Annual Rainfall Variability Research Articles

Disentangling the effects of climate and urban growth on streamflow for sustainable urban development: A stochastic approach to flow regime attribution

In urban watersheds, climate variability and change, urban growth, and stormwater management can act concurrently over time to shape and alter the streamflow dynamics. Yet, when assessing the impacts of urbanization on streamflow, these factors are rarely taken simultaneously into consideration. There is thus an emerging need for approaches that allow disentangling the hydrological impacts of land cover change from those due to climate, in the context of long-term, historical changes in urban landscapes. This is here termed flow regime attribution. We demonstrate in this study the ability of a stochastic mechanistic model to perform flow regime attribution. The modeling approach is applied to the Watts Branch watershed, located in metropolitan Washington D.C., United States. To carry out the flow regime attribution, the model is used to compute streamflow indicators of hydrological alteration and perform parameter sensitivity analysis. The application of the model shows that in Watts Branch urban growth drives the long-term temporal trend in streamflow. The mean and variance of streamflow increase at the end of the gauging period by 2 and 7 times, respectively, their value relative to an only climate (no urban growth) scenario. The results show that climate mainly amplifies or dampens the temporal trend according to wet/dry variations in annual rainfall. Further, the model facilitates the attribution process by allowing the derivation of streamflow indicators that directly depend on the model parameters. The proposed modeling approach may be useful for assessing the long-term flow behavior of urban watersheds, and informing sustainable urban development decisions.

Ecosystem heterogeneity and diversity mitigate Amazon forest resilience to frequent extreme droughts.

The impact of increases in drought frequency on the Amazon forest's composition, structure and functioning remain uncertain. We used a process- and individual-based ecosystem model (ED2) to quantify the forest's vulnerability to increased drought recurrence. We generated meteorologically realistic, drier-than-observed rainfall scenarios for two Amazon forest sites, Paracou (wetter) and Tapajós (drier), to evaluate the impacts of more frequent droughts on forest biomass, structure and composition. The wet site was insensitive to the tested scenarios, whereas at the dry site biomass declined when average rainfall reduction exceeded 15%, due to high mortality of large-sized evergreen trees. Biomass losses persisted when year-long drought recurrence was shorter than 2-7yr, depending upon soil texture and leaf phenology. From the site-level scenario results, we developed regionally applicable metrics to quantify the Amazon forest's climatological proximity to rainfall regimes likely to cause biomass loss >20% in 50yr according to ED2 predictions. Nearly 25% (1.8 million km2 ) of the Amazon forests could experience frequent droughts and biomass loss if mean annual rainfall or interannual variability changed by 2σ. At least 10% of the high-emission climate projections (CMIP5/RCP8.5 models) predict critically dry regimes over 25% of the Amazon forest area by 2100.

Farmers’ perspectives

PurposeUnderstanding farmers’ perceptions of how the climate is changing is vital to anticipating its impacts. Farmers are known to take appropriate steps to adapt only when they perceive change to be taking place. This study aims to analyse how African indigenous vegetable (AIV) farmers perceive climate change in three different agro-climatic zones (ACZs) in Kenya, identify the main differences in historical seasonal and annual rainfall and temperature trends between the zones, discuss differences in farmers’ perceptions and historical trends and analyse the impact of these perceived changes and trends on yields, weeds, pests and disease infestation of AIVs.Design/methodology/approachData collection was undertaken in focus group discussions (FGD) (N = 211) and during interviews with individual farmers (N = 269). The Mann–Kendall test and regression were applied for trend analysis of time series data (1980-2014). Analysis of variance and least significant difference were used to test for differences in mean rainfall data, while a chi-square test examined the association between farmer perceptions and ACZs. Coefficient of variation expressed as a percentage was used to show variability in mean annual and seasonal rainfall between the zones.FindingsFarmers perceived that higher temperatures, decreased rainfall, late onset and early retreat of rain, erratic rainfall patterns and frequent dry spells were increasing the incidences of droughts and floods. The chi-square results showed a significant relationship between some of these perceptions and ACZs. Meteorological data provided some evidence to support farmers’ perceptions of changing rainfall. No trend was detected in mean annual rainfall, but a significant increase was recorded in the semi-humid zone. A decreasing maximum temperature was noted in the semi-humid zone, but otherwise, an overall increase was detected. There were highly significant differences in mean annual rainfall between the zones. Farmers perceived reduced yields and changes in pest infestation and diseases in some AIVs to be prevalent in the dry season. This study’s findings provide a basis for local and timely institutional changes, which could certainly help in reducing the adverse effects of climate change.Originality/valueThis is an original research paper and the historical trends, farmers’ perceptions and effects of climate change on AIV production documented in this paper may also be representative of other ACZs in Kenya.

Trend and variability analysis of rainfall and temperature in the Tana basin region, Ethiopia

Abstract Global warming and climate variability are emerging as the foremost environmental problems in the 21st century, particularly in developing countries. Ethiopia is one of the countries located in the sub-Sahara region and climate variability has a significant impact on the economy of the country. The aim of this study is to characterize annual and seasonal rainfall and annual temperature variability, and to measure trends on both the spatial and the temporal scale for ten selected stations in the Tana basin region, Ethiopia. The Mann–Kendall test and Sen's slope estimator were used to assess trends and variability of rainfall and temperature. The spatial distribution of rainfall and temperature was determined using the inverse distance weighted technique. Results indicated that the amount of rainfall decreased for the majority of the stations. The annual rainfall showed significant decreasing trends with a magnitude ranging from −5.92 mm/year at Injibara to −9.74 mm/year in Wegera. However, a positive trend of annual rainfall was observed at Addis Zemen (1.81 mm/year). The minimum, maximum and mean temperatures have increased significantly for most of the stations. An increasing trend of annual maximum temperature was obtained between 1980 and 2015; an increase of 1.08°C was observed.

Is Fish Farming an Illusion for Lake Malawi Riparian Communities under Environmental Changes?

Global environmental changes have negatively affected many food systems while the demand for food has continued to rise. An urgent need exists to identify other sustainable means of producing food. This is a case in Malawi, where capture fisheries and agriculture are not supplying sufficient food. Fish farming food systems by communities who rely on inland fisheries have not been evaluated. Therefore, a study was conducted in two phases: January 2016 to May 2016 and in July 2017 to evaluate if fish farming could sustainably support livelihoods of Lake Malawi riparian communities. We used mixed methods to collect and analyze data. The data collection methods included explorative surveys, household survey interviews, focus group discussion and key informant interviews. Qualitative data was analyzed using content analysis for themes. This identified themes that were quantitatively analyzed using descriptive and inferential statistics. We observed that fish farming was dominated by men and also not the main occupation for the respondents despite owning fishponds. The respondents have water and land, which are prerequisite for any farming. The study also observed fish farming production challenges related to quality fingerlings, formulated diets, and extension services. Cases of food insecurity amongst the respondents were also prevalent due to lack of food to cover the entire year. Weak synergies existed between fish farming and agriculture restricting bio-resource flow and water usage between these two food systems, meaning the outcomes of the food systems provide unsustainable diets. Furthermore, water availability, money spent on food, and cassava cropping increased fish farming participation. Whereas operating a bicycle taxi, casual labor, former fish farming, as well as application of agricultural wastes negatively affected fish farming. On the other hand, extreme weather events (increased incidences of droughts and floods) attributed to inter annual rainfall variation also negatively affected fish farming. The responses from Lake Malawi riparian communities indicate that they merely look upon fish farming as an alternative to capture fisheries than as an illusion. Nonetheless, the research provides a theoretical platform to explore the potential to develop sustainable fish farming food system adapted to such changes. Therefore, we have brought new evidence that progress of fish farming in Malawi is being made, but there is a long way to go before it can be considered successful and sustainable.

The Influence of ENSO to the Rainfall Variability in North Sumatra Province

The El Niño Southern Oscillation (ENSO) is a global phenomenon that affects the variability of rainfall in North Sumatra. The influence of ENSO will be different for each region. This review will analyse the influence of ENSO activity on seasonal and annual rainfall variability. In this research, North Sumatra Province will be divided into 4 (four) regions based on topographical conditions, such as: East Coast (EC), East Slope (ES), Mountains (MT), and West Coast (WC). The method used was statistical and descriptive analysis. Data used in this research were rainfall data from 15 stations / climate observation posts which spread in North Sumatera region and also anomaly data of Nino 3.4 region from period 1981-2016. The results showed that the active El Niño had an effect on the decreasing the rainfall during the period of DJF, JJA and SON in East Coast, East Slope, and Mountains with the decreasing of average percentage of annual rainfall up to 7%. On the contrary, the active La Nina had an effect on the addition of rainfall during the period DJF and JJA in the East Coast and Mountains with the increasing of average percentage of annual rainfall up to 6%.

Spatial and temporal variability of rainfall: case of Bizerte-Ichkeul Basin (Northern Tunisia)

This study examines spatial and temporal variability of rainfall in Bizerte-Ichkeul Watershed. The basin, located in the extreme north of Tunisia, covers an area of 3084 km2. Thirteen rainfall stations, with continuous monthly precipitation records over the period (1970–2011), were considered in the analysis. Two methods were used. In the first, the dimensionless standardized precipitation ratio is applied to examine precipitation temporal variation. The second method is represented by continuous wavelet analysis for the precipitation spatial analysis and the identification of the origin of its variability. The study of temporal variability of annual rainfall showed severe persistent and recurrent drought episodes over the period (1977–2001). Wavelet analysis resulted in detecting the modes and origins of precipitation variability. Three energy bands were clearly identified: (1, 2–4, and 4–8 years) for the entire watershed. The visualization of the power distribution showed that the observed modes of variability are different in their power distributions from one station to another. The approach adopted allowed the identification of two groups with the same precipitation frequency and temporal variation. These groups were defined according to the difference in occurrence of the frequency band for each station.

Rainfall and Temperature in the Limpopo River Basin, Southern Africa: Means, Variations, and Trends from 1979 to 2013

Understanding temporal and spatial characteristics of regional climate is essential for decision making in water resource management. Established statistical and GIS techniques were used to evaluate annual and seasonal variations of rainfall and temperature in time and space from 1979 to 2013 in the Limpopo River Basin (LRB). Annual means of rainfall in the LRB varied between 160 and 1109 mm, generally from west to east of the basin during the study period. Annual minimum and maximum temperature ranged from 8 °C in the south to 20 °C in the east of the basin, and 23 °C in the south of the basin to 32 °C in the east. The respective coefficients of variation (CVs) of these variables showed an inverse pattern to the annual values, with rainfall having high CV values (28% to 70% from east to west of the basin) compared to temperature CV values. Seasonal variations followed similar patterns as annual variations for the individual variables examined. Trend analysis showed upward trends for both annual and seasonal rainfall in most parts of the basin, except for the winter season which showed a decreasing trend. Analysis of minimum temperature on an annual basis and for the winter season and spring season shows upward trends during the study period over the whole basin while minimum temperature for summer and autumn showed decreasing trends. Maximum temperature, by contrast, showed decreasing trends on an annual, summer, autumn, and spring basis but an increasing trend for winter during the study period in most parts of the basin.

Analysis and Modelling of Stormwater Volume Control Performance of Rainwater Harvesting Systems in Four Climatic Zones of China

Rainwater harvesting has been widely used to alleviate urban water scarcity and waterlogging problems. In this study, a water balance model is developed to continuously simulate the long-term (57 to 65 years) stormwater capture efficiency of rainwater harvesting systems for three water demand scenarios at four cities across four climatic zones of China. The impacts of the “yield after spillage” (YAS) and “yield before spillage” (YBS) operating algorithms, climatic conditions, and storage and demand fractions on stormwater capture efficiency of rainwater harvesting systems are analyzed. The YAS algorithm, compared with the YBS, results in more conservative estimations of stormwater capture efficiency of rainwater harvesting systems with relatively small storage tanks (e.g., ≤50 m3). The difference between stormwater capture efficiency calculated using the YBS and YAS algorithms can be remedied by increasing storage capacity and reduced by decreasing water demand rates. Higher stormwater capture efficiency can be achieved for rainwater harvesting systems with higher storage and demand fractions and located in regions with less rainfall. However, the lager variations in annual rainfall in arid zones may lead to unstable stormwater management performance of rainwater harvesting systems. The impacts of storage and demand fractions on stormwater capture efficiency of rainwater harvesting systems are interactive and dependent on climatic conditions. Based on the relationships among storage capacity, contributing area, water demand, and stormwater capture efficiency of rainwater harvesting systems, easy-to-use equations are proposed for the hydrologic design of rainwater harvesting systems to meet specific stormwater control requirements at the four cities.

Farmers’ Knowledge of Inter Annual Rainfall Variability and Adaptation Strategies in Nasarawa State, Nigeria

Farmers’ Knowledge of Inter Annual Rainfall Variability and Adaptation Strategies in Nasarawa State, Nigeria

Sensitivity of vegetation to annual rainfall variations over Nigeria

Distribution and composition of vegetation are strongly controlled by climatic conditions. Of the climatic elements, precipitation has been found to be a major influence on the abundance, health and productivity of vegetation. This study investigated the spatial and temporal pattern in the sensitivity of vegetation to rainfall in Nigeria between 1980 and 2009. Rainfall data collected from 23 observation points were analysed and spatially correlated with monthly Normalized Difference Vegetation Index (NDVI) values for the same period to estimate the responsiveness of vegetation to annual variations in rainfall. The results revealed a non-uniform pattern in the sensitivity of vegetation to rainfall across the country. Areas in the forest and guinea savannah zones with higher mean annual rainfall exhibited stronger correlation, while the Sudan savannah zone exhibited weaker correlations. However, further northwards in the Sahel fringes, the correlations between rainfall and vegetation were found to be very negligible (mostly zero), except for the forested wetland around the Lake Chad where stronger positive coefficient of correlation were found. The findings have implications for ecological studies, agricultural planning and other socio-economic activities, which are directly concerned with vegetation and vegetal cover. This knowledge could be put to use to ensure better planning and decisions that concern the environment.

Time‐since‐fire and climate interact to affect the structural recovery of an Australian semi‐arid plant community

AbstractHow does time‐since‐fire influence the structural recovery of semi‐arid, eucalypt‐dominated Murray‐Mallee shrublands after fire, and is recovery affected by spatial variation in climate? We assessed the structure and dynamics of a hummock grass, Triodia scariosa N.T. Burb, and mallee eucalypts – two key structural components of mallee shrublands – using a >100 year time‐since‐fire chronosequence. The relative influence of climatic variables, both individually and combined with time‐since‐fire, was modelled to account for spatial variation in the recovery of vegetation structural components. Time‐since‐fire was the primary determinant of the structural recovery of T. scariosa and eucalypts. However, climate, notably mean annual rainfall and rainfall variability, also influenced the recovery of the eucalypt overstorey, T. scariosa cover and mean hummock height. We observed that (i) the mean number of live eucalypt stems per individual decreased while mean individual basal area increased, (ii) cover of T. scariosa peaked at ~30 years post‐fire and gradually decreased thereafter, and (iii) the ‘hummock’ form of T. scariosa occurred throughout the chronosequence, whereas the ‘ring’ form tended not to occur until ~30 years post‐fire. Time‐since‐fire was the key determinant of the structural recovery of eucalypt‐dominated mallee shrublands, but there is geographical variation in recovery related to rainfall and its variability. Fire regimes are likely to have different effects across the geographic range of mallee shrublands.

Analysis of Historical Monthly, Seasonal and Annual Rainfall Variability (1990–2014) in Machakos Sub County, Kenya

Varying climatic conditions with increased uncertainty mainly in rainfall and temperature are salient features in marginal areas globally, adversely affecting farming activities particularly in Africa. Kenya experiences variations of annual rainfall with considerable uncertainty. These variations ranging from below 500mm to above 2000mm annually, cause droughts and floods respectively, affecting farming activities and yields. This study attempts to analyze historical monthly, seasonal and annual rainfall variability in Machakos sub County between 1990 – 2014. Studies on analysis of rainfall variability covered in Machakos have dwelt on broad and expansive areas of Eastern and South Eastern Kenya including Machakos, Kitui and Makueni Counties. Broad studies give generalized information which may not give true and adequate results for some specific areas. This has prompted the need to focus on a smaller area. This study focused on Machakos sub County, a semi arid area, East of Central Kenya highlands. Existing studies in the mentioned areas, mainly cover the period between 1930 - 1990. After this period, very scarce information exists. The main objective of the study was to establish the historical monthly, seasonal and annual rainfall variability in Machakos sub county between 1990 – 2014. Publications from Meteorological Department Nairobi and sub county Agricultural offices, provided rainfall data. Using SPSS, quantitative data was analyzed using descriptive statistics such as Means, Standard deviation and percentages. Inferential statistics using Pearson’s Correlation technique was used to determine relationships between variables. Coefficient of variability, Relative variability and Precipitation Concentration index measured Rainfall Variability. The study established that monthly, seasonal and annual rainfall distribution in Machakos sub County was highly variable, erratic and unpredictable over the 25 years with a CV of 24% and a PCI of 10. There is high temporal rainfall variability (CV of 24% and PCI of 10) in the sub County. Disseminating information on rainfall forecasts, rainwater harvesting for irrigation, proper cattle stocking, a forestation and reforestation programs were highly recommended.

Improvement of Hydroclimatic Projections over Southeast Spain by Applying a Novel RCM Ensemble Approach

Climate model outputs can be used as climate forcing for hydrological models to study the impact of climate change on the water cycle. This usually propagates cumulative uncertainties, transferring the errors from the climate models to the hydrological models. Then, methodologies are needed to evaluate the impact of climate change at basin scale by reducing the uncertainties involved in the modeling chain. The paper aims to assess the impact of climate change on the runoff, considering a novel approach to build a Regional Climate Model (RCM) ensemble as climate forcing for a parsimonious spatially distributed hydrological model. A semiarid basin of southeast of Spain was selected for the study. The RCM ensembles were built based on seasonal and annual variability of rainfall and temperature. If the runoff projections for 2021–2050 are compared to the 1961–1990 observed period, a significant decrease in runoff equal to −20% (p-value t-test 0.05) was projected. However, by changing the observed period to 1971–2000, a despicable change (2.5%) is identified. This fact demonstrates that trends based on short records are very sensitive to the beginning and end dates, due to the natural variability. Special attention should be paid to the selection of the period for impact studies.

THE INFLUENCE OF EL NIÑO ON RAINFALL VARIABILITY IN NORTH SUMATRA PROVINCE

El Niño is a global phenomenon that affects the variability of rainfall in the Province of North Sumatra, Indonesia. The impact of El Niño will be different for each region. This review will analyse the influence of El Niño activity on seasonal and annual rainfall variability. In this research, North Sumatra Province will be divided into 4 (four) regions based on its topographical conditions, such as: East Coast (EC), East Slope (ES), Mountains (MT), and West Coast (WC). The method used was statistical and descriptive analysis. Data used in this research were rainfall data from 15 stations/climate observation posts which is spread in North Sumatera region and anomaly data of Nino 3.4 region over the period 1981 – 2016. The results showed that the active El Niño had an effect on the decreasing of rainfall in the period of DJF, JJA, and SON in East Coast, East Slope, and Mountains with an average annual percentage decline up to 7%. Keywords : El Niño, rainfall, variability, and North Sumatra.

Importance of determining the climatic domains of sheep breeds

The main purpose of the study was to compare the capacity of the major sheep breeds in Morocco to cope with climate changes through the ranges of several climate parameters in which they can be found. We first delimitated the climatic ‘domains’ of each breed by constructing a database including altitude and climatic parameters (minima mean of the coldest month, maxima mean of the hottest month, annual rainfall, pluviothermic coefficient of Emberger Q2, annual minima mean and annual maxima mean) on a 30-year period using the representative stations of each breed distribution. The overlap between each breed combination was quantified through a canonical analysis that extracted the most discriminant parameters. The variance analysis of each climatic parameter evidenced two breeds remarkable by their tolerance. The first one is the Timahdite, mainly settled in areas over 1100 m, which can tolerate the greatest variations in annual rainfall and pluviothermic coefficient. In spite of this feature, this breed is endangered owing to the decreasing quality of pastures. The second one is the D’man which apparently can support high variations in extreme temperatures. In fact, this breed is not well adapted to pastures and requires a special microclimate offered by oases. The information reported in this study will be the basis for the establishment of characterization and selection strategies for Moroccan sheep.

Stochastic Modelling in Yearly Rainfall at Tirunelveli District, Tamil Nadu, India

Rainfall is natural phenomenon and major component of the water cycle and is responsible for depositing most of the fresh water on the earth. Several methods have been proposed by various researchers for modelling rainfall data. This paper deals with the variations of annual rainfall in Tirunelveli based on stochastic method. The study is investigate the changes in rainfall model in present, past form the chronological rainfall chain used to calculate the rainfall amount for successive years in future using the stochastic model in the Tirunelveli district, Tamil Nadu. The data collected from the 24 rain fall station situated the study area for the period of 44 years among 1971 to 2015. Stochastic modelling method predicts the future rainfall in 2025. The main intention of the current study is to explain the make use of first order Markov chain modelling for annual basis of rainfall measurement of Tirunelveli District.

Climatic variability and prediction of annual rainfall using stochastic time series model at Jhansi in central India

Climatic variability and prediction of annual rainfall using stochastic time series model at Jhansi in central India

Changes in regional grain yield responses to chemical fertilizer use in China over the last 20 years

A major challenge facing China is to meet the increasing food demand of its growing population in the face of decreasing arable land area, while sustaining or improving soil productivity and avoiding adverse environmental impacts from intensive agriculture. This study uses data from China Statistical Yearbooks to analyze trends in regional soil productivity and grain yields in the major grain-producing regions in North China (NC), Northeast China (NE), East China (EC), Central China (CC), and Southwest China (SW), associated with regional fertilizer use and annual climate variation in rainfall and mean temperature over the 20 years. During 1992-2012, the average fertilizer increase rates (in kg ha-1 year-1) were in the order of regions CC (6.6) > NC (4.8) > EC (2.4) > SW (2.1) > NE (1.3), while yield responses to fertilizer use (with regression model coefficients, in kg kg-1) were in the order: SW (-0.9)

Environmental controls of lucerne (Medicago sativa L.) growth across a climatic and edaphic gradient

Lucerne is one of the most valuable forage species because of its high productivity and nutritional traits. However, the knowledge of spatio-temporal variability and environmental controls of its growth generated from the simultaneous study of several sites and throughout several years is extremely scarce. Five-year biomass data were analyzed from four rain fed sites located across a climatic and edaphic gradient in Argentina. The aims proposed were to characterize annual and seasonal lucerne growth, to analyze environmental controls of spatial and emporal growth, and to compare water use efficiency (WUE) among sites. Annual growth differed significantly among sites, ranging between 7,514 and 14,262 kg DM/ha. This range at the spatial scale was mainly explained by variations in annual rainfall and WUE among sites. Seasonal growth depended on incident radiation and actual evapotranspiration. Interannual variability of lucerne growth was explained by precipitation occurred during the growing season in the driest sites, on sandy soils with less water retention capacity. Knowing the sources of variability of lucerne growth, would allow developing more efficient livestock management due to less uncertainty on the forage production dynamics.