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

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 effects of local topography. These variables were used to calculate atmospheric evaporative demand (AED) at the same spatial and temporal resolution. Two functions that represent AED were chosen: one is a standard form of potential evapotranspiration (PET) and the other is a derived PET measure used by hydrologists that includes the effect of water intercepted by the canopy (PETI). Temporal trends in these functions were calculated, with PET found to be increasing in all regions, and at an overall rate of 0.021 ± 0.021 mm day−1 decade−1 in Great Britain. PETI was found to be increasing at a rate of 0.019 ± 0.020 mm day−1 decade−1 in Great Britain, but this was not statistically significant. However, there was a trend in PETI in England of 0.023 ± 0.023 mm day−1 decade−1. The trends were found to vary by season, with spring PET increasing by 0.043 ± 0.019 mm day−1 decade−1 (0.038 ± 0.018 mm day−1 decade−1 when the interception correction is included) in Great Britain, while there is no statistically significant trend in other seasons. The trends were attributed analytically to trends in the climate variables; the overall positive trend was predominantly driven by rising air temperature, although rising specific humidity had a negative effect on the trend. Recasting the analysis in terms of relative humidity revealed that the overall effect is that falling relative humidity causes the PET to rise. Increasing downward short- and longwave radiation made an overall positive contribution to the PET trend, while decreasing wind speed made a negative contribution to the trend in PET. The trend in spring PET was particularly strong due to a strong decrease in relative humidity and increase in downward shortwave radiation in the spring.

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Simulation of future climate scenarios and the impact on the water availability in southern Brazil

Acta Scientiarum Technology ◽

10.4025/actascitechnol.v43i1.56026 ◽

2021 ◽

Vol 43 ◽

pp. e56026

Author(s):

Gabriela Leite Neves ◽

Jorim Sousa das Virgens Filho ◽

Maysa de Lima Leite ◽

Frederico Fabio Mauad

Keyword(s):

Climate Change ◽

Water Balance ◽

Water Availability ◽

Meteorological Data ◽

Future Climate ◽

Climate Scenarios ◽

Southern Brazil ◽

Climate Data ◽

Intergovernmental Panel ◽

The Impact

Water is an essential natural resource that is being impacted by climate change. Thus, knowledge of future water availability conditions around the globe becomes necessary. Based on that, this study aimed to simulate future climate scenarios and evaluate the impact on water balance in southern Brazil. Daily data of rainfall and air temperature (maximum and minimum) were used. The meteorological data were collected in 28 locations over 30 years (1980-2009). For the data simulation, we used the climate data stochastic generator PGECLIMA_R. It was considered two scenarios of the fifth report of the Intergovernmental Panel on Climate Change (IPCC) and a scenario with the historical data trend. The water balance estimates were performed for the current data and the simulated data, through the methodology of Thornthwaite and Mather (1955). The moisture indexes were spatialized by the kriging method. These indexes were chosen as the parameters to represent the water conditions in different situations. The region assessed presented a high variability in water availability among locations; however, it did not present high water deficiency values, even with climate change. Overall, it was observed a reduction of moisture index in most sites and in all scenarios assessed, especially in the northern region when compared to the other regions. The second scenario of the IPCC (the worst situation) promoting higher reductions and dry conditions for the 2099 year. The impacts of climate change on water availability, identified in this study, can affect the general society, therefore, they must be considered in the planning and management of water resources, especially in the regional context

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The Potential Impact of Climate Change on Oat Lodging in the UK and Republic of Ireland

Advances in Meteorology ◽

10.1155/2020/4138469 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16 ◽

Cited By ~ 3

Author(s):

Mohammadreza Mohammadi ◽

John Finnan ◽

Chris Baker ◽

Mark Sterling

Keyword(s):

Climate Change ◽

Meteorological Data ◽

Climate Data ◽

Republic Of Ireland ◽

Growing Period ◽

The United Kingdom ◽

Double Exponential ◽

The Republic ◽

The Uk ◽

The Impact

This paper examines the impact that climate change may have on the lodging of oats in the Republic of Ireland and the UK. Through the consideration of a novel lodging model representing the motion of an oat plant due to the interaction of wind and rain and integrating future predictions of wind and rainfall due to climate change, appropriate conclusions have been made. In order to provide meteorological data for the lodging model, wind and rainfall inputs are analysed using 30 years’ time series corresponding to peak lodging months (June and July) from 38 meteorological stations in the United Kingdom and the Irish Republic, which enables the relevant probability density functions (PDFs) to be established. Moreover, climate data for the next six decades in the British Isles produced by UK climate change projections (UKCP18) are analysed, and future wind and rainfall PDFs are obtained. It is observed that the predicted changes likely to occur during the key growing period (June to July) in the next 30 years are in keeping with variations, which can occur due to different husbandry treatments/plant varieties. In addition, the utility of a double exponential function for representing the rainfall probability has been observed with appropriate values for the constants given.

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Using ERA-Interim Reanalysis output for creating datasets of energy-relevant climate variables

10.5194/essd-2016-67 ◽

2017 ◽

Cited By ~ 1

Author(s):

Philip D. Jones ◽

Colin Harpham ◽

Alberto Troccoli ◽

Benoit Gschwind ◽

Thierry Ranchin ◽

...

Keyword(s):

Climate Change ◽

Wind Speed ◽

Relative Humidity ◽

Solar Radiation ◽

Climate Variables ◽

Climate Data ◽

Near Surface ◽

Bias Adjustment ◽

Dewpoint Temperature ◽

Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim Reanalysis is presented. A number of different bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting those calculated from ERA-Interim to those based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed and relative humidity, available at either 3 or 6 h timescales over the period 1979-2014. This dataset is available to anyone through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S), and can be accessed at present from (ftp://ecem.climate.copernicus.eu). The benefit of performing bias-adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against observations.

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Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables

Earth System Science Data ◽

10.5194/essd-9-471-2017 ◽

2017 ◽

Vol 9 (2) ◽

pp. 471-495 ◽

Cited By ~ 19

Author(s):

Philip D. Jones ◽

Colin Harpham ◽

Alberto Troccoli ◽

Benoit Gschwind ◽

Thierry Ranchin ◽

...

Keyword(s):

Climate Change ◽

Wind Speed ◽

Relative Humidity ◽

Solar Radiation ◽

Climate Variables ◽

Climate Data ◽

Near Surface ◽

Bias Adjustment ◽

Dewpoint Temperature ◽

Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis is presented. A number of different, variable-dependent, bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting ERA-Interim based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed, and relative humidity. These are available on either 3 or 6 h timescales over the period 1979–2016. The resulting bias-adjusted dataset is available through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S) and can be accessed at present from ftp://ecem.climate.copernicus.eu. The benefit of performing bias adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against gridded observational fields.

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Assessment of Flood Risk Map under Climate Change RCP8.5 Scenarios in Taiwan

Water ◽

10.3390/w14020207 ◽

2022 ◽

Vol 14 (2) ◽

pp. 207

Author(s):

Yun-Ju Chen ◽

Hsuan-Ju Lin ◽

Jun-Jih Liou ◽

Chao-Tzuen Cheng ◽

Yung-Ming Chen

Keyword(s):

Climate Change ◽

High Risk ◽

Flood Risk ◽

Spatial Scales ◽

Disaster Risk ◽

Decision Makers ◽

Risk Map ◽

Climate Data ◽

Risk Areas ◽

Flood Risk Map

Climate change has exerted a significant global impact in recent years, and extreme weather-related hazards and incidents have become the new normal. For Taiwan in particular, the corresponding increase in disaster risk threatens not only the environment but also the lives, safety, and property of people. This highlights the need to develop a methodology for mapping disaster risk under climate change and delineating those regions that are potentially high-risk areas requiring adaptation to a changing climate in the future. This study provides a framework of flood risk map assessment under the RCP8.5 scenario by using different spatial scales to integrate the projection climate data of high resolution, inundation potential maps, and indicator-based approach at the end of the 21st century in Taiwan. The reference period was 1979–2003, and the future projection period was 2075–2099. High-resolution climate data developed by dynamic downscaling of the MRI-JMA-AGCM model was used to assess extreme rainfall events. The flood risk maps were constructed using two different spatial scales: the township level and the 5 km × 5 km grid. As to hazard-vulnerability(H-V) maps, users can overlay maps of their choice—such as those for land use distribution, district planning, agricultural crop distribution, or industrial distribution. Mapping flood risk under climate change can support better informed decision-making and policy-making processes in planning and preparing to intervene and control flood risks. The elderly population distribution is applied as an exposure indicator in order to guide advance preparation of evacuation plans for high-risk areas. This study found that higher risk areas are distributed mainly in northern and southern parts of Taiwan and the hazard indicators significantly increase in the northern, north-eastern, and southern regions under the RCP8.5 scenario. Moreover, the near-riparian and coastal townships of central and southern Taiwan have higher vulnerability levels. Approximately 14% of townships have a higher risk level of flooding disaster and another 3% of townships will become higher risk. For higher-risk townships, adaptation measures or strategies are suggested to prioritize improving flood preparation and protecting people and property. Such a flood risk map can be a communication tool to effectively inform decision- makers, citizens, and stakeholders about the variability of flood risk under climate change. Such maps enable decision-makers and national spatial planners to compare the relative flood risk of individual townships countrywide in order to determine and prioritize risk adaptation areas for planning spatial development policies.

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

GEOGRAPHY ENVIRONMENT SUSTAINABILITY ◽

10.24057/2071-9388-2020-24 ◽

2020 ◽

Vol 13 (3) ◽

pp. 102-109

Author(s):

Maingey Yvonne ◽

Gilbert Ouma ◽

Daniel Olago ◽

Maggie Opondo

Keyword(s):

Climate Change ◽

Local Community ◽

Linear Trend ◽

Meteorological Data ◽

Household Survey ◽

Weather Data ◽

Climate Variables ◽

Local Perceptions ◽

Maximum Temperatures ◽

Negative Impacts

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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Coupled application of R and WetSpa models for assessment of climate change impact on streamflow of Werie Catchment, Tigray, Ethiopia

Journal of Water and Climate Change ◽

10.2166/wcc.2020.238 ◽

2020 ◽

Author(s):

Selam Kidanemariam ◽

Haddush Goitom ◽

Yigzaw Desta

Keyword(s):

Climate Change ◽

Baseline Period ◽

Future Climate ◽

Calibration Data ◽

Climate Variables ◽

Emission Scenarios ◽

Climate Data ◽

Validation Data ◽

Data Set ◽

Rcp 8.5

Abstract This research assesses the streamflow response of Werie River to climate change. Baseline (1980–2009) climate data of precipitation, maximum and minimum temperature were analyzed using delta based statistical downscaling approach in R software packages to predict future 90 years (2010–2099) periods under two emission scenarios of Representative Concentration Pathways (RCP) 4.5 and RCP 8.5, indicating medium and extremely high emission scenarios respectively. Generated future climate variables indicate Werie will experience a significant increase in precipitation, and maximum and minimum air temperature for both RCPs. Further, Water and Energy Transfer between Soil, Plants, and Atmosphere (WetSpa) was applied to assess the water balance of Werie River. The WetSpa model reproduced the streamflow well with performance statistics values of R2 = 0.84 and 0.85, Nash–Sutcliffe efficiency = 0.72 and 0.72, and model bias = –0.14 and –0.15 for the calibration data set of 1999–2010 and validation data of 2011–2014 respectively. Finally, by taking the downscaled future climate variables as input, WetSpa future prediction shows that there will an increase in the Werie catchment mean annual streamflow up to 29.6% for RCP 4.5 and 35.6% for RCP 8.5 compared to the baseline period.

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Streamflow into Beijing and Its Response to Climate Change and Human Activities over the Period 1956–2016

Water ◽

10.3390/w12030622 ◽

2020 ◽

Vol 12 (3) ◽

pp. 622 ◽

Cited By ~ 3

Author(s):

Xing Mu ◽

Hao Wang ◽

Yong Zhao ◽

Huan Liu ◽

Guohua He ◽

...

Keyword(s):

Climate Change ◽

Human Activities ◽

Potential Evapotranspiration ◽

Spatial Scales ◽

Meteorological Data ◽

Kendall Test ◽

Spatial And Temporal Variability ◽

Annual Streamflow ◽

Significant Downward Trend ◽

Human Effects

Streamflow is likely affected by climate change and human activities. In this study, hydro-meteorological data from six rivers upstream of Beijing, namely, the Yongdinghe, Baihe, Heihe, Chaohe, Juhe, and Jumahe Rivers, were analyzed to quantify the spatial and temporal variability of streamflow and their responses to climate change and human activities over the period of 1956–2016. The Mann–Kendall test and moving t-test were used to detect trends and changing points of the annual streamflow. Results showed that the streamflow into Beijing experienced a statistically significant downward trend (p < 0.05), abruptly changing after the early 1980s, owing to climate and human effects. The climate elasticities of the streamflow showed that a 10% decrease in precipitation would result in a 24.5% decrease in total streamflow, whereas a 10% decrease in potential evapotranspiration would induce a 37.7% increase in total streamflow. Human activities accounted for 87% of the reduction in total streamflow, whereas 13% was attributed to climate change. Lastly, recommendations are provided for adaptive management of water resources at different spatial scales.

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Application of LARS - WG downscaling model for building climate change scenarios in the Srepok watershed

Science and Technology Development Journal ◽

10.32508/stdj.v17i2.1320 ◽

2014 ◽

Vol 17 (2) ◽

pp. 108-122

Author(s):

Khoi Nguyen Dao ◽

Nhung Thi Hong Nguyen ◽

Canh Thanh Truong

Keyword(s):

Climate Change ◽

General Circulation ◽

Emission Scenario ◽

Circulation Model ◽

Climate Change Scenarios ◽

Climate Variables ◽

Climate Data ◽

Policy Makers ◽

Rain Gauges ◽

Temperature And Precipitation

There are statistical downscaling methods such as: SDSM, LARS-WG, WGEN…, used to convert information on climate variables from the simulation results of General Circulation Model (GCM) to build climate change scenarios for local region. In this study, we used the LARS-WG model and HadCM3 GCM for two emission scenarios: B1 (low emission scenario) and A1B (medium emission scenario) to generate future scenarios for temperature and precipitation at meteorological stations and rain gauges in the Srepok watershed. The LARS-WG model was calibrated and validated against observed climate data for the period 1980-2009, and the calibrated LARS-WG was then used to generate future climate variables for the 2020s (2011-2030), 2055s (2046-2065), and 2090s (2080-2099). The climate change scenarios suggested that the climate in the study area will become warmer and drier in the future. The results obtained in this study could be useful for policy makers in planning climate change adaptation strategies for the study area.

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