Future Weather Data Research Articles

Evaluating the utility of weather generators in crop simulation models for in-season yield forecasting

CONTEXTCrop yield forecasting is crucial for ensuring food security and adapting to the impacts of climate change, as it provides early insights into potential harvest outcomes and helps farmers and policymakers make informed decisions in the face of changing environmental conditions. The accuracy of the crop model–based yield forecasting frameworks is affected by the uncertainty in future weather data, which is often substituted with synthetic weather realizations generated by stochastic weather generators. OBJECTIVEThis study aims to assess the performance of three recent stochastic weather generators—Global Weather Generator (GWGEN), WeatherGEN, and R Multi-Sites Autoregressive Weather GENerator (RMAWGEN) — in producing synthetic weather realizations that accurately represent regional climate variations and their impact on winter wheat yield forecasting. METHODSWe utilized historical weather data from Daymet, an interpolation of daily meteorological observations that produces gridded datasets with a spatial resolution of 1 km. This data was used both as an input for the weather generators and for evaluating the performance of the generated weather realizations. Furthermore, the weather realizations generated by these weather generators across multiple winter wheat field sites in Kansas were employed in the calibrated Environmental Policy Integrated Climate (EPIC) crop model to assess the potential impact of variations in weather generators on the accuracy of crop yield forecasts. RESULTS AND CONCLUSIONSRMAWGEN and WeatherGEN excelled in accurately simulating rainy days and precipitation amounts, with WeatherGEN particularly effective in wet months and RMAWGEN performing best in dry months, showcased their proficiency in diverse weather conditions. RMAWGEN consistently showed lowest error across all variables, including precipitation, solar radiation, and both maximum and minimum temperatures. Except for GWGEN, both RMAWGEN and WeatherGEN demonstrate good agreement with Daymet in replicating spatial variability patterns. RMAWGEN notably outperformed other weather generators, particularly during the forecasting period. Consequently, it showed superior capabilities in forecasting crop yields closely matching the simulated results with Daymet data. SIGNIFICANCEThe findings of this study are crucial for selecting accurate weather data estimates for crop yield forecasting. Utilizing alternative sources such as ensembles of multiple weather generators or outputs from sub-seasonal multi-model forecast systems may further enhance the accuracy of crop yield forecasts.

Peak shaving at system level with a large district heating substation using deep learning forecasting models

The decarbonisation of urban district heating (DH) systems requires increased heating grid flexibility. Therefore, this article examines the optimised operation of a tank thermal energy storage (TTES) on the secondary side of a new DH substation for an industrial site in a German city, in order to shave the peaks of the whole DH system and thus reduce the need for heat-only boilers (HOB). The accuracy of heat load and return temperature forecasts for both the industrial consumer and the DH grid is critical to the performance of the optimisation-based operating strategy of the TTES. Therefore, long short-term memory neural networks are used in combination with continuous model updates through incremental learning to create two forecasting scenarios, one using only preceding data for the forecasts and the other including future weather data. The results show that high forecasting accuracy is most relevant for reducing the annual maximum peak, with a reduction of 2.8% in the preceding data scenario, 4% with future weather data and 7% in a benchmark with perfect forecasts. The economic viability of the storage through HOB heat savings is primarily affected by lower forecasting accuracy when the additional cost of HOB heat is less than 60 €/MWh.

Near-, medium- and long-term impacts of climate change on the thermal energy consumption of buildings in Finland under RCP climate scenarios

The building sector is critical in climate change mitigation by reducing its energy consumption. At the same time, warming climate requires adaptation measures from buildings, making it necessary to study the future thermal energy demand of buildings. This paper studies the impact of climate change on the thermal energy demand of buildings in a heating dominated climate of Finland, under “representative concentration pathway” (RCP) climate scenarios in near- (2030), medium- (2050) and long-term (2080) horizon. Additionally, the impact of the thermal performance level of the buildings is investigated. The thermal energy demand calculation is based on overarching standards from the European “energy performance of buildings” directive. The weather scenarios present both typical and extreme weather years, applied through morphing and future weather data from the Finnish Meteorological Institute. The results show that annual thermal energy demand in Finland in 2080 can decrease up to 74.6kWh/m2. In addition, the decrease in heating demand will be higher than the increase in cooling demand. Cooling can become the dominant peak in long-term evaluations, especially in South Finland (Vantaa) with passive buildings under RCP8.5 climate scenario. Renovation is the most efficient action to decrease energy intensity, the earliest it is done the better.

Impact assessment of climate change on naturally ventilated residential buildings in Lebanon — Overheating risk under future climate scenarios

In light of current climate change and global warming, indoor overheating poses a significant risk. Buildings in the Mediterranean climate heavily rely on natural ventilation to maintain acceptable indoor thermal conditions. This reliance poses an increased risk to built environments in this region, particularly those occupied by low-income populations who cannot afford conditioning systems. This study assesses the thermal performance of typical residential buildings in Lebanon in response to future climate change, considering various emission scenarios and climate zones. The study uses morphed future weather data and dynamic building simulations to assess indoor overheating and the potential for natural ventilation to establish comfortable indoor conditions. Findings indicate that indoor overheating occurrences in naturally ventilated apartments are expected to increase in both frequency and intensity in the future, across different emission scenarios, varying by climate. The risk of overheating was highest in inland region, followed by coastal then mountain regions. Regarding natural ventilation comfort hours, coastal climates saw a significant decrease (40% to 26% in the worst case), inland climates witnessed a slight reduction (27% to 23% in the worst case), and mountain climates observed a marginal increase (1% to 3%), accompanied by an increased risk of overheating during peak periods.

A revised morphing algorithm for creating future weather for building performance evaluation

Climate change is one of the greatest challenges the building industry faces. Engineers and architects require representative future weather data if they would like to see how their buildings and designs will fare under a changing climate. The most common method used to create future weather involves manipulating observations commonly known as morphing, but the most used algorithms can create implausible weather conditions due to their unbounded nature. Here, bounded morphing algorithms will be described and their effectiveness proved mathematically. The improved bounded method applies two additional conditions on the morphed distribution to the maximum and minimum values, in addition to the mean values. The benefits over the standard approach will also be illustrated considering the changes in the distribution of temperature and solar irradiation due to climate change. The improved algorithms outperform the standard morphing procedures in terms of preserving the underlying climate signal while not creating unrealistic or implausible weather conditions. This method should give engineers confidence that the generated future weather series are more robust and representative of potential future weather. Practical application: The use of future weather to inform building design is now commonplace within the industry. Reliable weather files are crucial to support and deliver strategies for decarbonisation and adaptation to climate change in the built environment and the wider industry. This article provides support for the use of revised morphing algorithms which result in improved future weather time series which can be used in building simulation. For example, when applied to the temperature, it can be used to produce more accurate representations of future temperature profiles due to climate change, and for building performance assessment, such as energy consumption and overheating. It plays an important role in producing reliable and realistic weather data for future-proof building design.

A multi-objective optimization framework for building performance under climate change

Most building energy simulations utilize typical meteorological year data, overlooking the impacts of climate change. This can lead to inaccurate predictions and suboptimal designs. This study develops an optimization framework to identify robust building designs under varying climate scenarios. A case study is presented for residential buildings in Huangshan, China under 2020, 2050 and 2020–2050 conditions. A general circulation model provides future weather data. Multi-objective optimization maximizes energy efficiency and minimizes life cycle costs. Machine learning models predict heating, cooling and cost as functions of envelope parameters. Optimization is performed using a genetic algorithm. Results reveal the optimum insulation thickness, wall properties and window specifications. Compared to 2020 data, the ideal roof insulation increases from 40 mm to 90 mm by 2050. The optimal heating transmittance decreases from 0.38 W/m2K to 0.19 W/m2K. The approach highlights the significant influence of evolving climate on building design. It provides architects with accurate projections to make informed design decisions for energy efficiency and cost savings over the building lifespan.

Cascaded reinforcement learning based supply temperature control

In this work, a Q-learning based supply temperature control approach for a demonstrator building is proposed. The purpose is to improve the temperature behaviour inside the building and to tackle comfort problems such as overheating and undercooling which cannot be coped with by the standard heating curve. The Q-learning controller considers predicted future weather data as system states. This can be shown to be superior to Q-learning controllers without weather prediction. Furthermore, in order for the controller to capture different thermal effects of different time constants, a cascaded control structure is designed: An inner Q-learning based controller deals with thermal effects of smaller time constants. It is wrapped by an outer slower Q-learning controller which can tackle effects of larger time constants. Therewith, further improvement of comfort can be achieved.

Enhancing school buildings energy efficiency under climate change: A comprehensive analysis of energy, cost, and comfort factors

Incorporating future weather predictions into building assessments is essential for enhancing resilience, energy efficiency, cost savings, comfort, and sustainable infrastructure development in response to climate change. This study investigates the interplay between climate change and building performance, primarily focusing on energy usage, cost implications, and occupant comfort. It examines how future weather conditions impact school buildings in different climates, analyzing energy, cost, and comfort aspects. The research underscores the significance of tailored climate adaptation strategies for various regions and emphasizes considering future performance, even for highly energy-efficient buildings. Employing a comprehensive simulation-based approach, the study implements and validates future weather data in a Turkish school building, incorporating envelope improvements and photovoltaic applications to boost energy efficiency. A distinctive feature is the rigorous validation of future weather predictions against current measured data, facilitating a regional-level assessment of climate change effects on building energy consumption. The study's novelty lies in its detailed evaluation of climate change's multifaceted impacts on buildings, innovative future climate data validation, and contribution to a more localized and climate-specific approach to addressing building energy-cost-comfort performance. Findings reveal that in hot climates, there is a potential for nearly doubling primary energy consumption, global costs, and CO2 emissions in the future for both cost-optimal and nearly zero-energy scenarios. Consequently, the savings would decrease from 53-63 % to 13–30 %. In contrast, in cold climates, the impact on these parameters differs slightly, with reduced primary energy consumption and CO2 emissions but higher global costs. Notably, a building retrofitted to a high energy efficiency level may experience a substantial increase in future energy consumption and global costs, approaching the levels of currently inefficient buildings.

Framework to assess climate change impact on heating and cooling energy demands in building stock: A case study of Belgium in 2050 and 2100

Climate change has a broad impact on different aspects of energy use in buildings. This study explores potential changes in future heating and cooling energy demands. Increasing comfort expectations resulting from events like the extraordinary summer heatwaves in Europe are accelerating this trend to develop future scenarios for a better understanding of the relationship between future climate changes and the cooling need. This study used future weather data to estimate the heating and cooling energy demands in the Belgian building stock by 2050 and 2100 under base and business-as-usual scenarios using a dynamic building simulation model. The study showed that heating energy demand in the base scenario is expected to decrease by 8% to 13% in the 2050s and 13% to 22% in the 2090s compared to the 2010s. Additionally, the cooling energy demand is expected to increase by 39% to 65% in the 2050s and by 61% to 123% in the 2090s compared to the 2010s. Retrofit strategies applied to different building types contribute to lower the increase in cooling energy demand in the business-as-usual scenario compared to the base scenario. The cooling energy demand for an average building in the business-as-usual scenario is expected to increase with a range of 25% to 71% in the 2050s compared to 45% to 92% in the base scenario and 77% to 154% in the 2090s compared to 72% to 198% in the base scenario compared to the 2010s. The findings of the study provide insights to mitigate the impacts of climate change on heating and cooling energy demands.

Impact assessment of climate change on energy performance and thermal load of residential buildings in New Zealand

While it is evident that climate change will have an impact on the energy demand for heating and cooling in buildings, the exact extent of this impact is not yet fully understood. Quantification of future cooling and heating need in buildings provides a basis for taking appropriate measures for building climate change adaptation. The focus of this study is to examine how future climate change scenarios will impact the heating and cooling of residential buildings across different climatic regions in New Zealand. The future weather data under changing climate were generated for six climatic zones of New Zealand employing the statistical downscaling method. The study used various climate change scenarios, which represent concentration pathways (RCPs), to generate weather data. Specifically, the RCP8.5 and RCP4.5 scenarios were employed in the building performance simulations for different prototypes of residential buildings. The results showed there would be a significant change in the thermal performance of residential buildings, with a noticeable increase in cooling load and a decrease in heating load. These changes include a maximum thermal load change of 3 kWh/m2 in Auckland by 2090, 2.7 kWh/m2 in Hamilton, 8.3 kWh/m2 in Wellington, 4.2 kWh/m2 in Rotorua, 11 kWh/m2 in Christchurch, and 11.6 kWh/m2 in Queenstown. The warmer climatic zones are expected to change from a heating dominated to a cooling-dominated zone. The results indicated the importance of considering present and future climatic conditions in design and establishing a foundation for actions for the resilience of buildings to climate change.

Building Adaptation Measures Using Future Climate Scenarios—A Scoping Review of Uncertainty Treatment and Communication

The global climate is changing. Predicting the impacts this will have on buildings is the first step in the process of finding suitable building adaptation measures. Future climate adaptation of buildings and infrastructure is a growing field of research, relying on both socio-economical and meteorological research for input values to the simulation models. Models producing hourly future weather data rely on global climate models which are based on emission scenarios made from assumptions of future political, social, and economic developments. Accounting for the uncertainties from these underlying models as much as possible, and communicating the uncertainties in the results, is obviously paramount for reliable conclusions from the building simulation models. This paper is a scoping review, investigating how 132 studies treat and communicate the string of uncertainties from underlying models connected to future weather file generation in the scientific literature on building adaptation research. The findings suggest that climate-model-induced uncertainties are often under-communicated, due to either insufficient analysis or neglect. The studies that included the most comprehensive analyses of the uncertainties frequently concluded that treatment of these is important for the reliability of the results, and neglecting this could lead to misleading conclusions.

Using urban building energy modeling to quantify the energy performance of residential buildings under climate change.

The building sector is facing a challenge in achieving carbon neutrality due to climate change and urbanization. Urban building energy modeling (UBEM) is an effective method to understand the energy use of building stocks at an urban scale and evaluate retrofit scenarios against future weather variations, supporting the implementation of carbon emission reduction policies. Currently, most studies focus on the energy performance of archetype buildings under climate change, which is hard to obtain refined results for individual buildings when scaling up to an urban area. Therefore, this study integrates future weather data with an UBEM approach to assess the impacts of climate change on the energy performance of urban areas, by taking two urban neighborhoods comprising 483 buildings in Geneva, Switzerland as case studies. In this regard, GIS datasets and Swiss building norms were collected to develop an archetype library. The building heating energy consumption was calculated by the UBEM tool-AutoBPS, which was then calibrated against annual metered data. A rapid UBEM calibration method was applied to achieve a percentage error of 2.7%. The calibrated models were then used to assess the impacts of climate change using four future weather datasets out of Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The results showed a decrease of 22%-31% and 21%-29% for heating energy consumption, an increase of 113%-173% and 95%-144% for cooling energy consumption in the two neighborhoods by 2050. The average annual heating intensity dropped from 81 kWh/m2 in the current typical climate to 57 kWh/m2 in the SSP5-8.5, while the cooling intensity rose from 12 kWh/m2 to 32 kWh/m2. The overall envelope system upgrade reduced the average heating and cooling energy consumption by 41.7% and 18.6%, respectively, in the SSP scenarios. The spatial and temporal distribution of energy consumption change can provide valuable information for future urban energy planning against climate change.

Phasic Development of Rice During the Present and Future Climatic Conditions in Central Zone of Kerala

Rice is a staple food and its demand is substantially increasing with the growth of the global population. Phenological development was found to play a significant role in the distribution of carbon among plant organs, which has an impact on rice yield. Temperature affects plant phenology, and the current rapid climate change has revived interest in understanding and modelling plant phenology response to the warming trend. Two rice varieties viz., Jyothi (short duration variety) and Jaya (medium duration variety) were raised at Agriculture Research Station, Mannuthy, during the kharif season of 2021 and phenological observations viz., days to 50% flowering and physiological maturity were recorded. The phenophase has been also predicted from the Info-Crop and CERES-DSSAT for both varieties during the experimental period for validation. To study the phenology changes in future conditions i.e., near (2030), mid (2050) and end (2080) century, future weather data has been downloaded from the GFDL-CM3 climate model under RCP 4.5 and RCP 8.5 scenarios. Using the projected weather data, the phenophase of both varieties has been predicted using Info-Crop and CERES-DSSAT models. During the base period, Jyothi took 71 days and Jaya took 75 days to reach 50% flowering, while the total duration was found to be 101 days in Jyothi and 108 days in Jaya. In the case of 50% flowering, CERES-DSSAT predicted more accurately for Jyothi while InfoCrop predicted more accurately for Jaya. The prediction of physiological maturity was found to be more accurate using CERES-DSSAT in Jaya and Info-Crop in Jyothi. Validation results showed that both models can be used to predict the phenophases of rice varieties. The predicted duration during base period was compared with future duration. In Info-Crop model, the days to 50% flowering of Jyothi variety in near, mid and end century is expected to reduce by 3, 3 and 4 days in RCP 4.5 scenario and 1, 3 and 4 days in RCP 8.5 scenario whereas for Jaya variety, it is by 2, 3 and 3 days in RCP 4.5 and 1, 3 and 3 days in RCP 8.5 scenario respectively. CERES-DSSAT model predicted reduction of 50% flowering duration of Jyothi variety in near, mid and century by 1, 2 and 3 days in RCP 4.5 scenario and 1, 2 and 2 days in RCP 8.5 scenario whereas for Jaya variety, the reduction is by 2, 2 and 3 days in RCP 4.5 scenario and 1, 2 and 3 days in RCP 8.5 scenario respectively. The physiological maturity is projected to shorten by 3-6 days in Jyothi and 4-5 days in Jaya, by the end century. Results showed that the temperature rise in future can cause the considerable reduction in duration to attain 50% flowering and physiological maturity of rice varieties.

Impact of climate change on the life cycle greenhouse gas emissions of cross-laminated timber and reinforced concrete buildings in China

With the urgency of addressing climate change, cross-laminated timber (CLT) has become a sustainable material to substitute reinforced concrete (RC) in building applications. There is a growing number of studies on the life cycle assessment of CLT buildings worldwide, mainly based on historical weather files. This study integrates climate change on life cycle greenhouse gas emissions (LCGHGE) in CLT and RC buildings in five climate zones, including the product and construction (P&C), operational and End-of-Life (EoL) stages. The CCWorldWeatherGen tool is used to generate future weather data (i.e., the 2020s, 2050s and 2080s) for the A2 emissions scenario. The simulation results show heating demands will decline significantly and cooling demands will rise in all cases. By the 2080s, global warming will result in an average LCGHGE of 33.09% and 38.90% higher than the baseline scenario for RC and CLT buildings, respectively. Strategies to reduce space cooling demand and decarbonize electricity will become increasingly important in the future. The average LCGHGE of CLT buildings is 27.53%, 26.22%, 24.67% and 22.19% lower than those of RC alternatives in the baseline, the 2020s, 2050s and 2080s, respectively. The study demonstrates that the lower greenhouse gas (GHG) emissions and the greater benefits from EoL from CLT buildings outweigh the operational GHG savings benefits from the thermal mass of RC buildings, even when climate impact is taken into account. Therefore, CLT buildings are a feasible way to reduce GHG emissions from buildings in China.

Future weather generator for building performance research: An open-source morphing tool and an application

Mathematical morphing of historical weather data to match the projected climate change scenario is a commonly used method to generate future weather files for building energy simulation. It is known for preserving the local weather characteristics, which is particularly important when designing or analyzing high-performance buildings. Researchers may use one of the available morphing tools. Unfortunately, current tools use future climate data from outdated climate models with lesser accuracy, coarser spatial resolution, and fewer climate scenarios than recent models. In addition, these tools are closed, preventing others from adding corrections, updating, and developing them further. This paper presents an open-source, cross-platform, and state-of-the-art morphing tool that generates future hourly weather data for the whole building performance simulation. The novel tool is applied to a real-case office building in Coimbra, Portugal. In the SSP5-8.5 scenario, simulations show thermal energy needs and electricity use will reduce by 60% in 2050 and 77% in 2080 for heating and will increase by 67% in 2050 and 121% in 2080 for cooling. As a result, the building's global electricity consumption will increase by 24% in 2050 and by 53% in 2080. The study is extended to other locations in Europe, concluding that the total HVAC energy needs will remain constant or even decrease in heating-dominated climates, mainly due to the significant drop in the heating demand in the future.

Review of future weather data for building simulations available in Japan and confirmation of its characteristics

Buildings use a large amount of energy, depending on the climate. To design buildings with high energy and thermal performance in the future, it is necessary to use weather data that reflect future climatic information. Some future weather files for building simulations have been developed. However, these datasets are based on different predictions, and each future weather file has a different creation process. Such methodological differences may lead to differences in predicting the energy and thermal performance of buildings. Understanding the characteristics of each data type is necessary for its appropriate use. However, limited information is available for properly utilizing future weather data for building simulations. This study aims to provide information on the characteristics of future weather data for better utilization. After thoroughly reviewing the existing data and creation methods, we propose a framework for understanding future weather data based on their creative process. We collected five types of future weather datasets available in Japan and compared their characteristics. One of these datasets is the future weather dataset based on climate information provided by the National Institute for Environmental Studies (NIES). We confirmed the degree of variation in each weather element and predicted cooling/heating demand using future weather data available in Japan.

Resiliency comparison of radiant cooling systems and all- air systems

Radiant systems have been proven to be an energy-efficient and resource-effective heating and cooling solution for buildings. A key feature of a thermally active building system (TABS), one type of a radiant cooling system, is its ability to activate and control the thermal mass of the building structure. The advantage of this feature is the peak load shifting effect by the thermal mass, which leads to energy saving compared to a conventional system, e.g., an all-air system. This feature of the radiant cooling system could be particularly beneficial under a heat wave and power outage event. Dynamic building simulations were carried out to quantify the resilience of TABS to heat waves and power outages. An all-air system (i.e., airconditioning) was used as the reference cooling system. The simulations were carried out using EnergyPlus. Future weather files (typical meteorological years and years with heat waves) developed in IEA EBC Annex 80 were used for the simulations. In both HVAC systems. Simulation results for future weather data resulted in a decrease in heating demand and an increase in cooling demand.

Simulation and Prediction of Groundwater Dynamics in Nagarjuna Sagar Right Canal (NSRC) Command for Normal and Extreme Groundwater Recharge Conditions

Groundwater Modeling is the best tool to optimize the different combinations and to select the best combination for sustainable groundwater management. Using computer models, best solution or scenario can optimize by creating model with real conditions. In this study, computer model Visual MODFLOW was applied to Nagarjuna Sagar Right Canal Command area to simulate the groundwater dynamics. The net area irrigated under Nagarjuna Sagar Right Canal is 4.75 lakh ha. The aquifer properties of various layers and boundary conditions were fed in to the model as input for calibration and validation of the model. After calibration and validation, the model was used to predict the groundwater dynamics in the study area using different recharge scenarios. The validated groundwater model was used to predict the groundwater levels in the study area for the years 2020, 2030 and 2040 with different recharge scenarios. Global climate model was used to generate future weather data. MarkSim developed by the International Centre for Tropical Agriculture (CIAT) was used to generate the weather parameters like maximum and minimum temperatures for the years 2020, 2030 and 2040. The different recharge scenarios with average, lowest and highest recharge over the study period and projected evapotranspiration have been used and revealed that the groundwater levels increase with the increase in recharge and decreases with the decrease in recharge. The results revealed that the groundwater depleted area would decrease with the highest recharge scenario and it would increase with the lowest recharge scenario in future. The deeper groundwater table would be expected in future at Chimakurthy and Thalluru villages of Prakasam district and Karempudi and Piduguralla villages of Guntur district in the study area. This condition may be due to the excess utilization of groundwater which is less than the groundwater recharge in particular villages. The construction of rainwater harvesting structures for artificial groundwater recharge in those areas should be carried out immediately.

MODELLING IMPACT OF CROPPING PATTERN AND URBAN AREAON GROUNDWATER RESOURSES OF NAGARJUNA SAGAR RIGHTCANAL COMMAND AREA USING VISUAL MODFLOW

Groundwater models can be used for the assessment of the impact of the different cropping pattern andchange in urban area on groundwater resource and modeling is the best tool to optimize the differentcombinations or scenarios and to select the best combination or scenario for management of sustainablegroundwater resources. In this study, three-dimensional finite-difference groundwater model VisualMODFLOW 2.8.1 was used to simulate the groundwater dynamics in Nagarjuna Sagar Right CanalCommand area. The net area irrigated under Nagarjuna Sagar Right Canal is 4.75 lakh ha. Calibration andvalidation of the model has been carried out and used to predict the static groundwater storage availabilityin the study area for different cropping pattern scenarios and change in the urban area. The validatedVisual MODFLOW 2.8.1 was used to simulate the groundwater dynamics in the study area for the years2030 and 2040 with different cropping pattern scenarios and change in the urban area. Global climatemodel MarkSim, which is developed by the International Centre for Tropical Agriculture (CIAT) was usedto generate future weather data for the years 2030 and 2040. The impact of change in cropping pattern onstorage of static groundwater resource in the study area for the years 2030 and 2040 was assessed with twoscenarios of change in rice cropped area decreased by 50 per cent and 100 per cent with this cropped areacultivated equally by the ID crops. These results showed that the lesser storage of static groundwaterresources in future lesser. The change in the cropping pattern affects the net recharge of the groundwaterwhich influences the groundwater levels. Increase in paddy area increases the recharge and rise ingroundwater level has been identified. In the study area, cropping pattern could be changed from rice cropto other irrigated dry crops to prevent the increase of groundwater resources which leads the water loggingcondition in the study area. The urbanization is one of the most significant factor considering for assessmentof the response on the groundwater levels and availability of static groundwater resources. The groundwaterrecharge is very less in case of increase in urban area and generates more runoff rather than the intake ofrainfall in the ground surface as having the impervious nature. In view of increase in urban area in thestudy area, the four scenarios are proposed with the combination of 10 per cent and 20 per cent decrease inrecharge and 50 per cent and 100 per cent increase in groundwater draft and simulated the groundwater fluctuations for the years 2030 and 2040. The impact of change in the urban area on storage of availablestatic groundwater resource in the study area for the years 2030 and 2040 was assessed with four scenarioscombination of groundwater recharge and groundwater pumping. The storage of static groundwaterresources would be expected in future lesser than the present storage of static groundwater resources in allscenarios due to increase in urban area as increased in impervious nature of the ground surface. The declinein available static groundwater resources would be expected 6.38 per cent and 7.08 per cent during 2030and 2040 respectively. The rapid expansion in the urban area and their settlements is also one of the reasonsfor the depletion of groundwater resources with low recharge and high groundwater draft.

Hourly air temperature projection in future urban area by coupling climate change and urban heat island effect

Global climate change and urban heat island (UHI) effect are changing the urban microclimate and ecosystem of building and outdoor environment. In this study, CMIP5 family, which was found to have the most consistent projection with the current worldwide observation till now, was chosen to evaluate climate change’s impact on urban climate. This research uses Principal Component Analysis and Gaussian Mixture Model based building type clustering method to obtain urban morphological data and input parameters of the UHI model, and the validated UHI model is then coupled to the projections from various global climate models (GCMs).It is found that future urban microclimate prediction after considering the UHI effect can significantly improve the prediction accuracy of air temperature when downscaling future weather data from various selected GCMs, indicating the UHI shall be considered in predicting future weather data in urban tissue. After using the best-fit climate change prediction model MRI-CGCM3 to couple with the UHI model, the projections of the heating degree days (HDD) and cooling degree days (CDD) of Shenzhen from 2020 to 2099 has shown that comparing with the typical meteorological year, the average HDD in Shenzhen will be reduced by 57.5% in 2020–2099 under the RCP8.5 scenario, and the CDD will increase by 25.1%, which will exert considerable impacts on building energy use in urban tissue.