# Rainfall runoff modelling in gauged and ungauged catchments pdf

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- Comparative evaluation of conceptual and physical rainfall–runoff models
- A soil-based approach to rainfall-runoff modelling in ungauged catchments for England and Wales
- A soil-based approach to rainfall-runoff modelling in ungauged catchments for England and Wales
- Predicting Surface Runoff from Catchment to Large Region

*Hydrology Research 1 April ; 49 2 : — Runoff prediction in ungauged catchments has been a challenging topic over recent decades. Great progress has been made in the field of regionalization study of hydrological models; however, there is no clear conclusion yet about the applicability of various methods in different regions and for different models.*

## Comparative evaluation of conceptual and physical rainfall–runoff models

The design of water resource structures needs long-term runoff data which is always a problem in developing countries due to the involvement of huge cost of operation and maintenance of gauge discharge sites.

Hydrological modelling provides a solution to this problem by developing relationship between different hydrological processes. In the past, several models have been propagated to model runoff using simple empirical relationships between rainfall and runoff to complex physical model using spatially distributed information and time series data of climatic variables. Several tests for goodness of fit have been applied to compare the performance of conceptual and semi-distributed physical models.

The analysis suggested that TANK model of RRL performed most appropriately among all the models applied in the analysis; however, SWAT model having spatial and climatic data can be used for impact assessment of change due to climate and land use in the basin. The rainfall—runoff modelling is an important and useful tool for hydrological research, water engineering and environment application. The runoff computation from ungauged or poorly gauged catchments is a serious challenge in developing countries like India where higher operation and maintenance cost differed gauging on small and medium rivers.

The knowledge-based or data-driven hydrological models were developed and used by researchers to extend runoff records and address modelling issues Kar et al. The hydrological model can be classified into three broad groups, namely metric, physical and conceptual models Beck et al. The rainfall—runoff relationships in metric models are essentially based on observations and without characterizing different processes involved in the hydrologic system Kokkonen and Jakeman The linearity assumption-based unit hydrograph theory Sherman , catchment characteristics-based rational formula for peak runoff given by JM Thomas — and reproduced by Loague , Strange tables, etc.

The metric models are observation-based models developed using observed runoff and catchment characteristics without considering much of hydrological processes.

The physical models represent different hydrological processes through mass, momentum and energy conservation equations. The physical models may be capable of considering the spatial variability of land use, slope, soil and climate to deal with the hydrological processes within the watershed semi- or fully distributed in nature.

The conceptual models are hydrological models that use simplified mathematical conceptualization of a system with the help of a number of interconnected storages used to represent different components of the hydrological process through recharge and depletion.

The conceptual models are usually lumped in nature and use the same value of parameters for the whole watershed and ignored the spatial variability of watershed characteristics. The conceptual models strongly rely on observed data, and results depend on the quality of input data used in the model. The finite difference or finite computation schemes are generally used to solve governing partial difference equations in these models.

As physical models are distributed in nature, they require large amount of topographical, soil, land use and climatic data to provide a framework to explore the changes in hydrological cycle due to human interferences and climate change for water resources management Leavesley ; Jiang et al. At the same time, these models can depict complex hydrological processes and predict different components of the hydrological cycle with an acceptable limit of efficiency.

Vaze et al. The requirement of high-resolution spatial and other data is low for metric models, moderate for the conceptual model, while high to very high in case of semi-distributed and fully distributed physical models. Similarly, metric models may have 1 to 5 parameters; conceptual models may have 4 to 20 parameters, while fully distributed models can have 10 to parameters to be adjusted for calibration.

Haque et al. In the study, five rainfall data sets, twenty-three different calibration data length and eight optimization techniques for gauge catchment have been employed and extended to the ungauged catchment using two regional prediction equations.

The uncertainties were compared with observed and model runoff by the AWBM and varied from 1. Zhang et al. The results indicated that multiple regression models were appropriate with precipitation, and potential evapotranspiration of the current month and precipitation of the last month were explanatory variables, but AWBM gave higher Nash—Sutcliffe efficiency. Kumar et al. Shoaib et al.

The Nash—Sutcliffe efficiency was used as a goodness-of-fit measure for selecting the best model. They suggested that the choice of model should be made on the basis of the objective of modelling. The main weakness of the model is the requirement of a wide range of data and non-spatial representation of the HRU inside each sub-catchment. Glavan and Pinter described strength, weakness, opportunities and threat SWOT of SWAT model and described the strength of SWAT model as an easily available online model that can combine water quality, quantity, agriculture land management, and climate change simultaneously.

The results of the analysis indicated that among all the methods, SUFI2 may be the most suitable method as it required the smallest runs to get appreciable prediction and model performance. Looking into the strength and weakness of different conceptual and semi-distributed physical models, the present study deals with the comparative evaluation of some conceptual and distribute models for performance in an Indian basin having problems of availability of data.

The study area for the present study is Tandula catchment up to Tandula reservoir in the Chhattisgarh state of India. Tandula reservoir is situated on the confluence of river Tandula with Sukha Nala in Balod district having a catchment area of The location map of the study area has been presented in Fig. The Tandula catchment is influenced by four rain gauge stations including Balod, Bhanpura, Chamra and Gondli in and around the catchment with the Thiessen weight of 0.

The daily reservoir level, releases and overflow from weir for the period from to have been used in a simple water balance model to compute daily runoff from the catchment.

The rainfall data of Balod, Bhanpura, Chamra and Gondli and climatic data of Raipur have been used in the analysis. The climatic data of Raipur climatic station were used in rainfall—runoff modelling. The parameters of these models can be optimized using any one of the techniques including uniform random sampling, genetic algorithm GA , shuffled complex evaluation SEC-UA , pattern search, multi-start pattern search, Rosenbrock search and Rosenbrock multi-start search.

The calibration and validation of these models can be done with any one of ten different objective functions including Nash—Sutcliffe efficiency Nash and Sutcliffe , sum of square error, root-mean-square error, root-mean-square difference about bias, absolute value of bias, sum of square roots, sum of square of the difference of square root and sum of absolute difference of the log-transformed data. The SWAT is a semi-distributed physical model capable of modelling hydrological processes including quantity, quality and sediment in a basin.

The SWAT model is a widely used model for runoff, recharge, sediment and nutrient flow in the basin. The TANK model in RRL consists of four vertical tanks in series where precipitation and evaporation are used as inputs in the top tank and evaporation is subtracted sequentially from all the tanks.

In this model, the top tank has two outlets, while all other tanks have only one outlet with runoff coefficient a xy at variable heights H xy.

Here, x is the number of tank and y is the number of outlets Fig. The total runoff is computed as the sum of runoff from all the tanks using the following equation:. The actual evapotranspiration ETA from each tank is the loss from the system and can be computed based on the depth of water C x in x th tank.

The following equation can be used to compute actual evapotranspiration ETA for a tank. Reproduced from RRL manual. The infiltration I x in these tanks can be computed using tank-wise infiltration coefficient b x and water levels in the corresponding tank with the help of the following equation:.

The sum of runoff computed from different tanks can be considered as total runoff from the basin. The first tank gives surface runoff, second tank intermediate runoff, while third and fourth tanks provide sub-base and base flow, respectively. The AWBM works on the basis of three independent surface storages for computation of partial runoff from a catchment Fig. The model independently calculates water balance for each surface storage using precipitation and evapotranspiration as inputs.

The whole catchment in this model can be divided into three partial areas using three parameters as A 1 , A 2 and A 3. The soil moisture for each partial area is computed by adding precipitation and deducting evapotranspiration. At any time, rainfall is added to each of the partial area or storage and after deducting evapotranspiration, the following water balance equation is used to compute storage and if the moisture becomes greater than the capacity of storage, the moisture in excess of capacity becomes runoff.

In this model, friction BF 1 of runoff in any partial storage gave base flow and reminder may provide surface runoff from that store using following equations.

Both the surface runoff from three partial storage and base flow are depleted on daily or hourly timescale with the help of surface flow recession coefficient KS and baseflow recession coefficient K in a linear manner using the following equations:. The routed surface and base flow at the outlet of the basin provide total runoff from the basin. The land use, soil, digital elevation model, climatic data are the prime input for the model, and it is able to predict sub-basin-wise runoff, groundwater contribution, base flow, crop growth, soil erosion, sediment yield, nutrient status, etc.

Santhi et al. The runoff yield for individual HRU and then for sub-watersheds are routed through river network using variable storage or Muskingum routing method. The model have hundreds of parameters for defining the spatial variability of hydrological characteristics of the basin, out of which some of the parameters vary by sub-basin, land use, or soil type, while others can be computed through field measurement, data or literature.

The SWAT model works on the principle of water balance, and two major components of the hydrological cycle are computed considering physical process within the basin.

The first phase computes the runoff, sediment, nutrients and pesticide loading to the main channel of each basin, while the second phase concentrates on routing for movement of generated runoff, sediment, nutrient and pesticide through a network of the channel to the outlet of the basin. The different components of the hydrological cycle in the SWAT model can be represented by the following water balance equation:.

The runoff computed for each HRU is routed to get total runoff at the outlet of the basin. The input precipitation in the model first undergoes interception and is computed by the canopy in the SCS curve number method or user-defined leaf area index in case of Green and Ampt method.

The infiltration in the SWAT model can be computed either directly through Green and Ampt method or by the remaining amount of water from precipitation after generation of daily runoff in case of SCS method. For computation of evaporation from soil and water separately, the method suggested by Ritchie is used, while the options of Hargreaves method Hargreaves et al.

The water percolated to the bottom root zone is partitioned in interflow and baseflow. The lateral subsurface flows or interflow is computed using a kinematic storage model in each soil layer, while baseflow or groundwater is muddled using two aquifer systems in the SWAT model.

The topmost aquifer which is considered as unconfined aquifer contributes return flow within the watershed, and underlined deep aquifer contributes outside of the watershed Arnold et al. The baseflow is only allowed when the amount of water stored in shallow aquifer reached to a prescribed level defined by the user and then baseflow which is the contribution to the main channel or reach is computed using the study-state response of groundwater flow suggested by Hooghoudt The application of models whether simple or complex depends on how well they can reproduce the response of the system.

The Nash—Sutcliffe efficiency NS , the coefficient of correlation C c and root-mean-square error RMSE were used as the goodness-of-fit criterions for selecting an appropriate model. The aerial rainfall series based on Thiessen weight of different stations and evapotranspiration computed from climatic data of Raipur station were used as inputs to the TANK model where data from to have been used for warming, to for calibration and to for validation of the model.

The auto-calibration has been done for optimization of parameters of the model, and then, genetic algorithm GA has been used to get further refined values of parameters. The graphical representation of observed and computed runoff obtained from TANK models during calibration and validation has been presented in Fig.

The Nash—Sutcliffe efficiency of the model during calibration and validation was found as 0. The model warming, calibration and validation of the model have been carried out for the periods of —, to and to , respectively. The calibration of the parameters of the AWBM was done initially by auto-calibration and then genetic algorithm to optimize the parameters. The Nash—Sutcliffe efficiency of the model was worked out as 0.

In the study, SWAT which is a semi-distributed physical model has been established using digital elevation model, land use and soil maps of Tandula catchment Fig.

After setting up of the model and writing the default values of all the parameters, a simulation run of the model has been made and exported to SWAT-CUP application for sensitivity, calibration and validation.

The sensitivity analysis has been carried out using t-stat larger absolute value and P value smaller absolute value. The comparison of observed and simulated runoff during calibration and validation has been presented in Fig. These models contain a sperate set of parameters and optimization technique. The performance of these models was evaluated on the basis of some standard goodness-of-fit criterions including root-mean-square error, the coefficient of correlation, Nash—Sutcliffe efficiency and coefficient of determination, etc.

The efficiency criteria of different models during calibration and validation indicated a satisfactory performance of all these models used in the study.

From the analysis, it has been found that RRL TANK model performed comparatively better in both calibration and validation, while SWAT model due to its complexity, multiple spatially distributed information and climatic data performed with the lowest efficiency.

As SWAT model has several parameters to depict hydrological processes above and below the earth, sometime could not be optimized and gave less efficiency. However, due to incorporation of land use, slope, soil and climatic data, the SWAT model can be used for impact assessment of land use change and climate change assessment in a basin.

## A soil-based approach to rainfall-runoff modelling in ungauged catchments for England and Wales

Predicting surface runoff from catchment to large region is a fundamental and challenging task in hydrology. This paper presents a comprehensive review for various studies conducted for improving runoff predictions from catchment to large region in the last several decades. This review summarizes the well-established methods and discusses some promising approaches from the following four research fields: 1 modeling catchment, regional and global runoff using lumped conceptual rainfall-runoff models, distributed hydrological models, and land surface models, 2 parameterizing hydrological models in ungauged catchments, 3 improving hydrological model structure, and 4 using new remote sensing precipitation data. It is a major component in regional and global hydrological cycle. It has direct impacts on human lives since it is a key water resource for agriculture, industry, urban water use, and so forth. It is crucial to understand complex relationships between rainfall and runoff processes and then to accurately estimate surface runoff for efficient design, planning, and management of catchments. This can be achieved using hydrological modeling that not only estimates continuous surface runoff, but also helps in understanding catchment behaviors and modeling impacts of climate and land use changes on surface water balance [ 1 , 2 ].

The design of water resource structures needs long-term runoff data which is always a problem in developing countries due to the involvement of huge cost of operation and maintenance of gauge discharge sites. Hydrological modelling provides a solution to this problem by developing relationship between different hydrological processes. In the past, several models have been propagated to model runoff using simple empirical relationships between rainfall and runoff to complex physical model using spatially distributed information and time series data of climatic variables. Several tests for goodness of fit have been applied to compare the performance of conceptual and semi-distributed physical models. The analysis suggested that TANK model of RRL performed most appropriately among all the models applied in the analysis; however, SWAT model having spatial and climatic data can be used for impact assessment of change due to climate and land use in the basin.

Request PDF | On Jan 1, , T Wagener and others published Rainfall-Runoff Modelling In Gauged And Ungauged Catchments | Find, read and cite all the.

## A soil-based approach to rainfall-runoff modelling in ungauged catchments for England and Wales

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### Predicting Surface Runoff from Catchment to Large Region

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