RESUMO
Groundwater level directly affects soil water content, which in turn impacts the growth, distribution, and succession of wetland vegetation. Based on the groundwater monitoring data during 2014 to 2018, we analyzed the inter- and intra-annual variations in groundwater level of the wetland in Poyang Lake. We set up a groundwater numerical model to investigate the spatial patterns of groundwater flow field and distribution of groundwater depth. The relationship between groundwater depth and distribution of typical vegetation was explored with Gaussian regression analysis. Results showed that groundwater level of the wetland showed inter-annual variation, with the trend highly consistent to the changes in lake water level. There was a lag of one month between the inter-annual change in groundwater depth and the seasonal distribution of precipitation. The annual groundwater level showed a decreasing trend from upstream to downstream. The range of groundwater depth in upstream area of the wetland was narrower (0.1-1.1 m) than that of the downstream area (0.1-5.6 m). The groundwater flow field was directed from the hilly upstream area to the relatively flat downstream lake area, with the flow direction consistent with the change in elevation. The ground-water depth decreased with decreasing distance to the lake water body, with an average value of 2.07 m in the study are. The optimal groundwater depths for the growth of Carex, Phragmites aus-tralis, and Artemisia capillaris communities were 1.1, 3.7 and 5.7 m, respectively. The typical vegetation distribution showed different responses to groundwater depth. The ecological width of A. capillaris communities was larger than the Carex and P. australis communities. The three types of community were overlapped in ecological niche when the groundwater depth ranged 1.1-5.7 m. The vegetation distribution index of Carex decreased rapidly, that of P. australis increased firstly and then decreased, while that of A. capillaris increased continually to the maximum and began to decrease when the groundwater depth reached 5.7 m.
Assuntos
Água Subterrânea , Lagos , China , Ecossistema , Áreas AlagadasRESUMO
Eutrophication of shallow lakes in the middle and lower reaches of the Yangtze River has become an increasingly serious problem. In this study, we investigated the temporal and spatial variations in nutrient loads (nitrogen, N and phosphorus, P) in the Changdang Lake Catchment located to the northwest of Lake Taihu through field sampling and laboratory analysis in 2016-2017. The results show the severity of the N and P pollution in the Changdang Lake catchment. The mean river water concentrations of TN, NO3--N, NH4+-N, TP, Chla, and permanganate index are (3.70±0.76) mg ·L-1, (1.81±0.42) mg ·L-1, (1.03±0.61) mg ·L-1, (0.38±0.31) mg ·L-1, (25.74±37.00) µg ·L-1, and (6.35±0.81) mg ·L-1, respectively. N pollution in the river is more severe in winter and spring than in summer and autumn whereas P pollution in the river is worse in autumn and winter than in spring and summer. Spatially, the magnitude of river N and P pollution follows the order of northern > northwestern > southern > eastern part of the study area. The rivers are in a state of moderate to severe eutrophication. The mean lake water concentrations of TN, NO3--N, NH4+-N, TP, Chla, and permanganate index are (2.25±0.94) mg ·L-1, (0.98±0.47) mg ·L-1, (0.19±0.14) mg ·L-1, (0.11±0.03) mg ·L-1, (18.71±8.76) µg ·L-1, and (4.59±1.09) mg ·L-1, respectively. The water quality in Changdang Lake is categorized as worse than class â ¢ for TN and TP concentrations, which show decreasing trends from the west to the east to the south of the lake. The lake is in a status of slight to moderate eutrophication. The lake water quality is affected by the combination of sewage discharge and non-point source pollutant losses. The inflow rivers including the Danjinlicao River, Tongji River, and Xuebu River are the dominant pollution sources for Changdang Lake. The Danjinlicao River transports 10-12 times the total N and P loads transported by Tongji and Xuebu rivers. Changes in land use and atmospheric deposition are the driving factors of the deterioration of water quality and eutrophication in the catchment.
Assuntos
Rios , Poluentes Químicos da Água , China , Monitoramento Ambiental , Eutrofização , Lagos , Nitrogênio/análise , Nutrientes , Fósforo/análise , Poluentes Químicos da Água/análiseRESUMO
Increasing indirect nitrous oxide (N2O) emission from river networks as a result of enhanced human activities on landscapes has become a global issue, as N2O has been widely recognized as an important ozone-depleting greenhouse gas. However, indirect N2O emissions from different rivers, particularly for those that drain completely different landscapes, are poorly understood. Here, we investigated the spatial-temporal variability of N2O emissions among the different rivers in the Chaohu Lake Basin of Eastern China. Our results showed that river reaches in urban watersheds are the hotspots of N2O production, with a mean N2O concentration of â¼410 nmol L-1, which is 9-18 times greater than those mainly draining forested (23 nmol L-1), agricultural (42 nmol L-1) and mixed (45 nmol L-1) landscapes. Riverine dissolved N2O was generally supersaturated with respect to the atmosphere. Such N2O saturation can best be explained by nitrogen availability, except for those in the forested watersheds, where dissolved oxygen is thought to be the primary predictor. The estimated N2O fluxes in urban rivers reached â¼471 µmol m-2 d-1, a value of â¼22, 13, and 11 times that in forested, agricultural and mixed watersheds, respectively. Averaged riverine N2O emission factors (EF5r) of the forested, agricultural, urban and mixed watersheds were 0.066%, 0.12%, 0.95% and 0.16%, respectively, showing different deviations from the default EF5r that released by IPCC in 2019. This points to a need for more field measurements with wider spatial coverage and finer frequency to further refine the EF5r and to better reveal the mechanisms behind indirect N2O emissions as influenced by watershed landscapes.
Assuntos
Óxido Nitroso/análise , Rios , Agricultura , China , Monitoramento AmbientalRESUMO
Many water quality models have been successfully used worldwide to predict nutrient losses from anthropogenically impacted catchments, but hydrological and nutrient simulations with limited data are difficult considering the transfer of model parameters and complication of model calibration and validation. This study aims: (i) to assess the performance capabilities of a new and relatively more advantageous model, namely, Hydrological Predictions for the Environment (HYPE), that simulates stream flow and nutrient load in agricultural areas by using a multi-site and multi-objective parameter calibration method and (ii) to investigate the temporal and spatial variations of total nitrogen (TN) and total phosphorous (TP) concentrations and loads with crop rotation by using the model for the first time. A parameter estimation tool (PEST) was used to calibrate parameters. Results show that the parameters related to the effective soil porosity were highly sensitive to hydrological modeling. N balance was largely controlled by soil denitrification processes. P balance was influenced by the sedimentation rate and production/decay of P in rivers and lakes. The model reproduced the temporal and spatial variations of discharge and TN/TP relatively well in both calibration (2006-2008) and validation (2009-2010) periods. Among the obtained data, the lowest Nash-Suttclife efficiency of discharge, daily TN load, and daily TP load were 0.74, 0.51, and 0.54, respectively. The seasonal variations of daily TN concentrations in the entire simulation period were insufficient, indicated that crop rotation changed the timing and amount of N output. Monthly TN and TP simulation yields revealed that nutrient outputs were abundant in summer in terms of the corresponding discharge. The area-weighted TN and TP load annual yields in five years showed that nutrient loads were extremely high along Hong and Ru rivers, especially in agricultural lands.