Inhomogeneity detection in the precipitation series: case of arid province of Pakistan

Ahmed, Kamal; Nawaz, Nadeem; Khan, Najeebullah; Rasheed, Balach; Baloch, Amdadullah

doi:10.1007/s10668-020-00910-y

Cited by 13 publications

(4 citation statements)

References 53 publications

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“…A standardized normal homogeneity test (SNHT) approach was used for homogeneity testing [40]. This approach was used to decrease the influence of non-climatic factors on climate series [41,42]. The relative and standalone tests were also used for this purpose.…”

Section: Dataset and Preprocessingmentioning

confidence: 99%

Spatiotemporal Precipitation Trends and Associated Large-Scale Teleconnections in Northern Pakistan

Rebi

Hussain

et al. 2023

Atmosphere

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The effects of climate change are unparalleled in magnitude, ranging from changing weather patterns that endanger food production to increasing sea levels that increase the likelihood of catastrophic flooding. Therefore, determining the extent of such variations on regional and local scales is imperative. We used monthly precipitation data from 25 meteorological stations in northern Pakistan (NP) to document the observed changes in seasonal and annual precipitation. The station density in the NP is small and unevenly distributed; therefore, ERA-5 reanalysis data were used to supplement the observed dataset to assess the spatial trends in NP. The non-parametric Mann–Kendall (MK), Sen’s Slope estimator (SSE), and Sequential Mann–Kendall (SQMK) tests were performed to assess the trends. In addition, the wavelet analysis technique was used to determine the association of precipitation with various oceanic indices from 1960 to 2016. Results indicate that maximum precipitation was shown in the annual and summer seasons. In NP, annual, winter, spring, and summer precipitation declined, while an increase in autumn was observed at a rate of 0.43 mm/decade between 1989 and 2016. The spatial trends for observed and ERA-5 reanalysis datasets were almost similar in winter, spring, and autumn; however, some disagreement was observed in both datasets in the summer and annual precipitation trends in NP during 1960–2016. Between 1989 and 2016, summer and annual precipitation increased significantly in Region III. However, seasonal and annual precipitation decreased in NP between 1960 and 2016. Moreover, there were no prominent trends in annual precipitation until the mid-1980s, but an apparent increase from 1985 onwards. Annual precipitation increased in all elevations except at the 500–1000 m zone. The ENSO (El Niño–Southern Oscillation) shared notable interannual coherences among all indices above 16–64 months. Inter-decadal coherence with the ENSO, AO (Arctic Oscillation), and PDO (Pacific Decadal Oscillation) in NP for 128 months and above. Generally, AO, AMO (Atlantic Multidecadal Oscillation), and NAO (North Atlantic Oscillation) exhibited less coherence with precipitation in NP. The regression of seasonal and annual precipitation revealed that winter and spring precipitation levels had higher linear regression with the AO and ENSO, respectively, while both the AO and ENSO also dominated at the annual scale. Similarly, the IOD and PDO indices had a higher influence in summer precipitation. The findings may help water resource managers and climate researchers develop a contingency plan for better water resource management policies in the face of changing climate change in Pakistan, particularly in NP.

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Section: Dataset and Preprocessingmentioning

confidence: 99%

Spatiotemporal Precipitation Trends and Associated Large-Scale Teleconnections in Northern Pakistan

Rebi

Hussain

et al. 2023

Atmosphere

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“…Since Pakistan is located in monsoon and westerly active zones, due to which the country is highly vulnerable to climate change‐induced precipitation extremes (Ullah et al ., 2018a; 2021; Fahad and Wang, 2019). The dynamic changes in precipitation have caused several hydro‐meteorological disasters in recent decades, which are expected to continue in the future with more catastrophic impacts (Ahmed et al ., 2019b; 2020). The evidence of the country's vulnerability to future climate change highlights the need for considerable attention towards the robust projections of precipitation variability, which can only be achieved through the selection of the best performing GCMs.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and projection of precipitation in Pakistan using the Coupled Model Intercomparison Project Phase 6 model simulations

Abbas

Ullah

et al. 2022

Intl Journal of Climatology

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This study aimed to evaluate the performance of global climate models (GCMs) from the family of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in the historical simulation of precipitation and select the best performing GCMs for future projection of precipitation in Pakistan under multiple shared socioeconomic pathways (SSPs). The spatiotemporal performance of GCMs was evaluated against the Climate Research Unit (CRU) data in simulating annual precipitation during 1951–2014, using the Taylor diagram and interannual variability skill (IVS). Moreover, the modified Mann–Kendall (mMK) and Sen's slope estimator (SSE) tests were employed to estimate significant trends in future precipitation for the period 2015–2100. Based on the comprehensive ranking index (CRI), the HadGEM3‐GC31‐MM model has the highest skill in simulating precipitation distributions followed by EC‐Earth3‐Veg‐LR, CNRM‐ESM2‐1, MPI‐ESM1‐2‐HR, CNRM‐CM6‐1, MRI‐ESM2‐0, CNRM‐CM6‐1‐HR, EC‐Earth3‐Veg, MCM‐UA‐1‐0, INM‐CM5‐0, KACE‐1‐0‐G, CAMS‐CSM1‐0, and HadGEM3‐GC31‐LL models. Furthermore, the projections of the best models ensemble mean (BMEM) showed that the study region will experience a substantial increase in precipitation under SSP3‐7.0 and SSP5‐8.5 but an indolent rise under SSP1‐2.6 and SSP2‐4.5 scenarios. The summer and annual precipitations exhibit a statistically significant increasing trend relative to the winter season under most scenarios. Moreover, the magnitude of monotonic trends in seasonal and annual precipitation progresses from low forcing scenario (SSP1‐2.6) to high forcing scenario (SSP5‐8.5). The findings of the study could provide a benchmark in selecting appropriate GCMs for future projection over a data scare region, like Pakistan. Moreover, the projected trends of future precipitation are crucial in devising adaption and mitigation actions towards sustainable planning of water resource management, food security, and disaster risk management.

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“…The standard normal homogeneity test (SNHT) was introduced by Alexandersson (1986) and is one of the most widely used methods to check the homogeneity of a time series (Tomozeiu et al ., 2000; Khaliq and Ouarda, 2007; Ghebreyesus and Sharif, 2021; Ahmed et al ., 2021a). The SNHT test identifies the breakpoints at the start and end of the time series.…”

Section: Methodsmentioning

confidence: 99%

Innovative polygon trend analysis of monthly precipitation (1952–2015) in the Hindukush‐Karakoram‐Himalaya river basins of Pakistan

Ahmed

Lü

Booij

et al. 2022

Intl Journal of Climatology

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The Hindukush-Karakoram-Himalayas (HKH) mountainous range supplies water to 10 river basins serving eight countries. The present study focuses on the river basins located within the HKH region in Pakistan. The modified Soil and Water Assessment Tool (SWAT+) was used to split the research region into 13 subbasins. The Mann-Kendall, Sen's slope, innovative polygon trend analysis (IPTA), and innovative trend analysis (ITA) methods were used for the monthly precipitation time series. The Shyok sub-basin (central Karakoram) experienced rising trends for all seasons, while the southwestern Karakoram (Hunza) showed a rising trend only for the winter. The Hindukush Mountain Range feeds five subbasins (Gilgit, Chitral, Swat, Kabul, and Kurram). The Gilgit and Kurram subbasins observed an upward trend for all seasons. A falling trend in spring and transition from spring (falling) to summer (rising) was observed for the Swat and Kabul sub-basins. The Chitral sub-basin received rising precipitation in spring, summer, and autumn, whereas no trend was observed during winter. In the southern Himalayas (Kanshi and Jhelum), no trend was observed in winter and autumn and a falling trend in spring and summer. The Kunhar sub-basin (southern Himalaya) showed rising trends in winter, spring, and autumn. In contrast, the Soan sub-basin (eastern Himalaya) has no trend in spring, and other seasons show rising trends. The sub-basin Upper Indus experienced a rising trend for autumn, winter, and summer, while a downward trend was observed in spring. The IPTA and ITA approaches are much more sensitive than the Mann-Kendall method in detecting trends. Although the innovative methods are mostly compatible with each other (90%), the IPTA presents additional information about trend transitions between successive parts of the time series. The results presented in this study are an advancement in the investigations made so far in this region and will help to understand the climate and hydrology of this region.

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Inhomogeneity detection in the precipitation series: case of arid province of Pakistan

Cited by 13 publications

References 53 publications

Spatiotemporal Precipitation Trends and Associated Large-Scale Teleconnections in Northern Pakistan

Spatiotemporal Precipitation Trends and Associated Large-Scale Teleconnections in Northern Pakistan

Evaluation and projection of precipitation in Pakistan using the Coupled Model Intercomparison Project Phase 6 model simulations

Innovative polygon trend analysis of monthly precipitation (1952–2015) in the Hindukush‐Karakoram‐Himalaya river basins of Pakistan

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