Trends In Climate Variables (Temperature And Rainfall) And Local Perceptions Of Climate Change In Lamu, Kenya

Community adaptation to the negative impacts of climate change benefits from an analysis of both the trends in climate variables and people’s perception of climate change. This paper contends that members of the local community have observed changes in temperature and rainfall patterns and that these perceptions can be positively correlated with meteorological records. This is particularly useful for remote regions like Lamu whereby access to weather data is spatially and temporally challenged. Linear trend analysis is employed to describe the change in temperature and rainfall in Lamu using monthly data obtained from the Kenya Meteorological Department (KMD) for the period 1974–2014. To determine local perceptions and understanding of the trends, results from a household survey are presented. Significant warming trends have been observed in the study area over the period 1974–2014. This warming is attributed to a rise in maximum temperatures. In contrast to temperature, a clear picture of the rainfall trend has not emerged. Perceptions of the local community closely match the findings on temperature, with majority of the community identifying a rise in temperature over the same period. The findings suggest that the process of validating community perceptions of trends with historical meteorological data analysis can promote adaptation planning that is inclusive and responsive to local experiences.

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Do local perceptions of climate variability and changes correspond to observed climate changes? – a comparative study from Nepal as one of the most climate vulnerable countries

Weather Climate and Society ◽

10.1175/wcas-d-21-0081.1 ◽

2021 ◽

Keyword(s):

Climate Change ◽

Climate Changes ◽

Climate Variables ◽

Focus Group Discussions ◽

Group Discussions ◽

Local Perceptions ◽

Temperature And Precipitation ◽

Local Residents ◽

Perceived Temperature ◽

People’S Perceptions

Abstract This study explored people’s perceptions of climate change by conducting interviews and focus group discussions with local residents of three ecological regions of Nepal, i.e., Mountain, Mid-hills and Low-land. Climatic measurements from meteorological stations of the regions were acquired for the period of 1988 to 2018. We compared the people’s perception with trends and variabilities of observed temperature and rainfall patterns. The results showed that over the last three decades, temperature and precipitation trends, and variability between regions varied largely corroborating with the local experiences. The temperature increased in Mountain, Mid-hills, and Low-land by 0.061° C yr−1, 0.063° C yr−1 and 0.017° C yr−1 respectively. On the contrary, rainfall reduced by −9.7 mm yr−1, −3.6 mm yr−1, and −0.04 mm yr−1 for the regions respectively. While the amount of rainfall decrease observed in the Mountain was highest, its variability was found relatively low; and vice-versa in Low-land. Approximately 88% interviewees perceived temperature rise, and 74% noticed rainfall decline. Local residents linked these changes with their livelihood activities and exemplified with, for example, crop’s quality and quantity; and birds’ migration. The results indicate that local understandings complement the scarce observational data and provides a reliable and additional foundations to determine changes in climatic variables. Moreover, the result infers that the small changes in climate variables have noticeable implications on human behavior change. Therefore, besides active participation of local communities, integrating local understanding is crucial in developing climate change related policies and strategies at local and national levels.

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Evaluating and modelling the impacts of climate change and reference datasets on river water temperatures for a hydropower system with two outlets

10.5194/egusphere-egu2020-3791 ◽

2020 ◽

Author(s):

Philippe Gatien

Keyword(s):

Climate Change ◽

Water Temperature ◽

Air Temperature ◽

River Flow ◽

Mean Squared Error ◽

Meteorological Data ◽

Delta Method ◽

Weather Data ◽

Monthly Basis ◽

Reanalysis Dataset

Water temperature modelling has become an essential tool in the management of ectotherm species downstream of dams in North American rivers. The main objective of this project is to compare different datasets and their ability to adequately simulate water temperatures in the Nechako River, (B.C., Canada) downstream of a major dam where the flow is not managed for hydroelectric production, but spills are programmed to cool the downstream reaches. This will ultimately lead to a reassessment of water management in the context of climate change to ensure the survival of fish migrating or living in the reaches located downstream of the dam during warm periods.Water in the Nechako River stems from the Nechako reservoir at the Skins lake spillway and flows into river through a series of lakes prior to reaching Finmoore, where federal regulations stipulate that water temperatures must be maintained below 20 &#176;C. The river has multiple tributaries on it&#8217;s 250 km journey including the Nautley river. The river flow is simulated using a 1D unsteady flow simulation and lateral inflows using HEC-RAS.Water temperature simulations are then conducted using different datasets. The first is a series of observed meteorological data spanning from 2017 to present day from two different weather stations near the river. The second dataset is ERA5, a reanalysis product that&#8217;s gridded every 0.25&#176;. Eleven stations nearest to the river were extracted over the same period as the observations. Both datasets were used to calibrate five parameters (dust coefficient, three wind function parameters and the Richardson number) three times using the mean absolute error (MAE), Nash-Sutcliffe coefficient (NS) and root mean squared error (RMSE) by comparing the observed and simulated temperatures near Finmoore.Individual calibrations were performed over each available summer from early June to late August and then validated over the rest of the data to ensure the robustness of the results.Overall, the reanalysis dataset outperformed the available observations for thermal representation of the river.To further understand the thermal model, a sensitivity analysis was performed on the different inputs (inflow water temperature, air temperature, wind speed, etc.). The model showed very little sensitivity to the characteristics of the inflow (temperature, volume) as the point of interest was so far downstream. In fact, environmental factors such as air temperature had a greater impact on water temperature than upstream conditions at the reservoir spillway. This effect seems to be mostly attributable to Cheslatta Lake with its long water residence time that can reach upwards of three days.The potential effects of climate change on water temperature were then investigated by modifying existing weather data like air temperature with the delta method on a monthly basis using the RCP8.5 emission scenario. Water temperatures increased throughout by roughly 2.5&#176;C downstream, near Finmoore.&#160;

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Local Perceptions and Trends of Climate Change in the Sikkim Himalaya, North-East India

Journal of Climate Change ◽

10.3233/jcc210008 ◽

2021 ◽

Vol 7 (2) ◽

pp. 1-26

Author(s):

Prachita Arora ◽

Sheikh Nawaz Ali ◽

P. Morthekai

Keyword(s):

Climate Change ◽

Meteorological Data ◽

Age Groups ◽

Positive Outcome ◽

Tourism Industry ◽

High Mountain ◽

Local Perceptions ◽

Mountain Areas ◽

North East ◽

Impacts Of Climate Change

The Himalayan high mountain areas are more vulnerable to climate change and the awareness of its impacts among the natives is very crucial as well as beneficial to stakeholders and policymakers. The impacts of climate change via food security, water availability, natural hazards, agriculture, and livelihoods have a direct relation or threat to the lives of high mountain communities, as these areas are experiencing the immediate and greatest impacts of climate change. Although the tourism industry has become the backbone of the economy in these areas, a significant increase in tourist footfall has also impacted the environment, livelihoods, culture and food habits. To understand the local perceptions of climate change, a binary question-based survey (interview) was conducted in six main subdivisions of North Sikkim, which is a biodiversity and tourism hotspot. The data revealed that irrespective of the locality (urban/rural) people are aware of climate change. Significant coherence in the responses among gender and age groups, and between remote and developed areas exist. The transhumant herder populations are also well aware of climate change (80%). Peoples’ perception about temperature change and the meteorological data are also consistent, however, a misperception is observed with the precipitation data. Decreasing snowfall patterns and increasing landslides in the higher altitudes are major concerns among the natives. The majority of people have denied any positive outcome of climate change and around 85% of the respondents are willing to participate at the community level in mitigation efforts to help curb climate change.

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Perception analysis of climate change and adaptations by smallholder farmers in Nepal

Journal of Agriculture and Environment ◽

10.3126/aej.v17i0.19866 ◽

2018 ◽

Vol 17 ◽

pp. 104-110

Author(s):

NP Ghimire ◽

M Aryal ◽

PP Regmi ◽

RB Thapa ◽

KR Pande ◽

...

Keyword(s):

Climate Change ◽

Food Security ◽

Low Income ◽

Negative Impact ◽

Smallholder Farmers ◽

Secondary Data ◽

Household Survey ◽

Low Income Countries ◽

Response Strategies ◽

Negative Impacts

Climate change is posturing warning on present and future food security in low income countries. But, the actual effect of the climate change is still unknown. This study examined the farmer’s perception on climate change and strategies employed to adapt using primary and secondary data collected through household survey and reported by government. Statistical analysis is used for exploring the adaptations by farmers for the negative impact of climate change on domestic production of major cereals crops. The results are discussed at district level empirically and major variables are found statistically significant. This study conclude that there is a need for adaptations strategy by government authority in environmental management and agricultural sustainability in Nepal to come to terms with negative impacts of climate change and likely positive and beneficial response strategies to global warming. The paper suggests some policy measures for improving adaptations and food security situation in the country and open up some areas for further research.

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Stepping inside the niche: microclimate data are critical for accurate assessment of species' vulnerability to climate change

Biology Letters ◽

10.1098/rsbl.2014.0576 ◽

2014 ◽

Vol 10 (9) ◽

pp. 20140576 ◽

Cited By ~ 39

Author(s):

Collin Storlie ◽

Andres Merino-Viteri ◽

Ben Phillips ◽

Jeremy VanDerWal ◽

Justin Welbergen ◽

...

Keyword(s):

Climate Change ◽

Statistical Downscaling ◽

Environmental Data ◽

Weather Data ◽

Daily Maximum ◽

Biologically Relevant ◽

Species Vulnerability ◽

Vulnerability To Climate Change ◽

Thermal Regimes ◽

Maximum Temperatures

To assess a species' vulnerability to climate change, we commonly use mapped environmental data that are coarsely resolved in time and space. Coarsely resolved temperature data are typically inaccurate at predicting temperatures in microhabitats used by an organism and may also exhibit spatial bias in topographically complex areas. One consequence of these inaccuracies is that coarsely resolved layers may predict thermal regimes at a site that exceed species' known thermal limits. In this study, we use statistical downscaling to account for environmental factors and develop high-resolution estimates of daily maximum temperatures for a 36 000 km 2 study area over a 38-year period. We then demonstrate that this statistical downscaling provides temperature estimates that consistently place focal species within their fundamental thermal niche, whereas coarsely resolved layers do not. Our results highlight the need for incorporation of fine-scale weather data into species' vulnerability analyses and demonstrate that a statistical downscaling approach can yield biologically relevant estimates of thermal regimes.

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IMPACT OF CLIMATE CHANGE ON CHINA’S RICE PRODUCTION — AN EMPIRICAL ESTIMATION BASED ON PANEL DATA (1979–2011) FROM CHINA’S MAIN RICE-PRODUCING AREAS

The Singapore Economic Review ◽

10.1142/s0217590817400240 ◽

2018 ◽

Vol 63 (03) ◽

pp. 535-553 ◽

Cited By ~ 3

Author(s):

DAN WANG ◽

YU HAO ◽

JIANPEI WANG

Keyword(s):

Climate Change ◽

Panel Data ◽

Temperature Difference ◽

Rice Production ◽

Weather Data ◽

Impact Of Climate Change ◽

Positive Effects ◽

Daily Weather Data ◽

Maximum Temperatures ◽

The Impact

Climate change is attracting increasing attention from the international community. To assess the impact of climate change on China’s rice production, this paper re-organizes the main rice-producing areas by adding up the annual production of the provincial level regions between 1979 and 2011, utilizes Cobb–Douglas function using daily weather data over the whole growing season. Our analysis of the panel data shows that minimum temperatures (Tmin), maximum temperatures (Tmax), temperature difference (TD) and precipitation (RP) are the four key climate determinants of rice production in China. Among these, temperature difference is surprisingly significant and all except maximum temperatures have positive effects. However, because the actual minimum temperatures and precipitation in China’s main rice-producing areas declined while the maximum temperatures and the temperature difference increased during our sample period, climate change has actually provided a negative contribution to the increase in China’s rice production.

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Productivity Impact of Climate Change on Primary Export Commodity in Africa

10.21203/rs.3.rs-1047410/v1 ◽

2021 ◽

Author(s):

Berihu Assefa Gebrehiwot ◽

Alebel B. Weldesilassie

Keyword(s):

Climate Change ◽

Technology Use ◽

Household Survey ◽

Farm Income ◽

Seasonal Temperature ◽

Climate Data ◽

Education Access ◽

Impact Of Climate Change ◽

Large Household ◽

Negative Impacts

Abstract Using a large household survey dataset and climate data, we estimated and analyzed the productivity impact of climate change on two strategic export commodities, cotton, and sugarcane, in Ethiopia. The productivity impact revealed that it is likely that climate change will significantly reduce the productivity of sugarcane and cotton. In addition, net revenue from sugarcane and cotton will also be reduced due to an increase in annual and seasonal temperature as well as changes in mean annual and seasonal rainfall. We have presented results for various climate scenarios and seasons. We also found that non–climate drivers of vulnerability such as gender, education, access to extension services and credit, technology use, access to land and non – farm income, and natural assets will exacerbate the negative impacts of climate change.

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Trends in evaporative demand in Great Britain using high-resolution meteorological data

10.5194/hess-2015-520 ◽

2016 ◽

Cited By ~ 2

Author(s):

Emma L. Robinson ◽

Eleanor M. Blyth ◽

Douglas B. Clark ◽

Jon Finch ◽

Alison C. Rudd

Keyword(s):

Climate Change ◽

Great Britain ◽

Spatial Scales ◽

Meteorological Data ◽

Temporal Trends ◽

Natural Environments ◽

Climate Variables ◽

Climate Data ◽

Evaporative Demand ◽

Local Topography

Abstract. Observations of climate are often available on very different spatial scales from observations of the natural environments and resources that are affected by climate change. In order to help bridge the gap between these scales using modelling, a new dataset of daily meteorological variables was created at 1 km resolution over Great Britain for the years 1961–2012, by interpolating coarser resolution climate data and including the effect of local topography. These variables were used to calculate evaporative demand at the same spatial and temporal resolution, both excluding (PET) and including (PETI) the effect of water intercepted by the canopy. Temporal trends in evaporative demand were calculated, with PET found to increase in all regions and PETI found to increase in England. The trends were found to vary by season, with spring evaporative demand increasing by 14 % (11 % when the interception correction is included) in Great Britain over the dataset, while there is no statistically significant trend in other seasons. The trends in PET were attributed analytically to trends in the climate variables, with the spring trend in evaporative demand being driven by radiation trends, particularly by increasing solar radiation.

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Local Perceptions of Climate Change and Adaptation Responses from Two Mountain Regions in Tanzania

Land ◽

10.3390/land10100999 ◽

2021 ◽

Vol 10 (10) ◽

pp. 999

Author(s):

Kaiza R. Kaganzi ◽

Aida Cuni-Sanchez ◽

Fatuma Mcharazo ◽

Emanuel H. Martin ◽

Robert A. Marchant ◽

...

Keyword(s):

Climate Change ◽

Crop Yields ◽

Meteorological Data ◽

Cloud Base ◽

Structured Interviews ◽

Local Perceptions ◽

Planting Dates ◽

Mountain Environments ◽

Crop Varieties ◽

Mount Kilimanjaro

Mountain environments and communities are disproportionately impacted by climate change. Changes in temperature are greater than at lower elevations, which affect the height of the cloud base and local rainfall patterns. While our knowledge of the biophysical nature of climate change in East Africa has increased in the past few years, research on Indigenous farmers’ perceptions and adaptation responses is still lacking, particularly in mountains regions. Semi-structured interviews were administered to 300 farmers on Mount Kilimanjaro (n = 150) and the Udzungwa Mountains (n = 150) in Tanzania across gender and wealth groups. Respondents in both mountains reported not only changes in rainfall and temperature, corresponding with meteorological data, but also a greater incidence of fog, wind, frost, and hailstorms—with impacts on decreased crop yields and increased outbreaks of pests. The most common adaptation strategies used were improved crop varieties and inputs. Wealthier households diversified into horticulture or animal rearing, while poorer households of Hehe ethnicity diversified to labour and selling firewood. Despite being climate change literate and having access to radios, most respondents used Indigenous knowledge to decide on planting dates. Our findings highlight how context and culture are important when designing adaptation options and argue for greater involvement of local stakeholders in adaptation planning using a science-with-society approach. Place-based results offer generalisable insights that have application for other mountains in the Global South.

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To what extent does the detection of climate change in Hungary depend on the choice of statistical methods?

GEM - International Journal on Geomathematics ◽

10.1007/s13137-020-00154-y ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Beatrix Izsák ◽

Tamás Szentimrey

Keyword(s):

Climate Change ◽

Trend Analysis ◽

Linear Trend ◽

Meteorological Data ◽

Warming Trend ◽

Data Series ◽

Data Sets ◽

Raw Data ◽

Temperature Trends ◽

Trend Data

AbstractThe trend analysis of meteorological time series has gained prominence in recent decades, the most common method being the so-called ‘linear analytical trend analysis’. Until the mid-1990s, trend analysis was commonly performed on non-homogenized data sets, which frequently led to erroneous conclusions. Nowadays, only homogenized data sets are examined, so it really is possible to detect climate change in long meteorological data sets. In this paper, the methodology of linear trend analysis is summarized, the way in which the model can be validated is demonstrated, and there is a discussion of the results obtained if unjustified discontinuities caused by changing measurement conditions, such as the relocation of stations, changes in measurement time, or instrument change occur. On the basis of an examination of records for the preceding 118 years, it is possible to state that both annual and seasonal mean temperature trends display a significant warming trend. In the case of homogenized data series, the change is significant over the entire territory of Hungary; in the case of raw data series, however, the change is not significant everywhere. The validity of the linear model is tested using the F-test, a task as yet carried out on the entire Hungarian data series, series comprising records for over 100 years. Furthermore, neither has a comparison been made of the trend data for raw data series and the homogenized data series with the help of information on station history to explore the causes of inhomogeneity.

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