WETLAND AND WATER RESOURCE MODELING AND ASSESSMENT: A Watershed Perspective - Chapter 11 pdf

As there are many types of lakes in the Yangtze River basin, and the causes of eutrophication are different for each type, Honghu Lake basin, which is located at the middle reach of the

11

Variations of Nitrogenous and Phosphorous

Nutrients in Honghu

Lake Basin, China

Feng Gui, Ge Yu, and Geying Lai

11.1 INTRODUCTION

In recent years, eutrophication has become an increasingly serious environmental problem in lake systems Excessive nutrient enrichment is the root cause of eutro-phication Although lakes naturally receive nutrient inputs from their catchments and the atmosphere, many human activities such as sewage inflows, runoff from agricultural fields, and industrial effluents have greatly accelerated the eutrophica-tion process To assess the relative roles of natural, climate-induced changes ver-sus human-related activities, such as the removal of vegetation, it is important to evaluate the natural trajectory of nutrient transportation over the catchments and its contribution to a lake’s eutrophication The eutrophication of the lakes in the most developed region in China, the mid-lower reaches of the Yangtze River, has brought great attention from the public, scholars, and government As there are many types

of lakes in the Yangtze River basin, and the causes of eutrophication are different for each type, Honghu Lake basin, which is located at the middle reach of the Yangtze River, was chosen as the study area Computational simulations with the SWAT (Soil and Water Assessment Tool) model were used to reflect the historical nutrient sedi-mentation and transportation processes With the application of the SWAT model,

the principle of nutrient transportation in the natural agricultural environment (the

environment under which the nutrient sedimentation and transportation processes are only controlled by natural factors, such as topography, climate changes, and natural vegetation cover, etc.) has been discussed in order to provide scientific basis for the mechanism research of lake water eutrophication

11.2 STUDY AREA

Honghu Lake, located at the middle reach of the Yangtze River (113°12’–113°26’E and 29°40’–29°58’N) (Figure 11.1), is typical of a shallow water lake As the larg-est lake in Jianghan Plain, its geological setting is a faulted depression between the

Yangtze River basin and the Dongjing River basin The Honghu Lake region covers both Honghu Lake County and Jianli County, an area of 344.4 km2.1,2 Due to the lake evolution and the impacts of human activities in recent centuries, Honghu Lake has shrunk significantly from its original coverage.3,4

Honghu Lake basin, located at the northernmost area of China’s subtropical zone, features a typical northern subtropical humid monsoon climate, with abundant diurnal heating and precipitation in the same season The average precipitation in the area is approximately 1100 to 1300 mm, 77% of which is in the summer season The average annual runoff in this basin is approximately 37.35 × 108 m3.1,5 The area receives water from Chang Lake, San Lake, and White Dew Lake, with a watershed area of 8265 km2

In the flood season, Honghu Lake receives water not only from the upstream area, but also from overflowing water from the downstream rivers Prior to the 1960s, building

of the floodgate of Xin Tankou, located downstream of Neijing River, the floodwater from the Yangtze River would overflow through the Neijing River to Honghu Lake (Figure 11.1) In the flood season, the basin area enlarges to 10,325 km2

11.3 INTRODUCTION OF THE SWAT MODEL

SWAT (Soil and Water Assessment Tool) is a river basin–scale hydrological model developed by the Agriculture Research Service of the U.S Department of Agricul-ture.6–8 Its GIS-based version (AVSWAT 2000) has strong functionality in spatial analysis and visualization The SWAT model can be used to evaluate the impact of land management practices on water, sediment, and agricultural-chemical yields in large, complex basins with a variety of soils and land cover on a time scale of 10 to

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Study area River

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Dongjing River Kint and Kou

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Yangtae River

Hang Lake

FIGURE 11.1 Location of study area

100 years There have been many successful applications of the SWAT model, with longtime series outputs to reconstruct the past environment or to predict future envi-ronmental changes.9–11

11.4 BOUNDARY CONDITIONS AND SIMULATION DESIGN

To simulate and evaluate the eutrophication of Honghu Lake basin under natural-agricultural environments, the boundary conditions were set as natural-natural-agricultural time scenarios, including the following major factors: (1) natural topography, slope, and channel, (2) climatic and hydrological factors (temperature, solar radiation, pre-cipitation, and runoff), and (3) biomass of natural vegetation

Based on these boundary values, the following data sets were compiled or cre-ated for the model simulation: a database of land topography and hydrological units were generated based on the 1:250,000 digital elevation models (DEM) published

by the National GIS Center of China.12 A stream network data set was created by digitizing 1:100,000 topography maps and using a “burn-in” method6 by which a stream network theme is superimposed onto the DEM to define the location of the stream network This feature is useful in situations where the DEM does not provide enough detail to allow the interface to accurately predict the location of the stream network.6 A 1:1,000,000 digital soil map with spatial and nonspatial information was obtained from the Institute of Soil Science, Chinese Academy of Science (ISSCAS),13

including information on sand, silt, organic material, soil pH value, total phospho-rus, available phosphophospho-rus, and bulk density Due to the difference in the soil texture system between the SWAT standard (U.S standard) and the standard of the second national soil survey in China, the dataset used in this study was transformed into the SWAT standard Soil bulk density, available water capacity of the soil layer (SOL_ AWC), and saturated hydraulic conductivity (SOL_K) were calculated using SPAW 6.1.14 The soil types were further classified into four different hydrological groups (A,

B, C, and D) according to the guidelines documented in the SWAT user manual.8

Land use/vegetation data, obtained from a 1:1,000,000 China vegetation map,15

were transformed to a grid file with a resolution of 10’x10’ to correspond to the plant nutrient data In this study, the vegetation data were used to provide land use infor-mation Within the study area, the major vegetation types are rice field and wetland Thus, the corresponding land use types were classified into rice land, water, and forested wetland, forested mixed

Meteorological data were collected from weather stations located within or near the basin, including daily precipitation, maximum and minimum temperature data from 1951 to 2000, as well as daily radiation, average wind speed, and humidity from 1980 to 2000 To generate meteorological data over the last 200 years, statisti-cal values (such as standard deviation, skew coefficient of the daily precipitation and temperature in a month, and the probability of a wet day after a dry day, etc.) reflect-ing the characteristics of the local climate were calculated by usreflect-ing the weather gen-erator tool in the SWAT model

All data mentioned above were transformed into the Arc/INFO grid format with

an Albers equal-area projection

11.4.2 SIMULATION PROCEDURE ANDDESIGN

For the simulation experiment, we designed two scenarios according to the differ-ence in watershed areas between the flood season (from June through August) and the non-flood season (from March through May as well as from September through February) (Figures 11.2 and 11.3)

We handle this source area change by adjusting the watershed outlet location as well as the number We selected one outlet location at the entrance of Honghu Lake for the winter, while for summer we chose an outlet downstream of Honghu Lake Our procedure for simulation began with the construction of a background and dynamic database of each subbasin, including geological sediment, topog-raphy, climate, hydrology, soil, and vegetation coverage We then subdivided the watershed into several subbasins Subbasins possess a geographic position in the watershed and are spatially related to each other We likewise subdivided the sub-basins into HRU (hydrologic response units) HRUs are portions of a subbasin that possess unique land use/management/soil attributes To acquire the function of the

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FIGURE 11.2 Watershed delineation and subbasin division for non-flood period

nutrient source and transport dynamic of the land surface process, we evaluated all

of the databases to the subbasin and HRU levels The next step involved running the model with a time step of 24 hours and a continuous simulation of 200 years

We then used sedimentary core records to compare and validate the simulation output

11.5 RESULTS AND DISCUSSION

The source area change has little effect on the nutrient concentration, but has a larger effect on the nutrient production and flow flux There is an increase of approximately 25% in the flow flux and nutrient production in the summer season experiment

11.5.1.1 Variability and Characteristics of Input Flow Flux

The results showed that the average yearly runoff flux was 46.1 × 108 m3 The runoff distribution within a year has its peak value during the period from April through September, in which the summer season (from June to August) contributes approxi-mately one-half of the entire year’s runoff (Figure 11.4a)

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FIGURE 11.3 Watershed delineation and subbasin division for flood period

11.5.1.2 Nutrient Changes in a Year

The annual means of TN (total nitrogen) and TP (total phosphorus) were plotted

in Figure 11.4 A negative correlation was observed between the flow flux and the concentration of TN and TP, a lag between the peak value of flow and nutrient con-centration Due to the confluence of a channel in a large subbasin, only a portion of the surface runoff will reach the main channel on the day it is generated, creating

a lag between the time the surface runoff was generated and the time it reaches the main channel

In the summer season when the rains are heaviest, the nutrient concentration reached its lowest value because the nutrients were diluted by an abundant flow flux The highest annual nutrient concentration occurred in spring, perhaps due to cultiva-tion activities during that period of time (Figures 11.4b, c)

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FIGURE 11.4 Simulation of annual mean of input flow flux, TN, TP

11.5.1.3 Variations of Nutrient Concentration over Time

The nutrient concentration changes through time were analyzed seasonally Both

TN and TP concentrations exhibited high variability The maximum TP concentra-tion appeared in the spring season and the minimum appeared in the summer, with

a relatively stable concentration in the time series The maximum TN concentration appeared in the spring and the minimum appeared in summer, with a slow increas-ing trend in the time series (Figure 11.5)

11.5.1.4 Annual TP and TN Production

The annual average TN and TP production of the Honghu Lake basin was 420.25 tons per year and 19.613 tons per year, respectively Analysis of the nutrient produc-tion showed that the producproduc-tion of TN had a slow increasing trend with time, while

TP had no obvious trend The nutrients in the Honghu Lake basin are characteristic

of an accumulated natural trend (Figure 11.6)

It is difficult to directly validate the 100-year simulations of nutrient production and concentration due to the lack of long-term observation data However, the simula-tions can be indirectly validated by comparing the data with long-term sedimentary nutrient records and establishing the statistical relations between them In this study,

we focused on the study results from the 84-cm-long sedimentary cores HN (for Honghu Lake North; collected at the northern part of Honghu Lake in November

2002 with a water depth of 3.2 m), and the relative 137Cs-dating data, with the 150-cm-long core H2-2002 (collected at the central part of Honghu Lake in 2002) as the reference.16,17 The analyses indicated that the sedimentation rate of Honghu Lake in the last 540 years was about 0.155 cm/a At 25 ~ 8 cm, the age approximately cor-responds to the years 1840 to 1950.16,17 Above 8 cm, the age corresponds to the year

1950 Chen Ping et al (2004) determined the sedimentation rate of the core H2-2002

to be approximately 0.092 ~ 0.129 cm/a at layer D (above 22 cm in the core), sug-gesting an age of approximately 150 years, from 1845 to 1992.16 The dates of the two cores are almost the same in each layer

Because the simulation covers the time period between 1840 and 1950, a natural-agricultural time, the simulated outputs could be validated by comparing the TN and

TP concentrations of relative layers with these age data of the cores as follows:

1 The nutrient concentrations in the core HN varied (Figure 11.7) Between

1840 and 1850, the TN concentrations increased while core depth decreased and the concentration variability was 1.20 ~ 1.77 g/kg Between 1959 and

2002, the nutrient concentration increased rapidly with the decrease of the depth, and the variability was 1.77 ~ 8.78 g/kg The TP concentration varied through the core profile, with a peak value of 65 ~ 70 cm at the topmost 4

cm The TP concentration also showed an increasing trend with the decrease

of depth, and reached 0.946 g/kg at the depth of 0.25 cm (Table 11.1)

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FIGURE 11.5 Change of nutrient concentration with time

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1000 TN annual production

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TP annual production

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FIGURE 11.6 Simulations of annual total phosphorus and total nitrogen production

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1840

FIGURE 11.7 Total nitogen and total phosphorus values in core HN (From Yao Shuchun,

Bin Xue, and Weilan Xia Human impact recorded on the sediment of Honghu Lake Journal

of Hohai University (Natural Sciences), 2004, 32 (Supplement): 154–159.

TP (g/kg)

TN (g/kg)

1950

2 The recent observations of water nutrients were obtained from the litera-ture3,4 and the measured data (the measured TN and TP concentrations of the Honghu Lake water) Table 11.1 shows the water nutrient information

We compared it with sedimentary records since the 1950s in order to exam-ine the relations between the water nutrients and the sedimentary nutrients

A regression was obtained as follows:

where Y is the total nitrogen of the three types in water (mg/l) and X is the TN

con-centration in the sedimentary core (g/kg)

The correlation coefficient R2 = 0.9557 was significant for the sample size With this statistical relation, the sedimentary nutrient was used to calculate the water nutrient

in a time series, which was then applied to validate the results of simulated nutrients

in the lake water

As shown inTable 11.2, during the period of 1840 to 1950, the variability of nitrogen in water was 0.13 ~ 0.23 mg/l, with an average value of 0.19 mg/l The simulated nitrogen concentration for the same period was 0.071 ~ 0.11 mg/l, with an average value of 0.09 mg/l

According to Table 11.2, the difference between the simulations and referenced calculations is approximately 50% This difference may have occurred because there are two sources of lake nutrients, the internal (lake) source and the external source, while the simulation only took into account the nitrogen from the external sources As such, the simulation output represents the level of the nutrients transported from the entire basin into the lake water and does not account for the nutrients produced from the lake water itself As many studies 18,19 have suggested that the internally sourced nutrients play an important role in lake eutrophication, these nutrients should be con-sidered in order to fully understand nutrient level changes in lake water While the contribution of internally sourced nutrient release in Honghu Lake cannot presently

TABLE 11.1 The water nitrogen concentrations statistically calculated based on the sedimentary nitrogen values.

Core TN (g/kg)

Calculated nitrogen

in water (mg/l)