large-scale simulations of climate variables (referred as the predictors) provided by a GCM to regional scale hydrologic variables (referred as the predictands). This general methodology is characterized by various uncertainties such as GCM and scenario uncertainty, uncertainty due to initial conditions of the GCMs, uncertainty due to downscaling methods, uncertainty due to hydrological model used for impact assessment and uncertainty resulting from multiple stake holders in a water resources system.
Economic Commentary, December 2018 enable the monitoring of water distribution and consumption efficiency have been successful in reducing water consumption in households. For water utility companies, the development of a smart grid network of meters connected to the Internet of Things (IoT) enables real-time monitoring of consumption, facilitating easier billing of customers and faster identification of leaks and hot spots for water waste. However, the overall uptake of smart water metering in the UK has been slow, in spite of the growing evidence of savings that such devices can achieve. This highlights that technology alone is an insufficient strategy to reducing water use, and that a focus on behaviour change is necessary. Paradoxically, water efficiency and savings strategies can result in an increase in consumption, where reductions in water costs via more efficient technologies can result in a rebound effect as users end up using more water in new areas. This has been reported in agriculture where more efficient irrigation technology has resulted in the expansion of cropped areas, ultimately resulting in an increase in overall water usage (Sears et al., 2018).
There is increasing evidence that the global climate is changing and that this will have implications for the future of water resources. The impacts of climatechange will be transmitted primarily via the global hydrosphere, whereby changes in rainfall patterns and the frequency and magnitude of extreme weather conditions (e.g., flood and drought) will result in significant challenges, including for the way we access, manage and use freshwater resources. In addition, water demand will continue to rise to support a growing global population and its resultant increases in food and energy needs. There are likely to be variations across the globe in climatechangeimpacts and these will further exacerbate existing spatial disparities in water availability. Water is a critical component for all aspects of life, and is particularly significant in many economic activities (e.g. agriculture, energy etc.). Changes in water availability and hydrological extremes will impact at regional and global scales on economic activity, supply chains, key industries and migration. While all regions of the world will be impacted by climate- induced water stress, regions with robust water policies and water management strategies, or at the leading edge of water-technologies may see opportunities. Here, we discuss the projected impacts of climatechange on water resources, and the challenges and opportunities this poses for economic activities in Scotland, including Scotland’s readiness to adapt to changes in water availability.
This paper presents global and regionalimpacts at different levels of increase in global mean temperature for over 30 indicators characterising impacts on heat extremes, water resources, river flooding and agriculture. It builds on Arnell et al. (2016b) by using CMIP5- generation climate model patterns and more indicators and extends substantially Arnell et al. (2018) by using a much wider range of indicators and a more recent climate reference period. The paper focuses on changes in the occurrence of physical climate hazards and the natural resource base at different levels of increase in global mean temperature, using indicators that are relevant to socio-economic impacts. Summary results at the global and continental levels are presented in the main part of the paper, and the Supplementary Material provides plots and tables at the regional scale. The results will be of value to those summarising and comparing impacts across sectors at different levels of global warming, for example in the IPCC ’ s Sixth Assessment Report currently in preparation. They provide quantitative evidence to supplement expert judgement as used in the Fifth Assessment Report and to help guide the assessment of impacts at different levels of warming as represented by the ‘reasons for concern’ framing.
Human health is also vulnerable to climatechange and variability, one of the direct effects is human fatality and injuries caused by extreme climatic events; floods and landslides, and indirectly by determining transmission of vector and water born disease, changes in food availability and quality and quantity of water. According to WHO (2010) changes in pattern of temperature and precipitation as a result of climatechange have been claiming 150,000 lives annually for the past 30 years. The report identifies malaria, cholera, Rift Valley fever, typhoid, malnutrition, scabies and jiggers infestations diseases that are likely to be impacted by the changing climate. A malaria risk model based on altitude found that climatechange may increase the number of people at risk especially amongst the rural population by 36% to 89% by 2050 which translates to a direct cost estimated at USD 45 - 99M annually (SEI, 2009). High temperatures cause heat stress, heat stroke and also restrict outdoor activities. A study carried out by Egondi et al. (2012), revealed that high ambient temperatures are the cause of increased mortality affecting the young and the elderly. Unless appropriate adaptation measures are adopted based on future projection, mortality due to high ambient temperature is likely to increase.
Tampa Bay Water and the Southwest Florida Water Man- agement District (SWFWMD) developed the Integrated Hy- drologic Model (IHM) simulation engine which integrates the EPA Hydrologic Simulation Program-Fortran (Bicknell et al., 2005) for surface water modeling with the U.S. Geo- logical Survey (USGS) MODFLOW96 (Harbaugh and Mc- Donald, 1996) for groundwater modeling. The IHM simu- lates the dynamic interaction of surface water and groundwa- ter systems within the INTB region, including all processes which affect flow and water levels in uplands, within the un- saturated soil, and within wetlands, rivers, and aquifers. In addition, the INTB model can account for variability in cli- mate and anthropogenic stresses such as land use change, groundwater pumping, and diversions to/from rivers, lakes, and wetlands.
Warming air and surface temperatures, land cover change, forest fire, and development are thawing permafrost throughout the Arctic and sub-arctic (Jorgenson et al. 2001, Hinzman et al. 2005, Romanovsky et al. 2010). Unfrozen fine-grained soils are more permeable than their frozen form and are associated with higher groundwater flow rates. Our research illustrates the magnitude of the potential impact of increased groundwater upwelling on river ice thickness under varying environmental conditions. The most significant drivers were vertical upwelling rate, snow depth, water column depth, air temperature, and groundwater temperature. Our findings indicated that relatively small changes in upwelling, groundwater temperature, or snow depth could have substantial impacts on ice thickness. Because ice melt is so sensitive to snow depth and because this parameter can increase in the span of hours, rapidly changing snow conditions affect the relative safety of winter travel on river ice.
structing climatechange scenarios from a single climate model and found that these different scenarios could lead to differences in runoff of 10 – 20%. They use a regionalclimate model as their primary downscaling method and compare results with different downscaling techniques, including simple interpolation of global-model results and a time slice experiment. They also examine the relative merits of using climate model data directly to assess impacts of climatechange versus applying a climatechange signal to an observed baseline climate. The reports of both Stone et al.  and Arnell et al.  address uncertainties relating to spatial scales of the scenarios, but our study goes one step further to explicitly look at error in impacts resulting from the RCM itself. The availability of reanalysis data over a data-rich region such as the continental United States allows comparison of impacts resulting from an RCM driven by reanalyzed observations versus impacts derived from observed surface data, thereby allowing RCM error to be quantified.
Many hydrological models resort to a simplistic approach to simulate the actual evapotranspiration, namely to an agro- nomic concept called potential evapotranspiration (PET), representative of constant crop and soil conditions. PET formulations are largely influenced by a changing climate (changes in the evaporative demand) and are thus a supple- mental source of uncertainty. However, scant research ad- dresses this question even if the diversity of PET formu- lations and concepts is compatible for intercomparison. As an example, Kay and Davies (2008) found that the Penman equation, compared to a simple temperature-based formula- tion (Oudin et al., 2005) in a climatechange context with A2 scenario, offers very different results for climate-change- impact modelling on water resources for the 2071–2100 pe- riod. They advised that the choice of a PET formulation af- fects hydrological projections. Bae et al. (2011) evaluated un- certainties from hydrological models and PET formulations on a Korean catchment. They compared three hydrological models, three PET formulations, and thirty-nine climate sce- narios for the 2020 and 2080 horizons. Their results showed
Delta), North Central Coast, South Central Coast, Central Highlands and the Southern Delta (Mekong Delta). The baseline period is 1980-1999. However, the application of the MAGICC/SCENGEN 5.3 model in the development of climatechange scenarios, which produces array files on a standard 2.5x2.5 degree latitude/longitude grid (300 by 300 km) displayed as maps, has a number of limitations as it is unable to accurately reflect the intra-regional nuances of climatechange in Viet Nam.
main reasons of ozone depletion is the increasing greenhouse gases in our environment. Ozone layer is a shield which protects us against the harmful UV rays of the sun which can cause diseases like skin cancer. Ozone depletion refers to both the slow decline of the total volume of ozone making up the ozone layer since the late 1970’s and a seasonal decrease in the ozone layer over the globe polar regional during the period. Greenhouse gases include chlorofluorocarbons which are the biggest contributors to ozone depletion. CFC’s are used in solvents and cleaners, refrigerators and aerosols. CFC’s are released into the air and they rise up to the stratosphere. There, their molecules destroy the ozone molecules. It has
prevalence of extreme events combined with an acceleration of warming, glacier retreat and sea-level rise, regional changes in mean precipitation, and increased risks of land degradation and crop loss from agricultural pests. There should be a determined effort from developed and developing countries to make industrialization environment friendly by reducing greenhouse gases pumping into the atmosphere. In the same fashion, awareness programmes on climatechange and its effects on various sectors viz., agriculture, health, infrastructure, water, forestry, fisheries, land and ocean biodiversity and sea level and the role played by human interventions in climatechange need to be taken up on priority basis. In the process, lifestyles of people should also be changed so as not to harm earth atmosphere continuum by pumping greenhouse gases. Reference:
The Governor’s Action Team on Energy and Climate’s final report also included a framework for adaptation. These initial recommendations for adaptation included supporting climate science research for Florida; integrating climatechange into local, regional, and state planning; protecting ecosystems and wetlands through land acquisition and nonstructural techniques; minimizing impacts to water quantity and quality; minimizing impacts to infrastructure and property by requiring design to consider climatechangeimpacts; and improving emergency response and preparedness capabilities. Furthermore, in 2008 the Florida Energy and Climate Commission was established to implement the recommendations contained in the Energy and Climate Action Plan. 67 However, recent political changes in state government have shifted momentum away from climatechange issues. Consequently, the
Several limitations apply to the above results. First, in our analysis changes in precipitation, temperature and river flow are defined based on regional averages. We do not take into account differences between river basins within the same region. These local effects are averaged out. Second, we use annual average precipitation, temperature and river flow data, therefore we do not consider changes in the seasonality nor extreme events. Third, we have made no attempt to address uncertainty in our scenarios, other than by the use of two emission scenarios from only one climate model, which could generate biased estimates. Forth, in our analysis we do not consider any cost or investment associated to the expansion of irrigated areas. Therefore, our results might overestimate the benefits of some scenarios. These issues should be addressed in future research.
One may ask why not simply resort to the air temperature forecasts at the grid points? One reason is that the environmental conditions at the grid point are generally different from those at the weather station, as they do not overlap. For example, in Fig. 5 , we observe a maximum gap of about 10 ◦ C around the median of the distributions, this because the weather station is situated at a lower altitude than the grid point. Another reason is that the observed air temperatures at the weather stations add information to a relatively coarse regionalclimate models.
The three scenario periods cover 30 years each, but none of these corresponds with the required timescale 2010-2060. The scenarios represent the expected future climate for a given scenario period as a whole and should generally not be used for other periods. However, in order to match the model time horizon, we had to combine as a first step the two 30-year scenario periods centering around 2035 and 2060. This resulted in an incongruent time series with an interrupted continuity at the turn of the year 2044/2045. Both problems were tackled by adjusting the daily change signals of all years except 2060. The adjustment factors were calculated with the aid of the only continuous time series from the CH2011 scenarios (Fischer et al. 2015). The dataset covers thirteen consecutively shifted future periods (shifted by 5 years) for 2010- 2039, 2015-2044, until 2070-2099. Unfortunately, it displays no daily data, but seasonal means of delta signals for daily maximum temperature for the evaluation points 2020, 2025, …, 2085. The spatial resolu- tion is regional, i.e. five regions covering Switzerland as a whole (West, East, South, West of the Alps, East of the Alps). We determined the required Swiss values as average of the regions, whereby the two Alpine regions were omitted for the same reason that we cut out meteorological stations at high altitudes from the historical data. The continuous dataset includes results from 10 individual model chains from which the values for upper/medium/lower were derived also in the same manner as for the historical data.
observed relationships, dynamic process-based models for par- ticular crop types, and generalized large and field scale process- based models. Crop models vary in their complexity, how they simulate dynamic processes (e.g., crop development), and which processes they simulate (e.g., high temperature stress around anthesis and/or microclimate). Crop models based on observed relationships have set parameters for a given cultivar (“genetic coefficients”), determined through field experiments; thus not accounting for parametric uncertainty. Regional scale crop mod- els may be optimized to observed yield data and a parameter ensemble may be used (Challinor et al., 2009a). Impact model studies are limited by the number of observable output variables that can be used for parameterization, regardless of the approach taken and the application.
Not even an enormous effort would guarantee the elucida- tion of these variables and their interactions in a field ex- periment examining potential effects on agricultural pro- duction. Therefore, it is crucial to develop agricultural system models that are calibrated efficiently and validated to investigate the combined effect of various chemical, physical, and biological processes (Ahuja et al., 2000; Kirschbaum, 2000; Zawadzki et al., 2005). Process-based crop models that simulate the development process of bio- mass and yield components are valued evaluating the im- pact of climatechange on crop growth and development. Crop models frequently used to simulate the growth and de- velopment of barley include CERES-barley (Otter-Nacke et al., 1991) in the Decision Support System for Agrotech- nology Transfer (DSSAT) (Jones et al., 2003), environmen- tal policy integrated climate (EPIC) model (Williams et al., 1998), SHOOTGROW (McMaster, 1993), and World Food Studies (WOFOST) model (Hijmans et al., 1994).
The average monthly time-series of precipitation, flow and evapotranspiration (ET) in the watershed are analysed to assess hydrologic trend in the watershed, as depicted in Fig. 4-4. The precipitation has lower seasonal variability as compared to other two variables. ET follows a normal distribution peaking up during the summer while the flow has a bi-modal distribution. The snow accumulated during the winter season starts melting in spring period causing high discharges in the river. As also evident from Fig. 4-3(a), the river attains the peak discharge annually during the spring period. Snowmelt makes the depth of streamflow during this period (March and April) in excess of precipitation, as seen in Fig. 4-4, allowing the runoff coefficient to exceed one. Summer period has relatively lower precipitation and high evapotranspiration (ET); in fact the ET is higher than the precipitation for May-August months. As the watershed has predominantly agriculture land use, the available precipitation is utilized to fulfil the crop evapotranspiration demands. Therefore, the river discharge during summer period is principally due to the baseflow contribution. The annual average baseflow contribution is close to half of the total streamflow as computed using the baseflow separation programme (Arnold and Allen 1999). ET starts to decrease with the falling temperatures during the autumn period and thus the river flow during this period is primarily precipitation driven. Accordingly, the increase in average discharge during November is in sync with the incremental precipitation during the corresponding period.