15,010 research outputs found

    Addressing Uncertainty in TMDLS: Short Course at Arkansas Water Resources Center 2001 Annual Conference

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    Management of a critical natural resource like water requires information on the status of that resource. The US Environmental Protection Agency (EPA) reported in the 1998 National Water Quality Inventory that more than 291,000 miles of assessed rivers and streams and 5 million acres of lakes do not meet State water quality standards. This inventory represents a compilation of State assessments of 840,000 miles of rivers and 17.4 million acres of lakes; a 22 percent increase in river miles and 4 percent increase in lake acres over their 1996 reports. Siltation, bacteria, nutrients and metals were the leading pollutants of impaired waters, according to EPA. The sources of these pollutants were presumed to be runoff from agricultural lands and urban areas. EPA suggests that the majority of Americans-over 218 million-live within ten miles of a polluted waterbody. This seems to contradict the recent proclamations of the success of the Clean Water Act, the Nation\u27s water pollution control law. EPA also claims that, while water quality is still threatened in the US, the amount of water safe for fishing and swimming has doubled since 1972, and that the number of people served by sewage treatment plants has more than doubled

    On the dialog between experimentalist and modeler in catchment hydrology

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    The dialog between experimentalist and modeler in catchment hydrology has been minimal to date. The experimentalist often has a highly detailed yet highly qualitative understanding of dominant runoff processes—thus there is often much more information content on the catchment than we use for calibration of a model. While modelers often appreciate the need for 'hard data' for the model calibration process, there has been little thought given to how modelers might access this 'soft' or process knowledge. We present a new method where soft data (i.e., qualitative knowledge from the experimentalist that cannot be used directly as exact numbers) are made useful through fuzzy measures of model-simulation and parameter-value acceptability. We developed a three-box lumped conceptual model for the Maimai catchment in New Zealand, a particularly well-studied process-hydrological research catchment. The boxes represent the key hydrological reservoirs that are known to have distinct groundwater dynamics, isotopic composition and solute chemistry. The model was calibrated against hard data (runoff and groundwater-levels) as well as a number of criteria derived from the soft data (e.g. percent new water, reservoir volume, etc). We achieved very good fits for the three-box model when optimizing the parameter values with only runoff (Reff=0.93). However, parameter sets obtained in this way showed in general a poor goodness-of-fit for other criteria such as the simulated new-water contributions to peak runoff. Inclusion of soft-data criteria in the model calibration process resulted in lower Reff-values (around 0.84 when including all criteria) but led to better overall performance, as interpreted by the experimentalist’s view of catchment runoff dynamics. The model performance with respect to soft data (like, for instance, the new water ratio) increased significantly and parameter uncertainty was reduced by 60% on average with the introduction of the soft data multi-criteria calibration. We argue that accepting lower model efficiencies for runoff is 'worth it' if one can develop a more 'real' model of catchment behavior. The use of soft data is an approach to formalize this exchange between experimentalist and modeler and to more fully utilize the information content from experimental catchments

    Rainfall modeling for integrating radar information into hydrological model

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    A spatial rainfall model was applied to radar data of air mass thunderstorms to yield a rainstorm representation as a set of convective rain cells. The modeled rainfall was used as input into hydrological model, instead of the standard radar-grid data. This approach allows a comprehensive linkage between runoff responses and rainfall structures. Copyright © 2005 Royal Meteorological Society

    Sensitivity analysis of a land surface scheme using multicriteria methods

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    Attempts to model surface-atmosphere interactions with greater physical realism have resulted in complex land surface schemes (LSS) with large numbers of parameters. A companion paper describes a multicriteria calibration procedure for extracting plot-scale estimates of the preferred ranges of these parameters from the various observational data sets that are now available. A complementary procedure is presented in this paper that provides an objective determination of the multicriteria sensitivity of the modeled variables to the parameters, thereby allowing the number of calibration parameters and hence the computational effort to be reduced. Two case studies are reported for the BATS model using data sets of typical quality but very different location and climatological regime (ARM-CART and Tucson). The sensitivity results were found to be consistent with the physical properties of the different environments, thereby supporting the reasonableness of the model formulation. Further, when the insensitive parameters are omitted from the calibration process, there is little degradation in the quality of the model description and little change in the preferred range of the remaining parameters. Copyright 1999 by the American Geophysical Union

    Global-scale regionalization of hydrologic model parameters

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    Current state-of-the-art models typically applied at continental to global scales (hereafter called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) simulation. For the first time, a scheme for regionalization of model parameters at the global scale was developed. We used data from a diverse set of 1787 small-to-medium sized catchments ( 10-10,000 km(2)) and the simple conceptual HBV model to set up and test the scheme. Each catchment was calibrated against observed daily Q, after which 674 catchments with high calibration and validation scores, and thus presumably good-quality observed Q and forcing data, were selected to serve as donor catchments. The calibrated parameter sets for the donors were subsequently transferred to 0.5 degrees grid cells with similar climatic and physiographic characteristics, resulting in parameter maps for HBV with global coverage. For each grid cell, we used the 10 most similar donor catchments, rather than the single most similar donor, and averaged the resulting simulated Q, which enhanced model performance. The 1113 catchments not used as donors were used to independently evaluate the scheme. The regionalized parameters outperformed spatially uniform (i.e., averaged calibrated) parameters for 79% of the evaluation catchments. Substantial improvements were evident for all major Koppen-Geiger climate types and even for evaluation catchments>5000 km distant from the donors. The median improvement was about half of the performance increase achieved through calibration. HBV with regionalized parameters outperformed nine state-of-the-art macroscale models, suggesting these might also benefit from the new regionalization scheme. The produced HBV parameter maps including ancillary data are available via

    Where should livestock graze? Integrated modeling and optimization to guide grazing management in the Cañete basin, Peru

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    Integrated watershed management allows decision-makers to balance competing objectives, for example agricultural production and protection of water resources. Here, we developed a spatially-explicit approach to support such management in the Cañete watershed, Peru. We modeled the effect of grazing management on three services – livestock production, erosion control, and baseflow provision – and used an optimization routine to simulate landscapes providing the highest level of services. Over the entire watershed, there was a trade-off between livestock productivity and hydrologic services and we identified locations that minimized this trade-off for a given set of preferences. Given the knowledge gaps in ecohydrology and practical constraints not represented in the optimizer, we assessed the robustness of spatial recommendations, i.e. revealing areas most often selected by the optimizer. We conclude with a discussion of the practical decisions involved in using optimization frameworks to inform watershed management programs, and the research needs to better inform the design of such programs

    Correcting the radar rainfall forcing of a hydrological model with data assimilation: application to flood forecasting in the Lez Catchment in Southern France

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    The present study explores the application of a data assimilation (DA) procedure to correct the radar rain- fall inputs of an event-based, distributed, parsimonious hy- drological model. An extended Kalman filter algorithm was built on top of a rainfall-runoff model in order to assimilate discharge observations at the catchment outlet. This work fo- cuses primarily on the uncertainty in the rainfall data and considers this as the principal source of error in the sim- ulated discharges, neglecting simplifications in the hydro- logical model structure and poor knowledge of catchment physics. The study site is the 114 km2 Lez catchment near Montpellier, France. This catchment is subject to heavy oro- graphic rainfall and characterised by a karstic geology, lead- ing to flash flooding events. The hydrological model uses a derived version of the SCS method, combined with a Lag and Route transfer function. Because the radar rainfall in- put to the model depends on geographical features and cloud structures, it is particularly uncertain and results in signifi- cant errors in the simulated discharges. This study seeks to demonstrate that a simple DA algorithm is capable of ren- dering radar rainfall suitable for hydrological forecasting. To test this hypothesis, the DA analysis was applied to estimate a constant hyetograph correction to each of 19 flood events. The analysis was carried in two different modes: by assimi- lating observations at all available time steps, referred to here as reanalysis mode, and by using only observations up to 3 h before the flood peak to mimic an operational environment, referred to as pseudo-forecast mode. In reanalysis mode, the resulting correction of the radar rainfall data was then com- pared to the mean field bias (MFB), a corrective coefficient determined using rain gauge measurements. It was shown that the radar rainfall corrected using DA leads to improved discharge simulations and Nash-Sutcliffe efficiency criteria compared to the MFB correction. In pseudo-forecast mode, the reduction of the uncertainty in the rainfall data leads to a reduction of the error in the simulated discharge, but un- certainty from the model parameterisation diminishes data assimilation efficiency. While the DA algorithm used is this study is effective in correcting uncertain radar rainfall, model uncertainty remains an important challenge for flood fore- casting within the Lez catchment

    Uncertainty in hydrological estimation

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    2021 Spring.Includes bibliographical references.Detailed hydrometeorologic analyses and uncertainty assessments are needed to aid water resources decision-making, to account for upstream-downstream linkages and dominant process scale for integrated land and water resources management and planning. The water balance is a fundamental concept in hydrology that inspires many tools for predicting the specific components including precipitation, streamflow, soil moisture, and groundwater storage. A water balance is typically expressed as an equation that relates water inputs, outputs, and storage of a system. The water balance model is applied to analyze the allocation of water among components of the hydrologic system. Knowledge on the components composing inputs and outputs in a water balance are essential to understanding watershed processes. While methods to measure and model water balance components continue to improve, all components of the balance have substantial uncertainty. Methods to analyze a water balance should acknowledge these uncertainties and consider how they propagate through water balance calculations in order to better assist water resources decisions. This research investigated four water balance components: (1) snowpack sublimation, (2) precipitation as snow, (3) precipitation as rain, and (4) stream discharge in mountainous watersheds in order to examine and build our knowledge of uncertainty in the water balance for mountainous environments. The research presented in this dissertation supports a theme that hydrology is a highly uncertain science, where uncertainty is a result of the hydrologic community's knowledge gap to accurately model physics of atmospheric and hydrologic processes. A finding of this work is that no component of the water balance can be quantified at watershed scale without estimating he associated uncertainty. Results highlight that mean cumulative snowpack sublimation uncertainty is 41% with individual input variable uncertainties in the range of 1 to 29%; simulated to observed basin mean snow depth was estimated within 15% for 10-years while extreme dry and wet years were within 5%; and forcing precipitation datasets used in hydrologic models to estimate streamflow have cumulative uncertainties in the range of 30 to 60%. Results of this dissertation identify the importance to account for uncertainty in water resources, i.e., Monte Carlo methods, to properly account for and quantify associated risks in water management and design infrastructure decisions

    Modelling water-harvesting systems in the arid south of Tunisia using SWAT

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    In many arid countries, runoff water-harvesting systems support the livelihood of the rural population. Little is known, however, about the effect of these systems on the water balance components of arid watersheds. The objective of this study was to adapt and evaluate the GIS-based watershed model SWAT (Soil Water Assessment Tool) for simulating the main hydrologic processes in arid environments. The model was applied to the 270-km(2) watershed of wadi Koutine in southeast Tunisia, which receives about 200 mm annual rain. The main adjustment for adapting the model to this dry Mediterranean environment was the inclusion of water-harvesting systems, which capture and use surface runoff for crop production in upstream subbasins, and a modification of the crop growth processes. The adjusted version of the model was named SWAT-WH. Model evaluation was performed based on 38 runoff events recorded at the Koutine station between 1973 and 1985. The model predicted that the average annual watershed rainfall of the 12-year evaluation period (209 mm) was split into ET (72%), groundwater recharge (22%) and outflow (6%). The evaluation coefficients for calibration and validation were, respectively, R-2 (coefficient of determination) 0.77 and 0.44; E (Nash-Sutcliffe coefficient) 0.73 and 0.43; and MAE (Mean Absolute Error) 2.6 mm and 3.0 mm, indicating that the model could reproduce the observed events reasonably well. However, the runoff record was dominated by two extreme events, which had a strong effect on the evaluation criteria. Discrepancies remained mainly due to uncertainties in the observed daily rainfall and runoff data. Recommendations for future research include the installation of additional rainfall and runoff gauges with continuous data logging and the collection of more field data to represent the soils and land use. In addition, crop growth and yield monitoring is needed for a proper evaluation of crop production, to allow an economic assessment of the different water uses in the watershed
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