Estimation of daily sunshine duration using support vector machines

Although sunshine duration (SD) is one of the most frequently measured meteorological parameters, there is a lack of measurements in some parts of the world. Hence, it should be estimated accurately for areas where no reliable measurement is possible. The main objective of this study is to evaluate the potential of support vector machine (SVM) approach for estimating daily SD. For this purpose, three different kernels of SVM, such as linear, polynomial, and radial basis function (RBF), were used. Different combinations of five related meteorological parameters, namely cloud cover, maximum temperature (T_max), minimum temperature (T_min), relative humidity (RH), and wind speed (WS), and one astronomic parameter, day length, were considered as the inputs of the models, and the output was obtained as daily SD. Simulated values of the models were compared with ground measured values, and concluded that the usage of the SVM-RBF estimator with combination of all input attributes produced the best results. The coefficient of determination, root mean square error, and mean absolute error were found to be 0.8435, 1.5105 h, and 1.0771 h, respectively, for the pooled four-year daily data set of 14 stations in Turkey. It was also deduced that accuracy increased as the number of attributes increased and the major contribution to this came from RH as compared with T_max, T_min, and WS. This study has shown that the SVM methodology can be a good alternative for conventional and artificial neural network methods for estimating daily SD.     

Comparison of Machine Learning Models Performance on Simulating Reservoir Outflow: A Case Study of Two Reservoirs in Illinois,U.S.A.

Reservoir outflow is an important variable for understanding hydrological processes and water resource management. Natural streamflow variation, in addition to the streamflow regulation provided by dams and reservoirs, can make streamflow difficult to understand and predict. This makes them a challenge to accurately simulate hydrologic processes at a daily scale. In this study, three Machine Learning (ML) algorithms, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were examined and compared to model reservoir outflow. Past, current, and future hydrologic and meteorological data were used as model inputs, and the outflow of next day was used as prediction. Simulation results demonstrated that all three models can reasonably simulate reservoir outflow. For Carlyle Lake, the coefficient of determination and Nash–Sutcliffe efficiency were each close to one for the three models. The coefficient of determination, relative mean bias, and root mean square error indicated that the SVM performed better than the RF and ANN, but the SVM output displayed a larger relative mean bias than that from RF and ANN. For Lake Shelbyville, the ANN model performed better than RF and SVM when considering the coefficient of determination, Nash–Sutcliffe efficiency, relative mean bias, and root mean square error. The study results demonstrate that the three ML algorithms (RF, SVM, and ANN) are all promising tools for simulating reservoir outflow. Both the accuracy and efficacy of the three ML algorithms are considered to support practitioners in planning reservoir management.     

The global solar radiation estimation and analysis of solar energy: Case study for Osmaniye,Turkey

This paper investigates the prediction of solar radiation model and actual solar energy in Osmaniye, Turkey. Four models were used to estimate using the parameters of sunshine duration and average temperature. In order to obtain the statistical performance analysis of models, the coefficient of determination (R²), mean absolute percentage error (MAPE), mean absolute bias error (MABE), and root mean square error (RMSE) were used. Results obtained from the linear regression using the parameters of sunshine duration and average temperature showed a good prediction of the monthly average daily global solar radiation on a horizontal surface. In order to obtain solar energy, daily and monthly average solar radiation values were calculated from the five minute average recorded values by using meteorological measuring device. As a result of this measurement, the highest monthly and yearly mean solar radiation values were 698 (April in 2013) and 549 (2014 year) W/m² respectively. On an annual scale the maximum global solar radiation changes from 26.38 MJ/m²/day by June to 19.19 MJ/m²/day by September in 2013. Minimum global solar radiation changes from 14.05 MJ/m²/day by October to 7.20 MJ/m²/day by January in 2013. Yearly average energy potential during the measurement period was 16.53 MJ/m²/day (in 2013). The results show that Osmaniye has a considerable solar energy potential to produce electricity.     

Comparison of ANN,MVR, and SWR models for computing thermal efficiency of a solar still

In this paper, the viability of modeling the instantaneous thermal efficiency (η_ith) of a solar still was determined using meteorological and operational data with an artificial neural network (ANN), multivariate regression (MVR), and stepwise regression (SWR). This study used meteorological and operational variables to hypothesize the effect of solar still performance. In the ANN model, nine variables were used as input parameters: Julian day, ambient temperature, relative humidity, wind speed, solar radiation, feed water temperature, brine water temperature, total dissolved solids of feed water, and total dissolved solids of brine water. The η_ith was represented by one node in the output layer. The same parameters were used in the MVR and SWR models. The advantages and disadvantages were discussed to provide different points of view for the models. The performance evaluation criteria indicated that the ANN model was better than the MVR and SWR models. The mean coefficient of determination for the ANN model was about 13% and14% more accurate than those of the MVR and SWR models, respectively. In addition, the mean root mean square error values of 6.534% and 6.589% for the MVR and SWR models, respectively, were almost double that of the mean values for the ANN model. Although both MVR and SWR models provided similar results, those for the MVR were comparatively better. The relative errors of predicted η_ith values for the ANN model were mostly in the vicinity of ±10%. Consequently, the use of the ANN model is preferred, due to its high precision in predicting η_ith compared to the MVR and SWR models. This study should be extremely beneficial to those coping with the design of solar stills.     

A NEW FLASHINESS INDEX: CHARACTERISTICS AND APPLICATIONS TO MIDWESTERN RIVERS AND STREAMS1

ABSTRACT: The term flashiness reflects the frequency and rapidity of short term changes in streamflow, especially during runoff events. Flashiness is an important component of a stream's hydrologic regime. A variety of land use and land management changes may lead to increased or decreased flashiness, often to the detriment of aquatic life. This paper presents a newly developed flashiness index, which is based on mean daily flows. The index is calculated by dividing the pathlength of flow oscillations for a time interval (i.e., the sum of the absolute values of day‐to‐day changes in mean daily flow) by total discharge during that time interval. This index has low interannual variability, relative to most flow regime indicators, and thus greater power to detect trends. Index values were calculated for 515 Midwestern streams for the 27‐year period from 1975 through 2001. Statistically significant increases were present in 22 percent of the streams, primarily in the eastern portion of the study area, while decreases were present in 9 percent, primarily in the western portion. Index values tend to decrease with increasing watershed area and with increasing unit area ground water inputs. Area compensated index values often shift at ecoregion boundaries. Potential index applications include evaluation of programs to restore more natural flow regimes.     

RIPARIAN MICROCLIMATE AND STREAM TEMPERATURE RESPONSE TO FOREST HARVESTING: A REVIEW1

Forest harvesting can increase solar radiation in the riparian zone as well as wind speed and exposure to air advected from clearings, typically causing increases in summertime air, soil, and stream temperatures and decreases in relative humidity. Stream temperature increases following forest harvesting are primarily controlled by changes in insolation but also depend on stream hydrology and channel morphology. Stream temperatures recovered to pre‐harvest levels within 10 years in many studies but took longer in others. Leaving riparian buffers can decrease the magnitude of stream temperature increases and changes to riparian microclimate, but substantial warming has been observed for streams within both unthinned and partial retention buffers. A range of studies has demonstrated that streams may or may not cool after flowing from clearings into shaded environments, and further research is required in relation to the factors controlling downstream cooling. Further research is also required on riparian microclimate and its responses to harvesting, the influences of surface/subsurface water exchange on stream and bed temperature regimes, biological implications of temperature changes in headwater streams (both on site and downstream), and methods for quantifying shade and its influence on radiation inputs to streams and riparian zones.     

River temperature sensitivity to hydraulic and meteorological parameters

A sensitivity analysis is performed to evaluate river temperature variations in response to changes in hydraulic and meteorological conditions. The effects of instream flow, river geometry, and weather factors on daily mean and daily maximum river temperatures are quantified by analytical solutions to a simplified model. The influence coefficient method is used to determine river temperature sensitivity. The sensitivity analysis presents quantitative evidence that river temperatures are more sensitive to instream flowrate, upstream inflow temperature, air temperature, humidity and solar radiation than to other parameters including wind speed and channel geometry and morphometry. It is found that the sensitivity of river temperatures to flow is as significant as that to weather. Daily maximum river temperature is more sensitive to flowrate than daily mean temperature. Adapting the concept of 'diminishing returns', a critical instream flowrate is identified, which divides high and low sensitivity of water temperatures to flowrate. The critical flowrate can be used to determine practically achievable and economically feasible flow requirements for summer river temperature control. The sensitivity results can assist in streamflow management and reservoir operation for protections of habitat and aquatic environment.     

AIR-WATER TEMPERATURE RELATIONSHIP IN ILLINOIS RIVER1

Application of harmonic analysis to daily mean air, water temperature records for a location indicates that the first harmonic accounts for a major portion of the total variance in the records. Water temperature residuals are well correlated with air temperature residuals. Parametric values of the mathematical model for predicting water temperatures from air temperature records are stable from year to year. The air-water temperature relationship appears to be a stationary linear process. Consequently, it is possible to predict water temperatures at a location from the ambient temperature records provided both air and water temperature records are available for another similarly situated water body.     

Riparian Ecosystem Management Model: Sensitivity to Soil,Vegetation, and Weather Input Parameters1

Abstract: The Riparian Ecosystem Management Model (REMM) was developed by the U.S. Department of Agriculture‐Agriculture Research Service (USDA‐ARS) and its cooperators to design and evaluate the efficiency of riparian buffer ecosystems for nonpoint source pollution reduction. REMM requires numerous inputs to simulate water movement, sediment transport, and nutrient cycling in the buffer system. In order to identify critical model inputs and their uncertainties, a univariate sensitivity analysis was conducted for nine REMM output variables. The magnitude of each input parameter was changed from ?50% to +50% from the baseline data in 12 intervals or, in some cases, the complete range of an input was tested. Baseline model inputs for the sensitivity analysis were taken from Gibbs Farm, Georgia, where REMM was tested using a 5‐year field dataset. Results of the sensitivity analysis indicate that REMM responses were most sensitive to weather inputs, with minimum daily temperature having the greatest impact on the nitrogen‐related outputs. For example, the 100% change (?50% to +50%) in minimum daily temperature input values yielded a 164.4% change in total nitrogen (N), a 109.3% change in total nitrate (NO₃), and a 127.1% change in denitrification. REMM was most sensitive to precipitation with regard to total flow leaving the riparian vegetative buffer zone (199.8%) and sediment yield (138.2%). Deep seepage (12.2%), volumetric water content (24.8%), and pore size index (6.5%) in the buffer soil profile were the most influential inputs for the output water movement. Sediment yield was most sensitive to Manning’s coefficient (46.6%), bare soil percent (40.7%), and soil permeability (6.1%). For vegetation, specific leaf area, growing degree day coefficients, and maximum root depth influenced the nitrogen related outputs. Overall results suggest that because of the high sensitivity to weather parameters, on‐site weather data is needed for model calibration and validation. The model’s relatively low sensitivity to vegetation parameters also appears to support the use of regional vegetation datasets that would simplify model implementation without compromising results.     

A basin-scale approach to estimating stream temperatures of tributaries to the lower Klamath River, California

Stream temperature is an important component of salmonid habitat and is often above levels suitable for fish survival in the Lower Klamath River in northern California. The objective of this study was to provide boundary conditions for models that are assessing stream temperature on the main stem for the purpose of developing strategies to manage stream conditions using Total Maximum Daily Loads. For model input, hourly stream temperatures for 36 tributaries were estimated for 1 Jan. 2001 through 31 Oct. 2004. A basin-scale approach incorporating spatially distributed energy balance data was used to estimate the stream temperatures with measured air temperature and relative humidity data and simulated solar radiation, including topographic shading and corrections for cloudiness. Regression models were developed on the basis of available stream temperature data to predict temperatures for unmeasured periods of time and for unmeasured streams. The most significant factor in matching measured minimum and maximum stream temperatures was the seasonality of the estimate. Adding minimum and maximum air temperature to the regression model improved the estimate, and air temperature data over the region are available and easily distributed spatially. The addition of simulated solar radiation and vapor saturation deficit to the regression model significantly improved predictions of maximum stream temperature but was not required to predict minimum stream temperature. The average SE in estimated maximum daily stream temperature for the individual basins was 0.9 +/- 0.6 degrees C at the 95% confidence interval.     

乌鲁木齐黑碳气溶胶浓度时间变化特征及影响因素分析

利用乌鲁木齐市环境空气超级站中MAAP-5012型黑碳仪对乌鲁木齐市黑碳气溶胶进行连续一年的监测,并结合乌鲁木齐环境空气质量城市站小时数据和日数据及气象数据对黑碳气溶胶变化情况进行综合分析。结果表明:2019年6月至2020年5月乌鲁木齐黑碳气溶胶浓度日均值为1 506(±1 096) ng/m~3,本底值为575 ng/m~3,与国内主要城市相比黑碳浓度相对较低;污染最严重的冬季黑碳浓度为2 414(±1 325) ng/m~3,本底值为825 ng/m~3,较十年前浓度下降明显。乌鲁木齐市黑碳气溶胶浓度随季节变化差异大,冬季>秋季>夏季>春季,24小时昼夜变化整体呈现"夜高昼低"和不典型的"双峰双谷"特征,"周末效应"特征显著。相关性分析结果表明:乌鲁木齐黑碳气溶胶与NO_2、CO的Pearson相关性系数均超过0.8,一元线性拟合度均超过0.7,黑碳气溶胶与环境空气中的污染物NO_2、CO具有高度的同源性。虽然气象因素中温度、湿度、风速与黑碳的Pearson相关性及一元线性拟合效果不佳,但气象因素仍是影响黑碳气溶胶浓度重要因素。     

GENERATION OF SYNTHETIC AND MISSING CLIMATIC DATA FOR PUERTO RICO1

ABSTRACT: Climatic data such as temperature, solar radiation, relative humidity, and wind speed have been widely used to estimate evapotranspiration. Moat of the solar radiation data and portions of the relative humidity data are either not available or missing from the records in Puerto Rico. Depending upon the availability and data characteristics of records, three methods (including a regression technique, an averaging of historical data, and a regional average) were used to generate missing data, and a time series analysis was used to synthesize a series of climatic data. The limitations and applicability of each method are discussed. The results showed that the time series analysis method can be successfully used to synthesize a series of monthly solar radiations for several stations. The regression technique and the regional average can be successfully applied to generate missing monthly solar radiation data. The regression technique and the averaging of historical data have been satisfactorily used to interpolate missing monthly relative humidity. The explained variance (R²) varied from 0.68 to 0.88, which are both significant at the 0.05 level of significance.     

气温和湿度在泸州市城区对臭氧浓度预测的运用研究

气温和湿度均为影响臭氧生成的要素,通过分析泸州市国控点数据,找出气温和湿度与臭氧浓度的关系,从而建立气温和湿度为变量的臭氧浓度预测模型.选取2019年泸州市主城区的气温和湿度数据,将气温按日最高温(Tmax)、日最低温(Tmin)和日平均气温(Tmean)划分,将湿度按四季划分,分别统计其与臭氧浓度的相关性.发现臭氧浓...     

Temperature and relative humidity distributions in a medium-size administrative town in southwest Nigeria

This study was carried out in one of the medium-sized public administrative towns in the southwestern part of Nigeria. Its aim is to highlight the effect of spatial distribution of settlements, population, and socio-economic activities on urban air temperature and humidity in the town. Temperature and relative humidity data from 1992 to 2001 were obtained from three meteorological stations in Akure, the Administrative Capital of Ondo State, Nigeria. The stations are located within the Federal Ministry of Aviation, Akure Airport (FMA), Federal University of Technology, Akure (FUTA) and Federal School of Agriculture (SOA). Air temperature and relative humidity measurements were also obtained from 27 points, which were cited to include road junctions, markets, built up areas, etc., using sling psychrometer. The data were subsequently analysed for spatial and temporal variations using statistical packages (SPSS and Microsoft Excel) and isolines. Actual vapour pressure and dew point temperature were computed using Magnus conversion formulae. The results obtained showed that spatial variation was insignificant, in terms of the temperature and humidity variables. The annual mean temperature (Tmean) ranged between 21.9 and 30.4 degrees C while minimum (Tmin) and maximum (Tmax) temperatures varied from 13 to 26 and 21.5-39.6 degrees C, respectively. Relative humidity (RH), actual vapour pressure (Es) and dew point temperature (Td) values also varied from 39.1% to 98.2%, 19.7-20.8 gm(-3), and 17.3-17.8 degrees C, respectively. A significant relationship (p>0.6; r<0.05) between Tmin, Es and Td was observed while the daytime 'urban heat island' intensity (UHI) ranged between 0.5 and 2.5 degrees C within the study period. The study concluded that there is influence of urban canopy on the microclimate of Akure, and hypothesizes that the urban dwellers may be subjected to some levels of weather related physiological disorderliness.     

Global solar radiation and energy yield estimation from photovoltaic power plants for small loads

Daily global solar radiation on a horizontal surface and duration of sunshine hours have been determined experimentally for five meteorological stations in Saudi Arabia, namely, Abha, Al-Ahsa, Al-Jouf, Al-Qaisumah, and Wadi Al-Dawaser sites. Five-years of data covering 1998–2002 period have been used. Suitable Angstrom models have been developed for the global solar radiation estimation as a function of the sunshine duration for each respective sites. Daily averages of monthly solar PV power outputs have been determined using the Angstrom models developed. The effect of the PV cell temperature on the PV efficiency has been considered in calculating the PV power output. The annual average PV output energy has been discussed in all five sites for small loads. The minimum and maximum monthly average values of the daily global solar radiation are found to be 12.09 MJ/m²/d and 30.42 MJ/m²/d for Al-Qaisumah and Al-Jouf in the months of December June, respectively. Minimum monthly average sunshine hours of 5.89 hr were observed in Al-Qaisumah in December while a maximum of 12.92 hr in Al-Jouf in the month of June. Shortest range of sunshine hours of 7.33–10.12 hr was recorded at Abha station. Minimum monthly average Solar PV power of 1.59 MJ/m²/day was obtained at Al-Qaisumah in the month of December and a maximum of 3.39 MJ/m²/day at Al-Jouf in June. The annual PV energy output was found to be 276.04 kWh/m², 257.36 kWh/m², 256.75 kWh/m², 245.44 kWh/m², and 270.95 kWh/m² at Abha, Al-Ahsa, Al-Jouf, Al-Qaisumah, and Wadi Al-Dawaser stations, respectively. It is found that the Abha site yields the highest solar PV energy among the five sites considered.     

格尔木地区集装箱内部温度-湿度条件研究

某些特殊物资对温度、湿度等环境条件有特殊的要求,特别是集装箱作为密闭的箱体,其内部环境条件更加恶劣。为了保证进藏特殊物资安全开展集装箱运输,作者在格尔木地区对集装箱内部温、湿度环境条件及其影响因素即大气环境温度、太阳辐射强度和风速进行了测试,最后运用统计分析方法建立了集装箱内部温度数学模型,得出了内部最恶劣温度范围,从而为特殊物资安全进箱提供理论依据。     

MULTITEMPORAL SCALE HYDROGRAPH PREDICTION USING ARTIFICIAL NEURAL NETWORKS1

Abstract: An artificial neural network (ANN) provides a mathematically flexible structure to identify complex nonlinear relationship between inputs and outputs. A multilayer perceptron ANN technique with an error back propagation algorithm was applied to a multitime-scale prediction of the stage of a hydro-logically closed lake, Devils Lake (DL), and discharge of the Red River of the North at Grand Forks station (RR-GF) in North Dakota. The modeling exercise used 1 year (2002), 5 years (1998–2002), and 27 years (1975–2002) of data for the daily, weekly, and monthly predictions, respectively. The hydrometeorological data (precipitations P_(t), P_(t-1), P_(t-2), P_(t-3), antecedent runoff/lake stage R_(t-1) and air temperature T_(t) were partitioned for training and for testing to predict the current hydro-graph at the selected DL and RR-GF stations. Performance of ANN was evaluated using three combinations of daily datasets (Input I = P_(t)), P_(t-l), P_(t-2), P_(t-3), T_(t) and R_(t-l); Input II = Input-l less P_(t) P_(t-l), P_(t-2), P_(t-3); and Input III = Input-II less T_(t)). Comparison of the model output using Input I data with the observed values showed average testing prediction efficiency (E) of 86 percent for DL basin and 46 percent for RR-GF basin, and higher efficiency for the daily than monthly simulations.     

Observation and simulation of net primary productivity in Qilian Mountain, western China

We modeled net primary productivity (NPP) at high spatial resolution using an advanced spaceborne thermal emission and reflection radiometer (ASTER) image of a Qilian Mountain study area using the boreal ecosystem productivity simulator (BEPS). Two key driving variables of the model, leaf area index (LAI) and land cover type, were derived from ASTER and moderate resolution imaging spectroradiometer (MODIS) data. Other spatially explicit inputs included daily meteorological data (radiation, precipitation, temperature, humidity), available soil water holding capacity (AWC), and forest biomass. NPP was estimated for coniferous forests and other land cover types in the study area. The result showed that NPP of coniferous forests in the study area was about 4.4 tCha(-1)y(-1). The correlation coefficient between the modeled NPP and ground measurements was 0.84, with a mean relative error of about 13.9%.     

CLIMATOLOGICAL WATERSHED CALIBRATION1

ABSTRACT: This study tests the hypothesis that climatic data can be used to develop a watershed model so that stream flow changes following forest harvest can be determined. Measured independent variables were precipitation, daily maximum and minimum temperature, and concurrent relative humidity. Computed variables were humidity deficit, saturated vapor pressure, and ambient vapor pressure. These climatic variables were combined to compute a monthly evaporation index. Finally, the evaporation index and monthly precipitation were regressed with measured monthly stream flow and the monthly estimates of stream flow were combined for the hydrologic year. A regression of predicted versus measured annual stream flow had a standard error of 1.5 inches (within 6.1 percent of the measured value). When 10, 15, and 20 years of data were used to develop the regression equations, predicted minus measured stream flow for the last 7 years of record (1972–1978) were within 16.8, 11.5, and 9.7 percent of the measured mean, respectively. Although single watershed calibration can be used in special conditions, the paired watershed approach is expected to remain the preferred method for determining the effects of forest management on the water resource.