Input Weather Data Research Articles

Optimization and performance evaluation of a novel absorption-based atmospheric water generator system for enhanced energy efficiency

Providing potable water poses a severe and chronic challenge for the human race. Conventional desalination systems are only feasible in large-scale production, requiring significant energy and a source of feeding water. Atmospheric water harvesting is a decentralized water production method that uses air humidity as a renewable water source. This technique holds promise for providing water even in hard-to-reach regions with no source of liquid water. However, the feasibility of this method declines in arid or semi-arid climates. In this paper, a novel absorption-based atmospheric water generator is proposed to improve the feasibility and cost-effectiveness of this technique to make it a viable method for producing fresh water, even in arid regions. The system is modeled using EES software to explore the effects of seven adjustable parameters on its performance. Subsequently, the modeled system is transferred into MATLAB through the utilization of artificial neural networks. A tri-objective optimization is then carried out using the genetic algorithm approach. The optimized system can produce 14016m3/year of fresh water, with a levelized cost of approximately 33.72$/m3, operating under hot and arid weather conditions (27°C and 25% RH). The data acquired from the system’s performance parameters is leveraged to derive correlations that are used to determine the system’s performance based on the input weather data. These correlations allow for the assessment of system feasibility across various regions and serve as a valuable shortcut in modeling similar systems for future research.

Modelling and performance evaluation of a parabolic trough solar water heating system in various weather conditions in South Africa

In recent years, various energy sources and methods have been used to heat water in domestic and commercial buildings. Water heating methods for large households or commercial buildings include electrical heating elements, gas heaters, and solar energy (solar concentrators, flat plate collectors, evacuated tube collectors, etc.). In recent decades, the focus of water heating has shifted to solar energy, which is abundantly available in most African countries. The weather condition of a region has a huge impact on the system's performance. South Africa is characterised by four different weather seasons (winter, spring, summer, and autumn), unlike most African countries. Therefore, this study focuses on the impact of weather seasons on the system's performance for water heating through the combined use of solar energy and solar concentrator techniques. The system performance was modelled by using Matlab Simulink®, where historical weather data for Pretoria, South Africa, was fed into the model. Based on the weather data input, the system behaviour varied per season due to the change in solar intensity. The average outlet temperatures of the absorber, in the order of magnitude, were 333, 332, 328, and 325 K during the autumn, summer, spring, and winter seasons, respectively. Similarly, the average storage tank temperatures, in the order of magnitude, were 366, 364, 363, and 360 K in spring, summer, autumn, and winter, respectively. From this study, it is concluded that the different weather seasons in South Africa, have a direct impact on the performance of the system. Irrespective of the season, the system produced the required volume of hot water required throughout the year.

Are the Noah and Noah-MP land surface models accurate for frozen soil conditions?

The accurate prediction of frozen soil conditions is a critical component of dynamic terrain characterization for engineering operations in cold regions. Currently the trusted resource for dynamic terrain within the US military uses gridded global soil conditions (i.e., temperature and moisture) generated by the Land Information System (LIS) architecture. LIS combines static terrain data with trusted surface weather information into the Noah land surface model to generate a continuous representation of the global surface conditions. Both the input weather data and the model output land surface state are adjusted to match the observed state using an advanced ensemble Kalman filter data assimilation technique to incorporate in situ and remote sensing observations. While this product has been robustly evaluated under unfrozen conditions only sporadic studies have investigated its ability to accurately simulate freeze/thaw cycles, and soil temperature and moisture under frozen conditions. Here, we present a broad comparison and evaluation of the simulated soil state against an in situ network of soil temperature and moisture distributed across the Continental United States that focuses on the winter season. This comparison includes both the Noah and the newer Noah with multi-parameterization (Noah-MP) land surface models. A comparison of the two models reveals substantial differences in simulated soil moisture, frozen content, and frozen soil strength. In general, Noah predicts colder soils with deeper frost depths, and frozen soils that are slightly stronger than Noah-MP. Initial results evaluating Noah and Noah-MP against in situ observations show that both models have significantly degraded model performance for frozen soil conditions compared to their performance for unfrozen soil. In particular, Noah has a significant and persistent cold bias of approximately −3 K when the soil temperature is below freezing. This cold bias is not present in Noah-MP and further analysis shows that this improvement was almost entirely caused by a better representation of thermal conduction through snow in Noah-MP. Specifically, we find that the bulk effective thermal conductivity of a snowpack is 2× greater in Noah, which allows for greater surface heat loss during the winter and, consequently, lower soil temperatures. This analysis reveals that the more advanced multi-layer snow model in Noah-MP substantially improves the representation of soil temperature during the cold season.

Effect of different generated weather climate data file on building energy consumption in different climates of Iran

Accurate estimation of building energy consumption is critical, and it can usually be calculated using simulation software like Energy Plus. The input weather data for software is typically a typical or reference year as a standard weather file, such as TMY2. To best estimate building energy consumption, TMY2 data must be generated intelligently. In the present study, first, several TMY2 data files have been generated from different periods (5–21 years) of available measured weather data (9 separate files). Available measured weather data is for 1998 to 2019. Also, a TMY2 file has been generated from the long-term average for the total period (1998–2019), and two TMY2 files have been generated using the CCWeatherGen software for 2020 and 2050. In the next step, the calculated building energy consumption from those files for an educational building will be compared using Energy Plus software. The primary purpose of this study is to investigate the impact of different generated TMY2 (from different weather data periods) on energy consumption and determine the best period to generate TMY2 files from the available period of 5 to 21 years for the capital of Iran (Tehran), which has a semi-arid climate, and Tabriz, which has a cold and mountainous climate. These cities have different climates. Results show that for Tehran, building heating energy consumption changes by about 10% from other generated TMY2 files (one file generated from 2009 to 2019 and another generated from the 2007 to 2019 period). The result shows that generating TMY2 files from different weather data periods affects building energy consumption. So, a suitable strategy for generating TMY2 files is necessary. As a new strategy, the present study proposes that TMY2 files should be generated based on building cooling and heating energy from recent years. Also, because climate change is an important parameter, a TMY2 file should be generated from recent years’ weather data to accurately predict building energy consumption.

Prediksi Luas Area Terbakar Menggunakan Fire Weather Index dan Frekuensi Titik Panas di Jambi

Forest fires occur every year in Indonesia, one of the regions with the highest forest fires is Jambi Province. Significant losses and negative impacts due to forest fires cause the need for an effort to prevent forest fires early on with the detection of forest and land fires. One method that can provide information about the level of forest fire based on daily weather data input is the Fire Weather Index (Fire Weather Index / FWI) system, which was first developed by Canada. This study aims to estimate burn area in the Jambi region by using temperature, rainfall, humidity, and wind speed data. Other supporting data are hotspot frequency data from NASA-FIRMS satellites and data fraction of burn area from GFED satellites on a daily scale of the period 2006-2016. In this study an analysis of the relationship between these data variables and burn area estimation was carried out using multiple linear regression methods then validated to see the level of suitability of the output model forecasts. The results showed that the predictor variables that had the highest relationship were hotspots frequency, Buildup Index (BUI), and FWI index with correlation values of 0.888, 0.739 and 0.753, respectively. The estimation model of the resulting burnt area is: Burned Area = -966.6146918 + (7.519631195 × BUI) + (147.4865469 × FWI) + (14.5373858 × Hotspots) + 116, with an RMSE value of 635.524 and MAE of 491.38

Using weather data in energy time series forecasting: the benefit of input data transformations

Renewable energy systems depend on the weather, and weather information, thus, plays a crucial role in forecasting time series within such renewable energy systems. However, while weather data are commonly used to improve forecast accuracy, it still has to be determined in which input shape this weather data benefits the forecasting models the most. In the present paper, we investigate how transformations for weather data inputs, i. e., station-based and grid-based weather data, influence the accuracy of energy time series forecasts. The selected weather data transformations are based on statistical features, dimensionality reduction, clustering, autoencoders, and interpolation. We evaluate the performance of these weather data transformations when forecasting three energy time series: electrical demand, solar power, and wind power. Additionally, we compare the best-performing weather data transformations for station-based and grid-based weather data. We show that transforming station-based or grid-based weather data improves the forecast accuracy compared to using the raw weather data between 3.7 and 5.2%, depending on the target energy time series, where statistical and dimensionality reduction data transformations are among the best.

The effect of components capacity loss on the performance of a hybrid PV/wind/battery/hydrogen stand-alone energy system

A stand-alone hybrid renewable energy system (HRES) comprising a photovoltaic (PV) array and a small wind turbine for energy production, a lead-acid battery for short-term energy storage, and a hydrogen subsystem for long-term energy storage is analyzed in this study. The hydrogen subsystem comprises a PEM electrolyzer for hydrogen production, a hydrogen compressor and a hydrogen tank for hydrogen storage, and a PEM fuel cell for power generation. Twenty-year hourly input weather data were acquired from the national meteorological service for the Mediterranean location of Split, Croatia. Two different end-user load profiles are considered, namely a year-around constant load profile and the stochastically variable load profile. Furthermore, a comparative analysis is performed for two different energy management strategies – one based on energy balance within the system, and the other based on double hysteresis loop control. The HRES performance is simulated using MATLAB. The dynamics of capacity loss of the batteries, PEM electrolyzer, and PEM fuel cell is a function of how the system operates, but at the same time, this dynamics of capacity loss affects the overall system performance. The double hysteresis control strategy results in a significantly longer battery lifetime for both load profiles considered, while the PV and battery loss of capacity causes more frequent electrolyzer and fuel cell operation for all considered cases.

Enhancement of the Advanced Weather Awareness System for the development of an Integrated Mission Management System in the COAST project

The COAST (Cost Optimized Avionics SysTem) project, funded by Clean Aviation Joint Undertaking, works toward the realization of cost-effective key technologies for cockpit and avionics of Small Air Transport (SAT) aircraft. In 2020 the design of a new technology started, the Integrated Mission Management System (IMMS), devoted to automatically optimize the trajectory while considering air-traffic, weather conditions, terrain and obstacles. It is aimed to implement into a unique system the functionalities of Trajectory Planning, Flight Reconfiguration, Tactical Separation and Weather Awareness, benefitting from the integration and interaction on-board of three technologies, individually developed and tested in the first phases of the COAST project: Flight Reconfiguration System (FRS, managing pilot’s incapacitation emergency), Tactical Separation System (TSS, managing tactical traffic separation and enhanced situational awareness) and Advanced Weather Awareness System (AWAS, devoted to provide on-board updated weather data). The present work focuses on the last one and more in detail on the description of new functionalities introduced to the baseline AWAS system, already demonstrated in flight in 2021, in order to allow its integration in IMMS. Specifically, enhancements to the AWAS technology were required to integrate new input weather data and generate additional information, needed for IMMS purposes. These data are produced on-ground and sent through satellite link to the AWAS on-board segment, which has been updated to manage and exchange them with the other components on the aircraft. All the achieved progresses in the development of the evolved version of the AWAS system presented in this work will be demonstrated and tested in flight during a dedicated campaign planned in 2023 in the framework of COAST project.

Effect of typical meteorological year selection on integrated daylight modeling and building energy simulation

The complete description of outdoor luminous and thermal environment is the basis for daylight utilization design with simulation tools. Nevertheless, Typical Meteorological Year (TMY) and generation method specifically developed for the energy simulation of daylight-utilized buildings is still unavailable currently. Luminous environment parameters have not been taken into consideration in existing TMY generation methods. In this study, the feasibility of existing TMY generation process has been examined. A generic office model implementing sided window daylighting is established. Historical meteorological data of Hong Kong from 1979 to 2007 have been collected and three existing weighting schemes are applied during the Typical Meteorological Month (TMM) selection procedures. Three TMY files for Hong Kong are generated and used to conduct integrated Climate-Based Daylight Modeling and building energy simulation. The result demonstrates that, on annual basis, the energy consumption results obtained from the generated TMY files are in good agreements with the long-term mean annual value. The maximum deviation of annual energy consumptions for the generated TMY files is only 1.8%. However, further analysis on monthly basis shows that all the three generated TMY files fail to fully represent the long-term monthly mean level. The maximum deviation of monthly energy consumptions for the generated TMY files can reach up to 11%. As the energy performance daylight utilization is subject to weather change, analysis on daily and monthly energy level is important, especially during design stage. The deficiency of existing TMM selection process and TMY generation method indicates the necessity to develop a corresponding typical weather data input with finer resolution for the energy simulation of daylight-related buildings.

Investigating the impact of urban microclimate on building thermal performance: A case study of dense urban areas in Hong Kong

Urban microclimate conditions could be an important factor influencing building thermal performance. However, most studies on urban building energy modeling (UBEM) use the typical meteorological year (TMY), often developed from observations of exposed/ rural sites, as input weather data. This study aims to assess the impacts of urban heat on building thermal performance by coupling a high-resolution urban climate simulation with UBEM. The simulated building thermal performance was compared with and without urban microclimate considerations, using weather data from the TMY, weather observations from an urban oasis, and outputs from the urban climate model. The physical parameters of typical building archetypes used in the UBEM were calibrated against indoor-measured data during summer days. Without considering urban microclimate, there was an underestimation of up to 140% of the average overheating risks of urban buildings. Furthermore, neighboring urban contexts and building ventilation rates considerably affected the thermal performance of individual buildings within high-density urban areas. The study reveals that neglecting the influence of the urban microclimate can result in a notable error in the building thermal performance assessment even at the urban level.

Temporal Resolution of Input Weather Data Strongly Affects an Off-Grid PV System Layout and Reliability

Renewable energy systems design using average year weather data is a standard approach that works well for grid-tied systems, but for stand-alone ones, it leads to dramatic mistakes. We considered the effect of meteorological data temporal resolution (5, 10, 15, 20, 30 min; 1, 2, 3, 4 h) on a stand-alone hybrid system’s layout in terms of equipment cost, power supply reliability and maximum duration of interruption for monitoring equipment in the Alps. We have shown that lifecycle costs could be strongly (order of magnitude) underestimated for off-grid systems, as well as their reliability overestimated. Lower temporal resolution data lead to the underestimation of energy storage charge–discharge cycles (considering depth of discharge too)—real batteries are to be replaced more often, which matches our practical experience as well. Even a 5 to 10 min decrease in weather data temporal resolution leads to the estimated annual expenses being halved. In general, we recommend using 30 min. resolution.

A Methodology of Creating a Synthetic, Urban-Specific Weather Dataset Using a Microclimate Model for Building Energy Modelling

The relationship between outdoor microclimate and indoor building conditions requires the input of hourly weather data on the typical meteorological characteristics of the specific location. These data, known as typical meteorological year (TMY), are mainly deduced from the multi-year records of meteorological stations outside urban centres, preventing the actual complex interactions between solar radiation, wind speed, and high urban density. These factors create the urban heat island effect and higher ambient air temperatures, skewing the assumptions for energy demand in buildings. This paper presents a computational method for assessing the effect of the urban climate in the generation of typical weather data for dynamic energy calculations. As such, the paper discusses an evaluation method of pairing ENVI-met 4 microclimate and IES-VE building energy modelling software to produce a typical urban specific weather dataset (USWDs) that reflects the actual microclimatic conditions. The ENVI-met results for the outdoor microclimate conditions were employed to determine the thermal boundaries for the IES-VE, and then used to compute the building’s energy consumption. The energy modelling that employed the USWDs achieved better performance compared to the TMY, as the former had just a 6% variation from the actual electricity consumption of the building compared to 15% for the latter.

Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland

Abstract. Even today, the assessment of avalanche danger is by and large a subjective yet data-based decision-making process. Human experts analyse heterogeneous data volumes, diverse in scale, and conclude on the avalanche scenario based on their experience. Nowadays, modern machine learning methods and the rise in computing power in combination with physical snow cover modelling open up new possibilities for developing decision support tools for operational avalanche forecasting. Therefore, we developed a fully data-driven approach to assess the regional avalanche danger level, the key component in public avalanche forecasts, for dry-snow conditions in the Swiss Alps. Using a large data set of more than 20 years of meteorological data measured by a network of automated weather stations, which are located at the elevation of potential avalanche starting zones, and snow cover simulations driven with these input weather data, we trained two random forest (RF) classifiers. The first classifier (RF 1) was trained relying on the forecast danger levels published in the official Swiss avalanche bulletin. To reduce the uncertainty resulting from using the forecast danger level as target variable, we trained a second classifier (RF 2) that relies on a quality-controlled subset of danger level labels. We optimized the RF classifiers by selecting the best set of input features combining meteorological variables and features extracted from the simulated profiles. The accuracy of the models, i.e. the percentage of correct danger level predictions, ranged between 74 % and 76 % for RF 1 and between 72 % and 78 % for RF 2. We assessed the accuracy of forecasts with nowcast assessments of avalanche danger by well-trained observers. The performance of both models was similar to the agreement rate between forecast and nowcast assessments of the current experience-based Swiss avalanche forecasts (which is estimated to be 76 %). The models performed consistently well throughout the Swiss Alps, thus in different climatic regions, albeit with some regional differences. Our results suggest that the models may well have potential to become a valuable supplementary decision support tool for avalanche forecasters when assessing avalanche hazard.

Balance point temperature and heating degree-days in different climate conditions for building energy efficiency applications

The precise calculation of heating degree-days (HDD) depends on the correct building balance point temperature, which however has often been limited by ignoring complex impacts of local climate and building properties. This research clarified the calculation process for building balance point temperature and HDD and provided experimental validation. Then, to explore the impacts of local climate and building thermal properties on balance point temperature and HDD, 31 representative cities with heating demands located along the Chinese coastline are investigated for the case study, involving Cold Zone and Hot Summer and Cold Winter Zone. The results show that as insulation regulations have become more stringent, the balance point temperature in the surveyed area has reduced by an average of 4.54 °C over the past four decades. For office buildings constructed since 2015, every 1 °C increase in outdoor monthly average temperature could result in an increment of calculated balance point temperature by 0.22 °C and 0.34 °C in these two regions, respectively. The deviation of HDD values calculated from the obtained balance point temperatures in Cold Zone is much wider than that in Hot Summer and Cold Winter Zone, with respective ranges from 617 to 1509 °C d and from 228 to 788 °C d. With great advantages of high accuracy of calculation results as well as low sampling frequency of input weather data, HDD calculated from monthly temperature data can be considered as a reliable indication for heating demand prediction as well as an index for building energy efficiency climate zoning.

Simulated irrigation water productivity and related profit effects in U.S. Southern High Plains cotton production

To explore management practices that increase irrigation water productivity (IWP) in U.S. Southern High Plains (SHP) cotton production, the CROPGRO-Cotton crop simulation model was used to evaluate the yield, IWP, and profit effects of irrigation amount and timing. Using 2005–2019 weather input data from 21 SHP weather stations, lint yields were simulated for each of the 315 station-years under unirrigated ‘dryland’ conditions and 18 increasing total irrigation (TIRR) levels. As TIRR was increased to 55.9 cm median lint yields asymptotically approached a maximum. However, irrigation above 35.6 cm increased the incidence of total irrigation plus growing season rainfall exceeding 100% of potential crop ET, leading to decreasing marginal yield effects and decreasing IWP. The highest median IWP (0.321 kg m−3) was found with both 33.0 and 35.6 cm of total irrigation, with 30.5 cm providing slightly lower IWP (0.320 kg m−3). In analyses of irrigated profitability under varying lint price and pumping cost conditions, 30.5 cm (12.0 in) of irrigation increases profits relative to dryland conditions under all but low lint price and high pumping cost conditions. But as TIRR is reduced the probability of these positive profit effects become similar to an evenly weighted coin flip at about 17.8 cm (7.0 in). Simulations that varied the timing of 30.5 cm of irrigation increased median IWP up to 0.434 kg m−3 by limiting irrigation to cotton’s reproductive and maturation periods, with no irrigation during the initial vegetative period. As a result, these simulations show that applying 30.5–35.6 cm (12.0–14.0 in) of irrigation during cotton’s reproductive and maturation phases, with little or no vegetative irrigation, maximizes IWP in SHP cotton production under current climate conditions.

Neural Network based Predictors for Evaporation Estimation at Jabalpur in Central India

Free water evaporation is an imperative parameter for estimation of crop water requirement, and irrigation scheduling. This study aims to evaluate different techniques to estimate evaporation with weather parameters inputs. Multilayer Perception (MLR), Radial Basis Function (RBF) based neural network, traditional statistical Linear Regression (LR) approach and conventional empirical methods of Linacre and Christianson were used to estimate the evaporation at Jabalpur station situated under Kymore Plateau and Satpura Hills Agro-climatic Zone of Madhya Pradesh in the Central India. The weather parameters considered for estimation of evaporation are temperature, humidity, sunshine hours and wind speed. Results indicate that MLP and RBF based models with input of all selected weather parameters is able to estimate evaporation much precisely than LR and empirical approaches. It was found that higher accuracy may be obtained with multiple weather data input and low accuracy with only temperature input. It was observed that with temperature used as input the performance accuracy reduces in estimating evaporation with the selected models. However, neural network approach seems to produce better results as compared to statistical and empirical approach. The neural network based model RBF found more efficient in estimation of evaporation as compared to MLP. This study suggests that evaporation can be estimated by RBF model of a station, where there is no standard instrument available for its observation.

An algorithm to assess the heating strategy of buildings in cold climates: a case study of Germany

Abstract Two-thirds of the final energy consumption of the EU residential sector goes towards space heating of buildings, yet a huge portion of the population still suffers from energy poverty. Identifying optimum heating strategies of current buildings would be a solution to this crisis, which is the main aim of the developed algorithm in this research. The algorithm incorporates a modified version of the simple hourly method from the ISO 13790 standard to determine the hourly heating load and indoor temperatures of buildings based on any heating strategy. Flexibility in the input of building and weather data make this tool versatile with practicality towards building users and policymakers. With this algorithm, a case study to evaluate three commonly used domestic heating strategies has been established for nine different residential buildings in typical cold winter conditions in Germany. Most EU households heat their buildings either continuously throughout the day at fixed temperatures, sporadically at fixed times or at peak loads during the evening. The continuous heating strategy is rated the best consuming minimal energy with consistent temperatures and optimal thermal comfort ranges. The sporadic and peak load heating strategies provide fluctuating indoor temperatures with high standard deviations of up to 8.70°C while consuming a similar cumulative energy to the continuous heating strategy. Additionally, both these aforementioned strategies augment energy poverty and promote indoor mould formation on the building envelope caused by water vapor condensation. Consequently, this algorithm can be applicable to any building type of any region.

Towards improving the global water application uniformity of centre pivots through lateral speed adjustment

An initial step is made towards improving the global water application uniformity of centre pivots by adjusting the lateral speed based on diurnal changes of wind drift and evaporation losses (WDEL). A large WDEL dataset was used to construct an artificial neural network (ANN) model capable of predicting the above ground application efficiency (AGAE ≈ 1- WDEL) from input weather data. Given its reasonably good accuracy (RMSE = 1.5%), the ANN model was then applied in heuristic, simple and dynamic algorithms that used forecasted or real-time weather parameters corresponding to the hourly time intervals of the pivot's revolution time. For each timing interval, the final output of the algorithms is a coefficient ( λ ), that when multiplied by the lateral speed, can ensure a more consistent water delivery depth across the field. The proposed algorithms were theoretically tested for extremely hot, dry, humid, and windy days. These efforts indicated that under the climatic conditions of the study, λ could potentially vary between 0.92% and 1.06% if the revolution time is about 24 h. The λ coefficient typically increased from 1:00 AM to 9:00 AM, then decreased until the sunset, between 19:00 to 21:00, and then increased again until midnight. This general trend for λ could however, be disrupted by sudden changes in the wind speed. Future studies should conduct extensive field work to test the efficacy of the proposed algorithms and quantify the improvement of centre pivot global water application uniformity through the lateral speed adjustment. • Centre pivot lateral speed can be adjusted to account for a changing application efficiency. • Two theoretical algorithms were developed to improve the global water application uniformity. • The first algorithm uses forecasted weather data while the second uses real time data. • The lateral speed adjustment factor varied within ±6% during a 24-hr cycle. • Centre pivot lateral speed adjustment is easy to implement.

EVALUATING HYDROLOGIC MODEL PERFORMANCE OF GLOBAL AND LOCAL WEATHER DATA INPUTS

Abstract. Runoffs from hydrologic models are often used in flood models, among other applications. These runoffs are converted from rainfall, signifying the importance of weather data accuracy. A common challenge for modelers is local weather data sparsity in most watersheds. Global weather datasets are often used as alternative. This study investigates the statistical significance and accuracy between using local weather data for hydrologic models and using the Climate Forecast System Reanalysis (CFSR), a global weather dataset. The Soil and Water Assessment Tool (SWAT) was used to compare the two weather data inputs in terms of generated discharges. Both long-term and event-based results were investigated to compare the models against absolute discharge values. The basin’s average total annual rainfall from the CFSR-based model (4062 mm) was around 1.5 times the local weather-based model (2683 mm). These basin precipitations yielded annual average flows of 53.4 cms and 26.7 cms for CFSR-based and local weather-based models, respectively. For the event-based scenario, the dates Typhoon Ketsana passed through the Philippine Area of Responsibility were checked. CFSR only read a spatially averaged maximum daily rainfall of 18.8 mm while the local gauges recorded 157.2 mm. Calibration and validation of the models were done using the observed discharges in Sto. Niño Station. The calibration of local weather-based model yielded satisfactory results for the Nash-Sutcliffe Efficiency (NSE), percent of bias (PBIAS), and ratio of the RMSE to the standard deviation of measured data (RSR). Meanwhile, the calibration of CFSR model yielded unsatisfactory values for all three parameters.

Effects of Bias-Corrected Regional Climate Projections and Their Spatial Resolutions on Crop Model Results under Different Climatic and Soil Conditions in Austria

The quality, reliability, and uncertainty of Austrian climate projections (ÖKS15) and their impacts on the results of the crop model DSSAT for three different orographic and climatic agricultural regions in Austria were analyzed. Cultivar-specific grain yields of winter wheat, spring barley, and maize were simulated for different soil classes to address three main objectives. First, the uncertainties of simulated crop yields related to the ÖKS15 projections were analyzed under current climate conditions. The climate projections revealed that the case study regions with higher humidity levels generally had lower yield deviations than the drier regions (yield deviations from −19% to +15%). Regarding the simulated crop types, spring barley was found to be less sensitive to the climate projections than rainfed maize, and the response was greater in regions with a low soil water storage capacity. The second objective was to simulate crop yields for the same cultivars using future climate projections. Winter wheat and spring barley tended to show increased yields by the end of the century due to an assumed CO2-fertilization effect in the range of 3–23%, especially under RCP 8.5. However, rainfed and irrigated maize were associated with up to 17% yield reductions in all three study regions due to a shortened growth period caused by warming. The third objective addressed the effects of crop model weather input data with different spatial resolutions (1 vs. 5, 11, and 21 km) on simulated crop yields using the climate projections. Irrigated grain maize and rainfed spring barley had the lowest simulated yield deviations between the spatial scales applied due to their better water supply conditions. The ranges of uncertainty revealed by the different analyses suggest that impact models should be tested with site representative conditions before being applied to develop site-specific adaptation options for Austrian crop production.