Evaluation of Filtering Methods for Hydrograph Separation in Small Agricultural Watersheds in Québec, Canada

HighlightsAgricultural hydrology is complex due to the management of surface and subsurface flow to increase productivity.This study provides an interpretation of hydrological functioning, using a geochemical tracer (electrical conductivity) as a reference method, for hydrograph separation and evaluation of filtering methods.Filtering method efficiency must be interpreted according to season, year, watershed relief, and management practices.Routine application of basic filtering concepts is not sufficient to address the heterogeneity of hydrological processes in agricultural watersheds.Abstract. Streamflow hydrographs summarize the behavior of watersheds. Their separation into quick and slow components requires hydrological knowledge of the specific drainage area. To better understand the hydrological response of 14 small agricultural watersheds in Québec, Canada, covering different physiographic attributes ranging from lowlands to hilly and steep landscapes, streamflow electrical conductivity was used as a geochemical tracer. These agricultural watersheds have undergone significant management practices, including artificial drainage. The objective of this research was to evaluate the performance of existing automated filter methods for hydrograph separation (BFLOW, UKIH, PART, FIXED, SLIDE, LOCMIN, and Eckhardt). The geochemical method was used as a reference for comparison with the filter methods. Comparison of the slow flow estimates from non-calibrated filters, using a MANOVA model, showed that the filter performance increased under conditions with high contributions of quick runoff to the stream, such as during snowmelt (spring season), during heavy precipitation, and in subwatersheds with landscape conditions more prone to quick runoff. However, filter performance decreased as hydrological processes predisposed more flow to slower pathways, typically in summer and fall, as well as in lowland landscapes generally associated with high rates of tile drainage rather than in hilly and steep relief. Underlying the filter assumptions is the classic concept of a rainfall event with quick runoff as the main source of the drainage area response. Thus, slow flow is associated with a low threshold response. Eckhardt filter simulations were in good agreement with the geochemical method after calibration, based on model statistical measures (R, NSE, and PBIAS). However, larger errors were associated with higher flow values. The slow flow overestimations were more pronounced during periods of extreme events, i.e., spring runoff and heavy precipitation. The linear concept of the Eckhardt filter yields no information on slow flow response behavior that could be useful in capturing its temporal variability. Because the routing of water has been managed to improve agricultural productivity, these hydrological modifications resulted in a more complex slow flow response. The performance of filtering methods is thus affected. Therefore, simplifications of filter assumptions are less likely to provide more effective estimates of slow flow. Furthermore, given the heterogeneity of hydrological processes due to seasonal climatic characteristics, the routine application of basic filter concepts is not sufficient to address the variable nature of the hydrological response. The variability scale of geochemical separation, from regional (agro-climatic) to local (adjacent watersheds), proved that it is always relevant to have adequate separation. However, the validation of filters without a tracer is limited and almost unsuitable for these agricultural watersheds. Keywords: Agricultural watershed, Artificial drainage, Electrical conductivity, Filtering method, Geochemical method, Hydrograph separation, MANOVA, Quick flow, Slow flow, Tile drainage.

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Regression Approaches for Hydrograph Separation: Implications for the Use of Discontinuous Electrical Conductivity Data

Water ◽

10.3390/w10091235 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1235 ◽

Cited By ~ 2

Author(s):

Antonia Longobardi ◽

Paolo Villani ◽

Domenico Guida ◽

Albina Cuomo

Keyword(s):

Electrical Conductivity ◽

Digital Filters ◽

Geochemical Data ◽

Hydrograph Separation ◽

Conductivity Measurements ◽

Conductivity Data ◽

Runoff Generation ◽

Filter Performance ◽

Recursive Digital Filters ◽

Electrical Conductivity Data

Understanding of runoff generation mechanisms affects the ability to manage streamflow quantity and quality issues. Concerning the baseflow in particular, measurements are almost never available and hydrograph separation is generally applied to characterize its relevant patterns. As an alternative to well-known recursive digital filters and mass balance filtering methods, this paper deals with the use of regression approaches, based on electrical conductivity measurements, as a proxy for total dissolved solids, to separate baseflow from total flow. Particular focus is placed on their flexibility and ability to adapt to discontinuous electrical conductivity data measurements. To illustrate this, we analyze a hydrochemical dataset collected from the Ciciriello experimental catchment (Southern Italy). The main findings are as follows: A comparative analysis suggests that the performance of regressive approaches in the case of daily electrical conductivity measurements is better than that of calibrated recursive digital filters. Weekly monitored electrical conductivity data led to performances comparable to the daily scale monitoring, and even monthly observation leads to a nonsignificant reduction in regression hydrograph filter performance; this shows how spot geochemical data monitoring may present valid and operational alternatives for characterization of baseflow in poorly gauged catchments.

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Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods

10.5194/hess-2020-460 ◽

2020 ◽

Author(s):

Yang Yang ◽

Ting Fong May Chui

Keyword(s):

Machine Learning ◽

Management Practices ◽

Hydrological Processes ◽

Rainfall Time Series ◽

Hydrograph Separation ◽

Learning Methods ◽

Drainage Systems ◽

Factors Affecting ◽

Machine Learning Methods ◽

Hydrological Responses

Abstract. Sustainable drainage systems (SuDS) are decentralized stormwater management practices that mimic the natural drainage processes. Their modeling is often challenged by insufficient data and unknown factors affecting the hydrological processes. This study uses machine learning methods to model directly the correlation between hydrological responses and rainfalls at fine temporal scales in two catchments of different sizes. A feature engineering method is developed to extract useful information from rainfall time series and is used in combination with a nested cross-validation procedure to derive high-quality models and to estimate their generalization errors. The SHAP method is adopted to explain the basis of each prediction, which is then used for estimating catchment response time and hydrograph separation. The explanations of the predictions provide valuable insights into the models’ behavior and the involved hydrological processes. Thus, interpreting machine learning models is found as a useful way to study catchment hydrology.

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Simulating cold-region hydrology in an intensively drained agricultural watershed in Manitoba, Canada, using the Cold Regions Hydrological Model

Hydrology and Earth System Sciences ◽

10.5194/hess-21-3483-2017 ◽

2017 ◽

Vol 21 (7) ◽

pp. 3483-3506 ◽

Cited By ~ 12

Author(s):

Marcos R. C. Cordeiro ◽

Henry F. Wilson ◽

Jason Vanrobaeys ◽

John W. Pomeroy ◽

Xing Fang ◽

...

Keyword(s):

Preferential Flow ◽

Model Development ◽

Agricultural Watershed ◽

Frozen Soil ◽

Red River ◽

Hydrological Processes ◽

Low Flow ◽

Northern Great Plains ◽

Agricultural Watersheds ◽

Cold Regions

Abstract. Etrophication and flooding are perennial problems in agricultural watersheds of the northern Great Plains. A high proportion of annual runoff and nutrient transport occurs with snowmelt in this region. Extensive surface drainage modification, frozen soils, and frequent backwater or ice-damming impacts on flow measurement represent unique challenges to accurately modelling watershed-scale hydrological processes. A physically based, non-calibrated model created using the Cold Regions Hydrological Modelling platform (CRHM) was parameterized to simulate hydrological processes within a low slope, clay soil, and intensively surface drained agricultural watershed. These characteristics are common to most tributaries of the Red River of the north. Analysis of the observed water level records for the study watershed (La Salle River) indicates that ice cover and backwater issues at time of peak flow may impact the accuracy of both modelled and measured streamflows, highlighting the value of evaluating a non-calibrated model in this environment. Simulations best matched the streamflow record in years when peak and annual discharges were equal to or above the medians of 6.7 m3 s−1 and 1.25  × 107 m3, respectively, with an average Nash–Sutcliffe efficiency (NSE) of 0.76. Simulation of low-flow years (below the medians) was more challenging (average NSE  <  0), with simulated discharge overestimated by 90 % on average. This result indicates the need for improved understanding of hydrological response in the watershed under drier conditions. Simulation during dry years was improved when infiltration was allowed prior to soil thaw, indicating the potential importance of preferential flow. Representation of in-channel dynamics and travel time under the flooded or ice-jam conditions should also receive attention in further model development efforts. Despite the complexities of the study watershed, simulations of flow for average to high-flow years and other components of the water balance were robust (snow water equivalency (SWE) and soil moisture). A sensitivity analysis of the flow routing model suggests a need for improved understanding of watershed functions under both dry and flooded conditions due to dynamic routing conditions, but overall CRHM is appropriate for simulation of hydrological processes in agricultural watersheds of the Red River. Falsifications of snow sublimation, snow transport, and infiltration to frozen soil processes in the validated base model indicate that these processes were very influential in stream discharge generation.

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Applying Nitrogen Site-Specifically Using Soil Electrical Conductivity Maps and Precision Agriculture Technology

The Scientific World JOURNAL ◽

10.1100/tsw.2001.95 ◽

2001 ◽

Vol 1 ◽

pp. 767-776 ◽

Cited By ~ 4

Author(s):

E.D. Lund ◽

M.C. Wolcott ◽

G.P. Hanson

Keyword(s):

Electrical Conductivity ◽

Case Studies ◽

Crop Yield ◽

Soil Texture ◽

Precision Agriculture ◽

Management Practices ◽

N Availability ◽

Soil Electrical Conductivity ◽

Variable Yield ◽

Wide Range

Soil texture varies significantly within many agricultural fields. The physical properties of soil, such as soil texture, have a direct effect on water holding capacity, cation exchange capacity, crop yield, production capability, and nitrogen (N) loss variations within a field. In short, mobile nutrients are used, lost, and stored differently as soil textures vary. A uniform application of N to varying soils results in a wide range of N availability to the crop. N applied in excess of crop usage results in a waste of the grower’s input expense, a potential negative effect on the environment, and in some crops a reduction of crop quality, yield, and harvestability. Inadequate N levels represent a lost opportunity for crop yield and profit. The global positioning system (GPS)-referenced mapping of bulk soil electrical conductivity (EC) has been shown to serve as an effective proxy for soil texture and other soil properties. Soils with a high clay content conduct more electricity than coarser textured soils, which results in higher EC values. This paper will describe the EC mapping process and provide case studies of site-specific N applications based on EC maps. Results of these case studies suggest that N can be managed site-specifically using a variety of management practices, including soil sampling, variable yield goals, and cropping history.

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Hydrological processes and water flux quantification in agricultural fields under different tillage and irrigation systems using water stable isotopes

10.5194/egusphere-egu21-11039 ◽

2021 ◽

Author(s):

Alba Canet-Marti ◽

Angela Morales-Santos ◽

Reinhard Nolz ◽

Günter Langergraber ◽

Christine Stumpp

Keyword(s):

Stable Isotopes ◽

Water Content ◽

Water Flow ◽

Drip Irrigation ◽

Management Practices ◽

Hydrological Processes ◽

Agricultural Fields ◽

Soil Profiles ◽

Water Fluxes ◽

Irrigation Systems

Sustainable agriculture should be based on management practices that improve resource usage efficiency and minimize harmful impacts on the environment while maintaining and stabilizing crop production. Both tillage and irrigation can have a great influence on hydrological processes within agroecosystems. However, it remains difficult to directly assess the effect of practices on water fluxes which has been mainly indirectly quantified by complex numerical modelling methods in the past. Therefore, the objective of the study was to use a space for time concept and measure oxygen and hydrogen isotopes (&#948;18O, &#948;2H) in the pore water of soil profiles as well as moisture contents for quantifying the soil water balance and fluxes. Covering all combinations, soil profiles and isotope analysis was performed for 16 sites planted with winter wheat and managed with different tillage (conventional tillage (CT), reduced tillage (RT), minimal tillage (MT), and no-tillage (NT)) and irrigation systems (hose reel boom irrigation with nozzles (BI), sprinkler irrigation (SI), drip irrigation (DI) and no irrigation (NI)). The results indicated that the more intense the tillage, the lower the water content. Among the irrigation systems, DI had the highest average water content. Tracing the minimum in the isotopic composition of the pores water within the depth profiles showed a deeper percolation of water in the CT fields, which indicates higher water flow velocity. Considering both water content and differences in water flow velocities resulted in water fluxes ranging from 90 to 151 mm yr-1. The losses due to evapotranspiration varied between 57 and 80%. The resulting evapotranspiration within tillage and irrigation variants decreased in the order RT>CT&#8776;MT>NT, and SI>BI>DI>NI. Thus, the method revealed that the lower water content in CT fields is a consequence of deeper water infiltration. Moreover, irrigation water contributed mostly to evapotranspiration, and drip irrigation showed the lowest evapotranspiration losses among irrigation systems. This study demonstrated that water stable isotopes can be used as indicators and are a promising method to quantify water fluxes in agricultural fields with great potential for evaluating management practices.

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A Multiscale Alignment Method for Ensemble Filtering with Displacement Errors

Monthly Weather Review ◽

10.1175/mwr-d-19-0170.1 ◽

2019 ◽

Vol 147 (12) ◽

pp. 4553-4565 ◽

Cited By ~ 1

Author(s):

Yue Ying

Keyword(s):

Turnover Time ◽

The State ◽

Filter Performance ◽

Filter Methods ◽

Displacement Vectors ◽

Efficient Data ◽

High Resolution Models ◽

Quasigeostrophic Model ◽

Ensemble Filtering ◽

Nonlinear Observation

Abstract High-resolution models nowadays simulate phenomena across many scales and pose challenges to the design of efficient data assimilation methods that reduce errors at all scales. Smaller-scale features experience rapid nonlinear error growth that gives rise to displacement errors, which cause suboptimal ensemble filter performance. Previous studies have started exploring methods that can reduce displacement errors. In this study, a multiscale alignment (MSA) method is proposed for ensemble filtering. The MSA method iteratively processes the model state from the largest to the smallest scales. At each scale, an ensemble filter is applied to update the state with observations, and the analysis increments are utilized to derive displacement vectors for each member that align the ensemble at smaller scales before the next iteration. The nonlinearity in smaller-scale priors is reduced by removing larger-scale displacement errors. Because the displacement vectors are derived from analysis increments in the state space rather than the nonlinear observation-space cost function formulated in previous studies, this method provides a less costly and more robust way to solve for the displacement vectors. Observing system simulation experiments using a two-layer quasigeostrophic model were conducted to provide a proof of concept of the MSA method. Results show that the MSA method significantly improves the accuracy of posteriors compared to the existing ensemble filter methods with or without multiscale localization. Advantage of the MSA method are more evident when the ensemble size is relatively small and the cycling period is comparable to the average eddy turnover time of the dynamical system.

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Convenient use of electrical conductivity measurements to investigate hydrological processes in Alpine headwaters

The Science of The Total Environment ◽

10.1016/j.scitotenv.2019.05.166 ◽

2019 ◽

Vol 685 ◽

pp. 37-49 ◽

Cited By ~ 7

Author(s):

Karina Cano-Paoli ◽

Gabriele Chiogna ◽

Alberto Bellin

Keyword(s):

Electrical Conductivity ◽

Hydrological Processes ◽

Conductivity Measurements

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Modeling the Effects of Future Hydroclimatic Conditions on Microbial Water Quality and Management Practices in Two Agricultural Watersheds

Transactions of the ASABE ◽

10.13031/trans.13630 ◽

2020 ◽

Vol 63 (3) ◽

pp. 753-770 ◽

Cited By ~ 1

Author(s):

Rory Coffey ◽

Jonathan Butcher ◽

Brian Benham ◽

Thomas Johnson

Keyword(s):

Water Quality ◽

Fecal Coliform ◽

Management Practices ◽

Quality Criteria ◽

Agricultural Watersheds ◽

Water Quality Criteria ◽

Recreational Water ◽

Microbial Water Quality ◽

Recreational Water Quality ◽

Nonpoint Sources

Highlights Increased fecal coliform (FC) loading from nonpoint sources is associated with wetter-warmer futures. Drier-warmer futures reduced FC loads but caused more recreational water quality criteria exceedances. More extensive BMP implementation may be needed to meet water quality goals. Abstract. Anticipated future hydroclimatic changes are expected to alter the transport and survival of fecally sourced waterborne pathogens, presenting an increased risk of recreational water quality impairments. Managing future risk requires an understanding of the interactions between fecal sources, hydroclimatic conditions, and best management practices (BMPs) at spatial scales relevant to decision makers. In this study, we used the Hydrologic Simulation Program FORTRAN (HSPF) to quantify potential fecal coliform (FC, an indicator of the potential presence of pathogens) responses to a range of mid-century climate scenarios and assess different BMP scenarios (based on reduction factors) for reducing the risk of water quality impairment in two small agricultural watersheds: the Chippewa watershed in Minnesota, and the Tye watershed in Virginia. In each watershed, simulations show a wide range of FC responses, driven largely by variability in projected future precipitation. Wetter future conditions, which drive more transport from nonpoint sources (e.g., manure application, livestock grazing), show increases in FC loads. Loads typically decrease in drier futures; however, higher mean FC concentrations and more recreational water quality criteria exceedances occur, likely caused by reduced flow during low-flow periods. Median changes across the ensemble generally show increases in FC load. BMPs that focus on key fecal sources (e.g., runoff from pasture, livestock defecation in streams) within a watershed can mitigate the effects of hydroclimatic change on FC loads. However, more extensive BMP implementation or improved BMP efficiency (i.e., higher FC reductions) may be needed to fully offset increases in FC load and meet water quality goals, such as total maximum daily loads and recreational water quality standards. Strategies for managing climate risk should be flexible and to the extent possible include resilient BMPs that function as designed under a range of future conditions. Keywords: Climate, HSPF, Management responses, Microbial water quality, Modeling, Watersheds.

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Modeling the Snowmelt Runoff Process of the Tizinafu River Basin, Northwest China, with GLDAS Data and Bayesian Uncertainty Analysis

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0162.1 ◽

2021 ◽

Vol 22 (1) ◽

pp. 169-182

Author(s):

Wenyi Xie ◽

Xiankui Zeng ◽

Dongwei Gui ◽

Jichun Wu ◽

Dong Wang

Keyword(s):

River Basin ◽

Digital Filter ◽

Local Minimum ◽

Northwest China ◽

Hydrograph Separation ◽

Snowmelt Runoff ◽

Baseflow Separation ◽

Filter Methods ◽

Relative Errors ◽

Streamflow Simulation

AbstractThe climate of the Tizinafu River basin is characterized by low temperature and sparse precipitation, and snow and glacier melt serve as the main water resource in this area. Modeling the snowmelt runoff process has great significance for local ecosystems and residents. The total streamflow of the Tizinafu River basin was divided into surface streamflow and baseflow. The surface streamflow was estimated using the routing model (RM) with Noah runoff data from Global Land Data Assimilation (GLDAS), and the parameter uncertainty of the RM was quantified through Markov chain Monte Carlo simulation. Additionally, the 10 commonly used baseflow separation methods of four categories [digital filter, hydrograph separation program (HYSEP), baseflow index, and Kalinlin methods] were used to generate the baseflow and were then evaluated by their performance in total streamflow simulation. The results demonstrated that the RM driven by GLDAS runoff data could reproduce the runoff process of the Tizinafu River basin. RM-Hl (local minimum HYSEP method) achieved the best performance in the total streamflow simulation, with Nash–Sutcliffe efficiency (NSE) coefficients of 0.82 and 0.93, relative errors of −0.40% and 10.50%, and observation inclusion ratios C of 62.07% and 68.52% for the calibration and verification periods, respectively. The local minimum HYSEP method was most suitable for describing the baseflow of the Tizinafu River basin among the 10 baseflow separation methods. However, digital filter methods exhibited weak performance in baseflow separation.

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