Seasonal Changes In Rainfall Research Articles

ASSESSMENT OF RAINFALL IMPACT ON GROUNDWATER VULNERABILITY FOR AGRICULTURAL PRODUCTION

The research study was conducted in Northern Lake Victoria basin to assess rainfall impact on groundwater as a fresh water source on 30 farms for agricultural production. Depth Aquifer Recharge and Transmissivity (DART) index quantitative method was applied that revealed that rainfall was directly related to groundwater vulnerability index. Positive DART index was realized during long rainfall season between April and June, and during short rainfall season in September and October. The other months had negative values during dry season. DART indices ranged between -8.40 and 2.50 with positive values representing more resilience to negative impact of erratic rainfall. There were two groundwater vulnerability Index Thresholds of -8.40 and -4.80 that marked onset of long and short rainfall seasons respectively. Mann-Kendall trend analysis and t-test performed indicated a downward trend and a significant difference of groundwater vulnerability index values because of seasonal rainfall changes that caused wells to dry up thus increasing water insecurity. The research study results confirmed the severity of rainfall patterns on groundwater recharge that can affect agricultural production and food security to households.

Changes in rainfall seasonality in Serbia from 1961 to 2020

Changes in rainfall seasonality in Serbia from 1961 to 2020

Two decades of endemic dengue in Bangladesh (2000-2022): trends, seasonality, and impact of temperature and rainfall patterns on transmission dynamics.

The objectives of this study were to compare dengue virus (DENV) cases, deaths, case-fatality ratio [CFR], and meteorological parameters between the first and the recent decades of this century (2000-2010 vs. 2011-2022) and to describe the trends, seasonality, and impact of change of temperature and rainfall patterns on transmission dynamics of dengue in Bangladesh. For the period 2000-2022, dengue cases and death data from Bangladesh's Ministry of Health and Family Welfare's website, and meteorological data from the Bangladesh Meteorological Department were analyzed. A Poisson regression model was performed to identify the impact of meteorological parameters on the monthly dengue cases. A forecast of dengue cases was performed using an autoregressive integrated moving average model. Over the past 23 yr, a total of 244,246 dengue cases were reported including 849 deaths (CFR = 0.35%). The mean annual number of dengue cases increased 8 times during the second decade, with 2,216 cases during 2000-2010 vs. 18,321 cases during 2011-2022. The mean annual number of deaths doubled (21 vs. 46), but the overall CFR has decreased by one-third (0.69% vs. 0.23%). Concurrently, the annual mean temperature increased by 0.49 °C, and rainfall decreased by 314 mm with altered precipitation seasonality. Monthly mean temperature (Incidence risk ratio [IRR]: 1.26), first-lagged rainfall (IRR: 1.08), and second-lagged rainfall (IRR: 1.17) were significantly associated with monthly dengue cases. The increased local temperature and changes in rainfall seasonality might have contributed to the increased dengue cases in Bangladesh.

Habitat partitioning among sympatric tinamous in semiarid woodlands of central Argentina.

Sympatric, phylogenetically related and morphologically similar species that overlap in their distributions at a regional scale display different patterns of co-occurrence at local assemblages. Occurrence of each species at local scales might be the result of interspecific competition for limiting resources. However, these patterns could also arise from species-specific habitat preferences along the abiotic or land use gradients. To assess the role of these mechanisms, we investigated niche partitioning among sympatric tinamou species occurring in semiarid woodlands of central Argentina. We used occupancy models incorporating habitat characteristics and interspecific interactions, while accounting for detectability, to examine the spatial overlap among elegant crested tinamous (Eudromia elegans), brushland tinamous (Nothoprocta cinerascens), and nothura tinamous (Nothura darwinii and N. maculosa) across a wide regional scale. In addition, we investigated time partitioning among these species by estimating the degree of overlap in their daily activity patterns. The regional distribution of the three species was influenced by the gradient in plant productivity and vegetation structure, in agreement with their broad habitat requirements. We also found that the occurrence of each species was presumably affected by the presence of one or two predator species. Models including interactions among tinamou species found weak negative and positive interactions among species pairs, suggesting that co-occurrence patterns were mainly driven by species-specific habitat use rather than interspecific competition. The three species were diurnal, showing two main peaks of activity, and overlapped widely in their overall diel activity, although subordinate species tended to shift their activity patterns to reduce encounters with the dominant tinamou species, suggesting some segregation in this niche dimension. Projected changes in rainfall seasonality and warmer conditions in this region could benefit elegant crested tinamous over the other two species, although climate and land use changes will likely have a negative impact on all tinamou species.

Impact of Atlantic multidecadal variability on rainfall intensity distribution and timing of the West African monsoon

Abstract. Previous studies agree on an impact of the Atlantic multidecadal variability (AMV) on the total seasonal rainfall amounts over the Sahel. However, whether and how the AMV affects the distribution of rainfall or the timing of the West African monsoon is not well known. Here we seek to explore these impacts by analyzing daily rainfall outputs from climate model simulations with an idealized AMV forcing imposed in the North Atlantic, which is representative of the observed one. The setup follows a protocol largely consistent with the one proposed by the Component C of the Decadal Climate Prediction Project (DCPP-C). We start by evaluating model's performance in simulating precipitation, showing that models underestimate it over the Sahel, where the mean intensity is consistently smaller than observations. Conversely, models overestimate precipitation over the Guinea coast, where too many rainy days are simulated. In addition, most models underestimate the average length of the rainy season over the Sahel; some are due to a monsoon onset that is too late and others due to a cessation that is too early. In response to a persistent positive AMV pattern, models show an enhancement in total summer rainfall over continental West Africa, including the Sahel. Under a positive AMV phase, the number of wet days and the intensity of daily rainfall events are also enhanced over the Sahel. The former explains most of the changes in seasonal rainfall in the northern fringe, while the latter is more relevant in the southern region, where higher rainfall anomalies occur. This dominance is connected to the changes in the number of days per type of event; the frequency of both moderate and heavy events increases over the Sahel's northern fringe. Conversely, over the southern limit, it is mostly the frequency of heavy events which is enhanced, thus affecting the mean rainfall intensity there. Extreme rainfall events are also enhanced over the whole Sahel in response to a positive phase of the AMV. Over the Sahel, models with stronger negative biases in rainfall amounts compared to observations show weaker changes in response to AMV, suggesting that systematic biases could affect the simulated responses. The monsoon onset over the Sahel shows no clear response to AMV, while the demise tends to be delayed, and the overall length of the monsoon season enhanced between 2 and 5 d with the positive AMV pattern. The effect of AMV on the seasonality of the monsoon is more consistent to the west of 10∘ W, with all models showing a statistically significant earlier onset, later demise, and enhanced monsoon season with the positive phase of the AMV. Our results suggest a potential for the decadal prediction of changes in the intraseasonal characteristics of rainfall over the Sahel, including the occurrence of extreme events.

Increasing heavy rainfall events and associated excessive soil water threaten a protein-source legume in dry environments of West Africa

The intensification of the hydrological cycle has increased heavy rainfall and drought events in a changing climate. However, compared to drought, the impacts of heavy rainfall on crop production are under-studied. Using field experimental data and a calibrated crop model CYGMA, we showed that excessive soil water associated with heavy rainfall events is having a detrimental effect on cowpea yields, even in the dry environments of West Africa where cowpea is an important, protein-rich cash crop. Cowpea yields are susceptible to heavy rainfall in areas with poorly drained soils, and to drought in soils that have a low water-retention capacity. The crop model captured of the main characteristics of the observed development, growth, and yield, as well as the characteristics of root-zone soil water contents and how they vary by soil type. The analysis of d4PDF factual and counterfactual climate model simulations revealed that heavy rainfall events associated with anthropogenic climate change have increased in recent decades, and that they are projected to increase in future. Further, changes in seasonal rainfall and the number of dry days would be largely absent from CMIP6 climate projections by mid-century. Reductions in cowpea yields due to excessive soil water is projected to become more frequent, and the potential damage in a 1-in-100 extremely wet year would be comparable to the damage currently experienced in droughts, irrespective of soil types. Simulations of the projected damage due to drought show that the situation will be similar to current levels, with drought remaining a major climate hazard. However, excessive soil water is projected to be a serious threat to food security in the region. Our findings indicate that, even in dry environments, cropping systems need to be implemented in order to reduce the susceptibility of soils to both drought and excessive soil water.

The Synergic Effects of Climate Variability on Rainfall Distribution over Hare Catchment of Ethiopia

Climate analysis at relevant time scales is important for water resources management, agricultural planning, flood risk assessment, ecological modeling, and climate change adaptation. This study analyzes the spatiotemporal variability of climate on rainfall distribution for the Hare catchment of Ethiopia. Numerous hydroclimatic variables and scenarios were developed to assess the pattern of rainfall during different seasons. The average annual precipitation varies between −37.3%, +33.1%, and −38.2%, +61.2%, for RCP 4.5 and RCP 8.5, respectively. The anticipated declines in mean seasonal rainfall changes for the Bega and Belg seasons range from −69.6% to 88.4% and from −60.6% to 15.2% for RCP 4.5 and RCP 8.5, respectively. Climate models predict that the average periodic precipitation considered for the Kiremt season will vary from −12.1% to 1.33%. The Belg, Kiremt, and Bega seasons will likely see a 28.2%, 12.2%, and 22.6% drop in mean seasonal precipitation, respectively. The decrease in stream flow accompanied by the aforementioned climate scenarios (RCP 4.5 and RCP 8.5) can be as high as 19.6% and 6.7%, respectively. Also, the amount of discharge will reduce in the near future because of a substantial reduction in rainfall and a rise in evapotranspiration in the catchment. This decline in stream flow has its own effect on the future availability of water resources. The research finding is vital to environmental protection authority, decision makers, and scientific community to undertake climate change adaption techniques for rain scare areas. A program combined with multi-RCMs to evaluate climate change effects on hydrometeorology generated a novel approach to this research with appropriate adaptation mechanisms.

Contrasting water-use strategies revealed by species-specific transpiration dynamics in the Caatinga dry forest.

In forest ecosystems, transpiration (T) patterns are important for quantifying water and carbon fluxes and are major factors in predicting ecosystem change. Seasonal changes in rainfall and soil water content can alter the sensitivity of sap flux density to daily variations in vapor pressure deficit (VPD). This sensitivity is species-specific and is thought to be related to hydraulic strategies. The aim of this work is to better understand how the sap flux density of species with low versus high wood density differ in their sensitivity to VPD and soil water content and how potentially opposing water-use strategies influence T dynamics, and ultimately, correlations to evapotranspiration (ET). We use hysteresis area analysis to quantify the sensitivity of species-specific sap flux density to changes in the VPD, breakpoint-based models to determine the soil water content threshold instigating a T response and multiscalar wavelet coherency to correlate T to ET. We found that low wood density Commiphora leptophloeos (Mart.) Gillett had a more dynamic T pattern, a greater sensitivity to VPD at high soil water content, required a higher soil water content threshold for this sensitivity to be apparent, and had a significant coherency correlation with ET at daily to monthly timescales. This behavior is consistent with a drought avoidance strategy. High wood density Cenostigma pyramidale (Tul.) E. Gagnon & G. P. Lewis, conversely, had a more stable T pattern, responded to VPD across a range of soil water content, tolerated a lower soil water content threshold to T, and had a significant coherency correlation with ET at weekly timescales. This behavior is consistent with a drought-tolerant strategy. We build on previous research to show that these species have contrasting water-use strategies that should be considered in large-scale modeling efforts.

Identifying nitrate sources and transformations in an agricultural watershed in Northeast China: Insights from multiple isotopes

Accurate identification of riverine nitrate sources is required for preventing and controlling nitrogen contamination in agricultural watersheds. The water chemistry and multiple stable isotopes (δ15N–NO3, δ18O–NO3, δ2H–H2O, and δ18O–H2O) of the river water and groundwater in an agricultural watershed in China's northeast black soil region were analyzed to better understand the sources and transformations of riverine nitrogen. Results showed that nitrate is an important pollutant that affects water quality in this watershed. Affected by factors such as seasonal rainfall changes and spatial differences in land use, the nitrate concentrations in the river water showed obvious temporal and spatial variations. The riverine nitrate concentration was higher in the wet season than in the dry season, and higher downstream than upstream. The water chemistry and dual nitrate isotopes revealed that riverine nitrate came primarily from manure and sewage (M&S). Results from the SIAR model showed that it accounted for more than 40% of riverine nitrate in the dry season. The proportional contribution of M&S decreased during the wet season due to the increased contribution of chemical fertilizers and soil nitrogen induced by large amounts of rainfall. The δ2H–H2O and δ18O–H2O signatures implied that interactions occurred between the river water and groundwater. Considering the large accumulation of nitrates in the groundwater, restoring groundwater nitrate levels is essential for controlling riverine nitrate pollution. As a systematic study on the sources, migration, and transformations of nitrate/nitrogen in agricultural watersheds in black soil regions, this research can provide a scientific support for nitrate pollution management in the Xinlicheng Reservoir watershed and provide a reference for other watersheds in black soil regions in the world with similar conditions.

Surface Subsidence Characteristics and Causes in Beijing (China) before and after COVID-19 by Sentinel-1A TS-InSAR

Surface subsidence is a serious threat to human life, buildings and traffic in Beijing. Surface subsidence is closely related to human activities, and human activities in Beijing area showed a decreasing trend during the Corona Virus Disease 2019 (COVID-19). To study surface subsidence in Beijing before and after the COVID-19 outbreak and its causes, a total of 51 Sentinel-1A SAR images covering Beijing from January 2018 to April 2022 were selected to derive subsidence information by Time Series Interferometry Synthetic Aperture Radar (TS-InSAR). The results of surface subsidence in Beijing demonstrate that Changping, Chaoyang, Tongzhou and Daxing Districts exhibited the most serious subsidence phenomenon before the COVID-19 outbreak. The four main subsidence areas form an anti-Beijing Bay that surrounds other important urban areas. The maximum subsidence rate reached −57.0 mm/year. After the COVID-19 outbreak, the main subsidence area was separated into three giant subsidence funnels and several small subsidence funnels. During this period, the maximum subsidence rate was reduced to −43.0 mm/year. Human activity decrease with the COVID-19 outbreak. This study effectively analysed the influence of natural factors on surface subsidence after excluding most of the human factors. The following conclusions are obtained from the analysis: (1) Groundwater level changes, Beijing’s geological structure and infrastructure construction are the main reasons for surface subsidence in Beijing. (2) Seasonal changes in rainfall and temperature indirectly affect groundwater level changes, thereby affecting surface subsidence in the area. (3) The COVID-19 outbreak in early 2020 reduced the payload of Beijing’s transportation facilities. It also slowed down the progress of various infrastructure construction projects in Beijing. These scenarios affected the pressure on the soft land base in Beijing and reduced the surface subsidence trend to some extent.

Long-term vegetation response to rainfall seasonality and fire in the heathlands and shrublands of the Cape Floristic Region (SW South Africa)

Mediterranean environments are biodiversity hotspots in which strongly seasonal winter rainfall regimes and fire play major roles in driving ecosystem dynamics. Global predictions forecast unreliability of winter rainfall and increases in summer rainfall that are expected to result in major changes in community structure. Mediterranean systems are difficult to model, and although ecophysiological responses can be studied at observational timescales, a long-term understanding is necessary to address uncertainties and refine predictive models at landscape scales. Here we provide a ∼ 1100 year-long palaeoecological reconstruction of vegetation (palynology), fire (sedimentary charcoal) and sedimentological change at a site adjacent to a multi-annual rainfall manipulation experiment designed to test plant population and community responses to altered seasonal regimes in the Greater Cape Floristic Region hotspot (southwestern Africa). We use these data to test whether long-term vegetation dynamics are controlled by changes in rainfall seasonality. We conclude that vegetation dynamics correlate with centennial-scale seasonality fluctuations, with transitions between two ecologically distinct fine-leaved shrub communities. These transitions are consistent with results of responses to experimental manipulations of summer rainfall. This study demonstrates the value of ecophysiological research in interpreting palaeoecological reconstructions and scaling up the results of observational research to answer long-term questions about environmental change.

Block level annual and seasonal rainfall changes over Indian Sundarbans during 1901-2100

Block level annual and seasonal rainfall changes over Indian Sundarbans during 1901-2100

Block level annual and seasonal rainfall changes over Indian Sundarbans during 1901-2100

Block level annual and seasonal rainfall changes over Indian Sundarbans during 1901-2100

Temperature-associated morphological changes in an African arid-zone ground squirrel

Abstract The ecology, life histories, and physiology of many animals are changing in response to human-induced climate change. As the Earth warms, the ability of an animal to thermoregulate becomes ecologically and physiologically significant. Morphological adaptations to warmer temperatures include larger appendages and smaller bodies. We examined morphological features in a ground squirrel, Xerus inauris, living in the arid zones of South Africa, to examine whether squirrels have responded to increases in temperature and changes in seasonal rainfall with morphological modifications over the last 18 years. We found that over time, absolute hindfoot length and proportional hindfoot length increased, while spine length decreased. These changes are consistent with ecogeographical rules (Allen’s rule and Bergmann’s rule) and provide evidence in support of “shape-shifting” in response to climatic warming. Body mass also increased with time; however, these changes were not consistent with Bergmann’s rule, indicating that mass is influenced by other ecological factors (e.g., resource availability). Our study adds to the growing evidence that animal morphologies are changing in response to changing climatic conditions, although it remains to be seen whether these changes are adaptive.

Multilayer Feedforward Artificial Neural Network Model to Forecast Florida Bay Salinity with Climate Change

Florida Bay is a large, subtropical estuary whose salinity varies from yearly and seasonal changes in rainfall and freshwater inflows. Water management changes during the 20th century led to a long-term reduction in inflows that increased mean salinity, and the frequency and severity of hypersalinity. Climate change may exacerbate salinity conditions in Florida Bay; however, future salinity conditions have not been adequately evaluated. Here, we employed a Multilayer Feedforward Artificial Neural Network model to develop baseline salinity models for nearshore and offshore sites. Then, we examined the impacts of climate change on salinity using forecasted changes in various input variables under two climate change scenarios, representative concentration pathways (RCP) 4.5 and 8.5. Salinity could rise by 30% and 70% under the RCP4.5 and RCP8.5 forecasts, respectively. Climate change affected nearshore salinity significantly more, which rapidly fluctuated between mesohaline (5 to 18 PSU) and metahaline (40 to 55 PSU) to hypersaline conditions (>55 PSU). Offshore salinities ranged between euhaline (30 to 40 PSU) to metahaline (40 to 55 PSU) conditions. Our study suggests that increased freshwater flow would help maintain suitable estuarine conditions in Florida Bay during climate change, while our novel modeling approach can guide further Everglades restoration efforts.

Projected changes in rainfall over Uganda based on CMIP6 models

Information about likely future patterns of climate variables is important in climate change mitigation and adaptation efforts. This study investigates future (2021–2100) changes in rainfall based on CMIP6 datasets over Uganda. The projection period is divided into two sub-periods: 2021–2060 (near future) and 2061–2100 (far future), relative to the baseline period (1985–2014). Two emission scenarios: SSP2-4.5 and SSP5-8.5, are considered. The results reveal a larger decrease (increase) in rainfall during March–April (November–December) under both SSPs. Moreover, an enhanced decline (increase) is projected under SSP2-4.5 (SSP5-8.5). The spatial distribution of future changes in seasonal rainfall reveals a decrease in MAM rainfall in the near future over most parts of the country under both emission scenarios. However, a recovery is exhibited towards the end of the century with more increase in the south-western parts of the country, and a higher magnitude under SSP5-8.5. In contrast, SON rainfall reveals wetter conditions during both timelines and emission scenarios. Maximum (minimum) wet conditions are expected in the north-western parts of the country (around the Lake Victoria basin). The linear trend analysis shows a non-significant (z = − 0.714) decreasing trend for MAM rainfall during the historical period. This pattern is reflected in the near future with z-scores of − 0.757 and − 1.281 under SSP2-4.5 and SSP5-8.5, respectively. However, a significant increase for MAM and annual rainfall (z-scores of 2.785 and 3.46, respectively) is projected towards the end of the century under SSP5-8.5. These findings provide guidance to policy makers in devising appropriate adaptation measures to cope with expected changes in the local climate. Given the increase in intensity and frequency of extreme rainfall over the study region, future work should focus on examining projected changes in rainfall extremes under different global warming scenarios with consideration of model performance and independence.

Detection and forecasting of shallow landslides: lessons from a natural laboratory

Rapid shallow landslides are a significant hillslope erosion mechanism and limited understanding of their initiation and development results in persistent risk to infrastructure. Here, we analyse the slope above the strategic A83 Rest and be Thankful road in the west of Scotland. An inventory of 70 landslides (2003–2020) shows three types of shallow landslide, debris flows, creep deformation, and debris falls. Debris flows dominate and account for 5,350 m3 (98%) of shallow-landslide source volume across the site. We use novel time-lapse vector tracking to detect and quantify slope instabilities, whilst seismometers demonstrate the potential for live detection and location of debris flows. Using on-slope rainfall data, we show that shallow-landslides are typically triggered by abrupt changes in the rainfall trend, characterised by high-intensity, long duration rainstorms, sometimes part of larger seasonal rainfall changes. We derive empirical antecedent precipitation (>62 mm) and intensity-duration (>10 h) thresholds over which shallow-landslides occur. Analysis shows the new thresholds are more effective at raising hazard alerts than the current management plan. The low-cost sensors provide vital notification of increasing hazard, the initiation of movement, and final failure. This approach offers considerable advances to support operational decision-making for infrastructure threatened by complex slope hazards.

How extreme could the near term evolution of the Indian Summer Monsoon rainfall be?

We provide a methodology to estimate possible extreme changes in seasonal rainfall for the coming decades. We demonstrate this methodology using Indian summer monsoon rainfall as an example. We use an ensemble of 1669 realizations of Indian summer monsoon rainfall from selected seasonal prediction systems to estimate internal variability and show how it can exacerbate or alleviate forced climate change. Our estimates show that for the next decade there is a ∼60% chance of wetting trends, whereas the chance of drying is ∼40%. Wetting trends are systematically more favoured than drying with the increasing length of the period. However, internal variability can easily negate or overwhelm the wetting trends to give temporary drying trends in rainfall. This provides a quantitative explanation for the varying trends in the past observational record of rainfall over India. We also quantify the likelihood of extreme trends and show that there is at least a 1% chance that monsoon rainfall could increase or decrease by one fifth over the next decade and that more extreme trends, though unlikely, are possible. We find that monsoon rainfall trends are influenced by trends in sea-surface temperatures over the Niño3.4 region and tropical Indian Ocean, and ∼1.5° cooling or warming of these regions can approximately double or negate the influence of climate change on rainfall over the next two decades. We also investigate the time-of-emergence of climate change signals in rainfall trends and find that it is unlikely for a climate change signal to emerge by the year 2050 due to the large internal variability of monsoon rainfall. The estimates of extreme rainfall change provided here could be useful for governments to prepare for worst-case scenarios and therefore aid disaster preparedness and decision-making.

The temporal response of dissolved heavy metals to landscape indices in the Le'an river, China

Heavy metals in watersheds are a serious concern due to their toxicity, abundance, and persistence in the environment, especially in mining areas. Source analyses and exploration of other related factors are one of the most important methods to help with effective prevention and control of heavy metal pollution in watersheds. In this study, the concentrations of Cr, Co, Ni, Cu, Zn, As, Cd, Sb, Ba and Pb were measured in the Le'an River, and PCA (principal component analysis) and APCS-MLR (absolute principal component scores - multivariate linear regression) methods were used to identify the sources of the dissolved heavy metals. Additionally, a CA (correlation analysis) method was used to explore the correlations between landscape indices and concentrations of heavy metals. Results show that the main sources for these dissolved heavy metals are mining activities, fertilizers, pesticides, and natural sources. Specific results of PCA and APCS-MLR suggest that Cu, Zn, Cd, Ba are mainly related to mining activities, Cr and Pb are due to fertilizers and pesticides, and Co and Ni are mainly due to natural sources. Correlations between landscapes and heavy metals revealed significant temporal variations, with the strongest responses of dissolved heavy metals to landscape indices appearing in December and March. The propensity of positive or negative responses of the heavy metals to landscape indices are determined by the sources, and their temporal variations may be related to the seasonal changes of rainfall and plant metabolism.

The impact of future climate change on water availability in Gusau, Zamfara State, Nigeria

Climate change has continued to pose challenging global environmental problems in the 21st century. For instance, studies have shown that water resources quantity and quality will be seriously degraded by the phenomenon. This paper examines the effect of climate change on future water availability in Gusau City, Zamfara State, Nigeria. The data obtained from three Regional Climate Models (RCMs) ensembles gridded at 0.5o x 0.5o spatial resolutions were used. The analysis was carried out using ArcGIS (ArcMap, version 10.5) software and comparison of the outcome of the three ensembles was made. The findings show future temperature increase as 1- 3oC by 2040, 3oC by 2070 and 2- 5oC by 2100. Furthermore, future rainfall will increase under rcp 4.5 and 8.5 by 6.7% - 20%, 10% - 20% and 10% - 30% by 2040, 2070v and 2100 respectively. It further shows an increase of +10% to + 60% in seasonal changes of rainfall in the months of April-May-June and July-August-September respectively. These will ultimately result in the increase in surface run off and aquifer recharge. Similarly, the quality of water will also be degraded through sedimentation and nitrogen loading from farms, transport and dissolution of heavy metals by flooding. Therefore, the study recommends for the involvement in storm water harvesting and storage processes by both Government and other large scale water users.