Using High‐Resolution Data to Assess Land Use Impact on Nitrate Dynamics in East African Tropical Montane Catchments

Abstract

Land use change alters nitrate (NO3‐N) dynamics in stream water by changing nitrogen cycling, nutrient inputs, uptake and hydrological flow paths. There is little empirical evidence of these processes for East Africa. We collected a unique 2 year high‐resolution data set to assess the effects of land use (i.e., natural forest, smallholder agriculture and commercial tea plantations) on NO3‐N dynamics in three subcatchments within a headwater catchment in the Mau Forest Complex, Kenya's largest tropical montane forest. The natural forest subcatchment had the lowest NO3‐N concentrations (0.44 ± 0.043 mg N L−1) with no seasonal variation. NO3‐N concentrations in the smallholder agriculture (1.09 ± 0.11 mg N L−1) and tea plantation (2.13 ± 0.19 mg N L−1) subcatchments closely followed discharge patterns, indicating mobilization of NO3‐N during the rainy seasons. Hysteresis patterns of rainfall events indicate a shift from subsurface flow in the natural forest to surface runoff in agricultural subcatchments. Distinct peaks in NO3‐N concentrations were observed during rainfall events after a longer dry period in the forest and tea subcatchments. The high‐resolution data set enabled us to identify differences in NO3‐N transport of catchments under different land use, such as enhanced NO3‐N inputs to the stream during the rainy season and higher annual export in agricultural subcatchments (4.9 ± 0.3 to 12.0 ± 0.8 kg N ha−1 yr−1) than in natural forest (2.6 ± 0.2 kg N ha−1 yr−1). This emphasizes the usefulness of our monitoring approach to improve the understanding of land use effects on riverine N exports in tropical landscapes, but also the need to apply such methods in other regions.

Tropical Montane Forest Conversion Is a Critical Driver for Sediment Supply in East African Catchments

“…Water level (“stage”) was used to determine stream discharge based on a site‐specific second‐order polynomial stage‐discharge relationship (Jacobs, Weeser, et al, 2018). The calibration was checked over a wide range of stream flows using salt‐dilution gauging (Shaw et al, 2011), an Accoustic Doppler Velocimeter (ADV; FlowTracker, SonTek, San Diego CA, USA) or an Acoustic Doppler Current Profiler (ADCP; RiverSurveyor S5, SonTek, San Diego, USA) depending on river size and discharge (Jacobs, Weeser, et al, 2018). Specific discharge (mm day −1 ) was determined by integrating instantaneous discharge taken at 10 min intervals over a day and relating it to the catchment area.…”

“…Using Thiessen polygons, we estimated the weighted contribution of rainfall of every tipping bucket in each catchment. A more detailed description of the study sites and instrumentation can be found in Jacobs, Weeser, et al (2018). Turbidity was measured in situ as a surrogate for suspended sediment concentrations using a ultraviolet/visible (UV/Vis) spectroscopy sensor (spectro::lyser, s::can Messtechnik GmbH, Vienna, Austria).…”

Assessment of hydrological pathways in East African montane catchments under different land use

“…The final set of solutes to be included in the EMMA was selected based on conservative behaviour of the solutes, which was assessed with bivariate scatter plots of all possible solute combinations, including stable isotopes of water. A solute was considered conservative when it showed at least one significant (p<0.01) linear relationship with another solute with R 2 > 0.5 (Hooper, 2003;James and Roulet, 2006). Principle component analysis was applied to the selected solutes to identify a mixing space that explained most of the variation in stream water solute concentrations.…”

Rainfall‐Runoff Modeling Using Crowdsourced Water Level Data

“…To develop the rating curve (equation ), 86 manual discharge measurements using the salt dilution method ( n = 82) and an Acoustic Doppler Current Profiler (RiverSurveyor S5, SonTek, San Diego CA, USA) ( n = 4) over a wide range of water levels ( h ) were conducted. Extrapolation below the water level of 0.236 m was done using a quadratic function through the lowest measured discharge and zero discharge (Jacobs, Weeser, et al, ). For water levels above the highest measured water level used to develop the rating curve (0.66 m), we extrapolated the discharge using the same rating curve (3.3% of the time).…”

“…For water levels above the highest measured water level used to develop the rating curve (0.66 m), we extrapolated the discharge using the same rating curve (3.3% of the time). To assess the discharge uncertainty we followed the procedure described in Jacobs, Weeser, et al (), where the uncertainty was estimated based on the standard deviation (SD) of repeated measurements (SD water level: 1 mm, SD ADCP: 6.2%, SD Salt Dilution: 6.9%). We assumed that the true values were within 3*SD and generated 10,000 random samples for each water level/discharge combination.…”