We develop and test a physically based semi-Lagrangian waterbodytemperaturemodel to apply climatological data and thermal pollution from river-based power plants to historical river flow data in order to better understand climatechangeimpacts on surface watertemperature and thermal power plant withdrawal allowances. The model is built for rapid assessment and use in Integrated Assessment Models. We first test the standalone model on a 190km river reach, the Delaware River, where we have detailed flow and temperature data. An R 2 of 0.88 is obtained on hourly data for this initial test. Next, we integrate the standalone temperaturemodel into a series of models— rainfall-runoff model, water demand model, water resource management model, and power plant uptake and release model—for the contiguous USA (CONUS), with about 19,000 segments total. With this system in place, we then validate the standalone watertemperaturemodel within the system for 16 river stations throughout the CONUS, where we have measured daily temperature data. The model performs reasonably well with a median R 2 of 0.88. A variety of climate and emissions scenarios are then applied to the model to test regions of higher vulnerability to river temperature environmental violations, making use of output from two GCMs and six emissions scenarios focusing on projections out to 2050. We find that the two GCMs project significantly different impacts to watertemperature, driven largely by the resulting changes in streamflow from the two models. We also find significantly different impacts on the withdrawal allowed by thermal power plants due to environmental regulations. Potential impacts on generation are between +3% and -4% by 2050 for the unconstrained emissions case and +3.5% to -2% for the stringent GHG mitigation policy (where 1% is equivalent to 32 TWh, or about 3 billion USD/year using 2005 electricity prices). We also find that once-through cooling plants are most vulnerable to climatechangeimpacts, with summer impacts ranging from -0.8% to -6% for the unconstrained emissions case and +2.1% to -3.7% for the stringent GHG emissions case.
The simulation results showed that the changes in this trait obeyed the wheat yield. As the maximum rate of harvest index in all scenarios and general circulation models was less than the current situation of the studied region ( Table 5 ). For the time period of 2080, the A1B scenario under the impact of gen- eral circulation model HadCM3 and the A2 scenario for the general circulation model IPCM4, showed the maximum change according to the rate of this trait in the current climatic condition of Mashhad (52). On the other hand for the period of 2080, the B1 scenario showed the least reduction of this trait in both general circulation models within the current situation of the studied region with 51% of harvest index. The reduction of wheat harvest index in the climatic change condition of Sis- tan in relation to its current situation, can illustrate this issue that the climatic changes ahead, which have more emphasis on the temperature rise on the basis of the mentioned scenarios between 1.5 and 5 degree centigrade in the next 100 years ( IPCC, 2007 ), have less inﬂuenced on wheat biomass produc- tion than the grain yield. It appears that the rise in temperature in climatechange condition has had negative impacts on the grain ﬁlling period by reducing the plant’s growth period ( Ta- ble 5 ), and resulted in the reduction of wheat yield within its current situation. Change in temperature and rainfall level, af- fects the plant photosynthesis, growth and absorption rate and water and nutrient distribution and as a result the leaf area in- dex ( Long, 1991 ).
Projected impacts of climatechange will depend on the combination of emissions scenarios, cli- mate forcings, and impact model used to assess the local impacts (Viner, 2003; Olesen et al., 2007). The assessment process begins with the selection of one or more emissions scenarios, normally adopted from the Special Report on Emissions Scenarios (SRES, Naki´cenovi´c et al., 2000), which are derived from four main storylines (A1, A2, B1, B2) describing different socio-economic, de- mographic and technological evolutions of our society, which have to be treated as being equally plausible (Schneider, 2002; Viner, 2003). Emissions are then converted to concentrations of green- house gases by gas-cycle models, and scenarios of future concentrations are then used to derive projections of climate response, usually through complex coupled atmosphere-ocean General Cir- culation Models (AOGCM). To overcome limitations related to spatial resolution of AOGCM, spa- tial and/or temporal downscaling techniques are carried out for limited areas and run for shorter periods, with the purpose of better reproducing temperature and precipitation fields. Finally, downscaled climate fields are used to drive impact models, and to obtain projected impacts and their corresponding predictive uncertainties. In particular, hydrological impact studies involve important decisions which effects are reflected into the final impacts. Firstly, the modeller select one or more model structure(s) to represent the main physical process undergoing in the catch- ment; secondly, one or many parameter sets are used to describe some effective properties of the catchment within the adopted model structure(s); then, parameter values are obtained by using one or more goodness-of-fit measure selected by the modeller, for comparing observed and sim- ulated values during a user-defined calibration period, with or without explicit consideration of errors in the input data used to drive the simulations. As a result, quantification of impacts of cli- mate change have to be seen as a ”cascade of uncertainty” (New and Hulme, 2000; Mearns et al., 2001; Schneider, 2002; Viner, 2003; Giorgi, 2005; Wilby, 2005), as shown in Figure 5.1, in which decisions taken in every step of the assessment process, going from emissions scenario to projected impacts, convey uncertainties that are unavoidably propagated to subsequent levels.
In this context, evaluating a wide range of divergent cli- mate projections may also not necessarily provide a useful overview of the possible impact of climatechange on water management issues. To develop a didactic demonstration that brings scientists and managers to a reflection on adaptation strategies to climatechange, it may be more suitable to limit the analysis to a multi-model ensemble mean of future pro- jections (see e.g. Knutti et al., 2010) or even adopt a model- free approach relying on a sensitivity analysis under different climate scenarios. Climate-related uncertainties could thus be restrained to the use of basic climate scenarios leading progressively to warmer and drier conditions. For instance, only decreasing trends in precipitation associated with a very probable increase in temperature could be considered to fo- cus impact studies towards a degradation of hydro-climatic conditions according to gradual thresholds. We believe this is of primary importance to improve our knowledge regard- ing vulnerability and resiliency of hydrological systems un- der possible climatechange.
Climatechange is a global concern as it may affect many aspects of life, including water supply. A tool used to modelclimate change’s impacts is called a General Circulation Model (GCM). GCMs project future scenarios including temperature and precipitation, but these are designed at a coarse resolution and require downscaling for employment for regional hydrologic modeling. There is a vast amount of research on downscaling and bias-correcting GCMs data, but it is unknown whether these techniques alter precipitation signals embedded in these models or reproduce climate states that are viable for water resource planning and management. Using the Tampa, Florida region for the case study, the first part of the research investigated 1) whether GCM and the downscaled, bias-corrected data were able to replicate important historical climate states; and 2) if climate state and/or transition probabilities in raw GCMs were preserved or lost in translation in the corrected downscaled data. This has an important implication in understanding the limitations of bias-correction methods and shortcomings of future projection scenarios. Results showed that the GCM, and downscaled and bias-corrected data did a poor job in capturing historical climate states for wet or dry states as well as the variability in precipitation including some extremes associated with El Niño events. Additionally, the corrected products ended up creating different cycles compared to the original GCMs. Since the corrected products did not preserve GCMs historical transition probabilities, more than likely similar types of deviations will occur for “future” predictions and therefore another correction could be applied if desired to reproduce the degree of spatial persistence of atmospheric features and climatic states that are hydrologically important.
Since the 1960s, heat balance models of the human body have become more and more accepted in the assessment of thermal comfort. The basis for these models is the human energy balance equation. One of the first and still very popular heat balance models is the comfort equation defined by Fanger (1972). Fanger introduced the thermal indices “Predicted Mean Vote” (PMV) and “Predicted Percentage Dissatisfied” (PPD) to help air-conditioning engineers create thermally comfortable indoor cli- mates. Two decades later, Jendritzky et al. (1990) managed to make Fanger’s approach applicable to outdoor conditions by assigning appropriate parameters to adjust the model the much more complex outdoor radiation conditions. This approach, which is also known as the “Klima Michel Model”, is now increasingly being applied. Since this model was designed only to estimate an integral index for the thermal component of climate and not to represent a realistic description of thermalbody conditions, it is able to work without the consideration of fundamental thermo-physi- ological regulatory processes. For example, in Fanger’s approach the mean skin tem- perature and sweat rate are quantified as “comfort values”, being only dependent on activity and not on climatic conditions (Höppe 1999).
Abstract: Currently, thermal power is the largest source of power in the world. Although the impacts of climatechange on coolingwater sufficiency in thermal power plants have been extensively assessed globally and regionally, their economic consequences have seldom been evaluated. In this study, the Asia-Pacific Integrated Model Computable General Equilibrium model (AIM/CGE) was used to evaluate the economic consequences of projected future coolingwater insufficiency on a global basis, which was simulated using the H08 global hydrological model. This approach enabled us to investigate how the physical impacts of climatechange on thermal power generation influence economic activities in regions and industrial sectors. To account for the uncertainty of climatechange projections, five global climate models and two representative concentration pathways (RCPs 2.6 and 8.5) were used. The ensemble-mean results showed that the global gross domestic product (GDP) loss in 2070–2095 due to coolingwater insufficiency in the thermal power sector was −0.21% (−0.12%) in RCP8.5 (RCP2.6). Among the five regions, the largest GDP loss of −0.57% (−0.27%) was observed in the Middle East and Africa. Medium-scale losses of −0.18% (−0.12%) and −0.14% (−0.12%) were found in OECD90 (the member countries of the Organization for Economic Co-operation and Development as of 1990) and Eastern Europe and the Former Soviet Union, respectively. The smallest losses of −0.05% (−0.06%) and −0.09% (−0.08%) were found in Latin America and Asia, respectively. The economic impact of coolingwater insufficiency was non-negligible and should be considered as one of the threats induced by climatechange.
In Figure 5.11, the PDFs of future spring runoff depths are shifted to the right for all GCMs. Also, the PDFs generally appear less spread in a future climate, compared to current climate for all hydrological models, except HYDROTEL. This is generally due to increasing spring temperatures and accumulated winter snow under the future climate conditions created by an increase in winter precipitation (see Figure 5.1) and will cause intense snowmelt and higher spring runoff. The coefficient of variation (CV) of average spring discharge (from March to May) for the future period was calculated for each PDF and average values produced by the hydrological model. The CV is a metric used to standardize the dispersion of PDFs and allows comparing distributions with different means. Average CV values obtained are 18.5%, 16.1%, 15.8% and 12.3%, respectively for HYDROTEL, HBV, HSAMI and HMETS. Although no statistical tests were conducted, CV values are higher for the distributed hydrological models than for the lumped models. This highlights the fact that explicitly recognizing the distributed properties of climate input and watershed physiographic characteristics may be responsible for creating more variability in the spring runoff volumes. Finally, extreme spring runoff events are similar for four of ten GCMs (i.e. BCCR, CNRM, INM and MPI) when comparing the control and simulated PDF curves. This indicates that the cold and dry GCMs (except MPI which is cold and wet models) tend to simulate conditions that lead to extreme spring floods which are similar to today’s climate.
Norris Lake, the largest reservoir on a tributary of the Tennessee River, with 809 miles of shoreline and 33,840 acres of water surface, is a popular tourist and recreation destination. In the 1930s, TVA established demonstration public parks at several locations on Norris Reservoir, including Cove Lake, Big Ridge, and the areas around Norris Dam. These parks later became the nucleus of Tennessee’s state park system supporting various recreation activities including hiking, boating, water skiing, swimming, and excellent fishing. Moreover, Norris Lake has various aquatic habitats in the watershed, and the Clinch River including the Powell River has one of the most diverse fish and mussel faunas in North America as Eckert (2010) documented that 20% of the 300 species in the U.S. are known from the Clinch River.
Abstract. An understanding of potential stream water qual- ity conditions under future climate is critical for the sustain- ability of ecosystems and the protection of human health. Changes in wetland water balance under projected climate could alter wetland extent or cause wetland loss (e.g., via in- creased evapotranspiration and lower growing season flows leading to reduced riparian wetland inundation) or altered land use patterns. This study assessed the potential climate- induced changes to in-stream sediment and nutrient loads in the snowmelt-dominated Sprague River, Oregon, west- ern US. Additionally, potential water quality impacts of combined changes in wetland water balance and wetland area under future climatic conditions were evaluated. The study utilized the Soil and Water Assessment Tool (SWAT) forced with statistical downscaling of general circulation model (GCM) data from the Coupled Model Intercompar- ison Project 5 (CMIP5) using the Multivariate Adaptive Constructed Analogs (MACA) method. Our findings sug- gest that, in the Sprague River, (1) mid-21st century nutri- ent and sediment loads could increase significantly during the high-flow season under warmer, wetter climate projec- tions or could change only nominally in a warmer and some- what drier future; (2) although water quality conditions under some future climate scenarios and no wetland loss may be
A number of studies also investigated the impacts of cli- mate change in the Middle East and particularly in the E- T basin, although none have concentrated specifically on the effects to snow water availability. Using a super-high- resolution GCM, Kitoh et al. (2008), concluded that by the end of this century, the Fertile Crescent (the arc area that cov- ers the headwater region of the E-T basin) will lose its cur- rent shape and may disappear altogether, due in part to sig- nificant decreases (29–73 %) in the annual discharge of the Euphrates River. Evans (2009) examined the performance and future predictions of 18 GCMs in the Middle East and found an overall temperature increase of almost four degrees K and a large decrease in precipitation associated with a de- crease in storm track activity in parts of Turkey, Syria, and northern Iraq by late 21st century. Similarly, ¨ Onol and Se- mazzi (2009) assessed the role of global warming in mod- ulating the future climate over the eastern Mediterranean re- gion using a regional climatemodel under Intergovernmental Panel on ClimateChange (IPCC) emission scenarios. Their results suggest a large decrease in precipitation over south- eastern Turkey where the headwaters of the E-T basin origi- nate. With these results in mind, the research presented here investigates the effect of projected climatic changes on snow water availability in the E-T basin, using a distributed hydro- logical model capable of capturing snow-water dynamics in mountainous watersheds. In particular, it focuses on changes in SWE as an aggregate measure of climatechangeimpacts on snow water availability that forms the major source of flow for the twin rivers. Unlike previous research, this study also represents a comprehensive assessment that uses 52 cli- mate change scenario realizations (13 models, two emission scenarios, and two time periods).
Agriculture is one of the most climate sensitive sectors as it is continuously and directly affected by temperature and precipitation.Climate changes pose significant economic and environmental risks worldwide. The economy of Odisha mainly depends on agriculture, and this in turn largely depends on available water resources. Climate variability is concerned with the changeability in the mean state and other statistics (such as Mean, Standard deviation, Coefficient of variation, Skewness, Kurtosis etc.)of climate elements on all spatial and temporal scales beyond those of individual weather events.Climate change is on the other hand is variability that continues over a longer period and statistically significant. Climatic variability particularly rainfall is the major factor influencing the agricultural productivity and sustainability in the tropics. Around 60% of the Indian agriculture is rain-dependent, distress-prone and vulnerable to climate. Constant increase in green house gas concentrations, since pre-industrial times, has led to positive radioactive force of the climate, tending to warm the surface.
The WBGT profile in fig.1 showed that the measured average WBGT in the high heat, medium heat, and low heat industry were 31° C, 30 °C & 29 °C during summer where maximum WBGTs were measured in the places where employees were working near furnaces and dryers. During winter the average WBGT 28.2°C, 27°C and 25 °C. This indicates the impact on ambient temperatures on indoor heat stress conditions. It is also clearly seen that in summer 94 % and winter only 37% of the workers are at the risk of heat stress recommended Threshold Limit Value (TLV) as per ACGIH guidelines  and hence they experienced thermal discomfort. Similarly high occupational heat stress proﬁles that exceed recommended TLVs have also been demonstrated in other studies conducted in India [3, 16] and around the world [2, 17-19]. From such evidence, it is suggested that occupational heat-protection and mitigation requires more attention and action in many regions of the world.
Rising global temperatures have threatened the operating conditions of Batang Padang hydropower reservoir system, Malaysia. It is therefore crucial to analyze how such changes in temperature and precipitation will affect water availability in the reservoir in the coming decades. Thus, to predict future climate data, including daily precipitation, and minimum and maximum temperature, a statistical weather generator (LARS-WG) is used as a downscaling model. Observed climate data (1984-2012) were employed to calibrate and validate the model, and to predict future climate data based on SRES A1B, A2, and B1 scenarios simulated by the General Circulation Model’s (GCMs) outputs in 50 years. The results show that minimum and maximum temperatures will increase around 0.3-0.7 ºC. Moreover, it is expected that precipitation will be lower in most months. These parameters greatly influence water availability and elevation in the reservoir, which are key factors in hydropower generation potential. In the absence of a suitable strategy for the operation of the hydropower reservoir, which does not consider the effects of climatechange, this research could help managers to modify their operation strategy and mitigate such effects.
Abstract. The sensitivity of some aspects of water quality to climatechange was assessed in the Seine River (France) with the biogeochemical model RIVERSTRAHLER, which describes the transformations and fluxes of C, N, P and Si between the main microbiological populations, the water col- umn and the sediment, along the entire river network. Point and diffuse sources are prescribed, stream temperature un- dergoes a sinusoidal annual cycle constrained by observa- tions, and runoff is calculated by a physically-based land sur- face model. The reference simulation, using meteorological forcing of 1986–1990 and point sources of 1991, compares very well with observations. The climatechange simulated by a general circulation model under the SRES emission sce- nario A2 was used to simulate the related changes in runoff and stream temperature. To this end, a statistical analysis was undertaken of the relationships between the water and air temperatures in the Seine watershed over 1993–1999, using 88 points that correctly sampled the variability of the tribu- taries. Most of stream temperature variance was explained by the lagged moving average of air temperature, with pa- rameters that depended on Strahler stream order. As an inter- esting simplification, stream temperature changes could be approximated by air temperature changes. This modelling framework was used to analyse of the relative influence of the water warming and discharge reduction induced by cli- mate change on biogeochemical water quality in Paris and downstream. Discharge reduction increased phytoplankton growth and oxygen deficits. Water warming decreased dis- solved oxygen, increased phytoplankton biomass during the growth period, and reduced it afterwards, when loss factors dominate. It was also shown that these impacts were en- hanced when point source inputs of nutrient and organic car- bon increased.
Compared to a BAU future, the results presented here show benefits of mitigating GHG emissions, as the overall damages to U.S. water resource uses from climatechange would be reduced. Our analysis shows that the largest impacts from climatechange are projected to be on non-consumptive uses (e.g., environmental flows and hydropower) and relatively lower-valued consumptive uses (e.g., agriculture), as water is reallocated during reduced water availability conditions to supply domestic, commercial, and indus- trial uses with higher marginal values. The primary driver of these changes is increased evaporation associated with warming temperatures. Lower GHG concentrations associated with a mitigation policy will result in a smaller rise in temperature and thus less damage to some of the water resource uses. Hydropower, environmental flow penalty, and agriculture, however, were shown to be sensitive to the change in runoff as well. In some regions with higher GHG concentrations, precipitation can increase; thus the relationship between GHGs and the water resource damages that result from changes in precipitation is more complex, and requires additional analysis to identify key drivers of change and to address areas of uncertainty.
Abstract: Climate can be defined as the ―expected weather‖ and when changes in the expected weather occur, we call these climate changes. With respect to the relations between the hydrological cycle and the climate system, every change on the climate could affect all meteorological parameters and this leads to change in the crop water requirement in agriculture. Considering this, a study was carried out to assess the impact of climatechange on crop water requirement for the crops grown in the Sukhi command area of Vadodara district, Gujarat. For this study, daily meteorological data like maximum temperature, minimum temperature, wind speed, sunshine hours and rainfall for the period 2003 to 2009 are used. Cropping pattern data and crop data was used for this study. Future climate data were predicted for the periods of 2011-2030, 2046-2065 and 2080-2099 considering A2 scenario of the Intergovernmental Panel on ClimateChange Special Report on Emissions Scenarios (IPCC SRES) using stochastic weather generator named Long Ashton Research Station Weather Generator (LARS-WG 5.0) considering HADCM3 (Hadley centre Unified Model 3) scenario file. Reference crop evapotranspiration (ETo) was determined using mean monthly meteorological data with the help of CROPWAT 8.0 and then crop water requirement (ETc) was determined. Results shows the clear effect of climatechange on crop water requirement of Rabi and Hot Weather crops. Results shows that crop water requirement of all Hot Weather crops (Millet, Ground nut, Maize, Small vegetables and Tomato) in all future periods is increasing as compared to base period 2003-2009. Crop water requirement of Rabi crops (Wheat, Sorghum, Maize, Small Vegetables, Tomato, Gram and Cowpeas) shows negligible decrease in crop water requirement in the period 2011-2020 but all crops shows considerable increasing water requirement in the period 2021-2030 including the periods 2046-2065 and 2080-2099 as compared to base period 2003-2009. For meeting the increasing water demand and to increase yield, over all water resources should be increased by doing water conservation practices effectively and also farmers’ should be motivated to use drip or sprinkler irrigation system instead of flooding methods.
Author Contributions: C.O. conducted the modelling, analysed the results and wrote the paper. T.A.C. supervised the research, advised on the methodologies, gave comments and corrected the manuscript. S.C. assisted in modelling and data processing from the model, M.G.K., M.E.A., T.P. gave comments and improved the manuscript. Funding: This study was funded by a grant from the John D. and Catherine T. MacArthur Foundation through a project entitled “Increasing community and biodiversity resilience to development and climate-change based threats on the Tonle Sap Lake” in partnership with Conservation International via a subproject “Managing pressures from the development of dams, land use conversion, and climatechange on riverine ecosystems of the Mekong’s Tonle Sap basin”, grant number 6001451. Dr. OEURNG Chantha was further supported by a Fulbright US-ASEAN Visiting Scholar Fellowship at the University of California, Berkeley, USA, and manuscript preparation was partially supported by the Collegium de Lyon—Institut des Etudes Avancées de l’Université de Lyon, the EURIAS Fellowship Programme, and the European Commission (Marie-Sklodowska-Curie Actions-COFUND Programme-FP7).
The climate models that we are using in this study are tools that have been developed to understand and to predict specific features of the real climate system of the Earth. In order to be useful for this purpose, it is necessary to evaluate the capability of such models to realistically represent these features [58, 59]. Therefore, before entering into the analysis of future changes in dH, we first evaluate its representation in the climate models for the reference period 1971-2000, assuming this to be representative of contemporary climate. Model evaluation is commonly based on the direct comparison between simulation results and measurements of individual observables (e.g. ,  and ). Here we compare the simulated drought hazard from individual models, as well as the mean (CMIP5-EMean) and median (CMIP5-EMed) of their ensemble, to the drought hazard computed with monthly precipitation totals from the GPCCv4-WATCH forcing data. Differences at each grid point were quantified by means of the Pearson product-moment correlation coefficient, r, a widely used measure of the degree of linear dependence between two datasets . The single model or ensemble statistic (mean or median) with the highest agreement is selected for projecting future changes in the geographic patterns and magnitudes of dH.
The CMIP5 projections are likely to provide unreliable estimates of the mean values and daily variations in pre- cipitation due to inherent limitations of the general circula- tion models (GCMs; Raty et al., 2014). Biases have already been identified in simulating the present-day observed In- dian summer monsoon climatologies (Sengupta and Rajee- van, 2013). Further, Lutz et al. (2014) found large uncertain- ties and variations between the annually averaged and sea- sonal precipitation projections over the Upper Ganges basin. In addition, GCMs were not built for the application of hy- drological impact studies. The runoff generation mechanism in GCMs is based on a simplistic representation of the hy- drological cycle, and several studies have shown that hydro- logical models driven directly by GCM model outputs do not perform well (Fowler et al., 2007). To diminish the im- pacts of GCM biases, several techniques that adjust the cli- mate projections and transform coarse-resolution GCM out- puts into finer-scale products suitable for hydrological appli- cations have been developed over recent years and plenty of studies have revised and evaluated these techniques (Fowler et al., 2007; Maraun et al., 2010; Teutschbein and Seibert, 2012; Raisanen and Raty, 2013; Raty et al., 2014). In this study, we applied the delta-change method to observed me- teorological datasets. This is a relatively simple approach, broadly used for transforming coarse-resolution GCM out- puts into finer-scale products suitable for hydrological ap- plications. The delta-change method was selected as it is a relatively straightforward to apply technique, it is computa- tionally efficient and can be applied to all variables. However, it also has a number of limitations: (a) it assumes a constant delta for each month, as it suggests that relative change is bet- ter simulated than absolute values; (b) it assumes a constant spatial pattern of the climatic variable and ignores changes in variability, as the calculated change factors (CFs) only scale the mean, maximum and minimum values; and (c) there is no change in the temporal sequence of wet and dry days (Fowler et al., 2007).