Journal articles on the topic 'Hydrological modelling'
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1
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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Romanowicz, Renata. "Scale issues in hydrological modelling." Engineering Structures 18, no. 11 (November 1996): 889. http://dx.doi.org/10.1016/0141-0296(96)84818-8.
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Trudgill, Stephen, Jason Ball, and R. I. Ferguson. "Excel modelling of hydrological systems." Earth Surface Processes and Landforms 19, no. 9 (1994): 815–17. http://dx.doi.org/10.1002/esp.3290190908.
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Vinogradov, Yu B., O. M. Semenova, and T. A. Vinogradova. "An approach to the scaling problem in hydrological modelling: the deterministic modelling hydrological system." Hydrological Processes 25, no. 7 (November 15, 2010): 1055–73. http://dx.doi.org/10.1002/hyp.7901.
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Felsberg, Anne, Zdenko Heyvaert, Jean Poesen, Thomas Stanley, and Gabriëlle J. M. De Lannoy. "Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling." Natural Hazards and Earth System Sciences 23, no. 12 (December 14, 2023): 3805–21. http://dx.doi.org/10.5194/nhess-23-3805-2023.
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Abstract. In this study we present a model for the global Probabilistic Hydrological Estimation of LandSlides (PHELS). PHELS estimates the daily hazard of hydrologically triggered landslides at a coarse spatial resolution of 36 km, by combining landslide susceptibility (LSS) and (percentiles of) hydrological variable(s). The latter include daily rainfall, a 7 d antecedent rainfall index (ARI7) or root-zone soil moisture content (rzmc) as hydrological predictor variables, or the combination of rainfall and rzmc. The hazard estimates with any of these predictor variables have areas under the receiver operating characteristic curve (AUC) above 0.68. The best performance was found with combined rainfall and rzmc predictors (AUC = 0.79), which resulted in the lowest number of missed alarms (especially during spring) and false alarms. Furthermore, PHELS provides hazard uncertainty estimates by generating ensemble simulations based on repeated sampling of LSS and the hydrological predictor variables. The estimated hazard uncertainty follows the behaviour of the input variable uncertainties, is about 13.6 % of the estimated hazard value on average across the globe and in time and is smallest for very low and very high hazard values.
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Chiew, F. H. S., H. Zheng, and J. Vaze. "Implication of calibration period on modelling climate change impact on future runoff." Proceedings of the International Association of Hydrological Sciences 371 (June 12, 2015): 3–6. http://dx.doi.org/10.5194/piahs-371-3-2015.
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Abstract. This paper explores the consideration and implication of calibration period on the modelled climate change impact on future runoff. The results show that modelled runoff and hydrologic responses can be influenced by the choice of historical data period used to calibrate and develop the hydrological model. Modelling approaches that do not take this into account may therefore underestimate the range and uncertainty in future runoff projections. Nevertheless, the uncertainty associated with the choice of hydrological models and consideration of calibration dataset for modelling climate change impact on runoff is likely to be small compared to the uncertainty in the future rainfall projections.
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Jeannin, Pierre-Yves, Guillaume Artigue, Christoph Butscher, Yong Chang, Jean-Baptiste Charlier, Lea Duran, Laurence Gill, et al. "Karst modelling challenge 1: Results of hydrological modelling." Journal of Hydrology 600 (September 2021): 126508. http://dx.doi.org/10.1016/j.jhydrol.2021.126508.
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Gunathilake, Miyuru B., Chamaka Karunanayake, Anura S. Gunathilake, Niranga Marasingha, Jayanga T. Samarasinghe, Isuru M. Bandara, and Upaka Rathnayake. "Hydrological Models and Artificial Neural Networks (ANNs) to Simulate Streamflow in a Tropical Catchment of Sri Lanka." Applied Computational Intelligence and Soft Computing 2021 (May 27, 2021): 1–9. http://dx.doi.org/10.1155/2021/6683389.
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Accurate streamflow estimations are essential for planning and decision-making of many development activities related to water resources. Hydrological modelling is a frequently adopted and a matured technique to simulate streamflow compared to the data driven models such as artificial neural networks (ANNs). In addition, usage of ANNs is minimum to simulate streamflow in the context of Sri Lanka. Therefore, this study presents an intercomparison between streamflow estimations from conventional hydrological modelling and ANN analysis for Seethawaka River Basin located in the upstream part of the Kelani River Basin, Sri Lanka. The hydrological model was developed using the Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS), while the data-driven ANN model was developed in MATLAB. The rainfall and streamflows’ data for 2003–2010 period have been used. The simulations by HEC-HMS were performed by four types of input rainfall data configurations, including observed rainfall data sets and three satellite-based precipitation products (SbPPs), namely, PERSIANN, PERSIANN-CCS, and PERSIANN-CDR. The ANN model was trained using three well-known training algorithms, namely, Levenberg–Marquadt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG). Results revealed that the simulated hydrological model based on observed rainfall outperformed those of based on remotely sensed SbPPs. BR algorithm-based ANN algorithm was found to be superior among the data-driven models in the context of ANN model simulations. However, none of the above developed models were able to capture several peak discharges recorded in the Seethawaka River. The results of this study indicate that ANN models can be used to simulate streamflow to an acceptable level, despite presence of intensive spatial and temporal data sets, which are often required for hydrologic software. Hence, the results of the current study provide valuable feedback for water resources’ planners in the developing region which lack multiple data sets for hydrologic software.
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Beven, Keith. "How to make advances in hydrological modelling." Hydrology Research 50, no. 6 (October 23, 2019): 1481–94. http://dx.doi.org/10.2166/nh.2019.134.
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Abstract After some background about what I have learned from a career in hydrological modelling, I present some opinions about how we might make progress in improving hydrological models in future, including how to decide whether a model is fit for purpose; how to improve process representations in hydrological models; and how to take advantage of Models of Everywhere. Underlying all those issues, however, is the fundamental problem of improving the hydrological data available for both forcing and evaluating hydrological models. It would be a major advance if the hydrological community could come together to prioritise and commission the new observational methods that are required to make real progress.
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Ocio, D., T. Beskeen, and K. Smart. "Fully distributed hydrological modelling for catchment-wide hydrological data verification." Hydrology Research 50, no. 6 (June 3, 2019): 1520–34. http://dx.doi.org/10.2166/nh.2019.006.
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Abstract Hydrological data scarcity and uncertainty is a fundamental challenge in hydrology, particularly in places with weak or declining investment in hydrometric networks. It is well established that fully distributed hydrological models can provide robust estimation of flows at ungauged locations, through local calibration and regionalisation using spatial datasets of physical properties. Even in situations where data are abundant, the existence of inconsistent information is not uncommon. The measurement, estimation or interpolation of rainfall, potential evapotranspiration and flow as well as the difficulty in monitoring artificial influences are all sources of potential inconsistency. Less studied but as important, distributed hydrological models, given their capability of capturing both the temporal and spatial dimensions of the water balance and runoff generation, are suitable tools to identify potential deficiencies in, and reliability of, input data. Three heavily modified catchments in the East of England such as the Ely Ouse, the Witham, and the Black Sluice have been considered, all of which have issues of data scarcity and uncertainty. This paper demonstrates not only the benefits of fully distributed modelling in addressing data availability issues but also in its use as a catchment-wide data validation tool that serves to maximise the potential of limited data and contributes to improved basin representation.
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Cecílio, Roberto Avelino, Wesley Augusto Campanharo, Sidney Sara Zanetti, Amanda Tan Lehr, and Alessandra Cunha Lopes. "Hydrological modelling of tropical watersheds under low data availability." Research, Society and Development 9, no. 5 (March 30, 2020): e100953262. http://dx.doi.org/10.33448/rsd-v9i5.3262.
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Hydrologic simulation is an important tool for the planning and management of water resources. However, the lack of input data, particularly soil and climate data, frequently complicates the application of hydrological models in Brazilian Atlantic Rainforest basins. The purpose of this study was to analyse the application of the VIC model, under the condition of low data availability, to predict the daily streamflow of two basins (Jucu and Santa Maria da Vitória). The results showed satisfactory statistical indexes only for the Santa Maria da Vitória basin. Due to data limitations and the simplified forms used to estimate these missing data, the model proved promising for understanding the hydrologic regime of these basins.
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Tabari, Hossein. "Statistical Analysis and Stochastic Modelling of Hydrological Extremes." Water 11, no. 9 (September 7, 2019): 1861. http://dx.doi.org/10.3390/w11091861.
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Analysis of hydrological extremes is challenging due to their rarity and small sample size and the interconnections between different types of extremes and gets further complicated by an untrustworthy representation of meso-scale processes involved in extreme events by coarse spatial and temporal scale models as well as biased or missing observations due to technical difficulties during extreme conditions. The special issue “Statistical Analysis and Stochastic Modelling of Hydrological Extremes”—motivated by the need to apply and develop innovative stochastic and statistical approaches to analyze hydrological extremes under current and future climate conditions —encompass 13 research papers. Case studies presented in the papers exploit a wide range of innovative techniques for hydrological extremes analyses. The papers focus on six topics: Historical changes in hydrological extremes, projected changes in hydrological extremes, downscaling of hydrological extremes, early warning and forecasting systems for drought and flood, interconnections of hydrological extremes and applicability of satellite data for hydrological studies. This Editorial provides an overview of the covered topics and reviews the case studies relevant for each topic.
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Döll, P., K. Berkhoff, H. Bormann, N. Fohrer, D. Gerten, S. Hagemann, and M. Krol. "Advances and visions in large-scale hydrological modelling: findings from the 11th Workshop on Large-Scale Hydrological Modelling." Advances in Geosciences 18 (October 22, 2008): 51–61. http://dx.doi.org/10.5194/adgeo-18-51-2008.
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Abstract. Large-scale hydrological modelling has become increasingly wide-spread during the last decade. An annual workshop series on large-scale hydrological modelling has provided, since 1997, a forum to the German-speaking community for discussing recent developments and achievements in this research area. In this paper we present the findings from the 2007 workshop which focused on advances and visions in large-scale hydrological modelling. We identify the state of the art, difficulties and research perspectives with respect to the themes "sensitivity of model results", "integrated modelling" and "coupling of processes in hydrosphere, atmosphere and biosphere". Some achievements in large-scale hydrological modelling during the last ten years are presented together with a selection of remaining challenges for the future.
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Daide, Fatima, Rachida Afgane, Abderrahim Lahrach, Abdel-Ali Chaouni, Mohamed Msaddek, and Ismail Elhasnaoui. "Application of the HEC-HMS hydrological model in the Beht watershed (Morocco)." E3S Web of Conferences 314 (2021): 05003. http://dx.doi.org/10.1051/e3sconf/202131405003.
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This work focused on the collection and preparation of the data required for the hydrological modelling of the Beht catchment area, which covers an area of 4560 km2 with a perimeter of 414 km, by combining the various spatial technologies, in particular geographical information systems (GIS), remote sensing, and digital terrain models (DTM), with hydrological models in order to prepare for spatial hydrological modelling used for flood forecasting. The methodology consists, at first, in the automatic extraction of the sub-basins and the drainage network. Then, edit these data using the HEC-GEO-HMS extension, and the preparation of the land use and land cover data for the elaboration of a Curve Number (CN) map of Beht watershed, then the import of the basin model into the Hydrologic Modeling System (HEC-HMS) to simulate the surface runoff using six extreme daily time series events.
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Graham, L. P., and S. Bergström. "Land surface modelling in hydrology and meteorology – lessons learned from the Baltic Basin." Hydrology and Earth System Sciences 4, no. 1 (March 31, 2000): 13–22. http://dx.doi.org/10.5194/hess-4-13-2000.
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Abstract. By both tradition and purpose, the land parameterization schemes of hydrological and meteorological models differ greatly. Meteorologists are concerned primarily with solving the energy balance, whereas hydrologists are most interested in the water balance. Meteorological climate models typically have multi-layered soil parameterisation that solves temperature fluxes numerically with diffusive equations. The same approach is carried over to a similar treatment of water transport. Hydrological models are not usually so interested in soil temperatures, but must provide a reasonable representation of soil moisture to get runoff right. To treat the heterogeneity of the soil, many hydrological models use only one layer with a statistical representation of soil variability. Such a hydrological model can be used on large scales while taking subgrid variability into account. Hydrological models also include lateral transport of water – an imperative if' river discharge is to be estimated. The concept of a complexity chain for coupled modelling systems is introduced, together with considerations for mixing model components. Under BALTEX (Baltic Sea Experiment) and SWECLIM (Swedish Regional Climate Modelling Programme), a large-scale hydrological model of runoff in the Baltic Basin is used to review atmospheric climate model simulations. This incorporates both the runoff record and hydrological modelling experience into atmospheric model development. Results from two models are shown. A conclusion is that the key to improved models may be less complexity. Perhaps the meteorological models should keep their multi-layered approach for modelling soil temperature, but add a simpler, yet physically consistent, hydrological approach for modelling snow processes and water transport in the soil. Keywords: land surface modelling; hydrological modelling; atmospheric climate models; subgrid variability; Baltic Basin
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Banda, Vincent Dzulani, Rimuka Bloodless Dzwairo, Sudhir Kumar Singh, and Thokozani Kanyerere. "Hydrological Modelling and Climate Adaptation under Changing Climate: A Review with a Focus in Sub-Saharan Africa." Water 14, no. 24 (December 10, 2022): 4031. http://dx.doi.org/10.3390/w14244031.
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Empirical evidence continues to show that climate change remains a threat to the stability of the hydrologic system. As the climate system interacts with the hydrologic cycle, one significant repercussion of global warming includes changes in water availability at both regional and local scales. Climate change adaptation is intrinsically difficult to attain due to the dynamic earth system and lack of a comprehensive understanding of future climate and its associated uncertainties. Mostly in developing countries, climate adaptation is hampered by scarcity of good quality and adequate hydro-meteorological data. This article provides a synopsis of the modelling chain applied to investigate the response of the hydrologic system under changing climate, which includes choosing the appropriate global climate models, downscaling techniques, emission scenarios, and the approach to be used in hydrologic modelling. The conventional criteria for choosing a suitable hydrological model are discussed. The advancement of emission scenarios including the latest Shared Socioeconomic Pathways and their role in climate modelling, impact assessment, and adaptation, are also highlighted. This paper also discusses the uncertainties associated with modelling the hydrological impacts of climate change and the plausible approaches for reducing such uncertainties. Among the outcomes of this review include highlights of studies on the commonly used hydrological models for assessing the impact of climate change particularly in the sub-Saharan Africa region and some specific reviews in southern Africa. Further, the reviews show that as human systems keep on dominating within the earth system in several ways, effective modelling should involve coupling earth and human systems models as these may truly represent the bidirectional feedback experienced in the modern world. The paper concludes that adequate hydro-meteorological data is key to having a robust model and effective climate adaptation measures, hence in poorly gauged basins use of artificial neural networks and satellite datasets have shown to be successful tools, including for model calibration and validation.
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Chang, Fi-John, and Shenglian Guo. "Advances in Hydrologic Forecasts and Water Resources Management." Water 12, no. 6 (June 24, 2020): 1819. http://dx.doi.org/10.3390/w12061819.
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The impacts of climate change on water resources management as well as the increasing severe natural disasters over the last decades have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resources management and the mitigation of natural disasters. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modelling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has the great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modelling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; and (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue can not only advance water sciences but can also support policy makers toward more sustainable and effective water resources management.
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Peel, Murray C., and Günter Blöschl. "Hydrological modelling in a changing world." Progress in Physical Geography: Earth and Environment 35, no. 2 (March 31, 2011): 249–61. http://dx.doi.org/10.1177/0309133311402550.
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Changing hydrological conditions due to climate, land use and infrastructure pose significant ongoing challenges to the hydrological research and water management communities. While, traditionally, hydrological models have assumed stationary conditions, there has been much progress since 2005 on model parameter estimation under unknown or changed conditions and on techniques for modelling in those conditions. There is an analogy between extrapolation in space (termed Prediction in Ungauged Basins, PUB), and extrapolation in time (termed Prediction in Ungauged Climates, PUC) that can be exploited for estimating model parameters. Methods for modelling changing hydrological conditions need to progress beyond the current scenario approach, which is reliant upon precalibrated models. Top-down methods and analysis of spatial gradients of a variable of interest, instead of temporal gradients (a method termed ‘Trading space for time’) show much promise for validating more complex model projections. Understanding hydrological processes and how they respond to change, along with quantification of parameter estimation and modelling process uncertainty will continue to be active areas of research within hydrology. Contributions from these areas will not only help inform future climate change impact studies about what will change and by how much, but also provide insight into why any changes may occur, what changes we are able to predict in a realistic manner, and what changes are beyond the current predictability of hydrological systems.
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Overgaard, J., D. Rosbjerg, and M. B. Butts. "Land-surface modelling in hydrological perspective." Biogeosciences Discussions 2, no. 6 (December 13, 2005): 1815–48. http://dx.doi.org/10.5194/bgd-2-1815-2005.
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Abstract. A comprehensive review of energy-based land-surface modelling, as seen from a hydrological perspective, is provided. We choose to focus on energy-based approaches, because in comparison to the traditional potential evapotranspiration models, these approaches allow for a stronger link to remote sensing and atmospheric modelling. New opportunities for evaluation of distributed land-surface models through application of remote sensing are discussed in detail, and the difficulties inherent in various evaluation procedures are presented. Remote sensing is the only source of distributed data at scales that correspond to hydrological modelling scales. Finally, the dynamic coupling of hydrological and atmospheric models is explored, and the future perspectives of such efforts are discussed.
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Olayinka, D. N., and H. E. Irivbogbe. "Estimation of Hydrological Outputs using HEC-HMS and GIS." July 2017 1, no. 2 (July 2017): 390–402. http://dx.doi.org/10.36263/nijest.2017.02.0054.
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Estimating runoff and understanding of the relationship between rainfall and runoff are of great importance in the management of flood. Several computer based hydrological models have been developed and used in simulating runoff in various watersheds in different parts of the world and in water resource studies. This study focuses on the combination of Geographic Information System (GIS) with Hydrologic Engineering Center –Hydrologic Modelling System (HEC-HMS) hydrological model to simulate runoff process of the adjoining areas of the Lagos Island and Eti-Osa Local Government Areas (LGAs). The study makes use of LIDAR Digital Elevation Model (DEM), drainage data and land use map for catchment delineation and hydrological modelling, using HECGeoHMS and ArcGIS 10.2. In HEC-HMS 4.2.1, the delineated catchment with all hydrological parameters and average daily rainfall data, are used to simulate and compute rainfall runoff volume, peak discharges for 10 months (between Jan to October) and a total of three years (2012, 2015 and 2017) were considered. Direct runoff volume and depth estimation for the years under review were determined. Results show that the peak discharge occurred on the 2nd of July 2012 at a rate of 14m3/s with an estimated runoff volume at the basin outlet of 39,669.70 x 103m3 (this date tallies with the severe flood events that occurred in that year). The study shows that estimating hydrological outputs is possible with the use of HEC-HMS and GIS. It recommends the application of such technologies in the prediction and development of basic flood warning systems for the area.
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Haberlandt, U. "From hydrological modelling to decision support." Advances in Geosciences 27 (August 23, 2010): 11–19. http://dx.doi.org/10.5194/adgeo-27-11-2010.
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Abstract. Decision support for planning and management of water resources needs to consider many target criteria simultaneously like water availability, water quality, flood protection, agriculture, ecology, etc. Hydrologic models provide information about the water balance components and are fundamental for the simulation of ecological processes. Objective of this contribution is to discuss the suitability of classical hydrologic models on one hand and of complex eco-hydrologic models on the other hand to be used as part of decision support systems. The discussion is based on results from two model comparison studies. It becomes clear that none of the hydrologic models tested fulfils all requirements in an optimal sense. Regarding the simulation of water quality parameters like nitrogen leaching a high uncertainty needs to be considered. Recommended for decision support is a hybrid metamodel approach, which comprises a hydrologic model, empirical relationships for the less dynamic processes and makes use of simulation results from complex eco-hydrologic models through second-order modelling at a generalized level.
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Bergström, Sten. "Principles and Confidence in Hydrological Modelling." Hydrology Research 22, no. 2 (April 1, 1991): 123–36. http://dx.doi.org/10.2166/nh.1991.0009.
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General principles in development and application of hydrological models are discussed and related to the confidence in the results. The presentation is mainly based on the experience from the work with the HBV and PULSE models at the Swedish Meteorological and Hydrological Institute between 1971 and 1990 but has also been influenced by other modelling work. It covers a discussion on the optimal complexity of models, use of observations, calibration, control and sensitivity analysis. Special attention is given to the uncertainties encountered when using hydrological models for the simulation of extreme floods and long-term scenario simulations. Finally a few ethical problems in modelling are mentioned.
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Dawson, C. W., and R. L. Wilby. "Hydrological modelling using artificial neural networks." Progress in Physical Geography 25, no. 1 (March 1, 2001): 80–108. http://dx.doi.org/10.1191/030913301674775671.
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Lana-Renault, Noemí, and D. Karssenberg. "PyCatch: component based hydrological catchment modelling." Cuadernos de Investigación Geográfica 39, no. 2 (July 8, 2013): 315. http://dx.doi.org/10.18172/cig.1993.
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Bobba, A. G., and D. C. L. Lam. "Hydrological modelling of acidified Canadian watersheds." Ecological Modelling 50, no. 1-3 (March 1990): 5–32. http://dx.doi.org/10.1016/0304-3800(90)90040-n.
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Dawson, C. W., and R. L. Wilby. "Hydrological modelling using artificial neural networks." Progress in Physical Geography: Earth and Environment 25, no. 1 (March 2001): 80–108. http://dx.doi.org/10.1177/030913330102500104.
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This review considers the application of artificial neural networks (ANNs) to rainfall-runoff modelling and flood forecasting. This is an emerging field of research, characterized by a wide variety of techniques, a diversity of geographical contexts, a general absence of intermodel comparisons, and inconsistent reporting of model skill. This article begins by outlining the basic principles of ANN modelling, common network architectures and training algorithms. The discussion then addresses related themes of the division and preprocessing of data for model calibration/validation; data standardization techniques; and methods of evaluating ANN model performance. A literature survey underlines the need for clear guidance in current modelling practice, as well as the comparison of ANN methods with more conventional statistical models. Accordingly, a template is proposed in order to assist the construction of future ANN rainfall-runoff models. Finally, it is suggested that research might focus on the extraction of hydrological ‘rules’ from ANN weights, and on the development of standard performance measures that penalize unnecessary model complexity.
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KITE, G. W., and A. PIETRONIRO. "Remote sensing applications in hydrological modelling." Hydrological Sciences Journal 41, no. 4 (August 1996): 563–91. http://dx.doi.org/10.1080/02626669609491526.
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Ballard, C. E., N. McIntyre, H. S. Wheater, J. Holden, and Z. E. Wallage. "Hydrological modelling of drained blanket peatland." Journal of Hydrology 407, no. 1-4 (September 2011): 81–93. http://dx.doi.org/10.1016/j.jhydrol.2011.07.005.
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Chappell, Nick, and Les Ternan. "Low path dimensionality and hydrological modelling." Hydrological Processes 6, no. 3 (July 1992): 327–45. http://dx.doi.org/10.1002/hyp.3360060307.
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Farzana, Syeda Zehan. "Uncertainty in hydrological modelling: A review." International Journal of Hydrology Research 8, no. 1 (February 17, 2023): 1–13. http://dx.doi.org/10.18488/ijhr.v8i1.3297.
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Availability of hydrological data and various soft wares for developing models make easy way to answer frequently asked questions to hydrologists. A great deal of concentration has given to the development of models in the last decades. But the thorough study regarding uncertainty of simulations has not carried out in comparison with the development of models. Uncertainty in models emanates from input data, calibrated data, parameters and from the structure of models. The sources of uncertainty, cause of generation and how these can be dealt with are reviewed here. This also comprises a review about five different methods viz. Monte Carlo sampling, Bayesian approach, Generalized Likelihood Uncertainty Estimation, Bootstrap Approach and Machine learning methods which were applied in the estimation of the model and parameter uncertainty. This will indicate the comparison between the methods which were applied to measure the uncertainty of hydrological models and highlight the strengths and weaknesses of the methods in identifying the usefulness of the models. By the comparison of the methods the improvement of the model reliability, slackening of the prediction error of the hydrological models can be suggested. By a proper quantification of uncertainty of data applied for the building up and evaluation of models, model performance can be improved, cost can be reduced and unambiguous results can lead the proper water resources management.
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Uhlenbrook, Stefan, Alberto Montanari, and João L. M. P. de Lima. "Preface to the special issue: “Hydrological processes and distributed hydrological modelling”." Physics and Chemistry of the Earth, Parts A/B/C 28, no. 6-7 (January 2003): 225. http://dx.doi.org/10.1016/s1474-7065(03)00031-7.
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Tyl, Radovan, Vojtěch Svoboda, Petr Šercl, Martin Pecha, and Dominik Míka. "Rainfall-runoff modelling in the hydrological practice at the CHMI." Meteorologické zprávy 76, no. 5 (December 5, 2023): 148–57. http://dx.doi.org/10.59984/mz.2023.05.02.
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Rainfall-runoff modelling is widely used in the hydrological practice of the CHMI. The presented text offers an insight into work of the Surface Water Department. The use of hydrological modelling procedures is described on specific selected examples such as the derivation of theoretical flood waves, the evaluation and verification of past flood events or its use in several applications operated at the CHMI. Hydrological modelling is also an integral part of scientific and research activities with a focus on updating methodological procedures and harmonizing input data.
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Abrahart, R. J., and L. M. See. "Neural network modelling of non-linear hydrological relationships." Hydrology and Earth System Sciences 11, no. 5 (September 20, 2007): 1563–79. http://dx.doi.org/10.5194/hess-11-1563-2007.
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Abstract. Two recent studies have suggested that neural network modelling offers no worthwhile improvements in comparison to the application of weighted linear transfer functions for capturing the non-linear nature of hydrological relationships. The potential of an artificial neural network to perform simple non-linear hydrological transformations under controlled conditions is examined in this paper. Eight neural network models were developed: four full or partial emulations of a recognised non-linear hydrological rainfall-runoff model; four solutions developed on an identical set of inputs and a calculated runoff coefficient output. The use of different input combinations enabled the competencies of solutions developed on a reduced number of parameters to be assessed. The selected hydrological model had a limited number of inputs and contained no temporal component. The modelling process was based on a set of random inputs that had a uniform distribution and spanned a modest range of possibilities. The initial cloning operations permitted a direct comparison to be performed with the equation-based relationship. It also provided more general information about the power of a neural network to replicate mathematical equations and model modest non-linear relationships. The second group of experiments explored a different relationship that is of hydrological interest; the target surface contained a stronger set of non-linear properties and was more challenging. Linear modelling comparisons were performed against traditional least squares multiple linear regression solutions developed on identical datasets. The reported results demonstrate that neural networks are capable of modelling non-linear hydrological processes and are therefore appropriate tools for hydrological modelling.
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Savenije, H. H. G. "<i>HESS Opinions</i> "The art of hydrology"<sup>*</sup>." Hydrology and Earth System Sciences Discussions 5, no. 6 (November 14, 2008): 3157–67. http://dx.doi.org/10.5194/hessd-5-3157-2008.
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Abstract. Hydrological modelling is the same as developing and encoding a hydrological theory. A hydrological model is not a tool but a theory. The whole discussion about the inadequacy of hydrological models we have witnessed of late, is related to the wrong concept of what a model is. Good models don't exist. Instead, hydrological research should focus on improving models and enhancing understanding. The process of modelling should be top-down, learning from the data. There is always a need for calibration, which implies that we need tailor-made and site-specific models. Only flexible models are fit for this modelling process, as opposed to most of the "established" models, "one-size-fits-all" models or "models of everywhere". The process of modelling requires imagination, inspiration, creativity, ingenuity, experience and skill. These are qualities that belong to the field of art. Hydrology is an art as much as it is science and engineering.
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Mendoza, Manuel, Gerardo Bocco, and Miguel Bravo. "Spatial prediction in hydrology: status and implications in the estimation of hydrological processes for applied research." Progress in Physical Geography: Earth and Environment 26, no. 3 (September 2002): 319–38. http://dx.doi.org/10.1191/0309133302pp335ra.
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Based on a review of research, the linkages between distributed hydrological modelling (DHM) remote sensing (RS) and geographical information system (GIS) techniques, coupled with geomorphological knowledge are discussed. While presenting characteristics of the models, techniques, and supporting analytical tools of geographical hydrology, the emphasis is on the estimation of hydrological variables. The first is limited to the spatialization and integration of low resolution meteorological data with hydrological models in a GIS environment. The second includes research in the calculation of precipitation, evapotranspiration, radiation, etc., from the digital analyses of remote sensing data, to feed either lumped or spatially distributed models. The third links the tools of GIS and RS with hydrological modelling; usually it makes intensive use of the tools of GIS for several scales of spatial modelling. The last group integrates GIS, RS and hydrological modelling supported by the delimitation and characterization of environmental units, generally to detailed and semi-detailed scales.
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Wang, Jian Ping, Jian Chen, and Chun Hong Li. "Research for Hydrological Modelling System Technology Based on OpenMI." Advanced Materials Research 347-353 (October 2011): 1806–15. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.1806.
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There has been a variety wide of hydrological simulation models, each model has its own characteristics. Hydrological modelling considers more and more detailed physical mechanism. And involved professional models also increased great significantly. Therefore, it is particularly important to establish an open unified model interface standards to make a data communication among different models and quickly formulate modelling systems suitable for the characteristics of target watershed. OpenMI emerged at the right moment to satisfy the requirements of above situations. The thesis bases on OpenMI to design hydrological modelling systems and inherits OpenMI framework of design, request/feedback mechanism and hierarchy facing to modules. This design can solve the assembly link problem of cross-language and multi-type models and make a beneficial attempt for hydrological modelling system efficient integration.
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Lane, S. N. "Acting, predicting and intervening in a socio-hydrological world." Hydrology and Earth System Sciences Discussions 10, no. 8 (August 16, 2013): 10659–717. http://dx.doi.org/10.5194/hessd-10-10659-2013.
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Abstract. This paper asks a simple question: if humans and their actions co-evolve with hydrological systems (Sivapalan and Blöschl, 2012), what is the role of those humans who are simultaneously hydrological scientists, bound up within this system? To put it more directly, can we, as socio-hydrologists study the socio-hydrological world in isolation from that world in a way that mirrors the supposed separation between scientists and society? I answer this question, in the negative, from three linked perspectives. The first draws directly upon science-technology studies to make a case to the (socio-hydrological) community that we need to be sensitive to constructivist accounts of science in general and hydrology in particular. I review three positions taken by such accounts and apply them to hydrological science, supported with specific examples: (a) the philosophical critique of the claimed abstraction of scientists and scientific activity from the socio-hydrological world; (b) the way in which hydrological science is embedded in wider societal decision-making; and (c) the recognition that socio-hydrological knowledge is much more distributed than we as (socio-)hydrologists commonly recognise. For the second perspective, I consider predictive modelling as a socio-hydrological practice. I draw upon wider studies of the practice of modelling, coupled to empirical evidence for one element of hydrological modelling, roughness parameterisation, to consider how it is that socio-hydrological modellers come to believe in the predictive models that they use. This will show that if predictive modelling is to be more than analytical, that if it is to effect more sustainable socio-hydrological futures, then we need to rethink the basic tenets of how we practice predictive modelling. These first two perspectives are themselves, in combination, analytical, prone to the criticism that they cause us to degenerate into an "anything goes" relationship with the world around us. Thus, in a third perspective I explicitly challenge this degeneration by setting out a number of practices that might be valuable for doing prediction within a socio-hydrological system. These include: (1) working with conflict and controversy in hydrological science, rather than trying to eliminate them; (2) using hydrological events to avoid becoming paradigm-bound; (3) being empirical and experimental but in a socio-hydrological sense; and (4) co-producing socio-hydrological predictions. I will show how this might be done through a project that specifically developed predictive models for making interventions in river catchments to increase high river flow attenuation, in which I found myself becoming detached from my normal disciplinary networks and attached to the co-production of a predictive hydrological model with communities normally excluded from the practice of hydrological science.
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Luo, Min, Tie Liu, Fanhao Meng, Yongchao Duan, Yue Huang, Amaury Frankl, and Philippe De Maeyer. "Proportional coefficient method applied to TRMM rainfall data: case study of hydrological simulations of the Hotan River Basin (China)." Journal of Water and Climate Change 8, no. 4 (June 15, 2017): 627–40. http://dx.doi.org/10.2166/wcc.2017.080.
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Abstract A low-density rain gauge network is always a major obstacle for hydrological modelling, particularly for alpine and remote regions. The availability of the Tropical Rainfall Measuring Mission (TRMM) rainfall products provides an opportunity for hydrological modelling, although the results must be validated and corrected before they can be used in further applications. In this paper, the combination of proportional coefficients with cross-checking by hydrological modelling was proposed as a method to improve the quality of TRMM data in a rural mountainous region, the Hotan River Basin. The performance of the Soil and Water Assessment Tool (SWAT) model was examined using streamflow and snow cover measurements. The corrected results suggest that the proportional coefficient approach could effectively improve the TRMM data quality. A verification of the hydrological model outputs indicated that the simulated streamflow was consistent with the observed runoff. Moreover, the modelled snow cover patterns presented similar spatial and temporal variations to the remotely sensed snow cover, and the correlation coefficient ranged from 0.63 to 0.98. The results from the TRMM correction and hydrological simulation approach indicated that this method can significantly improve the precision of TRMM data and can meet the requirements of hydrological modelling.
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Tarek, Mostafa, François P. Brissette, and Richard Arsenault. "Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America." Hydrology and Earth System Sciences 24, no. 5 (May 14, 2020): 2527–44. http://dx.doi.org/10.5194/hess-24-2527-2020.
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Abstract. The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released its most advanced reanalysis product, the ERA5 dataset. It was designed and generated with methods giving it multiple advantages over the previous release, the ERA-Interim reanalysis product. Notably, it has a finer spatial resolution, is archived at the hourly time step, uses a more advanced assimilation system and includes more sources of data. This paper aims to evaluate the ERA5 reanalysis as a potential reference dataset for hydrological modelling by considering the ERA5 precipitation and temperatures as proxies for observations in the hydrological modelling process, using two lumped hydrological models over 3138 North American catchments. This study shows that ERA5-based hydrological modelling performance is equivalent to using observations over most of North America, with the exception of the eastern half of the US, where observations lead to consistently better performance. ERA5 temperature and precipitation biases are consistently reduced compared to ERA-Interim and systematically more accurate for hydrological modelling. Differences between ERA5, ERA-Interim and observation datasets are mostly linked to precipitation, as temperature only marginally influences the hydrological simulation outcomes.
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Götzinger, J., J. Jagelke, R. Barthel, and A. Bárdossy. "Integration of water balance models in RIVERTWIN." Advances in Geosciences 9 (September 26, 2006): 85–91. http://dx.doi.org/10.5194/adgeo-9-85-2006.
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Abstract. In the project RIVERTWIN climate, hydrologic, groundwater and water quality models are integrated in order to evaluate river basin management plans established for the implementation of the EU Water Framework Directive. In such integrated models, which try to simulate all relevant processes in a river basin realistically, modelling of the water balance plays a key role. Therefore the integration of hydrological and groundwater models requires special attention. In this case study, the hydrological model simulates discharge and daily groundwater recharge in a high spatial resolution. Using the latter as input, the groundwater model calculates groundwater levels and groundwater runoff, which is then returned to the hydrological model. Such integration on the meso-scale brings up new problems such as commensurability, verification and compatibility of internal state variables and fluxes, but also provides the possibility to analyse the underlying assumptions and simplifications. As an example of this modelling approach the simulation of groundwater recharge, groundwater levels and groundwater runoff in the Neckar catchment are discussed and the problems of the current integration concept are described.
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Todini, E. "History and perspectives of hydrological catchment modelling." Hydrology Research 42, no. 2-3 (April 1, 2011): 73–85. http://dx.doi.org/10.2166/nh.2011.096.
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This paper presents a brief historical excursus on the development of hydrological catchment models together with a number of possible future perspectives. Given the wide variety of available hydrological models which, according to the embedded level of prior physical information, vary from the simple input–output lumped models to complex physically meaningful ones, the paper suggests how to accommodate and to reconcile the different approaches. This can be performed by better clarifying the roles and the limitations of the different models through objective benchmarks or test-beds characterizing the diverse potential hydrological applications. Furthermore, when dealing with hydrological forecasting, the reconciliation can be obtained in terms of forecasting uncertainty, by developing Bayesian frameworks to combine together models of different nature in order to assess and reduce predictive uncertainty.