Hasil untuk "River, lake, and water-supply engineering (General)"

Bogdan Kulig, Agnieszka Klimek-Kopyra, Anna Ślizowska et al.

Various devices and applications are used for the rapid assessment of plant nitrogen nutrition, which give an approximate indication of leaf chlorophyll saturation by giving the relative chlorophyll content or leaf greenness intensity. In this study, chlorophyll content and leaf greenness determined by three devices were compared: SPAD-502 (spectrum technology), Hydro N-Tester, and Samsung smartphone (RGB app). Additionally, laboratory determination of chlorophyll content was compared to soil-plant analysis development (SPAD) values. Based on the results obtained, indices characterising the vegetative or direct state were calculated and the values obtained with these devices were compared. The crops tested were soya, potatoes, wheat and sunflower. The results show a close relationship between the size of the SPAD index and RGB light sources of colour the intensity of red (R), green (G) and blue (B). The indices IPCA and R+G–2B showed a very high negative correlation with SPAD readings (−0.82 and −0.83). Statistical analysis showed that SPAD readings obtained from the two chlorophyll meters showed a high correlation regardless of the crop species tested (R2 = 0.98). The correlation analysis also showed the possibility of substituting equipment and vegetation indices based on readings taken with a smartphone, with an accuracy not much inferior to standard chlorophyll meters. This situation could occur in case of failure or absence of the standard device.

Danial Khari, Aslan Egdernezhad, Niazali Ebrahimipak

IntroductionWater resources are strongly influenced by the hydrological cycle and the estimation of evapotranspiration as the main component of the hydrological cycle plays an important role in water resources management. This phenomenon is nonlinear and very difficult to estimate in the sense that there are many parameters involved in its estimation. There are many methods to estimate evapotranspiration none of which is free from problems. Some of these methods, such as Lysimeter, are costly and time-consuming, and others, such as empirical methods only have local value. Therefore, the application of a method which is able to model this phenomenon based on its entity and with minimum data seems necessary. In recent years, the use of artificial intelligence models to simulate various problems has become very popular. In terms of performance, artificial neural networks are very efficient models whose computational speed is completely independent of the mathematical complexity of the algorithms or the method used. The purpose of this research is to compare artificial neural network models, neural network model optimized with genetic algorithm and experimental models in estimation of reference evaporation and transpiration using meteorological data in Ramhormoz synoptic station. Materials and MethodsAs mentioned above, the aim of this study was to compare the models of artificial neural network (ANN), artificial neural network optimized with genetic algorithm (ANN + GA) and experimental models (Hargreaves-Samani, Blaney-Criddle and Irmak) in estimating reference evapotranspiration compared to the results obtained from the Penman-Monteith FAO standard model by using Meteorological data in Ramhormoz synoptic station. For this purpose, meteorological parameters of Ramhormoz synoptic station were collected monthly during the years 2011 to 2018. This information includes: minimum temperature, maximum temperature, average temperature, wind speed at 2 meters, minimum relative humidity, maximum relative humidity and was sunny hours.Artificial neural networks are simplified models of the working system of the human brain, which are not comparable to natural systems. These models try to imitate human thought processes.The process of using artificial neural network models includes three stages of training, verification and testing. In the present study, 70% of the data was considered for training, 10% for validation and 20% for testing. Also, the stimulus function considered for the training and test phase is the sigmoid tangent.To extract better results from the artificial neural network model, it is necessary to optimize the parameters used. To determine the most optimal parameters required for the artificial neural network model, such as the number of layers, neurons and the weight of the layers, a lot of time is spent on their calibration using the trial and error method. For this reason, in this research, the combination of artificial neural network model and genetic algorithm (ANN+GA) was used in order to achieve the optimal parameters of the artificial neural network model. Minimizing the amount of simulation error as a function of the objective function and the number of iterations was considered as the stopping condition of the optimization algorithm. Results and DiscussionOverall, the results showed that artificial neural network models to empirical models used to model higher correlation with the Penman-Monteith FAO model. In addition, among the neural network models used, the integrated neural network model with the genetic algorithm has a higher correlation with the Penman-Monteith FAO model. So that the value of R2 in Blaney Kridel, Hargreaves Samani, Airmak, ANN and ANN+GA models is 0.65, 0.819, 0.781, 0.969 and 0.973, respectively. The results of using scenarios using meteorological parameters as input for ANN and ANN + GA models showed that the highest accuracy of estimating reference evapotranspiration in both models is related to the scenario with input data such as temperature. The minimum is the maximum temperature, wind speed at a height of 2 meters, minimum relative humidity, maximum relative humidity and sunny hours, and the lowest accuracy of the model was in a scenario with two inputs of maximum temperature and minimum temperature. Among the experimental models, Hargreaves-Samani, Irmak and Blaney-Criddle models had the highest correlation with the standard Penman-Monteith FAO model, respectively. ConclusionEvapotranspiration is one of the important factors in the hydrological cycle and among the determining factors of energy equations on the earth's surface and water balance. In this regard, many researchers tried to estimate the amount of evaporation and transpiration with a suitable approximation using a cheap and easier method for different regions. The purpose of this research is to compare artificial neural network (ANN) models, artificial neural network optimized with genetic algorithm (ANN+GA) and experimental models (Blaney-Criddle, Hargreaves Samani and Irmak) in estimating reference evaporation and transpiration compared to the obtained results. It was done from the standard Penman-Monteith-FAO model, using meteorological data at Ramhormoz synoptic station. The general results of this research showed that the artificial neural network models have a higher correlation with the Penman-Manteith-Fau model than the used experimental models. In addition, among the used neural network models, the integrated neural network model with genetic algorithm has a higher correlation with the Penman-Manteith-Fau model than the artificial neural network model. Also, among the experimental models used, respectively, Hargreaves Samani, Irmak, and Blaney-Criddle models have the highest correlation with the standard Penman-Monteith-Fau method. In line with the results of the present research, it is suggested to compare the results of experimental models and artificial neural network with the data obtained from the evaporation pan.

Patrice Carbonneau, Simone Bizzi

Abstract Much progress has been made over the last decade to build global inventories of freshwater resources. However, existing freshwater inventories are generally produced for a fixed period in time and/or do not discriminate lakes and rivers. The emergence of deep learning methods and Big Data platforms such as Google Earth Engine offers a potential solution. Here we presenta unique global raster dataset of 6.7 terapixels at a spatial resolution of 10 m produced with a deep learning workflow. This dataset uses Sentinel-2 imagery downloaded from Google Earth Engine to delineatefreshwater as separate semantic classes of rivers and lakes. Ourstudy site covers the non-polar globe, 89% of the terrestrial landmass, with repeat surveys for the months of April, July and October 2021. This gives us the first global-scale direct count of lakes larger than 1 hectare as ~7.3 million. Also the repeat surveys allow us topresent the firstintra-annual ranges for the areas distinctly occupied by rivers and lakes where we find that basins influenced by the Asian summer monsoon (e.g. the Ganges) closely followed by the Amazon display the largest intra-annual range of river area per unit basin area. Finally, we find that whilst maximum of lake water surface occurs in October 2021, the maximum river surfaceis in July 2021 leading us to conclude that the global maximum occurrences of river waterand lake water are not synchronous.

GAO Shan

ZHENG Wenkui, LI Tao, GAO Shan et al.

Onosakponome ., Ogaga Robert

Water quality levels of Otamiri River was studied to seek explanations for the variations of water quality indicators in the river and also tried to evaluate the influence of urban land use activities and season on the water quality and to examine the water quality of Otamiri River for agricultural and domestic uses. Water sampling and laboratory analyses were carried out using standard procedures to determine the concentrations of selected parameters in the sample. The data obtained ranged from 21.10 to 98.00µS/cm for Electrical Conductivity, 9.70 to 45.10mg/l for TDS, 5.60 to 20.80mg/l total hardness, 0.00 to 3.06mg/l NO3- , 0.15 to 6.50mg/l PO4, 0.00 to 12.63mg/l SO4 2- , and 14.48 to 32.96mg/l Cl- . Other results ranged from 12.03 to 19.26mg/l BOD5, TSS 40.50 to 1793.50mg/l, turbidity 7.86 to 96.40NTU and pH 6.48 to 6.90 at 29?C. The data clearly show that areas outside the influence of urban activities still maintain their pristine environmental conditions with water quality parameters falling within permissible limits. The water from these areas can sustain aquatic life and are safe for human consumption. However, the results further showed that the midstream area that receive wastes from urban land use activities was polluted with parameters such as NO3- , PO4- , Cl- , BOD5, TDS, and COD being higher than the permissible limit. This demonstrates the influence of urban land use on the water quality of Otamiri River.

GUILHERME SANTOS, ROBERTO EDUARDO KIRCHHEIM, EDUARDO LAZZAROTTO et al.

A zona de convergência do Atlântico Sul (ZCAS) é um fenômeno meteorológico intrasazonal formado por uma faixa de nebulosidade de direção NW-SE, que vai do W da região Amazônica até a região SE do Brasil entre os meses de novembro e abril. Provoca elevados níveis de precipitação, principalmente no período de verão e possui papel fundamental na distribuição das chuvas nas regiões SE e Centro W. Sendo as águas meteóricas a principal fonte de recarga dos aquíferos, é provável que uma mudança no regime das chuvas provocadas pela atuação (ou não) da ZCAS possa afetar diretamente os níveis de água nos aquíferos e consequentemente a disponibilidade dessas águas para a manutenção dos níveis de base dos rios. Como forma de identificar a origem das águas subterrâneas com os prováveis eventos que as geraram, foi utilizada a assinatura isotópica de δ18O (Oxigênio-18) e δ2H (Deutério) das amostras dos poços de monitoramento RIMAS (operada e mantida pela CPRM) do Sistema Aquífero Bauru (SAB) e do Sistema Aquífero Guarani (SAG) nos Estados de São Paulo, Paraná e Mato Grosso do Sul.

Nourbakhsh Zahra, Yousefi Hossein

A conceptual model was proposed in the present study, which highlighted important independent and dependent variables in order to managing the groundwater quality. Furthermore, the methods of selection of variable and collection of related data were explained. The study was carried out in the Tajan Plain, north of Iran; 50 drinking wells were considered as sampling points. In this model the Analytical Hierarchy Process (AHP) was proposed to select the indicator water quality parameters. According to expert opinions and characteristics of the study area ten factors were chosen as variables influencing the quality of groundwater (land use types, lithology units, geology units, distance of wells to the outlet, distance to the residential areas, direction toward the residential areas, depth of the groundwater table, the type of aquifer, transmissivity and population). Geographic Information System (AecGIS 9.3) was used to manage the spatial-based variables and the data of non-spatial-based variables were obtained from relevant references. A database, which contains all collected data related to groundwater quality management in the studied area, was created as the output of the model. The output of this conceptual model can be used as an input for quantitative and mathematical models. Results show that 6 parameters (sulphate, iron, nitrate, electrical conductivity, calcium, and total dissolved solids (TDS) were the best indicators for groundwater quality analysis in the area. More than 50% of the wells were drilled in the depth of groundwater table about 5 meters, in this low depth pollutants can load into the wells and also 78% of the wells are located within 5 km from the urban area; it can be concluded from this result that the intensive urban activities could affect groundwater quality.

Evandro Flavio Soares Benevides, Carlos Vitor Barcelos, Mardon Borges Mendes

Os rejeitos do beneficiamento do minério de ferro, normalmente são lançados nas barragens de rejeito que são projetadas para este fim. Na Mina de Fábrica, os rejeitos foram lançados nas cavas da Mina Segredo devido à exaustão da barragem Forquilha III antes do tempo previsto. O lançamento de rejeito nestas cavas elevou o nível d’água nos sumps e posteriormente o nível d’água dos aquíferos do entorno das cavas. Os dados de monitoramento interpretados, mostram a alteração de nível d’água nos Inas (indicadores de nível d’água) e piezômetros do entorno das cavas, porém existem nascentes no entorno das cavas que não sofreram variação na vazão de descarga. Os impactos da disposição de rejeitos, no regime de fluxo da água subterrânea foram o alvo da investigação.

K. McCoy, B. White, R. M. Yager et al.

VIVIANE CRISTINA VIEIRA DA CUNHA, BRENO ALEXANDRE PEREIRA, DANIELA ALCÂNTRA MACHADO et al.

Os estudos hidrogeológicos aqui apresentados buscaram fornecer elementos para a compreensão das condições de circulação e armazenamento da água subterrânea em uma área de explotação de minério de ferro no sudoeste do Quadrilátero Ferrífero (Mina Casa de Pedra). Foram individualizadas sete unidades hidrogeológicas, sendo a principal delas o Aquífero Cauê que pode ser subdividido em seis compartimentos hidrogeológicos distintos. As análises isotópicas de Deutério, Oxigênio-18 e Trítio forneceram importantes informações sobre o fluxo subterrâneo, no geral de norte para sul, nos diferentes compartimentos do Aquífero Cauê. Os estudos resultaram na elaboração do modelo numérico de água subterrânea, que possibilitou a quantificação do impacto sob a disponibilidade hídrica decorrentes do desaguamento da Mina Casa de Pedra.

De-min Yang, Bing Wang, Hong-yang Ren et al.

The degradation efficiencies and mechanism of ozonation for the degradation of sodium acetate in aqueous solution were investigated under atmospheric pressure at room temperature (293 K). The effects of the initial pH value, reaction time, and concentrations of HCO3−, CO32− CaCl2 and Ca(OH)2 on the removal rate of chemical oxygen demand (COD) were studied. The results indicated that ozonation obviously improved the degradation rate of sodium acetate when the pH value of the solution was not less than 8.5. A suitable long reaction time may be helpful in increasing the COD removal rate, and a removal rate of 36.36% can be obtained after a 30-minute treatment. The COD removal rate increased firstly and decreased subsequently with the increase of the HCO3− concentration (from 0 to 200 mg/L), and under the same experimental condition it reached the optimum 34.66% at the HCO3− concentration of 100 mg/L. The COD removal rate was 5.26% lower when the concentration of HCO3− was 200 mg/L than when there was no HCO3−. The COD removal rate decreased by 15.68% when the CO32− concentration increased from 0 to 200 mg/L. CO32− has a more obvious scavenging effect in inhibiting the formation of hydroxyl radicals than HCO3−. CaCl2 and Ca(OH)2 could increase the degradation efficiency of sodium acetate greatly, and the COD removal rates reached 65.73% and 83.46%, respectively, after a 30-minute treatment, 29.37% and 47.10% higher, respectively, than with single ozone oxidation. It was proved that the degradation of sodium acetate in the ozonation process followed the mechanism of oxidization with hydroxyl free radicals (·OH).

G. Heritage