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

doi:10.15302/J-FASE-2018243

Front. Agr. Sci. Eng.

0, Vol.

Issue () : 420-431 https://doi.org/10.15302/J-FASE-2018243

REVIEW

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

Muhammad WASEEM(

), Frauke KACHHOLZ, Jens TRÄNCKNER

Faculty of Agriculture and Environmental Sciences, University of Rostock, 18059 Rostock, Germany

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Abstract

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.

Keywords diffuse pollution ECO Lab HSPF lowland catchment MIKE 11 MIKE SHE modeling tools SWAT SWIM Tollense River water quality

Corresponding Author(s): Muhammad WASEEM

Online First Date: 02 November 2018 Issue Date: 19 November 2018

Cite this article:

Muhammad WASEEM,Frauke KACHHOLZ,Jens TRÄNCKNER. Suitability of common models to estimate hydrology and diffuse water pollution in North-eastern German lowland catchments with intensive agricultural land use[J]. Front. Agr. Sci. Eng. , 0, (): 420-431.

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https://academic.hep.com.cn/fase/EN/10.15302/J-FASE-2018243
https://academic.hep.com.cn/fase/EN/Y0/V/I/420

Fig.1 Tollense area of investigation (Tollense River catchment from Klempenow to Demmin). Source: Rivers, lakes, and land use data was kindly provided by LUNG-MV (Landesamt für Umwelt, Naturschutz und Geologie Mecklenburg Vorpommern).

Fig.2 Relationship between precipitation, measured surface and estimated base flow with NO₃-N concentrations in Gehmkow Augraben at surface water quality monitoring station (MS) Lindenburg^[²⁰^]

Fig.3 Desired hydrological and hydraulic processes in the study area of the Tollense River catchment

Tab.1 Ability to model the desired hydrological processes by selected modeling tools

Tab.2 Governing equations, spatial and temporal resolution of selected modeling tools

Tool	Category	Parameters
SWAT	Climate (6)	Rainfall, air temperature, solar radiation, wind speed, evapotranspiration and humidity/dew point
	Hydrology and hydrogeology (7)	Water table height, hydraulic conductivity, groundwater extraction, initial shallow aquifer storage, recharge water, drain spacing, and irrigation
	Soil data (7)	Layer thickness, bulk density, initial soil water content, field capacity^, wilting point^, hydraulic conductivity, and porosity
	Land use and vegetation (7)	Land use, vegetation type, vegetation height, leaf area index, root depth, fertilizing rate, and crop management
	Topography (6)	Area, elevation, land surface slope length, land surface slope steepness, hill slope length, and hill slope steepness
MIKE SHE	Climate (5)	Rainfall, air temperature, solar radiation, wind speed, and grass reference evaporation
	Hydrology and hydrogeology (9)	Water table height, hydraulic conductivity (x-, y- and z-directions), specific yield, specific storage, groundwater extraction, initial shallow aquifer storage, recharge water, drain spacing, and irrigation
	Soil data (6)	Layer thickness, bulk density, initial soil water content, field capacity, wilting point, and hydraulic conductivity
	Land use and vegetation (5)	Land use, vegetation type, leaf area index, root depth, and fertilizer application rates
	Topography (1)	Digital elevation model
HSPF	Climate (6)	Rainfall, air temperature, solar radiation, wind speed, evapotranspiration, and humidity/dew point
	Hydrology and hydrogeology (3)	Active groundwater storage, interflow storage, and lower zone storage
	Soil data (3)	Layer thickness, bulk density, and initial soil water content
	Land use and vegetation (2)	Land use, and vegetation type
	Topography (4)	Area, elevation, land surface slope length, land surface slope steepness
SWIM	Climate (6)	Rainfall, air temperature, solar radiation, wind speed, evapotranspiration, and humidity/dew point
	Hydrology and hydrogeology (6)	Water table height, hydraulic conductivity, specific yield, groundwater extraction, drain spacing, Irrigation
	Soil data (7)	Layer thickness, bulk density, initial soil water content, field capacity, wilting point, hydraulic conductivity, and porosity
	Land use and vegetation (5)	Land use, vegetation type, leaf area index, root depth, and fertilizer application rates
	Topography (7)	Area, elevation, land surface slope length, land surface slope steepness, hill slope length, hill slope steepness, and hill slope width

Tab.3 Input data required by the selected hydrological and water quality models [²²,²³,³⁷,³⁸,⁵³–⁵⁵,⁶³–⁶⁷]

Item	Relevant models
Item	SWAT	SWIM	ECO Lab^*	HSPF
Initial soil nitrogen
Organic N	•	•	•	•
$N O 3 −$	•	•	•	•
$N H 4 +$		•		•
Point sources
Organic N	•		•
$N O 3 −$	•		•
$N O 2 −$	•		•
$N H 4 +$	•		•
Fertilizer nitrogen (crop-specific)
Organic N	•		•
Active organic N		•	•
Inorganic N	•		•
$N H 4 +$	•		•
In-stream nitrogen
Organic N	•		•
$N O 3 −$	•		•	•
$N O 2 −$	•		•	•
$N H 4 +$	•		•
Atmospheric deposition
$N O 3 −$ in rain	•	•
$N H 4 +$ in rain	•

Tab.4 Input required for predicting nitrogen transformations and transport in surface and subsurface water [³⁹,⁵³–⁵⁵,⁶²–⁶⁷]

Fig.4 Summary of minimum input parameters required by the selected models

Item	Relevant models
Item	SWAT	SWIM	ECO Lab	HSPF
Soil nitrogen
Organic N			•	•
$N O 3 −$			•	•
$N H 4 +$			•	•
Transport through surface runoff
$N O 3 −$ in water	•	•	•	•
$N H 4 +$ in water			•	•
Transport through interflow
$N O 3 −$	•	•	•	•
$N H 4 +$			•	•
Inorganic N			•
Transport through subsurface drainage flow
Inorganic N			•
Transport through groundwater flow
$N O 3 −$	•		•	•
$N H 4 +$			•	•
Transformation
Fixation	•		•
Nitrification	•		•	•
Ammonia volatilization	•		•	•
Denitrification	•	SWIM	•	•
Adsorption and desorption
Total N	•			•

Tab.5 Prediction of nitrogen transformations and transport in surface and subsurface waters by the selected models [⁵⁹–⁶²]

Tab.6 Openness, availability of graphical user interface and online support for selected models [⁵³–⁶⁷]

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