Spatio-Temporal Variability of Drought in Pakistan Using Standardized Precipitation Evapotranspiration Index

Pakistan is among the top ten countries adversely affected by climate change. More specifically, there is concern that climate change may cause longer and severer spells of droughts. To quantify the change in the characteristics of droughts in Pakistan over the years, we have evaluated spatio-temporal trends of droughts in Pakistan over the period 1902–2015 using Standardized Precipitation Evapotranspiration Index (SPEI). Additionally, the Spatial “K” luster Analysis using Tree Edge Removal (SKATER) method was employed to regionalize droughts into five contiguous zones. The run theory was then applied to each zone to identify drought events and characterize them in terms of duration, severity, intensity, and peak. Moreover, the Modified Mann–Kendall trend test was applied to identify statistically significant trends in SPEI and drought characteristics in each zone. It was found that the southern areas of Pakistan, encompassing Sindh and most of Baluchistan, have experienced a decrease in SPEI, indicating a drying trend. Central Pakistan has witnessed a wetting trend as demonstrated by an increase in SPEI over time, whereas no statistically significant trend was observed for the northern areas of Pakistan. On a zonal basis, the longest duration drought to occur in Pakistan lasted 22 months in zone 5 (Sindh) from 1968 to 1970. In addition, the drought of 1920 and 2000 can be said to be the worst drought in the history of the region as it affected all the zones and lasted for more than 10-months in three zones.

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Exploring the Evolution of Drought Characteristics in Balochistan, Pakistan

Applied Sciences ◽

10.3390/app10030913 ◽

2020 ◽

Vol 10 (3) ◽

pp. 913 ◽

Cited By ~ 1

Author(s):

Shoaib Jamro ◽

Falak Naz Channa ◽

Ghulam Hussain Dars ◽

Kamran Ansari ◽

Nir Y. Krakauer

Keyword(s):

Climate Change ◽

Drought Index ◽

Risk Index ◽

Trend Test ◽

Standardized Precipitation Evapotranspiration Index ◽

Long Duration ◽

Tree Edge ◽

Edge Removal ◽

The 1960S

In the wake of a rapidly changing climate, droughts have intensified, in both duration and severity, across the globe. The Germanwatch long-term Climate Risk Index ranks Pakistan among the top 10 countries most affected by the adverse effects of climate change. Within Pakistan, the province of Balochistan is among the most vulnerable regions due to recurring prolonged droughts, erratic precipitation patterns, and dependence on agriculture and livestock for survival. This study aims to explore how the characteristics of droughts have evolved in the region from 1902–2015 using 3-month and 12-month timescales of a popular drought index, the Standardized Precipitation Evapotranspiration Index (SPEI). The region was divided into six zones using Spatial “K”luster Analysis using Tree Edge Removal (SKATER) method, and run theory was applied to characterize droughts in terms of duration, severity, intensity, and peak. The results of the non-parametric Mann–Kendall trend test applied to SPEI indicate prevailing significant negative trends (dryer conditions) in all the zones. Balochistan experienced its most severe droughts in the 1960s and around 2000. The effects of climate change are also evident in the fact that all the long duration droughts occurred after 1960. Moreover, the number of droughts identified by 3-month SPEI showed a significant increase after 1960 for all six zones. The same trend was found in the 12-month SPEI but for only three zones.

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Spatial-temporal trends analysis of flood events in the Republic of Armenia in the context of climate change

Journal of Water and Climate Change ◽

10.2166/wcc.2020.244 ◽

2020 ◽

Vol 11 (S1) ◽

pp. 289-309 ◽

Cited By ~ 1

Author(s):

Hrachuhi Galstyan ◽

Shamshad Khan ◽

Hovik Sayadyan ◽

Artur Sargsyan ◽

Tatevik Safaryan

Keyword(s):

Climate Change ◽

Flood Hazard ◽

Methodological Approach ◽

Temporal Trends ◽

Geographical Information ◽

Trend Test ◽

Flood Events ◽

Temperature And Precipitation ◽

The Republic ◽

Events Data

Abstract The primary goal of the study is to analyze the spatial-temporal trends and distribution of flood events in the context of climate change in Armenia. For that purpose, some meteorological parameters, physical-geographical factors and the flood events data were studied for the 1994–2019 period. The linear trends demonstrate an increasing tendency of air temperature and precipitation. Those trends expressed increased flood occurrences, especially for the 2000s, whereas the Mann–Kendall (MK) trend test reveals no significant change. The number of flood events reaches its maximum in 2011 with its peak in May. Out of 191 flood events, half of the occurrences are recorded in the flat areas and southern aspects of the mountains (22% of the country's territory). There is a certain clustering of flood events in the areas with up to 5° slopes (66% of flood events). The most flood vulnerable areas were analyzed and mapped via Geographical Information System (GIS). The GIS-based mapping shows the location of flood vulnerable areas in the central, northern parts of the country, and the coastal areas of Lake Sevan. Our methodological approach elaborates the localization of flood-prone sites. It can be used for the flood hazard assessment mapping and risk management.

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Assessment of the impact of climate change on the freshwater availability of Kaduna River basin, Nigeria

Journal of Water and Land Development ◽

10.2478/jwld-2018-0047 ◽

2018 ◽

Vol 38 (1) ◽

pp. 105-114 ◽

Cited By ~ 2

Author(s):

Gloria C. Okafor ◽

Kingsley N. Ogbu

Keyword(s):

Climate Change ◽

River Basin ◽

Water Availability ◽

Hydrological Cycle ◽

Temporal Trends ◽

Kendall Test ◽

Well Being ◽

Trend Test ◽

Climate Indices ◽

The Impact

AbstractChanges in runoff trends have caused severe water shortages and ecological problems in agriculture and human well-being in Nigeria. Understanding the long-term (inter-annual to decadal) variations of water availability in river basins is paramount for water resources management and climate change adaptation. Climate change in Northern Nigeria could lead to change of the hydrological cycle and water availability. Moreover, the linkage between climatic changes and streamflow fluctuations is poorly documented in this area. Therefore, this study examined temporal trends in rainfall, temperature and runoff records of Kaduna River basin. Using appropriate statistical tools and participatory survey, trends in streamflow and their linkages with the climate indices were explored to determine their amplifying impacts on water availability and impacts on livelihoods downstream the basin. Analysis indicate variable rainfall trend with significant wet and dry periods. Unlike rainfall, temperature showed annual and seasonal scale statistically increasing trend. Runoff exhibit increasing tendency but only statistically significant on annual scale as investigated with Mann–Kendall trend test. Sen’s estimator values stood in agreement with Mann–Kendall test for all variables. Kendall tau and partial correlation results revealed the influence of climatic variables on runoff. Based on the survey, some of the hydrological implications and current water stress conditions of these fluctuations for the downstream inhabitants were itemized. With increasing risk of climate change and demand for water, we therefore recommend developing adaptive measures in seasonal regime of water availability and future work on modelling of the diverse hydrological characteristics of the entire basin.

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Contrasting effects of climate change on seasonal survival of a hibernating mammal

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1918584117 ◽

2020 ◽

Vol 117 (30) ◽

pp. 18119-18126 ◽

Cited By ~ 3

Author(s):

Line S. Cordes ◽

Daniel T. Blumstein ◽

Kenneth B. Armitage ◽

Paul J. CaraDonna ◽

Dylan Z. Childs ◽

...

Keyword(s):

Climate Change ◽

Population Dynamics ◽

Life History ◽

Environmental Conditions ◽

Temporal Trends ◽

Life History Strategies ◽

Environmental Drivers ◽

Age Classes ◽

History Of ◽

Colorado Rocky Mountains

Seasonal environmental conditions shape the behavior and life history of virtually all organisms. Climate change is modifying these seasonal environmental conditions, which threatens to disrupt population dynamics. It is conceivable that climatic changes may be beneficial in one season but result in detrimental conditions in another because life-history strategies vary between these time periods. We analyzed the temporal trends in seasonal survival of yellow-bellied marmots (Marmota flaviventer) and explored the environmental drivers using a 40-y dataset from the Colorado Rocky Mountains (USA). Trends in survival revealed divergent seasonal patterns, which were similar across age-classes. Marmot survival declined during winter but generally increased during summer. Interestingly, different environmental factors appeared to drive survival trends across age-classes. Winter survival was largely driven by conditions during the preceding summer and the effect of continued climate change was likely to be mainly negative, whereas the likely outcome of continued climate change on summer survival was generally positive. This study illustrates that seasonal demographic responses need disentangling to accurately forecast the impacts of climate change on animal population dynamics.

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Cultural implications of late Holocene climate change in the Cuenca Oriental, Mexico

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1405653112 ◽

2015 ◽

Vol 112 (6) ◽

pp. 1693-1698 ◽

Cited By ~ 37

Author(s):

Tripti Bhattacharya ◽

Roger Byrne ◽

Harald Böhnel ◽

Kurt Wogau ◽

Ulrike Kienel ◽

...

Keyword(s):

Climate Change ◽

Regional Climate ◽

Political Factors ◽

X Ray ◽

Cultural Implications ◽

Monsoonal Precipitation ◽

History Of ◽

Drying Trend ◽

Maar Lake

There is currently no consensus on the importance of climate change in Mesoamerican prehistory. Some invoke drought as a causal factor in major cultural transitions, including the abandonment of many sites at 900 CE, while others conclude that cultural factors were more important. This lack of agreement reflects the fact that the history of climate change in many regions of Mesoamerica is poorly understood. We present paleolimnological evidence suggesting that climate change was important in the abandonment of Cantona between 900 CE and 1050 CE. At its peak, Cantona was one of the largest cities in pre-Columbian Mesoamerica, with a population of 90,000 inhabitants. The site is located in the Cuenca Oriental, a semiarid basin east of Mexico City. We developed a subcentennial reconstruction of regional climate from a nearby maar lake, Aljojuca. The modern climatology of the region suggests that sediments record changes in summer monsoonal precipitation. Elemental geochemistry (X-ray fluorescence) and δ18O from authigenic calcite indicate a centennial-scale arid interval between 500 CE and 1150 CE, overlaid on a long-term drying trend. Comparison of this record to Cantona’s chronology suggests that both the city’s peak population and its abandonment occurred during this arid period. The human response to climate change most likely resulted from the interplay of environmental and political factors. During earlier periods of Cantona’s history, increasing aridity and political unrest may have actually increased the city’s importance. However, by 1050 CE, this extended arid period, possibly combined with regional political change, contributed to the city’s abandonment.

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Regionalization and Spatiotemporal Variation of Drought in China Based on Standardized Precipitation Evapotranspiration Index (1961–2013)

Advances in Meteorology ◽

10.1155/2015/950262 ◽

2015 ◽

Vol 2015 ◽

pp. 1-18 ◽

Cited By ~ 25

Author(s):

Xiongfei Liu ◽

Shixin Wang ◽

Yi Zhou ◽

Futao Wang ◽

Wenjun Li ◽

...

Keyword(s):

Southern China ◽

Central China ◽

Tibet Plateau ◽

Severe Drought ◽

Standardized Precipitation Evapotranspiration Index ◽

Spatiotemporal Variations ◽

Drought Duration ◽

Mk Test ◽

Tree Edge ◽

Edge Removal

China is considered to be one of the most drought prone countries. This study is dedicated to analyzing the regionalization and spatiotemporal variations of drought based on the Standardized Precipitation Evapotranspiration Index, which covers the period 1961–2013 across 810 stations in China. Using Spatial “K”luster Analysis by Tree Edge Removal method, China was divided into eight regions: southwest (SW), northeast (NE), north (N), southeast (SE), Yangtze River (YR), northwest (NW), central China (C), and Tibet Plateau (TP). The spatiotemporal variations of drought characteristics indicated that the drought count in NE and C was generally high. Southern China and NW had suffered long drought duration and extreme severity. The MK test results show that stations with significant drying trends mainly locate in SW, N, NW, and C. The severe drought frequency was very high in 1990s and 2000s. Furthermore, more attention should be paid to abnormal less precipitation in summer and abnormal high temperature in spring in SW, NE, N, and C. Besides, abnormal less precipitation is the main factor of drought in SE and YR in whole year. This study is anticipated to support the water resources management, and to promote the realization of environmental protection and agricultural production.

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Climatic suitability of Aedes albopictus in Europe referring to climate change projections: comparison of mechanistic and correlative niche modelling approaches

Eurosurveillance ◽

10.2807/1560-7917.es2014.19.6.20696 ◽

2014 ◽

Vol 19 (6) ◽

Cited By ~ 47

Author(s):

D Fischer ◽

S M Thomas ◽

M Neteler ◽

N B Tjaden ◽

C Beierkuhnlein

Keyword(s):

Climate Change ◽

Aedes Albopictus ◽

Temporal Trends ◽

Climatic Suitability ◽

Niche Modelling ◽

Climatic Constraints ◽

Spatio Temporal ◽

Basic Approaches ◽

Set Up ◽

Modelling Approaches

The Asian tiger mosquito, Aedes albopictus, is capable of transmitting a broad range of viruses to humans. Since its introduction at the end of the 20th century, it has become well established in large parts of southern Europe. As future expansion as a result of climate change can be expected, determining the current and projected future climatic suitability of this invasive mosquito in Europe is of interest. Several studies have tried to detect the potential habitats for this species, but differing data sources and modelling approaches must be considered when interpreting the findings. Here, various modelling methodologies are compared with special emphasis on model set-up and study design. Basic approaches and model algorithms for the projection of spatio-temporal trends within the 21st century differ substantially. Applied methods range from mechanistic models (e.g. overlay of climatic constraints based on geographic information systems or rather process-based approaches) to correlative niche models. We conclude that spatial characteristics such as introduction gateways and dispersal pathways need to be considered. Laboratory experiments addressing the climatic constraints of the mosquito are required for improved modelling results. However, the main source of uncertainty remains the insufficient knowledge about the species' ability to adapt to novel environments.

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Spatio-temporal variation of extreme indices derived from observed and reanalysis products for detection of climate change across India

10.5194/egusphere-egu2020-5837 ◽

2020 ◽

Author(s):

Sachidanand Kumar ◽

Kironmala Chanda ◽

Srinivas Pasupuleti

Keyword(s):

Climate Change ◽

Extreme Precipitation ◽

Daily Precipitation ◽

Temporal Trends ◽

Maximum Temperature ◽

Daily Minimum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Spatio Temporal ◽

Precipitation And Temperature

AbstractThis article reports the research findings in a recent study (Kumar et al., 2020) that utilizes eight indices of climate change recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) for analyzing spatio-temporal trends in extreme precipitation and temperature at the daily scale across India. Observed gridded precipitation (1971-2017) and temperature (1971-2013) datasets from India Meteorological Department (IMD) are used along with reanalysis products from Climate Prediction Centre (CPC). The trends are estimated using non-parametric Mann-Kendall (MK) test and regression analysis. The trends in &#8216;wet days&#8217; (daily precipitation greater than 95th percentile) and &#8216;dry days&#8217; (daily precipitation lower than 5th percentile) are examined considering the entire year (annual) as well as monsoon months only (seasonal). At the annual scale, about 13% of the grid locations indicated significant trend (either increasing or decreasing at 5% significance level) in the index R95p (rainfall contribution from extreme &#8216;wet days&#8217;) while 20% of the locations indicated significant trend in R5p (rainfall contribution from extreme &#8216;dry days&#8217;). For the seasonal analysis (June to September), the corresponding figures are nil and 21% respectively. The spatio-temporal trends in &#8216;warm days&#8217; (daily maximum temperature greater than 95th percentile), &#8216;warm nights&#8217; (daily minimum temperature greater than 95th percentile), &#8216;cold days&#8217; (daily maximum temperature lower than 5th percentile) and &#8216;cold nights&#8217; (daily minimum temperature lower than 5th percentile) are also investigated for the aforementioned period. The number of &#8216;warm days&#8217; per year increased significantly at 14% of the locations, while the number of &#8216;cold days&#8217;, &#8216;warm nights&#8217; and &#8216;cold nights&#8217; per year decreased significantly at several (42%, 34% and 39%) of the locations. The extreme temperature indices are also investigated for the future using CanESM2 projected data for RCP8.5 after suitable bias correction. Most of the locations (49% to 84%) indicate significant increasing (decreasing) trend in &#8216;warm days&#8217; (&#8216;cold days&#8217;) in the three epochs, 2006-2040, 2041-2070 and 2071-2100. Moreover, most locations (60% to 81%) show an increasing trend in &#8216;warm nights&#8217; and a decreasing trend in &#8216;cold nights&#8217; in all the epochs. A similar investigation for the historical and future periods using CPC data as the reference indicates that the trends, on comparison with IMD observations, seem to be in agreement for temperature extremes but spatially more extensive in case of CPC precipitation extremes.Keywords: extreme precipitation and temperature, climate change indices, spatio-temporal variation, IndiaReferences:Kumar S., Chanda, K., Srinivas P., (2020), Spatiotemporal analysis of extreme indices derived from daily precipitation and temperature for climate change detection over India, Theoretical and Applied Climatology, Springer, In press, DOI: 10.1007/s00704-020-03088-5.

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Bayesian uncertainty quantification of spatio-temporal trends in soil organic carbon using INLA and SPDE

10.5194/egusphere-egu2020-9154 ◽

2020 ◽

Author(s):

Nicolas P.A. Saby ◽

Thomas Opitz ◽

Bifeng Hu ◽

Blandine Lemercier ◽

Hocine Bourennane

Keyword(s):

Climate Change ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Spatial Patterns ◽

Temporal Trends ◽

Model Parameters ◽

Spatial And Temporal Variability ◽

Statistical Inferences ◽

Spatio Temporal ◽

General Response

The assumption of spatial and temporal stationarity does not hold for many ecological and environmental processes. This is particularly the case for many soil processes like carbon sequestration, often driven by factors such as biological dynamics, climate change and anthropogenic influences. For better understanding and predicting such phenomena, we develop a Bayesian inference framework that combines the integrated nested Laplace approximation (INLA) with the stochastic partial differential equation approach (SPDE). We put focus on modeling complex temporal trends varying through space with an accurate assessment of uncertainties, and on spatio-temporal mapping of processes that are only partially observed.We model observed data through a latent (i.e., unobserved) smooth process whose additive components are endowed with Gaussian process priors. We use the SPDE approach to implement flexible sparse-matrix approximations of the Mat&#233;rn covariance for spatial fields. The separate specification of the spatially varying linear trend allows us to conduct component-specific statistical inferences (range and variance estimates, standard errors, confidence bounds), and to provide maps to stakeholders for time-invariant spatial patterns, spatial patterns in slopes of time trends, and the associated uncertainties. For observed data following a Gaussian distribution, we add independent measurement errors, but more general response distributions of the data can be implemented. We also include in our model covariate information on parent material, climate and seasonality.The INLA method and its implementation in the R-INLA library provide a rich toolbox for statistical space-time modelling while sidestepping typical convergence problems arising with simulation-based techniques using Markov Chain Monte&#8211;Carlo codes for large and complex hierarchical models such as ours. Uncertainties arising in model parameters and in pointwise spatio-temporal predictions are naturally captured in the posterior distributions computed through INLA using appropriate approximation techniques, and we can communicate on them through maps of various properties. Moreover, INLA also allows for direct simulation from the estimated posterior model, such that we can conduct statistical inferences on more complex functionals of the multivariate predictive distributions by analogy with MCMC frameworks.Soil organic carbon is a major compartment of the global carbon cycle and small variations of its level can largely impact atmospheric CO2 concentrations. In the context of global climate change, it is important to be able to quantify and explain spatial and temporal variability of SOC in order to forecast future changes. In this work, we used this approach to study possible trends in space and time of soil carbon stock of three agricultural fields in France. Fitted models reveal signi&#64257;cant temporal trends with strong spatial heterogeneity. The Mat&#233;rn model and SPDE approach provide a flexible framework with respect to field design.

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