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Journal articles on the topic 'Hydrological models'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

Singh, Shailesh Kumar, and András Bárdossy. "Calibration of hydrological models on hydrologically unusual events." Advances in Water Resources 38 (March 2012): 81–91. http://dx.doi.org/10.1016/j.advwatres.2011.12.006.

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2

Visser-Quinn, Annie, Lindsay Beevers, and Sandhya Patidar. "Replication of ecologically relevant hydrological indicators following a modified covariance approach to hydrological model parameterization." Hydrology and Earth System Sciences 23, no. 8 (2019): 3279–303. http://dx.doi.org/10.5194/hess-23-3279-2019.

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Abstract. Hydrological models can be used to assess the impact of hydrologic alteration on the river ecosystem. However, there are considerable limitations and uncertainties associated with the replication of ecologically relevant hydrological indicators. Vogel and Sankarasubramanian's 2003 (Water Resources Research) covariance approach to model evaluation and parameterization represents a shift away from algorithmic model calibration with traditional performance measures (objective functions). Using the covariance structures of the observed input and simulated output time series, it is possib

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3

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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4

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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5

Babel, L. V., and D. Karssenberg. "Hydrological models are mediating models." Hydrology and Earth System Sciences Discussions 10, no. 8 (2013): 10535–63. http://dx.doi.org/10.5194/hessd-10-10535-2013.

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Abstract. Despite the increasing role of models in hydrological research and decision-making processes, only few accounts of the nature and function of models exist in hydrology. Earlier considerations have traditionally been conducted while making a clear distinction between physically-based and conceptual models. A new philosophical account, primarily based on the fields of physics and economics, transcends classes of models and scientific disciplines by considering models as "mediators" between theory and observations. The core of this approach lies in identifying models as (1) being only p

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6

Pandi, Dinagarapandi, Saravanan Kothandaraman, and Mohan Kuppusamy. "Hydrological models: a review." International Journal of Hydrology Science and Technology 12, no. 3 (2021): 223. http://dx.doi.org/10.1504/ijhst.2021.117540.

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7

Kuppusamy, Mohan, Saravanan Kothandaraman, and Dinagarapandi Pandi. "Hydrological models: a review." International Journal of Hydrology Science and Technology 12, no. 3 (2021): 223. http://dx.doi.org/10.1504/ijhst.2021.10040160.

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8

Götzinger, J., J. Jagelke, R. Barthel, and A. Bárdossy. "Integration of water balance models in RIVERTWIN." Advances in Geosciences 9 (September 26, 2006): 85–91. http://dx.doi.org/10.5194/adgeo-9-85-2006.

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Abstract. In the project RIVERTWIN climate, hydrologic, groundwater and water quality models are integrated in order to evaluate river basin management plans established for the implementation of the EU Water Framework Directive. In such integrated models, which try to simulate all relevant processes in a river basin realistically, modelling of the water balance plays a key role. Therefore the integration of hydrological and groundwater models requires special attention. In this case study, the hydrological model simulates discharge and daily groundwater recharge in a high spatial resolution.

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9

Caetano, Jordana Moura, and Derblai Casaroli. "Tendências dos modelos hidrológicos integrados aos sistemas de informações geográficas a partir da cienciometria." Comunicata Scientiae 7, no. 3 (2016): 406. http://dx.doi.org/10.14295/cs.v7i3.1197.

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The remote sensing and Geographic Information Systems (GIS) development have encouraged and improved the use and spansion of hydrological models worldwide. This development allows the use of hydrological models, simulating watersheds systems operation in a more simple, economical and realistic way. In order to maximize this integration, new computational tools, hydrological models and GIS are being developed. This study aimed to apply the scientometric study to quantify and verify the tendencies of the scientific publications of hydrological models and their integration with geographic informa

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10

Cirulis, Arnis, Edmunds Jansons, Oskars Java, and Andris Lapans. "COMPARISON OF PEATLAND HYDROLOGICAL MODELS." RURAL DEVELOPMENT 2019 2021, no. 1 (2022): 65–72. http://dx.doi.org/10.15544/rd.2021.016.

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11

Sood, Aditya, and Vladimir Smakhtin. "Global hydrological models: a review." Hydrological Sciences Journal 60, no. 4 (2015): 549–65. http://dx.doi.org/10.1080/02626667.2014.950580.

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12

Anderson, M. G., and C. C. M. Rogers. "Catchment scale distributed hydrological models." Progress in Physical Geography: Earth and Environment 11, no. 1 (1987): 28–51. http://dx.doi.org/10.1177/030913338701100102.

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13

Andréassian, Vazken, Julien Lerat, Nicolas Le Moine, and Charles Perrin. "Neighbors: Nature’s own hydrological models." Journal of Hydrology 414-415 (January 2012): 49–58. http://dx.doi.org/10.1016/j.jhydrol.2011.10.007.

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14

Devia, Gayathri K., B. P. Ganasri, and G. S. Dwarakish. "A Review on Hydrological Models." Aquatic Procedia 4 (2015): 1001–7. http://dx.doi.org/10.1016/j.aqpro.2015.02.126.

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15

Ostojski, Mieczysław. "Application of hydrological and hydraulic models for hydrological data transfer." Acta Geophysica 61, no. 3 (2013): 690–705. http://dx.doi.org/10.2478/s11600-012-0097-6.

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16

ABEBE, A. J., and R. K. PRICE. "Managing uncertainty in hydrological models using complementary models." Hydrological Sciences Journal 48, no. 5 (2003): 679–92. http://dx.doi.org/10.1623/hysj.48.5.679.51450.

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17

Xin, Zhuohang, Ke Shi, Chenchen Wu, Lu Wang, and Lei Ye. "Applicability of Hydrological Models for Flash Flood Simulation in Small Catchments of Hilly Area in China." Open Geosciences 11, no. 1 (2019): 1168–81. http://dx.doi.org/10.1515/geo-2019-0089.

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Abstract Flash flood in small catchments of hilly area is an extremely complicated nonlinear process affected by catchment properties and rainfall spatio-temporal variation characteristics including many physical-geographical factors, and thus accurate simulation of flash flood is very difficult. Given the fact that hundreds of hydrological models are available in the literature, how to choose a suitable hydrological model remains an unsolved task. In this paper, we selected five widely used hydrological models including three lumped hydrologic models, a semi-distributed hydrological model and

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18

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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19

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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20

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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21

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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22

Arnold, S., S. Attinger, K. Frank, and A. Hildebrandt. "Parameterization and uncertainty in coupled ecohydrological models." Hydrology and Earth System Sciences Discussions 6, no. 3 (2009): 4155–207. http://dx.doi.org/10.5194/hessd-6-4155-2009.

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Abstract. In this paper we develop and apply a conceptual ecohydrological model to investigate the effects of model structure and parameter uncertainty on the prediction of vegetation structure and hydrological dynamics. The model is applied for a typical water limited riparian ecosystem along an ephemeral river: the middle section of the Kuiseb River in Namibia. We modelled this system by coupling an ecological model with a conceptual hydrological model. The hydrological model is storage based with stochastical forcing from the flood. The ecosystem is modelled with a population model, and rep

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23

Toledo, Camila, Enrique Muñoz, and Mauricio Zambrano-Bigiarini. "Comparison of Stationary and Dynamic Conceptual Models in a Mountainous and Data-Sparse Catchment in the South-Central Chilean Andes." Advances in Meteorology 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/526158.

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In recent years, it has been documented that climatic variability influences hydrological processes; however, these influences, such as hydrologic dynamics, have not yet been incorporated into models, which have been assumed as stationary with regard to climatic conditions. In this study, the temporal variability of hydrological processes and their influence on the water balance of a mountainous and data-sparse catchment in Chile are observed and modeled through the comparison of a stationary (time-invariant parameters) and dynamic (time-variant parameters) model. Since conceptual models are t

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24

Zhang, Lei, Desmond Ofosu Anim, and Amos T. Kabo-Bah. "Integration of Geographical Information Science (GIS) Technology in Hydrological Modeling: A Critical Review." Advanced Materials Research 838-841 (November 2013): 2284–91. http://dx.doi.org/10.4028/www.scientific.net/amr.838-841.2284.

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Current trends in hydrological modeling depend on spatial datasets, complex computational tasks and representation. As the need for the development of hydrological modeling capabilities have evolved, its integration with geographic information system (GIS) has provided a significant contribution to the efforts of hydrologic models. It serves the role of providing support in data capturing and improving hydrological modeling efforts by giving tools for effective analysis. This integration of GIS technology and hydrological modeling has resulted in great value and presents potential benefits to

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25

Đukić, Vesna, and Ranka Erić. "SHETRAN and HEC HMS Model Evaluation for Runoff and Soil Moisture Simulation in the Jičinka River Catchment (Czech Republic)." Water 13, no. 6 (2021): 872. http://dx.doi.org/10.3390/w13060872.

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Due to the improvement of computation power, in recent decades considerable progress has been made in the development of complex hydrological models. On the other hand, simple conceptual models have also been advanced. Previous studies on rainfall–runoff models have shown that model performance depends very much on the model structure. The purpose of this study is to determine whether the use of a complex hydrological model leads to more accurate results or not and to analyze whether some model structures are more efficient than others. Different configurations of the two models of different c

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26

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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27

Kamp, R. G., and H. H. G. Savenije. "Hydrological model coupling with ANNs." Hydrology and Earth System Sciences 11, no. 6 (2007): 1869–81. http://dx.doi.org/10.5194/hess-11-1869-2007.

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Abstract. There is an increasing need for model coupling. However, model coupling is complicated. Scientists develop and improve models to represent physical processes occurring in nature. These models are built in different software programs required to run the model. A software program or application represents part of the system knowledge. This knowledge is however encapsulated in the program and often difficult to access. In integrated water resources management it is often necessary to connect hydrological, hydraulic or ecological models. Model coupling can in practice be difficult for ma

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28

Rushworth, Alastair M., Adrian W. Bowman, Mark J. Brewer, and Simon J. Langan. "Distributed Lag Models for Hydrological Data." Biometrics 69, no. 2 (2013): 537–44. http://dx.doi.org/10.1111/biom.12008.

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29

Zlate, Ionel. "Rainfall field estimates for hydrological models." Atmospheric Research 42, no. 1-4 (1996): 229–36. http://dx.doi.org/10.1016/0169-8095(95)00065-8.

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30

Zhao, Lingling, Jun Xia, Chong-yu Xu, Zhonggen Wang, Leszek Sobkowiak, and Cangrui Long. "Evapotranspiration estimation methods in hydrological models." Journal of Geographical Sciences 23, no. 2 (2013): 359–69. http://dx.doi.org/10.1007/s11442-013-1015-9.

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31

KLEMEŠ, V. "Operational testing of hydrological simulation models." Hydrological Sciences Journal 31, no. 1 (1986): 13–24. http://dx.doi.org/10.1080/02626668609491024.

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32

Jin, Xin, and Yanxiang Jin. "Calibration of a Distributed Hydrological Model in a Data-Scarce Basin Based on GLEAM Datasets." Water 12, no. 3 (2020): 897. http://dx.doi.org/10.3390/w12030897.

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The calibration of hydrological models is often complex in regions with scarce data, and generally only uses site-based streamflow data. However, this approach will yield highly generalised values for all model parameters and hydrological processes. It is therefore necessary to obtain more spatially heterogeneous observation data (e.g., satellite-based evapotranspiration (ET)) to calibrate such hydrological models. Here, soil and water assessment tool (SWAT) models were built to evaluate the advantages of using ET data derived from the Global Land surface Evaporation Amsterdam Methodology (GLE

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33

Tran, Hop Quang, and Zoltán Zsolt Fehér. "Water balance calculation capability of hydrological models." Acta Agraria Kaposváriensis 26, no. 1 (2022): 37–53. http://dx.doi.org/10.31914/aak.2877.

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Currently, in the world, there are many different hydrological models built and developed to solve problems related to the hydrological cycle. Each model has its specific mathematical foundations to describe physical processes in nature. Therefore, each model has its various characteristics: setting up the model, input data requirements, model calibration and verification, and output results. Water balance is still playing an important role in the effective management and use of water resources for agriculture. Based on the results of the hydrological parameter’s calculation, the water balance

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34

Kumar, Dilip, and Rajib Kumar Bhattacharjya. "Evaluating two GIS-based semi-distributed hydrological models in the Bhagirathi-Alkhnanda River catchment in India." Water Policy 22, no. 6 (2020): 991–1014. http://dx.doi.org/10.2166/wp.2020.159.

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Abstract The hydrological models are used for simulating the runoff of a river basin based on available rainfall data and other parameters. Over the years, several hydrological models have been developed in different parts of the world. Two such semi-distributed hydrologic models are SWAT and HEC-HMS. In this study, a comparative analysis has been carried out to evaluate the performance of these two distributed hydrological models as a flood forecasting tool. The Alkhnanda and Bhagirathi rivers, which flow into the Tehri Reservoir, Uttarakhand and pass through Tehri, Uttarkashi and Chamoli dis

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35

Arsenault, Richard, Jean-Luc Martel, Frédéric Brunet, François Brissette, and Juliane Mai. "Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models." Hydrology and Earth System Sciences 27, no. 1 (2023): 139–57. http://dx.doi.org/10.5194/hess-27-139-2023.

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Abstract. This study investigates the ability of long short-term memory (LSTM) neural networks to perform streamflow prediction at ungauged basins. A set of state-of-the-art, hydrological model-dependent regionalization methods are applied to 148 catchments in northeast North America and compared to an LSTM model that uses the exact same available data as the hydrological models. While conceptual model-based methods attempt to derive parameterizations at ungauged sites from other similar or nearby catchments, the LSTM model uses all available data in the region to maximize the information cont

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36

Aqnouy, Mourad, Mohamed Ahmed, Gebiaw T. Ayele, Ismail Bouizrou, Abdelmounim Bouadila, and Jamal Eddine Stitou El Messari. "Comparison of Hydrological Platforms in Assessing Rainfall-Runoff Behavior in a Mediterranean Watershed of Northern Morocco." Water 15, no. 3 (2023): 447. http://dx.doi.org/10.3390/w15030447.

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This research evaluates the applicability of different types of hydrological models to simulate discharge behavior scenarios in a northern Moroccan watershed, Oued Laou watershed (OLW). In this context, an improved understanding of the runoff mechanisms through hydrological modeling of the OLW can assist in the hazard risk management and facilitate the effective planning of water resources. For that end, a multitude of hydrological models were used to perform a very efficient modelling, and a comparative approach was adopted. Comparison of the models allowed the determination of potential sour

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Corzo Perez, G. A., M. H. J. van Huijgevoort, F. Voß, and H. A. J. van Lanen. "On the spatio-temporal analysis of hydrological droughts from global hydrological models." Hydrology and Earth System Sciences 15, no. 9 (2011): 2963–78. http://dx.doi.org/10.5194/hess-15-2963-2011.

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Abstract. The recent concerns for world-wide extreme events related to climate change have motivated the development of large scale models that simulate the global water cycle. In this context, analysis of hydrological extremes is important and requires the adaptation of identification methods used for river basin models. This paper presents two methodologies that extend the tools to analyze spatio-temporal drought development and characteristics using large scale gridded time series of hydrometeorological data. The methodologies are classified as non-contiguous and contiguous drought area ana

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Corzo Perez, G. A., M. H. J. van Huijgevoort, F. Voß, and H. A. J. van Lanen. "On the spatio-temporal analysis of hydrological droughts from global hydrological models." Hydrology and Earth System Sciences Discussions 8, no. 1 (2011): 619–52. http://dx.doi.org/10.5194/hessd-8-619-2011.

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Abstract. The recent concerns for world-wide extreme events related to climate change phenomena have motivated the development of large scale models that simulate the global water cycle. In this context, analyses of extremes is an important topic that requires the adaptation of methods used for river basin and regional scale models. This paper presents two methodologies that extend the tools to analyze spatio-temporal drought development and characteristics using large scale gridded time series of hydrometeorological data. The methodologies are distinguished and defined as non-contiguous and c

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Gui, Hanliang, Zhiguo Wu, and Chunping Zhang. "Comparative Study of Different Types of Hydrological Models Applied to Hydrological Simulation." CLEAN – Soil, Air, Water 49, no. 8 (2021): 2000381. http://dx.doi.org/10.1002/clen.202000381.

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Gao, Hongkai, John L. Sabo, Xiaohong Chen, et al. "Landscape heterogeneity and hydrological processes: a review of landscape-based hydrological models." Landscape Ecology 33, no. 9 (2018): 1461–80. http://dx.doi.org/10.1007/s10980-018-0690-4.

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Li, K. Y., M. T. Coe, and N. Ramankutty. "Investigation of Hydrological Variability in West Africa Using Land Surface Models." Journal of Climate 18, no. 16 (2005): 3173–88. http://dx.doi.org/10.1175/jcli3452.1.

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Abstract The availability of freshwater is a particularly important issue in Africa where large portions of the continent are arid or semiarid and climate is highly variable. Sustainable water resource management requires the assessment of hydrological variability in response to nature climate fluctuation. In this study, a land surface model, the Integrated Biosphere Simulator (IBIS), and a hydrological routing model, the Hydrological Routing Algorithm (HYDRA), are used to investigate the hydrological variability in two large basins, the Lake Chad basin (LCB) and the Niger River basin (NRB), l

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42

Li, Lu Xiu, Song Tao Zhang, and Xu Zhao Wang. "Application and Development of Stochastic Hydrological Models in China." Applied Mechanics and Materials 212-213 (October 2012): 290–93. http://dx.doi.org/10.4028/www.scientific.net/amm.212-213.290.

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By reviewing the application of stochastic hydrological models in flood control safety design, risk analysis, water conservancy project planning and scheduling in recent years in China, it is presented that stochastic hydrological models are good tools for optimizing decisions in planning, design and operation of water resources development and utilization. Also, it deals with the problems which are difficult to solve in traditional hydrological calculation method. Besides, it is focused on some work which should be improvedand popularized about stochastic hydrological models in the future.

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Shaikh, Wajid Ali, Syed Feroz Shah, Muhammad Anwar Solangi, and Siraj Muhammed Pandhiani. "Forecasting Analysis of GMDH model with LSSVM and MARS models for Hydrological Datasets (Case study)." Indian Journal of Science and Technology 12, no. 39 (2019): 1–6. http://dx.doi.org/10.17485/ijst/2019/v12i39/147941.

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44

Wanders, Niko, Stephan Thober, Rohini Kumar, et al. "Development and Evaluation of a Pan-European Multimodel Seasonal Hydrological Forecasting System." Journal of Hydrometeorology 20, no. 1 (2019): 99–115. http://dx.doi.org/10.1175/jhm-d-18-0040.1.

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Abstract Hydrological forecasts with a high temporal and spatial resolution are required to provide the level of information needed by end users. So far high-resolution multimodel seasonal hydrological forecasts have been unavailable due to 1) lack of availability of high-resolution meteorological seasonal forecasts, requiring temporal and spatial downscaling; 2) a mismatch between the provided seasonal forecast information and the user needs; and 3) lack of consistency between the hydrological model outputs to generate multimodel seasonal hydrological forecasts. As part of the End-to-End Demo

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Zhang, Yongqiang, and David Post. "How good are hydrological models for gap-filling streamflow data?" Hydrology and Earth System Sciences 22, no. 8 (2018): 4593–604. http://dx.doi.org/10.5194/hess-22-4593-2018.

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Abstract. Gap-filling streamflow data is a critical step for most hydrological studies, such as streamflow trend, flood, and drought analysis and hydrological response variable estimates and predictions. However, there is a lack of quantitative evaluation of the gap-filled data accuracy in most hydrological studies. Here we show that when the missing data rate is less than 10 %, the gap-filled streamflow data obtained using calibrated hydrological models perform almost the same as the benchmark data (less than 1 % missing) when estimating annual trends for 217 unregulated catchments widely spr

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Shu, Lele, Paul Ullrich, Xianhong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li. "rSHUD v2.0: advancing the Simulator for Hydrologic Unstructured Domains and unstructured hydrological modeling in the R environment." Geoscientific Model Development 17, no. 2 (2024): 497–527. http://dx.doi.org/10.5194/gmd-17-497-2024.

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Abstract. Hydrological modeling is a crucial component in hydrology research, particularly for projecting future scenarios. However, achieving reproducibility and automation in distributed hydrological modeling research for modeling, simulation, and analysis is challenging. This paper introduces rSHUD v2.0, an innovative, open-source toolkit developed in the R environment to enhance the deployment and analysis of the Simulator for Hydrologic Unstructured Domains (SHUD). The SHUD is an integrated surface–subsurface hydrological model that employs a finite-volume method to simulate hydrological

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Xing, Zikang, Miaomiao Ma, Zhicheng Su, Juan Lv, Peng Yi, and Wenlong Song. "A review of the adaptability of hydrological models for drought forecasting." Proceedings of the International Association of Hydrological Sciences 383 (September 16, 2020): 261–66. http://dx.doi.org/10.5194/piahs-383-261-2020.

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Abstract. Drought intensity and frequency are increasing in recent years in multiple regions across the world due to global climate change and consequently drought forecasting research has received more and more attention. Previous studies on drought forecasting mostly focus on meteorological drought based on precipitation and temperature. However, the trend of predicting agriculture and hydrological drought, which consider soil moisture and runoff, have developed rapidly in recent years. Hydrological drought forecasting is based on the hydrological models and the model structure plays a role

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Qi, Wenyan, Jie Chen, Chongyu Xu, and Yongjing Wan. "Finding the Optimal Multimodel Averaging Method for Global Hydrological Simulations." Remote Sensing 13, no. 13 (2021): 2574. http://dx.doi.org/10.3390/rs13132574.

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Global gridded precipitations have been extensively considered as the input of hydrological models for runoff simulations around the world. However, the limitations of hydrologic models and the inaccuracies of the precipitation datasets could result in large uncertainty in hydrological forecasts and water resource estimations. Therefore, it is of great importance to investigate the hydrological value of a weighted combination of hydrological models driven by different precipitation datasets. In addition, due to the diversities of combination members and climate conditions, hydrological simulat

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Kambarbekov, G. М., and A. Ye Baimaganbetov. "USING ARTIFICIAL INTELLIGENCE FOR HYDROLOGICAL MODELLING." Geography and water resources, no. 1 (March 28, 2024): 58–62. http://dx.doi.org/10.55764/2957-9856/2024-1-58-62.8.

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Hydrological modelling plays a critical role in managing water resources, especially in arid and semi-arid regions where water scarcity is a major challenge. With the emergence of artificial intelligence (AI), hydrological modelling has experienced a significant transformation in recent years. This paper reviews the recent advances in AI-based hydrological modelling and examines its potential applications in water resource management. The study highlights the role of AI in enhancing the accuracy of hydrological models and facilitating more efficient and sustainable water management practices.

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Birhanu, Dereje, Hyeonjun Kim, Cheolhee Jang, and Sanghyun Park. "Does the Complexity of Evapotranspiration and Hydrological Models Enhance Robustness?" Sustainability 10, no. 8 (2018): 2837. http://dx.doi.org/10.3390/su10082837.

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In this study, five hydrological models of increasing complexity and 12 Potential Evapotranspiration (PET) estimation methods of different data requirements were applied in order to assess their effect on model performance, optimized parameters, and robustness. The models were applied over a set of 10 catchments that are located in South Korea. The Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm was implemented to calibrate the hydrological models for each PET input while considering similar objective functions. The hydrological models’ performance was satisfactory for each

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