Climate, Volume 3, Issue 1 (March 2015) – 15 articles , Pages 1-263

Infrastructure such as dams and reservoirs are critical water-supply features in several regions of the world. However, ongoing population growth, increased demand and climate variability/change necessitate the better understanding of these systems, particularly in terms of their long-term trends. The Sooke Reservoir (SR) of British Columbia, Canada is one such reservoir that currently supplies water to ~300,000 people, and is subject to considerable inter and intra-annual climatic variations. The main objectives of this study are to better understand the characteristics of the SR through an in-depth assessment of the contemporary water balance when the basin was intensively monitored (1996–2005), to use standardized runoff to select the best timescale to compute the Standard Precipitation (SPI) and Standard Precipitation Evaporation Indices (SPEI) to estimate trends in water availability over 1919 to 2005. Estimates of runoff and evaporation were validated by comparing simulated change in storage, computed by adding inputs and subtracting outputs from the known water levels by month, to observed change in storage. Water balance closure was within ±11% of the monthly change in storage on average when excluding months with spill pre-2002. The highest evaporation, dry season (1998) and lowest precipitation, wet season (2000/2001) from the intensively monitored period were used to construct a worst-case scenario to determine the resilience of the SR to drought. Under such conditions, the SR could support Greater Victoria until the start of the third wet season. The SPEI and SPI computed on a three-month timescale had the highest correlation with the standardized runoff, R² equaled 0.93 and 0.90, respectively. A trend toward drier conditions was shown by SPEI over 1919 to 2005, while moistening over the same period was shown by SPI, although trends were small in magnitude. This study contributes a validated application of SPI and SPEI, giving more credit to their trends and estimated changes in drought. Full article

Evidence is mounting that the temporal dynamics of the climate system are changing at the same time as the average global temperature is increasing due to multiple climate forcings. A large number of extreme weather events such as prolonged cold spells, heatwaves, droughts and floods have been recorded around the world in the past 10 years. Such changes in the temporal scaling behaviour of climate time-series data can be difficult to detect. While there are easy and direct ways of analysing climate data by calculating the means and variances for different levels of temporal aggregation, these methods can miss more subtle changes in their dynamics. This paper describes multi-scale entropy (MSE) analysis as a tool to study climate time-series data and to identify temporal scales of variability and their change over time in climate time-series. MSE estimates the sample entropy of the time-series after coarse-graining at different temporal scales. An application of MSE to Central European, variance-adjusted, mean monthly air temperature anomalies (CRUTEM4v) is provided. The results show that the temporal scales of the current climate (1960–2014) are different from the long-term average (1850–1960). For temporal scale factors longer than 12 months, the sample entropy increased markedly compared to the long-term record. Such an increase can be explained by systems theory with greater complexity in the regional temperature data. From 1961 the patterns of monthly air temperatures are less regular at time-scales greater than 12 months than in the earlier time period. This finding suggests that, at these inter-annual time scales, the temperature variability has become less predictable than in the past. It is possible that climate system feedbacks are expressed in altered temporal scales of the European temperature time-series data. A comparison with the variance and Shannon entropy shows that MSE analysis can provide additional information on the statistical properties of climate time-series data that can go undetected using traditional methods. Full article

Landslides, floods, and droughts are recurring natural disasters in Nepal related to too much or too little water. The summer monsoon contributes more than 80% of annual rainfall, and rainfall spatial and inter-annual variation is very high. The Gandaki River, one of the three major rivers of Nepal and one of the major tributaries of the Ganges River, covers all agro-ecological zones in the central part of Nepal. Time series tests were applied for different agro-ecological zones of the Gandaki River Basin (GRB) for rainfall trends of four seasons (pre-monsoon, monsoon, post-monsoon and winter) from 1981 to 2012. The non-parametric Mann-Kendall and Sen’s methods were used to determine the trends. Decadal anomalies relative to the long-term average were analyzed using the APHRODITE precipitation product. Trends in number of rainy days and timing of the monsoon were also analyzed. We found that the post-monsoon, pre-monsoon and winter rainfalls are decreasing significantly in most of the zones but monsoon rainfall is increasing throughout the basin. In the hill region, the annual rainfall is increasing but the rainy days do not show any trend. There is a tendency toward later departure of monsoon from Nepal, indicating an increase in its duration. These seasonally and topographically variable trends may have significant impacts for the agriculture and livestock smallholders that form the majority of the population in the GRB. Full article

Using 1 km satellite remote sensing observations, this paper examines the clouds, aerosols, water vapor and surface skin temperature over Beijing to understand the possible urban system contributions to the extreme rainfall event on 21 July 2012 (i.e., 721 event). Remote sensing measurements, with the advantage of high spatial resolution and coverage, reveal three key urban-related mechanisms: (a) the urban heat island effect (UHI) resulted in strong surface convection and high level cloud cover over Beijing; (b) urban aerosol amount peaked before the rainfall, which “seeded” the clouds and invigorated precipitation; and (c) urban tall buildings provided additional lift for the air mass and provided heat at the underlying boundary to keep the rainfall system alive for a long duration precipitation (>10 hours). With the existing rainfall system moving from the northwest and abundant water vapor was transported from the southeast into Beijing, the urban canyon-lifting, aerosol, and UHI effects all enhanced this extreme rainfall event. This work proves that urban system is responsible, at least partly, for urban rainfall extremes and thus should be considered for urban extreme rainfall prediction in the future. Full article

This study analyzed the trends of extreme daily rainfall indices over the Indochina Peninsula from 1960 to 2007. The trends were obtained from high-resolution gridded daily rainfall data compiled by APHRODITE with coordinates of 4°N–25°N and 90E°–112°E. The indices were selected from the list of climate change indices recommended by ETCCDI, which is a joint group of WMO CCl, CLIVAR and JCOMM. The indices are based on the number of heavy rainfall days (≥10 mm), number of very heavy rainfall days (≥20 mm), number of extremely heavy rainfall days (≥25 mm), consecutive dry days (<1 mm), consecutive wet days (≥1 mm), daily maximum rainfall, five-day maximum rainfall, annual wet-day rainfall total, Simple Daily Intensity Index, very wet days, and extremely wet days. The indices were simulated by calculating different extreme characteristics according to wet and dry conditions, frequency, and intensity. Linear trends were calculated by using a least squares fit and significant or non-significant trends were identified using the Mann–Kendall test. The results of this study revealed contrasting trends in extreme rainfall in eastern and western Indochina Peninsula. The changes in extreme rainfall events in the east primarily indicate positive trends in the number of heavy rainfall days, very heavy rainfall days, extremely heavy rainfall days, consecutive wet days and annual wet-day rainfall total, with significant trends at times. These events correlated with the northeastern monsoon that influences the Indochina Peninsula from October to February annually. The results in the west primarily indicate negative trends in consecutive wet days, where significant trends were correlated with decreasing number of annual wet-day rainfall total, heavy rainfall days, very heavy rainfall days, and extremely heavy rainfall days. Daily maximum rainfall, five-day maximum rainfall, very wet days, and extremely wet days show random positive (negative) significant (non-significant) trends, while the simple daily intensity index shows positive trends that dominate the southern part of the Indochina Peninsula, with some grids show significant trends. Full article

Changes in annual rainfall in five sub-regions of the Argentine Pampa Region (Rolling, Central, Mesopotamian, Flooding and Southern) were examined for the period 1941 to 2010 using data from representative locations in each sub-region. Dubious series were adjusted by means of a homogeneity test and changes in mean value were evaluated using a hydrometeorological time series segmentation method. In addition, an association was sought between shifts in mean annual rainfall and changes in large-scale atmospheric pressure systems, as measured by the Atlantic Multidecadal Oscillation (AMO), the Pacific Decadal Oscillation (PDO) and the Southern Oscillation Index (SOI). The results indicate that the Western Pampas (Central and Southern) are more vulnerable to abrupt changes in average annual rainfall than the Eastern Pampas (Mesopotamian, Rolling and Flooding). Their vulnerability is further increased by their having the lowest average rainfall. The AMO showed significant negative correlations with all sub-regions, while the PDO and SOI showed significant positive and negative correlations respectively with the Central, Flooding and Southern Pampa. The fact that the PDO and AMO are going through the phases of their cycles that tend to reduce rainfall in much of the Pampas helps explain the lower rainfall recorded in the Western Pampas sub-regions in recent years. This has had a significant impact on agriculture and the environment. Full article

Predicting the future climate and its impacts on the global environment is model based, presenting a level of uncertainty. Alternative robust approaches of analyzing high volume climate data to reveal underlying regional and local trends are increasingly incorporating satellite data. This study uses a centered log-ratio (clr) transformation approach and robust principal component analysis (PCA), on a long-term Normalized Difference Vegetation Index (NDVI) dataset to test its applicability in analyzing large multi-temporal data, and potential to recognize important trends and patterns in regional climate. Twenty five years of NDVI data derived by Global Inventory Modeling and Mapping Studies (GIMMS) from 1982 to 2006 were extracted for 88 subwatersheds in central Kenya and statistically analyzed. Untransformed (raw) and clr transformed NDVI data were evaluated using robust PCA. The robust PCA compositional biplots of the clr transformed long-term NDVI data demonstrated the finest spatial-temporal display of observations identifying climate related events that impacted vegetation activity and observed variations in greenness. The responses were interpreted as normal conditions, El Niño Southern Oscillation (ENSO) events of El Niño and La Niña, and drought events known to influence the moisture level and precipitation patterns (high, low, normal) and therefore the level of vegetation greenness (NDVI value). More drought events (4) were observed between 1990 and 2006, a finding corroborated by several authors and linked to increasing climate variability. Results are remarkable, emphasizing the need for appropriate data transformation prior to PCA, dealing with huge complex datasets, to enhance pattern recognition and meaningful interpretation of results. Through improved analysis of past data, uncertainty is decreased in modeling future trends. Full article

Lack of suitable observational data makes bias correction of high space and time resolution regional climate models (RCM) problematic. We present a method to construct pseudo-observational precipitation data bymerging a large scale constrained RCMreanalysis downscaling simulation with coarse time and space resolution observations. The large scale constraint synchronizes the inner domain solution to the driving reanalysis model, such that the simulated weather is similar to observations on a monthly time scale. Monthly biases for each single month are corrected to the corresponding month of the observational data, and applied to the finer temporal resolution of the RCM. A low-pass filter is applied to the correction factors to retain the small spatial scale information of the RCM. The method is applied to a 12.5 km RCM simulation and proven successful in producing a reliable pseudo-observational data set. Furthermore, the constructed data set is applied as reference in a quantile mapping bias correction, and is proven skillful in retaining small scale information of the RCM, while still correcting the large scale spatial bias. The proposed method allows bias correction of high resolution model simulations without changing the fine scale spatial features, i.e., retaining the very information required by many impact models. Full article

The influence of future climate change on the occurrence of heat waves and its implications for heat wave-related mortality due to ischemic heart diseases (IHD) in Germany is studied. Simulations of 19 regional climate models with a spatial resolution of 0.25° × 0.25° forced by the moderate climate change scenario A1B are analyzed. Three model time periods of 30 years are evaluated, representing present climate (1971–2000), near future climate (2021–2050), and remote future climate (2069–2098). Heat waves are defined as periods of at least three consecutive days with daily mean air temperature above the 97.5th percentile of the all-season temperature distribution. Based on the model simulations, future heat waves in Germany will be significantly more frequent, longer lasting and more intense. By the end of the 21st century, the number of heat waves will be tripled compared to present climate. Additionally, the average duration of heat waves will increase by 25%, accompanied by an increase of the average temperature during heat waves by about 1 K. Regional analyses show that stronger than average climate change effects are observed particularly in the southern regions of Germany. Furthermore, we investigated climate change impacts on IHD mortality in Germany applying temperature projections from 19 regional climate models to heat wave mortality relationships identified in a previous study. Future IHD excess deaths were calculated both in the absence and presence of some acclimatization (i.e., that people are able to physiologically acclimatize to enhanced temperature levels in the future time periods by 0% and 50%, respectively). In addition to changes in heat wave frequency, we incorporated also changes in heat wave intensity and duration into the future mortality evaluations. The results indicate that by the end of the 21st century the annual number of IHD excess deaths in Germany attributable to heat waves is expected to rise by factor 2.4 and 5.1 in the acclimatization and non-acclimatization approach, respectively. Even though there is substantial variability across the individual model simulations, it is most likely that the future burden of heat will increase considerably. The obtained results point to public health interventions to reduce the vulnerability of the population to heat waves. Full article

This paper provides a holistic literature review of climate change and variability in Ghana by examining the impact and projections of climate change and variability in various sectors (agricultural, health and energy) and its implication on ecology, land use, poverty and welfare. The findings suggest that there is a projected high temperature and low rainfall in the years 2020, 2050 and 2080, and desertification is estimated to be proceeding at a rate of 20,000 hectares per annum. Sea-surface temperatures will increase in Ghana’s waters and this will have drastic effects on fishery. There will be a reduction in the suitability of weather within the current cocoa-growing areas in Ghana by 2050 and an increase evapotranspiration of the cocoa trees. Furthermore, rice and rooted crops (especially cassava) production are expected to be low. Hydropower generation is also at risk and there will be an increase in the incidence rate of measles, diarrheal cases, guinea worm infestation, malaria, cholera, cerebro-spinal meningitis and other water related diseases due to the current climate projections and variability. These negative impacts of climate change and variability worsens the plight of the poor, who are mostly women and children. Full article

A comparative analysis of the sensitivity of NDVI and EVI to rainfall indicators has been carried out for different land use/covers in the Southwest of Burkina Faso. Three classified land use/covers maps for 1999, 2006 and 2011 were produced and change detection was applied to locate persistent areas. Thereafter monthly vegetation indices of plots of 750 × 750 m² were extracted from 2001 to 2011 for persistent woodland, mixed vegetation, and agricultural area within 5 km radius around four rain gauges. Furthermore, correlation analysis to measure the relationship between vegetation indices and rainfall indicators was performed. The results indicate some similarities between NDVI and EVI. Both indices, for all land use/covers, showed significant and strong positive correlation with the rainfall indicators. In general, NDVI was more sensitive to rainfall than EVI in the study area, but the difference between the Pearson’s coefficient values of both vegetation indices was insignificant. The findings of this work agree with some previous studies, but contrasting conclusions were also noted in literature. Hence wider spatial investigation will be necessary to confirm the results of this paper. Full article

The study aims to assess climate change impacts on streamflow drought in a catchment upstream of Tseng-Wen Reservoir which is the main water supplier in southern Taiwan. A singular-value-decomposition statistical downscaling method and a stochastic weather generator were applied to six different general circulation models for rainfall and temperature downscaling. Two emission climate change scenarios (A1B and B1) were used for rainfall and temperature projections during the two periods, 2010–2045 and 2081–2100. After rainfall and temperature projections, the HBV-based hydrological model was used to simulate future streamflows. By using the threshold level method, the characteristics of streamflow drought during the baseline period (1975–2000) and the future periods were calculated and compared. Important findings include: (1) the start and end times of scenario droughts occur earlier than those of baseline droughts; (2) streamflow under future scenarios during the dry period tends to decrease in January and February, but to increase in March and April; and (3) most of general circulation models’ results support that the drought duration and magnitude tend to decrease for a given return period in the future, and the occurrence probability of severer drought tends to decrease. Full article

In this study, we examined the individual and socio-environmental factors that mediate differential self-reported experiences of climate change in coastal communities in Lagos, Nigeria. Binary complementary log-log multivariate regression was used to model residents’ experiences of changing rainfall patterns, ocean surges, and flood events. An analysis of both compositional and contextual factors showed that there were urban communities where vulnerability to flooding tends to be clustered, and that this was not fully explained by the characteristics of the people of whom the community was composed. This study, thus, underscores the importance and complex nature of the interaction between personal and socio-environmental determinants in shaping climate change experiences and vulnerability of individuals across coastal neighbourhoods. Key findings suggest certain sub-populations as well as geographic clusters in Lagos require special attention from disaster mitigation experts and policy makers. Full article

Evaluating groundwater levels and recharge patterns is part of sustainably managing the water supply and predicting the possibility of water shortages, especially in light of climate change, extreme events (floods/droughts), increasing population and development. In the northern region of Ghana, groundwater is increasingly relied upon as a source of potable water for rural populations, but seasonal and inter-annual fluctuations of groundwater levels and recharge patterns are not always known. The fluctuation of groundwater levels on a seasonal basis shows that groundwater levels at all sites increase in response to seasonal precipitation. On an annual basis, all sites show an overall net decline of groundwater levels over the study period, which may be associated with below-average departures of precipitation during five of the seven study years. The variability of recharge patterns among five sites is attributed to the spatio-temporal variability of precipitation and hydrogeologic site uniqueness. The overarching potential benefit of this study is to facilitate closing knowledge gaps and contribute to a foundation for a more robust evaluation of groundwater resources in the area, especially as more data become available. Full article