Runoff processes and streamflow modelling

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Modified CN: Sensitivity analysis was performed to estimate the accuracy of calibration and validation results of hydrology model. Since in this study the CN method has been used for estimation of losses, sensitivity analysis is performed to determine the effective parameters for calibration of the loss model to achieve better results. Since a large number of data are put into the rainfall-runoff model, the two parameters include curve number and initial abstractions are used for sensitivity analysis in HEC-HMS. The analysis was optimized according to objective functions of peak weighted root mean square.

Woodward et al. The Eq. Time of concentration: The standard lag time is defined as the length of time between the centroid of precipitation mass and the peak flow of the hydrograph. This can be estimated by getting the time between the centroid of the storm and the inflection point of the hydrograph or via calibration. Hydrograph represents the changes in runoff through the time. In this study, SCS dimensionless hydrograph was used to generate hydrograph for a long time daily rainfall over Klang watershed.

The parameters of the method are: Time of concentration, lag time, Duration of the excess rainfall, Time to peak flow, Peak flow. The relevant equations are defined as:. Meteorological model: To define the meteorological model in HEC-HMS for Klang watershed, the gauge weight method was used to allocate the climatic parameters for each sub-basin Meenue et al. The meteorological model used Monthly average Evapo-Transpiration ET method for the rainfall-runoff simulation.

The empirical Hargreaves method Salazar et al. It is based on the air temperature and requires the maximum and minimum air temperature to calculate ET. Many studies have shown the role of the Evapo-Transpiration into hydrology modelling Zhao et al. This method was used as its simplicity and modest data requirement which made it attractive for the hydrology modelling Hargreaves and Samani, Eq. The mean air temperature in the Hargreaves equation is calculated as an average of Tmax and Tmin.

Generating hydrological watershed characterization: Catchment delineations have been driven for Klang watershed to extract hydrological parameters using as input into HEC-HMS hydrology model. These data have been developed using GIS spatially. Soil type as one of the significant layer affecting rainfall loss were classified based on Soil Group Classification SCS system.

Figure 3 illustrates the overlaid map of the landuse and soil layers indicating CN values. The Curve number parameters for using in sensitivity analysis are calculated based on the CN 0. The value of the two parameters CN and Initial abstraction changed to determine their effects on peak discharge of flood. In this method the standard lag is defined as the length of time between the centroid of precipitation mass and the peak flow of the resulting the hydrograph. Basin lag is considered as 0. Table 5 gives the lag time, potential soil storage and initial abstraction calculated for each sub basin of Klang watershed.

Meteorological model: The meteorological model used Monthly average Evapo-transpiration ET method for the rainfall-runoff simulation. The daily evaporation from Batu dam station for the years was used. Table 6 shows the daily and monthly ET calculated for the Batu dam station. The 17 years daily evaporation time series in the Batu dam station was used. Model calibration and validation: The daily rainfall data for the 23 rainfall gages through the long period were used for calibration and validation of HEC-HMS simulation for Klang watershed.

The numbers of 16 years from were selected for the calibration and the 11 year lengths from for the validation in HEC-HMS program as the same period calibration and validation of rainfall downscaling in SDSM. Some statistical efficiency criteria are used to perform evaluation of the calibration and validation results between model outputs and observed data which are Root mean square error RMSE , Coefficient of determination r 2 and Correlation coefficient r to indicate the goodness of fit between simulated and observed data.

The calibration of the rainfall-runoff model in HEC-HMS for Klang watershed is performed by comparing the modelled daily streamflows with the observed flow at the Sulaiman discharge gauge. Table 7 gives the statistics of the daily observed and modelled streamflow at the Sulaiman discharge station for the calibration and validation periods.

As the table, maximum and mean values of daily flows are underestimated during calibration and validation periods. The plots of daily and monthly flow modelling are illustrated in figures indicate that flows are well simulated. However most of daily high flows simulated in calibration and validation periods are underpredicted. Then it was attempted to estimate the magnitude of underprediction of high flows using sensitivity analysis to address the uncertainty involved in the modelling. The discrepancy of daily flow modelling at the Sulaiman streamflow station has already been observed by Kavvas et al.

Table 8 gives the performance assessment for the daily and monthly discharges in the calibration and validation periods. The calibration and validation results represented a good fit between the observed and simulated daily discharges.

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Thus it can be concluded that HEC-HMS model responds well in simulation of hydrological processes in Klang watershed using meteorological observation data. However, there has no significant differences between two CN and CN 0. Using CN 0. In order to determine the efficiency and suitability of modified CN loss method used there has been attempted to make estimation on the results by some correlation coefficients and error indices. Although the characteristics of the hydrological watershed used in the rainfall-runoff modelling in Klang watershed are assumed constant throughout the simulation period.

Flood hydrograph is best calibrated for peak discharge with the modified ratio of initial abstraction to maximum potential retention in SCS model. Therefore, CN 0. Abood, M. Thamer, A.

Ghazali, A. Mahmud and L. Sidek, Impact of infiltration methods on the accuracy of rainfall-runoff simulation. Applied Sci. Akbari, A. Samah and F. Othman, Practical use of SRTM digital elevation dataset in the urban-watershed modeling. Spatial Hydrol. Desa, M. Niemczynowicz, Spatial variability of rainfall in Kuala Lumpur, Malaysia: Long and short term characteristics.

Pingel and J. Devries, Hargreaves, G. Samani, Reference crop evapotranspiration from temperature. Applied Eng. Chen and N. Ohara, Kumar, R. Chatterjee, R. Singh, A. Lohani and S. Kumar, With these new functions, WEPP application to watersheds has made the outputs more feasible. It processes a m or lower DEM based on the D8 method. The basic watershed unit is produced by overlaying land use or management , which represents soil planning and practices under human activities, the soils map, and the digital elevation model. Finally, the parameters of soil and vegetation are input under TopWEPP to obtain the simulation results.

WEPP has been applied to several regions around the world for runoff and sediment yield predictions from agricultural Defersha; Melesse; McClain, ; Kim et al. These cited authors showed that GeoWEPP has produced better performance when compared with other models, according to statistical indicators taking into consideration. Jong van Lier et al. The results showed a successful use of GeoWEPP for prediction of spatially soil erosion and runoff with georeferenced maps as the model's outputs for this hydrological unit.

Despite that the WEPP model has been developed for US pedological and weather conditions, it can successfully be applied in other regions, Brazil included. However, we need to consider carefully the parameters that the model requires as these are poorly determined under field conditions for tropical soils as well as new equations to estimate such values based on soil properties under the tropical and subtropical conditions Reichert; Norton, Nunes and Cassol showed that the use of soil sand and clay contents, as suggested by the WEPP model, has proved that they are not adequate to estimate the interrill soil erodibility in Oxisols with different clay contents in the State of Rio Grande do Sul, Brazil.

Machado et al. Based on theirs results, the WEPP was not properly calibrated for this location, requiring some adaptations and characterization of soil properties required by the model for the tropical soils found in Brazil. A team of researchers have worked in the development of the Lavras Simulation of Hydrology LASH model since in order to make available a conceptually-based hydrological model to estimate stream flows in tropical and subtropical watersheds.

However, when the researchers first discussed the model structure, they drew the conclusion that the great challenge would be the necessity of developing a model compatible with the reality of developing countries in terms of both hydrological processes and lack of data base availability. The soil-water balance equation is the most fundamental concept in the LASH structure regardless of its spatial and temporal discretization. This equation is used to update the current soil water storage value A t at each time step TS and in each hydrological response unit HRU watershed, subwatershed or grid cell.

It takes into account the main hydrological processes also referred to as components of interest at watershed scale: rainfall R , interception of rainfall IR , evapotranspiration ET , infiltration I , capillary rise CR , surface runoff D S , sub-surface runoff D SS , and base flow D B. All of these hydrological processes have their values updated at each time step and for every HRU where there is a specific algorithm for each of them.

Overview of Runoff

It is worthwhile to mention that there is an interrelationship among different components. ET is estimated using the Penman-Monteith equation Allen et al. Its usage in LASH is intended to estimate crop evapotranspiration considering vegetation-related parameters such as height, albedo, leaf area index LAI , stomatal resistance, and aerodynamic resistance, besides the effective rooting depth. Rainfall is stored on the vegetation cover until maximum interception storage I max is reached, which is calculated for each HRU as a linear function of LAI.

LASH considers the interception reservoir in which it is emptied after each time step as a function of ET ratio. The Penman-Monteith equation is applied for the water intercepted by vegetation separately from the water stored in the soil when the interception reservoir cannot hold any more water, rainfall reaches soil surface directly; otherwise, the rainfall depth that reaches soil surface is reduced by LASH taking into account how much water this reservoir still can store.

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This method requires information on R, initial abstraction I a , maximum potential soil water storage S , and antecedent soil water storage M. The main alterations are associated with the approaches employed to compute I a and S. The S value of each HRU is updated in every time step from the difference between maximum soil water storage A m and At, which is given by the soil water balance equation. LASH considers the area of each HRU to convert D S into its corresponding surface runoff volume V S , which in turn is transformed into surface runoff discharge Q S by using the time of concentration and a parameter related to the residence time of water in the surface reservoir C S.

Aiming to convert this runoff depth into discharge, the model considers the time of concentration and another parameter related to the residence time of water in the surface reservoir Csup to reduce the uncertainties, which also needs to be calibrated. T B is associated with an aquifer depletion coefficient, which can be obtained from an observed hydrograph if a long drought period is available. Although it was not mentioned in the above paragraph, the method of linear reservoirs is employed in LASH model to transform the runoff volume of each component into the respective discharge, thus accounting for the delayed effect within each HRU.

Finally, LASH combines Q t values resulting from different HRUs by using the linear Muskingam-Kunge model to route them through the drainage network in order to consider its accumulation effects on hydrograph behavior. Details about this routing method can be found in several hydrology books. The LASH model has many parameters, which can be calibrated depending on the scenario established by the hydrologist. From the work of Beskow et al. Each step described before is run over a cell whose size will be defined by the users, as presented in the following figure Figure 2 , elaborated by Beskow et al.

Basically, this procedure allows the identification of the most sensitivity parameters. Thus, the model is run considering each one of the parameters having a statistic of precision as the target, normally, the Nash-Sutcliff coefficient C NS. ArcLASH generates drainage network, soils and land-use maps within the LASH environment, avoiding the users to make a mistake that normally occurs due to different coordinates system, with errors accumulating because of the Digital Elevation Model references, among others.

SWAT has been one of the most applied models around the world. According to Neitsch et al. For simulation of the hydrological cycle elements, SWAT works with different soil layers, which are defined by the users. Suking to simulate the components of the streamflows, SWAT is structured by the following systems:. However, for estimating the peak discharges, SWAT employs the rational method. In order to proceed with this simulation, SWAT uses a kinetic wave model for mass balance, which requires the following properties: soil moisture at field capacity for the considered soil layer, soil saturation capacity, soil saturated hydraulic conductivity, steepness of the hydrological unit and drainable porosity.

However, for estimation of the base flow component, the model considers only the shallow aquifer contribution.

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For that, the hydrograph coefficient of depletion is needed as well as the distance between water basin divisor and the main stream channel, besides hydraulic conductivity of the aquifers. The model estimates the evaporation from soils and transpiration from plants. SWAT simulates both evaporation and transpiration by means of the Penman-Monteith equation, considering some physiological plants parameters and a coefficient given by a threshold established by the soil moisture. In addition, SWAT considers water movement to the unsaturated adjacent layers as a function of the water demand by evapotranspiration.

For this purpose, SWAT requires the soil erodibility values for each soil type from the user. For propagation of the sediments by the channels, SWAT considers two physical processes: sedimentation deposition and transportation, simultaneously. For the first, Stokes' law is considered based on the maximum velocity of the water on the channel. This calculation is carried out at the sub-basin level. Thus, the maximum concentration that can be transported by the channels conc sedmax is calculated adjusting the potential equation as a function of two parameters and maximum velocity of water on the channel.

If conc sedmax is lesser than the sediment load estimated by MUSLE the sediment transport will be predominant. The streamflows are routed in the drainage network using the Variable Storage method, developed by Williams This method considers a trapezoidal section for the channel and the velocity of water estimates by Manning equation.

However, the model can also uses the Muskingham routing method. SWAT is classified as a semi-distributed model, with the watershed divided into sub-basins. These sub-basins are parameterized by the Hydrologic Response Units HRU , which are defined by the users as a function of several combinations between land use, soil type and slope. This spatial sub-divisions allow the model to better be calibrated as well as to generate outputs spatially distributed by sub-basins or even by hydrologic units.

Thus, the streamflows are estimated for each HRU and then routed into the drainage network, for which a hydrologically consistent Digital Elevation Model is necessary. Among them, the SUFI-2 algorithm stands out due to its capability to account for all sources of uncertainty on the parameter ranges such as uncertainty in driving variables e.

When acceptable values of goodness of fit indices are reached, then the parameter uncertainties are considered the calibrated parameter ranges and the best simulation is calculated. After the automatic calibration, the parameters of the best simulation can be modified, manually, by: replacing the parameter previously calibrated; adding an absolute value to the calibrated parameter; and relative change, which implies in a correction factor of the parameter.

Table 2 presents parameters that normally have been calibrated in the SWAT applications to simulate streamflows for Brazilian conditions Andrade; Mello; Beskow, ; Pinto et al. Table 3 summarizes some results from SWAT application to simulate streamflows for Brazilian watersheds. Its first application was concentrated in mountainous catchments located in Northwestern USA, representing the hydrology, the weather and the vegetation of temperate regions Wigmosta; Vail; Lettenmaier, Afterwards, other studies in USA have contributed for the consolidation of the model, highlighting Bowling and Lettenmaier and Doten et al.

DHSVM was initially designed for mountainous regions and has been applied to different research purposes, such as:. Shortly, DHSVM is a parametric physical based distributed model, and runs aiming to represent the effects of topography, soil and plant on the different water fluxes in a watershed, which is derived from a Terrain Digital Model TDM. Thus, DHSVM takes into account spatial variability and the heterogeneity of each grid cell throughout the watershed basin.

The model runs over a grid of cells whose horizontal resolution varies depending on the Terrain Digital Model and the size of basin. This resolution has varied from 5 to 30 m to smaller basins up to km 2 and m for the others from to 10, km 2. According to the temporal resolution of the weather data sets, which can be lower than a daily time step, the model gives a simultaneous solution to water and energy balance equations for each cell.

SPHY: Spatial Processes in HYdrology – FutureWater

These computational cells are hydrological connected through the sub-surface and surface flows. In addition, the topographical characterization is taken into consideration to control the absorbed solar radiation, to describe the orographic effect on the precipitation pattern, the effects on air temperature and on the dynamics of water throughout the basin. It is worthily to mention that the vegetation parameters and soil properties are also distributed by cells.

Maximum infiltration rate is determined based on the maximum quantity of water that can be infiltrated at each time step and the water movement on unsaturated soils is simulated considering multi-layers and each layer of vegetation can extract water from one or more soil layers. Darcy Law simulates water percolation through soil layers by means of Brooks-Corey equation to estimate hydraulic conductivity;. In Table 4 , a list of some remarkable applications of the DHSVM along with the most important features of the respective study, are presented. AnnAGNPS model was developed to analyze and to provide estimates of runoff with primary emphasis upon non-point source pollutant loadings from agricultural watersheds and to compare the effects of various conservation alternatives Li et al.

The basic modeling components are hydrology, sediment, nutrient, and pesticide transport Pease; Oduor; Padmanabhan, Within the model, runoff using the SCS curve number equation is calculated United States Departement of Agriculture-Soil Conservation Service-Usda-Scs, and modified daily based upon tillage operations, soil moisture, and crop stage Chahor et al. Evapotranspiration is a function of potential evapotranspiration, calculated using the Penman-Monteith equation and soil-water content Fares, Li et al.

The estimated runoff data for the and years were used to calibrate and validate the annual runoff. Nash-Sutcliffe coefficient of model efficiency and coefficient of determination R 2 values for the calibration and validation runoff were both 0. Observed nutrient data from July to September and December to December were used to calibrate and validate the monthly nutrient load. Nash-Sutcliffe efficiency coefficient values for calibration and validation of the monthly nitrogen and phosphorus load were 0. Based on these results, the AnnAGNPS model presented an acceptable tool for runoff and nutrient yield estimations at this hydrological site in China.

Pease, Oduor and Padmanabhan used the AnnAGNPS model at the Pipestem Creek Watershed in North Dakota and found a high degree of accuracy of the model when runoff was modeled, which was demonstrated by the low values of systematic errors obtained from the coefficient of performance. In tropical conditions, Shamshad et al. The authors found a good correlation between the predicted and observed data, which suggested that the AnnAGNPS model can be capable of being used as a valuable tool for planning and management of studied watersheds in that country.

Only few works in tropical conditions exemplify the difficulty in obtaining the appropriate number of parameters that are needed in calibration of this model, especially in Brazil, where these type of data have been scarce. In order to model natural systems, like hydrological and sediment cycles, many challenges have been faced by the scientists. Besides, water quality as another of models' output, is fundamental for the natural resources management and planning, especially for drinking water.

Many studies have been conducted at the watershed level simulating the impacts on hydrology water quality, erosion and sediment transport from removal of the native vegetation e. Based on these results, it is possible to evaluate how much the society will need to pay for protection of headwaters streams aiming to preserve the quality and quantity of water.

This process is known as "ecological services", in which models have been useful in evaluating the hydrological cycle and quantifying its effects. With the development of geospatial technologies, the use and application of hydrology models have had a new and significant improvements not only to solve hydraulic engineering problems but also as a powerful tool to support the solutions for environmental issues, like the groundwater recharge process, soil erosion and transport, eco-hydrology interactions, among others.

The results from hydrological and erosion models simulation of processes have been the primary information for land-use planning in the watershed basins, especially to better understanding how the hydrological cycle, soils and vegetation interact between themselves. Demonstrating the possible impacts negatives or positives generated by removal of species or changing soil management from agricultural to forestry and livestock activities has helped to understand how better to conserve the environment.

Based on the advent of new computational technologies, the outputs from models can provide a much better understanding by scientists and land managers, and consequently, give more support for the actions of the field level engineers. One of the most significant state-of-art studies involves hydrologic models to simulate climate change impacts on the hydrology of watershed basins, allowing the discussion about how the hydrological cycle, water erosion and sediment transport can be affected in the future.

In this context, for example, Viola et al. In addition, floods will become more severe in the region due to excessive concentration of rainfalls during the summer. The use of hydrologic models has been a widely effective tool in order to support decision makers dealing with watersheds related to several economic and social activities, like public water supply, energy generation, and water availability for irrigation, among others. The application of these tools is based on the understanding of the hydrological cycle and its prediction. GIS and remote sense tools, searching to obtain a distributed model in both space and time, have supported the models, aiming to reduce the uncertainties and providing basis for simulation of different scenarios in the watersheds.

In this sense, one of the most relevant state-of-art of knowledge in the earth natural sciences is associated to the simulation of different impacts from both climate and land-use change scenarios on the hydrology, erosion and sediment transport in the watersheds. Nevertheless, the majority of models have presented limitations mainly related to simulate adequately the long-term impacts from different land-uses changes.

As an example, the impacts from pasture instead of native forest or vice-versa even after a very adequate performance during the calibration and validation process, need to be considered taking into consideration the limitations for modeling dynamically the infiltration process as it is directly influenced by the respective land-use throughout the time. These limitations are associated with simplifications adopted in the models' structure, such as:.

One of these processes is related to the soil infiltrability behavior after deforestation. There are a number of studies demonstrating the role of native forest litter and biological activities on the preferential flows formation in the soil profile, which has promoted much greater infiltration capacity of the soils. These preferential flows are formed from micro-to macro-scales in the watersheds and influence the overall water budget. The models cannot simulate this process adequately as this interaction soil - biological activities is very uncertain to be modeled. Thus, the water budget outputs, mainly those related with base flow, will not be adequately simulated, conducting to wrong conclusions.

Finally, we understand that the models have been changed and evaluated in a significant manner in recent years, highlighting the use of remote sense, GIS and automatic calibration process, allowing the models to be capable of simulating a given current land-use in the watersheds. However, all hydrology models have almost the same physical structure, which is not enough for simulating problems related to the long-term effects of different land-uses. That is our challenge for the future: to understand entirely the hydrology cycle, e. Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure.

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Journal of Soil and Water Conservation, 37 2 , Soil erosion prediction in the Grande River Basin, Brazil using distributed modeling. Catena, 79 1 , Performance of a distributed semi-conceptual hydrological model under tropical watershed conditions. Development, sensitivity and uncertainty analysis of LASH model. Scientia Agricola, 68 3 , The future of distributed models e model calibration and uncertainty prediction. Hydrological Processes, 6 3 , Access in: Oct The effects of forest roads and harvest on catchment hydrology in a mountainous maritime environment.

Land use and watersheds: human influence on hydrology and geomorphology in urban and forest areas. Washington: American Geophysical Union, Agricultural Water Management, 1 , CHU, H. Modelling the hydrologic effects of dynamic land-use change using a distributed hydrologic model and a spatial land-use allocation model.

Hydrological Processes, 24 18 , Distributed hydrological modeling of a micro-scale rainforest watershed in Amazonia: model evaluation and advances in calibration using the new HAND terrain model. Journal of Hydrology, 10 , CUO, L. Effects of a century of land cover and climate change on the hydrology of the Puget Sound basin. Hydrological Processes, 23 6 , Hydrological Processes, 22 21 , DEB, S. An overview of some hydrological watershed models. Wallingford, U. Computers and Geosciences, 31 10 , Modeling the effects of climate change projections on streamflow in the Nooksack River basin, northwest Washington.

Hydrological Processes, 28 20 , A spatially distributed model for the dynamic prediction of sediment erosion and transport in mountainous forested watersheds. Water Resources Research, 42 4 :W, DUN, S. Applying online WEPP to assess forest watershed hydrology. Cerne, 17 4 ,

Runoff processes and streamflow modelling Runoff processes and streamflow modelling
Runoff processes and streamflow modelling Runoff processes and streamflow modelling
Runoff processes and streamflow modelling Runoff processes and streamflow modelling
Runoff processes and streamflow modelling Runoff processes and streamflow modelling
Runoff processes and streamflow modelling Runoff processes and streamflow modelling
Runoff processes and streamflow modelling Runoff processes and streamflow modelling
Runoff processes and streamflow modelling Runoff processes and streamflow modelling
Runoff processes and streamflow modelling Runoff processes and streamflow modelling
Runoff processes and streamflow modelling

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