Phosphorus Fractions and Its Summer's Release Flux from Sediment in the China's Three Gorges Reservoir

Abstract

The amounts and forms of phosphorus (P) in sediments, orthophosphate (Pi) concentrations as well as Pi release fluxes across the sediment-water interface were investigated from five sampling sites in the Three-Gorges Reservoir (TGR). The Total P (TP) contents of surface sediments (0-2 cm) ranged from 415.5 to 1047.9 mg/kg, and the predominant fraction was HCl-P (Ca-bound P). The main rank order of P-fractions was HCl-P > Res-P (Residual-P) > NaOH-P (Al-adsorbed P) > BD-P (Redox sensitive P) > NH4Cl-P (Labile P), and ranged from 164.5 to 687.2 mg/kg, 96.0 to 140.8 mg/kg, 75.8 to 111.5 mg/kg, 34.0 to 104.8 mg/kg, and 5.9 to 15.9 mg/kg, respectively. The pH values ranged from 6.4 to 8.2 across sediment-water interface and sediment pH values were lower than those of the corresponding overlying water. The average Pi concentrations of pore water were higher than those of the open water and overlying water. Based on calculations using the Fick's first law, sediment in Xiangxi River (XX03) functioned as a P sink, with Pi flux of-0.003 mg/m 2 /d, while sediments from Modaoxi03 (MDX03), Daning02 (DN02), Caotanghe (CTH) and Changjiang Badong (CJBD), had Pi fluxes of 0.013 mg/m 2 /d, 0.009 mg/m 2 /d, 0.004 mg/m 2 /d and 0.007 mg/m 2 /d, respectively, and served as an internal P source in summer.

Influence of turbid flood water release on sediment deposition and phosphorus distribution in the bed sediment of the Three Gorges Reservoir, China

“…4 obtained from tributaries and water fluctuating zones (Wang et al, 2009;Luo et al, 2015;Zhuo et al, 2017a;Pan et al, 2018). Accumulation of PP in the parts of surface sediment in these environments increases the potential ecological risk in the reservoir due to sediment P release (Wang et al, 2009;Wang et al, 2015;Han et al, 2018). The sediment or soil in the water level fluctuating zone of the TGR exhibits a seasonal change between the "sink" and "source" for P, and soil organic phosphorus (Org-P)…”

“…Bio-P can be used to evaluate the potential risk of sediment P release for water column eutrophication (Wang et al, 2015). Regression analysis was conducted to determine the relationship between the Bio-P content in column sediment and key sediment physiochemical parameters such as OM and the MPS.…”

“…P in the sediment layer included P bound to the sediment particles and that dissolved in the pore water, and the partitioning is significantly affected by redox conditions (Patrick and Khalid, 1974). P associated with Fe/Al (hydr) oxides is the fundamental Bio-P fraction in the sediment, which is sensitive to the changes in the redox value at the sediment-water interface (Wang et al, 2015). The particulate FeOOH is reduced to the dissolved Fe 2+…”

Discrete principal‐monotonicity inference for hydro‐system analysis under irregular nonlinearities, data uncertainties, and multivariate dependencies. Part II: Application to streamflow simulation in the Xingshan Watershed, China

“…Because of the existence of temporal heterogeneity in hydrological systems, the calibration efficiency may not be very high. Data uncertainties as well as other types of uncertainties may lead to instability of the DPMI modelling efficiencies (Ahmadi et al ., ; Nourani et al ., ; Rahmani and Zarghami, ; Shen et al ., ; Wang et al ., ). Simplifications in the process of DPMI , such as quantification of the correspondence between predictors and streamflow as the principal monotonicity, may also decrease the capability of DPMI at reflecting real‐world hydrological systems.…”

Discrete principal‐monotonicity inference for hydro‐system analysis under irregular nonlinearities, data uncertainties, and multivariate dependencies. Part I: methodology development

“…In DPMI, the responsive relationship from influencing factors (named as predictors) to the hydrological variable of interest (named as the predictand) will be discretized as interrelated end nodes, i.e. groups of paired samples of predictors and the predictand, under irregular nonlinearities, data uncertainties, and multivariate dependencies (Ahmadi et al ., ; Nourani et al ., ; Rahmani and Zarghami, ; Shen et al ., ; Wang et al ., ). In detail, a discrete distribution transformation approach will be developed to enable transformation of non‐normally distributed predictand samples as a normal distribution and invertible restoration of the simulated predictand values as the original non‐normal distribution.…”