Download or read book Evaluating the Performance of Process based and Machine Learning Models for Rainfall runoff Simulation with Application of Satellite and Radar Precipitation Products written by Amrit Bhusal and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hydrology Modeling using HEC-HMS (Hydrological Engineering Centre-Hydrologic Modeling System) is accepted globally for event-based or continuous simulation of the rainfall-runoff operation. Similarly, Machine learning is a fast-growing discipline that offers numerous alternatives suitable for hydrology research's high demands and limitations. Conventional and process-based models such as HEC-HMS are typically created at specific spatiotemporal scales and do not easily fit the diversified and complex input parameters. Therefore, in this research, the effectiveness of Random Forest, a machine learning model, was compared with HEC-HMS for the rainfall-runoff process. In addition, Point gauge observations have historically been the primary source of the necessary rainfall data for hydrologic models. However, point gauge observation does not provide accurate information on rainfall's spatial and temporal variability, which is vital for hydrological models. Therefore, this study also evaluates the performance of satellite and radar precipitation products for hydrological analysis. The results revealed that integrated Machine Learning and physical-based model could provide more confidence in rainfall-runoff and flood depth prediction. Similarly, the study revealed that radar data performance was superior to the gauging station's rainfall data for the hydrologic analysis in large watersheds. The discussions in this research will encourage researchers and system managers to improve current rainfall-runoff simulation models by application of Machine learning and radar rainfall data.

Download or read book Evaluating Satellite and Radar Based Precipitation Data for Rainfall runoff Simulation written by Abhiru Aryal and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Climate change and urbanization causes the increasing challenges of flooding in urban watersheds. Even the rivers identified as non-vulnerable are causing catastrophic damage due to heavy flooding. So, several satellite and radar-based precipitation data are considered to study the watersheds with no gauge station or need recent precipitation data. Weather Radar (NEXRAD)arch, the accuracy of satellite-based precipitation data, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR), and radar-based precipitation data, Next Generation Weather Radar (NEXRAD), is evaluated in rainfall-runoff simulation considering Hydrological Engineering Centre-Hydrologic Modeling System (HEC-HMS) and Personal Computer Storm Water Management Model (PCSWMM), respectively. The primary research proposes a framework for modeling the rainfall-runoff process using PERSIANN-CDR and a floodplain map in an ungauged urban watershed. The one-dimensional Hydrologic Engineering Centre-River Analysis System (HEC-RAS) model generates a flood inundation map for the pertinent flooding occurrences from the acquired peak hydrograph, providing a quantifiable display of the inundation extent percentage. The second research uses the PCSWMMs to show the extent of flooding. It also employs the compromise programming method (CPM) to rank the most critical sub-catchments based on three parameters: slope, surface area, and impervious area. Three low-impact development (LID) strategies over the watershed determine the best flood management option. Therefore, the overall study presents a comprehensive framework for flood management in urban watersheds that integrates satellite precipitation data, hydrologic modeling, and LID strategies. The framework can provide an accurate flood-prone zone and help prioritize critical sub-catchments for flood management options. The study proposes using HEC-HMS and PCSWMM models to simulate and analyze interactions between rainfall, runoff, and the extent of the flood zone. Furthermore, LID can be applied to reduce flooding in urban watersheds. Overall, the framework can be helpful for policymakers and system managers to build the watershed's resilience during catastrophic flooding events caused by climate change and urbanization.

Download or read book Performance Assessment of Satellite Rainfall Products for Hydrologic Modeling written by Hojjat Seyyedi and published by . This book was released on 2014 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Impacts of Global Change on the Hydrological Cycle in West and Northwest Africa written by Peter Speth and published by Springer Science & Business Media. This book was released on 2010-08-12 with total page 692 pages. Available in PDF, EPUB and Kindle. Book excerpt: Africa is highly vulnerable to the impacts of climate change. In particular shortage of fresh water is expected to be the dominant water problem for West and Northwest Africa of the 21th century. In order to solve present and projected future problems concerning fresh water supply, a highly interdisciplinary approach is used in the book. Strategies are offered for a sustainable and future-oriented water management. Based on different scenarios, a range of management options is suggested with the aid of Information Systems and Spatial Decision Support Systems for two river catchments in Northwest and West Africa: the wadi Drâa in south-eastern Morocco and the Ouémé basin in Benin. The selected catchments are representative in the sense: "what can be learnt from these catchments for other similar catchments?

Download or read book Rainfall runoff Modelling in Gauged and Ungauged Catchments written by Thorsten Wagener and published by World Scientific. This book was released on 2004 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: This important monograph is based on the results of a study on the identification of conceptual lumped rainfall-runoff models for gauged and ungauged catchments. The task of model identification remains difficult despite decades of research. A detailed problem analysis and an extensive review form the basis for the development of a Matlab? modelling toolkit consisting of two components: a Rainfall-Runoff Modelling Toolbox (RRMT) and a Monte Carlo Analysis Toolbox (MCAT). These are subsequently applied to study the tasks of model identification and evaluation. A novel dynamic identifiability approach has been developed for the gauged catchment case. The theory underlying the application of rainfall-runoff models for predictions in ungauged catchments is studied, problems are highlighted and promising ways to move forward are investigated. Modelling frameworks for both gauged and ungauged cases are developed. This book presents the first extensive treatment of rainfall-runoff model identification in gauged and ungauged catchments.

Download or read book Uncertainty of Global Precipitation Datasets and Its Propagation in Hydrological Simulations written by Md Abul Ehsan Bhuiyan and published by . This book was released on 2018 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate estimates of precipitation at the global scale are vital for a variety of hydrometeorological applications. Quantification of the error sources along with characterization of the error propagation in hydrological simulations are required for promoting use of satellite and reanalysis precipitation estimates in hydrological applications.In this study we address the remotely-sensed precipitation products uncertainty characterization based ona machine learning tree-based model, Quantile Regression Forests (QRF). We first apply the model to satellitepassive microwave estimates from the TRMM satellite. Reference precipitation was based on high-resolution (5 min/1 km) rainfall fields derived from the NOAA/National Severe Storms Laboratory multi-radar multi-sensor system. The model was evaluated using a K-fold validation experiment using systematic and random error statistics of the model-adjusted TRMM passive microwave rainfall point estimates, and ensemble verification statistics of the corresponding prediction intervals. Then, this framework was utilized to combine dynamic and static land surface variables together with multiple global precipitation sources to stochastically generate improved precipitation ensembles (combined product) over complex terrain. Input to the model included multiple global satellite precipitation products; an atmospheric reanalysis precipitation product; and other auxiliary variables including a daily soil moisture dataset, specific humidity and a terrain elevation dataset. The model performance was demonstrated over three mountainous study areas (Peruvian and Colombian Andes and the Blue Nile in East Africa) based on 13 years (2000-2012) ofreference rainfall data derived from in situ rain gauge networks. Results showed that the proposed blending framework could significantly reduce the error andadequately characterize the uncertainty of the combined product. In the last section of this study we investigate the impact of the combined product in hydrological simulations. The Iberian Peninsula was chosen as the study area, which has precipitation and climate variability due to complex orography influenced by both Atlantic and Mediterranean climates.Comparisons of the precipitation product-driven hydrological simulations by a distributed hydrological model against reference-driven streamflow simulations by the same model showed that the magnitude of systematic and random errors for the combined product was significantly lower than those for the individual precipitation products. Moreover, this blending framework rendered a detailed investigation of the precipitation error propagation into multi-hydrologic model simulations, which was accomplished using four global-scale land surface models (JULES, ORCHIDEE, HTESSEL and SURFEX) and one global hydrologic model (WaterGAP3). Through this analysis we investigated the error characteristics of different precipitation forcing datasets (satellite, reanalysis, and combined product) and their error propagation in different hydrologic variables (surface/subsurface runoff, evapotranspiration).

Download or read book Satellite Precipitation Measurement written by Vincenzo Levizzani and published by Springer Nature. This book was released on 2020-04-14 with total page 797 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a complete overview of the measurement of precipitation from space, which has made considerable advancements during the last two decades. This is mainly due to the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) mission, CloudSat and a carefully maintained constellation of satellites hosting passive microwave sensors. The book revisits a previous book, Measuring Precipitation from Space, edited by V. Levizzani, P. Bauer and F. J. Turk, published with Springer in 2007. The current content has been completely renewed to incorporate the advancements of science and technology in the field since then. This book provides unique contributions from field experts and from the International Precipitation Working Group (IPWG). The book will be of interest to meteorologists, hydrologists, climatologists, water management authorities, students at various levels and many other parties interested in making use of satellite precipitation data sets.

Download or read book Rainfall Runoff Modeling Using Artificial Neural Networks written by Jagadeesh Anmala and published by LAP Lambert Academic Publishing. This book was released on 2010-07 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book addresses a two-pronged approach for the determination of a watershed's response by developing a physically-based model and a neural network-based model. For the physically-based model, the watershed is partitioned into a series of one-dimensional overland flow planes and channel elements, and water is routed over these elements in a cascading fashion. A system of partial differential equations under the kinematic wave approximation was used to describe surface water movement. The applicability of ANNs was investigated by developing a neural network-based runoff predictive model. The performance of ANNs, with different architectures, was evaluated using monthly precipitation and temperature data (input) and watershed runoff (output) for 3 medium-sized watersheds - El Dorado, Marion, and Council Grove in Kansas, USA. The prediction of watershed response was also studied using several existing empirical rainfall-runoff models. The advantage of ANNs over the physically-based models is that they require only input and output data for mapping of an unknown function such as rainfall-runoff relationship. In the case of physically-based models a lot more data is required.

Download or read book Runoff Simulation Using Radar Rainfall Data written by John Charles Peters and published by . This book was released on 1996 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Application of Recurrent Neural Networks to Rainfall runoff Processes written by Tsung-yi Pan and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this chapter, the application of a DLRNN is demonstrated to simulate rainfall-runoff processes and recognize the transition of UHs in hydrology. Although most neural networks are black-box models that lack physical meanings of weights, the DLRNN developed in this chapter connects its weights with UHs that reveal the physical concepts from the network based on the special structure of RNNs. Without trial and error method, the structure and the weights of DLRNN can be quickly determined through a modified form of system identification that combines indirect system identification with the subspace algorithm. Then, the DLRNN learning algorithm based on the interchange of the roles of the network state variables and the weight matrix is derived for on-line training. In this chapter, the DLRNN introduced can not only simulate rainfall-runoff processes, but also recognize the transition of UHs. Owing to the feedback connections, DLRNN performs rainfall-runoff simulations as dynamic systems, and the advantage of DLRNN's dynamic feature has been proven after the comparison between DLRNN and FNN. The investigation of the connections between weights and physical meanings is an extension of neural networks applied in hydrological field due to the linearization of the RNN. Based on the linearization, weights of DLRNN are treated as Markov parameters to realize the transition of UHs. Through on-line learning, DLRNN modifies the weights to capture the relation between rainfall and runoff every time step, and the transition of rainfall-runoff processes can be emerged based on the changes of UHs. Furthermore, a modified system identification that combines indirect system identification with subspace algorithm is described to calibrate the DLRNN. This method determines the quantity of neurons in hidden layer and the weights of the network. It overcomes the drawback of costing time by traditional trial and error search for optimum structure of DLRNN. Additionally, the different forms of DLRNN have also been discussed herein. The results show that the performances of DLRNNs in different forms are close. Hence, the transformation of canonical form can be ignored in the flowchart of simulation via DLRNN. Finally, four criteria have been applied to evaluate the performance of rainfall-runoff simulation via DLRNN. The results show that the performance is satisfactory and DLRNN is competent to simulate dynamic systems, like rainfall-runoff processes.

Download or read book Application of the Precipitation runoff Modeling System to the Ah shi sle pah Wash Watershed San Juan County New Mexico written by H. R. Hejl and published by . This book was released on 1989 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Evaluating the Performance of Different Artificial Intelligence Techniques for Forecasting written by Muhammad Saifullah and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The forecasting plays key role for the water resources planning. Most suitable technique is Artificial intelligence techniques (AITs) for different parameters of weather forecasting and generated runoff. The study compared AITs (RBF-SVM and M5 model tree) to understand the rainfall runoff process in Jhelum River Basin, Pakistan. The rainfall and runoff of Jhelum river used from 1981 to 2012. The Different rainfall and runoff dataset combinations were used to train and test AITs. The data record for the period 1981,Äì2001 used for training and then testing. After training and testing, modeled runoff and observed data was evaluated using R2, NRMSE, COE and MSE. During the training, the dataset C2 and C3 were found to be 0.71 for both datasets using M5 model. Similar results were found for dataset of C3 using RBF-SVM. Over all, C3 and C7 were performed best among all the dataset. The M5 model tree was performed better than other applied techniques. GEP has also exhibited good results to understand rainfall runoff process. The RBF-SVM performed less accurate as compare to other applied techniques. Flow duration curve (FDCs) were used to compare the modeled and observed dataset of Jhelum River basin. For High flow and medium high flows, GEP exhibited well. M5 model tree displayed the better results for medium low and low percentile flows. RBF-SVM exhibited better for low percentile flows. GEP were found the accurate and highly efficient DDM among the AITs applied techniques. This study will help understand the complex rainfall runoff process, which is stochastic process. Weather forecasting play key role in water resources management and planning.

Download or read book Deep Learning for Operational Streamflow Forecasts Or More Specifically written by Jonathan Martin Frame and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation investigates deep learning (DL) and combining hydrologic process-based (PB) models with DL for a hybrid (HB) modeling approach (often referred to as ''physics-informed machine learning" or ''theory-guided learning") for improving the predictive performance of streamflow in the U.S. National Water Model. An in-depth analysis is made of the benefits of DL and the potential drawbacks of the HB models. No evidence is found supporting the use HB models over the "pure" DL models in the use cases analyzed. The performance of the HB models is found to degrade in ungauged basins, whereas the DL models do not. The DL models are the best performing models for predicting extremely high runoff events, even when such events are not included in the training set. Adding physics inspired constraints to data-driven models causes a loss of system information relative to the DL models. As such, a "pure" DL model, specifically the Long Short-Term Memory (LSTM), is chosen as one of the core modules for the Next Generation (Nextgen) U.S. National Water Model. The LSTM (via Nextgen) is applied to simulate streamflow for a three-year period across the 191,020 km^2 New England region.

Download or read book The Estimation of Rainfall for Flood Forecasting Using Radar and Rain Gage Data written by William J. Charley and published by . This book was released on 1988 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Use of Rainfall simulator Data in Precipitation runoff Modeling Studies written by Gregg C. Lusby and published by . This book was released on 1983 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Overview and Bibliography of Methods for Evaluating the Surface water infiltration Component of the Rainfall runoff Process written by R. B. King and published by . This book was released on 1992 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Rainfall Runoff Modelling written by Keith J. Beven and published by John Wiley & Sons. This book was released on 2012-01-30 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rainfall-Runoff Modelling: The Primer, Second Edition is the follow-up of this popular and authoritative text, first published in 2001. The book provides both a primer for the novice and detailed descriptions of techniques for more advanced practitioners, covering rainfall-runoff models and their practical applications. This new edition extends these aims to include additional chapters dealing with prediction in ungauged basins, predicting residence time distributions, predicting the impacts of change and the next generation of hydrological models. Giving a comprehensive summary of available techniques based on established practices and recent research the book offers a thorough and accessible overview of the area. Rainfall-Runoff Modelling: The Primer Second Edition focuses on predicting hydrographs using models based on data and on representations of hydrological process. Dealing with the history of the development of rainfall-runoff models, uncertainty in mode predictions, good and bad practice and ending with a look at how to predict future catchment hydrological responses this book provides an essential underpinning of rainfall-runoff modelling topics. Fully revised and updated version of this highly popular text Suitable for both novices in the area and for more advanced users and developers Written by a leading expert in the field Guide to internet sources for rainfall-runoff modelling software