Evaluation of nutrient loads from a mountain forest including storm runoff loads

Abstract

Water quality and flow rates at a weir installed on the end of Aburahi-S Experimental Watershed (3.34 ha) were measured once a week from 2001 to 2003 and in appropriate intervals from 30 min to 6 h during five storm runoff events caused by each rainfall from 8 mm to 417 mm. The average annual loads of total nitrogen (TN) and total phosphorus (TP) were calculated to be 19.0 and 0.339 kg ha(-1) y(-1) from the periodical data by using the integration interval-loads method (ILM), which did not properly account for storm runoff loads. Three types of L(Q) equations (L = aQ(b)) were derived from correlations between loading rates L and flow rates Q obtained from the periodic observation and from storm runoff observation. L(Q) equation method (LQM), which was derived from the storm runoff observation and allowed for the hysteresis of discharge of materials, gave 9.68 and 0.159 kg ha(-1) y(-1), respectively, by substitution of the sequential hourly data of flow rates. L(R) equation (L = c(R - r)d) was derived from the correlations between the loads and the effective rainfall depth (R - r) measured during the storm runoff events, and L(R) equation method (LRM) calculated 9.83 +/- 1.68 and 0.175 +/- 0.0761 kg ha(-1) y(-1), respectively, by using the rainfall data for the past 16 years. The atmospheric input-fluxes of TN and TP were 16.5 and 0.791 kg ha(-1) y(-1).

Evaluation of Loading Rate of Nitrogen from Rice-Paddies by Small Watershed Method

“…method was developed in order to evaluate storm-runoff loads by using precipitations in cases of limited data of concentrations and flow-rates Kunimatsu et al, 2006). L(R)-equations were derived from the correlation between the storm-runoff load L Si and corresponding rainfall depth R i measured during the storm runoff event i.…”

Evaluation of Nutrient Loads from a Citrus Orchard in Japan

“…This equation is called the L-Q equation model (e.g., Komai et al, 2003;Kunimatsu et al, 2006) and shows the relationships between nutrient load (L) and water discharge (Q). We decided the optimum coefficients (a and n) of two sets (Cases 3 and 4) by applying the least-squares method to antilogarithmic and logarithmic regression curves.…”

“…The results of our evaluation by weekly field research and the automatic water level records showed that the Case 2 evaluation gave a higher discharge than in Case 1. Kunimatsu et al (2006) calculated cv (coefficient of variation) values of the annual loads in Mano River (watershed area of 16.4 km 2 ), under various conditions of observation frequency (from every day to every 30 days) by the same method as Case 2 of our study. Kunimatsu et al (2006) also reported the cv values of the annual loads calculated from the data of weekly (7-days interval) observation were 0.54 for total nitrogen and 0.74 for total phosphorus, and that of discharge was 0.23.…”

“…Kunimatsu et al (2006) calculated cv (coefficient of variation) values of the annual loads in Mano River (watershed area of 16.4 km 2 ), under various conditions of observation frequency (from every day to every 30 days) by the same method as Case 2 of our study. Kunimatsu et al (2006) also reported the cv values of the annual loads calculated from the data of weekly (7-days interval) observation were 0.54 for total nitrogen and 0.74 for total phosphorus, and that of discharge was 0.23. They designated that the annual loads calculated with Case 2 vary widely depending on only the observation frequency.…”

The impact of Eurasian dust storms and anthropogenic emissions on atmospheric nutrient deposition rates in forested Japanese catchments and adjacent regional seas

“…The product of concentration and catchment outflow is an appropriate method for calculating fluxes of dissolved matter, as shown by the Cl-balance (Table 1). However, in the context of a Japanese catchment adjacent to Kutsuki, this approach may overestimate TN and TP fluxes due to the strong variability of particulate matter during rain runoff events (Kunimatsu et al, 2006a). In particular, suspended particulate matter (SM), PN and PP are highly sensitive to particulate matter variability.…”