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.