Relevant bibliographies by topics / Hydrological modelling

Academic literature on the topic 'Hydrological modelling'

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Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Hydrological modelling"

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Javadinejad, Safieh. "A review on homogeneity across hydrological regions." Resources Environment and Information Engineering 3, no. 1 (2021): 124–37. http://dx.doi.org/10.25082/reie.2021.01.004.

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Hydrologic classification is the method of scientifically arranging streams, rivers or catchments into groups with the most similarity of flow regime features and use it to recognize hydrologically homogenous areas. Previous homogeneous attempts were depended on overabundance of hydrologic metrics that considers features of variability of flows that are supposed to be meaningful in modelling physical progressions in the basins. This research explains the techniques of hydrological homogeneity through comparing past and existing methods; in addition it provides a practical framework for hydrological homogeneity that illustrates serious elements of the classification process.
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Kambarbekov, G. М., and A. Ye Baimaganbetov. "USING ARTIFICIAL INTELLIGENCE FOR HYDROLOGICAL MODELLING." Geography and water resources, no. 1 (March 28, 2024): 58–62. http://dx.doi.org/10.55764/2957-9856/2024-1-58-62.8.

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Hydrological modelling plays a critical role in managing water resources, especially in arid and semi-arid regions where water scarcity is a major challenge. With the emergence of artificial intelligence (AI), hydrological modelling has experienced a significant transformation in recent years. This paper reviews the recent advances in AI-based hydrological modelling and examines its potential applications in water resource management. The study highlights the role of AI in enhancing the accuracy of hydrological models and facilitating more efficient and sustainable water management practices. The results suggest that AI-based hydrological models have the potential to revolutionize the way water resources are managed, and that future research in this area is warranted.
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Nordin, N. A. S., Z. Hassan, N. M. Noor, A. N. Kamarudzaman, and A. S. A. Ahmadni. "Assessing Hydrological Response in the Timah-Tasoh Reservoir Sub-Catchments: Calibration and Validation using the HEC-HMS Model." IOP Conference Series: Earth and Environmental Science 1303, no. 1 (February 1, 2024): 012029. http://dx.doi.org/10.1088/1755-1315/1303/1/012029.

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Abstract Hydrological modelling is a tool that is frequently used for assessing the hydrological response of a basin as a result of precipitation. It is also a vital component as water resources and environmental planning management. The study deals with calibrating and validating the hydrological response in the sub-catchments of the Timah-Tasoh reservoir using the hydrological model named Hydrologic Engineering Center – Hydrologic Modelling System (HEC-HMS). This study uses the SCS Curve Number, the SCS Unit Hydrograph, the constant monthly baseflow, and lag routing for the model development. The model was simulated for ten (10) years for calibration and nine (9) years for validation. The model calibration and validation efficiency were assessed using the coefficient of correlation (R). The findings show that the HEC-HMS model performs satisfactorily in simulating the observed daily inflow series, with the R-value of 0.4902-0.5139 during calibration and 0.5047-0.5559 during validation process. Thus, the result obtained from this study can be used as a preliminary development of hydrological modelling of the catchment of the Timah-Tasoh reservoir and can be used for extend application such as water inflow forecasting, impact of land use to the reservoir and others.
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Tyralis, Hristos, and Georgia Papacharalampous. "Quantile-Based Hydrological Modelling." Water 13, no. 23 (December 3, 2021): 3420. http://dx.doi.org/10.3390/w13233420.

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Predictive uncertainty in hydrological modelling is quantified by using post-processing or Bayesian-based methods. The former methods are not straightforward and the latter ones are not distribution-free (i.e., assumptions on the probability distribution of the hydrological model’s output are necessary). To alleviate possible limitations related to these specific attributes, in this work we propose the calibration of the hydrological model by using the quantile loss function. By following this methodological approach, one can directly simulate pre-specified quantiles of the predictive distribution of streamflow. As a proof of concept, we apply our method in the frameworks of three hydrological models to 511 river basins in the contiguous US. We illustrate the predictive quantiles and show how an honest assessment of the predictive performance of the hydrological models can be made by using proper scoring rules. We believe that our method can help towards advancing the field of hydrological uncertainty.
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Chadalawada, Jayashree, and Vladan Babovic. "Review and comparison of performance indices for automatic model induction." Journal of Hydroinformatics 21, no. 1 (December 6, 2017): 13–31. http://dx.doi.org/10.2166/hydro.2017.078.

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Abstract One of the more perplexing challenges for the hydrologic research community is the need for development of coupled systems involving integration of hydrologic, atmospheric and socio-economic relationships. Given the demand for integrated modelling and availability of enormous data with varying degrees of (un)certainty, there exists growing popularity of data-driven, unified theory catchment scale hydrological modelling frameworks. Recent research focuses on representation of distinct hydrological processes using mathematical model components that vary in a controlled manner, thereby deriving relationships between alternative conceptual model constructs and catchments’ behaviour. With increasing computational power, an evolutionary approach to auto-configuration of conceptual hydrological models is gaining importance. Its successful implementation depends on the choice of evolutionary algorithm, inventory of model components, numerical implementation, rules of operation and fitness functions. In this study, genetic programming is used as an example of evolutionary algorithm that employs modelling decisions inspired by the Superflex framework to automatically induce optimal model configurations for the given catchment dataset. The main objective of this paper is to identify the effects of entropy, hydrological and statistical measures as optimization objectives on the performance of the proposed approach based on two synthetic case studies of varying complexity.
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Kunstmann, H., J. Krause, and S. Mayr. "Inverse distributed hydrological modelling of alpine catchments." Hydrology and Earth System Sciences Discussions 2, no. 6 (December 1, 2005): 2581–623. http://dx.doi.org/10.5194/hessd-2-2581-2005.

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Abstract. Even in physically based distributed hydrological models, various remaining parameters must be estimated for each sub-catchment. This can involve tremendous effort, especially when the number of sub-catchments is large and the applied hydrological model is computationally expensive. Automatic parameter estimation tools can significantly facilitate the calibration process. Hence, we combined the nonlinear parameter estimation tool PEST with the distributed hydrological model WaSiM. PEST is based on the Gauss-Marquardt-Levenberg method, a gradient-based nonlinear parameter estimation algorithm. WaSiM is a fully distributed hydrological model using physically based algorithms for most of the process descriptions. WaSiM was applied to the alpine/prealpine Ammer River catchment (southern Germany, 710 km2) in a 100×100 m2 horizontal resolution. The catchment is heterogeneous in terms of geology, pedology and land use and shows a complex orography (the difference of elevation is around 1600 m). Using the developed PEST-WaSiM interface, the hydrological model was calibrated by comparing simulated and observed runoff at eight gauges for the hydrologic year 1997 and validated for the hydrologic year 1993. For each sub-catchment four parameters had to be calibrated: the recession constants of direct runoff and interflow, the drainage density, and the hydraulic conductivity of the uppermost aquifer. Additionally, five snowmelt specific parameters were adjusted for the entire catchment. Altogether, 37 parameters had to be calibrated. Additional a priori information (e.g. from flood hydrograph analysis) narrowed the parameter space of the solutions and improved the non-uniqueness of the fitted values. A reasonable quality of fit was achieved. Discrepancies between modelled and observed runoff were also due to the small number of meteorological stations and corresponding interpolation artefacts in the orographically complex terrain. A detailed covariance analysis was performed allowing to derive confidence bounds for all estimated parameters. The correlation between the estimated parameters was in most cases negligible, showing that parameters were estimated independently from each other.
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Kunstmann, H., J. Krause, and S. Mayr. "Inverse distributed hydrological modelling of Alpine catchments." Hydrology and Earth System Sciences 10, no. 3 (June 7, 2006): 395–412. http://dx.doi.org/10.5194/hess-10-395-2006.

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Abstract. Even in physically based distributed hydrological models, various remaining parameters must be estimated for each sub-catchment. This can involve tremendous effort, especially when the number of sub-catchments is large and the applied hydrological model is computationally expensive. Automatic parameter estimation tools can significantly facilitate the calibration process. Hence, we combined the nonlinear parameter estimation tool PEST with the distributed hydrological model WaSiM. PEST is based on the Gauss-Marquardt-Levenberg method, a gradient-based nonlinear parameter estimation algorithm. WaSiM is a fully distributed hydrological model using physically based algorithms for most of the process descriptions. WaSiM was applied to the alpine/prealpine Ammer River catchment (southern Germany, 710 km2 in a 100×100 m2 horizontal resolution. The catchment is heterogeneous in terms of geology, pedology and land use and shows a complex orography (the difference of elevation is around 1600 m). Using the developed PEST-WaSiM interface, the hydrological model was calibrated by comparing simulated and observed runoff at eight gauges for the hydrologic year 1997 and validated for the hydrologic year 1993. For each sub-catchment four parameters had to be calibrated: the recession constants of direct runoff and interflow, the drainage density, and the hydraulic conductivity of the uppermost aquifer. Additionally, five snowmelt specific parameters were adjusted for the entire catchment. Altogether, 37 parameters had to be calibrated. Additional a priori information (e.g. from flood hydrograph analysis) narrowed the parameter space of the solutions and improved the non-uniqueness of the fitted values. A reasonable quality of fit was achieved. Discrepancies between modelled and observed runoff were also due to the small number of meteorological stations and corresponding interpolation artefacts in the orographically complex terrain. Application of a 2-dimensional numerical groundwater model partly yielded a slight decrease of overall model performance when compared to a simple conceptual groundwater approach. Increased model complexity therefore did not yield in general increased model performance. A detailed covariance analysis was performed allowing to derive confidence bounds for all estimated parameters. The correlation between the estimated parameters was in most cases negligible, showing that parameters were estimated independently from each other.
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Bhattacharya, Biswa, Maurizio Mazzoleni, and Reyne Ugay. "Flood Inundation Mapping of the Sparsely Gauged Large-Scale Brahmaputra Basin Using Remote Sensing Products." Remote Sensing 11, no. 5 (March 1, 2019): 501. http://dx.doi.org/10.3390/rs11050501.

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Sustainable water management is one of the important priorities set out in the Sustainable Development Goals (SDGs) of the United Nations, which calls for efficient use of natural resources. Efficient water management nowadays depends a lot upon simulation models. However, the availability of limited hydro-meteorological data together with limited data sharing practices prohibits simulation modelling and consequently efficient flood risk management of sparsely gauged basins. Advances in remote sensing has significantly contributed to carrying out hydrological studies in ungauged or sparsely gauged basins. In particular, the global datasets of remote sensing observations (e.g., rainfall, evaporation, temperature, land use, terrain, etc.) allow to develop hydrological and hydraulic models of sparsely gauged catchments. In this research, we have considered large scale hydrological and hydraulic modelling, using freely available global datasets, of the sparsely gauged trans-boundary Brahmaputra basin, which has an enormous potential in terms of agriculture, hydropower, water supplies and other utilities. A semi-distributed conceptual hydrological model was developed using HEC-HMS (Hydrologic Modelling System from Hydrologic Engineering Centre). Rainfall estimates from Tropical Rainfall Measuring Mission (TRMM) was compared with limited gauge data and used in the simulation. The Nash Sutcliffe coefficient of the model with the uncorrected rainfall data in calibration and validation were 0.75 and 0.61 respectively whereas the similar values with the corrected rainfall data were 0.81 and 0.74. The output of the hydrological model was used as a boundary condition and lateral inflow to the hydraulic model. Modelling results obtained using uncorrected and corrected remotely sensed products of rainfall were compared with the discharge values at the basin outlet (Bahadurabad) and with altimetry data from Jason-2 satellite. The simulated flood inundation maps of the lower part of the Brahmaputra basin showed reasonably good match in terms of the probability of detection, success ratio and critical success index. Overall, this study demonstrated that reliable and robust results can be obtained in both hydrological and hydraulic modelling using remote sensing data as the only input to large scale and sparsely gauged basins.
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Donnelly, Chantal, Jörgen Rosberg, and Kristina Isberg. "A validation of river routing networks for catchment modelling from small to large scales." Hydrology Research 44, no. 5 (October 27, 2012): 917–25. http://dx.doi.org/10.2166/nh.2012.341.

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Underpinning all hydrological simulations is an estimate of the catchment area upstream of a point of interest. Locally, the delineation of a catchment and estimation of its area is usually done using fine scale maps and local knowledge, but for large-scale hydrological modelling, particularly continental and global scale modelling, this level of detailed data analysis is not practical. For large-scale hydrological modelling, remotely sensed and hydrologically conditioned river routing networks, such as HYDRO1k and HydroSHEDS, are often used. This study evaluates the accuracy of the accumulated upstream area in each gridpoint given by the networks. This is useful for evaluating the ability of these data sets to delineate catchments of varying scale for use in hydrological models. It is shown that the higher resolution HydroSHEDS data set gives better results than the HYDRO1k data set and that accuracy decreases with decreasing basin scale. In ungauged basins, or where other local catchment area data are not available, the validation made in this study can be used to indicate the likelihood of correctly delineating catchments of different scales using these river routing networks.
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Liu, Yue, Jian-yun Zhang, Amgad Elmahdi, Qin-li Yang, Xiao-xiang Guan, Cui-shan Liu, Rui-min He, and Guo-qing Wang. "Transferability of a lumped hydrologic model, the Xin'anjiang model based on similarity in climate and geography." Water Supply 21, no. 5 (February 25, 2021): 2191–201. http://dx.doi.org/10.2166/ws.2021.055.

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Abstract Hydrological experiments are essential to understanding the hydrological cycles and promoting the development of hydrologic models. Model parameter transfers provide a new way of doing hydrological forecasts and simulations in ungauged catchments. To study the transferability of model parameters for hydrological modelling and the influence of parameter transfers on hydrological simulations, the Xin'anjiang model (XAJ model), which is a lumped hydrologic model based on a saturation excess mechanism that has been widely applied in different climate regions of the world, was applied to a low hilly catchment in eastern China, the Chengxi experimental watershed (CXEW). The suitability of the XAJ model was tested in the eastern branch catchment of CXEW and the calibrated model parameters of the eastern branch catchment were then transferred to the western branch catchment and the entire watershed of the CXEW. The results show that the XAJ model performs well for the calibrated eastern branch catchment at both daily and monthly scales on hydrological modelling with the NSEs over 0.6 and the REs less than 2.0%. Besides, the uncalibrated catchments of the western branch catchment and the entire watershed of the CSEW share similarities in climate (the precipitation) and geography (the soil texture and vegetation cover) with the calibrated catchment, the XAJ model and the transferred model parameters can capture the main features of the hydrological processes in both uncalibrated catchments (western catchments and the entire watershed). This transferability of the model is useful for a scarce data region to simulate the hydrological process and its forecasting.
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Dissertations / Theses on the topic "Hydrological modelling"

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Hammond, Michael John. "Uncertainty issues in hydrological modelling." Thesis, University of Bristol, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435429.

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Whitelaw, A. S. "Hydrological modelling using variable source areas." Thesis, University of Bristol, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.384524.

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Vitolo, Claudia. "Exploring data mining for hydrological modelling." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/30773.

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Technological advances in computer science, namely cloud computing and data mining, are reshaping the way the world looks at data. Data are becoming the drivers of discoveries and strategic developments. In environmental sciences, for instance, big volumes of information are produced by monitoring networks, satellites and model simulations and are processed to uncover hidden patterns, correlations and trends to, ultimately, support policy and decision making. Hydrologists, in particular, use models to simulate river discharges and estimate the concentration of pollutants as well as the risk of floods and droughts. The very first step of any hydrological modelling exercise consists of selecting an appropriate model. However, the choice is often made by the modeller based on his/her expertise rather than on the model's suitability to reproduce the most important processes for the area under study. Since this approach defeats the ''scientific method'' for its lack of reproducibility and consistency across experts as well as locations, a shift towards a data-driven selection process is deemed necessary. This work presents the design, development and testing results of a completely novel data mining algorithm, called AMCA, able to automatically identify the most suitable model configurations for a given catchment, using minimum data requirements and an inventory of model structures. In the design phase a transdisciplinary approach was adopted, borrowing techniques from the fields of machine learning, signal processing and marketing. The algorithm was tested on the Severn at Plynlimon flume catchment, in the Plynlimon study area (Wales, UK). This area was selected because of its reliable measurements and the homogeneity of its soils and vegetation. The Framework for Understanding Structural Errors (FUSE) was used as sample model inventory, but the methodology can easily be adapted to others, including more sophisticated model structures. The model configuration problem, that the AMCA attempts to solve, can be categorised as ''fully unsupervised'' if there is no prior knowledge of interactions and relationships amongst observed data at a certain location and available model structures and parameters. Therefore, the first set of tests was run on a synthetic dataset to evaluate the algorithm's performance against known outcomes. Most of the component of the synthetic model structure were clearly identified by the AMCA, which allowed to proceed with further testing using observed data. Using real observations, the AMCA efficiently selected the most suitable model structures and, when coupled with association rule mining techniques, could also identify optimal parameter ranges. The performance of the ensemble suggested by the combination of AMCA and association rules was calibrated and validated against four widely used models (Topmodel, ARNOVIC, PRMS and Sacramento). The ensemble configuration always returned the best average efficiency, characterised by the narrowest spread and, therefore, lowest uncertainty. As final application, the full set of FUSE models was used to predict the effect of land use changes on catchment flows. The predictive uncertainty improved significantly when the prior distributions of model structures and parameters were conditioned using the AMCA approach. It was also noticed that such improvement is due to constrains applied to both model and parameter space, however the parameter space seems to contribute more. These results confirm that a considerable part of the uncertainty in prediction is due to the definition of the prior choice of the model configuration and that more objective ways to constrain the prior using formal data-driven techniques are needed. AMCA is, however, a procedure that can only be applied to gauged catchment. Future experiments could test whether AMCA configurations could be regionalised or transferred to ungauged catchments on the basis of catchment characteristics.
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Refsgaard, Jens Christian. "Hydrological modelling and river basin management." København : GEUS, 2007. http://www.geus.dk/program-areas/water/denmark/rapporter/geus_special_rap_1_2007.pdf.

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Thyer, Mark Andrew. "Modelling long-term persistence in hydrological time series." Diss., 2000, 2000. http://www.newcastle.edu.au/services/library/adt/public/adt-NNCU20020531.035349/index.html.

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Tsegaw, Aynalem Tassachew. "Short term Distributed Hydrological Modelling of Gaula Catchment." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for vann- og miljøteknikk, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-12597.

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Testing and trying out of the applicability and utility of watershed hydrological models in different; catchment sizes, hydro-geologic conditions, soil conditions and with different time resolutions is necessary for a range of spatial scales to assess the utility of these models in water shade management means like flood protection, land slide prevention, erosion control etc. The main purpose of this thesis is to tryout TOPLAND hydrological model, i.e. the new developments to the LANDPINE model allowing for using TOPMODEL distributed runoff generation, with different precipitation input methods. It focuses on the simulation of precipitation events with time resolution of one hour. Short term time resolution event simulations are important to capture flow events in small and large catchments; since these events are responsible for local flood, land slide etc., especially in areas where they are strongly localized. The model simulation has been carried out using three different precipitation input methods; gauge IDW interpolation, gauge simulated and radar based precipitation data for the selected hourly events of 2006 (27-07-2006 00:00 to 29-07-2006 23:00) and 2009 (19-07-2009 05:00 to 25-07-2009 20:00). 2009 Event The 2009 event is characterized by high peak and uniformly distributed event. For the bias corrected radar precipitation, the objective method of result comparison showed an excellent correspondence between observed and simulated flows with NS (R2) of 0.98, correlation (R2) of 0.98 and PBIAS of 0.48% at the calibration point (Gaulfoss). The bias corrected radar precipitation also showed a very good performance of the model at the interior uncalibrated gauging stations with average values of NS (R2) 0.85, correlation (R2) 0.93 and PBIAS 16.6% of the HugdalBru, Lillebudal and Eggafoss gauging stations. The gauge IDW interpolation and gauge simulated precipitation input methods also showed a very good performance of the model both at the calibration and internal uncalibrated gauging stations. 2006 Event The 2006 event is characterized by low peak and unevenly distributed (localized) event. The bias corrected radar precipitation is the only precipitation input method that made possible for calibration of the model. The objective method of result comparison showed a very good result for NS (R2) of 0.96, correlation (R2) of 0.97 and PBIAS of 5.1% at the calibration point (Gaulfoss). At the internal uncalibrated gauging stations, the correlation and PBIAS showed a good performance with average correlation (R2) of 0.77 and PBIAS of 21.3% and a poor average NS (R2) of 0.3.
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Cardoso, Lopes de Almeida Susana Margarida. "The value of regionalised information for hydrological modelling." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/28086.

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In many areas of the world, the absence of streamflow data to calibrate hydrological models limits the ability to make reliable streamflow predictions. Whilst a large and increasing number of regions are insufficiently gauged, there are also many highly monitored catchments. Transferring the knowledge gained in data-rich areas to data-scarce regions offers possibilities to overcome the absence of streamflow observations. In this thesis knowledge is transferred in the form of signatures, which reflect hydrological response characteristics of a particular catchment. Several signatures may be required to capture different aspects of catchment functional behaviour. Using a large dataset of catchments, observed signatures are regressed against physical and climatic catchment descriptors. Signatures for an ungauged location with known descriptors are then estimated utilising the derived relationships. A Bayesian procedure is subsequently used to condition a conceptual model for the ungauged catchment on the estimated signatures with formal uncertainty estimation. Particular challenges related to the Bayesian approach include the selection of signatures, and specification of the prior distribution and the likelihood functions. A methodological development is based on an initial transformation of the commonly adopted uniform parameter prior into a prior that maps to a uniform signature distribution, aimed at cases where limited prior knowledge regarding the model structure adequacy and the parameters distribution exist. The suggested methodology contributes to improved estimation of response signatures, and is particularly relevant when regionalised information is highly uncertain. A further contribution of this thesis refers to the integration of several regionalised signatures into the model, accounting for the inter-signature error covariance structure. By increasing the number and regionalisation quality of signatures in the conditioning process, better predictions are obtained. Additionally, the consideration of the inter-signature error structure may improve the results when correlations between errors are shown to be strong. When regionalised signatures are integrated into the model, it is shown that model structural inadequacy has a strong effect on the prediction quality.
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Blasone, Roberta-Serena. "Parameter estimation and uncertainty assessment in hydrological modelling." Kgs. Lyngby, 2007. http://www.er.dtu.dk/publications/fulltext/2007/MR2007-105.pdf.

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Harvey, David Peter. "A generic modelling framework component for hydroinformatics systems." Thesis, University of Bristol, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.271764.

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Selling, Benjamin. "Modelling Hydrological Impacts of Forest Clearcutting through Parameter Regionalization." Thesis, Uppsala universitet, Luft-, vatten och landskapslära, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-267402.

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The aim of this thesis was to test and evaluate whether parameter regionalization of a hydrological model can be used to model the impact of forest clearcutting on streamflow in Sweden. This is an important task to be able to perform water management and impact assessments adequately. The HBV conceptual rainfall-runoff model was applied for 218 Swedish catchments of different sizes that were spread across the country and covered a wide range of different forest cover percentages. The modelling approach included calibration of the model for each catchment using a genetic algorithm and then associating the resulting optimal parameter values with the percentage of forest cover. The obtained relationship between different model parameters and forest cover was validated with help of a paired catchment study site in northern Sweden where a clear cut was done in 2006: calibrated optimal parameter sets of pre- and post-clearcutting conditions were compared to parameter sets obtained from the Sweden-wide analysis. Correlations were found for about half of the fifteen hydrological model parameters, but the validation with the paired catchment study site could only partially confirm these obtained relationships. The results suggest that the adopted parameter regionalization approach is too basic. However, some of the results seem promising and emphasize the need for further research and development of the approach to provide a more reasonable method to model the impact of forest clearcutting on streamflow.
Det huvudsakliga målet med detta examensarbete var att testa och utvärdera om parameterregionalisering av en hydrologisk modell kan vara en lämplig metod för att modellera och kvantifiera påverkan från skogsavverkning på vattenbalansen i Sverige. Detta är en viktig uppgift för att kunna hantera våra vattenresurser och utföra konsekvensanalyser på ett tillfredsställande sätt. En konceptuell hydrologisk modell tillämpades på 218 avrinningsområden av olika storlekar och som var geografiskt utspridda i hela Sverige där även andelen skog i avrinningsområdena hade ett brett spektrum. Den använda modelleringsmetoden innefattade kalibrering av varje avrinningsområde genom att använda en genetisk algoritm, varefter de optimala parametervärdeana korrelerades mot andelen skog i avrinningsområdet. Idén med denna metod är att använda dessa potentiella samband för att justera modellparametrarna och därmed simulera en skogsavverkning. De erhållna sambanden mellan modellparametrarna och skogstäcket validerades med hjälp av data från en försöksstudie i norra Sverige där en skogsavverkning gjordes under 2006. Skillnaden mellan de bäst fungerande parametervärdena före och efter skogsavverkningen jämfördes med de tidigare sambanden från andra avrinningsområden i Sverige. Signifikant korrelation hittades för ungefär hälften av de 15 hydrologiska modellparametrarna, men valideringen mot den riktiga skogsavverkningen kunde bara delvis bekräfta de erhållna sambanden. Resultaten visar att detta sätt att använda parameterregionalisering antagligen är för grundläggande. Vissa resultat är ändå lovande och fortsatt forskning och utvidgning av metoden är nödvändig för att kunna tillhandahålla en rimlig metod för att kvantifiera en skogsavverknings effekter på vattenbalansen.
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Books on the topic "Hydrological modelling"

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B, Abbott Michael, and Refsgaard Jens Christian, eds. Distributed hydrological modelling. Dordrecht: Kluwer Academic, 1996.

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Abbott, Michael B., and Jens Christian Refsgaard, eds. Distributed Hydrological Modelling. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-0257-2.

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Thangarajan, M., Th Surendranath Singh, and L. Minaketan Singh. Modelling hydrological system. Imphal: Manipur Science & Technology Council, 2008.

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Remesan, Renji, and Jimson Mathew. Hydrological Data Driven Modelling. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09235-5.

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Viviroli, Daniel. The hydrological modelling system PREVAH. Bern: University of Berne, Switzerland, Institute of Geographiy, 2007.

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D, Kalma Jetse, and Sivapalan Murugesu, eds. Scale issues in hydrological modelling. Chichester: Wiley, 1995.

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Sorooshian, Soroosh, Kuo-Lin Hsu, Erika Coppola, Barbara Tomassetti, Marco Verdecchia, and Guido Visconti, eds. Hydrological Modelling and the Water Cycle. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-77843-1.

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Singh, Vijay P., Rajendra Singh, Pranesh Kumar Paul, Deepak Singh Bisht, and Srishti Gaur. Hydrological Processes Modelling and Data Analysis. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1316-5.

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Wheater, Howard, Soroosh Sorooshian, and K. D. Sharma, eds. Hydrological Modelling in Arid and Semi-Arid Areas. Cambridge: Cambridge University Press, 2007. http://dx.doi.org/10.1017/cbo9780511535734.

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Świątek, Dorota, and Tomasz Okruszko, eds. Modelling of Hydrological Processes in the Narew Catchment. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19059-9.

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Book chapters on the topic "Hydrological modelling"

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BEVEN, KEITH, JAMES BATHURST, ENDA O'CONNELL, IAN LITTLEWOOD, JIM BLACKIE, and MARK ROBINSON. "Hydrological Modelling." In Progress in Modern Hydrology: Past, Present and Future, 216–39. Chichester, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781119074304.ch7.

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Hansen, M., and P. Gravesen. "Geological Modelling." In Distributed Hydrological Modelling, 193–214. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0257-2_10.

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Thorsen, M., J. Feyen, and M. Styczen. "Agrochemical Modelling." In Distributed Hydrological Modelling, 121–41. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0257-2_7.

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Lørup, J. K., and M. Styczen. "Soil Erosion Modelling." In Distributed Hydrological Modelling, 93–120. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0257-2_6.

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Refsgaard, J. C., and M. B. Abbott. "The Role of Distributed Hydrological Modelling in Water Resources Management." In Distributed Hydrological Modelling, 1–16. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0257-2_1.

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Deckers, F., and C. B. M. Te Stroet. "Use Of GIS And Database with Distributed Modelling." In Distributed Hydrological Modelling, 215–32. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0257-2_11.

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Sørensen, H. R., J. Klucovska, J. Topolska, T. Clausen, and J. C. Refsgaard. "An Engineering Case Study - Modelling the Influences of Gabcikovo Hydropower Plant on the Hydrology and Ecology in the Slovakian Part of the River Branch System of Zitny Ostrov." In Distributed Hydrological Modelling, 233–53. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0257-2_12.

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Beven, Keith J. "A Discussion of Distributed Hydrological Modelling." In Distributed Hydrological Modelling, 255–78. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0257-2_13.

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Refsgaard, J. C., B. Storm, and M. B. Abbott. "Comment on ’A Discussion of Distributed Hydrological Modelling’ by K. Beven." In Distributed Hydrological Modelling, 279–87. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0257-2_14.

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Beven, Keith J. "Response to comments on ‘a discussion of distributed hydrological modelling’ by j c refsgaard et al." In Distributed Hydrological Modelling, 289–95. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0257-2_15.

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Conference papers on the topic "Hydrological modelling"

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"Disentangling hydrological mixtures." In 25th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2023. http://dx.doi.org/10.36334/modsim.2023.athukorala550.

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Allahyaripour, Forough, Mohammad Azmi, Shahab Araghinejad, and Reza Aasemi. "Probabilistic Multivariate Forecasting of Hydrological Variables." In Applied Simulation and Modelling. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.715-011.

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"Modelling hydrological change due to wildfires." In 24th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2021. http://dx.doi.org/10.36334/modsim.2021.j8.partington.

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"Applying rainfall ensembles to explore hydrological uncertainty." In 23rd International Congress on Modelling and Simulation (MODSIM2019). Modelling and Simulation Society of Australia and New Zealand, 2019. http://dx.doi.org/10.36334/modsim.2019.k14.kumari2.

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"Process-based hydrological modelling in different permafrost environments." In 22nd International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2017. http://dx.doi.org/10.36334/modsim.2017.l9.lebedeva.

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"Modelling hydrological impact of remotely sensed vegetation change." In 25th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2023. http://dx.doi.org/10.36334/modsim.2023.zheng658.

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"Exposing a Hydrological Simulation Model on the web." In 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.c4.leighton.

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"Building a modern, all-purpose hydrological forecasting system." In 25th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2023. http://dx.doi.org/10.36334/modsim.2023.matala.

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Napolitano, Francesco, and Fabio Russo. "Preface of the “Mathematical Modelling of Hydrological Sciences”." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2015 (ICNAAM 2015). Author(s), 2016. http://dx.doi.org/10.1063/1.4952214.

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Koshinchanov, Georgy, and Snezhanka Balabanova. "Hydrological modelling using remote sensing techniques in Bulgaria." In Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019), edited by Giorgos Papadavid, Kyriacos Themistocleous, Silas Michaelides, Vincent Ambrosia, and Diofantos G. Hadjimitsis. SPIE, 2019. http://dx.doi.org/10.1117/12.2533155.

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Reports on the topic "Hydrological modelling"

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de Vries, Sander C. WFLOW_LINTUL: raster-based simulation of rice growth in the WFLOW/OpenStreams hydrological modelling platform : user manual and description of core model code. Wageningen: Wageningen Research (WR) business unit Agrosystems Research, 2018. http://dx.doi.org/10.18174/461276.

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de Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331097.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model development. Canada in 3D (C3D) is a vision and road map for transforming the Geological Survey of Canada's (GSC) work practice by leveraging emerging 3D technologies. Primarily the transformation from 2D geological mapping, to a well-structured 3D modelling practice that is both data-driven and knowledge-driven. It is tempting to imagine that advanced 3D computational methods, coupled with Artificial Intelligence and Big Data tools will automate the bulk of this process. To effectively apply these methods there is a need, however, for data to be in a well-organized, classified, georeferenced (3D) format embedded with key information, such as spatial-temporal relations, and earth process knowledge. Another key challenge for C3D is the relative infancy of 3D geoscience technologies for geological inference and 3D modelling using sparse and heterogeneous regional geoscience information, while preserving the insights and expertise of geoscientists maintaining scientific integrity of digital products. In most geological surveys, there remains considerable educational and operational challenges to achieve this balance of digital automation and expert knowledge. Emerging from the last two decades of research are more efficient workflows, transitioning from cumbersome, explicit (manual) to reproducible implicit semi-automated methods. They are characterized by integrated and iterative, forward and reverse geophysical modelling, coupled with stratigraphic and structural approaches. The full impact of research and development with these 3D tools, geophysical-geological integration and simulation approaches is perhaps unpredictable, but the expectation is that they will produce predictive, instructive models of Canada's geology that will be used to educate, prioritize and influence sustainable policy for stewarding our natural resources. On the horizon are 3D geological modelling methods spanning the gulf between local and frontier or green-fields, as well as deep crustal characterization. These are key components of mineral systems understanding, integrated and coupled hydrological modelling and energy transition applications, e.g. carbon sequestration, in-situ hydrogen mining, and geothermal exploration. Presented are some case study examples at a range of scales from our efforts in C3D.
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de Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331871.

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Abstract:
Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model development. Canada in 3D (C3D) is a vision and road map for transforming the Geological Survey of Canada's (GSC) work practice by leveraging emerging 3D technologies. Primarily the transformation from 2D geological mapping, to a well-structured 3D modelling practice that is both data-driven and knowledge-driven. It is tempting to imagine that advanced 3D computational methods, coupled with Artificial Intelligence and Big Data tools will automate the bulk of this process. To effectively apply these methods there is a need, however, for data to be in a well-organized, classified, georeferenced (3D) format embedded with key information, such as spatial-temporal relations, and earth process knowledge. Another key challenge for C3D is the relative infancy of 3D geoscience technologies for geological inference and 3D modelling using sparse and heterogeneous regional geoscience information, while preserving the insights and expertise of geoscientists maintaining scientific integrity of digital products. In most geological surveys, there remains considerable educational and operational challenges to achieve this balance of digital automation and expert knowledge. Emerging from the last two decades of research are more efficient workflows, transitioning from cumbersome, explicit (manual) to reproducible implicit semi-automated methods. They are characterized by integrated and iterative, forward and reverse geophysical modelling, coupled with stratigraphic and structural approaches. The full impact of research and development with these 3D tools, geophysical-geological integration and simulation approaches is perhaps unpredictable, but the expectation is that they will produce predictive, instructive models of Canada's geology that will be used to educate, prioritize and influence sustainable policy for stewarding our natural resources. On the horizon are 3D geological modelling methods spanning the gulf between local and frontier or green-fields, as well as deep crustal characterization. These are key components of mineral systems understanding, integrated and coupled hydrological modelling and energy transition applications, e.g. carbon sequestration, in-situ hydrogen mining, and geothermal exploration. Presented are some case study examples at a range of scales from our efforts in C3D.
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Frey, S., G. Stonebridge, S. Berg, D. Steinmoeller, D. Lapen, O. Khader, A. Erler, and E. Sudicky. Applications of a regional-scale integrated modelling platform towards watershed-level hydrologic insights. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2019. http://dx.doi.org/10.4095/313583.

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The Modelling the Flow of the Mekong. Vientiane, Lao PDR: Mekong River Commission Secretariat, November 2009. http://dx.doi.org/10.52107/mrc.ajhz5z.

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Annual Mekong Hydrology, Flood and Drought Report 2018. Vientiane, Lao PDR: Mekong River Commission Secretariat, July 2020. http://dx.doi.org/10.52107/mrc.ajg3u4.

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The report will replaces the Annual Mekong Flood Report to provide an annual summary on different hydrological subjects, ranging from flood, hydrology, and drought recognition to monitoring and early warning, remote sensing, modelling, and water management.
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