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1
Zafari, Najibullah, Ashok Sharma, Dimuth Navaratna, Varuni M. Jayasooriya, Craig McTaggart, and Shobha Muthukumaran. "A Comparative Evaluation of Conceptual Rainfall–Runoff Models for a Catchment in Victoria Australia Using eWater Source." Water 14, no. 16 (August 16, 2022): 2523. http://dx.doi.org/10.3390/w14162523.
Повний текст джерелаАнотація:
Hydrological modelling at a catchment scale was conducted to investigate the impact of climate change and land-use change individually and in combination with the available streamflow in the Painkalac catchment using an eWater Source hydrological model. This study compares the performance of three inbuilt conceptual models within eWater Source, such as the Australian water balance model (AWBM), Sacramento and GR4J for streamflow simulation. The three-model performance was predicted by bivariate statistics (Nash–Sutcliff efficiency) and univariate (mean, standard deviation) to evaluate the efficiency of model runoff predictions. Potential evapotranspiration (PET) data, daily rainfall data and observed streamflow measured from this catchment are the major inputs to these models. These models were calibrated and validated using eight objective functions while further comparisons of these models were made using objective functions of a Nash–Sutcliffe efficiency (NSE) log daily and an NSE log daily bias penalty. The observed streamflow data were split into three sections. Two-thirds of the data were used for calibration while the remaining one-third of the data was used for validation of the model. Based on the results, it was observed that the performance of the GR4J model is more suitable for the Painkalac catchment in respect of prediction and computational efficiency compared to the Sacramento and AWBM models. Further, the impact of climate change, land-use change and combined scenarios (land-use and climate change) were evaluated using the GR4J model. The results of this study suggest that the higher climate change for the year 2065 will result in approximately 45.67% less streamflow in the reservoir. In addition, the land-use change resulted in approximately 42.26% less flow while combined land-use and higher climate change will produce 48.06% less streamflow compared to the observed flow under the existing conditions.
2
Adhikary, Sajal Kumar, Nitin Muttil, and Abdullah Gokhan Yilmaz. "Improving streamflow forecast using optimal rain gauge network-based input to artificial neural network models." Hydrology Research 49, no. 5 (December 5, 2017): 1559–77. http://dx.doi.org/10.2166/nh.2017.108.
Повний текст джерелаАнотація:
Abstract Accurate streamflow forecasting is of great importance for the effective management of water resources systems. In this study, an improved streamflow forecasting approach using the optimal rain gauge network-based input to artificial neural network (ANN) models is proposed and demonstrated through a case study (the Middle Yarra River catchment in Victoria, Australia). First, the optimal rain gauge network is established based on the current rain gauge network in the catchment. Rainfall data from the optimal and current rain gauge networks together with streamflow observations are used as the input to train the ANN. Then, the best subset of significant input variables relating to streamflow at the catchment outlet is identified by the trained ANN. Finally, one-day-ahead streamflow forecasting is carried out using ANN models formulated based on the selected input variables for each rain gauge network. The results indicate that the optimal rain gauge network-based input to ANN models gives the best streamflow forecasting results for the training, validation and testing phases in terms of various performance evaluation measures. Overall, the study concludes that the proposed approach is highly effective to achieve the enhanced streamflow forecasting and could be a viable option for streamflow forecasting in other catchments.
3
Rezaie-Balf, Mohammad, and Ozgur Kisi. "New formulation for forecasting streamflow: evolutionary polynomial regression vs. extreme learning machine." Hydrology Research 49, no. 3 (March 27, 2017): 939–53. http://dx.doi.org/10.2166/nh.2017.283.
Повний текст джерелаАнотація:
Abstract Streamflow forecasting is crucial in hydrology and hydraulic engineering since it is capable of optimizing water resource systems or planning future expansion. This study investigated the performances of three different soft computing methods, multilayer perceptron neural network (MLPNN), optimally pruned extreme learning machine (OP-ELM), and evolutionary polynomial regression (EPR) in forecasting daily streamflow. Data from three different stations, Soleyman Tange, Perorich Abad, and Ali Abad located on the Tajan River of Iran were used to estimate the daily streamflow. MLPNN model was employed to determine the optimal input combinations of each station implementing evaluation criteria. In both training and testing stages in the three stations, the results of comparison indicated that the EPR technique would generally perform more efficiently than MLPNN and OP-ELM models. EPR model represented the best performance to simulate the peak flow compared to MLPNN and OP-ELM models while the MLPNN provided significantly under/overestimations. EPR models which include explicit mathematical formulations are recommended for daily streamflow forecasting which is necessary in watershed hydrology management.
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SIQUEIRA, HUGO, LEVY BOCCATO, ROMIS ATTUX, and CHRISTIANO LYRA. "UNORGANIZED MACHINES FOR SEASONAL STREAMFLOW SERIES FORECASTING." International Journal of Neural Systems 24, no. 03 (February 19, 2014): 1430009. http://dx.doi.org/10.1142/s0129065714300095.
Повний текст джерелаАнотація:
Modern unorganized machines — extreme learning machines and echo state networks — provide an elegant balance between processing capability and mathematical simplicity, circumventing the difficulties associated with the conventional training approaches of feedforward/recurrent neural networks (FNNs/RNNs). This work performs a detailed investigation of the applicability of unorganized architectures to the problem of seasonal streamflow series forecasting, considering scenarios associated with four Brazilian hydroelectric plants and four distinct prediction horizons. Experimental results indicate the pertinence of these models to the focused task.
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Mazzoleni, M., M. Verlaan, L. Alfonso, M. Monego, D. Norbiato, M. Ferri, and D. P. Solomatine. "Can assimilation of crowdsourced streamflow observations in hydrological modelling improve flood prediction?" Hydrology and Earth System Sciences Discussions 12, no. 11 (November 3, 2015): 11371–419. http://dx.doi.org/10.5194/hessd-12-11371-2015.
Повний текст джерелаАнотація:
Abstract. Monitoring stations have been used for decades to properly measure hydrological variables and better predict floods. To this end, methods to incorporate such observations into mathematical water models have also being developed, including data assimilation. Besides, in recent years, the continued technological improvement has stimulated the spread of low-cost sensors that allow for employing crowdsourced and obtain observations of hydrological variables in a more distributed way than the classic static physical sensors allow. However, such measurements have the main disadvantage to have asynchronous arrival frequency and variable accuracy. For this reason, this study aims to demonstrate how the crowdsourced streamflow observations can improve flood prediction if integrated in hydrological models. Two different types of hydrological models, applied to two case studies, are considered. Realistic (albeit synthetic) streamflow observations are used to represent crowdsourced streamflow observations in both case studies. Overall, assimilation of such observations within the hydrological model results in a significant improvement, up to 21 % (flood event 1) and 67 % (flood event 2) of the Nash–Sutcliffe efficiency index, for different lead times. It is found that the accuracy of the observations influences the model results more than the actual (irregular) moments in which the streamflow observations are assimilated into the hydrological models. This study demonstrates how networks of low-cost sensors can complement traditional networks of physical sensors and improve the accuracy of flood forecasting.
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Yilmaz, Abdullah Gokhan, Serter Atabay, Kimia Haji Amou Assar, and Monzur Alam Imteaz. "Climate Change Impacts on Inflows into Lake Eppalock Reservoir from Upper Campaspe Catchment." Hydrology 8, no. 3 (July 24, 2021): 108. http://dx.doi.org/10.3390/hydrology8030108.
Повний текст джерелаАнотація:
Climate change has significant effects on societies and ecosystems. Due to the strong link between climate and the hydrological cycle, water resources is one of the most affected fields by climate change. It is of great importance to investigate climate change effects on streamflows by producing future streamflow projections under different scenarios to create adaptation measures and mitigate potential impacts of climate change. The Upper Campaspe Catchment (UCC), located at North Central Victoria in Australia, is a significant catchment as it provides a large portion of total inflow to the Lake Eppalock Reservoir, which supplies irrigation to the Campaspe Irrigation district and urban water to Bendigo, Heathcote, and Ballarat cities. In this study, climate change effects on monthly streamflows in the UCC was investigated using high resolution future climate data from CSIRO and MIROC climate models in calibrated IHACRES hydrological model. The IHACRES model was found to be very successful to simulate monthly streamflow in UCC. Remarkable streamflow reductions were projected based on the climate input from both models (CSIRO and MIROC). According to the most optimistic scenario (with the highest projected streamflows) by the MIROC-RCP4.5 model in near future (2035–2064), the Upper Campaspe River will completely dry out from January to May. The worst scenario (with the lowest streamflow projection) by the CSIRO-RCP8.5 model in the far future (2075–2104) showed that streamflows will be produced only for three months (July, August, and September) throughout the year. Findings from this study indicated that climate change will have significant adverse impacts on reservoir inflow, operation, water supply, and allocation in the study area.
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Li, Yujie, Zhongmin Liang, Yiming Hu, Binquan Li, Bin Xu, and Dong Wang. "A multi-model integration method for monthly streamflow prediction: modified stacking ensemble strategy." Journal of Hydroinformatics 22, no. 2 (November 7, 2019): 310–26. http://dx.doi.org/10.2166/hydro.2019.066.
Повний текст джерелаАнотація:
Abstract In this study, we evaluate elastic net regression (ENR), support vector regression (SVR), random forest (RF) and eXtreme Gradient Boosting (XGB) models and propose a modified multi-model integration method named a modified stacking ensemble strategy (MSES) for monthly streamflow forecasting. We apply the above methods to the Three Gorges Reservoir in the Yangtze River Basin, and the results show the following: (1) RF and XGB present better and more stable forecast performance than ENR and SVR. It can be concluded that the machine learning-based models have the potential for monthly streamflow forecasting. (2) The MSES can effectively reconstruct the original training data in the first layer and optimize the XGB model in the second layer, improving the forecast performance. We believe that the MSES is a computing framework worthy of development, with simple mathematical structure and low computational cost. (3) The forecast performance mainly depends on the size and distribution characteristics of the monthly streamflow sequence, which is still difficult to predict using only climate indices.
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Samadi, S. Zahra. "Assessing the sensitivity of SWAT physical parameters to potential evapotranspiration estimation methods over a coastal plain watershed in the southeastern United States." Hydrology Research 48, no. 2 (July 4, 2016): 395–415. http://dx.doi.org/10.2166/nh.2016.034.
Повний текст джерелаАнотація:
One of the key inputs of a hydrologic budget is the potential evapotranspiration (PET), which represents the hypothetical upper limit to evapotranspirative water losses. However, different mathematical formulas proposed for defining PET often produce inconsistent results and challenge hydrological estimation. The objective of this study is to investigate the effects of the Priestley–Taylor (P–T), Hargreaves, and Penman–Monteith methods on daily streamflow simulation using the Soil and Water Assessment Tool (SWAT) for the southeastern United States. PET models are compared in terms of their sensitivity to the SWAT parameters and their ability to simulate daily streamflow over a five-year simulation period. The SWAT model forced by these three PET methods and by gauged climatic dataset showed more deficiency during low and peak flow estimates. Sensitive parameters vary in magnitudes with more skew and bias in saturated soil hydraulic conductivity and shallow aquifer properties. The results indicated that streamflow simulation using the P–T method performed well especially during extreme events’ simulation.
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Li, Xue, Jian Sha, You-meng Li, and Zhong-Liang Wang. "Comparison of hybrid models for daily streamflow prediction in a forested basin." Journal of Hydroinformatics 20, no. 1 (November 29, 2017): 191–205. http://dx.doi.org/10.2166/hydro.2017.189.
Повний текст джерелаАнотація:
Abstract Accurate forecasting of daily streamflow is essential for water resource planning and management. As a typical non-stationary time series, it is difficult to avoid the effects of noise in the hydrological data. In this study, the wavelet threshold de-noising method was applied to pre-process daily flow data from a small forested basin. The key factors influencing the de-noising results, such as the mother wavelet type, decomposition level, and threshold functions, were examined and determined according to the signal to noise ratio and mean square error. Then, three mathematical techniques, including an optimized back-propagation neural network (BPNN), optimized support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS), were used to predict the daily streamflow based on raw data and wavelet de-noising data. The performance of the three models indicated that a wavelet de-noised time series could improve the forecasting accuracy. The SVR showed a better overall performance than BPNN and ANFIS during both the training and validating periods. However, the estimation of low flow and peak flow indicated that ANFIS performed best in the prediction of low flow and that SVR was slightly superior to the others for forecasting peak flow.
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Makarieva, Olga, Nataliia Nesterova, Ali Torabi Haghighi, Andrey Ostashov, and Anastasiia Zemlyanskova. "Challenges of Hydrological Engineering Design in Degrading Permafrost Environment of Russia." Energies 15, no. 7 (April 4, 2022): 2649. http://dx.doi.org/10.3390/en15072649.
Повний текст джерелаАнотація:
The study shows that the current network of hydrometeorological observation in the permafrost zone of Russia is insufficient to provide data for the statistical approaches adopted at the state level for engineering surveys and calculations. The alternative to the financially costly and practically impossible expansion of the monitoring network is the development of hydrological research stations and the implementation of new methods for calculating streamflow characteristics based on mathematical modeling. The data of the Kolyma Water-Balance Station, the first research basin in the world in a permafrost environment (1948–1997), and the process-based hydrological model Hydrograph are applied to simulate streamflow hydrographs in remote mountainous permafrost basins. The satisfactory results confirm that mathematical modeling may substitute or replace statistical approaches in the conditions of extreme data insufficiency. The improvement of the models in a changing climate requires the renewal of historical observations at currently abandoned research stations in Russian permafrost regions. The study is important for forming the state policy in climate change adaptation and mitigation measures.
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Lambert, M. F., J. P. Whiting, and A. V. Metcalfe. "A non-parametric hidden Markov model for climate state identification." Hydrology and Earth System Sciences 7, no. 5 (October 31, 2003): 652–67. http://dx.doi.org/10.5194/hess-7-652-2003.
Повний текст джерелаАнотація:
Abstract. Hidden Markov models (HMMs) can allow for the varying wet and dry cycles in the climate without the need to simulate supplementary climate variables. The fitting of a parametric HMM relies upon assumptions for the state conditional distributions. It is shown that inappropriate assumptions about state conditional distributions can lead to biased estimates of state transition probabilities. An alternative non-parametric model with a hidden state structure that overcomes this problem is described. It is shown that a two-state non-parametric model produces accurate estimates of both transition probabilities and the state conditional distributions. The non-parametric model can be used directly or as a technique for identifying appropriate state conditional distributions to apply when fitting a parametric HMM. The non-parametric model is fitted to data from ten rainfall stations and four streamflow gauging stations at varying distances inland from the Pacific coast of Australia. Evidence for hydrological persistence, though not mathematical persistence, was identified in both rainfall and streamflow records, with the latter showing hidden states with longer sojourn times. Persistence appears to increase with distance from the coast. Keywords: Hidden Markov models, non-parametric, two-state model, climate states, persistence, probability distributions
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Tran, Hoang, Elena Leonarduzzi, Luis De la Fuente, Robert Bruce Hull, Vineet Bansal, Calla Chennault, Pierre Gentine, Peter Melchior, Laura E. Condon, and Reed M. Maxwell. "Development of a Deep Learning Emulator for a Distributed Groundwater–Surface Water Model: ParFlow-ML." Water 13, no. 23 (December 1, 2021): 3393. http://dx.doi.org/10.3390/w13233393.
Повний текст джерелаАнотація:
Integrated hydrologic models solve coupled mathematical equations that represent natural processes, including groundwater, unsaturated, and overland flow. However, these models are computationally expensive. It has been recently shown that machine leaning (ML) and deep learning (DL) in particular could be used to emulate complex physical processes in the earth system. In this study, we demonstrate how a DL model can emulate transient, three-dimensional integrated hydrologic model simulations at a fraction of the computational expense. This emulator is based on a DL model previously used for modeling video dynamics, PredRNN. The emulator is trained based on physical parameters used in the original model, inputs such as hydraulic conductivity and topography, and produces spatially distributed outputs (e.g., pressure head) from which quantities such as streamflow and water table depth can be calculated. Simulation results from the emulator and ParFlow agree well with average relative biases of 0.070, 0.092, and 0.032 for streamflow, water table depth, and total water storage, respectively. Moreover, the emulator is up to 42 times faster than ParFlow. Given this promising proof of concept, our results open the door to future applications of full hydrologic model emulation, particularly at larger scales.
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Ghanim, Abdulnoor A. J., Salmia Beddu, Teh Sabariah Binti Abd Manan, Saleh H. Al Yami, Muhammad Irfan, Salim Nasar Faraj Mursal, Nur Liyana Mohd Kamal, et al. "Prediction of Runoff in Watersheds Located within Data-Scarce Regions." Sustainability 14, no. 13 (June 30, 2022): 7986. http://dx.doi.org/10.3390/su14137986.
Повний текст джерелаАнотація:
The interest in the use of mathematical models for the simulation of hydrological processes has largely increased especially in the prediction of runoff. It is the subject of extreme research among engineers and hydrologists. This study attempts to develop a simple conceptual model that reflects the features of the arid environment where the availability of hydrological data is scarce. The model simulates an hourly streamflow hydrograph and the peak flow rate for any given storm. Hourly rainfall, potential evapotranspiration, and streamflow record are the significant input prerequisites for this model. The proposed model applied two (2) different hydrologic routing techniques: the time area curve method (wetted area of the catchment) and the Muskingum method (catchment main channel). The model was calibrated and analyzed based on the data collected from arid catchment in the center of Jordan. The model performance was evaluated via goodness of fit. The simulation of the proposed model fits both (a) observed and simulated streamflow and (b) observed and simulated peak flow rate. The model has the potential to be used for peak discharges’ prediction during a storm period. The modeling approach described in this study has to be tested in additional catchments with appropriate data length in order to attain reliable model parameters.
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Koycegiz, Cihangir, Meral Buyukyildiz, and Serife Yurdagul Kumcu. "Spatio-temporal analysis of sediment yield with a physically based model for a data-scarce headwater in Konya Closed Basin, Turkey." Water Supply 21, no. 4 (January 19, 2021): 1752–63. http://dx.doi.org/10.2166/ws.2021.016.
Повний текст джерелаАнотація:
Abstract There are many empirical, semi-empirical and mathematical methods that have been developed to estimate sediment yield by researchers. In the last decades, the advancement in computer technologies has increased the use of mathematical models as they can solve the system more rapidly and accurately. The Soil and Water Assessment Tool (SWAT) is one of the physically based hydrological models that is preferred to compute sediment yield. In this study, spatial and temporal analysis of sediment yield in the Çarşamba Stream located at the Konya Closed Basin has been investigated using the SWAT model. Streamflow and sediment data collected during the 2003–2015 time period have been used in the analysis. Consequently, the SWAT presented satisfactory results compared with R2 = 0.68, Nash–Sutcliffe Efficiency (NSE) = 0.68 in calibration and R2 = 0.76, NSE = 0.66 in validation. According to the model results, spatial asymmetry in terms of sediment yield was determined in the sub-basins of the study area.
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Mazzoleni, Maurizio, Martin Verlaan, Leonardo Alfonso, Martina Monego, Daniele Norbiato, Miche Ferri, and Dimitri P. Solomatine. "Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?" Hydrology and Earth System Sciences 21, no. 2 (February 14, 2017): 839–61. http://dx.doi.org/10.5194/hess-21-839-2017.
Повний текст джерелаАнотація:
Abstract. Monitoring stations have been used for decades to properly measure hydrological variables and better predict floods. To this end, methods to incorporate these observations into mathematical water models have also been developed. Besides, in recent years, the continued technological advances, in combination with the growing inclusion of citizens in participatory processes related to water resources management, have encouraged the increase of citizen science projects around the globe. In turn, this has stimulated the spread of low-cost sensors to allow citizens to participate in the collection of hydrological data in a more distributed way than the classic static physical sensors do. However, two main disadvantages of such crowdsourced data are the irregular availability and variable accuracy from sensor to sensor, which makes them challenging to use in hydrological modelling. This study aims to demonstrate that streamflow data, derived from crowdsourced water level observations, can improve flood prediction if integrated in hydrological models. Two different hydrological models, applied to four case studies, are considered. Realistic (albeit synthetic) time series are used to represent crowdsourced data in all case studies. In this study, it is found that the data accuracies have much more influence on the model results than the irregular frequencies of data availability at which the streamflow data are assimilated. This study demonstrates that data collected by citizens, characterized by being asynchronous and inaccurate, can still complement traditional networks formed by few accurate, static sensors and improve the accuracy of flood forecasts.
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Tran, Vinh Ngoc, and Jongho Kim. "Toward an Efficient Uncertainty Quantification of Streamflow Predictions Using Sparse Polynomial Chaos Expansion." Water 13, no. 2 (January 15, 2021): 203. http://dx.doi.org/10.3390/w13020203.
Повний текст джерелаАнотація:
Reliable hydrologic models are essential for planning, designing, and management of water resources. However, predictions by hydrological models are prone to errors due to a variety of sources of uncertainty. More accurate quantification of these uncertainties using a large number of ensembles and model runs is hampered by the high computational burden. In this study, we developed a highly efficient surrogate model constructed by sparse polynomial chaos expansion (SPCE) coupled with the least angle regression method, which enables efficient uncertainty quantifications. Polynomial chaos expansion was employed to surrogate a storage function-based hydrological model (SFM) for nine streamflow events in the Hongcheon watershed of South Korea. The efficiency of SPCE is investigated by comparing it with another surrogate model, full polynomial chaos expansion (FPCE) built by a well-known, ordinary least square regression (OLS) method. This study confirms that (1) the performance of SPCE is superior to that of FPCE because SPCE can build a more accurate surrogate model (i.e., smaller leave-one-out cross-validation error) with one-quarter the size (i.e., 500 versus 2000). (2) SPCE can sufficiently capture the uncertainty of the streamflow, which is comparable to that of SFM. (3) Sensitivity analysis attained through visual inspection and mathematical computation of the Sobol’ index has been of great success for SPCE to capture the parameter sensitivity of SFM, identifying four parameters, α, Kbas, Pbas, and Pchn, that are most sensitive to the likelihood function, Nash-Sutcliffe efficiency. (4) The computational power of SPCE is about 200 times faster than that of SFM and about four times faster than that of FPCE. The SPCE approach builds a surrogate model quickly and robustly with a more compact experimental design compared to FPCE. Ultimately, it will benefit ensemble streamflow forecasting studies, which must provide information and alerts in real time.
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Benke, Kurt K., and Nathan J. Robinson. "Quantification of Uncertainty in Mathematical Models: The Statistical Relationship between Field and Laboratory pH Measurements." Applied and Environmental Soil Science 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/5857139.
Повний текст джерелаАнотація:
The measurement of soil pH using a field portable test kit represents a fast and inexpensive method to assess pH. Field based pH methods have been used extensively for agricultural advisory services and soil survey and now for citizen soil science projects. In the absence of laboratory measurements, there is a practical need to model the laboratory pH as a function of the field pH to increase the density of data for soil research studies and Digital Soil Mapping. The accuracy and uncertainty in pH field measurements were investigated for soil samples from regional Victoria in Australia using both linear and sigmoidal models. For samples in water and CaCl2 at 1 : 5 dilutions, sigmoidal models provided improved accuracy over the full range of field pH values in comparison to linear models (i.e., pH < 5 or pH > 9). The uncertainty in the field results was quantified by the 95% confidence interval (CI) and 95% prediction interval (PI) for the models, with 95% CI < 0.25 pH units and 95% PI = ±1.3 pH units, respectively. It was found that the Pearson criterion for robust regression analysis can be considered as an alternative to the orthodox least-squares modelling approach because it is more effective in addressing outliers in legacy data.
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Guinaldo, Thibault, Simon Munier, Patrick Le Moigne, Aaron Boone, Bertrand Decharme, Margarita Choulga, and Delphine J. Leroux. "Parametrization of a lake water dynamics model MLake in the ISBA-CTRIP land surface system (SURFEX v8.1)." Geoscientific Model Development 14, no. 3 (March 10, 2021): 1309–44. http://dx.doi.org/10.5194/gmd-14-1309-2021.
Повний текст джерелаАнотація:
Abstract. Lakes are of fundamental importance in the Earth system as they support essential environmental and economic services, such as freshwater supply. Streamflow variability and temporal evolution are impacted by the presence of lakes in the river network; therefore, any change in the lake state can induce a modification of the regional hydrological regime. Despite the importance of the impact of lakes on hydrological fluxes and the water balance, a representation of the mass budget is generally not included in climate models and global-scale hydrological modeling platforms. The goal of this study is to introduce a new lake mass module, MLake (Mass-Lake model), into the river-routing model CTRIP to resolve the specific mass balance of open-water bodies. Based on the inherent CTRIP parameters, the development of the non-calibrated MLake model was introduced to examine the influence of such hydrological buffer areas on global-scale river-routing performance. In the current study, an offline evaluation was performed for four river networks using a set of state-of-the-art quality atmospheric forcings and a combination of in situ and satellite measurements for river discharge and lake level observations. The results reveal a general improvement in CTRIP-simulated discharge and its variability, while also generating realistic lake level variations. MLake produces more realistic streamflows both in terms of daily and seasonal correlation. Excluding the specific case of Lake Victoria having low performances, the mean skill score of Kling–Gupta efficiency (KGE) is 0.41 while the normalized information contribution (NIC) shows a mean improvement of 0.56 (ranging from 0.15 to 0.94). Streamflow results are spatially scale-dependent, with better scores associated with larger lakes and increased sensitivity to the width of the lake outlet. Regarding lake level variations, results indicate a good agreement between observations and simulations with a mean correlation of 0.56 (ranging from 0.07 to 0.92) which is linked to the capability of the model to retrieve seasonal variations. Discrepancies in the results are mainly explained by the anthropization of the selected lakes, which introduces high-frequency variations in both streamflows and lake levels that degraded the scores. Anthropization effects are prevalent in most of the lakes studied, but they are predominant for Lake Victoria and are the main cause for relatively low statistical scores for the Nile River However, results on the Angara and the Neva rivers also depend on the inherent gap of ISBA-CTRIP process representation, which relies on further development such as the partitioned energy budget between the snow and the canopy over a boreal zone. The study is a first step towards a global coupled land system that will help to qualitatively assess the evolution of future global water resources, leading to improvements in flood risk and drought forecasting.
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De OLIVEIRA, Guilherme Garcia, Dejanira Luderitz SALDANHA, and Laurindo Antonio GUASSELLI. "MODELS FOR SPATIALIZATION AND FORECASTING OF FLOODED AREAS IN THE SÃO SEBASTIÃO DO CAÍ URBAN ZONE, RIO GRANDE DO SUL STATE, BRAZIL." Pesquisas em Geociências 38, no. 2 (August 31, 2011): 132. http://dx.doi.org/10.22456/1807-9806.26379.
Повний текст джерелаАнотація:
The study aims at developing models for the spatialization and forecasting of floods in the urban area of São Sebastião do Caí, RS, Brazil. For the calculation of return period (RP), and in order to analyze the seasonality of floods, streamflow data from the station located in the city were used. However, for the development of a mathematical model for flood forecasting, the time series of a station upstream was also used in order to perform a regression with the quotas recorded in both seasons. For the identification of flood plains, a digital terrain model was produced based on elevation data in scales between 1:2,000 and 1:10,000. The QuickBird satellite image (spatial resolution of 0.61 m) was used only for the spatialization of the land use and land cover reached by each flood scenario. Mapping and 3D simulation of the areas affected by flooding were obtained for RP of 2, 5, 10 and 30 years. The following results are most significant: i) the river water level rises between 9.28 m and 11.98 m for RP of 2 to 30 years; ii) along the historical series, 75% of floods have occurred between June and October; iii) the mathematical model for flood forecasting showed an average error of 0.72 m, and the accuracy varies between 0.62 m and 1.84 m, according to the expected magnitude; iv) it was observed that 93 hectares of urban area in São Sebastião do Caí are hit by floods with a RP of 30 years (23% of the urban area); v) modelling of a recent flood event dated of 24/09/2007 has resulted in similar values for the simulated and observed flooded area.
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Das, Sushil K., Amimul Ahsan, Md Habibur Rahman Bejoy Khan, Muhammad Atiq Ur Rehman Tariq, Nitin Muttil, and Anne W. M. Ng. "Impacts of Climate Alteration on the Hydrology of the Yarra River Catchment, Australia Using GCMs and SWAT Model." Water 14, no. 3 (February 1, 2022): 445. http://dx.doi.org/10.3390/w14030445.
Повний текст джерелаАнотація:
A rigorous evaluation of future hydro-climatic changes is necessary for developing climate adaptation strategies for a catchment. The integration of future climate projections from general circulation models (GCMs) in the simulations of a hydrologic model, such as the Soil and Water Assessment Tool (SWAT), is widely considered as one of the most dependable approaches to assess the impacts of climate alteration on hydrology. The main objective of this study was to assess the potential impacts of climate alteration on the hydrology of the Yarra River catchment in Victoria, Australia, using the SWAT model. The climate projections from five GCMs under two Representative Concentration Pathway (RCP) scenarios—RCP 4.5 and 8.5 for 2030 and 2050, respectively—were incorporated into the calibrated SWAT model for the analysis of future hydrologic behaviour against a baseline period of 1990–2008. The SWAT model performed well in its simulation of total streamflow, baseflow, and runoff, with Nash–Sutcliffe efficiency values of more than 0.75 for monthly calibration and validation. Based on the projections from the GCMs, the future rainfall and temperature are expected to decrease and increase, respectively, with the highest changes projected by the GFDL-ESM2M model under the RCP 8.5 scenario in 2050. These changes correspond to significant increases in annual evapotranspiration (8% to 46%) and decreases in other annual water cycle components, especially surface runoff (79% to 93%). Overall, the future climate projections indicate that the study area will become hotter, with less winter–spring (June to November) rainfall and with more water shortages within the catchment.
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Dibike, Yonas B., and Paulin Coulibaly. "TDNN with logical values for hydrologic modeling in a cold and snowy climate." Journal of Hydroinformatics 10, no. 4 (October 1, 2008): 289–300. http://dx.doi.org/10.2166/hydro.2008.049.
Повний текст джерелаАнотація:
Watershed runoff in areas with heavy seasonal snow cover is usually estimated using physically based conceptual hydrologic models. Such simulation models normally require a snowmelt algorithm consisting of a surface energy balance and some accounting of internal snowpack processes to be part of the modeling system. On the other hand, artificial neural networks are flexible mathematical structures that are capable of identifying such complex nonlinear relationships between input and output datasets from historical precipitation, temperature and streamflow records. This paper presents the findings of a study on using a form of time-delayed neural network, namely time-lagged feedforward neural network (TLFN), that implicitly accounts for snow accumulation and snowmelt processes through the use of logical values and tapped delay lines. The logical values (in the form of symbolic inputs) are used to implicitly include seasonal information in the TLFN model. The proposed method has been successfully applied for improved precipitation–runoff modeling of both the Chute-du-Diable reservoir inflows and the Serpent River flows in northeastern Canada where river flows and reservoir inflows are highly influenced by seasonal snowmelt effects. The study demonstrates that the TLFN with logical values is capable of modeling the precipitation–runoff process in a cold and snowy climate by relying on ‘logical input values’ and tapped delay lines to implicitly recognize the temporal input–output patterns in the historical data. The study results also show that, once the appropriate input patterns are identified, the time-lagged neural network based models performed quite well, especially for spring peak flows, and demonstrated comparable performance in simulating the precipitation–runoff processes to that of a physically based hydrological model, namely HBV.
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Abdulai, Patricia Jitta, and Eun-Sung Chung. "Uncertainty Assessment in Drought Severities for the Cheongmicheon Watershed Using Multiple GCMs and the Reliability Ensemble Averaging Method." Sustainability 11, no. 16 (August 8, 2019): 4283. http://dx.doi.org/10.3390/su11164283.
Повний текст джерелаАнотація:
The consequence of climate variations on hydrology remains the greatest challenging aspect of managing water resources. This research focused on the quantitative approach of the uncertainty in variations of climate influence on drought pattern of the Cheongmicheon watershed by assigning weights to General Circulation Models (GCMs) based on model performances. Three drought indices, Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI) and Streamflow Drought Index (SDI) are used for three durations 3-, 6- and 9-months. This study included 27 GCMs from Coupled Model Intercomparison Project 5 (CMIP5) and considered three future periods (2011–2040, 2041–2070 and 2071–2100) of the concentration scenario of Representation Concentration Pathway (RCP) 4.5. Compared to SPEI and SDI, SPI identified more droughts in severe or extreme categories of shorter time scales than SPEI or SDI. The results suggested that the discrepancy in temperature plays a significant part in characterizing droughts. The Reliability Ensemble Averaging (REA) technique was used to give a mathematical approximation of associated uncertainty range and reliability of future climate change predictions. The uncertainty range and reliability of Root Mean Square Error (RMSE) varied among GCMs and total uncertainty ranges were between 50% and 200%. This study provides the approach for realistic projections by incorporating model performance ensemble averaging based on weights from RMSE.
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"Intercomparison of process-based physical and mathematical models in data-scarce semi-arid region of Eritrea." Water sector of Russia: problems, technologies, management, no. 1, 2021 (2021): 86–112. http://dx.doi.org/10.35567/1999-4508-2021-1-6.
Повний текст джерелаАнотація:
Watershed models simulate natural hydrological and biogeochemical processes within watersheds as well as quantify the impact of human activities on these processes. Among them, rainfall-runoff models have been widely applied for generating hydrological responses using reanalysis datasets as forcing variables in data-scare regions. In the present study, Soil and Water Assessment Tool model and rainfall-runoff model were employed to simulate streamflow from a small watershed with arid and semi-arid climate. As such, models that provide reliable streamflow predictions in the region as well as whose errors and uncertainties are within acceptable ranges could be identified. The intercomparison of the models’ performances indicated that the Soil and Water Assessment Tool model relatively outperformed the rainfall-runoff model. However, while most of the statistical evaluations proved an acceptable performance of the Soil and Water Assessment Tool model, significant amounts of uncertainties during calibration and validation procedures were noticed. Among the possible sources of errors, errors due to forcing variables were highly likely to be responsible for unsatisfactory performances of the selected models. In this regard, to minimize model uncertainty and thereupon improve its performance, ground-based data collection need to be boosted up. Besides, the study highlighted the need for further investigation on the possible mechanisms of properly applying reanalysis datasets in arid and semi-arid regions.
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Bhatt, Ankit, and Ajay Pradhan. "Uncertainty of Streamflow Forecasting with the Climate Change Scenario in India." Journal of Water Engineering and Management 1, no. 3 (2020). http://dx.doi.org/10.47884/jweam.v1i3pp01-06.
Повний текст джерелаАнотація:
Streamflow and rainfall estimates have utmost importance to compute detailed water availability and hydrology for many sectors such as agriculture, water management, and food security. There are various models developed over the years for runoff estimation but among them only a few models incorporate climate change factors. Snowmelt and rainfall are the main sources of surface as well as groundwater resource and the main inputs in runoff models for estimation of streamflow. There are numerous factors which leads to climate change which intern affects the distribution on rainfall on spatial and temporal scales and the rate of melting of snows in the Himalayan region. Uncertainties in projected changes in the hydrological systems arise from internal variability in the climatic system, uncertainty about future greenhouse gas and aerosol emissions, the translations of these emissions into climate change by global climate models, and hydrological model uncertainty. Projections become less consistent between models as the spatial scale decreases. The uncertainty of climate model projections for freshwater assessments is often taken into account by using multi-model ensembles. The multi-model ensemble approach is, however, not a guarantee of reducing uncertainty in mathematical models. In recent years the floods have occurred due to high intensity rainfall occurred in a very short time, but in several cases the flooding has also occurred because the rainfall has fallen at times when all the storage systems have not been emptied after the previous rainfall. This is what we call coupled rainfall. There is currently no recommendation for how to take coupled rainfall account when applying the climate change scenario. It is estimated that such changes represent at a large scale, and cannot be applied to shorter temporal and smaller spatial scales. In areas where rainfall and runoff are very low (e.g., desert areas), small changes in runoff can lead to large percentage changes. In some regions, the sign of projected changes in runoff differs from recently observed trends. Moreover, in some areas with projected increases in runoff, different seasonal effects are expected, such as increased wet season runoff and decreased dry season runoff. Studies using results from fewer climate models can be considerably different from the other models
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Panant, Charoen, and Samakkee Boonyawat. "A Simplified Rainfall-Streamflow Network Model on Multivariate Regression Analysis for Water Level Forecasting in Klong Luang (KGT.19 Station) Sub-watershed, Chon Buri Province, Thailand." Applied Environmental Research, October 17, 2014, 53–65. http://dx.doi.org/10.35762/aer.2014.36.4.6.
Повний текст джерелаАнотація:
A simplifiedrainfall-streamflownetwork model based on multivariate linear regression (MLR) analysishas been proposed. To determine significant coefficients of streamflow network, eleven MLR models were examined. The study’s three objectives were 1) to develop a novel a mathematical model based on MLR analysis for forecasting optimal water levels;2) to determine the most significant coefficient of rainfall-streamflow network among in the area of interest in the vicinity of Klong Luang sub-watershedKGT.19 station; and3) to apply the optimal MLR model forwater level andflood forecasting mapsin Klong Luang Sub-watershed. We used Geographic Information System (GIS) and Remotely Sensed Data (RS) data recorded from Klong Luang (KGT.19 Station) sub-watershed, and Phanat Nikhom, Chonburi, Ban Bueng and Phan Thong districts, in Chonburi Province, Thailand.The findings indicated that the MLR based Model No. 8 is the most applicable and effective. The proposed model also could be applied in water level forecasting, water resource management, flood hazard planning, and flood early warning.
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Of Algebraic Statistics, Journal. "Editorial Messages." Journal of Algebraic Statistics 1, no. 1 (April 30, 2010). http://dx.doi.org/10.18409/jas.v1i1.3.
Повний текст джерелаАнотація:
Just as it has been continually happening in the world of mathematical sciences, the group of mathematical scientists led by (for example) Professor Eyup Cetin and his colleagues (who are responsible for the remarkably successful journal, The European Journal of Pure and Applied Mathematics ) have apparently broken the boundaries between pure and applied mathematics by establishing a new journal, the Journal of Algebraic Statistics . I am sure that both the mathematical as well as statistical communities at large will heartily welcome such an interesting and potentially useful addition to the list of broad-based journals in the mathematical sciences.I do sincerely wish the Journal of Algebraic Statistics every success in its endeavor to attract and publish high-quality papers which are aimed essentially and substantially at significantly bridging the gaps between the various areas within the disciplines of the mathematical and statistical sciences.Hari M. Srivastava, University of Victoria , Canada* * *The present moment seems a very appropriate one to launch a new journal on algebraic statistics. In fact many fields of mathematics are considering with interest concrete applications of well developed theories towards the solution of problems coming from everyday science and technology. This applies in particular to certain branches of algebraic geometry.I wish to the new journal a good success.Fabrizio Catanese, University of Bayreuth, Germany* * *Algebraic Statistics is a rapidly growing discipline, and presents many opportunities for research and applications. The newly launched Journal of Algebraic Statistics will bring together researchers working on problems in this area and as such is highly welcome.I congratulate the Editors for bringing it out and wish them and the journal success.Arjun K. Gupta, Bowling Green State University, USA* * *Many people think that Algebra and Statistics have really nothing in common, except some applications of Linear Algebra to Statistics. This is far away from the truth. A main purpose of this new journal is to uncover the numerous connections between these fields, and hence to advance both Statistics and Algebra.Many of these connections were not intended in the beginning and came as pleasant surprises. The applications go in both directions and bring new ideas and method from one area to the other.I want to congratulate the founders and Editors-in-Chief of this new journal for establishing it and for promoting the study of this fascinating interplay.Günter Pilz, Johannes Kepler University, Austria* * *Modern Algebra is central to all fields of mathematics, and impacts engineering fields such as coding theory and cryptography. Likewise, Statistics touches on all aspects of modern science. The intersection of these two fields, Algebraic Statistics, is becoming important in a number of application areas in the form of random walks on groups, random matrix theory, multivariate statistical analysis, geometric probability, and topological analysis of large data sets.Though efforts in these different areas have been published over the past half century in a variety of venues, having one place to go where readers interested in the theory and application of both Algebra and Statistics will enable significant advances by providing a hub from which connections to the broader literature can be more easily made. The Journal of Algebraic Statistics has the potential to be such a forum, and I look forward to the success of this new journal.Gregory S. Chirikjian, Johns Hopkins University, USA* * *I would like to congratulate the editorial team for the inaugural issue of the Journal of Algebraic Statistics .Algebraic Statistics is the emerging new field focused on the applications of algebraic geometry and its computational tools in the study of statistical models. Algebraic Statistics is built around the observation that many statistical models are (semi)-algebraic sets. The study of the geometry and equations of these algebraic sets can be useful for making statistical inferences, thus the areas of interest include categorical data analysis, experimental design, graphical models, maximum likelihood estimation, and Bayesian methods.Also some work shows applications of Algebraic Statistics to problems in computational biology. Nearly all statistical models for discrete random variables fall into the category above, and many models for continuous random variables can be treated this way as well. Thus, it is likely that these algebraic statistical techniques will be useful in many more areas of computational and mathematical biology such as systems biology, evolutionary biology, functional genomics, bioinformatics, and epidemiology.Algebraic Statistics is an exciting field and attracts many younger researchers. Thus I wish for theJournal of Algebraic Statistics to be very successful.Ruriko Yoshida, University of Kentucky, USA
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Jadhav, Sandeep, Ahmed Imran, and Marjia Haque. "Application of six sigma and the system thinking approach in COVID-19 operation management: a case study of the victorian aged care response centre (VACRC) in Australia." Operations Management Research, October 7, 2022. http://dx.doi.org/10.1007/s12063-022-00323-2.
Повний текст джерелаАнотація:
AbstractCOVID-19 has posed many unique and critical challenges in various contexts and circumstances. This often led the stakeholders and decision-makers to depart from traditional thinking and the business-as-usual processes and to come up with innovative approaches to tackle various mission-critical situations within a short time frame. In this paper, a real-life case study of COVID-19 operation management following a multi-disciplinary, multi-stakeholder novel integrated approach in aged care facilities in Victoria, Australia, is presented which yielded significant and positive outcomes. The purpose of the intervention was to develop an integrated system performance approach through the application of various quality management tools and techniques to achieve organizational excellence at the aged care centers. The case involved the use of mathematical models along with statistical tools and techniques to address the specific problem scenario. A system-wide management plan was proposed, involving various agencies across several residential aged care facilities during the pandemic. A three-step methodological framework was developed, where Six Sigma, a system thinking approach, and a holistic metric were proposed to manage the value chain of the pandemic management system. The experimental result analyses showed significant improvement in the management process, suggesting the validity and potential of this holistic approach to stabilize the situation and subsequently set the conditions for operations excellence within the sectors. The model offers new insight into the existing body of knowledge and offers an efficient approach to achieving operational excellence in any organization or business regardless of its type, shape and complexity, which can help practitioners in managing complex, mission-critical situations like a pandemic.