March April May Research Articles

Impact of Arctic amplification on declining spring dust events in East Asia

Dust aerosols play key roles in affecting regional and global climate through their direct, indirect, and semi-direct effects. Dust events have decreased rapidly since the 1980s in East Asia, particularly over northern China, primarily because of changes in meteorological parameters (e.g. surface wind speed and precipitation). In this study, we found that winter (December–January–February) Arctic amplification associated with weakened temperature gradients along with decreased zonal winds is primarily responsible for the large decline in following spring (March–April–May) dust event occurrences over northern China since the mid-1980s. A dust index was developed for northern China by combining the daily frequency of three types of dust event (dust storm, blowing dust, and floating dust). Using the empirical orthogonal function (EOF) analysis, the first pattern of dust events was obtained for spring dust index anomalies, which accounts for 56.2% of the variability during 1961–2014. Moreover, the enhanced Arctic amplification and stronger Northern Hemisphere annular mode (NAM) in winter can result in the anticyclonic anomalies over Siberia and Mongolia, while cyclonic anomalies over East Europe in spring. These results are significantly correlated with the weakened temperature gradients, increased precipitation and soil moisture, and decreased snow cover extent in the mid-latitude over Northern Hemisphere. Based on the future predictions obtained from the Fifth Climate Models Intercomparison Project (CMIP5), we found that the dust event occurrences may continually decrease over northern China due to the enhanced Arctic amplification in future climate.

ASSESSMENT OF SOLAR PV POWER POTENTIAL IN THE ASIA PACIFIC REGION WITH REMOTE SENSING CONSIDERING THE EFFECTS OF HIGH TEMPERATURE, DUST AND SNOW

Abstract. The last half century has witnessed the increasing trend of renewable energy utilization with solar photovoltaic (PV) systems as one of the most popular option. Solar PV continues to supplement the main grid in powering both commercial establishments (mainly for reduced electricity expense) as well as residential houses in isolated areas (for basic energy requirement such as for lighting purposes). The objective of this study is to assess the available solar PV power (PPV) potential considering the effects of high temperature, dust and snow in the Asia Pacific region. The PPV potential was estimated considering the effects of the said meteorological parameters using several satellite data including shortwave radiation from Advanced Himawari Imager 8 (AHI8), MOD04 aerosol data from Moderate Resolution Imaging Spectroradiometer (MODIS), precipitation rate from Global Satellite Mapping of Precipitation (GSMaP), air temperature from NCEP/DOE AMIP-II Reanalysis-2 data, and snow water equivalent (SWE) from Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). The model is validated by comparing its outputs with the measured PV power from two solar PV installations in Bangkok, Thailand and Perth, Australia. Results show that maximum PPV is estimated at 2.5 GW (cell efficiency of 17.47%) for the region with the maximum decrease in PPV estimated to be about < 2%, 22% and 100% due to high temperature (temperature coefficient of power = 0.47%/K), dust and snow, respectively. Moreover, areas in India and Northern China were observed to experience the effects of both dust and temperature during March-April-May (MAM) season. Meanwhile, countries located in the higher latitudes were severely affected by snow while Australia by high temperature during Dec-Jan-Feb (DJF) season. The model has a mean percentage prediction error (PPE) range of 5% to18% and 7% to 23% in seasonal and monthly estimations, respectively. Outputs from this study can be used by stakeholders of solar PV in planning for small-scale or large-scale solar PV projects in the solar rich region of Asia Pacific.

Influences of the Atlantic and Pacific Oceans on Rainy Season Precipitation for the Southernmost Caribbean Small Island State, Trinidad

Seasonal rainfall in the Caribbean Basin is known to be modulated by sea surface temperature anomalies (SSTAs) in the Atlantic and Pacific Oceans, and particularly those in the Equatorial Pacific and Atlantic and the Tropical North Atlantic. However, little is known about how these major oceans influence the seasonal precipitation of individual small island states within the region as climate variability at the island-scale may differ from the Caribbean as a whole. Correlation and composite analyses were determined using monthly rainfall data for the southernmost island of the Caribbean, Trinidad, and an extended area of global SSTAs. In addition to the subregions that are known to modulate Caribbean rainfall, our analyses show that sea surface temperatures (SSTs) located in the subtropical South Pacific, the South Atlantic, and the Gulf of Mexico also have weak (r2 < 0.5) yet significant influences on the islands’ early rainy season (ERS) and late rainy season (LRS) precipitation. Composite maps confirm that the South Pacific, South Atlantic, and the Gulf of Mexico show significant SSTAs in December–January–February (DJF) and March–April–May (MAM) prior to the ERS and the LRS. Statistical models for seasonal forecasting of rainfall at the island scale could be improved by using the SSTAs of the Pacific and Atlantic subregions identified in this study.

The role of the Atlantic Multidecadal Oscillation precondition in the teleconnection of different El Niño‐Southern Oscillation types and impacts on the 15°N–15°S South American sector precipitation

AbstractThe Eastern Pacific (EP) and Central Pacific (CP) El Niño‐Southern Oscillation (ENSO) types and their impacts on the tropical North Atlantic (TNA) SST variability and 15°N–15°S South American precipitation during the warm and cold phases of the Atlantic Multidecadal Oscillation (WAMO and CAMO) were evaluated during the 1901–2012 period. The results show more frequent ENSO events during the CAMO. The El Niño (EN) (La Niña [LN]) events, regardless of type (EP or CP), during the WAMO (CAMO) were accompanied by a warming (cooling) in the TNA after its mature phases. In these cases, extratropical teleconnection patterns are established through variations in the Pacific/North America (PNA) teleconnection pattern and are accompanied by variations in the Walker circulation. For the EN (LN) in the CAMO (WAMO), the tropical teleconnections occur predominant, through the Walker cell and the zonal inter‐basin gradient, which is intensified due to the SST gradient between the eastern equatorial Pacific (non‐neutral anomalies) and the equatorial Atlantic (neutral anomalies). These circulation pattern changes affect the precipitation patterns in the 15°N–15°S South American sector during December–January–February (D(0)JF(+1)) and March–April–May (MAM(+1)). The EP EN (EPEN) events are associated with the intensification of the negative precipitation anomalies in northeastern Brazil (NEB) during the WAMO and in the central part of the Amazon during the CAMO. In the case of CP EN (CPEN) events, the greatest differences between the AMO phases occur during MAM(+1), with reverse sign anomalies over northwestern South America. In the case of LN events, the largest differences occur in NEB, with reduced rainfall in the WAMO, regardless of type EP or CP. The results presented here highlight the role of low frequency oscillations in defining the teleconnection patterns between tropical Pacific and Atlantic Oceans, not discussed previously.

Projected changes in rainfall and temperature over the Philippines from multiple dynamical downscaling models

AbstractTo help meet increasing demands for high‐resolution climate change projections in the Philippines, this study provides the results of multiple dynamically downscaled climate model simulations for projected changes in rainfall and temperature over the country by the mid‐21st century (2036–2065) relative to the baseline period (1971–2000), under the RCP8.5 scenario. The model‐simulated seasonal means of temperature, rainfall, and low‐level wind patterns were first compared with observations during the baseline period. Comparisons made between the model‐derived and APHRODITE observation‐based gridded temperature and rainfall data indicate that the dynamically downscaled simulations provide an overall improvement from their driving global climate models in capturing the spatial patterns of rainfall over the country, and the spatial and temporal characteristics of the country's mean temperature. Future climate projections show that the country's climate is expected to become warmer by the mid‐21st century, with a multi‐model ensemble mean increase of 1.2 to 1.9°C, relative to the baseline period, projected for many parts of the country and across most seasons. Slightly higher increases are projected during the country's hottest season, March–April–May. However, there are large differences in the models' projected rainfall changes by the mid‐21st century across seasons and regions. For most parts of the country, the multi‐model ensemble includes simulations that show increases and simulations that show decreases in rainfall. Nevertheless, there is a tendency of model projections towards wetter conditions over northern and central sections of the country (particularly in the December–January–February season) and drier conditions in the southern region of the country in almost all seasons. The results demonstrate the need for communities in the Philippines to adapt to a future warmer climate and prepare for a range of possible future changes in rainfall and temperature.

Seasonal and Annual Rainfall Variability and Their Impact on Rural Water Supply Services in the Wami River Basin, Tanzania

In some parts of Africa, rainfall variability has resulted in widespread droughts and floods, thus posing a substantial challenge to water availability in rural areas, especially drinking water. Therefore, due to increasing water demands, increases in the population, and economic development, water supply systems are under constant stress. One of the critical uncertainties surrounding the effects of rainfall variability in Africa is the significant impact that it imposes on rural water supply services. The present study analyzes the trends in annual and seasonal rainfall time series in the Wami River Basin to see if there have been any significant changes in the patterns during the period 1983–2017 and how they affect the access to water supply services in rural areas. The study analyzes the trends of rainfall series of three stations using simple regression, Mann–Kendal Test and Sen’s Slope Estimator. The water point mapping datasets were analyzed considering seasonal variation. The analysis showed a statistically significant positive trend in annual rainfall at Kongwa and March–April–May (MAM) seasonal rainfall at Dakawa. The maximum increase in annual rainfall occurred at Kongwa (5.3 mm year−1) and for MAM seasonal data at Dakawa (4.1 mm year−1). Water points were found to be significantly affected by seasonal changes, both in terms of availability and quality of water. There also exists a strong relationship between rural water services and seasons.

Direct and indirect seasonal rainfall forecasts for East Africa using global dynamical models

AbstractRegional‐scale seasonal climate outlooks are typically produced using forecast information either local to the region or from another area with teleconnections to the region. Dynamical global long‐range forecast (LRF) systems can provide both types of information, and these two approaches are compared in the context of seasonal rainfall forecasts for two adjoining areas in the Greater Horn of Africa region in tropical East Africa. The direct method utilizes the unprocessed LRF outputs for the region. For the “indirect” method, canonical correlation analysis is used and works by identifying patterns in historical LRF predictions over large tropical domains that relate to observed variability in the East Africa regions. For a given year, projections of the LRF forecasts onto those patterns can then be employed to construct the regional forecasts. This approach takes advantage of the tendency for LRF systems to have greater skill for large‐scale variability than for smaller regional‐scale features. Using case studies, it is found the two approaches contain complementary information: the indirect approach can provide notable skill benefits in years with strong large‐scale forcing while in some years, particularly when large‐scale signals are weak, the “direct” forecast are superior. Skill comparisons over many years found that, although results are region/season dependent, in general the indirect approach has higher skill overall—with improvements equal to or greater than those afforded by a 1‐month reduction in lead time with the direct approach. Results for both methods used separately and in combination are provided for the March–April–May and September‐to‐December seasons in the two regions, using data from currently operational dynamical LRF systems. Skill was best for the September‐to‐December season in the southern region, using “indirect” forecasts. The “direct” approach was better than “indirect” for the March‐to‐May season in the northern region. In general, combination did not produce a substantial benefit.

Projecting Wet Season Rainfall Extremes Using Regional Climate Models Ensemble and the Advanced Delta Change Model: Impact on the Streamflow Peaks in Mkurumudzi Catchment, Kenya

Each year, many African countries experience natural hazards such as floods and, because of their low adaptative capabilities, they hardly have the means to face the consequences, and therefore suffer huge economic losses. Extreme rainfall plays a key role in the occurrence of these hazards. Therefore, climate projection studies should focus more on extremes in order to provide a wider range of future scenarios of extremes which can aid policy decision making in African societies. Some researchers have attempted to analyze climate extremes through indices reflecting extremes in climate variables such as rainfall. However, it is difficult to assess impacts on streamflow based on these indices alone, as most hydrological models require daily data as inputs. Others have analyzed climate projections through general circulation models (GCMs) but have found their resolution too coarse for regional studies. Dynamic downscaling using regional climate models (RCMs) seem to address the limitation of GCMs, although RCMs might still lack accuracy due to the fact that they also contain biases that need to be eliminated. Given these limitations, the current study combined both dynamic and statistical downscaling methods to correct biases and improve the reproduction of high extremes by the models. This study’s aim was to analyze extreme high flows under the projection of extreme wet rainfall for the horizon of 2041 of a Kenyan South Coast catchment. The advanced delta change (ADC) method was applied on observed data (1982–2005), control (1982–2005) and near future (2018–2041) from an ensemble mean of multiple regional climate models (RCMs). The created future daily rainfall time series was introduced in the HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) hydrological model and the generated future flow were compared to the baseline flow at the gaging station 3KD06, where the observed flow was available. The findings suggested that in the study area, the RCMs, bias corrected by the ADC method, projected an increase in rainfall wet extremes in the first rainy season of the year MAMJ (March–April–May–June) and a decrease in the second rainy season OND (October–November–December). The changes in rainfall extremes, induced a similar change pattern in streamflow extremes at the gaging station 3KD06, meaning that an increase/decrease in rainfall extremes generated an increase/decrease in the streamflow extremes. Due to lack of long-term good quality data, the researchers decided to perform a frequency analysis for up to a 50 year return period in order to assess the changes induced by the ADC method. After getting a longer data series, further analysis could be done to forecast the maximum flow to up to 1000 years, which could serve as design flow for different infrastructure.

FLOOD IMPACT-BASED FORECASTING FOR EARLY WARNING AND EARLY ACTION IN TANA RIVER BASIN, KENYA

Abstract. Kenya is mostly affected by floods during the March-April-May (MAM) and October-November-December (OND) rainfall. This often occurs along river basins such as the Tana river basin, leading to disruption of people’s livelihoods, loss of lives, infrastructure destruction and interruption of economic activities. This study used openly available data on flood exposure, vulnerability, lack of coping capacity, flood impacts and observed satellite rainfall to analyse and predict forecast-based impacts in Tana river. Earth observation satellites including LANDSAT, sentinel 1 and 2 were acquired based on credible flood event dates to validate flood exposure and flood events. The community risk assessment (CRA) approach was used to delineate communities at high risk of floods using combination of data on vulnerability, flood exposure and lack of coping capacity. Using an ordinary least squares (OLS) predictive model, observed satellite rainfall was used as a covariate in order to predict flood impacts on communities with high flood risk scores in Tana river. Weighted scores from the CRA dimensions were summed up with forecasted hazards from the OLS model in order to derive a flood impact-based forecast. The flood impact information is to be used in forecast-based action through early warning, early action protocols thereby reducing impacts of potential floods in communities living in high flood risk areas based on the flood risk map.

Heat wave occurrences over Senegal during spring: Regionalization and synoptic patterns

AbstractBased on 12 Senegalese stations of the Global Summary of the Day (GSOD) database (1979–2014), heat waves (HW) are defined for each station in spring (March–April–May, the hottest season in Senegal) as the daily maximum temperature (Tx), minimum temperature (Tn), or average apparent temperature of the day (AT), exceeding the corresponding 95% mobile percentile for at least three consecutive days. A hierarchical cluster analysis used to regionalize HW in these 12 stations is applied to simultaneous occurrences of daily temperature peaks over their 95% mobile percentiles. Three homogeneous zones of four stations each are identified (Zone 1, Zone 2 and Zone 3), from west (Atlantic coastline) to east (inland Senegal). Atmospheric circulation associated with HW is assessed through composites of ERA‐Interim deseasonalized anomalies, with the start date of each HW in each zone used as a reference. The main pattern controlling the presence of HW in Senegal consists in positive pressure anomalies centred around the strait of Gibraltar, promoting easterly to northeasterly wind anomalies. This causes higher temperatures in the three zones of Senegal, and lower temperatures and drier air over the central Sahel. This pattern is opposite to that characteristic of HW in the central Sahel shown in previous studies. From Zone 1 to Zone 3, the temperature and moisture patterns are shifted to the east while pressure anomalies weaken drastically. Night‐time Tn‐HW are characterized by higher water vapour contents than daytime Tx‐HW, corroborating and complementing previous studies over the Sahel. These HW patterns are close to the canonical mode of intra‐seasonal atmospheric variability over Senegal.

A rain on snow climatology and temporal analysis for the eastern United States

ABSTRACTRain-on-snow (ROS) has the potential to produce devastating floods by enhancing runoff from snowmelt. Although a common phenomenon across the eastern United States, little research has focused on ROS in this region. This study used a gridded observational snow dataset from 1960–2009 to establish a comprehensive seasonal climatology of ROS for this region. Additionally, different rain and snow thresholds were compared while considering temporal trends in ROS occurrence at four grid cells representing individual locations. Results show most ROS events occur in MAM (March-April-May). ROS events identified with rainfall >1 cm are more frequent near the east coast and events identified with >1 cm snow loss are more common in higher latitudes and/or elevations. Decreasing trends in DJF (December-January-February) ROS events were identified near the coastal areas, with increasing trends in the northern portion of the domain. Significant decreasing trends in MAM ROS are likewise present on a regional scale. Factors playing a role in snowpack depth and rainfall, such as movement of storm tracks in this region, should be considered with future work to discern mechanisms causing the changes in ROS frequency.

Dependence of Rainfall on Solar Activity Features

In the present paper, we have studied the statistical analysis between all India homogeneous rainfall (RF) with sunspot number (SN) during 1900-2014 (115-year period). We have calculated correlations coefficient of rainfall with sunspot number (SN) for annual and seasonal months: January, February (JF); March April May (MAM); June July August September (JJAS) and October November December (OND) and we have obtained high correlation ranging between 0.75 to 0.95. Our results show that rainfall is strongly influenced by sunspot number. Our study also indicates that occurrence of solar activity features play an important role for variability of rainfall.

Dependence of Rainfall on Solar Activity Features

In the present paper, we have studied the statistical analysis between all India homogeneous rainfall (RF) with sunspot number (SN) during 1900-2014 (115-year period). We have calculated correlations coefficient of rainfall with sunspot number (SN) for annual and seasonal months: January, February (JF); March April May (MAM); June July August September (JJAS) and October November December (OND) and we have obtained high correlation ranging between 0.75 to 0.95. Our results show that rainfall is strongly influenced by sunspot number. Our study also indicates that occurrence of solar activity features play an important role for variability of rainfall.

Estimations of global shortwave direct aerosol radiative effects above opaque water clouds using a combination of A-Train satellite sensors

Abstract. All-sky direct aerosol radiative effects (DARE) play a significant yet still uncertain role in climate. This is partly due to poorly quantified radiative properties of aerosol above clouds (AAC). We compute global estimates of shortwave top-of-atmosphere DARE over opaque water clouds (OWCs), DAREOWC, using observation-based aerosol and cloud radiative properties from a combination of A-Train satellite sensors and a radiative transfer model. There are three major differences between our DAREOWC calculations and previous studies: (1) we use the depolarization ratio method (DR) on CALIOP (Cloud–Aerosol Lidar with Orthogonal Polarization) Level 1 measurements to compute the AAC frequencies of occurrence and the AAC aerosol optical depths (AODs), thus introducing fewer uncertainties compared to using the CALIOP standard product; (2) we apply our calculations globally, instead of focusing exclusively on regional AAC “hotspots” such as the southeast Atlantic; and (3) instead of the traditional look-up table approach, we use a combination of satellite-based sensors to obtain AAC intensive radiative properties. Our results agree with previous findings on the dominant locations of AAC (south and northeast Pacific, tropical and southeast Atlantic, northern Indian Ocean and northwest Pacific), the season of maximum occurrence and aerosol optical depths (a majority in the 0.01–0.02 range and that can exceed 0.2 at 532 nm) across the globe. We find positive averages of global seasonal DAREOWC between 0.13 and 0.26 W m−2 (i.e., a warming effect on climate). Regional seasonal DAREOWC values range from −0.06 W m−2 in the Indian Ocean offshore from western Australia (in March–April–May) to 2.87 W m−2 in the southeast Atlantic (in September–October–November). High positive values are usually paired with high aerosol optical depths (>0.1) and low single scattering albedos (<0.94), representative of, for example, biomass burning aerosols. Because we use different spatial domains, temporal periods, satellite sensors, detection methods and/or associated uncertainties, the DAREOWC estimates in this study are not directly comparable to previous peer-reviewed results. Despite these differences, we emphasize that the DAREOWC estimates derived in this study are generally higher than previously reported. The primary reasons for our higher estimates are (i) the possible underestimate of the number of dust-dominated AAC cases in our study; (ii) our use of Level 1 CALIOP products (instead of CALIOP Level 2 products in previous studies) for the detection and quantification of AAC aerosol optical depths, which leads to larger estimates of AOD above OWC; and (iii) our use of gridded 4∘×5∘ seasonal means of aerosol and cloud properties in our DAREOWC calculations instead of simultaneously derived aerosol and cloud properties from a combination of A-Train satellite sensors. Each of these areas is explored in depth with detailed discussions that explain both the rationale for our specific approach and the subsequent ramifications for our DARE calculations.

Unraveling the Role of Human Activities and Climate Variability in Water Level Changes in the Taihu Plain Using Artificial Neural Network

Water level, as a key indicator for the floodplain area, has been largely affected by the interplay of climate variability and human activities during the past few decades. Due to a nonlinear dependence of water level changes on these factors, a nonlinear model is needed to more realistically estimate their relative contribution. In this study, the attribution analysis of long-term water level changes was performed by incorporating multilayer perceptron (MLP) artificial neural network. We took the Taihu Plain in China as a case study where water level series (1954–2014) were divided into baseline (1954–1987) and evaluation (1988–2014) periods based on abrupt change detection. The results indicate that climate variables are the dominant driver for annual and seasonal water level changes during the evaluation period, with the best performance of the MLP model having precipitation, evaporation, and tide level as inputs. In the evaluation period, the contribution of human activities to water level changes in the 2000s is higher than that in the 1990s, which indicates that human activities, including the rapid urbanization, are playing an important role in recent years. The influence of human activities, especially engineering operations, on water level changes in the 2000s is more evident during the dry season (March-April-May (MAM) and December-January-February (DJF)).

Changing aerosol loadings over Central Himalayan region (2007–2016) – A satellite perspective

The paper presents the variation of aerosol columnar variation and vertical profiles over the study area encompassing a portion of Central Himalayan region and the surrounding planar areas. The analysis is carried out using decadal (2007–2016) Level-2 CALIOP (onboard CALIPSO), Level-3 MODIS (onboard Aqua/Terra) and MISR (onboard Terra) datasets. Four seasons have been considered, namely, DJF (December-January-February), MAM (March-April-may), JJA (June-July-August) and SON (September-October-November). The CALIOP data has been screened using quality flags, i.e., CAD score, aerosol layer fraction and extinction quality checks. There are also signatures of elevated aerosol layers, especially during winter (DJF) and post-monsoon (SON) seasons. Latitudinal and longitudinal variations of the vertical structure have also been presented. The HYSPLIT back-trajectory analysis has been carried out in order to explore the changes in the sources of the aerosols during different seasons. It is seen that as we go higher up in the altitudes, the long range transport becomes more dominant. Compared to the other seasons, the air masses are more localized in JJA season. The changing aerosol amounts at different heights of the atmosphere have also been discussed in terms of decadal trends for the four seasons. These trends have been supplemented by the AOD trends from the passive satellite sensors. We find that aerosol amounts have increased in the last decade during DJF and SON seasons, when the atmospheric conditions are more stable. On the contrary, aerosol amounts have significantly decreased in JJA season throughout the vertical column. The effect of changing amounts of aerosols may have impacts on the snow cover variability as well. In this connection, the seasonal trends in snow cover have also been explored during the considered decadal period. Especially during the DJF and SON seasons, decreasing trends in snow cover are observed when the aerosol amounts have been found to be increased.

Ecosystem Productivity and Water Stress in Tropical East Africa: A Case Study of the 2010–2011 Drought

Characterizing the spatiotemporal patterns of ecosystem responses to drought is important in understanding the impact of water stress on tropical ecosystems and projecting future land cover transitions in the East African tropics. Through the analysis of satellite measurements of solar-induced chlorophyll fluorescence (SIF) and the normalized difference vegetation index (NDVI), soil moisture, rainfall, and reanalysis data, here we characterize the 2010–2011 drought in tropical East Africa. The 2010–2011 drought included the consecutive failure of rainy seasons in October–November–December 2010 and March–April–May 2011 and extended further east and south compared with previous regional droughts. During 2010–2011, SIF, a proxy of ecosystem productivity, showed a concomitant decline (~32% lower gross primary productivity, or GPP, based on an empirical SIF–GPP relationship, as compared to the long-term average) with water stress, expressed by lower precipitation and soil moisture. Both SIF and NDVI showed a negative response to drought, and SIF captured the response to soil moisture with a lag of 16 days, even if it had lower spatial resolution and much smaller energy compared with NDVI, suggesting that SIF can also serve as an early indicator of drought in the future. This work demonstrates the unique characteristics of the 2010–2011 East African drought and the ability of SIF and NDVI to track the levels of water stress during the drought.

Analysis of the diurnal to seasonal variability of solar radiation in Douala, Cameroon

One of the solutions for resolving the problem of energy production deficit in Central Africa is to promote renewable energy sources. The knowledge of the solar variability represents a determining factor for design, dimensioning, performance assessment, and energetic management of renewable energy conversion systems. In this work, we analyze the behaviour of solar radiation from diurnal to seasonal time scales for the region of Douala, the largest industrial city of Cameroon. Observed data of temperature, sunshine duration and precipitation, and satellite estimates of solar radiation (from Soda Solar Project) and cloudiness (acquired from Meteosat Second Generation) were used. The results show that the solar radiation annual cycle at Douala can be decomposed into four seasons: the main dry season in December–January–February (DJF) which corresponds to the most illuminated season, the main rainy season in June–July–August–September (JJAS) which is the least illuminated, and two intermediate periods, March–April–May (MAM) and October–November (ON) which correspond to semi-illuminated periods. Using a hierarchical clustering analysis (HCA), we found that Douala usually experiences five main types of solar radiation diurnal cycles depicting very bright to obscure days. A characterization of sky conditions during these five diurnal cycles shows a predominance of low and high opaque clouds during obscure days.

Spatial mapping and modeling of arsenic contamination of groundwater and risk assessment through geospatial interpolation technique

Spatial interpolation technique is useful for spatial mapping with sparse data procured from vantage in situ sampling sources. Through spatial interpolation, wall-to-wall mapping of the arsenic concentration in groundwater was accomplished for the whole of the study area by using known concentration value at nearby locations (aquifers) under homogenous terrain conditions. This present study proposes an empirical methodology through interpolation approach for spatial mapping of seasonal and annual groundwater arsenic contamination in the district North 24 Parganas, which happens to be the one of the worst arsenic-affected districts of West Bengal, India, in Bengal Basin. Two types of interpolation approach, Thiessen polygon and Kriging, have been used for spatial mapping of arsenic distribution. On the basis of spatial distribution map, classification has been done for the entire district into seven arsenic concentration zones with various levels of contaminations from arsenic in groundwater (0.01 mg/L as WHO-declared maximum limit for safe zone). In this study, a total of six seasonal (pre-/post-monsoon) data from 2006 to 2008 have been interpreted to examine temporal changes of arsenic concentration in groundwater, and finally, the future trend is projected. Future trend assessment of arsenic contamination has been performed through statistical analysis fitting a linear regression equation. Through this study, it is revealed that the unaffected blocks in the pre-monsoon season (March–April–May) of year 2006 became significantly affected by the end of year 2008. From regression model, it has been predicted that if this trend continues, then, after ten years 2/3 blocks of the said districts will be arsenic affected.

On soil moisture deficit, low precipitation, and temperature extremes impacts on rainfed cereal productions in Iran

In this study, rainfed wheat and barley yields response to severity and persistence of cold temperature extremes in the January-February-March (JFM) and high temperature, low rainfall, and soil water shortage extremes in the October-November-December (OND) and March-April-May (MAM) was evaluated. For this purpose, the associations between the yields and the extreme indices were tested using Pearson’s correlation coefficient for 10 sites located in the western Iran over 1982–2013. The FAO-56 soil water balance model was employed to develop the soil water deficit extreme indices. The impacts of extremes on the yields were also investigated under El Nino–Southern Oscillation (ENSO) phases. The yields were anti-correlated significantly (p < 0.05) with the soil water shortage and rainfall deficit extreme indices, but not with the temperature-related extreme indices, for the majority of sites. On average, a more negative association also existed between the yields and the soil moisture-related indices relative to the rainfall and temperature extreme indices. The yields seem, therefore, to be more adversely impacted by agricultural droughts. Compared with the OND extremes, the high temperature, precipitation shortage, and soil water deficit extremes in MAM had more negative effects on the yields. This can be attributable to strong sensitivity of reproductive processes commonly occurring during MAM to such stresses. In most cases, the yields were more adversely influenced by the short-term extremes. The results also revealed that more frequent occurrence of high temperature and drought-related extremes during La Nina can well explain the substantial yield loss in cold ENSO years.