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Artículos de revistas sobre el tema "Hydrologic models"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 25 de julio de 2025

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1

Sahu, Chinmayee, and Surendra Kumar Chandniha. "A Brief Review on Hydrological Modelling." International Journal of Environment and Climate Change 15, no. 1 (2025): 352–68. https://doi.org/10.9734/ijecc/2025/v15i14697.

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The interaction between water, climate, soil and land use is primary to the hydrological modelling concept. Hydrological models include spatial and temporal features. Hydrologists utilize hydrologic models as a primary tool for a variety of tasks including managing water resources, managing urban and rural areas, modelling ground water and more. In order to implement hydrologic models with ease, it is necessary to thoroughly comprehend their properties which have been developed and improved through the years. It is difficult to categorize hydrologic models precisely and various hydrologists ma

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2

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 (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 clima

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3

Mendoza, Pablo A., Martyn P. Clark, Naoki Mizukami, et al. "Effects of Hydrologic Model Choice and Calibration on the Portrayal of Climate Change Impacts." Journal of Hydrometeorology 16, no. 2 (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

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4

Sarker, Shiblu, and Olkeba Tolessa Leta. "Review of Watershed Hydrology and Mathematical Models." Eng 6, no. 6 (2025): 129. https://doi.org/10.3390/eng6060129.

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This study provides a comprehensive overview of watershed hydrology and mathematical models, focusing on its hydrological features and the implementation of hydrological modeling for effective water resource modeling and assessment, planning, and management. The study presents a thorough review of the primary transport mechanisms of water within a watershed, particularly the river network, and examines its physical and stochastic characteristics. It also discusses the derivation of governing equations for various hydrological processes within a watershed, including evaluating their applicabili

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5

Ganoulis, J. "Modeling Hydrologic Phenomena [Free opinion]." Revue des sciences de l'eau 9, no. 4 (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 ph

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6

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 (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 a

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7

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 (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 actu

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8

Pawitan, Hidayat, and Muh Taufik. "Non-linear Routing Scheme at Grid Cell Level for Large Scale Hydrologic Models: A Review." Agromet 35, no. 2 (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 geomorph

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9

Perra, Enrica, Monica Piras, Roberto Deidda, et al. "Multimodel assessment of climate change-induced hydrologic impacts for a Mediterranean catchment." Hydrology and Earth System Sciences 22, no. 7 (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 bala

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10

Ravi, Naik. "Spatial Variability of Rainfall and Classification of Peninsular Indian Catchments." International Journal of Advanced Engineering and Nano Technology (IJAENT) 10, no. 12 (2023): 8–15. https://doi.org/10.35940/ijaent.F4214.12101223.

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<strong>Abstract: </strong>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

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11

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 (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 fro

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12

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 (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 unco

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13

Simeone, Caelan, Sydney Foks, Erin Towler, Timothy Hodson, and Thomas Over. "Evaluating Hydrologic Model Performance for Characterizing Streamflow Drought in the Conterminous United States." Water 16, no. 20 (2024): 2996. http://dx.doi.org/10.3390/w16202996.

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Hydrologic models are the primary tools that are used to simulate streamflow drought and assess impacts. However, there is little consensus about how to evaluate the performance of these models, especially as hydrologic modeling moves toward larger spatial domains. This paper presents a comprehensive multi-objective approach to systematically evaluating the critical features in streamflow drought simulations performed by two widely used hydrological models. The evaluation approach captures how well a model classifies observed periods of drought and non-drought, quantifies error components duri

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14

V, Chavan Vaishnavi. "Evaluation of Municipal Solid Waste Landfill by HELP Model and Leachate Modelling by Hydrus-1d: A Review." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 79–82. https://doi.org/10.22214/ijraset.2024.65712.

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The effective management of municipal solid waste (MSW) landfills is vital for minimizing their environmental and public health impacts. The Hydrologic Evaluation of Landfill Performance (HELP) model and HYDRUS-1D are widely used tools for modeling landfill hydrology and leachate dynamics, respectively. This review consolidates recent advances in their application for evaluating MSW landfills. The HELP model's capabilities in simulating landfill hydrological behavior and HYDRUS-1D’s precision in modeling leachate transport through porous media are explored. The paper highlights the advantages,

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15

Wang, Jie, Guoqing Wang, Amgad Elmahdi, et al. "Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods." Open Geosciences 13, no. 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 m

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16

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 (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 a

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17

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 (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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18

Pietroniro, A., V. Fortin, N. Kouwen, 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 (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 Lau

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19

Pietroniro, A., V. Fortin, N. Kouwen, 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 (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 Lau

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20

Tanmay, Dhar, Mario M. Miglietta Dr., and Bhupendra S. Rawat Dr. "Calibration of WRF-Hydro for Bhagirathi-Alaknanda Basin." International Journal of Engineering and Advanced Technology (IJEAT) 11, no. 5 (2022): 24–29. https://doi.org/10.35940/ijeat.E3524.0611522.

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<strong>Abstract: </strong>Complemented with the coupling of the most sensitive and challenging interaction between terrestrial hydrology and atmosphere, the Bhagirathi-Alaknanda basin of the Garhwal Himalaya requires advanced dynamic and most comprehensively coupled atmospheric-hydrological models for simulation of streamflows. WRF-Hydro model which is enhanced by integrating most advanced set of hydrologic physics parameterization accounting for lateral water flow occurring on the land surface is the most compatible for this basin. This paper illustrates the development and the calibration o

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21

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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22

Ford, David T., and Darryl W. Davis. "HYDROLOGIC ENGINEERING CENTER PLANNING MODELS." Journal of the American Water Resources Association 21, no. 1 (1985): 135–44. http://dx.doi.org/10.1111/j.1752-1688.1985.tb05359.x.

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23

Vepraskas, M. J., R. L. Huffman, and G. S. Kreiser. "Hydrologic models for altered landscapes." Geoderma 131, no. 3-4 (2006): 287–98. http://dx.doi.org/10.1016/j.geoderma.2005.03.010.

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24

Gunathilake, Miyuru B., Chamaka Karunanayake, Anura S. Gunathilake, et al. "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 th

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25

Olaleye, Oluwatobi, Olayiwola Akintola, Rafiu Jımoh, Olukemi Gbadebo, and Oluwaseun Faloye. "REVIEW AND COMPARATIVE STUDY OF HYDROLOGICAL MODELS FOR RAINFALL-RUNOFF MODELLING." International Journal of Environment and Geoinformatics 11, no. 3 (2024): 119–29. http://dx.doi.org/10.30897/ijegeo.1514176.

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Water is considered as an important resources for human existence on the earth. In order to simulate or optimized hydrological data for various water resources management, several hydrological models are very useful to attain this aim for water resources management and as a decision support tools. A rainfall-runoff model is a quantitative prototype explaining the rainfall-runoff interactions at basin scale. The hydrological models have peculiarities in terms of capabilities for various water resources management. This paper tends to reviewed over fifty (50) papers that are peculiar to hydrolog

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26

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 (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

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27

Shen, Chaopeng, Eric Laloy, Amin Elshorbagy, et al. "HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community." Hydrology and Earth System Sciences 22, no. 11 (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 interpr

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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 (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

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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 (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 se

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Harpold, Adrian A., Michael L. Kaplan, P. Zion Klos, et al. "Rain or snow: hydrologic processes, observations, prediction, and research needs." Hydrology and Earth System Sciences 21, no. 1 (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 spat

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Thompson, S. E., M. Sivapalan, C. J. Harman, et al. "Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene." Hydrology and Earth System Sciences Discussions 10, no. 6 (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 chang

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32

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 (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 ha

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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 (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 dat

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Thompson, S. E., M. Sivapalan, C. J. Harman, et al. "Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene." Hydrology and Earth System Sciences 17, no. 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 cha

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35

Caja, CC, NL Ibunes, JA Paril, et al. "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

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Johnson, K. A., and N. Sitar. "Hydrologic conditions leading to debris-flow initiation." Canadian Geotechnical Journal 27, no. 6 (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

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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). Deta

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38

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 (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 d

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39

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 (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

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40

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 (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 resu

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41

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 (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 resu

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42

Daide, Fatima, Thomas Hasiotis, Soumaya Nabih, et al. "Assessing Hydrological Alterations and Environmental Flow Components in the Beht River Basin, Morocco, Using Integrated SWAT and IHA Models." Hydrology 12, no. 5 (2025): 109. https://doi.org/10.3390/hydrology12050109.

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This study presents a comprehensive analysis of hydrological alterations and environmental flow components in the Beht River basin in northwest Morocco, using a coupled approach involving the Soil and Water Assessment Tool (SWAT) for hydrological modeling, the Indicators of Hydrologic Alteration (IHA) for flow regime assessment, and the Standardized Precipitation Index (SPI) for drought characterization. The SWAT model, run on a daily time step, showed satisfactory performance in terms of statistical criteria for both calibration and validation periods, despite encountering limitations, and pr

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43

Rajaram, Harihar, and Konstantine P. Georgakakos. "Recursive parameter estimation of hydrologic models." Water Resources Research 25, no. 2 (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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45

Bouraoui, Faycal, and Mary Leigh Wolfe. "Application of hydrologic models to rangelands." Journal of Hydrology 121, no. 1-4 (1990): 173–91. http://dx.doi.org/10.1016/0022-1694(90)90231-l.

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46

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 (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 t

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47

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 (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

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48

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 system

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49

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

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50

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 (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 Under

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