatic indices included the annual maximum number of consecutive days with precipitation intensity < 1 mm/day (CDD1) or < 5 mm/day (CDD5), annual sum of PET for days with PET rate > 5 mm/day (SPETD5), and spell of high evaporative demand (or annual maximum number of consecutive days with PET > 5 mm/day) (CDPET5). Attributes of the variability in meteorological dry conditions were investigated. Trend and variability were investigated using an approach based on the cumulative sum of difference (CSD) between exceedance and non-exceedance counts of data points. The magnitudes of the linear decrease or increase in the precipitation and PET indices were determined using Sen’s method. To apply these methods, the CSD-based Sub (Trend) and Variability Analysis Tool (CSD-VAT) was used. The long-term mean of the extracted indices showed Kyoga and Victoria to be the driest and wettest WMZs, respectively. An increase in the number of dry days (indicating increasing severity of meteorological dry conditions) was confined to the Karamoja region or the eastern parts of the Upper Nile and Kyoga WMZs. However, the rest of the country generally experienced negative trends in the precipitation and PET indices. Extreme precipitation indices of Kyoga and the Upper Nile WMZs exhibited consecutive positive and negative sub-trends over the early 1980s and from 1985 to 2013, respectively. Victoria and Albert WMZs mainly exhibited negative sub-trends for both precipitation and PET indices especially from the mid-1980s to the end of the data. Across Victoria and Albert WMZs, we found that the variability in extreme precipitation indices such as CDD1 and CDD5 was significant (p<0.05). However, these WMZs were characterized by insignificant (p>0.05) variability of the PET indices such as SPETD5 and CDPET5. Changes in the precipitation and PET indices were significantly (p<0.05) found to be positively linked to the quasi-biennial oscillation. The meteorological drought indicators were significantly (p<0.05) negatively correlated with the Indian Ocean dipole and Atlantic multidecadal oscillation. Our findings based on NCEP’s CFSR data show that variation in sub-trends of the meteorological dry conditions across Uganda resonates well with the changes in some indicators of large-scale ocean-atmosphere conditions. This is important for planning predictive adaptation to the impacts of climate variability on water resources applications which are sensitive to the severity of meteorological dry conditions. Apart from our findings, we introduced a number of extreme PET indices which can be used for analysis of meteorological dry conditions or drought. © Korean Society of Civil Engineers 2022 |
| abstract_unstemmed |
Abstract This study analysed trends and variability in extreme precipitation and potential evapotranspiration (PET) indices across Uganda. We made use of daily precipitation and temperature datasets from 1979 to 2013 obtained from the Climate Forecast System Reanalysis (CFSR) products of the National Centres for Environmental Prediction (NCEP). The PET was estimated using the Hargreaves method based on the minimum and maximum temperature. Analysis was focused on the level of Water Management Zones (WMZs). Examples of the extracted extreme climatic indices included the annual maximum number of consecutive days with precipitation intensity < 1 mm/day (CDD1) or < 5 mm/day (CDD5), annual sum of PET for days with PET rate > 5 mm/day (SPETD5), and spell of high evaporative demand (or annual maximum number of consecutive days with PET > 5 mm/day) (CDPET5). Attributes of the variability in meteorological dry conditions were investigated. Trend and variability were investigated using an approach based on the cumulative sum of difference (CSD) between exceedance and non-exceedance counts of data points. The magnitudes of the linear decrease or increase in the precipitation and PET indices were determined using Sen’s method. To apply these methods, the CSD-based Sub (Trend) and Variability Analysis Tool (CSD-VAT) was used. The long-term mean of the extracted indices showed Kyoga and Victoria to be the driest and wettest WMZs, respectively. An increase in the number of dry days (indicating increasing severity of meteorological dry conditions) was confined to the Karamoja region or the eastern parts of the Upper Nile and Kyoga WMZs. However, the rest of the country generally experienced negative trends in the precipitation and PET indices. Extreme precipitation indices of Kyoga and the Upper Nile WMZs exhibited consecutive positive and negative sub-trends over the early 1980s and from 1985 to 2013, respectively. Victoria and Albert WMZs mainly exhibited negative sub-trends for both precipitation and PET indices especially from the mid-1980s to the end of the data. Across Victoria and Albert WMZs, we found that the variability in extreme precipitation indices such as CDD1 and CDD5 was significant (p<0.05). However, these WMZs were characterized by insignificant (p>0.05) variability of the PET indices such as SPETD5 and CDPET5. Changes in the precipitation and PET indices were significantly (p<0.05) found to be positively linked to the quasi-biennial oscillation. The meteorological drought indicators were significantly (p<0.05) negatively correlated with the Indian Ocean dipole and Atlantic multidecadal oscillation. Our findings based on NCEP’s CFSR data show that variation in sub-trends of the meteorological dry conditions across Uganda resonates well with the changes in some indicators of large-scale ocean-atmosphere conditions. This is important for planning predictive adaptation to the impacts of climate variability on water resources applications which are sensitive to the severity of meteorological dry conditions. Apart from our findings, we introduced a number of extreme PET indices which can be used for analysis of meteorological dry conditions or drought. © Korean Society of Civil Engineers 2022 |
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| container_issue |
12 |
| title_short |
Changes in Meteorological Dry Conditions across Water Management Zones in Uganda |
| url |
https://dx.doi.org/10.1007/s12205-022-0122-5 |
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Kerudong, Paskwale Acayerach |
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| doi_str |
10.1007/s12205-022-0122-5 |
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2024-07-03T20:00:39.751Z |
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