Black Brook Watershed Research Articles

SWAT Setup with Long-Term Detailed Landuse and Management Records and Modification for a Micro-Watershed Influenced by Freeze-Thaw Cycles

In the widely used soil and water assessment tool (SWAT), the standard hydrological response units (HRUs) delineation method has low spatial resolution with respect to model inputs and outputs and renders difficulties in using long-term detailed landuse and management records. In addition, the modified universal soil loss equation (MUSLE) uses a constant K-factor which cannot address seasonal variation in soil erodibility caused by freeze-thaw cycles in cold regions. The current study presents a simple method to incorporate detailed landuse and management inputs in SWAT. The method delineates HRUs based on field boundaries and associates each HRU with a particular field. As a result, long-term detailed records can be incorporated into the SWAT management files. In addition, the existing MUSLE in SWAT was modified by introducing a variable K-factor to address effects of freeze-thaw cycles on soil erosion for cold regions. This modified version of SWAT was calibrated and validated for an agricultural micro-watershed, i.e., Black Brook Watershed in New Brunswick, Canada. The results showed that, compared with the standard HRU-delineation method, field-based HRU-delineation method was able to improve landuse and management practice input accuracy for SWAT and save time and effort for long-term simulation, and provide high resolution outputs in the watershed. As a result, the field-based HRU-delineation method can facilitate decision making not only at the subbasin scale but also at the field scale. In addition, results showed that sediment loading simulation accuracy was improved with the modified-MUSLE compared with the original-MUSLE.

A new soil-temperature module for SWAT application in regions with seasonal snow cover

Summary Accurate estimates of soil temperature are important for quantifying hydrological and biological processes in hydrological models. Soil temperature predictions in the widely used Soil and Water Assessment Tool (SWAT) have large prediction errors when applied to regions with significant snow cover during winter. In this study, a new physically-based soil-temperature module is developed as an alternative to the empirical soil-temperature module currently used in SWAT. The physically-based module ​simulates soil temperature in different soil layers as a result of energy transfer between the atmosphere and soil (or snow) interface. The modified version of SWAT with the new soil-temperature module in place, introduces only three new parameters over the original soil-temperature module. Both the original and new soil-temperature modules are tested against field data from the Black Brook Watershed, a small watershed in Atlantic Canada. The results indicate that both versions of soil-temperature module ​are able to provide acceptable predictions of temperature in different layers of the soil during non-winter seasons. However, the original module severely underestimates soil temperatures in winter (within −10 to −20 °C), while the new module produces results that are more consistent with field measurements (within −2 to 2 °C). In addition, unlike its counterpart, the new module ​is able to simulate freeze–thaw cycles in the soil profile. Ice-water content variations in winter are reasonably simulated by the new module for different snow cover scenarios. In general, modified-SWAT improves prediction accuracy on baseflow discharge compared with the original-SWAT, due to improved estimates of soil temperature during winter. The new physically-based soil-temperature module has greatly improved the ability of SWAT to predict soil temperatures under seasonal snow cover, which is essential to the application of the model in regions like Atlantic Canada.

Structural controls on groundwater flow in a fractured bedrock aquifer underlying an agricultural region of northwestern New Brunswick, Canada

A hydrogeological study was conducted in northwestern New Brunswick, Canada, to improve the predictability of fracture-dominated groundwater flow within folded bedrock composed of fine-grained turbidites. Borehole televiewer logging and outcrop mapping, integrated with hydraulic packer tests revealed enhanced hydraulic conductivity associated with northeasterly striking bedding-plane fractures formed during folding and flexural slip. These fractures impart azimuthal anisotropy to the aquifer because of moderately dipping fold limbs. High-angle fractures form a well-developed non-stratabound network, comprising two open fracture sets striking NNE parallel to the current direction of principal stress, and WNW parallel to the direction of principal stress that dominated during the Acadian orogeny. The subset of fractures showing significant oxidation, deemed most important to the groundwater flow system, is dominated by bedding-plane and high-angle fractures striking near-parallel to the maximum principal stress direction, resulting in extensional opening and enhanced hydraulic conductivities. An equivalent porous media model, incorporating anisotropy and varying hydraulic conductivity with depth, indicates that horizontal flow dominates the aquifer with relatively minor exchange between different model layers. These findings have implications for understanding flow directions in the Black Brook Watershed and elsewhere in the Matapedia Basin where fractures formed under similar stress conditions.

Data requirements for using combined conductivity mass balance and recursive digital filter method to estimate groundwater recharge in a small watershed, New Brunswick, Canada

Summary Estimation of baseflow and groundwater recharge rates is important for hydrological analysis and modelling. A new approach which combines recursive digital filter (RDF) model with conductivity mass balance (CMB) method was considered to be reliable for baseflow separation because the combined method takes advantages of the reduced data requirement for RDF method and the reliability of CMB method. However, it is not clear what the minimum data requirements for producing acceptable estimates of the RDF model parameters are. In this study, 19-year record of stream discharge and water conductivity collected from the Black Brook Watershed (BBW), NB, Canada were used to test the combined baseflow separation method and assess the variability of parameters in the model over seasons. The data requirements and potential bias in estimated baseflow index (BFI) were evaluated using conductivity data for different seasons and/or resampled data segments at various sampling durations. Results indicated that the data collected during ground-frozen season are more suitable to estimate baseflow conductivity ( C bf ) and data during snow-melting period are more suitable to estimate runoff conductivity ( C ro ). Relative errors of baseflow estimation were inversely proportional to the number of conductivity data records. A minimum of six-month discharge and conductivity data is required to obtain reliable parameters for current method with acceptable errors. We further found that the average annual recharge rate for the BBW was 322 mm in the past twenty years.

Generation of soil drainage equations from an artificial neural network-analysis approach

Zhao, Z., MacLean, D. A., Bourque, C. P.-A., Swift, D. E. and Meng, F.-R. 2013. Generation of soil drainage equations from an artificial neural network-analysis approach. Can. J. Soil Sci. 93: 329–342. Soil properties, especially soil drainage, are known to be related to topo-hydrologic variables derived from digital elevation models (DEM), such as vertical slope position, slope steepness, sediment delivery ratio, and topographic wetness index. Such relationships typically are strongly non-linear and thus difficult to define with conventional statistical methods. In this study, we used artificial neural network (ANN) models to establish relationships between soil drainage classes and DEM-generated topo-hydrologic variables and subsequently formulated the relationships to generate soil drainage equations for soil mapping. A high-resolution field soil map of the Black Brook Watershed in northwest New Brunswick, Canada, was used to calibrate/validate the ANN models, and the obtained equations. Independent data from an experimental farm, about 180 km away, were also used for validation. Results indicated that vertical slope position was the best predictor of soil drainage classes (r=0.55), followed by slope steepness (r=0.44), sediment delivery ratio (r=0.39), and topographic wetness index (r=0.38). The obtained soil drainage equations fitted well to the ANN model predictions (r2=0.78–0.99; root mean squared error=0.39–4.55). Analyses indicated that soil drainage equations clearly reflected the actual relationships between soil drainage classes and DEM-generated topo-hydrologic variables, and have the potential to minimize bias originated from over-training the ANN models when applied outside the area of calibration, especially when the ranges of input variables were outside of the range of calibration data.

Using the Soil and Water Assessment Tool to Estimate Achievable Water Quality Targets through Implementation of Beneficial Management Practices in an Agricultural Watershed

Runoff from crop production in agricultural watersheds can cause widespread soil loss and degradation of surface water quality. Beneficial management practices (BMPs) for soil conservation are often implemented as remedial measures because BMPs can reduce soil erosion and improve water quality. However, the efficacy of BMPs may be unknown because it can be affected by many factors, such as farming practices, land-use, soil type, topography, and climatic conditions. As such, it is difficult to estimate the impacts of BMPs on water quality through field experiments alone. In this research, the Soil and Water Assessment Tool was used to estimate achievable performance targets of water quality indicators (sediment and soluble P loadings) after implementation of combinations of selected BMPs in the Black Brook Watershed in northwestern New Brunswick, Canada. Four commonly used BMPs (flow diversion terraces [FDTs], fertilizer reductions, tillage methods, and crop rotations), were considered individually and in different combinations. At the watershed level, the best achievable sediment loading was 1.9 t ha(-1) yr(-1) (89% reduction compared with default scenario), with a BMP combination of crop rotation, FDT, and no-till. The best achievable soluble P loading was 0.5 kg ha(-1) yr(-1) (62% reduction), with a BMP combination of crop rotation and FDT and fertilizer reduction. Targets estimated through nonpoint source water quality modeling can be used to evaluate BMP implementation initiatives and provide milestones for the rehabilitation of streams and rivers in agricultural regions.

Watershed-level analysis of exceedance frequencies for different management strategies

Exceedance of water-quality standards is important in assessing water quality. The effectiveness of soil conservation Beneficial Management Practices (BMPs) should be measured according to the BMPs' impact on exceedance frequencies. However, estimating exceedance frequencies for different management scenarios with field measurements is practically impossible due to difficulties in obtaining adequate data for analysing different combinations of BMPs. The objective of this modeling research was to analyse exceedance frequencies for different management strategies applied in the Black Brook Watershed (BBW). Daily concentrations of total suspended sediments (TSS) and soluble phosphorous (sol-P) were predicted with the Soil and Water Assessment Tool (SWAT) and assessed against water-quality standards from the Canadian Council of Ministers of the Environment (CCME) and National Agri-Environmental Standards Initiative-Ideal Performance Standards (NAESI-IPS). The investigated BMPs included conservation tillage, reduced fertilizer application, crop rotation, flow diversion terraces (FDT) and the combination of all four BMPs. The results indicated that FDT was the most effective at reducing exceedance frequencies of TSS and sol-P. Under the different management scenarios, we calculated the annual exceedance frequencies of TSS and sol-P concentrations above the CCME (20–45% and 10–26%) and NAESI-IPS (32–55% and 20–38%).

Effects of snowmelt on phosphorus and sediment losses from agricultural watersheds in Eastern Canada

Snowmelt is the most important hydrological event in cold climates. However, snowmelt effects on suspended sediment (SS) and phosphorus (P) loss are poorly documented in Canada. Using two agricultural watersheds in Eastern Canada, this study aimed to quantify SS and P loss during the snowmelt period and to investigate how snowmelt contributes SS and P loss. Water samples were collected from the outlets of the Bras d’Henri watershed (BHW, 2007–2009) and Black Brook watershed (BBW, 2008–2009) and measured for SS and P concentrations. Hydrological parameters (precipitation, snow water equivalent, and runoff discharge), soil frozen status and soil temperature were also measured. Results revealed inter-annual variation of snowmelt conditions and SS and P losses in each watershed. The 2008 snowmelt in BHW and BBW mainly occurred on unfrozen soils, while the 2007 and 2009 snowmelts in BHW and 2009 snowmelt in BBW mainly on frozen soils. In BHW, 2008 snowmelt caused much higher median concentrations of SS, total P (TP), dissolved P (DP) and particulate P (PP) in stream water than 2007 and 2009; ratios of PP fractions in TP were variable with events but the median values were similar, suggesting both DP and PP important contrubutors to TP loss. In BBW, the median concentration of dissolved reactive phosphorus (DRP) in stream water was greater in 2008 snowmelt than in 2009 snowmelt; PP dominated TP loss. This study also suggests that soil state (i.e. frozen status) and rainfall were the most important factors influencing SS and P losses during snowmelt. Furthermore, snowmelt P export represented more than 20% of the total annual P export in BHW, and more than 12% of the annual DRP export in BBW. Thus, we strongly recommend adopting Best Management Practices (BMPs) that specifically target sediment and P loss during snowmelt.

Hydrology and water quality across gradients of agricultural intensity in the Little River watershed area, New Brunswick, Canada

Impacts of suspended sediment and agrochemicals on water quality comprise one of the dominant environmental concerns affecting the agricultural sector today. Three sub- watersheds within the Little River watershed (New Brunswick, Canada) were instrumented to continuously monitor discharge and sample water for suspended sediment and agrochemi- cal analysis with the objective of determining the impacts of different agriculture and forestry intensities on water yield and quality on a long-term basis. The subwatersheds included (1) Black Brook watershed (BBW), a predominantly agricultural (65%) watershed with potato as the major crop; (2) Little River watershed (LRW), a combination of forest (77%) and agricultural (16%) land uses; and (3) Upper Little River watershed (ULRW), a forest- and wetland-dominated watershed (94%). Data from 2003 to 2007 showed that average water yields were 0.588, 0.849, and 0.901 million m 3 km -2 y -1 (53.78, 77.65, and 82.41 million ft 3 mi -2 yr -1 ) for the BBW, LRW, and ULRW, respectively, indicating that water yield decreased with increasing agricultural intensity. Average suspended sediment yield in the receiving water decreased from high agricultural intensity to low agricultural intensity, with values of 181.60, 121.60, and 57.00 Mg km -2 y -1 (470.34, 314.94, and 147.63 tn mi -2 yr -1 ) for the BBW, LRW, and ULRW, respectively, as did flow-weighted sediment concentra- tion (0.33, 0.13, and 0.06 mg L -1 for the BBW, LRW, and ULRW, respectively). Average flow-weighted nitrate-nitrogen concentration in the water of the BBW was 4.39 mg L -1 compared to 1.24 mg L -1 in the LRW and 0.45 mg L -1 in the ULRW; ortho-phosphorus in the BBW was 56.43 μg L -1 compared to 24.29 μg L -1 in the LRW and 19.56 μg L -1 in the ULRW. Major ions such as potassium, magnesium, and calcium followed similar trends to those observed for suspended sediments and dissolved nutrients across the watersheds. Water pH showed little variation among watersheds, but water conductivity and water temperature were highly related to the intensity of agriculture. While the differences across the watersheds were generally consistent, differences among years were variable-dependent. Except for the ortho-phosphorus load, chemical loads of nitrogen, potassium, calcium, and magnesium were closely related to annual precipitation, but chemical concentrations did not show much correlation with either rainfall amount or erosivity. The robust time-series data available for this study enabled meaningful assessment of agricultural impacts at a landscape scale.

Using artificial neural network models to produce soil organic carbon content distribution maps across landscapes

Soil organic carbon (SOC) content is an important soil quality indicator that plays an important role in regulating physical, chemical and biological properties of soil. Field assessment of SOC is time consuming and expensive. It is difficult to obtain high-resolution SOC distribution maps that are needed for landscape analysis of large areas. An artificial neural network (ANN) model was developed to predict SOC based on parameters derived from digital elevation model (DEM) together with soil properties extracted from widely available coarse resolution soil maps (1:1 000 000 scale). Field estimated SOC content data extracted from high-resolution soil maps (1:10 000 scale) in Black Brook Watershed in northwestern New Brunswick, Canada, were used to calibrate and validate the model. We found that vertical slope position (VSP) was the most important variable that determines distributions of SOC across the landscape. Other variables such as slope steepness, and potential solar radiation (PSR) also had significant influence on SOC distributions. The prediction of the selected two-input-node SOC model (VSP and coarse resolution soil map recorded SOC as inputs) had a correlation coefficient of 0.92 with measured values, and model predicted SOC values had 47.9% of the total points within ±0.5% of the measured values and 70.6% within ±1% of the measured values. The prediction od the selected four-input-node model (VSP, slope steepness, PSR and coarse resolution SOC values as inputs) had a correlation coefficient of 0.98 with measured values and model predicted SOC values had 75% of the total points within ±0.5% of the measured values and 87% within ±1% of the measured values. The prediction of the five-input-nodes model with soil drainage as additional input had a correlation coefficient of 0.99 with measured values, and model predicted SOC values had 87% of the total points within ±0.5% of the measured values and 98% of the total points within ±1% of the measured values. The calibrated SOC prediction model was used to produce a high-resolution SOC map for the Black Brook Watershed and the resulting SOC distribution map is considered to be realistic. Results indicated that DEM-derived hydrological parameters together with widely available coarse resolution soil map data could be used to produce high-resolution SOC maps with the ANN method.Key words: Soil organic carbon, artificial neural network model, high-resolution soil maps, digital elevation model, vertical slope position

A Watershed‐scale Assessment of Cost‐Effectiveness of Sediment Abatement with Flow Diversion Terraces

Soil conservation beneficial management practices (BMPs) are effective at controlling soil loss from farmlands and minimizing water pollution in agricultural watersheds. However, costs associated with implementing and maintaining these practices are high and often deter farmers from using them. Consequently, it is necessary to conduct cost-benefit analysis of BMP implementation to assist decision-makers with planning to provide the greatest level of environmental protection with limited resources and funding. The Soil and Water Assessment Tool (SWAT) was used to evaluate the efficacy of flow diversion terraces (FDT) in abating sediment yield at the outlet of Black Brook Watershed (BBW), northwestern New Brunswick. Different FDT-implementation scenarios were expressed as the ratio of land area protected by FDT to the total cultivated area. From this analysis, we found that average annual sediment yield decreased exponentially with increased FDT protection. When the proportion of FDT-protected areas was low, sediment reductions caused by FDT increased sharply with increasing use of FDT. Similarly, marginal sediment yield abatement costs (dollar per tonne of sediment reduction) increased exponentially with increasing proportion of FDT-protected area. The results indicated that increasing land protection with FDT from 6 to 50% would result in a reduction of about 2.1 tonne ha(-1) yr(-1) and costs of sediment reduction increased from $7 to $12 per tonne. Increasing FDT-protected cropland from 50 to 100%, a reduction of about 0.9 tonne of sediment ha(-1) yr(-1) would occur and the costs would increase from $12 to $53 per tonne of sediment yield reduction.

Impacts of Accuracy and Resolution of Conventional and LiDAR Based DEMs on Parameters Used in Hydrologic Modeling

Digital elevation model (DEM) is often used for hydrologic modeling, land use planning, engineering design and environmental protection. Research is required to assess the need of updating existing conventional DEM using higher resolution and more accurate DEMs, including light detection and ranging (LiDAR) DEM. The objective of this study was to evaluate effects of DEM accuracy and resolution on hydrologic parameters and modeling in an agriculture-dominated watershed. DEMs compared included 1 m and 10 m LiDAR based DEMs, and a conventional 10 m DEM obtained with aerial photogrammetry method. Hydrologic parameters assessed included elevation, sub-basin area and boundaries, drainage networks, slope and slope length. DEM derived hydrological parameters were used to estimate soil loss in Black Brook Watershed, New Brunswick using Revised Universal Soil Loss Equation (RUSLE). Results indicated that DEM resolution had substantial influence on the sub-basins boundaries, sub-basin area, and distribution of water flow lines. Field investigation confirmed that most of the water flow lines derived from 1 m LiDAR based DEM were accurate and a number of flow diversion terraces (FDT) failures had been identified with help of LiDAR 1 m DEM. Both conventional and LiDAR based 10 m DEM could not identify the impacts of soil conservation structures such as diversion terraces. The RUSLE predicted soil loss using 1 m LiDAR based DEM was considered to be better because both conventional and LiDAR based 10 m DEMs could not reflect the impact of FDTs on reducing soil loss.

Assessing the impacts of flow diversion terraces on stream water and sediment yields at a watershed level using SWAT model

Around the world, soil erosion is a major concern for the sustainability of agricultural systems and a threat to the integrity of aquatic ecosystems. Soil conservation Beneficial Management Practices (BMPs) have been widely used to reduce soil loss from cultivated lands and minimize degradation in water quality. As an effective BMP, flow diversion terraces (FDT) have been widely implemented in Canada and their impacts at field levels have been well documented. The objective of this study was to use the Soil and Water Assessment Tool (SWAT) to assess the efficacy of FDT systems on maintaining surface water quality at the watershed level in the Black Brook Watershed (BBW) in northwestern New Brunswick. The SWAT model was calibrated with three years of data (1992–1994) when limited amount of FDT had been implemented in the watershed. For this period, we found that SWAT performed well in predicting the seasonal variation of water yield (R2=0.91) and moderately well for sediment yield (R2=0.5). For key water variables, the SWAT model captured the variation of soluble phosphorous very well (R2=0.81). However, the SWAT model over-predicted the amount of nitrate loading (R2=0.02), and poorly represented the general trend of nitrate. The calibrated model was used to assess seasonal and annual effects of FDT on water quality for the entire watershed during the period of 1995–2005. The results indicated that FDT implemented in BBW contributed to the reduction of sediment yield by 4tonneha−1yr−1 on average, which represented a reduction of 56%. We also found that the FDT system as a BMP not only reduced the sediment loading at a watershed level, but also reduced water yield during the summer growing seasons. FDT systems contributed to the reduction of water yield by 158mmyr−1, which represented a reduction of 20%.

Model prediction of soil drainage classes based on digital elevation model parameters and soil attributes from coarse resolution soil maps

High-resolution soil drainage maps are important for crop production planning, forest management, and environmental assessment. Existing soil classification maps tend to only have information about the dominant soil drainage conditions and they are inadequate for precision forestry and agriculture planning purposes. The objective of this research was to develop an artificial neural network (ANN) model for producing soil drainage classification maps at high resolution. Soil profile data extracted from coarse resolution soil maps (1:1 000 000 scale) and topographic and hydrological variables derived from digital elevation model (DEM) data (1:35 000 scale) were considered as candidates for inputs. A high-resolution soil drainage map (1:10 000) of the Black Brook Watershed (BBW) in northwestern New Brunswick (NB), Canada, was used to train and validate the ANN model. Results indicated that the best ANN model included average soil drainage classes, average soil sand content, vertical slope position (VSP), sediment delivery ratio (SDR) and slope steepness as inputs. It was found that 52% of model-predicted drainage classes were exactly the same as field assessment observations and 94% of model-predicted drainage classes were within ±1 class. In comparison, only 12% of maps indicated drainage classes were the same as field assessment observations based on coarse resolution soil maps and only 55% of points were within ±1 class of field assessed drainage classes. Results indicated that the model could be used to produce high-resolution soil drainage maps at relatively low cost. Key words: Soil drainage, artificial neural network model, ANN model, high-resolution soil maps, DEM, hydrology model

Predict soil texture distributions using an artificial neural network model

High-resolution soil maps are important for planning agriculture crop production, forest management, hydrological analysis and environmental protection. However, high-resolution soil maps are generally only available for small areas because obtaining these maps through field survey is time consuming and expensive. The objective of this study was to develop an artificial neural network (ANN) model to predict soil texture (sand, clay and silt contents) based on soil attributes obtained from existing coarse resolution soil maps combined with hydrographic parameters derived from a digital elevation model (DEM). The calibrated ANN model then can be used to produce high-resolution soil maps in area with similar conditions without additional field surveys. The hydrographic parameters derived from DEM were soil terrain factor, sediment delivery ratio and vertical slope position. Field measured soil texture in the Black Brook Watershed (BBW) in northwestern New Brunswick, Canada was used to train and test the ANN model. Results indicated that the Levenberg–Marquardt optimization algorithm was better than the commonly used training method based on the resilient back-propagation algorithm. The root mean square errors between model predictions and field determination were 4.0 for clay and 6.6 for sand contents. The relative overall accuracy (within ±5% of field measurement) was 88% for clay content and 81% for sand content. The trained ANN model has been tested in an experimental farm located in southeastern NB about 180 km from the Black Brook Watershed where the model was first calibrated. Results indicated that with proper training, the ANN model can be used in the areas where the model was calibrated (for interpolations), or other areas provided that the relative range of input parameters were similar to the region where the model was calibrated.