Recent Advances in the Modeling of Hydrologic Systems
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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.
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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.
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.
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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.
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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.
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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  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 ,  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 ,  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.