Recent Advances in the Modeling of Hydrologic Systems

Free download. Book file PDF easily for everyone and every device. You can download and read online Recent Advances in the Modeling of Hydrologic Systems file PDF Book only if you are registered here. And also you can download or read online all Book PDF file that related with Recent Advances in the Modeling of Hydrologic Systems book. Happy reading Recent Advances in the Modeling of Hydrologic Systems Bookeveryone. Download file Free Book PDF Recent Advances in the Modeling of Hydrologic Systems at Complete PDF Library. This Book have some digital formats such us :paperbook, ebook, kindle, epub, fb2 and another formats. Here is The CompletePDF Book Library. It's free to register here to get Book file PDF Recent Advances in the Modeling of Hydrologic Systems Pocket Guide.

2019: Recent Advances in big data machine learning in Hydrology

Most of the studies have evaluated the impact of land use change by considering catchments in a single climatic condition. However, it is equally important to analyze the variation in the hydrologic response in catchments with different land use characteristics and climatic conditions. Several studies revealed that the conversion of forest land to grass land or crop land leads to reduction in ET value and an associated increase in surface flow.

Recently, Legesse et al. The results of climate scenario study showed that the influence of climate variability is more significant when compared to land use change. These scenario-based studies do not try to project the real future changes, but are attempts to assess the implications of possible future changes. This shows that, more emphasis is necessary towards the development of models which can predict future changes in climate and land use pattern in more realistic manner. Lin et al. Since, LUCC models are reasonably good at forecasting the near-future changes in LULC pattern by considering drivers such as demographic, socio-economic, national policies, etc.

However, many researchers preferred scenario-based forecasting due to difficulties and uncertainties associated with downscaling techniques and representation of detailed spatial features in climate models. On the other hand, the integration of variety of models is an improvement over the use of single scenario. Also, the present study has reviewed the importance of comparison of models in identifying possible sources of uncertainties in hydrologic modeling.

Based on the present review, it can be concluded that the variation between the simulating efficiency of the models can be attributed to uncertainty in the calibration strategy, model input and structure and parameterization. When the aim of the study is to tackle different aspects in single modeling framework, then it is more appropriate to integrate two or more models with different functionalities.


  1. Journal metrics.
  2. AboutHydrology: Recent advances in big data machine learning in Hydrology!
  3. 1776.
  4. 12222 Fall Cyberseminar Series.
  5. Recent Advances in the Modeling of Hydrologic Systems | D.S Bowles | Springer;

In the present review, an attempt has been made to understand the importance of hydrologic models in simulating hydrologic responses such as stream flow, ET, ground water flow, subsurface flow, etc. Based on this review, the following important points are highlighted: 1 The physically based semi-distributed and distributed hydrologic simulation models are more suitable for studying the effect of land use change, as land use pattern is heterogeneous in nature.

The integration of land use change models and climate change models GCM and RCM with hydrologic models can improve the efficiency of predicting the hydrologic response. Since, these models are capable of providing more realistic forecasts. All the models are associated with uncertainties; therefore, the comparison of models based on the evaluation criterion can help in identifying the uncertainties.

The accurate estimation of model parameters plays critical role by influencing the accuracy of model prediction.

Table of contents

To date, studies have been conducted to know the hydrologic changes in single hydroclimatic condition; very few studies have been carried out related to comparative evaluation in different hydroclimatic conditions. Therefore, it is equally important to analyze the variation in the hydrologic response in catchments with different land use characteristics and climatic conditions. You are free to: Share — copy and redistribute the material in any medium or format.

Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. No additional restrictions You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

We use cookies to improve your website experience. To learn about our use of cookies and how you can manage your cookie settings, please see our cookie policy. By continuing to use the website, you consent to our use of cookies. More information Accept. Cogent Geoscience. Authors 2. Close G. Dwarakish dwaraki. Ganasri ganasri gmail. About the author s Dr.

Recent Advances in the Modeling of Hydrologic Systems - Google Books

Download PDF. Cite this article as:. Article Figures and tables References. Abstract Abstract Hydrologic modeling plays a very important role in assessing the seasonal water availability, which is necessary to take decisions in water resources management. Public Interest Statement The conservation of land and water resources is significant for the sustainable development of mankind. Introduction Water is one of the essential components of the environment and requires proper planning and management to achieve its sustainable utilization.

Table 1. Scenario-based simulation of hydrological response in a catchment It is of interest to simulate the effect of possible changes in climate variables and land use that may occur in the near-future by considering scenario conditions. Table 2. Author s name Scenario details 1 Thanapakpawin et al. Model comparison and performance evaluation The comparison of models enables the identification of possible sources of uncertainty in hydrologic modeling and acts as valuable basis for the investigation of the effects of different model structures on model prediction Cornelissen et al.

Table 3. The performance of different models in estimating the runoff Serial No. Discussion The present review is concerned with the modeling approaches to assess the impact of land use changes on hydrologic response at catchment scale and also discusses the importance of scenario-based studies. Conclusions In the present review, an attempt has been made to understand the importance of hydrologic models in simulating hydrologic responses such as stream flow, ET, ground water flow, subsurface flow, etc.

Funding Funding. The authors received no direct funding for this research. References Abushandi, E. Water Resources Management , 27 , — SHE: Towards a methodology for physically-based distributed forecasting in hydrology. Journal of Applied Remote Sensing , 6 , 63— Uncertainty assessment through a precipitation dependent hydrologic uncertainty processor: An application to a small catchment in southern Italy. Journal of Hydrology , , 38— Advances in Water Resources , 32 , — An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow simulation.

Journal of Hydrology , , — Using artificial neural network approach for modelling rainfall—runoff due to typhoon.

Account Options

Journal of Earth System Science , , — The best relationship between lumped hydrograph parameters and urbanized factors. Natural Hazards , 56 , — Modeling the potential impacts of climate change on streamflow in agricultural watersheds of the Midwestern United States. Journal of Hydrology , , 73— Assessing hydrological impact of potential land use change through hydrological and land use change modeling for the Kishwaukee River basin USA. Journal of Environmental Management , 88 , — Applied hydrology ed.

A comparison of hydrological models for assessing the impact of land use and climate change on discharge in a tropical catchment. Effects of large-scale changes in land cover on the discharge of the Tocantins River, Southeastern Amazonia. Impact of land-cover and climate changes on runoff of the source regions of the Yellow River. Journal of Geographical Sciences , 14 , — Modelling the hydrological response of a Mediterranean medium-sized headwater basin subject to land cover change: The Cardener River basin NE Spain.

Application of a distributed physically-based hydrological model to a medium size catchment. Hydrology and Earth System Sciences , 4 , 47— Assessing hydrological impacts of climate change: Modeling techniques and challenges. The Open Hydrology Journal , 4 , — Physically based hydrologic modeling: Is the concept realistic? Water Resources Research , 26 , — Evaluating the suitability of TRMM satellite rainfall data for hydrological simulation using a distributed hydrological model in the Weihe River catchment in China.

Journal of Geographical Sciences , 25 , — Predicting runoff from rainfall using neural network. In Engineering hydrology pp. Integrating hydrologic modeling and land use projections for evaluation of hydrologic response and regional water supply impacts in semi-arid environments. Environmental Earth Science , 65 , — Study on runoff simulation of the upstream of Minjiang River under future climate change scenarios. Natural Hazards , 75 , — Comparison of hydrological impacts of climate change simulated by six hydrological models in the Dongjiang Basin, South China.

Hydrologic modeling impacts of post-mining land use changes on streamflow of Peace River, Florida. Chinese Geographical Science. Delineating hydrologic response units in large upland catchments and its evaluation using soil moisture simulations. Environmental Modelling and Software , 46 , — Coupled modeling of hydrologic and hydrodynamic processes including overland and channel flow. Advances in Water Resources , 37 , — Evaluation of distributed hydrologic impacts of temperature-index and energy-based snow models. Advances in Water Resources , 56 , 77— Water Resources Management , 29 , — Hydrological response of a catchment to climate and land use changes in Tropical Africa: Case study South Central Ethiopia.

Journal of Hydrology , , 67— Impacts of land use change and climate variability on hydrology in an agricultural catchment on the Loess Plateau of China. Journal of Hydrology , , 35— Developing and comparing optimal and empirical land-use models for the development of an urbanized watershed forest in Taiwan.

Landscape and Urban Planning , 92 , — The effects of changing the resolution of land-use modeling on simulations of land-use patterns and hydrology for a watershed land-use planning assessment in Wu-Tu, Taiwan. Landscape and Urban Planning , 87 , 54— Assessing the effect of land use change on catchment runoff by combined use of statistical tests and hydrological modelling: Case studies from Zimbabwe. Evaluation of hydrologic equilibrium in a mountainous watershed: Incorporating forest canopy spatial adjustment to soil biogeochemical processes.

Advances in Water Resources , 24 , — Intermittent reservoir daily-inflow prediction using lumped and distributed data multi-linear regression models. Journal of Earth System Sciences , , — Impacts of land-use change on hydrologic responses in the Great Lakes region. Journal of Hydrology , , 71— PROMET—Large scale distributed hydrological modelling to study the impact of climate change on the water flows of mountain watersheds. Land-use forecasting and hydrologic model integration for improved land-use decision support.


  • Java 101 for Hydrologists!
  • Journal list menu.
  • Journal of Hydrology.
  • Journal of Environmental Management , 84 , — Hydrologic response to scenarios of climate change in sub watersheds of the Okanagan basin, British Columbia. Journal of Hydrology , , 79— Sensitivity of land cover parameter in runoff estimation using GIS. Uncertainty in rainfall—runoff model simulations and the implications for predicting the hydrologic effects of land-use change. Land-use impacts on storm-runoff generation: scenarios of land-use change and simulation of hydrological response in a meso-scale catchment in SW-Germany.

    Journal of Hydrology , , 80— Study of parameter stability of a lumped hydrologic model in a context of climatic variability. Study of runoff response to land use change in the East River basin in South China. Stochastic Environmental Research and Risk Assessment. Potential of episodic flows in some four representative non-perennial river flow catchments in semi arid Laikipia district, Kenya. Environmental Monitoring and Assessment , 45 , — Modelling the effects of land use modifications on runoff.

    Water Resources Research , 6 , — Modeling the impact of land use change on the hydrology of a rural watershed. Journal of Hydrology , , 97— Assimilating scatterometer soil moisture data into conceptual hydrologic models at the regional scale. Hydrology and Earth System Sciences , 10 , — Theoretical and Applied Climatology , , — Sensitivity of a lumped and semi-distributed hydrological model to several methods of rainfall interpolation on a large basin in West Africa.

    Journal of Hydrology , , 96— Performance evaluation of modified versions of SCS curve number method for two watersheds of Maharashtra, India. Artificial neural networks for event based rainfall—runoff modeling. Journal of Water Resource and Protection , 04 , — Detecting changes in streamflow response to changes in non-climatic catchment conditions: Farm dam development in the Murray—Darling basin, Australia. Journal of Hydrology , , 84— A process-based, distributed hydrologic model based on a large-scale method for surface—subsurface coupling.

    Advances in Water Resources , 33 , — Artificial neural network based runoff prediction model for a reservoir. International Journal of Engineering Research and Technology , 1 3 , 1—4. Current Science , , — The impact of land use change on catchment hydrology in large catchments: The Comet River, Central Queensland, Australia. A data-driven algorithm for constructing artificial neural network rainfall—runoff models.

    Journal metrics

    Hydrological Processes , 16 , — Comparison of semi-distributed, GIS-based hydrological models for the prediction of streamflow in a large catchment. Detecting the effect of land-use change on streamflow, sediment and nutrient losses by distributed hydrological simulation. Predicting plausible impacts of sets of climate and land use change scenarios on water resources. Applied Geography , 32 , — Rainfall—runoff modeling of recent hydroclimatic change in a subtropical lake catchment: Laguna Mar Chiquita, Argentina.

    Water Resources Management , 26 , — Assessment of the uncertainties of a conceptual hydrologic model by using artificially generated flows. Slovak Journal of Civil Engineering , 20 , 35— Land use change modelling: current practice and research priorities. GeoJournal , 61 , — Paddy and Water Environment , 8 , — Environmental Modelling and Software , 24 , — Simulating the hydrological responses to climate change of the Xiang River basin, China. Theoretical and Applied Climatology. An event-based approach to understanding the hydrological impacts of different land uses in semi-arid catchments.

    Journal of Hydrology , — , 50— Hydrological impacts of land use change in three diverse South African catchments. Journal of Hydrology , — , — H Climate change effects on the hydrologic regime within the Churchill-Nelson River Basin. Chapter Projected change—Models and methodology pp. Assessing the impact of future land-use changes on hydrological processes in the Elbow River watershed in southern Alberta, Canada. Parameterisation of a simple semi-distributed model for assessing the impact of land-use on hydrologic response. Journal of Hydrology , , 16— A review on monthly water balance models for water resources investigations.

    Water Resources Management , 12 , 31— For example, a watershed model could be represented using tributaries as boxes with arrows pointing toward a box that represents the main river. The conceptual model would then specify the important watershed features e. Model scope and complexity is dependent on modeling objectives, with greater detail required if human or environmental systems are subject to greater risk. Systems modeling can be used for building conceptual models that are then populated using mathematical relationships. Prior to the advent of computer models, hydrologic modeling used analog models to simulate flow and transport systems.

    Unlike mathematical models that use equations to describe, predict, and manage hydrologic systems, analog models use non-mathematical approaches to simulate hydrology. Two general categories of analog models are common; scale analogs that use miniaturized versions of the physical system and process analogs that use comparable physics e. Scale models offer a useful approximation of physical or chemical processes at a size that allows for greater ease of visualization.

    Scale models commonly use physical properties that are similar to their natural counterparts e. Yet, maintaining some properties at their natural values can lead to erroneous predictions. This usually involves matching dimensionless ratios e.

    Looking for other ways to read this?

    Groundwater flow can be visualized using a scale model built of acrylic and filled with sand, silt, and clay. Some physical aquifer models are between two and three dimensions, with simplified boundary conditions simulated using pumps and barriers. The analogs to fluid flow are the flux of electricity , heat , and solutes , respectively. The analogs to hydraulic conductivity are electrical conductivity , thermal conductivity , and the solute diffusion coefficient. An early process analog model was an electrical network model of an aquifer composed of resistors in a grid.

    Electrical conductivity paper [7] can also be used instead of resistors. Statistical models are a type of mathematical model that are commonly used in hydrology to describe data, as well as relationships between data. Statistical moments e. These moments can then be used to determine an appropriate frequency distribution , [12] which can then be used as a probability model.

    The frequency of extremal events, such as severe droughts and storms, often requires the use of distributions that focus on the tail of the distribution, rather than the data nearest the mean. These techniques, collectively known as extreme value analysis , provide a methodology for identifying the likelihood and uncertainty of extreme events. The standard method for determining peak discharge uses the log-Pearson Type III log-gamma distribution and observed annual flow peaks.

    The degree and nature of correlation may be quantified, by using a method such as the Pearson correlation coefficient , autocorrelation , or the T-test. Regression analysis is used in hydrology to determine whether a relationship may exist between independent and dependent variables. Bivariate diagrams are the most commonly used statistical regression model in the physical sciences, but there are a variety of models available from simplistic to complex.

    Factor Analysis and Principal Component Analysis are multivariate statistical procedures used to identify relationships between hydrologic variables,. Convolution is a mathematical operation on two different functions to produce a third function. With respect to hydrologic modeling, convolution can be used to analyze stream discharge's relationship to precipitation.

    Convolution is used to predict discharge downstream after a precipitation event.

    Time-series analysis is used to characterize temporal correlation within a data series as well as between different time series. Many hydrologic phenomena are studied within the context of historical probability. Within a temporal dataset, event frequencies, trends, and comparisons may be made by using the statistical techniques of time series analysis.

    Markov Chains are a mathematical technique for determine the probability of a state or event based on a previous state or event. Markov Chains were first used to model rainfall event length in days in , [32] and continues to be used for flood risk assessment and dam management. Conceptual models represent hydrologic systems using physical concepts. The conceptual model is used as the starting point for defining the important model components. The relationships between model components are then specified using algebraic equations , ordinary or partial differential equations , or integral equations.

    The model is then solved using analytical or numerical procedures. The linear-reservoir model or Nash Model is widely used for rainfall-runoff analysis. The model uses a cascade of linear reservoirs along with a constant first-order storage coefficient, K , to predict the outflow from each reservoir which is then used as the input to the next in the series.

    The model combines continuity and storage-discharge equations, which yields an ordinary differential equation that describes outflow from each reservoir. The continuity equation for tank models is:. The storage storage-discharge relationship is:. Combining these two equation yields. Instead of using a series of linear reservoirs, also the model of a non-linear reservoir can be used. Governing equations are used to mathematically define the behavior of the system.

    Algebraic equations are likely often used for simple systems, while ordinary and partial differential equations are often used for problems that change in space in time. Examples of governing equations include:. Manning's equation is an algebraic equation that predicts stream velocity as a function of channel roughness, the hydraulic radius, and the channel slope:. Darcy's Law describes steady, one-dimensional groundwater flow using the hydraulic conductivity and the hydraulic gradient:.

    Groundwater flow equation describes time-varying, multidimensional groundwater flow using the aquifer transmissivity and storativity:. Advection-Dispersion equation describes solute movement in steady, one-dimensional flow using the solute dispersion coefficient and the groundwater velocity:.

    Poiseuille's Law describes laminar, steady, one-dimensional fluid flow using the shear stress:. Cauchy's integral is an integral method for solving boundary value problems:. Exact solutions for algebraic, differential, and integral equations can often be found using specified boundary conditions and simplifying assumptions.