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Guilpart, Etienne, Vahid Espanmanesh, Amaury Tilmant, and François Anctil. "Combining split-sample testing and hidden Markov modelling to assess the robustness of hydrological models." Hydrology and Earth System Sciences 25, no. 8 (August 30, 2021): 4611–29. http://dx.doi.org/10.5194/hess-25-4611-2021.
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Abstract. The impacts of climate and land-use changes make the stationary assumption in hydrology obsolete. Moreover, there is still considerable uncertainty regarding the future evolution of the Earth’s climate and the extent of the alteration of flow regimes. Climate change impact assessment in the water sector typically involves a modelling chain in which a hydrological model is needed to generate hydrologic projections from climate forcings. Considering the inherent uncertainty of the future climate, it is crucial to assess the performance of the hydrologic model over a wide range of climates and their corresponding hydrologic conditions. In this paper, numerous, contrasted, climate sequences identified by a hidden Markov model (HMM) are used in a differential split-sample testing framework to assess the robustness of a hydrologic model. The differential split-sample test based on a HMM classification is implemented on the time series of monthly river discharges in the upper Senegal River basin in West Africa, a region characterized by the presence of low-frequency climate signals. A comparison with the results obtained using classical rupture tests shows that the diversity of hydrologic sequences identified using the HMM can help with assessing the robustness of the hydrologic model.
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Mendoza, Pablo A., Martyn P. Clark, Naoki Mizukami, Andrew J. Newman, Michael Barlage, Ethan D. Gutmann, Roy M. Rasmussen, Balaji Rajagopalan, Levi D. Brekke, and Jeffrey R. Arnold. "Effects of Hydrologic Model Choice and Calibration on the Portrayal of Climate Change Impacts." Journal of Hydrometeorology 16, no. 2 (April 1, 2015): 762–80. http://dx.doi.org/10.1175/jhm-d-14-0104.1.
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Abstract The assessment of climate change impacts on water resources involves several methodological decisions, including choices of global climate models (GCMs), emission scenarios, downscaling techniques, and hydrologic modeling approaches. Among these, hydrologic model structure selection and parameter calibration are particularly relevant and usually have a strong subjective component. The goal of this research is to improve understanding of the role of these decisions on the assessment of the effects of climate change on hydrologic processes. The study is conducted in three basins located in the Colorado headwaters region, using four different hydrologic model structures [PRMS, VIC, Noah LSM, and Noah LSM with multiparameterization options (Noah-MP)]. To better understand the role of parameter estimation, model performance and projected hydrologic changes (i.e., changes in the hydrology obtained from hydrologic models due to climate change) are compared before and after calibration with the University of Arizona shuffled complex evolution (SCE-UA) algorithm. Hydrologic changes are examined via a climate change scenario where the Community Climate System Model (CCSM) change signal is used to perturb the boundary conditions of the Weather Research and Forecasting (WRF) Model configured at 4-km resolution. Substantial intermodel differences (i.e., discrepancies between hydrologic models) in the portrayal of climate change impacts on water resources are demonstrated. Specifically, intermodel differences are larger than the mean signal from the CCSM–WRF climate scenario examined, even after the calibration process. Importantly, traditional single-objective calibration techniques aimed to reduce errors in runoff simulations do not necessarily improve intermodel agreement (i.e., same outputs from different hydrologic models) in projected changes of some hydrological processes such as evapotranspiration or snowpack.
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Ganoulis, J. "Modeling Hydrologic Phenomena [Free opinion]." Revue des sciences de l'eau 9, no. 4 (April 12, 2005): 421–34. http://dx.doi.org/10.7202/705260ar.
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With the aim of suggesting some practical rules for the use of hydrological models, G. De MARSILY in his "free opinion" (Rev. Sci. Eau 1994, 7(3): 219-234) proposes a classification of hydrologic models into two categories: - models built on data (observable phenomena) and ; - models without any available observations (unobservable phenomena). He claims that for the former group of observable phenomena, models developed through a learning process as well as those based on the underlying physical laws are of the black box type. For the latter group of unobservable phenomena, he suggests that physically-based hydrologic models be developed. Physically-based hydrologic models should introduce to the phenomenological laws the correct empirical coefficients, which correspond to the proper time and space scales (GANOULIS, 1986). Well-known examples are Darcy's permeability coefficient on the macroscopic scale as derived from the Navier-Stokes equations on the local scale and the macroscopic dispersion coefficients in comparison with the local Fickian diffusion coefficients. Misuse of these models by confusing the proper time and space scales and determining the coefficients by calibration is not a sufficient reason to consider them as belonging to the black box type. Black box type hydrologic models, although very useful when data are available, remain formally empirical. They fail to give correct answers when serious constraints of unity in place, time and action are not fulfilled. Concerning the second class of models, we may notice that purely unobservable phenomena without any available data do not really exist in hydrology. In the case of very rare events and complex systems, such as radioactivity impacts and forecasting of changes on a large scale, physically-based models with adequate parameters may be used to integrate scarce information from experiments and expert opinions in a Bayesian probabilistic framework (APOSTOLAKIS, 1990). The most important feature of hydrologic models capable of describing real hydrologic phenomena, is the possibility of handling imprecision and natural variabilities. Uncertainties may be seen in two categories: aleatory or noncognitive, and epistemic or cognitive. Probabilistic hydrologic models are more suitable for dealing with aleatory uncertainties. Fuzzy logic-based models may quantify epistemic uncertainties (GANOULIS et al., 1996). The stochastic and fuzzy modeling approaches are briefly explained in this free opinion as compared to the deterministic physically-based hydrologic modeling.
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Abbas, Ather, Laurie Boithias, Yakov Pachepsky, Kyunghyun Kim, Jong Ahn Chun, and Kyung Hwa Cho. "AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods." Geoscientific Model Development 15, no. 7 (April 8, 2022): 3021–39. http://dx.doi.org/10.5194/gmd-15-3021-2022.
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Abstract. Machine learning has shown great promise for simulating hydrological phenomena. However, the development of machine-learning-based hydrological models requires advanced skills from diverse fields, such as programming and hydrological modeling. Additionally, data pre-processing and post-processing when training and testing machine learning models are a time-intensive process. In this study, we developed a python-based framework that simplifies the process of building and training machine-learning-based hydrological models and automates the process of pre-processing hydrological data and post-processing model results. Pre-processing utilities assist in incorporating domain knowledge of hydrology in the machine learning model, such as the distribution of weather data into hydrologic response units (HRUs) based on different HRU discretization definitions. The post-processing utilities help in interpreting the model's results from a hydrological point of view. This framework will help increase the application of machine-learning-based modeling approaches in hydrological sciences.
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Naik, M. Ravi, and Dr MVSS Giridhar. "Spatial Variability of Rainfall and Classification of Peninsular Indian Catchments." International Journal of Advanced Engineering and Nano Technology 10, no. 12 (December 30, 2023): 8–15. http://dx.doi.org/10.35940/ijaent.f4214.12101223.
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The strength and success of hydrological analysis depend upon the quantity and quality of observed data. In the recent past, the availability of advanced computing facilities and measurement techniques had a great impact on the field of hydrology, especially in hydrologic analysis and hydrologic modeling. In spite of such growth, the present hydrologic modeling has certain challenges: complexity (involving a large number of parameters), applicability to a specific region (difficult to generalize for other regions), and lack of understanding of the connection between model theories and the actual system. The general solution of simplifying the models in terms of developing a classification framework has been discussed and focused on in the present study. It will greatly help to overcome the hydrologic modeling challenges and provides a better understanding of the hydrologic process. In general, classification is a way of grouping entities which has similar characteristics. The importance of applying nonlinear dynamics and chaos methods for classification has been realized in the recent past; since such studies provide exclusive information on hidden characteristics such as complexity, nonlinearity, dimensionality, etc. Of hydrological processes. The hydrologic processes are complex. In this study, information regarding the complexity is extracted by statistical analysis and linear methods such as Autocorrelation Function, and Average Mutual Information. 367 gridded rainfall stations over Peninsular Indian basins are used to investigate the applicability of different methods used in the study.
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Pawitan, Hidayat, and Muh Taufik. "Non-linear Routing Scheme at Grid Cell Level for Large Scale Hydrologic Models: A Review." Agromet 35, no. 2 (August 12, 2021): 60–72. http://dx.doi.org/10.29244/j.agromet.35.2.60-72.
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New tools and concepts in the form of mathematical models, remote sensing and Geographic Information System (GIS), communication and telemetering have been developed for the complex hydrologic systems that permit a different analysis of processes and allow watershed to be considered as an integrated planning and management unit. Hydrological characteristics can be generated through spatial analysis, and ready for input into a distributed hydrologic models to define adequately the hydrological response of a watershed that can be related back to the specific environmental, climatic, and geomorphic conditions. In the present paper, some recent development in hydrologic modeling will be reviewed with recognition of the role of horizontal routing scheme in large scale hydrologic modeling. Among others, these developments indicated the needs of alternative horizontal routing models at grid scale level that can be coupled to land surface parameterization schemes that presently still employed the linear routing model. Non-linear routing scheme will be presented and discussed in this paper as possible extension.
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Perra, Enrica, Monica Piras, Roberto Deidda, Claudio Paniconi, Giuseppe Mascaro, Enrique R. Vivoni, Pierluigi Cau, Pier Andrea Marras, Ralf Ludwig, and Swen Meyer. "Multimodel assessment of climate change-induced hydrologic impacts for a Mediterranean catchment." Hydrology and Earth System Sciences 22, no. 7 (July 30, 2018): 4125–43. http://dx.doi.org/10.5194/hess-22-4125-2018.
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Abstract. This work addresses the impact of climate change on the hydrology of a catchment in the Mediterranean, a region that is highly susceptible to variations in rainfall and other components of the water budget. The assessment is based on a comparison of responses obtained from five hydrologic models implemented for the Rio Mannu catchment in southern Sardinia (Italy). The examined models – CATchment HYdrology (CATHY), Soil and Water Assessment Tool (SWAT), TOPographic Kinematic APproximation and Integration (TOPKAPI), TIN-based Real time Integrated Basin Simulator (tRIBS), and WAter balance SImulation Model (WASIM) – are all distributed hydrologic models but differ greatly in their representation of terrain features and physical processes and in their numerical complexity. After calibration and validation, the models were forced with bias-corrected, downscaled outputs of four combinations of global and regional climate models in a reference (1971–2000) and future (2041–2070) period under a single emission scenario. Climate forcing variations and the structure of the hydrologic models influence the different components of the catchment response. Three water availability response variables – discharge, soil water content, and actual evapotranspiration – are analyzed. Simulation results from all five hydrologic models show for the future period decreasing mean annual streamflow and soil water content at 1 m depth. Actual evapotranspiration in the future will diminish according to four of the five models due to drier soil conditions. Despite their significant differences, the five hydrologic models responded similarly to the reduced precipitation and increased temperatures predicted by the climate models, and lend strong support to a future scenario of increased water shortages for this region of the Mediterranean basin. The multimodel framework adopted for this study allows estimation of the agreement between the five hydrologic models and between the four climate models. Pairwise comparison of the climate and hydrologic models is shown for the reference and future periods using a recently proposed metric that scales the Pearson correlation coefficient with a factor that accounts for systematic differences between datasets. The results from this analysis reflect the key structural differences between the hydrologic models, such as a representation of both vertical and lateral subsurface flow (CATHY, TOPKAPI, and tRIBS) and a detailed treatment of vegetation processes (SWAT and WASIM).
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Janicka, Ewelina, Jolanta Kanclerz, Tropikë Agaj, and Katarzyna Gizińska. "Comparison of Two Hydrological Models, the HEC-HMS and Nash Models, for Runoff Estimation in Michałówka River." Sustainability 15, no. 10 (May 12, 2023): 7959. http://dx.doi.org/10.3390/su15107959.
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Floods are among the most devastating natural disasters in small suburban catchments. These phenomena, causing loss of life and massive property damage, pose a serious threat to the economy. Hydrological modeling is extremely important in terms of climate change, and the use of appropriate modeling can be a useful tool for flood risk prevention and mitigation. Rainfall–runoff modeling requires the selection of an appropriate hydrological model in order to obtain satisfactory results. Hydrological models are used in water resource planning and management to estimate catchment runoff. Small uncontrolled catchments play a particularly important role in hydrological phenomena, since changes in them affect flows in the recipient. Hydrologists are particularly interested in developing hydrological models that can be made with a minimum of data and parameters. Nash models and the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) are examples of simple and most practical hydrologic models. These models were used in this paper to study geographic and qualitative changes in precipitation runoff due to land cover changes. The modeling was carried out for two spatial aspects relating to the years 1940 and 2018. The model allowed for the simulation of the river flow that can occur under different rainfall probabilities. The analysis of the results was used to evaluate the hydrological models used. The hundred-year flow modeled with the Nash model for 1940 was 13.4 m3∙s−1, whereas the second model gave slightly lower flow values. In addition, modeling the flow for 2018 (after changing the land cover) highlighted the increase in the flow value for both models, where again the flow volume was slightly higher for the Nash model and amounted to about 19 m3∙s−1. The flow differences for individual models were not too large. This made it possible to conclude that the simulated outflow hydrographs are in good agreement, and this means that the models accurately reproduce the flow of the Michałówka River. The study showed that rapid urbanization adversely affects hydrological processes. In addition, the study showed that a well-distributed model can outperform a global flood forecasting model, especially in terms of magnitude, as in the current study example.
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Carleton, Tyler J., and Steven R. Fassnacht. "Linking Hydrologic and Hydraulic Data with Models to Assess Flow and Channel Alteration at Hog Park, Wyoming USA." Hydrology 7, no. 2 (May 23, 2020): 29. http://dx.doi.org/10.3390/hydrology7020029.
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Transbasin diversions and dams allow for water uses when and where there is high demand and low supply, but can come with an expense to the environment. This paper presents a linkage of hydrologic and hydraulic modeling and datasets to assess the hydrologic and hydraulic stability within a transbasin watershed as an approach for meeting water use targets and safeguarding environmental sustainability. The approach used a Prediction in Ungauged Basin (PUB) regionalization technique that completed the parameterization of a study watershed hydrologic model by transferring calibrated parameters from a reference watershed hydrologic model. This resulted in a long-term, simulated natural flow record that was compared to the measured modified flow record for the same time period to assess flow alteration. In the sensitive reach, hydraulic modeling results tracked channel response from before hydrologic modification to baseline using repeated survey years during the hydrologic modification. The combined assessment of hydrology and hydraulics highlighted the relation between flow regime and channel form.
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Valdés-Pineda, Rodrigo, Juan B. Valdés, Sungwook Wi, Aleix Serrat-Capdevila, and Tirthankar Roy. "Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting." Hydrology 8, no. 4 (December 20, 2021): 188. http://dx.doi.org/10.3390/hydrology8040188.
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The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed.
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Wang, Jie, Guoqing Wang, Amgad Elmahdi, Zhenxin Bao, Qinli Yang, Zhangkang Shu, and Mingming Song. "Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods." Open Geosciences 13, no. 1 (January 1, 2021): 401–15. http://dx.doi.org/10.1515/geo-2020-0239.
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Abstract Ensemble hydrologic forecasting which takes advantages of multiple hydrologic models has made much contribution to water resource management. In this study, four hydrological models (the Xin’anjiang model (XAJ), Simhyd, GR4J, and artificial neural network (ANN) models) and three ensemble methods (the simple average, black box-based, and binomial-based methods) were applied and compared to simulate the hydrological process during 1979–1983 in three representative catchments (Daixi, Hengtangcun, and Qiaodongcun). The results indicate that for a single model, the XAJ model and the GR4J model performed relatively well with averaged Nash and Sutcliffe efficiency coefficient (NSE) values of 0.78 and 0.83, respectively. For the ensemble models, the results show that the binomial-based ensemble method (dynamic weight) outperformed with water volume error reduced by 0.8% and NSE value increased by 0.218. The best performance on runoff forecasting occurs in the Hengtang catchment by integrating four hydrologic models based on binomial ensemble method, achieving the water volume error of 2.73% and NSE value of 0.923. Finding would provide scientific support to water engineering design and water resources management in the study areas.
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Dooge, J. C. I. "Hydrologic models and climate change." Journal of Geophysical Research 97, no. D3 (1992): 2677. http://dx.doi.org/10.1029/91jd02156.
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Ford, David T., and Darryl W. Davis. "HYDROLOGIC ENGINEERING CENTER PLANNING MODELS." Journal of the American Water Resources Association 21, no. 1 (February 1985): 135–44. http://dx.doi.org/10.1111/j.1752-1688.1985.tb05359.x.
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Vepraskas, M. J., R. L. Huffman, and G. S. Kreiser. "Hydrologic models for altered landscapes." Geoderma 131, no. 3-4 (April 2006): 287–98. http://dx.doi.org/10.1016/j.geoderma.2005.03.010.
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Pietroniro, A., V. Fortin, N. Kouwen, C. Neal, R. Turcotte, B. Davison, D. Verseghy, et al. "Using the MESH modelling system for hydrological ensemble forecasting of the Laurentian Great Lakes at the regional scale." Hydrology and Earth System Sciences Discussions 3, no. 4 (August 29, 2006): 2473–521. http://dx.doi.org/10.5194/hessd-3-2473-2006.
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Abstract. Environment Canada has been developing a community environmental modelling system (Modélisation Environmentale Communautaire – MEC), which is designed to facilitate coupling between models focusing on different components of the earth system. The ultimate objective of MEC is to use the coupled models to produce operational forecasts. MESH (MEC – Surface and Hydrology), a configuration of MEC currently under development, is specialized for coupled land-surface and hydrological models. To determine the specific requirements for MESH, its different components were implemented on the Laurentian Great Lakes watershed, situated on the Canada–U.S. border. This experiment showed that MESH can help us better understand the behaviour of different land-surface models, test different schemes for producing ensemble streamflow forecasts, and provide a means of sharing the data, the models and the results with collaborators and end-users. This modelling framework is at the heart of a testbed proposal for the Hydrologic Ensemble Prediction Experiment (HEPEX) which should allow us to make use of the North American Ensemble Forecasting System (NAEFS) to improve streamflow forecasts of the Great Lakes tributaries, and demonstrate how MESH can contribute to a Community Hydrologic Prediction System (CHPS).
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Pietroniro, A., V. Fortin, N. Kouwen, C. Neal, R. Turcotte, B. Davison, D. Verseghy, et al. "Development of the MESH modelling system for hydrological ensemble forecasting of the Laurentian Great Lakes at the regional scale." Hydrology and Earth System Sciences 11, no. 4 (May 3, 2007): 1279–94. http://dx.doi.org/10.5194/hess-11-1279-2007.
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Abstract. Environment Canada has been developing a community environmental modelling system (Modélisation Environmentale Communautaire – MEC), which is designed to facilitate coupling between models focusing on different components of the earth system. The ultimate objective of MEC is to use the coupled models to produce operational forecasts. MESH (MEC – Surface and Hydrology), a configuration of MEC currently under development, is specialized for coupled land-surface and hydrological models. To determine the specific requirements for MESH, its different components were implemented on the Laurentian Great Lakes watershed, situated on the Canada-US border. This experiment showed that MESH can help us better understand the behaviour of different land-surface models, test different schemes for producing ensemble streamflow forecasts, and provide a means of sharing the data, the models and the results with collaborators and end-users. This modelling framework is at the heart of a testbed proposal for the Hydrologic Ensemble Prediction Experiment (HEPEX) which should allow us to make use of the North American Ensemble Forecasting System (NAEFS) to improve streamflow forecasts of the Great Lakes tributaries, and demonstrate how MESH can contribute to a Community Hydrologic Prediction System (CHPS).
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Singh, Kuldeep. "Stream Order Delineation using SRTM 30 meter Resolution Digital Elevation Model (DEM) and Hydrology Tools in ArcGIS 10.3 and QGIS: Mapping of Drainage Pattern of Mandi District, Himachal Pradesh, India." Asian Review of Civil Engineering 10, no. 2 (November 5, 2021): 9–17. http://dx.doi.org/10.51983/tarce-2021.10.2.3118.
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The paper describes step by step watershed and stream network delineation based on digital elevation models using the Hydrology tools in ArcGIS and online services for Hydrology and Hydrologic data. The 30-meter resolution SRTM image of Himachal Pradesh was downloaded from open topology website. This was further processed in QGIS and ArcGIS 10.3 software. The different hydrological processes and data management tools were used like, fill, Flow direction; flow accumulation, map algebra, stream orders, stream feature and stream dissolve in order to get the final map of Mandi drainage pattern.
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Harpold, Adrian A., Michael L. Kaplan, P. Zion Klos, Timothy Link, James P. McNamara, Seshadri Rajagopal, Rina Schumer, and Caitriana M. Steele. "Rain or snow: hydrologic processes, observations, prediction, and research needs." Hydrology and Earth System Sciences 21, no. 1 (January 2, 2017): 1–22. http://dx.doi.org/10.5194/hess-21-1-2017.
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Abstract. The phase of precipitation when it reaches the ground is a first-order driver of hydrologic processes in a watershed. The presence of snow, rain, or mixed-phase precipitation affects the initial and boundary conditions that drive hydrological models. Despite their foundational importance to terrestrial hydrology, typical phase partitioning methods (PPMs) specify the phase based on near-surface air temperature only. Our review conveys the diversity of tools available for PPMs in hydrological modeling and the advancements needed to improve predictions in complex terrain with large spatiotemporal variations in precipitation phase. Initially, we review the processes and physics that control precipitation phase as relevant to hydrologists, focusing on the importance of processes occurring aloft. There is a wide range of options for field observations of precipitation phase, but there is a lack of a robust observation networks in complex terrain. New remote sensing observations have the potential to increase PPM fidelity, but generally require assumptions typical of other PPMs and field validation before they are operational. We review common PPMs and find that accuracy is generally increased at finer measurement intervals and by including humidity information. One important tool for PPM development is atmospheric modeling, which includes microphysical schemes that have not been effectively linked to hydrological models or validated against near-surface precipitation-phase observations. The review concludes by describing key research gaps and recommendations to improve PPMs, including better incorporation of atmospheric information, improved validation datasets, and regional-scale gridded data products. Two key points emerge from this synthesis for the hydrologic community: (1) current PPMs are too simple to capture important processes and are not well validated for most locations, (2) lack of sophisticated PPMs increases the uncertainty in estimation of hydrological sensitivity to changes in precipitation phase at local to regional scales. The advancement of PPMs is a critical research frontier in hydrology that requires scientific cooperation between hydrological and atmospheric modelers and field scientists.
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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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Sivapalan, Murugesu. "From engineering hydrology to Earth system science: milestones in the transformation of hydrologic science." Hydrology and Earth System Sciences 22, no. 3 (March 7, 2018): 1665–93. http://dx.doi.org/10.5194/hess-22-1665-2018.
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Abstract. Hydrology has undergone almost transformative changes over the past 50 years. Huge strides have been made in the transition from early empirical approaches to rigorous approaches based on the fluid mechanics of water movement on and below the land surface. However, progress has been hampered by problems posed by the presence of heterogeneity, including subsurface heterogeneity present at all scales. The inability to measure or map the heterogeneity everywhere prevented the development of balance equations and associated closure relations at the scales of interest, and has led to the virtual impasse we are presently in, in terms of development of physically based models needed for hydrologic predictions. An alternative to the mapping of heterogeneity everywhere is a new Earth system science view, which sees the heterogeneity as the end result of co-evolutionary hydrological, geomorphological, ecological, and pedological processes, each operating at a different rate, which help to shape the landscapes that we find in nature, including the heterogeneity that we do not readily see. The expectation is that instead of specifying exact details of the heterogeneity in our models, we can replace it (without loss of information) with the ecosystem function that they perform. Guided by this new Earth system science perspective, development of hydrologic science is now addressing new questions using novel holistic co-evolutionary approaches as opposed to the physical, fluid mechanics based reductionist approaches that we inherited from the recent past. In the emergent Anthropocene, the co-evolutionary view has expanded further to involve interactions and feedbacks with human-social processes as well. In this paper, I present my own perspective of key milestones in the transformation of hydrologic science from engineering hydrology to Earth system science, drawn from the work of several students and colleagues of mine, and discuss their implication for hydrologic observations, theory development, and predictions.
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Shen, Chaopeng, Eric Laloy, Amin Elshorbagy, Adrian Albert, Jerad Bales, Fi-John Chang, Sangram Ganguly, et al. "HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community." Hydrology and Earth System Sciences 22, no. 11 (November 1, 2018): 5639–56. http://dx.doi.org/10.5194/hess-22-5639-2018.
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Abstract. Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens. Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn, stimulate advances in machine learning as well.
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Höge, Marvin, Andreas Scheidegger, Marco Baity-Jesi, Carlo Albert, and Fabrizio Fenicia. "Improving hydrologic models for predictions and process understanding using neural ODEs." Hydrology and Earth System Sciences 26, no. 19 (October 11, 2022): 5085–102. http://dx.doi.org/10.5194/hess-26-5085-2022.
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Abstract. Deep learning methods have frequently outperformed conceptual hydrologic models in rainfall-runoff modelling. Attempts of investigating such deep learning models internally are being made, but the traceability of model states and processes and their interrelations to model input and output is not yet fully understood. Direct interpretability of mechanistic processes has always been considered an asset of conceptual models that helps to gain system understanding aside of predictability. We introduce hydrologic neural ordinary differential equation (ODE) models that perform as well as state-of-the-art deep learning methods in stream flow prediction while maintaining the ease of interpretability of conceptual hydrologic models. In neural ODEs, internal processes that are represented in differential equations, are substituted by neural networks. Therefore, neural ODE models enable the fusion of deep learning with mechanistic modelling. We demonstrate the basin-specific predictive performance for 569 catchments of the continental United States. For exemplary basins, we analyse the dynamics of states and processes learned by the model-internal neural networks. Finally, we discuss the potential of neural ODE models in hydrology.
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Pande, Saket, Luis A. Bastidas, Sandjai Bhulai, and Mac McKee. "Parameter-dependent convergence bounds and complexity measure for a class of conceptual hydrological models." Journal of Hydroinformatics 14, no. 2 (October 18, 2011): 443–63. http://dx.doi.org/10.2166/hydro.2011.005.
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We provide analytical bounds on convergence rates for a class of hydrologic models and consequently derive a complexity measure based on the Vapnik–Chervonenkis (VC) generalization theory. The class of hydrologic models is a spatially explicit interconnected set of linear reservoirs with the aim of representing globally nonlinear hydrologic behavior by locally linear models. Here, by convergence rate, we mean convergence of the empirical risk to the expected risk. The derived measure of complexity measures a model's propensity to overfit data. We explore how data finiteness can affect model selection for this class of hydrologic model and provide theoretical results on how model performance on a finite sample converges to its expected performance as data size approaches infinity. These bounds can then be used for model selection, as the bounds provide a tradeoff between model complexity and model performance on finite data. The convergence bounds for the considered hydrologic models depend on the magnitude of their parameters, which are the recession parameters of constituting linear reservoirs. Further, the complexity of hydrologic models not only varies with the magnitude of their parameters but also depends on the network structure of the models (in terms of the spatial heterogeneity of parameters and the nature of hydrologic connectivity).
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Thompson, S. E., M. Sivapalan, C. J. Harman, V. Srinivasan, M. R. Hipsey, P. Reed, A. Montanari, and G. Blöschl. "Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene." Hydrology and Earth System Sciences Discussions 10, no. 6 (June 20, 2013): 7897–961. http://dx.doi.org/10.5194/hessd-10-7897-2013.
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Abstract. Globally, many different kinds of water resources management issues call for policy and infrastructure based responses. Yet responsible decision making about water resources management raises a fundamental challenge for hydrologists: making predictions about water resources on decadal-to-century long timescales. Obtaining insight into hydrologic futures over 100 yr timescales forces researchers to address internal and exogenous changes in the properties of hydrologic systems. To do this, new hydrologic research must identify, describe and model feedbacks between water and other changing, coupled environmental subsystems. These models must be constrained to yield useful insights, despite the many likely sources of uncertainty in their predictions. Chief among these uncertainties are the impacts of the increasing role of human intervention in the global water cycle – a defining challenge for hydrology in the Anthropocene. Here we present a research agenda that proposes a suite of strategies to address these challenges. The research agenda focuses on the development of co-evolutionary hydrologic modeling to explore coupling across systems, and to address the implications of this coupling on the long-time behavior of the coupled systems. Three research directions support the development of these models: hydrologic reconstruction, comparative hydrology and model-data learning. These strategies focus on understanding hydrologic processes and feedbacks over long timescales, across many locations, and through strategic coupling of observational and model data in specific systems. We highlight the value of use-inspired and team-based science that is motivated by real-world hydrologic problems but targets improvements in fundamental understanding to support decision-making and management.
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Xu, Xiaoyong, Jonathan Li, and Bryan A. Tolson. "Progress in integrating remote sensing data and hydrologic modeling." Progress in Physical Geography: Earth and Environment 38, no. 4 (June 5, 2014): 464–98. http://dx.doi.org/10.1177/0309133314536583.
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Remote sensing and hydrologic modeling are two key approaches to evaluate and predict hydrology and water resources. Remote sensing technologies, due to their ability to offer large-scale spatially distributed observations, have opened up new opportunities for the development of fully distributed hydrologic and land-surface models. In general, remote sensing data can be applied to land-surface and hydrologic modeling through three strategies: model inputs (basin information, boundary conditions, etc.), parameter estimation (model calibration), and state estimation (data assimilation). There has been an intensive global research effort to integrate remote sensing and land/hydrologic modeling over the past few decades. In particular, in recent years significant progress has been made in land/hydrologic remote sensing data assimilation. Hence there is a demand for an up-to-date review on these efforts. This paper presents an overview of research efforts to combine hydrologic/land models and remote sensing products (mainly including precipitation, surface soil moisture, snow cover, snow water equivalent, leaf area index, and evapotranspiration) over the past decade. This paper also discusses the major challenges remaining in this field, and recommends the directions for further research efforts.
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Subramani, T., and K. A.Niasi. "Study of Hydrological Parameter with Respect to DEM Using GIS & RS in Nelliampathy Hill, Kerala." International Journal of Engineering & Technology 7, no. 3.10 (July 15, 2018): 125. http://dx.doi.org/10.14419/ijet.v7i3.10.15643.
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Digital elevation models (DEMs) represent the total topography, surface flow is one of the more important data sources for deriving variables used by numerous hydrologic models. A lot of research has been directed to address vulnerability related with error in digital height models (DEMs) and the spread of blunder to determined terrain parameters. This audit unites a discourse of research in major topical regions identified with DEM vulnerability that influence the utilization of DEMs for hydrologic applications. The work is to give some understanding into the characterization of elevation data quality and the relationship amongst topography and water assets models. A key characteristic of circulated displaying is the spatially factor portrayal of the watershed as far as topography, vegetative, or land use/cover, soils and impenetrable territories and the subordinate model parameters that represent the hydrologic procedures of infiltration, evapotranspiration, and runoff. In our study, application of DEM and deriving hydrological parameters using remote sensing and GIS technology at Nelliampathy hill, Kerala.
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Thompson, S. E., M. Sivapalan, C. J. Harman, V. Srinivasan, M. R. Hipsey, P. Reed, A. Montanari, and G. Blöschl. "Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene." Hydrology and Earth System Sciences 17, no. 12 (December 12, 2013): 5013–39. http://dx.doi.org/10.5194/hess-17-5013-2013.
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Abstract. Globally, many different kinds of water resources management issues call for policy- and infrastructure-based responses. Yet responsible decision-making about water resources management raises a fundamental challenge for hydrologists: making predictions about water resources on decadal- to century-long timescales. Obtaining insight into hydrologic futures over 100 yr timescales forces researchers to address internal and exogenous changes in the properties of hydrologic systems. To do this, new hydrologic research must identify, describe and model feedbacks between water and other changing, coupled environmental subsystems. These models must be constrained to yield useful insights, despite the many likely sources of uncertainty in their predictions. Chief among these uncertainties are the impacts of the increasing role of human intervention in the global water cycle – a defining challenge for hydrology in the Anthropocene. Here we present a research agenda that proposes a suite of strategies to address these challenges from the perspectives of hydrologic science research. The research agenda focuses on the development of co-evolutionary hydrologic modeling to explore coupling across systems, and to address the implications of this coupling on the long-time behavior of the coupled systems. Three research directions support the development of these models: hydrologic reconstruction, comparative hydrology and model-data learning. These strategies focus on understanding hydrologic processes and feedbacks over long timescales, across many locations, and through strategic coupling of observational and model data in specific systems. We highlight the value of use-inspired and team-based science that is motivated by real-world hydrologic problems but targets improvements in fundamental understanding to support decision-making and management. Fully realizing the potential of this approach will ultimately require detailed integration of social science and physical science understanding of water systems, and is a priority for the developing field of sociohydrology.
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Caja, CC, NL Ibunes, JA Paril, AR Reyes, JP Nazareno, CE Monjardin, and FA Uy. "Effects of Land Cover Changes to the Quantity of Water Supply and Hydrologic Cycle using Water Balance Models." MATEC Web of Conferences 150 (2018): 06004. http://dx.doi.org/10.1051/matecconf/201815006004.
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The hydrologic cycle is a recurring consequence of different forms of movement of water and changes of its physical state on a given area of the earth. The land cover of a certain area is a significant factor affecting the watershed hydrology. This also affects the quantity of water supply within the watershed. This study assessed the impacts of the changing land cover of the Ipo watershed, a part of the Angat-Ipo-La Mesa water system which is the main source of Metro Manila’s water supply. The environmental impacts were assessed using the interaction of vegetation cover changes and the output flow rates in Ipo watershed. Using hydrologic modelling system, the hydrological balance using rainfall, vegetation and terrain data of the watershed was simulated. Over the years, there has been a decreasing land cover within the watershed caused mostly by deforestation and other human activities. This significant change in the land cover resulted to extreme increase in water discharge at all streams and rivers in the watershed and the water balance of the area were affected as saturation and shape of the land terrain changes.
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Hollaus, M., W. Wagner, and K. Kraus. "Airborne laser scanning and usefulness for hydrological models." Advances in Geosciences 5 (December 16, 2005): 57–63. http://dx.doi.org/10.5194/adgeo-5-57-2005.
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Abstract. Digital terrain models form the basis for distributed hydrologic models as well as for two-dimensional hydraulic river flood models. The technique used for generating high accuracy digital terrain models has shifted from stereoscopic aerial-photography to airborne laser scanning during the last years. Since the disastrous floods 2002 in Austria, large airborne laser-scanning flight campaigns have been carried out for several river basins. Additionally to the topographic information, laser scanner data offer also the possibility to estimate object heights (vegetation, buildings). Detailed land cover maps can be derived in conjunction with the complementary information provided by high-resolution colour-infrared orthophotos. As already shown in several studies, the potential of airborne laser scanning to provide data for hydrologic/hydraulic applications is high. These studies were mostly constraint to small test sites. To overcome this spatial limitation, the current paper summarises the experiences to process airborne laser scanner data for large mountainous regions, thereby demonstrating the applicability of this technique in real-world hydrological applications.
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Johnson, K. A., and N. Sitar. "Hydrologic conditions leading to debris-flow initiation." Canadian Geotechnical Journal 27, no. 6 (December 1, 1990): 789–801. http://dx.doi.org/10.1139/t90-092.
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Mitigation of the hazards posed by debris flows requires an understanding of the mechanisms leading to their initiation. The objectives of this study were to evaluate and document the hydrologic response of a potential debris-flow source area to major rainstorms and to evaluate whether traditional models of hillslope hydrology can account for the observed response. A field site in an area of previous debris-flow activity was instrumented and monitored for two winter seasons. Hydrologic responses for a wide variety of antecedent conditions were recorded, including two storm events that produced well-defined positive pore-pressure pulses at the site and initiated numerous debris flows in the immediate vicinity of the site. The observed hydrologic response was highly dependent on antecedent moisture conditions which can be characterized by soil matric suction measurements. The pressure-head pulses observed had a magnitude of approximately 50 cm of water, were transient, traveled downslope, and exhibited some spatial variability. Traditional models of hillslope hydrology do not fully account for the positive pore-pressure pulses observed high on the hillslope. Key words: debris flow, hillslope hydrology, pore pressure, antecedent moisture, tensiometer, piezometer, field investigation.
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Rajaram, Harihar, and Konstantine P. Georgakakos. "Recursive parameter estimation of hydrologic models." Water Resources Research 25, no. 2 (February 1989): 281–94. http://dx.doi.org/10.1029/wr025i002p00281.
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K. W. Migliaccio and P. Srivastava. "Hydrologic Components of Watershed-Scale Models." Transactions of the ASABE 50, no. 5 (2007): 1695–703. http://dx.doi.org/10.13031/2013.23955.
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Bouraoui, Faycal, and Mary Leigh Wolfe. "Application of hydrologic models to rangelands." Journal of Hydrology 121, no. 1-4 (December 1990): 173–91. http://dx.doi.org/10.1016/0022-1694(90)90231-l.
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Maneta, M. P., and N. L. Silverman. "A Spatially Distributed Model to Simulate Water, Energy, and Vegetation Dynamics Using Information from Regional Climate Models." Earth Interactions 17, no. 11 (August 1, 2013): 1–44. http://dx.doi.org/10.1175/2012ei000472.1.
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Abstract Studies seeking to understand the impacts of climate variability and change on the hydrology of a region need to take into account the dynamics of vegetation and its interaction with the hydrologic and energy cycles. Yet, most of the hydrologic models used for these kinds of studies assume that vegetation is static. This paper presents a dynamic, spatially explicit model that couples a vertical energy balance scheme (surface and canopy layer) to a hydrologic model and a forest growth component to capture the dynamic interactions between energy, vegetation, and hydrology at hourly to daily time scales. The model is designed to be forced with outputs from regional climate models. Lateral water transfers are simulated using a 1D kinematic wave model. Infiltration is simulated using the Green and Ampt approximation to Richard's equation. The dynamics of soil moisture and energy drives carbon assimilation and forest growth, which in turn affect the distribution of energy and water through leaf dynamics by altering light interception, shading, and enhanced transpiration. The model is demonstrated in two case studies simulating energy, water, and vegetation dynamics at two different spatial and temporal scales.
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Sehgal, Vinit, Venkataramana Sridhar, Luke Juran, and Jactone Arogo Ogejo. "Integrating Climate Forecasts with the Soil and Water Assessment Tool (SWAT) for High-Resolution Hydrologic Simulations and Forecasts in the Southeastern U.S." Sustainability 10, no. 9 (August 29, 2018): 3079. http://dx.doi.org/10.3390/su10093079.
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This study provides high-resolution modeling of daily water budget components at Hydrologic Unit Code (HUC)-12 resolution for 50 watersheds of the South Atlantic Gulf (SAG) region in the southeastern U.S. (SEUS) by implementing the Soil and Water Assessment Tool (SWAT) model in the form of a near real-time, semi-automated framework. A near real-time hydrologic simulation framework is implemented with a lead time of nine months (March–December 2017) by integrating the calibrated SWAT model with National Centers for Environmental Prediction coupled forecast system model version 2 (CFSv2) weather data to forecast daily water balance components. The modeling exercise is conducted as a precursor for various future hydrologic studies (retrospective or forecasting) for the region by providing a calibrated hydrological dataset at high spatial (HUC-12) and temporal (1-day) resolution. The models are calibrated (January 2003–December 2010) and validated (January 2011–December 2013) for each watershed using the observed streamflow data from 50 United States Geological Survey (USGS) gauging stations. The water balance analysis for the region shows that the implemented models satisfactorily represent the hydrology of the region across different sub-regions (Appalachian highlands, plains, and coastal wetlands) and seasons. While CFSv2-driven SWAT models are able to provide reasonable performance in near real-time and can be used for decision making in the region, caution is advised for using model outputs as the streamflow forecasts display significant deviation from observed streamflow for all watersheds for lead times greater than a month.
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Herman, J. D., J. B. Kollat, P. M. Reed, and T. Wagener. "Technical Note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models." Hydrology and Earth System Sciences 17, no. 7 (July 24, 2013): 2893–903. http://dx.doi.org/10.5194/hess-17-2893-2013.
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Abstract. The increase in spatially distributed hydrologic modeling warrants a corresponding increase in diagnostic methods capable of analyzing complex models with large numbers of parameters. Sobol' sensitivity analysis has proven to be a valuable tool for diagnostic analyses of hydrologic models. However, for many spatially distributed models, the Sobol' method requires a prohibitive number of model evaluations to reliably decompose output variance across the full set of parameters. We investigate the potential of the method of Morris, a screening-based sensitivity approach, to provide results sufficiently similar to those of the Sobol' method at a greatly reduced computational expense. The methods are benchmarked on the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) over a six-month period in the Blue River watershed, Oklahoma, USA. The Sobol' method required over six million model evaluations to ensure reliable sensitivity indices, corresponding to more than 30 000 computing hours and roughly 180 gigabytes of storage space. We find that the method of Morris is able to correctly screen the most and least sensitive parameters with 300 times fewer model evaluations, requiring only 100 computing hours and 1 gigabyte of storage space. The method of Morris proves to be a promising diagnostic approach for global sensitivity analysis of highly parameterized, spatially distributed hydrologic models.
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Herman, J. D., J. B. Kollat, P. M. Reed, and T. Wagener. "Technical note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models." Hydrology and Earth System Sciences Discussions 10, no. 4 (April 5, 2013): 4275–99. http://dx.doi.org/10.5194/hessd-10-4275-2013.
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Abstract. The increase in spatially distributed hydrologic modeling warrants a corresponding increase in diagnostic methods capable of analyzing complex models with large numbers of parameters. Sobol' sensitivity analysis has proven to be a valuable tool for diagnostic analyses of hydrologic models. However, for many spatially distributed models, the Sobol' method requires a prohibitive number of model evaluations to reliably decompose output variance across the full set of parameters. We investigate the potential of the method of Morris, a screening-based sensitivity approach, to provide results sufficiently similar to those of the Sobol' method at a greatly reduced computational expense. The methods are benchmarked on the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) model over a six-month period in the Blue River Watershed, Oklahoma, USA. The Sobol' method required over six million model evaluations to ensure reliable sensitivity indices, corresponding to more than 30 000 computing hours and roughly 180 gigabytes of storage space. We find that the method of Morris is able to correctly identify sensitive and insensitive parameters with 300 times fewer model evaluations, requiring only 100 computing hours and 1 gigabyte of storage space. Method of Morris proves to be a promising diagnostic approach for global sensitivity analysis of highly parameterized, spatially distributed hydrologic models.
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Astuti, Anik Juli Dwi, Sofie Annys, Mekete Dessie, Jan Nyssen, and Stefaan Dondeyne. "To What Extent Is Hydrologic Connectivity Taken into Account in Catchment Studies in the Lake Tana Basin, Ethiopia? A Review." Land 11, no. 12 (November 30, 2022): 2165. http://dx.doi.org/10.3390/land11122165.
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Knowledge of hydrologic connectivity is important to grasp the hydrological response at a basin scale, particularly as changes in connectivity can have a negative effect on the environment. In the context of a changing climate, being able to predict how changes in connectivity will affect runoff and sediment transport is particularly relevant for land-use planning. Many studies on hydrology, geomorphology and climatology have been conducted in the Lake Tana Basin in Ethiopia, which is undergoing rapid development and significant environmental changes. This systematic literature review aims at assessing to what extent the hydrologic connectivity has been taken into account in such research, and to identify research gaps relevant to land and water management. On the Web of Science and Scopus databases, 135 scientific articles covering those topics were identified. Aspects of hydrologic connectivity were mostly implicitly taken into account based on process-based, statistical and descriptive models. Amongst the drivers of changing connectivity, the climate was covered by a large majority of publications (64%). Components of structural hydrologic connectivity were accounted for by considering geomorphology (54%) and soils (47%), and to a lesser extent, hydrography (16%) and geology (12%). Components of functional connectivity were covered by looking at surface water fluxes (61%), sediment fluxes (18%) and subsurface water fluxes (13%). While numerous studies of the Lake Tana Basin accounted for the hydrologic connectivity implicitly, these related predominantly to functional components. The structural components are given less attention, while in the context of a changing climate, better insights into their influence on the hydrologic seem most relevant. Better knowledge of the static aspect of connectivity is particularly important for targeting appropriate soil and water conservation strategies. Being able to explicitly assess the ‘structural connectivity’ is therefore of direct relevance for land management and land-use policy.
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Wu, Rui, Lei Yang, Chao Chen, Sajjad Ahmad, Sergiu M. Dascalu, and Frederick C. Harris Jr. "MELPF version 1: Modeling Error Learning based Post-Processor Framework for Hydrologic Models Accuracy Improvement." Geoscientific Model Development 12, no. 9 (September 23, 2019): 4115–31. http://dx.doi.org/10.5194/gmd-12-4115-2019.
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Abstract. This paper studies how to improve the accuracy of hydrologic models using machine-learning models as post-processors and presents possibilities to reduce the workload to create an accurate hydrologic model by removing the calibration step. It is often challenging to develop an accurate hydrologic model due to the time-consuming model calibration procedure and the nonstationarity of hydrologic data. Our findings show that the errors of hydrologic models are correlated with model inputs. Thus motivated, we propose a modeling-error-learning-based post-processor framework by leveraging this correlation to improve the accuracy of a hydrologic model. The key idea is to predict the differences (errors) between the observed values and the hydrologic model predictions by using machine-learning techniques. To tackle the nonstationarity issue of hydrologic data, a moving-window-based machine-learning approach is proposed to enhance the machine-learning error predictions by identifying the local stationarity of the data using a stationarity measure developed based on the Hilbert–Huang transform. Two hydrologic models, the Precipitation–Runoff Modeling System (PRMS) and the Hydrologic Modeling System (HEC-HMS), are used to evaluate the proposed framework. Two case studies are provided to exhibit the improved performance over the original model using multiple statistical metrics.
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Javadinejad, Safieh, Rebwar Dara, and Neda Dolatabadi. "Runoff coefficient estimation for various catchment surfaces." Resources Environment and Information Engineering 3, no. 1 (2022): 145–55. http://dx.doi.org/10.25082/reie.2021.01.005.
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The definition of runoff coefficient is the portion of rainfall that turn into direct runoff throughout an occurrence, and it is a significant perception in engineering hydrology and is extensively applied for design and as a diagnostic variable to show runoff creation in catchments. Event runoff coefficients may also be applied in event‐based developed flood frequency models that measure flood frequencies from rainfall frequencies and are valuable for recognizing the flood frequency controls in a specific hydrologic or climatic regime. Only a few previous studies worked on hydrological systems and processes deeply at catchment scale. Also in many catchments because of lacking data sets, analysis of land use change and water management and risks causes uncertainty in predictions of hydrological processes can be decreased. This problem is more important for predicting hydrology of ungauged basins in developing countries. The purpose of this study is to review predicting hydrology of ungauged basins.
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Parajka, J., V. Naeimi, G. Blöschl, W. Wagner, R. Merz, and K. Scipal. "Assimilating scatterometer soil moisture data into conceptual hydrologic models at the regional scale." Hydrology and Earth System Sciences Discussions 2, no. 6 (December 22, 2005): 2739–86. http://dx.doi.org/10.5194/hessd-2-2739-2005.
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Abstract. This paper examines the potential of scatterometer data from ERS satellites for improving hydrological simulations in both gauged and ungauged catchments. We compare the soil moisture dynamics simulated by a semidistributed hydrologic model in 320 Austrian catchments with the soil moisture dynamics inferred from the satellite data. The most apparent differences occur in the Alpine areas. Assimilating the scatterometer data into the hydrologic model during the calibration phase improves the relationship between the two soil moisture estimates without any significant decrease in runoff model efficiency. For the case of ungauged catchments, assimilating scatterometer data does not improve the daily runoff simulations but does provide more consistent soil moisture estimates. If the main interest is in obtaining estimates of catchment soil moisture, reconciling the two sources of soil moisture information seems to be of value because of the different error structures.
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Parajka, J., V. Naeimi, G. Blöschl, W. Wagner, R. Merz, and K. Scipal. "Assimilating scatterometer soil moisture data into conceptual hydrologic models at the regional scale." Hydrology and Earth System Sciences 10, no. 3 (May 17, 2006): 353–68. http://dx.doi.org/10.5194/hess-10-353-2006.
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Abstract. This paper examines the potential of scatterometer data from ERS satellites for improving hydrological simulations in both gauged and ungauged catchments. We compare the soil moisture dynamics simulated by a semidistributed hydrologic model in 320 Austrian catchments with the soil moisture dynamics inferred from the satellite data. The most apparent differences occur in the Alpine areas. Assimilating the scatterometer data into the hydrologic model during the calibration phase improves the relationship between the two soil moisture estimates without any significant decrease in runoff model efficiency. For the case of ungauged catchments, assimilating scatterometer data does not improve the daily runoff simulations but does provide more consistent soil moisture estimates. If the main interest is in obtaining estimates of catchment soil moisture, reconciling the two sources of soil moisture information seems to be of value because of the different error structures.
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Ehsan Bhuiyan, Md Abul, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, Jan Polcher, Clément Albergel, Emanuel Dutra, Gabriel Fink, Alberto Martínez-de la Torre, and Simon Munier. "Assessment of precipitation error propagation in multi-model global water resource reanalysis." Hydrology and Earth System Sciences 23, no. 4 (April 15, 2019): 1973–94. http://dx.doi.org/10.5194/hess-23-1973-2019.
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Abstract. This study focuses on the Iberian Peninsula and investigates the propagation of precipitation uncertainty, and its interaction with hydrologic modeling, in global water resource reanalysis. Analysis is based on ensemble hydrologic simulations for a period spanning 11 years (2000–2010). To simulate the hydrological variables of surface runoff, subsurface runoff, and evapotranspiration, we used four land surface models (LSMs) – JULES (Joint UK Land Environment Simulator), ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems), SURFEX (Surface Externalisée), and HTESSEL (Hydrology – Tiled European Centre for Medium-Range Weather Forecasts – ECMWF – Scheme for Surface Exchanges over Land) – and one global hydrological model, WaterGAP3 (Water – a Global Assessment and Prognosis). Simulations were carried out for five precipitation products – CMORPH (the Climate Prediction Center Morphing technique of the National Oceanic and Atmospheric Administration, or NOAA), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), 3B42V(7), ECMWF reanalysis, and a machine-learning-based blended product. As a reference, we used a ground-based observation-driven precipitation dataset, named SAFRAN, available at 5 km, 1 h resolution. We present relative performances of hydrologic variables for the different multi-model and multi-forcing scenarios. Overall, results reveal the complexity of the interaction between precipitation characteristics and different modeling schemes and show that uncertainties in the model simulations are attributed to both uncertainty in precipitation forcing and the model structure. Surface runoff is strongly sensitive to precipitation uncertainty, and the degree of sensitivity depends significantly on the runoff generation scheme of each model examined. Evapotranspiration fluxes are comparatively less sensitive for this study region. Finally, our results suggest that there is no single model–forcing combination that can outperform all others consistently for all variables examined and thus reinforce the fact that there are significant benefits to exploring different model structures as part of the overall modeling approaches used for water resource applications.
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Newman, Andrew J., Amanda G. Stone, Manabendra Saharia, Kathleen D. Holman, Nans Addor, and Martyn P. Clark. "Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system." Hydrology and Earth System Sciences 25, no. 10 (October 25, 2021): 5603–21. http://dx.doi.org/10.5194/hess-25-5603-2021.
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Abstract. This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain. The major components of the stochastic hydrologic modeling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100 000 years at two watersheds representing different hydroclimates across the western USA. A total of 10 hydrologic model structures were configured, calibrated, and run within the Framework for Understanding Structural Errors (FUSE) modular modeling framework for each of the two watersheds. Model parameters and initial conditions were derived from long-term calibrated simulations using a 100 member historical meteorology ensemble. A stochastic event-based hydrologic modeling workflow was developed using the calibrated models in which millions of flood event simulations were performed for each basin. The analysis of variance method was then used to quantify the relative contributions of model structure, model parameters, initial conditions, and precipitation inputs to flood magnitudes for different return periods. Results demonstrate that different components of the modeling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare floods, while initial conditions are most influential for more frequent events. However, the hydrological model structure and structure–parameter interactions together play an equally important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.
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Fenicia, F., D. P. Solomatine, H. H. G. Savenije, and P. Matgen. "Soft combination of local models in a multi-objective framework." Hydrology and Earth System Sciences Discussions 4, no. 1 (January 19, 2007): 91–123. http://dx.doi.org/10.5194/hessd-4-91-2007.
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Abstract. Conceptual hydrologic models are useful tools as they provide an interpretable representation of the hydrologic behaviour of a catchment. Their representation of catchment's hydrological processes and physical characteristics, however, implies simplification of the complexity and heterogeneity of reality. As a result, those models often show a lack of flexibility in reproducing the vast spectrum of catchment responses. Hence, the accuracy in reproducing certain aspects of the system behaviour is often paid in terms of a lack of accuracy in the representation of other aspects. By acknowledging the structural limitations of those models, a modular approach to hydrological simulation is proposed. Instead of using a single model to reproduce the full range of catchment responses, multiple models are used, each of them assigned to a specific task. The approach is here demonstrated in the case where the different models are associated with different parameter realizations within a fixed model structure. We show that using a composite "global" model, obtained by a combination of individual "local" models, the accuracy of the simulation is improved. We argue that this approach can be useful because it partially overcomes the structural limitations that a conceptual model might exhibit. The approach is shown in application to the discharge simulation of the experimental Alzette River basin in Luxembourg, with a conceptual model that follows the structure of the HBV model.
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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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P. C. Beeson, P. C. Doraiswamy, A. M. Sadeghi, M. Di Luzio, M. D. Tomer, J. G. Arnold, and C. S. T. Daughtry. "Treatments of Precipitation Inputs to Hydrologic Models." Transactions of the ASABE 54, no. 6 (2011): 2011–20. http://dx.doi.org/10.13031/2013.40652.
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Thiemann, M., M. Trosset, H. Gupta, and S. Sorooshian. "Bayesian recursive parameter estimation for hydrologic models." Water Resources Research 37, no. 10 (October 2001): 2521–35. http://dx.doi.org/10.1029/2000wr900405.
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Yapo, Patrice Ogou, Hoshin Vijai Gupta, and Soroosh Sorooshian. "Multi-objective global optimization for hydrologic models." Journal of Hydrology 204, no. 1-4 (January 1998): 83–97. http://dx.doi.org/10.1016/s0022-1694(97)00107-8.
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Vogel, Richard M. "Stochastic watershed models for hydrologic risk management." Water Security 1 (July 2017): 28–35. http://dx.doi.org/10.1016/j.wasec.2017.06.001.
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