Material and methods
Study area
The study area (Xitiaoxi Watershed) was located within Lake Taihu Basin
in eastern China (Fig. 1). The watershed included 24 mountain
sub-watersheds (30°23’N~30°45’N and
119°14’E~119°48’E), and 143 lowland artificial
sub-watersheds (30°45’N~31°5’N and
119°48’E~120°9’E). The elevations range from 0 m
(lowland) to 1576 m (mountain) above sea level.
The mountain watersheds are located at southwest of the study area, with
areas of 1,367 km2. River Xitiaoxi contributed 27.7%
of the water resources in Lake Taihu (Chen
et al., 2019). Annually average precipitation is 1465.8 mm
(Chen et al., 2019). Forestlands account
for 74% of the mountain watersheds, following 15% paddy lands, 4%
residential areas and small areas of surface water and grasslands.
The lowland watersheds (polders) are located at the northeast with a
total area of 743 km2. To develop agriculture and
protect villages from floods, these polders are enclosed by dikes.
Therefore, water exchange between rivers and polders are manually
controlled by pumps. The main cultivate land (69%) is paddy field with
rice–wheat rotation, other land use types of polder systems contain dry
lands (14%), ponds (6%) and residential areas (11%). During rice
seasons (from May to Nov.), supplementary water source from surrounding
rivers need to pour into paddy lands for irrigation. Meantime,
artificial drainage is closed, and excess runoffs are delivered into
ponds for keeping the soil moisture saturation. In case that the water
level of ponds is too high to harm the crop growth during heavy rainfall
events, pond water will be exported into surrounding rivers by pumps.
During wheat season, runoff water would not kept in ponds due to its
useless for wheat.
Fig. 1 Location of the study area
(Xitiaoxi Watershed) with the distribution of hydrological and weathers
stations.
Data
The required dataset for these two models included meteorological and
hydrological data, land use, and Digital Elevation Model (DEM). 1)
Meteorological data were collected based on the national weather station
(Huzhou: No.58450). The precipitation data were obtained from 13
automatic rain gauges (Fig. 1). The pan evaporation was substituted by
the reference evapotranspiration (ET0) using Penman
Equation. 2) Among hydrological data, daily water level was measured
using a water level logger. The discharge for mountain watersheds was
verified using the daily runoff data from 2009 to 2012 at the national
station of Hengtangcun. 3) Land use and digital elevation model (DEM)
with a spatial resolution of 30 m were obtained from the satellite image
interpretation of 2010 and International Scientific and Technical Data
Mirror Site, Computer Network Information Center, Chinese Academy of
Sciences (http://www.gscloud.cn)
respectively. Above-mentioned data were rescaled to an identical
resolution of 100 m for hydrological modelling. We used DEM to delineate
the boundaries of the mountain watersheds and to classify the slope.
Table 1 Data collected for the Xin’anjiang model and Nitrogen Dynamic
Polder (NDP) model.
Model description
Two hydrological models (Xin’anjiang and NDP) were used to simulate the
hydrological processes in the mountain watersheds and lowland artificial
watersheds, respectively, and to compare their different responses to
climate and land use change. Xin’anjiang model, a widely used
hydrological model for simulating streamflow in the humid and semi-humid
regions in China, was used to simulate discharge for mountain watersheds
at a daily time scale. NDP model was specially developed by
Huang et al. (2018a) to describe the
unique processes of hydrological and nitrogen dynamics in lowland
polders. In this study, it was used to distinguish the water balance
components in lowland watersheds.
Raster-based Xin’anjiang
model
The Raster-based Xin’anjiang model was developed based on the original
Xin’anjiang model originally developed by
Zhao (1984), incorporating the merits of
the original conceptual rainfall-runoff model
(Huang et al., 2018b). Its core concept
was runoff formation on the repletion of storage capacity, which implied
that the excess rainfall became the runoff until the soil water content
of the aeration zone reached its field capacity
(Yao et al., 2014). For each cell, the
outflow was simulated based on four modules: evapotranspiration module,
runoff generation module, runoff separation module and runoff routing
module. Total runoff was separated into three components including
surface, interflow, and groundwater runoff.
To develop the raster-based Xin’anjiang model for the mountain
watersheds, the following inputs were required: 1) initial data :
it included initial discharge of surface water, interflow and
groundwater, tension water storage and free water storage. A
spatial
resolution of 100×100 m was used for the model. 2) Forcing data :
weather conditions included the variables of sunshine hours, wind
direction, wind speed, average air temperature, precipitation, maximum
and minimum air temperature in a day. 3) Boundary data : the daily
runoff and the locations of streamflows should be input. 4) Model
parameters : there were 14 parameters in this model. Value ranges of
these parameters as well as their calibrated values were given in
Supporting Information.
NDP model
The model was developed based on water balance equations in four
land-use types (residential area, surface water area, paddy and dry
land). It included the hydrological processes of precipitation,
irrigation, evaporation, evapotranspiration, surface runoff,
infiltration, water exchange between groundwater and surface water.
Notably, water management modules describing the artificial drainage of
polder systems were also included. Flood drainage, culvert drainage and
seepage acted as outflow pathways to surrounding rivers.
The initial conditions of NDP included the areas of four land use types
in each polder. In case that hydrological component was selected, the
initial water level and land use types were required. Input data
included time series meteorological data and parameters. NDP included 28
parameters in the water balance and water management modules, all the
parameter values were obtained from previous studies in a typical polder
located not far (about 38 km) from the study area (See Supporting
Information).
Forecasting hydrological response in the context of climate
and land use
change
The Scenario Model Intercomparison Project (ScenarioMIP) is the primary
activity within Phase 6 of the Coupled Model Intercomparison Project
(CMIP6) that will provide multi-model climate projections based on
alternative scenarios of future emissions and land use changes produced
with integrated assessment models. BCC-CSM2-MR configured for CMIP6 are
used for generating precipitation and temperature for three developed
alternative future societal development pathways (the SSPs) and
emissions and land use scenarios based on RCPs
(O’Neill et al., 2016). Historical
simulations from 2000 to 2014 were as the reference period, and four
distinct periods including 2029~2032,
2049~2052, 2069~2072 and
2089~2092 were chosen for future scenario runs
representing the 2030s, 2050s, 2070s and 2090s, respectively. The
predicted climate scenarios indicated a 3.45% increase in annual
precipitation for the 2030s,
a
7.54% increase for the 2050s,
a
16.18% increase for the 2070s, and a 14.43% increase for the 2090s.
Temperature outputs indicated a 0.98 °C increase for the 2030s, a 1.63
°C increase
for
the 2050s, a 2.39 °C increase for the 2070s and a 3.03 °C increase for
the 2090s. Compared with BCC-CSM1.1m from CMIP5, BCC-CSM2-MR shows
significant improvements in many aspects including the tropospheric air
temperature and precipitation at global and regional scales in East Asia
(Wu et al., 2019), which fit for our
research.
Developed land use scenarios for the 2050s are all under the ‘current
rate’ according to Chen et al. (2009). In
mountain watersheds, the most frequent land use changes will occur in
expanding 272.4% residential area at the expense of cultivate land and
forest land by 86.5 km2 and 50.8 km2
respectively, following by the conversion between cultivate land and
forest land. In lowland artificial watershed, converting 36.1% (221.7
km2) cultivate land to residential land is
overwhelming than other land-use conversion patterns (See Supporting
Information).