Jerry Mount, Y. Sermet, Christopher S. Jones, Keith E. Schilling, P. Gassman, Larry J. Weber, W. Krajewski, Ibrahim Demir
The Upper Mississippi Information System (UMIS) is a cyberinfrastructure framework designed to support large-scale real-time water-quality data integration, analysis, and visualization for the Upper Mississippi River Basin (UMRB). UMIS is intended to directly address three of the Grand Challenges for Engineering including (1) understanding access to clean drinking water, (2) management of the nitrogen cycle, and (3) engineering the tools of scientific discovery. The UMIS is designed to provide significant immediate and long-term impacts including a central platform for data access, integration, discovery, and adoption of cyberinfrastructure tools and services. The UMIS demonstrates that public data aggregators and central repositories can provide important services to anyone interested in water-quality research or education. In addition, working across multiple scales (e.g., state, region, county, or watershed) allows researchers to understand broad and narrow effects of water-quality strategies. Exploration of data across these scales encourages the development of problem-based research questions that can eventually provide feedback to public policies.
{"title":"An integrated cyberinfrastructure system for water quality resources in the Upper Mississippi River Basin","authors":"Jerry Mount, Y. Sermet, Christopher S. Jones, Keith E. Schilling, P. Gassman, Larry J. Weber, W. Krajewski, Ibrahim Demir","doi":"10.2166/hydro.2024.079","DOIUrl":"https://doi.org/10.2166/hydro.2024.079","url":null,"abstract":"\u0000 \u0000 The Upper Mississippi Information System (UMIS) is a cyberinfrastructure framework designed to support large-scale real-time water-quality data integration, analysis, and visualization for the Upper Mississippi River Basin (UMRB). UMIS is intended to directly address three of the Grand Challenges for Engineering including (1) understanding access to clean drinking water, (2) management of the nitrogen cycle, and (3) engineering the tools of scientific discovery. The UMIS is designed to provide significant immediate and long-term impacts including a central platform for data access, integration, discovery, and adoption of cyberinfrastructure tools and services. The UMIS demonstrates that public data aggregators and central repositories can provide important services to anyone interested in water-quality research or education. In addition, working across multiple scales (e.g., state, region, county, or watershed) allows researchers to understand broad and narrow effects of water-quality strategies. Exploration of data across these scales encourages the development of problem-based research questions that can eventually provide feedback to public policies.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shravan Kumar S. M., Manish Pandey, N. V. Umamahesh
In this study, various regression models were utilized to predict total sediment yield in tons, while their performance was evaluated for accuracy and reliability. The dataset utilized contains numerous predictors that have been standardized and processed through principal component analysis to improve model performance. Models evaluated here include linear regression, normalized linear regression, PCA, PCC with generalized ridge regression, kernel ridge regression, multivariate regression, lasso regression approaches such as CA-ANN or ANN, and more. Results suggest that the artificial neural network (ANN) model achieved the lowest mean squared error (MSE), 113.641; this suggests superior predictive capability compared to other models. Although environmental data were complex and relationships complex, an ANN model showed less error, followed closely by CA-ANN with an MSE of 124.83. Traditional models such as linear or lasso regression revealed larger errors with negative squared values that indicated poor fits to data. This exhaustive analysis not only showcases the power of advanced machine-learning techniques in environmental modeling but also stresses the significance of selecting models based on data characteristics and specific environmental phenomena studied. Furthermore, its insights could assist environmental planners and advocates with better prediction and management of soil erosion and sediment transport for planning purposes and conservation efforts.
{"title":"Predicting total upland sediment yield using regression and machine learning models for improved land management and water conservation","authors":"Shravan Kumar S. M., Manish Pandey, N. V. Umamahesh","doi":"10.2166/hydro.2024.159","DOIUrl":"https://doi.org/10.2166/hydro.2024.159","url":null,"abstract":"\u0000 In this study, various regression models were utilized to predict total sediment yield in tons, while their performance was evaluated for accuracy and reliability. The dataset utilized contains numerous predictors that have been standardized and processed through principal component analysis to improve model performance. Models evaluated here include linear regression, normalized linear regression, PCA, PCC with generalized ridge regression, kernel ridge regression, multivariate regression, lasso regression approaches such as CA-ANN or ANN, and more. Results suggest that the artificial neural network (ANN) model achieved the lowest mean squared error (MSE), 113.641; this suggests superior predictive capability compared to other models. Although environmental data were complex and relationships complex, an ANN model showed less error, followed closely by CA-ANN with an MSE of 124.83. Traditional models such as linear or lasso regression revealed larger errors with negative squared values that indicated poor fits to data. This exhaustive analysis not only showcases the power of advanced machine-learning techniques in environmental modeling but also stresses the significance of selecting models based on data characteristics and specific environmental phenomena studied. Furthermore, its insights could assist environmental planners and advocates with better prediction and management of soil erosion and sediment transport for planning purposes and conservation efforts.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hiwa Farajpanah, Arash Adib, Morteza Lotfirad, Hassan Esmaeili-Gisavandani, Mohammad Mehdi Riyahi, Arash Zaerpour
The main goal of this study is to enhance the precision and reliability of monthly runoff forecasts within the complex Navrood watershed, situated in northern Iran. The innovative use of a waveform matching algorithm is a defining feature of this study. This approach is vital in optimizing the selection of the mother wavelet, which is a critical component in wavelet analysis. This is a significant divergence from established techniques in hydrological research, indicating a paradigm change in the area. To thoroughly assess model performance, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) is applied. This all-encompassing evaluation guarantees not only astounding precision but also a near-perfect fit with the ideal solution. The findings highlight the remarkable precision attained by using the hybrid multiresolution analysis (MRA) methodology. The proposed methodology involves the integration of the maximal overlap discrete wavelet transform (MODWT) with a random forest (RF) model, referred to as MRA–RF. The obtained Nash–Sutcliffe efficiency (NSE) score of 0.94 is noteworthy. Furthermore, the model exhibits a low mean absolute error (MAE) of just 0.36 m3/s, a strong p-factor of 73.5%, and a significant d-factor of 37.9% during extensive testing.
{"title":"A novel application of waveform matching algorithm for improving monthly runoff forecasting using wavelet–ML models","authors":"Hiwa Farajpanah, Arash Adib, Morteza Lotfirad, Hassan Esmaeili-Gisavandani, Mohammad Mehdi Riyahi, Arash Zaerpour","doi":"10.2166/hydro.2024.128","DOIUrl":"https://doi.org/10.2166/hydro.2024.128","url":null,"abstract":"\u0000 \u0000 The main goal of this study is to enhance the precision and reliability of monthly runoff forecasts within the complex Navrood watershed, situated in northern Iran. The innovative use of a waveform matching algorithm is a defining feature of this study. This approach is vital in optimizing the selection of the mother wavelet, which is a critical component in wavelet analysis. This is a significant divergence from established techniques in hydrological research, indicating a paradigm change in the area. To thoroughly assess model performance, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) is applied. This all-encompassing evaluation guarantees not only astounding precision but also a near-perfect fit with the ideal solution. The findings highlight the remarkable precision attained by using the hybrid multiresolution analysis (MRA) methodology. The proposed methodology involves the integration of the maximal overlap discrete wavelet transform (MODWT) with a random forest (RF) model, referred to as MRA–RF. The obtained Nash–Sutcliffe efficiency (NSE) score of 0.94 is noteworthy. Furthermore, the model exhibits a low mean absolute error (MAE) of just 0.36 m3/s, a strong p-factor of 73.5%, and a significant d-factor of 37.9% during extensive testing.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141657124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study examines the influence of emergent vegetation on flow dynamics in natural river systems using large eddy simulation. It specifically addresses alterations in flow velocity and turbulence characteristics within open channels with varying densities of vegetation. The findings reveal a marked reduction in flow velocity within vegetated areas due to the obstruction posed by vegetation and the dynamics of shear layers. The flow alteration manifests in two distinct phases: the formation of a stable wake region behind densely vegetated patches and a more gradual velocity recovery in areas with sparser vegetation. In non-vegetated downstream zones, flow velocity tends to stabilize over a distance. Regarding turbulence, the study identifies differing patterns: enhanced vortex structures and accelerated energy dissipation in sparsely vegetated areas, contrasted with reduced turbulence in densely vegetated patches. Quadrant analysis further elucidates that ejections and inward interactions are primary contributors to Reynolds stress within vegetation patches, whereas outward interactions and sweeps become dominant in downstream regions. These insights offer a deeper understanding of how aquatic vegetation shapes river hydrodynamics, providing valuable information for effective river management and ecological restoration strategies.
{"title":"Impacts of emergent rigid vegetation patches on flow characteristics of open channels","authors":"Hunan Qiu, Peng Li, Wen Wang, Peng Shi, Jiao Zhang, Hao Chen","doi":"10.2166/hydro.2024.009","DOIUrl":"https://doi.org/10.2166/hydro.2024.009","url":null,"abstract":"\u0000 This study examines the influence of emergent vegetation on flow dynamics in natural river systems using large eddy simulation. It specifically addresses alterations in flow velocity and turbulence characteristics within open channels with varying densities of vegetation. The findings reveal a marked reduction in flow velocity within vegetated areas due to the obstruction posed by vegetation and the dynamics of shear layers. The flow alteration manifests in two distinct phases: the formation of a stable wake region behind densely vegetated patches and a more gradual velocity recovery in areas with sparser vegetation. In non-vegetated downstream zones, flow velocity tends to stabilize over a distance. Regarding turbulence, the study identifies differing patterns: enhanced vortex structures and accelerated energy dissipation in sparsely vegetated areas, contrasted with reduced turbulence in densely vegetated patches. Quadrant analysis further elucidates that ejections and inward interactions are primary contributors to Reynolds stress within vegetation patches, whereas outward interactions and sweeps become dominant in downstream regions. These insights offer a deeper understanding of how aquatic vegetation shapes river hydrodynamics, providing valuable information for effective river management and ecological restoration strategies.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141661541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qiang Sun, Xu Wang, Yuebin Wu, Ying Xu, Zhihao Wang
A stepwise method is one of the efficient approaches in the calibration of transient flow in viscoelastic pipes. However, there is the lack of comprehensive research on the calibration method for retarded time and creep compliance. Therefore, the sensitivity of the order of magnitude of the creep parameters in the presence of interactions to transient flow pressure damping and phase in the case of the two-element Kelvin–Voigt model is investigated. Also, the calibration methods of retarded time and creep compliance in the stepwise method of transient flow parameter calibration for viscoelastic pipes are proposed. The results indicate that the creep compliances do not affect the phase of the pressure fluctuations when the selected retarded times are greater than the order of magnitude 10−1. For the first Kelvin–Voigt element, when the order of magnitude of the retarded time exceeds 10−2, an increase in creep compliance results in an increase in the degree of damping of pressure fluctuations. When the order of magnitude of the retarded time is less than 10−2, the rule is reversed. For the second Kelvin–Voigt element, an increase in creep compliance results in an increase in the degree of damping of transient flow pressure fluctuations independent of the retarded time.
{"title":"Sensitivity of creep parameters to pressure fluctuation of transient flow in viscoelastic pipes","authors":"Qiang Sun, Xu Wang, Yuebin Wu, Ying Xu, Zhihao Wang","doi":"10.2166/hydro.2024.116","DOIUrl":"https://doi.org/10.2166/hydro.2024.116","url":null,"abstract":"\u0000 \u0000 A stepwise method is one of the efficient approaches in the calibration of transient flow in viscoelastic pipes. However, there is the lack of comprehensive research on the calibration method for retarded time and creep compliance. Therefore, the sensitivity of the order of magnitude of the creep parameters in the presence of interactions to transient flow pressure damping and phase in the case of the two-element Kelvin–Voigt model is investigated. Also, the calibration methods of retarded time and creep compliance in the stepwise method of transient flow parameter calibration for viscoelastic pipes are proposed. The results indicate that the creep compliances do not affect the phase of the pressure fluctuations when the selected retarded times are greater than the order of magnitude 10−1. For the first Kelvin–Voigt element, when the order of magnitude of the retarded time exceeds 10−2, an increase in creep compliance results in an increase in the degree of damping of pressure fluctuations. When the order of magnitude of the retarded time is less than 10−2, the rule is reversed. For the second Kelvin–Voigt element, an increase in creep compliance results in an increase in the degree of damping of transient flow pressure fluctuations independent of the retarded time.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141658629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Among various monthly water balance models, one of the models that has the simplest structure and offers a well-behaved conceptual platform is the GR2M. Despite the widespread use of the model with two-free parameters, the fact that it tends to produce relatively large errors in peak flow months necessitates some modifications to the model. The reason for the mentioned simulation deficiencies could be that the relationship between the routing reservoir and the external environment of the basin is controlled by a single parameter, making the storage–discharge relationship linear. Therefore, in this study, least squares support vector regression, one of the nonlinear data-driven models, has replaced the routing part of the GR2M to enhance the monthly runoff simulation. The performance of the three-parameter hybrid model (GR3M), which was developed by considering the parameter parsimony point of view and including a machine learning (ML)-based nonlinear routing scheme, was examined in some locations in the Gediz River Basin in western Turkey. Statistical performance measures have shown that GR3M, which both leverages the capabilities of an ML model and blends conceptual outputs within a nested scheme, clearly outperforms the original GR2M. The proposed modification has brought significant improvements, especially to high-flow simulations.
在各种月度水平衡模型中，GR2M 是结构最简单的模型之一，它提供了一个良好的概念 平台。尽管该模型的两个自由参数得到了广泛应用，但它在流量高峰月往往会产生相对较大的误差，因此有必要对模型进行一些修改。造成上述模拟缺陷的原因可能是，路由水库与流域外部环境之间的关系由单一参数控制，使得蓄水-排水关系呈线性。因此，在本研究中，非线性数据驱动模型之一的最小二乘支持向量回归取代了 GR2M 的路由部分，以提高月径流模拟效果。从参数简约性角度出发，开发了三参数混合模型（GR3M），其中包括基于机器学习（ML）的非线性路由方案，并在土耳其西部盖迪兹河流域的一些地点对该模型的性能进行了检验。统计性能指标表明，GR3M 既利用了 ML 模型的功能，又在嵌套方案中融合了概念输出，其性能明显优于最初的 GR2M。拟议的修改带来了显著的改进，尤其是在大流量模拟方面。
{"title":"Internal structure modification of a simple monthly water balance model via incorporation of a machine learning-based nonlinear routing","authors":"U. Okkan, Zeynep Beril Ersoy, O. Fistikoglu","doi":"10.2166/hydro.2024.010","DOIUrl":"https://doi.org/10.2166/hydro.2024.010","url":null,"abstract":"\u0000 \u0000 Among various monthly water balance models, one of the models that has the simplest structure and offers a well-behaved conceptual platform is the GR2M. Despite the widespread use of the model with two-free parameters, the fact that it tends to produce relatively large errors in peak flow months necessitates some modifications to the model. The reason for the mentioned simulation deficiencies could be that the relationship between the routing reservoir and the external environment of the basin is controlled by a single parameter, making the storage–discharge relationship linear. Therefore, in this study, least squares support vector regression, one of the nonlinear data-driven models, has replaced the routing part of the GR2M to enhance the monthly runoff simulation. The performance of the three-parameter hybrid model (GR3M), which was developed by considering the parameter parsimony point of view and including a machine learning (ML)-based nonlinear routing scheme, was examined in some locations in the Gediz River Basin in western Turkey. Statistical performance measures have shown that GR3M, which both leverages the capabilities of an ML model and blends conceptual outputs within a nested scheme, clearly outperforms the original GR2M. The proposed modification has brought significant improvements, especially to high-flow simulations.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141669617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiangtao Yang, S. Soares-Frazão, Zhenming Shi, Danyi Shen
Landslide dams are formed by the blockage of rivers with landslides, or any debris flows. Such dams are prone to failure and therefore, simulation of their failure and induced morphological changes is of great importance for hazard mitigation. Multi-layer and non-uniform sediments are usually observed in landslide dam materials, but these are rarely considered in simulations. In this paper, a two-dimensional numerical model considering multi-layered and non-uniform sediments is proposed. The model solves the shallow water equations, the Exner equation considering the different size fractions and Hirano's active layer equation based on a finite volume scheme in a weakly coupled approach. Two experimental tests are used to assess the applicability and accuracy of the model. The results show that it can well simulate the flow and morphological changes, as well as the surface coarsening and fining phenomena related to multi-layer and non-uniform sediments. The numerical results of dam failure process and morphological changes are in overall good agreement with the experiments, but the peak discharge and bed elevation tend to be underestimated when finer material is considered. Finally, the influence of sediments fractions and active layer depth on numerical results, as well as the limitations of the model are discussed.
{"title":"A 2D numerical model for simulating landslide dam failure and induced morphological changes considering multi-layer and non-uniform sediments","authors":"Jiangtao Yang, S. Soares-Frazão, Zhenming Shi, Danyi Shen","doi":"10.2166/hydro.2024.103","DOIUrl":"https://doi.org/10.2166/hydro.2024.103","url":null,"abstract":"\u0000 Landslide dams are formed by the blockage of rivers with landslides, or any debris flows. Such dams are prone to failure and therefore, simulation of their failure and induced morphological changes is of great importance for hazard mitigation. Multi-layer and non-uniform sediments are usually observed in landslide dam materials, but these are rarely considered in simulations. In this paper, a two-dimensional numerical model considering multi-layered and non-uniform sediments is proposed. The model solves the shallow water equations, the Exner equation considering the different size fractions and Hirano's active layer equation based on a finite volume scheme in a weakly coupled approach. Two experimental tests are used to assess the applicability and accuracy of the model. The results show that it can well simulate the flow and morphological changes, as well as the surface coarsening and fining phenomena related to multi-layer and non-uniform sediments. The numerical results of dam failure process and morphological changes are in overall good agreement with the experiments, but the peak discharge and bed elevation tend to be underestimated when finer material is considered. Finally, the influence of sediments fractions and active layer depth on numerical results, as well as the limitations of the model are discussed.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141669984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A novel approach is presented for explicit assessment of the friction factor of the Darcy–Weisbach formula for a pipe turbulent flow, a topic being especially useful in practical applications requiring a large number of pipes that have to be calculated a great many times in a short time. In such applications, in fact, an explicit formula shortening computation time, with respect to a trial-and-error solution of the Colebrook–White formula, is very advisable. To this aim, in the paper analytical simplicity is pursued besides accuracy, the latter being kept high enough for practical purposes. Unlike previous studies, the ratio between the actual friction factor and that relating to a fully turbulent flow is analysed as a function of the relative roughness and the relative Reynolds number (i.e., the ratio between the actual Reynolds number and the Reynolds number separating the transition regime from the fully turbulent regime). By processing a dataset obtained by systematically solving the C-W formula over suited ranges of the Reynolds number and the relative roughness, two expressions are obtained: a simpler first-step accuracy expression giving generally acceptable accuracy for most engineering practical purposes; and a second-step accuracy expression allowing adequately high accuracy for all situations.
{"title":"A new approach for explicit approximation of the Colebrook–White formula for pipe flows","authors":"G. Ferreri","doi":"10.2166/hydro.2024.280","DOIUrl":"https://doi.org/10.2166/hydro.2024.280","url":null,"abstract":"\u0000 A novel approach is presented for explicit assessment of the friction factor of the Darcy–Weisbach formula for a pipe turbulent flow, a topic being especially useful in practical applications requiring a large number of pipes that have to be calculated a great many times in a short time. In such applications, in fact, an explicit formula shortening computation time, with respect to a trial-and-error solution of the Colebrook–White formula, is very advisable. To this aim, in the paper analytical simplicity is pursued besides accuracy, the latter being kept high enough for practical purposes. Unlike previous studies, the ratio between the actual friction factor and that relating to a fully turbulent flow is analysed as a function of the relative roughness and the relative Reynolds number (i.e., the ratio between the actual Reynolds number and the Reynolds number separating the transition regime from the fully turbulent regime). By processing a dataset obtained by systematically solving the C-W formula over suited ranges of the Reynolds number and the relative roughness, two expressions are obtained: a simpler first-step accuracy expression giving generally acceptable accuracy for most engineering practical purposes; and a second-step accuracy expression allowing adequately high accuracy for all situations.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141681755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Voon Hao Chai, Ren Jie Chin, Lloyd Ling, Yuk Feng Huang, Eugene Zhen Xiang Soo
Satellite precipitation estimations (SPEs) have become important to estimate rainfall in remote and inaccessible areas. The study evaluates two high-resolution SPEs (IMERG and CHIRPS) in Peninsular Malaysia from 2011 to 2020. In situ rain gauge observation data were used as reference data, and a series of statistic indices were used to evaluate the performance of SPEs. In order to identify the source of error in the SPEs, an error decomposition technique was proposed whereby the bias is segregated into four different independent components. The study found that IMERG outperformed CHIRPS, with both satellites performing well in the east coast region but poor in the central region. A superior correlation between the SPEs and rain gauge observations was found during the northeast monsoon. The false bias has shown the widest range compared to other error components, indicating that it is the main contributor to the total bias of both SPEs in Peninsular Malaysia.
{"title":"Regional and seasonal assessment of biases on high-resolution satellite precipitation estimations in peninsular Malaysia: 2011–2020","authors":"Voon Hao Chai, Ren Jie Chin, Lloyd Ling, Yuk Feng Huang, Eugene Zhen Xiang Soo","doi":"10.2166/hydro.2024.084","DOIUrl":"https://doi.org/10.2166/hydro.2024.084","url":null,"abstract":"\u0000 \u0000 Satellite precipitation estimations (SPEs) have become important to estimate rainfall in remote and inaccessible areas. The study evaluates two high-resolution SPEs (IMERG and CHIRPS) in Peninsular Malaysia from 2011 to 2020. In situ rain gauge observation data were used as reference data, and a series of statistic indices were used to evaluate the performance of SPEs. In order to identify the source of error in the SPEs, an error decomposition technique was proposed whereby the bias is segregated into four different independent components. The study found that IMERG outperformed CHIRPS, with both satellites performing well in the east coast region but poor in the central region. A superior correlation between the SPEs and rain gauge observations was found during the northeast monsoon. The false bias has shown the widest range compared to other error components, indicating that it is the main contributor to the total bias of both SPEs in Peninsular Malaysia.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141684475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Predicting water levels in urban storm-water sewer systems is a critical study that could provide vital information to help reduce the risk of flooding. This study proposed a new ensemble model based on the integration of a meta-learner model, residual-error corrections, and a multiple-output framework. To achieve the meta-learner model, three multiple-output data-driven-based (MOD) sewer flooding models employing support vector regression (SVR), k-nearest neighbor regression (KNR), and categorical gradient boosting regression (CGBR) techniques were constructed and applied to predict the short-duration evolution of water levels at seven storm-water gauging sites in Taipei city, Taiwan, considering 10-min datasets spanning nearly 6 years (2016–2021). The Bayesian optimization algorithm was utilized in the training phases for all the models to avoid overfitting or underfitting. Enhancing the analysis of feature importance was also conducted to explore model interpretability based on the SHapley Additive exPlanation (SHAP) algorithm. The outputs of storm-water management model (SWMM) were used as benchmark solutions. For the model validation phase, the proposed integrated model improved the lead-time-averaged Nash–Sutcliffe efficiency of single KNR, SVR, and CGBR models by 174.5, 42.4, and 69.4%, respectively, showing that the proposed accurate model could be useful for urban flood warning systems.
{"title":"Short-duration prediction of urban storm-water levels using the residual-error ensemble correction technique","authors":"Wen-Dar Guo, Wei-Bo Chen","doi":"10.2166/hydro.2024.255","DOIUrl":"https://doi.org/10.2166/hydro.2024.255","url":null,"abstract":"\u0000 \u0000 Predicting water levels in urban storm-water sewer systems is a critical study that could provide vital information to help reduce the risk of flooding. This study proposed a new ensemble model based on the integration of a meta-learner model, residual-error corrections, and a multiple-output framework. To achieve the meta-learner model, three multiple-output data-driven-based (MOD) sewer flooding models employing support vector regression (SVR), k-nearest neighbor regression (KNR), and categorical gradient boosting regression (CGBR) techniques were constructed and applied to predict the short-duration evolution of water levels at seven storm-water gauging sites in Taipei city, Taiwan, considering 10-min datasets spanning nearly 6 years (2016–2021). The Bayesian optimization algorithm was utilized in the training phases for all the models to avoid overfitting or underfitting. Enhancing the analysis of feature importance was also conducted to explore model interpretability based on the SHapley Additive exPlanation (SHAP) algorithm. The outputs of storm-water management model (SWMM) were used as benchmark solutions. For the model validation phase, the proposed integrated model improved the lead-time-averaged Nash–Sutcliffe efficiency of single KNR, SVR, and CGBR models by 174.5, 42.4, and 69.4%, respectively, showing that the proposed accurate model could be useful for urban flood warning systems.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141269916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：