Drainwat-Based Methods for Estimating Nitrogen Transport in Poorly Drained Watersheds

Amatya, Devendra M.; Chescheir, G. M.; Fernandez, G. P.; Skaggs, R. W.; Gilliam, J. W.

doi:10.13031/2013.16100

Cited by 22 publications

(4 citation statements)

References 18 publications

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“…These models can be linked or incorporated into larger-scale models to simulate relatively small watersheds while taking advantage of the process-based nature of most of the field-scale models. For example, DAISY has been linked to MIKESHE (Gregersen et al, 2007), and watershed-scale models have been developed based on DRAINMOD (Fernandez et al, 2005;Amatya et al, 2004).…”

Section: Field-scale Modelsmentioning

confidence: 99%

“…DRAINMOD-based watershed-scale models have been developed by linking the process-based, field-scale DRAINMOD hydrology model (Skaggs et al, 2012) with hydraulic transport subroutines for routing outflows through drainage canal and stream networks. These models, with various levels of complexity for flow and nitrogen transport, have been applied to poorly or artificially drained watersheds of several thousand hectares (Konyha and Skaggs, 1992;Amatya et al, 1997Amatya et al, , 2004Fernandez et al, 2002Fernandez et al, , 2005Fernandez et al, , 2006Fernandez et al, , 2007. Most recently, one of these models was applied to assess the hydrologic effects of conversion of forest lands into agricultural croplands (Kim et al, 2013) and to assess the hydrologic impacts of climate change (Amatya et al, 2006).…”

Section: Drainmodmentioning

confidence: 99%

“…All of the currently available DRAINMOD watershed-scale models rely on empirically derived, published, or measured field N concentration data rather than simulating these data within the model, except for the model developed recently for agricultural lands using DRAINMOD-NII (Negm et al, 2014). Linking the recently developed DRAINMOD-FOREST (Tian et al, 2012a(Tian et al, , 2012b), a comprehensive model fully integrated for simulating the processes of hydrology, nitrogen, carbon, and productivity in forest ecosystems, with DRAINMOD-NII (Youssef et al, 2005) as a core N and C submodel with the available in-stream flow and nutrient transport and transformation routines of various complexities (Fernandez et al, 2002(Fernandez et al, , 2005(Fernandez et al, , 2006(Fernandez et al, , 2007Amatya et al, 2004) would correct this deficiency. While these models are limited to simulating hydrologic and transport processes on poorly drained lands, they are capable of simulating backwater effects in streams and channels, which are characteristic of coastal systems .…”

Section: Drainmodmentioning

confidence: 99%

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Hydrological Processes and Model Representation: Impact of Soft Data on Calibration

2015

Trans. ASABE

149

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Hydrologic and water quality models are increasingly used to determine the environmental impacts of climate variability and land management. Due to differing model objectives and differences in monitored data, there are currently no universally accepted procedures for model calibration and validation in the literature. In an effort to develop accepted model calibration and validation procedures or guidelines, a special collection of 22 research articles that present and discuss calibration strategies for 25 hydrologic and water quality models was previously assembled. The models vary in scale temporally as well as spatially from point source to the watershed level. One suggestion for future work was to synthesize relevant information from this special collection and to identify significant calibration and validation topics. The objective of this article is to discuss the importance of accurate representation of model processes and its impact on calibration and scenario analysis using the information from these 22 research articles and other relevant literature. Models are divided into three categories: (1) flow, heat, and solute transport, (2) field scale, and (3) watershed scale. Processes simulated by models in each category are reviewed and discussed. In this article, model case studies are used to illustrate situations in which a model can show excellent statistical agreement with measured stream gauge data, while misrepresented processes (water balance, nutrient balance, sediment source/sinks) within a field or watershed can cause errors when running management scenarios. These errors may be amplified at the watershed scale where additional sources and transport processes are simulated. To account for processes in calibration, a diagnostic approach is recommended using both hard and soft data. The diagnostic approach looks at signature patterns of behavior of model outputs to determine which processes, and thus parameters representing them, need further adjustment during calibration. This overcomes the weaknesses of traditional regression-based calibration by discriminating between multiple processes within a budget. Hard data are defined as long-term, measured time series, typically at a point within a watershed. Soft data are defined as information on individual processes within a budget that may not be directly measured within the study area, may be just an average annual estimate, and may entail considerable uncertainty. The advantage of developing soft data sets for calibration is that they require a basic understanding of processes (water, sediment, nutrient, and carbon budgets) within the spatial area being modeled and constrain the calibration.

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Section: Field-scale Modelsmentioning

confidence: 99%

Section: Drainmodmentioning

confidence: 99%

Section: Drainmodmentioning

confidence: 99%

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Hydrological Processes and Model Representation: Impact of Soft Data on Calibration

2015

Trans. ASABE

149

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“…Although the standard version of DRAINMOD (Skaggs et al, 2012) does not use LAI, daily LAI was a direct input parameter in the physically based Penman-Monteith (P-M) canopy evaporation and transpiration modules of the extended forestry version of DRAIN-MOD (Amatya and Skaggs, 2001). Daily LAI was an input variable for estimating daily P-M-based PET input in the watershed-scale versions of DRAINMOD (Amatya et al, 1997(Amatya et al, , 2004Kim et al, 2012Kim et al, , 2013. Most recently, LAI was simulated as a physically based daily input variable to simulate potential transpiration using the P-M method as well as tree growth within the comprehensive DRAIN-MOD-FOREST model integrated for simulating hydrology, nutrients, and productivity of forests (Tian et al, 2012).…”

Section: Leaf Area Indexmentioning

confidence: 99%

Parameterization Guidelines and Considerations for Hydrologic Models

2015

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Imparting knowledge of the physical processes of a system to a model and determining a set of parameter values for a hydrologic or water quality model application (i.e., parameterization) are important and difficult tasks. An exponential increase in the literature has been devoted to the use and development of these models over the years. Few articles, however, have been devoted to developing general parameterization guidelines to assist in hydrologic model application, which is the main objective of this article along with discussing a few important parameters and extracting several case studies from the literature. The following guidelines were extracted from reviewing a special collection of 22 articles along with other relevant literature: (1) use site-specific measured or estimated parameter values where possible, (2) focus on the most uncertain and sensitive parameters, (3) minimize the number of optimized parameters, (4) constrain parameter values to within justified ranges, (5) use multiple criteria to help optimize parameter values, (6) use "soft" data to optimize parameters, and (7) use a warm-up period to reduce model dependence on initial condition state variables. A few soil and hydrology related parameters common to many models are briefly described along with a discussion of measurement and estimation methods and parameter sensitivity (curve number, Manning's "n", soil bulk density and porosity, soil hydraulic conductivity, soil field capacity and wilting point, and leaf area index). Weather and management inputs are also discussed, as they are critical hydrologic system information that must be imparted to the model. Several case studies from previously reported research illustrate implementation of the parameterization guidelines. This research will help improve model parameterization, resulting in more consistency, better representation of the field or watershed, and a reduced range of parameter value sets resulting in acceptable model simulations.

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Impact of Seasonality on Artificial Drainage Discharge under Temperate Climate Conditions

Hirt¹,

Wetzig²,

Amatya³

et al. 2011

International Review of Hydrobiology

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Artificial drainage systems affect all components of the water and matter balance. For the proper simulation of water and solute fluxes, information is needed about artificial drainage discharge rates and their response times. However, there is relatively little information available about the response of artificial drainage systems to precipitation. To address this need, we analysed 11 datasets from artificial drainage study sites (daily or hourly resolution), one daily dataset from an open ditch system, and three datasets from rainfall simulations on tile-drained fields.When we considered all 11 artificial drainage study sites, we found that artificial drainage discharge responded to 70% of all rainfall events during the year, and that the response rate differed significantly between 56% summer and 84% in winter. A median of 23% of the yearly precipitation rate is discharged by artificial drainage systems, varying from 9% of the precipitation in summer to 54% of the precipitation in winter. The artificial drainage systems usually started to respond within the first hour under rain fed conditions, and the response time increased at lower rainfall intensities ( < 1 mm h -1 ). The peak outflow normally occurred within the first two days. The influence of soil texture and land use on artificial drainage discharge rates could not be reproduced properly, due to the spatial high variability caused by other site-specific properties.

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Drainwat-Based Methods for Estimating Nitrogen Transport in Poorly Drained Watersheds

Cited by 22 publications

References 18 publications

Hydrological Processes and Model Representation: Impact of Soft Data on Calibration

Hydrological Processes and Model Representation: Impact of Soft Data on Calibration

Parameterization Guidelines and Considerations for Hydrologic Models

Impact of Seasonality on Artificial Drainage Discharge under Temperate Climate Conditions

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