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This project centered on the development of a linear reservoir response model for an unspecified watershed in Oklahoma. I was tasked with analyzing a rainfall hyetograph and corresponding watershed outflow from a past storm event, determining a range of appropriate parameters from this analysis, using these parameter to develop a series of different linear watershed models, calibrating these models to match the given hyetograph of the past rainfall event, and applying these models to predict the outflow of a future storm event. A significant portion of this project required assumptions inferred from the brief watershed description and the observed characteristics of the outflow curve of the watershed’s response.
Tikrit Journal of Engineering Science, 2023
Journal of Water Management Modeling, 2018
This project compared the suitability of two hydrological models for the Flint River watershed (FRW). The Flint River flows into Wheeler Lake, which drains to the Tennessee River, a major source of water in northern Alabama. Two very widely used hydrological models, the Soil and Water Assessment Tool (SWAT) and the Storm Water Management Model (SWMM), were selected for this study. Both models were calibrated and validated for FRW. The calibration parameters were selected based on past research studies which used the same hydrologic models. The calibration parameters for SWMM and SWAT included basin, subbasin, soil, groundwater, channel and land use parameters. During calibration, both models were run at daily and monthly time steps, where simulated streamflows were compared with observed streamflows (years 2004-2013) and various statistical parameters were computed. While comparing simulated and observed monthly streamflows, SWAT showed better performance (r = 0.86-0.97, R 2 = 0.73-0.93, bias = 12.2%, RMSE = 5.6 m 3 /s-8.9 m 3 /s) than SWMM (r = 0.70, R 2 = 0.50, RMSE = 2 m 3 /s-56 m 3 /s, bias = 6.2%-8.4%). However, both models showed better performance for monthly streamflows than for daily streamflows. The evaluation determined that SWAT provides a more suitable model than SWMM when applied to a mixed land use watershed like FRW.
Hydrological Sciences Journal, 2018
The complexities of the Prairie watersheds, including potholes, drainage interconnectivities, changing land-use patterns, dynamic watershed boundaries and hydro-meteorological factors, have made hydrological modelling on Prairie watersheds one of the most complex task for hydrologists and operational hydrological forecasters. In this study, four hydrological models (WATFLOOD, HBV-EC, HSPF and HEC-HMS) were developed, calibrated and tested for their efficiency and accuracy to be used as operational flood forecasting tools. The Upper Assiniboine River, which flows into the Shellmouth Reservoir, Canada, was selected for the analysis. The performance of the models was evaluated by the standard statistical methods: the Nash-Sutcliffe efficiency coefficient, correlation coefficient, root mean squared error, mean absolute relative error and deviation of runoff volumes. The models were evaluated on their accuracy in simulating the observed runoff for calibration and verification periods (2005-2015 and 1994-2004, respectively) and also their use in operational forecasting of the 2016 and 2017 runoff.
Obtaining representative meteorological data for watershed-scale hydrological modelling can be difficult and time consuming. Land-based weather stations do not always adequately represent the weather occurring over a watershed, because they can be far from the watershed of interest and can have gaps in their data series, or recent data are not available. This study presents a method for using the Climate Forecast System Reanalysis (CFSR) global meteorological dataset to obtain historical weather data and demonstrates the application to modelling five watersheds representing different hydroclimate regimes. CFSR data are available globally for each hour since 1979 at a 38-km resolution. Results show that utilizing the CFSR precipitation and temperature data to force a watershed model provides stream discharge simulations that are as good as or better than models forced using traditional weather gauging stations, especially when stations are more than 10 km from the watershed. These results further demonstrate that adding CFSR data to the suite of watershed modelling tools provides new opportunities for meeting the challenges of modelling un-gauged watersheds and advancing real-time hydrological modelling. a 'Replace' indicates that values were replaced within an initial range published in the literature, and 'percent' indicates that values were determined by adjusting the base initialization default variables by a certain percentage.
Journal of Spatial …, 2003
Advances in scientific knowledge and new techniques of remote sensing permit a better understanding of the physical land features governing hydrologic processes, and make possible efficient, large-scale hydrologic modeling. The need for land-cover and hydrologic response change detection at a larger scale and at times of the year when hydrologic studies are critical makes satellite imagery the most cost effective, efficient and reliable source of data. In this work, remotely-sensed data and geographic information system (GIS) tools were used to estimate the changes in runoff response for three watersheds (Etonia, Econlockhatchee, and S-65A subbasins) in Florida. Land-use information from Digital Orthophoto Quarter Quadrangles (DOQQ), Landsat Thematic Mapper, and Enhanced Thematic Mapper Plus were analyzed for the years 1984, 1990, 1995, and 2000. Spatial distribution of land-cover was assessed over time. The corresponding infiltration excess runoff response of the study areas due to these changes was estimated using the United States Department of Agriculture, Natural Resources Conservation Service Curve Number (USDA-NRCS-CN) method. A Digital Elevation Model-GIS technique was used to predict stream response to runoff events based on the travel time from each grid cell to the watershed outlet. The method was applied to a representative watershed (Simms Creek) in the Etonia sub-basin to study the effect of land-cover on storm runoff response. Simulated and observed runoff volume and hydrographs were compared. Isolated storms, with volumes of not less than 12.75 mm (0.5 inch) were selected (the minimum amount of rainfall volume recommended for the NRCS-CN method). Results show that the model predicts the total runoff volume with an average efficiency of 98%. The model is applicable to ungaged watersheds and useful for predicting runoff hydrographs resulting from changes in the land-cover.
Floods caused by hurricane storms are responsible for tremendous economic and property losses in the United States. To minimize flood damages associated with large hurricane-season storms, it is important to be able to predict streamflow amount in response to storms for a range of hydroclimatological conditions. However, this is challenging considering that streamflow response exhibits appreciable variability even for hurricane-season storms that deliver similar precipitation amounts. As such, better estimates of event responses require refined understanding of the causes of flood response variability. Here, a physically based, distributed hydrologic model and supporting hydrologic datasets are used to identify and evaluate dominant hydrologic controls on streamflow amount variability. The analysis indicates that variability in flood response in the Lake Michie watershed is primarily driven by antecedent soil moisture conditions near the land surface and evapotranspiration during postevent streamflow recession periods, which in turn is a function of precipitation history and prevailing vegetation and meteorological conditions. Presented results and ensuing analyses could help prioritize measurements during observation campaigns and could aid in risk management by providing look-up diagrams to quickly evaluate flood responses given prior information about hurricane storm size.
Water Resources Management, 2010
Among several hydrological models developed over the years, the most widely used technique for estimating direct runoff depth from storm rainfall i.e., the United States Department of Agriculture (USDA) Soil Conservation Service's (SCS) Curve Number (CN) method was adopted in the present study. In addition, the Muskingum method, which continues to be popular for routing of runoff in river network, was used in the developed model to route surface runoffs from different subbasin outlet points up to the outlet point of the catchment. SCS CN method in combination with Muskingum routing technique, however, required a detailed knowledge of several important properties of the watershed, namely, soil type, land use, antecedent soil water conditions, and channel information, which may not be readily available. Due to this complexity of semi-distributed conceptual approach (SCS CN method) and non-linearity involved in rainfall-runoff modeling, researchers also attempted another less data requiring approach for runoff prediction, i.e., the neural network approach, which is inherently suited to problems that are mathematically difficult to describe. The purpose of this study was to compare the rainfall-runoff modeling performance of semi-distributed conceptual SCS CN method (in combination with Muskingum routing technique) with that of empirical
Hydrological Processes, 2005
A reliable prediction of hydrologic models, among other things, requires a set of plausible parameters that correspond with physiographic properties of the basin. This study proposes a parameter estimation approach, which is based on extracting, through hydrograph diagnoses, information in the form of indices that carry intrinsic properties of a basin. This concept is demonstrated by introducing two indices that describe the shape of a streamflow hydrograph in an integrated manner. Nineteen mid-size (223-4790 km 2) perennial headwater basins with a long record of streamflow data were selected to evaluate the ability of these indices to capture basin response characteristics. An examination of the utility of the proposed indices in parameter estimation is conducted for a five-parameter hydrologic model using data from the Leaf River, located in Fort Collins, Mississippi. It is shown that constraining the parameter estimation by selecting only those parameters that result in model output which maintains the indices as found in the historical data can improve the reliability of model predictions. These improvements were manifested in (a) improvement of the prediction of low and high flow, (b) improvement of the overall total biases, and (c) maintenance of the hydrograph's shape for both long-term and short-term predictions.
Journal of Irrigation and Drainage Engineering-asce, 2006
Data from over 1,600 storms at 91 stations in Texas are analyzed to evaluate an instantaneous unit hydrograph IUH model for rainfall-runoff models. The model is fit to observed data using two different merit functions: a sum of squared errors function, and an absolute error at the peak discharge time QpMAX function. The model is compared to two other models
2021
Determinate the runoff of a watershed is a challenge due to the complexity of representing all “inlets” and “outlets” involved in a rainfall–runoff model. Therefore, methodologies applied for this purpose should have a good representation of the variables that most influence in this process. One of the models used to calculate the design flow is the (USDA in Urban Hydrology for Small. Technical release, no 55 (TR-55). Soil Conservation Service. Washigton, DC, http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Urban+Hydrology+for+Small+watersheds#1 , 1986), which considers the analysis changes in soil coverage, time of concentration (tc), and recurrence period (T). In this way, this study sought to evaluate the hydrological behavior of a watershed with an increase in soil waterproofing. These modifications were correlated with the variation of runoff coefficients (CN), modifications of the periods of recurrence indicated by the literature, and different equations of the ti...
Environmental Modelling & Software, 2014
Selection of strategies that help reduce riverine inputs requires numerical models that accurately quantify hydrologic processes. While numerous models exist, information on how to evaluate and select the most robust models is limited. Toward this end, we developed a comprehensive approach that helps evaluate watershed models in their ability to simulate flow regimes critical to downstream ecosystem services. We demonstrated the method using the Soil and Water Assessment Tool (SWAT), the Hydrological Simulation ProgrameFORTRAN (HSPF) model, and Distributed Large Basin Runoff Model (DLBRM) applied to the Maumee River Basin (USA). The approach helped in identifying that each model simulated flows within acceptable ranges. However, each was limited in its ability to simulate flows triggered by extreme weather events, owing to algorithms not being optimized for such events and mismatched physiographic watershed conditions. Ultimately, we found HSPF to best predict river flow, whereas SWAT offered the most flexibility for evaluating agricultural management practices.
The concepts, structure, theoretical development, and data requirements of the precipitation-runoff modeling system (PRMS) are described. The precipitation-runoff modeling system is a modular-design, deterministic, distributed-parameter modeling system developed to evaluate the impacts of various combinations of precipitation, climate, and land use on streamflow, sediment yields, and general basin hydrology. Basin response to normal and extreme rainfall and snowmelt can be simulated to evaluate changes in waterbalance relationships, flow regimes, flood peaks and volumes, soil-water relationships, sediment yields, and groundwater recharge. Parameter-optimization and sensitivity analysis capabilities are provided to fit selected model parameters and evaluate their individual and joint effects on model output. The modular design provides a flexible framework for continued model-system enhancement and hydro!ogic-modeling research and development.
s u m m a r y A methodology based on Parallel Linear Reservoir (PLR) models is presented. To carry it out has been implemented within the software SHEE (Simulation of Hydrological Extreme Events), which is a tool for the analysis of hydrological processes in catchments with the management and display of DEM and datasets. The algorithms of the models pass throughout the cells and drainage network, by means of the Watershed Traversal Algorithm (WTA) that runs the entire drainage network of a basin in both directions , upwards and downwards, which is ideal for incorporating the models of the hydrological processes of the basins into its structure. The WTA methodology is combined with another one based on models of Parallel Linear Reservoirs (PLR) whose main qualities include: (1) the models are defined by observing the recession curves of actual hydrographs, i.e., the watershed actual responses; (2) the models serve as a way to simulate the routing through the watershed and its different reservoirs; and (3) the models allow calculating the water balance, which is essential to the study of actual events in the watershed. A complete hydrometeorological event needs the combination of several models, each one of which represents a hydrological process. The PLR model is a routing model, but it also contributes to the adjustment of other models (e.g., the rainfall–runoff model) and allows establishing a distributed model of effective rainfall for an actual event occurred in a basin. On the other hand, the proposed formulation solves the rainfall distribution problem for each deposit in the reservoir combination models.
Journal of Hydrology, 2004
This study investigates an approach that combines physically-based and conceptual model features in two stages of distributed modeling: model structure development and estimation of spatially variable parameters. The approach adds more practicality to the process of model parameterization, and facilitates an easier transition from current lumped model-based operational systems to more powerful distributed systems. This combination of physically-based and conceptual model features is implemented within the Hydrology Laboratory Research Modeling System (HL-RMS). HL-RMS consists of a well-tested conceptual water balance model applied on a regular spatial grid linked to physically-based kinematic hillslope and channel routing models. Parameter estimation procedures that combine spatially distributed and 'integrated' basin-outlet properties have been developed for the water balance and routing components. High-resolution radar-based precipitation data over a large region are used in testing HL-RMS. Initial tests show that HL-RMS yields results comparable to well-calibrated lumped model simulations in several headwater basins, and it outperforms a lumped model in basins where spatial rainfall variability effects are significant. It is important to note that simulations for two nested basins (not calibrated directly, but parameters from the calibration of the parent basin were applied instead) outperformed lumped simulations even more consistently, which means that HL-RMS has the potential to improve the accuracy and resolution of river runoff forecasts. Published by Elsevier B.V.
Hydrology and Earth System Sciences, 2015
We present a community data set of daily forcing and hydrologic response data for 671 small-to mediumsized basins across the contiguous United States (median basin size of 336 km 2 ) that spans a very wide range of hydroclimatic conditions. Area-averaged forcing data for the period 1980-2010 was generated for three basin spatial configurations -basin mean, hydrologic response units (HRUs) and elevation bands -by mapping daily, gridded meteorological data sets to the subbasin (Daymet) and basin polygons (Daymet, Maurer and NLDAS). Daily streamflow data was compiled from the United States Geological Survey National Water Information System. The focus of this paper is to (1) present the data set for community use and (2) provide a model performance benchmark using the coupled Snow-17 snow model and the Sacramento Soil Moisture Accounting Model, calibrated using the shuffled complex evolution global optimization routine. After optimization minimizing daily root mean squared error, 90 % of the basins have Nash-Sutcliffe efficiency scores ≥ 0.55 for the calibration period and 34 % ≥ 0.8. This benchmark provides a reference level of hydrologic model performance for a commonly used model and calibration system, and highlights some regional variations in model performance. For example, basins with a more pronounced seasonal cycle generally have a negative low flow bias, while basins with a smaller seasonal cycle have a positive low flow bias. Finally, we find that data points with extreme error (defined as individual days with a high fraction of total error) are more common in arid basins with limited snow and, for a given aridity, fewer extreme error days are present as the basin snow water equivalent increases.
2008
A fundamental tradeoff exists in watershed modeling between a model's flexibility for representing watersheds with different characteristics versus its potential for overparameterization. This study uses global sensitivity analysis to investigate how a commonly used intermediate-complexity model, the Sacramento Soil Moisture Accounting Model (SAC-SMA), represents a wide range of watersheds with diverse physical and hydroclimatic characteristics.
Environmental Monitoring and Assessment, 2007
Negligence to consider the spatial variability of rainfall could result in serious errors in model outputs. The objective of this study was to examine the uncertainty of both runoff and pollutant transport predictions due to the input errors of rainfall. This study used synthetic data to represent the "true" rainfall pattern, instead of interpolated precipitation. It was conducted on a synthetic case area having a total area of 20 km 2 with ten subbasins. Each subbasin has one rainfall gauge with synthetic precipitation records. Six rainfall storms with varied spatial distribution were generated. The average rainfall was obtained from all of the ten gauges by the arithmetic average method. The input errors of rainfall were induced by the difference between the actual rainfall pattern and estimated average rainfall. The results show that spatial variability of rainfall can cause uncertainty in modeling outputs of hydrologic, which would be transport to pollutant export predictions, when uniformity of rainfall is assumed. Since rainfall is essential information for predicting watershed responses, it is important to consider the properties of rainfall, particularly spatial rainfall variability, in the application of hydrologic and water quality models.
Journal of the American Water Resources Association, 2003
A synthetic triangular hyetograph for a large data base of Texas rainfall and runoff is needed. A hyetograph represents the temporal distribution of rainfall intensity at a point or over a watershed during a storm. Synthetic hyetographs are estimates of the expected time distribution for a design storm and principally are used in small watershed hydraulic structure design. A data base of more than 1,600 observed cumulative hyetographs that produced runoff from 91 small watersheds (generally less than about 50 km 2 ) was used to provide statistical parameters for a simple triangular shaped hyetograph model. The model provides an estimate of the average hyetograph in dimensionless form for storm durations of 0 to 24 hours and 24 to 72 hours. As a result of this study, the authors concluded that the expected dimensionless cumulative hyetographs of 0 to 12 hour and 12 to 24 hour durations were sufficiently similar to be combined with minimal information loss. The analysis also suggests that dimensionless cumulative hyetographs are independent of the frequency level or return period of total storm depth and thus are readily used for many design applications. The two triangular hyetographs presented are intended to enhance small watershed design practice in applicable parts of Texas. (KEY TERMS: surface water hydrology; hyetograph; design storm; small watersheds).
Water, 2020
Depending on the purpose of the study, aggregated hydrological models are preferred over distributed models because they provide acceptable results in terms of precision and are easy to run, especially in data scarcity scenarios. To obtain acceptable results in terms of hydrological process representativeness, it is necessary to understand and assess the models. In this study, the relative importance of the parameters of the Hydrologiska Byråns Vattenbalansavdelning (HBV) model is analyzed using sensitivity analysis to detect if the simulated processes represent the predominant hydrological processes at watershed scale. As a case study, four watersheds with different hydrological regimes (glacial and pluvial) and therefore different dominant processes are analyzed. The results show that in the case of the rivers with a glacial regime, the model performance depends highly on the snow module parameters, while in the case of the rivers with a pluvial regime, the model is sensitive to t...
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