Impact of artificial subsurface drainage on groundwater travel times and baseflow discharge in an agricultural watershed,Iowa (USA)

Pollutant delivery through artificial subsurface drainage networks to streams is an important transport mechanism, yet the impact of drainage tiles on groundwater hydrology at the watershed scale has not been well documented. In this study, we developed a two‐dimensional, steady‐state groundwater flow model for a representative Iowa agricultural watershed to simulate the impact of tile drainage density and incision depth on groundwater travel times and proportion of baseflow contributed by tile drains. Varying tile drainage density from 0 to 0.0038 m^?1, while maintaining a constant tile incision depth at 1.2 m, resulted in the mean groundwater travel time to decrease exponentially from 40 years to 19 years and increased the tile contribution to baseflow from 0% to an upper bound of 37%. In contrast, varying tile depths from 0.3 to 2.7 m, while maintaining a constant tile drainage density of 0.0038 m^?1, caused mean travel times to decrease linearly from 22 to 18 years and increased the tile contribution to baseflow from 30% to 54% in a near‐linear manner. The decrease in the mean travel time was attributed to decrease in the saturated thickness of the aquifer with increasing drainage density and incision depth. Study results indicate that tile drainage affects fundamental watershed characteristics and should be taken into consideration when evaluating water and nitrate export from agricultural regions. Copyright © 2011 John Wiley & Sons, Ltd.     

Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed

This paper reports on an evaluation of the use of artificial neural network (ANN) models to forecast daily flows at multiple gauging stations in Eucha Watershed, an agricultural watershed located in north‐west Arkansas and north‐east Oklahoma. Two different neural network models, the multilayer perceptron (MLP) and the radial basis neural network (RBFNN), were developed and their abilities to predict stream flow at four gauging stations were compared. Different scenarios using various combinations of data sets such as rainfall and stream flow at various lags were developed and compared for their ability to make flow predictions at four gauging stations. The input vector selection for both models involved quantification of the statistical properties such as cross‐, auto‐ and partial autocorrelation of the data series that best represented the hydrologic response of the watershed. Measured data with 739 patterns of input–output vector were divided into two sets: 492 patterns for training, and the remaining 247 patterns for testing. The best performance based on the RMSE, R² and CE was achieved by the MLP model with current and antecedent precipitation and antecedent flow as model inputs. The MLP model testing resulted in R² values of 0·86, 0·86, 0·81, and 0·79 at the four gauging stations. Similarly, the testing R² values for the RBFNN model were 0·60, 0·57, 0·58, and 0·56 for the four gauging stations. Both models performed satisfactorily for flow predictions at multiple gauging stations, however, the MLP model outperformed the RBFNN model. The training time was in the range 1–2 min for MLP, and 5–10 s for RBFNN on a Pentium IV processor running at 2·8 GHz with 1 MB of RAM. The difference in model training time occurred because of the clustering methods used in the RBFNN model. The RBFNN uses a fuzzy min‐max network to perform the clustering to construct the neural network which takes considerably less time than the MLP model. Results show that ANN models are useful tools for forecasting the hydrologic response at multiple points of interest in agricultural watersheds. Copyright © 2008 John Wiley & Sons, Ltd.     

Modeling of non‐point source nitrogen pollution from 1979 to 2008 in Jiaodong Peninsula,China

Efforts to reduce land‐based non‐point source (NPS) pollutions from watersheds to coastal waters are ongoing all around the world. In this study, annual yield of NPS nitrogen (NPS‐N) pollution in Jiaodong Peninsula, China from 1979 to 2008 was estimated. The results showed that: from 1979 to 2008, NPS‐N yields exhibited significant inter‐annual variations and an increasing trend on decadal scale. High NPS‐N yield was mainly found in east and south parts, as well as the urbanized coastal regions in Jiaodong Peninsula. Among the 32 river basins, the three largest basins yielded more than 41.16% of the NPS‐N. However, some small coastal watersheds along the South Yellow Sea and Jiaozhou Bay had higher per unit area yield. Most of the small watersheds characterized by seasonal runoff had coastal waters pertain to mild and moderate pollution levels. The ratio of watershed area to shoreline length and the up‐stream land use had significant impacts on NPS‐N flux through the shoreline. Among the four adjacent coastal areas of Jiaodong Peninsula, Jiaozhou Bay was the most noteworthy one not only because of high levels of land‐based NPS‐N pollution but also because of its nearly enclosed structure. The combination between integrated coastal zone management and integrated river basin management, land use planning and landscape designing in Jiaodong Peninsula is recommended. Copyright © 2013 John Wiley & Sons, Ltd.     

Artificial neural network modelling of concentrations of nitrogen,phosphorus and dissolved oxygen in a non‐point source polluted river in Zhejiang Province,southeast China

A back‐propagation algorithm neural network (BPNN) was developed to synchronously simulate concentrations of total nitrogen (TN), total phosphorus (TP) and dissolved oxygen (DO) in response to agricultural non‐point source pollution (AGNPS) for any month and location in the Changle River, southeast China. Monthly river flow, water temperature, flow travel time, rainfall and upstream TN, TP and DO concentrations were selected as initial inputs of the BPNN through coupling correlation analysis and quadratic polynomial stepwise regression analysis for the outputs, i.e. downstream TN, TP and DO concentrations. The input variables and number of hidden nodes of the BPNN were then optimized using a combination of growing and pruning methods. The final structure of the BPNN was determined from simulated data based on experimental data for both the training and validation phases. The predicted values obtained using a BPNN consisting of the seven initial input variables (described above), one hidden layer with four nodes and three output variables matched well with observed values. The model indicated that decreasing upstream input concentrations during the dry season and control of NPS along the reach during average and flood seasons may be an effective way to improve Changle River water quality. If the necessary water quality and hydrology data are available, the methodology developed here can easily be applied to other case studies. The BPNN model is an easy‐to‐use modelling tool for managers to obtain rapid preliminary identification of spatiotemporal water quality variations in response to natural and artificial modifications of an agricultural drainage river. Copyright © 2009 John Wiley & Sons, Ltd.     

Hydrological footprints of urban developments in the Lake Simcoe watershed,Canada: a combined paired‐catchment and change detection modelling approach

Urban sprawl and regional climate variability are major stresses on surface water resources in many places. The Lake Simcoe watershed (LSW) Ontario, Canada, is no exception. The LSW is predominantly agricultural but is experiencing rapid population growth because of its proximity to the Greater Toronto area. This has led to extensive land use changes that have impacted its water resources and altered run‐off patterns in some rivers draining to the lake. Here, we use a paired‐catchment approach, hydrological change detection modelling and remote sensing analysis of satellite images to evaluate the impacts of land use change on the hydrology of the LSW (1994 to 2008). Results show that urbanization increased up to 16% in Lovers Creek, the most urban‐impacted catchment. Annual run‐off from Lovers Creek increased from 239 to 442 mm/year in contrast to the reference catchment (Black River at Washago) where run‐off was relatively stable with an annual mean of 474 mm/year. Increased annual run‐off from Lovers Creek was not accompanied by an increase in annual precipitation. Discriminant function analysis suggests that early (1992–1997; pre‐major development) and late (2004–2009; fully urbanized) periods for Lovers Creek separated mainly based on model parameter sets related to run‐off flashiness and evapotranspiration. As a result, parameterization in either period cannot be used interchangeably to produce credible run‐off simulations in Lovers Creek because of greater scatter between the parameters in canonical space. Separation of early and late‐period parameter sets for the reference catchment was based on climate and snowmelt‐related processes. This suggests that regional climatic variability could be influencing hydrologic change in the reference catchment, whereas urbanization amplified the regional natural hydrologic changes in urbanizing catchments of the LSW. Copyright © 2014 John Wiley & Sons, Ltd.     

Long‐term effects of management practices on water‐driven soil erosion in an intense agricultural sub‐watershed: monitoring and modelling

The overarching objective of this research was to provide an improved understanding of the role of land use and associated management practices on long‐term water‐driven soil erosion in small agricultural watersheds by coupling the established, physically based, distributed parameter Water Erosion Prediction Project (WEPP) model with long‐term hydrologic, land use and soil data. A key step towards achieving this objective was the development of a detailed methodology for model calibration using physical ranges of key governing parameters such as effective hydraulic conductivity, critical hydraulic shear stress and rill/inter‐rill erodibilities. The physical ranges for these governing parameters were obtained based on in situ observations within the South Amana Sub‐Watershed (SASW) (～26 km²) of the Clear Creek, IA watershed where detailed documentation of the different land uses was available for a period of nearly 100 years. A quasi validation of the calibrated model was conducted through long‐term field estimates of water and sediment discharge at the outlet of SASW and also by comparing the results with data reported in the literature for other Iowa watersheds exhibiting similar biogeochemical properties. Once WEPP was verified, ‘thought experiments’ were conducted to test our hypothesis that land use and associated management practices may be the major control of long‐term erosion in small agricultural watersheds such as SASW. Those experiments were performed using the dominant 2‐year crop rotations in the SASW, namely, fall till corn–no till bean (FTC‐NTB), no till bean–spring till corn (NTB‐STC) and no till corn–fall till bean (NTC‐FTB), which comprised approximately 90% of the total acreage in SASW. Results of this study showed that for all crop rotations, a strong correspondence existed between soil erosion rates and high‐magnitude precipitation events during the period of mid‐April and late July, as expected. The magnitude of this correspondence, however, was strongly affected by the crop rotation characteristics, such as canopy/residue cover provided by the crop, and the type and associated timing of tillage. Tillage type (i.e. primary and secondary tillages) affected the roughness of the soil surface and resulted in increases of the rill/inter‐rill erodibilities up to 35% and 300%, respectively. Particularly, the NTC‐FTB crop rotation, being the most intense land use in terms of tillage operations, caused the highest average annual erosion rate within the SASW, yielding quadrupled erosion rates comparatively to NTB‐STC. The impacts of tillage operation were further exacerbated by the timing of the operations in relation to precipitation events. Timing of operations affected the ‘life‐time’ of residue cover and as a result, the degree of protection that residue cover offers against the water action on the soil surface. In the case of NTC‐FTB crop rotation, dense corn residue stayed on the ground for only 40 days, whereas for the other two rotations, corn residue provided a protective layer for nearly 7 months, lessening thus the degree of soil erosion. The cumulative effects of tillage type and timing in conjunction with canopy/residue cover led to the conclusion that land management practices can significantly amplify or deamplify the impact of precipitation on long‐term soil erosion in small agricultural watersheds. Copyright © 2009 John Wiley & Sons, Ltd.     

Estimating source regions for snowmelt runoff in a Rocky Mountain basin: tests of a data‐based conceptual modeling approach

In many mountain basins, river discharge measurements are located far away from runoff source areas. This study tests whether a basic snowmelt runoff conceptual model can be used to estimate relative contributions of different elevation zones to basin‐scale discharge in the Cache la Poudre, a snowmelt‐dominated Rocky Mountain river. Model tests evaluate scenarios that vary model configuration, input variables, and parameter values to determine how these factors affect discharge simulation and the distribution of runoff generation with elevation. Results show that the model simulates basin discharge well (NSCE and R >0.90) when input precipitation and temperature are distributed with different lapse rates, with a rain‐snow threshold parameter between 0 and 3.3 °C, and with a melt rate parameter between 2 and 4 mm °C^?1 d^?1 because these variables and parameters can have compensating interactions with each other and with the runoff coefficient parameter. Only the hydrograph recession parameter can be uniquely defined with this model structure. These non‐unique model scenarios with different configurations, input variables, and parameter values all indicate that the majority of basin discharge comes from elevations above 2900 m, or less than 25% of the basin total area, with a steep increase in runoff generation above 2600 m. However, the simulations produce unrealistically low runoff ratios for elevations above 3000 m, highlighting the need for additional measurements of snow and discharge at under‐sampled elevations to evaluate the accuracy of simulated snow and runoff patterns. Copyright © 2013 John Wiley & Sons, Ltd.     

Evaluation of water‐use policies for baseflow recovery during droughts in an agricultural intensive karst watershed: Case study of the lower Apalachicola–Chattahoochee–Flint River Basin,southeastern United States

The lower Apalachicola–Chattahoochee–Flint River Basin in the Southeast United States represents a major agricultural area underlain by the highly productive karstic Upper Floridan aquifer (UFA). During El Niño Southern Oscillation‐induced droughts, intense groundwater withdrawal for irrigation lowers streamflow in the Flint River due to its hydraulic connectivity with the UFA and threatens the habitat of the federally listed and endangered aquatic biota. This study assessed the compounding hydrologic effects of increased irrigation pumping during drought years (2010–2012) on stream–aquifer water exchange (stream–aquifer flux) between the Flint River and UFA using the United States Geological Survey modular finite element groundwater flow model. Principal component and K‐means clustering analyses were used to identify critical stream reaches and tributaries that are adversely affected by irrigation pumping. Additionally, the effectiveness of possible water restriction scenarios on stream–aquifer flux was also analysed. Moreover, a cost–benefit analysis of acreage buyout procedure was conducted for various water restriction scenarios. Results indicate that increased groundwater withdrawal in Water Year 2011 decreased baseflow in the lower Apalachicola–Chattahoochee–Flint River Basin, particularly, in Spring Creek, where irrigation pumping during April, June, and July changed the creek condition from a gaining to losing stream. Results from sensitivity analysis and simulated water restrictions suggest that reducing pumping in selected sensitive areas is more effective in streamflow recovery (approximately 78%) than is reducing irrigation intensity by a prescribed percentage of current pumping rates, such as 15% or 30%, throughout the basin. Moreover, analysis of acreage buyout indicates that restrictions on irrigation withdrawal can have significant impacts on stream–aquifer flux in the Basin, especially in critical watersheds such as Spring and Ichawaynochaway Creeks. The proposed procedure for ranking of stream reaches (sensitivity analysis) in this study can be replicated in other study areas/models. This study provides useful information to policymakers for devising alternate irrigation water withdrawal policies during droughts for maintaining flow levels in the study area.