Modelling the effect of different agricultural practices on stream nitrogen load in central Germany

Jomaa, Seifeddine; Jiang, Sanyuan; Thraen, Daniela; Rode, Michael

doi:10.1186/s13705-016-0077-9

Cited by 16 publications

(12 citation statements)

References 35 publications

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“…Another reason that may explain this is the high turbidity that characterises the water in the catchment, which can speed up the denitrification process. In this regard, these findings are similar to those reported by Jiang 395 et al (2014) and Jomaa et al (2016) in nested mesoscale catchments in central Germany.…”

Section: Seasonal Distribution Of Flows and Nutrientssupporting

confidence: 81%

“…The optimal value of PBIAS is 0, whereas the positive and negative PBIAS values indicate the model over-estimation and 255 under-estimation of the flow, respectively (Gupta et al, 1999;Jomaa et al, 2016). Moriasi et al (2007) highlighted that, for a monthly time-step, the absolute PBIAS value ≤ 25% and NSE > 0.5 for streamflow can be judged as satisfactory.…”

mentioning

confidence: 99%

“…The focus here, is to apply a readily available "off the shelf" model and to test its 70 applicability in this setting. Thus, following the bilateral collaboration in water resources management between the Department of Water and Sanitation of South Africa and the Ministry of Environment and Energy of Sweden, the successful application of the HYPE model in simulating the transport and dynamics of water, nitrogen and phosphorus in catchments of different scales (Strömqvist et al, 2012;Jiang et al, 2014;Jomaa et al, 2016); and the lack of in-stream processes in ACRU-NPS. This study sought to test the capabilities of HYPE model.…”

mentioning

confidence: 99%

“…Moreover, graphical visualisation techniques were also used on time-series data, to examine the capability of the model to represent the 260 peak and the base flow events (Moriasi et al, 2007;Crout et al, 2008). These evaluation criteria were used in other applications of the HYPE model (Strömqvist et al, 2012;Jomaa et al, 2016;Yin et al, 2016). Moreover, another evaluation criterion, comparing the average daily simulated and observed flows, was used.…”

mentioning

confidence: 99%

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Assessment of the Hype Model for Simulation of Water and Nutrients in the Upper uMngeni River Catchment in South Africa

Namugize

Jewitt

Clark

et al. 2017

Preprint

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Abstract. Most studies considering water quality pollution in the upper reaches of the uMngeni Catchment have relied on the 10 physical grab sampling of water and the subsequent laboratory analysis of chemical determinants. However, this provides limited spatial and temporal information. Thus, the objectives of this study are to assess the capability of the Hydrological Predictions for the Environment (HYPE) model in simulating streamflow, dissolved inorganic nitrogen (DIN) and total phosphorus (TP), in the fast developing uMngeni Catchment in KwaZulu-Natal province, South Africa. The model was set up and calibrated, following a stepwise approach and then validated. Results indicated that the simulation of discharge is most 15 sensitive to the parameters related to evapotranspiration and the water-holding capacity of the soil, while DIN and TP are affected by plant uptake and initial pools of nutrients. DIN is also affected by denitrification. Runoff was captured well during the calibration (1989)(1990)(1991)(1992)(1993)(1994)(1995) and validation periods , with a Nash-Sutcliffe efficiency (NSE) greater than 0.0 in eight of the nine stations and a Pearson's correlation coefficient (r) of > 0.5 at all the sub-catchments. High streamflow events were represented well, low streamflows were over-simulated. The accumulative streamflows were over-predicted in the 20 downstream sub-catchments, with an absolute percentage of bias (PBIAS) of > 25 %. The transport and dynamics of DIN and TP vary differently and they are driven by hydrological and biochemical processes. The concentration of TP follows the pattern of the streamflow, whereas DIN shows an inconsistent variation. The values of DIN decrease from upstream to downstream, while the TP values increase from the headwaters to the outlet of the catchment. Agricultural activities were found to be the largest source of DIN, while the TP is mainly ascribed to the point sources of pollution. 25

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Section: Seasonal Distribution Of Flows and Nutrientssupporting

confidence: 81%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Assessment of the Hype Model for Simulation of Water and Nutrients in the Upper uMngeni River Catchment in South Africa

Namugize

Jewitt

Clark

et al. 2017

Preprint

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“…Managing water quality is challenging and implies to deal with both point and non-point source and ineffective for larger areas. Hydrological and water quality models used in several studies [26][27][28] require vast amounts of data, are complex, costly and time consuming to calibrate, and therefore only applied on one single or few catchments. Statistical methods tend to be simpler, easier to apply, and more efficient than physically-based hydrologic/water quality models when observed data are limited in time and when datasets are covering many different catchments [29].…”

Section: Introductionmentioning

confidence: 99%

Linking Forest Cover to Water Quality: A Multivariate Analysis of Large Monitoring Datasets

Brogna

Michez

Jacobs

et al. 2017

Water

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Forested catchments are generally assumed to provide higher quality water. However, this hypothesis must be validated in various contexts as interactions between multiple land use and land cover (LULC) types, ecological variables and water quality variables render this relationship highly complex. This paper applies a straightforward multivariate approach on a typical large monitoring dataset of a highly managed and densely populated area (Wallonia, Belgium; 10-year dataset), quantifying forest cover effects on nine physico-chemical water quality variables. Results show that forest cover explains about one third of the variability of water quality and is positively correlated with higher quality water. When controlling for spatial autocorrelation, forest cover still explains 9% of water quality. Unlike needle-leaved forest cover, broad-leaved forest cover presents an independent effect from ecological variables and explains independently 4.8% of water quality variability while it shares 5.8% with cropland cover. This study demonstrates clear independent effects of forest cover on water quality, and presents a method to tease out independent LULC effects from typical large multivariate monitoring datasets. Further research on explanatory variables, spatial distribution effects and water quality datasets could lead to effective strategies to mitigate pollution and reach legal targets.

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Improving nitrate load estimates in an agricultural catchment using Event Response Reconstruction

Jomaa

Aboud

Dupas

et al. 2018

Environ Monit Assess

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Low-frequency grab sampling cannot capture fine dynamics of stream solute concentrations, which results in large uncertainties in load estimates. The recent development of high-frequency sensors has enabled monitoring solute concentrations at sub-hourly time scales. This study aimed to improve nitrate (NO) load estimates using high-resolution records (15-min time interval) from optical sensors to capture the typical concentration response to storm events. An empirical model was developed to reconstruct NO concentrations during storm events in a 100-km agricultural catchment in Germany. Two years (Jan 2002 to Dec 2002 and Oct 2005 to Sep 2006) of high-frequency measurements of NO concentrations, discharge and precipitation were used. An Event Response Reconstruction (ERR) model was developed using NO concentration descriptor variables and predictor variables calculated from discharge and precipitation records. Fourteen events were used for calibration, and 27 events from four periods of continuous records of high-frequency measurement were used for validation. During all selected storm events, NO concentration decreased during flow rise and increased during the recession phase of the hydrograph. Three storm descriptor variables were used to describe these dynamics: relative change in concentration between initial and minimum NO concentrations (rdN), time to maximum change in NO concentration (TdN) and time to 50% recovery of NO concentration (TN ). The ERR consisted of building linear models of discharge and precipitation to predict these three descriptors. The ERR approach greatly improved NO load estimates compared to linear interpolation of grab sampling data (error decreased from 10 to 1%) or flow-weighted estimation of load (error is 7%). This study demonstrated that ERR based on a few months of high-resolution data enables accurate load estimates from low-frequency NO data.

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Modelling the effect of different agricultural practices on stream nitrogen load in central Germany

Cited by 16 publications

References 35 publications

Assessment of the Hype Model for Simulation of Water and Nutrients in the Upper uMngeni River Catchment in South Africa

Assessment of the Hype Model for Simulation of Water and Nutrients in the Upper uMngeni River Catchment in South Africa

Linking Forest Cover to Water Quality: A Multivariate Analysis of Large Monitoring Datasets

Improving nitrate load estimates in an agricultural catchment using Event Response Reconstruction

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