4. Results and
discussion
The results will be demonstrated on the sub-basin level. HRUs priority
level of management practices is presented below. Further, suggestions
were discussed to achieve the best conservation of the lake ecosystem.
4.1 Sub-basin level BMPs
The SWAT model quantified the spatial distribution of N&P emissions in
the Kolleru Lake catchment. This study examined that the diffuse
pollution from agricultural runoff is an essential contribution to the
total loads of nitrate-nitrogen (NO3-N) and total
phosphorus (TP). According to Fig. 3a, the amount of
NO3-N is extremely different in each sub-basin ranged
from 3.5 kg/ha/yr to 429 kg/ha/yr, respectively. Among the five river
sub-basins, the NO3-N was the highest in the Ramileru
basin, with up to 429 kg/ha/yr in some sub-basins, and the lowest in the
Gunderu basin, with less than 8.5 kg/ha/yr in each sub-basin. The
average range of each tributary river basin ranked from high to low
based on the load intensities is outlined in Table 3. However, the
annual average load of NO3-N in the Ramileru basin is
238.8 kg/ha/yr. The amount is larger than 40 kg/ha/yr in most sub-basins
of Budameru and Thammileru. For example, 55.6% of the
NO3-N export from the entire catchment came from
sub-basins No. 19, 14, 16, 13, 12, 5, 17, and 8, each contributing
>28.7 kg/ha/yr of the areal NO3-N export.
NO3-N in the lake mainly originates from the chemical
fertilizers used in the Kolleru Lake catchment, where the agricultural
land majorly accounts for paddy cultivation.
According to Fig. 3b, the spatial distribution of mean annual TP in the
Kolleru Lake catchment varied from one sub-basin to another, ranging
from 1.1 kg/ha/yr to 91.5 kg/ha/yr respectively. The highest TP load was
established in the Thammileru basin, with up to 45 kg/ha/yr in some
sub-basins, and the corresponding lowest values within the Gunderu
basin, with less than 5.5 kg/ha/yr in each sub-basin. The Thammileru
basin is accounted for the highest annual precipitation, which enabled
the large wet deposition of P. Similar to the NO3-N, the
highest contribution of TP origin from the sub-basins No. 19, 14, 13,
11, 15, 5, 6, and 8, accounted >16.5 kg/ha/yr annually. The
cause of the difference in sub-basin loads was observed in the Kolleru
lake catchment related to human activities. Additionally, the soil data
obtained from the National Bureau of Soil Survey identified that N and P
distribution in the soil types do have close spatial interaction with
diffuse pollution. The higher intensity load of these soils is
associated with higher export amounts of pollutants from sub-basins.
Therefore, this must be considered for conservation practices. Moreover,
the agricultural land was disturbed by the frequent cropping and
harvesting as well as by fertilizer application. The TP load from medium
to maximum variation of the sub-basins is similar to the
NO3-N, which is accounted onto the mainstream channel.
The high proportion of agricultural land use has a crucial factor in
NO3-N & TP exports. Many catchments worldwide show an
explicit positive correlation between N & P loss and cropland
percentages (Li et al., 2018; Chen et al., 2017; Harrison et al., 2009).
In the Kolleru Lake catchment medium to maximum variations of
NO3-N & TP loads in each sub-basin level was observed,
following the percentage of land uses (Fig. 4). Sub-basins with a higher
percentage of paddy fields result in higher N & P exports in the
Ramileru and the Thammileru basins. Li et al. (2018) also show a low
percentage of paddy fields, resulting in less amount of TN. However, the
intensity of frequent fertilizer applications significantly impacts the
sub-basins nutrient level exports and catchment characteristics as well.
4.2 Determination of HRU level
BMPs
The BMPs priority areas were identified following the methodology of
Izydorczyk et al., 2019, Piniewski et al., 2015) by SWAT on the HRU
level as paddy cultivated lands where the amounts of
NO3-N & TP emissions are the highest. Here, the
priority levels were divided into two types according to the area, which
is under net irrigated, gross irrigated, and the rain-fed regions. The
first BMPs priority level is the area cultivated more than once a year;
emission in selected HRUs ranged from 10.5 to 28.3 kg/ha for
NO3-N, while for TP, the emission level ranged from 3.2
to 9.8 kg/ha. The second BMPs priority level is where the cropping
intensity is higher than 50 % under gross cropped areas ranged from 1.2
to 10.5 kg/ha for NO3-N, while for TP ranged from 0.5 to
3.2 kg/ha (Fig. 5a and b).
According to Fig. 5a, the majority of selected HRUs of
NO3-N were clustered around the lake area. Subsequently,
they cause the eutrophication of the lake and led to increasing weed
distribution. On the priority of HRUs distribution, higher
NO3-N load contributing areas were concentrated in the
northern and middle-western villages of the catchment. Among them, the
outstanding villages were located in the Ramileru and the Thammileru
basins. In these two sub-basins, specific topographic features play an
essential role in the highest NO3-N emission. Besides,
the main inflow rivers contributing the water to the lake run through
these villages, are the Budameru River (5.5% of total
NO3-N in 2010), the Thammileru River (22.7% of total
NO3-N in 2010), and partially the Ramileru River (19.2%
of total NO3-N in 2010). Moreover, the diversified
irrigation network canals connected to the mainstream of the river can
easily extract the nutrient onto the river and nitrate loads into the
lake. In contrast, the flow contribution of NO3-N from
the eastern villages are low, because of the migration ability of
pollutants are limited there.
According to Fig. 5b, the TP emissions are spatially distributed and
partially overlapped with the regions of NO3-N. The
majority of TP emissions are primarily concentrated in the middle
reaches of the catchment. Approximately 534 village communities were
located in the catchment area. Most of the regions are under gross
irrigated. At certain stages, early-season drought changed the behavior
of the farmers to apply the water-soluble fertilizers (NPK-nitrogen,
phosphorus, and potassium), the ratio of 19-19-19, 20-20-20, and
21-21-21 to supplement nutrition. During the time, the soil absorbs the
N and P to enrich the plant growth, and unleash the soluble compounds
during the flooding period by surface runoff. In the catchment,
topographic properties play a key role, because of moderate slopes as
well as more than >46.7% of the catchment area is mostly
extended to the well-drained condition, hence, nonporous in nature,
contributed to high NO3-N & TP emissions as a result of
surface water runoff.
4.3 Temporal characteristics of diffuse
pollutants
The annual amount of NO3-N & TP, including streamflow,
were simulated. Fig. 6a illustrates the annual distribution of diffuse
pollution from 2008 to 2014 in the Kolleru Lake catchment. The
distribution of the NO3-N was very uneven between
different years. During wet years higher peak values van be observed
than in the dry years. The NO3-N was relatively
consistent with the runoff. Therefore, to assess the possible relation
between the NO3-N and the runoff, a simple Pearson’s
correlation analysis was performed. The results show a strong
correlation between the NO3-N and the streamflow
(r=0.89, p<0.01), which means that the NO3-N
was primarily governed by the runoff (Fig. 6b). Hence, the result was
justified with other studies (Qin et al., 2018; Navarro et al., 2014;
Helmreich et al., 2010). The correlation between the TP and the runoff
(Fig. 6c) is also high (r=0.84, p<0.01), but lower than the
NO3-N and the runoff. This can be attributed to the
agricultural water diversion system, and a mode of severe nutrient
transport. However, during the wet period (July 2010, August 2011), the
runoff is relatively high and subsequently resulted in a high nutrient
export, which can be transported by a stream network and accumulated
near to the downstream area of the Lake. NO3-N sources
are the chemical fertilizers used in agricultural fields, especially for
paddy cultivation followed by Cotton, Maize, and Chillies, in the
Kolleru Lake catchment. The upward trend of NO3-N load
in June 2010, resulting from the heavy precipitation recorded during
that month, according to the data derived from the Indian Meteorological
Department, might be responsible for the higher nitrate export load.
Industrial pollution, excessive fertilizer application, and chemical
usage of fishponds to enrich the fish growth contribute in significant
quantities to the nutrient loads. The primary reasons for high nutrient
flow in the catchment are both frequent land-use changes, intensive
paddy cultivation, and the two large rivers Krishna and Godavari.
4.4 Suggestions for pollution mitigation
measures
Suggestions for adequate pollution mitigation measures can be drawn from
the results of critical sub-basins and the HRU priority areas as well.
This study emphasized that improved agricultural management practices
are necessary for the whole catchment area. There are numerous methods
to improve the agricultural practices that can be adopted by farmers to
prevent nutrient losses from croplands (Izydorczyk et al., 2018).
However, management practices can be targeted on agricultural lands and
the development of proper land use planning and zoning practices in
sub-basins. Furthermore, the implementation of buffer strips and the
management of water margins to reduce surface runoff from fields are
essential measures to achieve environmental improvements (Izydorczyk et
al., 2018; McCracken et al., 2012, Zhang et al., 2010; Anbumozhi et al.,
2005). The buffer width, the slope gradient, and the vegetation type are
difficult to site conditions for designing an adequate buffer. However,
an increasing buffer width would increase sediment removal efficiency
(Zhang et al., 2010). Mainly, vegetated buffers are widely used for good
agricultural practices to reduce diffuse source pollution from runoff
(Balestrini et al., 2011). However, these effective mitigation buffer
measures of nutrient losses are still rarely implemented in India
(Anbumozhi et al., 2005; Bhojvaid et al., 1996).
In order to reduce the chemical pesticide consumption in Andhra Pradesh
state, between 1999 and 2005, the European Union had conducted the
“Non-Pesticide Management in Andhra Pradesh, India” in the cooperative
project of the German Council for Sustainable Development and Centre for
Sustainable Agriculture (CSA). The potential outcome of this program was
to enlighten the farmers to use natural pesticides, such as neem
(Azadirachta indica ) and chili-garlic extracts, rather than
intensive use of chemical fertilizers. Therefore, the positive results
caused increased biodiversity, no adverse environmental effects,
preventing soil erosion, and improving soil fertility. This study
further suggests the implementation of the ”Non-pesticide management”
practices in the Kolleru Lake catchment. However, this kind of
institutional practice for empowering rural people, imparting training
to farmers, and laying demonstrations are essential for sustainable
management growth. Besides, the catchment comprises of 534 villages, and
not even more than >20% adopted the conventional
irrigation methods. Moreover, this study identified the HRUs level
priority areas along with critical sub-basin measures, which should be
analyzed and implemented. For minimizing the environmental crisis, also
a forest area has suggested around the 3ft contour level of the lake.
Thus, it provides shelters for the 20 million immigrant, international
birds as well as to conserve the environmental lake ecosystem.
Additionally, the Government of India notified that only the traditional
method of fishing activities should be permitted around the lake,
following the law of G.O.Ms.No.120, dated 4.10.1999. For this purpose,
the Kolleru Fisherman Cooperative Society (KFCS) should adhere to the
standards laid down by the Ministry of Environment, Government of India,
to bring back the Kolleru Lake to its near pristine condition. Moreover,
adequate steps should be taken for stoppage and regulation of industrial
pollutants from nearby towns. Furthermore, the villages surrounded by
the lake must be classified as zones for BMPs.
4.5 Limitations
Because the Kolleru Lake catchment is an ungauged type, sufficient
calibration, and validation of the SWAT model are limited.
Unfortunately, there exists still a lack of observed data for nutrient
load, especially for the discharge depending on nutrient load. However,
the study was conducted based on original data, acquired from Indian
Organizations, promising the results obtained from the SWAT model. This
is the first study conducted for the whole Kolleru Lake catchment level,
regards certain assumptions that were made in terms of catchment
delineation boundaries and the crop fertilization period. Field
investigations on the interaction of pollutant loads with the runoff
should be taken into consideration for a better calculation of the
pollutant load.