Groundwater Contribution to Surface Water Contamination in a North German Low Land Catchment with Intensive Agricultural Land Use

Waseem, Muhammad; Koegst, Tatyana; Tränckner, Jens

doi:10.4236/jwarp.2018.103014

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…Surface discharges are mainly fed by groundwater, and groundwater flow is a dominant pathway in the Augraben River catchment. Higher groundwater contributions to surface water discharges in lowland Augraben River catchment are following the hydrological studies conducted in lowland catchments in north-eastern Germany [1,[51][52][53]. Overland flow or direct runoff is very low in comparison to other water balance components.…”

Section: Methods Strengthsmentioning

confidence: 62%

Improved Representation of Flow and Water Quality in a North-Eastern German Lowland Catchment by Combining Low-Frequency Monitored Data with Hydrological Modelling

et al. 2020

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Achievements of good chemical and ecological status of groundwater (GW) and surface water (SW) bodies are currently challenged mainly due to poor identification and quantification of pollution sources. A high spatio-temporal hydrological and water quality monitoring of SW and GW bodies is the basis for a reliable assessment of water quality in a catchment. However, high spatio-temporal hydrological and water quality monitoring is expensive, laborious, and hard to accomplish. This study uses spatio-temporally low resolved monitored water quality and river discharge data in combination with integrated hydrological modelling to estimate the governing pollution pathways and identify potential transformation processes. A key task at the regarded lowland river Augraben is (i) to understand the SW and GW interactions by estimating representative GW zones (GWZ) based on simulated GW flow directions and GW quality monitoring stations, (ii) to quantify GW flows to the Augraben River and its tributaries, and (iii) to simulate SW discharges at ungauged locations. Based on simulated GW flows and SW discharges, NO3-N, NO2-N, NH4-N, and P loads are calculated from each defined SW tributary outlet (SWTO) and respective GWZ by using low-frequency monitored SW and GW quality data. The magnitudes of NO3-N transformations and plant uptake rates are accessed by estimating a NO3-N balance at the catchment outlet. Based on sensitivity analysis results, Manning’s roughness, saturated hydraulic conductivity, and boundary conditions are mainly used for calibration. The water balance results show that 60–65% of total precipitation is lost via evapotranspiration (ET). A total of 85–95% of SW discharge in Augraben River and its tributaries is fed by GW via base flow. SW NO3-N loads are mainly dependent on GW flows and GW quality. Estimated SW NO3-N loads at SWTO_Ivenack and SWTO_Lindenberg show that these tributaries are heavily polluted and contribute mainly to the total SW NO3-N loads at Augraben River catchment outlet (SWO_Gehmkow). SWTO_Hasseldorf contributes least to the total SW NO3-N loads. SW quality of Augraben River catchment lies, on average, in the category of heavily polluted river with a maximum NO3-N load of 650 kg/d in 2017. Estimated GW loads in GWZ_Ivenack have contributed approximately 96% of the total GW loads and require maximum water quality improvement efforts to reduce high NO3-N levels. By focusing on the impacts of NO3-N reduction measures and best agricultural practices, further studies can enhance the better agricultural and water quality management in the study area.

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Section: Methods Strengthsmentioning

confidence: 62%

Improved Representation of Flow and Water Quality in a North-Eastern German Lowland Catchment by Combining Low-Frequency Monitored Data with Hydrological Modelling

et al. 2020

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“…To quantify the inflow of nutrients, Gehmkow Augraben, a characteristic tributary of the Tollense River, was studied based on available monitoring data [20] . Twenty-five percent to 45% of the nutrient content in the Gehmkow Augraben derives from the base flow.…”

Section: Tollense River Catchment and Water Quality Analysismentioning

confidence: 99%

“…Furthermore, it is necessary to quantify the land use impacts on the nutrients concentrations in the groundwater and then via groundwater and drainage flow in the Tollense River, as the available mass balances are insufficient to understand the dynamics and transport patterns of nutrients through surface and subsurface sources. Figure 2 [20] shows the overall water quality and nutrient concentrations in Gehmkow Augraben at the Lindenburg flow and surface water quality monitoring station.…”

Section: Tollense River Catchment and Water Quality Analysismentioning

confidence: 99%

“…HSPF has the ability to represent contributions of nutrients, sediment, pesticides, conservatives and faecal coliforms from agricultural areas, and to constantly simulate and predict water quality in pervious and impervious land surfaces, streams and in well-mixed Fig. 2 Relationship between precipitation, measured surface and estimated base flow with NO 3 -N concentrations in Gehmkow Augraben at surface water quality monitoring station (MS) Lindenburg [20] impoundments [21,24,25] . It is capable of simulating the water quality processes in a watershed by taking into account both diffuse and point sources of pollution [26,27] .…”

Section: Hydrological Simulation Program-fortran (Hspf)mentioning

confidence: 99%

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Suitability of common models to estimate hydrology and diffuse water pollution in North-eastern German lowland catchments with intensive agricultural land use

Waseem

Kachholz

Tränckner

2018

Front. Agr. Sci. Eng.

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Various process-based models are extensively being used to analyze and forecast catchment hydrology and water quality. However, it is always important to select the appropriate hydrological and water quality modeling tools to predict and analyze the watershed and also consider their strengths and weaknesses. Different factors such as data availability, hydrological, hydraulic, and water quality processes and their desired level of complexity are crucial for selecting a plausible modeling tool. This review is focused on suitable model selection with a focus on desired hydrological, hydraulic and water quality processes (nitrogen fate and transport in surface, subsurface and groundwater bodies) by keeping in view the typical lowland catchments with intensive agricultural land use, higher groundwater tables, and decreased retention times due to the provision of artificial drainage. In this study, four different physically based, partially and fully distributed integrated water modeling tools, SWAT (soil and water assessment tool), SWIM (soil and water integrated model), HSPF (hydrological simulation program-FORTRAN) and a combination of tools from DHI (MIKE SHE coupled with MIKE 11 and ECO Lab), have been reviewed particularly for the Tollense River catchment located in North-eastern Germany. DHI combined tools and SWAT were more suitable for simulating the desired hydrological processes, but in the case of river hydraulics and water quality, the DHI family of tools has an edge due to their integrated coupling between MIKE SHE, MIKE 11 and ECO Lab. In case of SWAT, it needs to be coupled with another tool to model the hydraulics in the Tollense River as SWAT does not include backwater effects and provision of control structures. However, both SWAT and DHI tools are more data demanding in comparison to SWIM and HSPF. For studying nitrogen fate and transport in unsaturated, saturated, and river zone, HSPF was a better model to simulate the desired nitrogen transformation and transport processes. However, for nitrogen dynamics and transformations in shallow streams, ECO Lab had an edge due its flexibility for inclusion of user-desired water quality parameters and processes. In the case of SWIM, most of the input data and governing equations are similar to SWAT but it does not include water bodies (ponds and lakes), wetlands and drainage systems. In this review, only the processes that were needed to simulate the Tollense River catchment were considered, however the resulted model selection criteria can be generalized to other lowland catchments in Australia, North-western Europe and North America with similar complexity.

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“…Various researchers have hence used different techniques to estimate pollutant loads from diffuse sources. One such approach, which has been widely used, is through the determination of pollutant flux based on the base flow separation [12]. However, such an approach could not be feasible for data-scarce areas like Ethiopia, which has little hydrometeorological and water quality data.…”

Section: Introductionmentioning

confidence: 99%

Integral Application of Chemical Mass Balance and Watershed Model to Estimate Point and Nonpoint Source Pollutant Loads in Data-Scarce Little Akaki River, Ethiopia

Angello

Behailu²,

Tränckner

2020

Sustainability

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The quality of Little Akaki River in Addis Ababa (Ethiopia) is deteriorating significantly due to uncontrolled waste released from point and diffuse sources. In this study, pollution load from these sources was quantified by integrating chemical mass balance analysis (CMB) and the watershed model of pollution load (PLOAD) for chemical oxygen demand, biochemical oxygen demand, total dissolved solid, total nitrogen, nitrate, and phosphate. Water samples monitored bimonthly at 15 main channel monitoring stations and 11-point sources were used for estimation of pollutant load using FLUX32 software in which the flow from the soil and water assessment tool (SWAT) model calibration, measured instantaneous flow, and constituent concentration were used as input. The SWAT simulated the flow quite well with a coefficient of determination (R2) of 0.78 and 0.82 and Nash-Sutcliff (NSE) of 0.76 and 0.80 during calibration and validation, respectively. The uncharacterized nonpoint source load calculated by integrating CMB and PLOAD showed that the contribution of nonpoint source prevails at the middle and downstream segments of the river. Maximum chemical oxygen demand (COD) load from uncharacterized nonpoint sources was calculated at the monitoring station located below the confluence of two rivers (near German Square). On the other hand, high organic pollution load, biochemical oxygen demand (BOD) load, was calculated at a station upstream of Aba Samuel Lake, whereas annual maximum total dissolved solid (TDS), total nitrogen (TN), and phosphate load (PO4-P) from the nonpoint source in Little Akaki River (LAR) were found at a river section near Kality Bridge and maximum NOX load was calculated at station near German Square. The integration of the CMB and PLOAD model in this study revealed that the use of area-specific pollutant export coefficients would give relatively accurate results than the use of mean and median ECf values of each land use.

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Groundwater Contribution to Surface Water Contamination in a North German Low Land Catchment with Intensive Agricultural Land Use

Cited by 4 publications

References 19 publications

Improved Representation of Flow and Water Quality in a North-Eastern German Lowland Catchment by Combining Low-Frequency Monitored Data with Hydrological Modelling

Improved Representation of Flow and Water Quality in a North-Eastern German Lowland Catchment by Combining Low-Frequency Monitored Data with Hydrological Modelling

Suitability of common models to estimate hydrology and diffuse water pollution in North-eastern German lowland catchments with intensive agricultural land use

Integral Application of Chemical Mass Balance and Watershed Model to Estimate Point and Nonpoint Source Pollutant Loads in Data-Scarce Little Akaki River, Ethiopia

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