Relevant bibliographies by topics / Streamflow – Swaziland – Mathematical models

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Academic literature on the topic 'Streamflow – Swaziland – Mathematical models'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Streamflow – Swaziland – Mathematical models"

1

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.

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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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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.

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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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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.

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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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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.

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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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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.

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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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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.

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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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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.

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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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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.

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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.

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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.

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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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Dissertations / Theses on the topic "Streamflow – Swaziland – Mathematical models"

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Mahanama, Sarith Prasad Panditha. "Distributed approach of coupling basin scale hydrology with atmospheric processes." Thesis, Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22088817.

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Ahn, Taejin 1957. "A procedure for the determination of a flow duration curve at an ungaged basin." Thesis, The University of Arizona, 1987. http://hdl.handle.net/10150/276585.

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The purpose of this study is to develop a method for predicting monthly flow duration curves for ungaged basins that are suitable for estimating average annual flow, and installed capacity and average annual energy generation at potential sites for hydropower development. The procedures were tested by developing monthly rainfall duration curves for five sample watersheds and then developing flow duration curves from the rainfall data. The methods were evaluated by comparing the predicted monthly flow duration curves to daily and monthly flow duration curves based on field data from the selected sites because a plant's potential energy output can be computed directly from a flow duration curve. The methods tested fit duration curves based on field data reasonably well and are suitable for preliminary evaluation of hydropower developments in ungaged basins.
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Vionnet, Leticia Beatriz, and Thomas Maddock. "Modeling of Ground-Water Flow and Surface/Ground-Water Interaction for the San Pedro River Basin Part I Mexican Border to Fairbank, Arizona." Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ), 1992. http://hdl.handle.net/10150/614152.

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Many hydrologic basins in the southwest have seen their perennial streamflows turn to ephemeral, their riparian communities disappear or be jeopardized, and their aquifers suffer from severe overdrafts. Under -management of ground -water exploitation and of conjunctive use of surface and ground waters are the main reasons for these events.
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Tian, Ying, and 田英. "Macro-scale flow modelling of the Mekong River with spatial variance." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38735556.

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Vionnet, Leticia Beatriz, Thomas III Maddock, and David C. Goodrich. "Investigations of stream-aquifer interactions using a coupled surface-water and ground-water flow model." Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ), 1997. http://hdl.handle.net/10150/615700.

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A finite element numerical model is developed for the modeling of coupled surface-water flow and ground-water flow. The mathematical treatment of subsurface flows follows the confined aquifer theory or the classical Dupuit approximation for unconfined aquifers whereas surface-water flows are treated with the kinematic wave approximation for open channel flow. A detailed discussion of the standard approaches to represent the coupling term is provided. In this work, a mathematical expression similar to Ohm's law is used to simulate the interacting term between the two major hydrological components. Contrary to the standard approach, the coupling term is incorporated through a boundary flux integral that arises naturally in the weak form of the governing equations rather than through a source term. It is found that in some cases, a branch cut needs to be introduced along the internal boundary representing the stream in order to define a simply connected domain, which is an essential requirement in the derivation of the weak form of the ground-water flow equation. The fast time scale characteristic of surface-water flows and the slow time scale characteristic of ground-water flows are clearly established, leading to the definition of three dimensionless parameters, namely, a Peclet number that inherits the disparity between both time scales, a flow number that relates the pumping rate and the streamflow, and a Biot number that relates the conductance at the river-aquifer interface to the aquifer conductance. The model, implemented in the Bill Williams River Basin, reproduces the observed streamflow patterns and the ground-water flow patterns. Fairly good results are obtained using multiple time steps in the simulation process.
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Vionnet, Leticia Beatriz, and Leticia Beatriz Vionnet. "Investigation of stream-aquifer interactions using a coupled surface water and groundwater flow model." Diss., The University of Arizona, 1995. http://hdl.handle.net/10150/187414.

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A finite element numerical model is developed for the modeling of coupled surface-water flow and ground-water flow. The mathematical treatment of subsurface flows follows the confined aquifer theory or the classical Dupuit approximation for unconfined aquifers whereas surface-water flows are treated with the kinematic wave approximation for open channel flow. A detailed discussion of the standard approaches to represent the coupling term is provided. In this work, a mathematical expression similar to Ohm's law is used to simulate the interacting term between the two major hydrological components. Contrary to the standard approach, the coupling term is incorporated through a boundary flux integral that arises naturally in the weak form of the governing equations rather than through a source term. It is found that in some cases, a branch cut needs to be introduced along the internal boundary representing the stream in order to define a simply connected domain, which is an essential requirement in the derivation of the weak form of the ground-water flow equation. The fast time scale characteristic of surface-water flows and the slow time scale characteristic of ground-water flows are clearly established, leading to the definition of three dimensionless parameters, namely, a Peclet number that inherits the disparity between both time scales, a flow number that relates the pumping rate and the streamflow, and a Biot number that relates the conductance at the river-aquifer interface to the aquifer conductance. The model, implemented in the Bill Williams River Basin, reproduces the observed streamflow patterns and the ground-water flow patterns. Fairly good results are obtained using multiple time steps in the simulation process.
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Imam, Bisher 1960. "Evaluation of disaggregation model in arid land stream flow generation." Thesis, The University of Arizona, 1989. http://hdl.handle.net/10150/277033.

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A Disaggregation model was tested for arid land stream flow generating. The test was performed on data from Black River, near Fort Apache, Arizona. The model was tested in terms of preserving the relevant historical statistics on both monthly and daily levels, the monthly time series were disaggregated to a random observation of their daily components and the daily components were then reaggregated to yield monthly values. A computer model (DSGN) was developed to perform the model implementation. The model was written and executed on the Macintosh plus personal computer Data from two months were studied; the October data represented the low flow season, while the April data represented the high flow season. Twenty five years of data for each month was used. The generated data for the two months was compared with the historical data.
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Tang, Philip Kwok Fan. "Stochastic Hydrologic Modeling in Real Time Using a Deterministic Model (Streamflow Synthesis and Reservoir Regulation Model), Time Series Model, and Kalman Filter." PDXScholar, 1991. https://pdxscholar.library.pdx.edu/open_access_etds/4580.

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The basic concepts of hydrologic forecasting using the Streamflow Synthesis And Reservoir Regulation Model of the U.S. Army Corps of Engineers, auto-regressive-moving-average time series models (including Greens' functions, inverse functions, auto covariance Functions, and model estimation algorithm), and the Kalman filter (including state space modeling, system uncertainty, and filter algorithm), were explored. A computational experiment was conducted in which the Kalman filter was applied to update Mehama local basin model (Mehama is a 227 sq. miles watershed located on the North Santiam River near Salem, Oregon.), a typical SSARR basin model, to streamflow measurements as they became available in simulated real time. Among the candidate AR and ARMA models, an ARMA(l,l) time series model was selected as the best-fit model to represent the residual of the basin model. It was used to augment the streamflow forecasts created by the local basin model in simulated real time. Despite the limitations imposed by the quality of the moisture input forecast and the design and calibration of the basin model, the experiment shows that the new stochastic methods are effective in significantly improving the flood forecast accuracy of the SSARR model.
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Koterba, Michael T. "Differential influences of storm and watershed characteristics on runoff from ephemeral streams in southeastern Arizona." Diss., The University of Arizona, 1987. http://hdl.handle.net/10150/191126.

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Relationships between thunderstorm and watershed variables and runoff from or within semiarid watersheds at Walnut Gulch, Arizona were examined. Variables showing greater sensitivity to basin and storm size were better flow predictors. Stepwise regression with three increasingly nonlinear algebraic models showed mean storm depth was the best simple predictor of runoff. Predictions improved using storm volume, a product of storm depth and areal extent. Initial runoff to streams was best described as a highly nonlinear function of storm and watershed variables. Runoff from a basin was a more linearized function of similar variables. The above differences were ascribed to channel transmission losses, reductions in runoff moving down initially dry channels. For a given basin and small storms, loss to runoff ratios exceeded 10:1 and were highly variable. Ratios were similar and less than 0.5:1 for storms centrally located over a basin and generating sufficient initial runoff to minimize flow variation due to losses. Losses increased disproportionately with basin size. Antecedent rainfall and first summer flows also affected rainfall runoff relationships in a differential manner. Wet conditions enhanced runoff more from larger versus smaller storms. First summer flows were less than expected probably because of higher soil infiltration and channel losses at the onset of summer storms. Overall, as storm size decreased or basin area increased, initial runoff was more often a localized phenomenon and downstream flow more dependent on storm depth, extent, location, and seasonal timing and basin channel losses, but less dependent on antecedent rainfall. Consequently, storm depth accounted for only 60% to 70% of the variation in flows while storm volume, antecedent rainfall, channel losses, and first summer flows explained 80% to 90%. Finally, oversimplifying storm or watershed variables or analytical methods led to errors in assessing their affect on runoff. It was also determined that current arguments supporting a recommendation to delete smaller, frequent annual floods to better fit remaining data to flood frequency curves were oversimplified. Distributed rainfall - runoff models with channel losses and regional storm depth - area - frequency data may be the way to develope flood curves for semiarid basins with short runoff records.
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Devkota, Bishnu Hari. "A new Lagrangian model for the dynamics and transport of river and shallow water flows." University of Western Australia. Centre for Water Research, 2005. http://theses.library.uwa.edu.au/adt-WU2005.0108.

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This study presents a new Lagrangian model for predicting dynamics and transport in rivers and shallow water flows. A hydrostatic model is developed for the prediction of rivers and floodplain flow and lateral interactions between them. The model is extended to the Boussinesq weakly non-linear, non-hydrostatic model for the simulation of solitary waves and undular bores. A model for advection-diffusion transport of tracers in open channel flow is also presented. The simulation results are compared against an analytical solution and published laboratory data, field data and theoretical results. It is demonstrated that the Lagrangian moving grid eliminates numerical diffusion and oscillations; the model is dynamically adaptive, providing higher resolution under the wave by compressing the parcels (grid). It also allows flow over dry beds and moving boundaries to be handled efficiently. The hydrostatic model results have shown that the model accurately simulates wave propagation and non-linear steepening until wave breaking. The model is successfully applied to simulate flow and lateral interactions in a compound channel and flood wave movement in a natural river. The non-hydrostatic model has successfully reproduced the general features of solitary waves such as the balance between non-linearity and wave dispersion and non-linear interactions of two solitary waves by phase-shift. Also, the model successfully reproduced undular bores (high frequency short waves) from a long wave and the predicted maximum height of the leading wave agreed very well with the published results. It is shown that the simple second order accurate Lagrangian scheme efficiently simulates dispersive waves without any numerical diffusion. Lagrangian modeling of advection-diffusion transport of Gaussian tracer distributions, top hat tracer distributions and steep fronts (step function) in steady, uniform flow has provided exact results and has shown that the scheme allows the use of a large time step without any numerical diffusion and oscillations, including for the advection of steep fronts. The scheme can handle large Courant numbers (results are presented for Cr = 0 to 20) and the entire range of grid Peclet numbers from zero to infinity. The model is successfully applied to tracer transport due to flow induced by simple waves, solitary waves and undular bores
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Books on the topic "Streamflow – Swaziland – Mathematical models"

1

Sikonia, W. G. Mass-conserving method of characteristics for streamflow modeling. Washington, DC: U.S. G.P.O., 1992.

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Miroslav, Procházka. Matematické modelování průměrných měsíčních průtoků. Praha: Výzkumný ústav vodohospodářský ve Státním zemědělském nakl., 1989.

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Kelly, Valerie J. Western pilot study: Ecologically relevant quantification of streamflow regimes in western streams. Washington, DC: United States Environmental Protection Agency, Office of Research and Development, 2006.

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Boczar, Jerzy. Zastosowanie modelu matematycznego do prognozy jakości wody w rzece w warunkach przepływów dwukierunkowych. Szczecin: Wydawn. Uczelniane Politechniki Szczecińskiej, 1991.

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Ries, Kernell G. Methods for estimating low-flow statistics for Massachusetts streams. Northborough, MA: U.S. Dept. of the Interior, U.S. Geological Survey, 2000.

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Hess, Glen W. Simulation of streamflow, middle Humboldt River, north-central Nevada. Carson City, Nev: U.S. Department of the Interior, U.S. Geological Survey, 2002.

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Ries, Kernell G. Methods for estimating low-flow statistics for Massachusetts streams. Northborough, Mass: U.S. Dept. of the Interior, U.S. Geological Survey, 2000.

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Ries, Kernell G. Methods for estimating low-flow statistics for Massachusetts streams. Northborough, Mass: U.S. Dept. of the Interior, U.S. Geological Survey, 2000.

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Koltun, G. F. Techniques for estimating selected streamflow characteristics of rural, unregulated streams in Ohio. Columbus, Ohio (6480 Doubletree Ave., Columbus 43229-1111): U.S. Dept. of the Interior, U.S. Geological Survey, 2001.

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Duwe, K. Mathematisches Modell des Alpenrhein-Einstroms in den Bodensee. [Reichenau]: Internationale Gewässerschutzkommission für den Bodensee, 1999.

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