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Analysis of changes in ecosystem services value in the YRBThe results showed that from 1990 to 2020, the total ecosystem services value in the YRB showed a dynamic trend of decrease-increase–decrease, with overall increasing trend, and a total increase of 31.85 × 1010 USD, with an average annual increase of 1.14 × 1010 USD (Table 2). This changing trend is consistent with land use cover change in the area. In 30a, YRB cultivated land decreased by 8663 km2, due to rapid urbanization. In addition, after year 2000, China began to implement the policy of returning farmland to forest and grassland on a large scale, which accelerated the reduction of cultivated land. Results again showed that the forest area increased by 30,933,093 km2, indicating that the implementation of “returning farmland to forest and grassland”policy achieved great results, thus increased the value of ecosystem services generated by forest land by 167.66 × 1010 USD. Grassland increased by 738 km2, as corresponding ESV increased by 28.73 × 1010 USD, while unused land decreased by 8131 km2, with 9.52 × 1010 USD ESV decrease. In general, the ecological protection and management measures in the YRB have achieved remarkable results, and ecosystem service values has been significantly improved due to forest and grassland increase.Table 2 The value of ecosystem services in the YRB from 1990 to 2020.Full size tableIn terms of ecosystem service structure in the YRB (Table 3), the relative proportions of various ESVs did not change significantly, resulting in relatively stable ESV structure. Soil conservation and waste disposal are the most important among them, accounting for about 37% of ESV’s total value. The YRB ecosystem, as can be seen, emphasises the importance of soil conservation and waste disposal in the basin, with Climate regulation, Biodiversity conservation, and Entertainment accounting for only 11.99 percent of the total. Various services have changed to varying degrees during the study period. Waste disposal and climate regulation, for example, have suffered losses of 22.23 × 1010 USD and 20.29 × 1010 USD, respectively.The rest of the services showed an upward trend, among which the value of the Food production service increased the most, which was 19.03 × 1010 USD, owing to the obvious increase of the forest land and grassland area in the YRB.Table 3 The value of individual ecosystem functions in the YRB from 1990 to 2020.Full size tableSpatial distribution and variation characteristics of ecosystem services in the YRBThe total ESV value of the study area and changes in the value of each service could not reflect their spatial differences. To describe the temporal and spatial distribution pattern of ESV in the study area, the natural breakpoint method was used. This method was further used to classify ESV with reference to existing studies, and divided the area into four levels: low-value, lower-value, higher-value, and high-value areas. Takin the three-level watershed of the YRB as the statistical unit for analysis, the result showed that the higher the level, the higher the ESV. As shown in Fig. 2, from 1990 to 2020, the spatial characteristics of ESV were relatively stable. The YRB’s upper reaches, from Shizuishan to the north bank of Hekou Town, the Fenhe River Basin, from Hekou Town to Longmen, and the Jinghe River Basin are all rich in high-values. The forest and grassland are relatively concentrated in the above-mentioned areas, the ESV coefficient is high, and the watershed area is large, resulting in a high total ESV. The higher value areas are mainly distributed in the areas from Longyang Gorge to Lanzhou main stream basin, the Daxia River and Tao River basin, and the Wei River basin. The area above Baoji Gorge and the inflow area fall in the transition zone between the high-value area and the lower-value area. For example, the transition area between the Loess and Qinghai-Tibet Plateaus is a higher-value area. The lower-value area mainly includes the Huangshui River Basin, the Datong River Basin, the basin below Lanzhou, and the Guanzhong Plain area. Thus, the unused land in this area is widely distributed. Due to the large area of construction land in the Guanzhong Plain, the ecosystem service value has shrunk. The low-value area is found in the YRB’s lower reaches, which contains the most extensive and large area of construction land in the basin, has a poor ecosystem service function, and is also the YRB’s most economically developed area. In terms of changes in the value of watershed ecosystem services, the number of watersheds at the ESV level did not change significantly between 1990 and 2020. The average ESV of the watershed is 40.52 × 1010 USD. There were 7 high-value, 5 higher-value, 12 lower-value, and 5 low-value watersheds respectively. Figure 2Spatial distribution of ESV changes in YRB from 1990 to 2020. (a) 1990, (b) 2000, (c) 2010, (d) 2020.Full size imageThe hotspot analysis revealed the spatial agglomeration characteristics and ESV evolution law in the YRB from 1990 to 2020 (Fig. 3). In most of the YRB, the ESV accumulation characteristics were not significant in space, and significant areas were dominated with high and low ESV accumulation. The Maqu-Longyangxia River Basin, Daxia River and Tao River Basin, the Datong River Basin, and Fen River Basin were the five core areas where ESV had the highest value. The Inner River, YRB’s northern and eastern margins, and the lower reaches are primarily low-value agglomeration areas. The high-value agglomeration area and low-value agglomeration area did not change significantly in space from 1990 to 2020, but the number of grids in each decreased from 647 to 627 and 699 to 681, respectively. In general, the YRB’s high-value agglomeration areas are strewn about, whereas the low-value agglomeration areas are scattered.Figure 3Spatial agglomeration characteristics of ESV in the YRB from 1990 to 2020. (a) 1990, (b) 2000, (c) 2010, (d) 2020.Full size imageFrom 1990 to 2020, the barycenter coordinates of the ESV in the YRB remained stable between 106.78°–106.94° E and 36.40°–36.65° N (Fig. 4). During the study period, the ESV barycenter coordinates showed a transfer trajectory of first to southwest, then to northeast, and then to southwest. From the perspective of overall transfer direction, ESV barycenter shifted from northeast of Huanxian County to southwest from 1990 to 2020. The ESV in the northeast decreased, while that in southeast increased. From 1995 to 2000 and from 2000 to 2005, the migration distance of ESV barycenter in the YRB was longer by 16.33 km and 15.75 km, respectively, while the barycenter migration distance of ESV from 2005 to 2020 was shorter.Figure 4Barycenter coordinates of ecosystem services in the YRB from 1990 to 2020.Full size imageResponse of ecosystem services to land-use changeThe area of land use type change in the YRB increased by 64,356 km2 between 1990 and 2020. Each land type’s area has changed to varying degrees. Cultivated land and construction land are the two land types that have seen the most changes. The area of cultivated land has shrunk by 8663 km2, while the area of construction land has grown by 13,109 km2. In comparison to water, forest, and grassland, unused land has undergone significant transformations. However, in comparison to 1990, it shrunk by 8131 km2 in 2020. The forest increased by 3093 km2 while grassland increased by 738 km2. Ecosystem services are significantly impacted by changes in land use types. Using the spatial analysis method, the researcher introduces a resilience index to reflect ESV’s response to land-use change in this paper. During 1990–2000 and 2000–2010, average elasticity of ESV change in the YRB relative to land use change was 0.27 and 0.44, respectively, but dropped to 0.04 during 2010–2020. This indicates that the disturbance capacity of land-use change on ecosystem services was low between 1990 and 2000, but increased between 2000 and 2010. Land-use change has had less of an impact on ecosystem services since 2010. The range of changes in land land-use types was wide during this time, but the average elasticity index was low because there were so many different types of land land-use changes, such as the conversion of forest land and cultivated land to construction land, and the conversion of forest land and water area to cultivated land. The decrease in ESV caused by the change in land use per unit area was minor. Furthermore, the forest land and grassland in the river basin have been effectively increased, as ESV has increased. Overall, the value of ecosystem services has remained relatively constant.Accurate spatial statistics on the elasticity index from 1990 to 2020 was carried out (Fig. 5). The elastic index of the upper YRB and Loess Plateau is higher, and the impact of land use change on ecosystem services is more apparent in this region, according to the findings. This is mainly due to the implementation of large-scale ecological engineering measures in response to vegetation degradation in the upper reaches of the YRB and soil erosion in the middle reaches (Loess Plateau), by the Chinese government. In addition, Lanzhou New District, Guanzhong Plain, and the lower Yellow River region also showed higher elasticity index. The above-mentioned regional development and construction, as well as human activities, have resulted in a rapid increase in construction land, resulting in a significant decline in ecosystem services and a higher resilience index as a result of rapid urbanisation.Figure 5Spatial distribution of elastic coefficients in the YRB from 1990 to 2020.Full size imageIn general, the land use types in the YRB have changed dramatically, and land type conversion is very common. The conversion of ecological land to urban construction land, as well as the conversion of unused and cultivated land to ecological land, has resulted in significant changes in ecosystem service value. This demonstrates that the basin’s ecological construction projects have yielded positive environmental results. More

Global fire activity is changing in many areas as temperatures increase and land use intensifies1,2,3,4,5. This is sparking an increase in attention given to fire activity and fire ecology. However, the availability of data for spatially delineated fire events is limited or non-existent in many countries6, with most global fire data coming from satellite-based active fire detections7,8 and gridded burned area products9,10. The lack of products containing delineated events has led to many global studies about fire ecology that are computationally-intensive, coarse-scale trend analyses1,4.A key advantage of datasets like Monitoring Trends in Burn Severity (MTBS)11 or the Fire Occurrence Dataset12 lies in their ease of use. Since its inception in 2007 MTBS has been cited 947 times in peer-reviewed studies according to a Google Scholar search at the time of this writing, despite documented limitations for scientific use of some facets of the product13. The MTBS dataset is regularly updated, easy to find on the internet, and it is free, fast and easy to download and use. Many environmental scientists and resource managers do not have the computational budget or expertise in big data or remote sensing to deal with the challenges one must overcome to process large fire datasets. This is especially true for cases when all that is needed is a shapefile of fire perimeters that can be used to map fire history. Other global fire perimeter datasets have been produced from satellite-derived burned area products14,15, but these are only available in yearly or monthly global shapefiles. Often field-based studies of fire effects require an entire time series over study areas that are only a few hundred km in diameter16 or a single ecoregion17. The end user who wants to understand the fire history for their region would have to download yearly shapefiles with a global extent, clip all of those shapefiles to their area of interest, and then combine them into one shapefile, just to get started. We suspect that the lack of accessible fire perimeter datasets that are easy to download and use contributes to a disparity in research, where fire ecology studies are conducted mostly in developed countries that have either research infrastructure capable of handling big data or longer-term government records, or temperate forested regions that have substantial tree-ring records18.There are two existing global perimeter products, the Global Fire Atlas (GFA) (Andela et al.14) and the Global Wildfire Information System (GWIS) (Artes et al.15). Both were created by applying spatiotemporal flooding algorithms to the MODIS MCD64 Burned Area Product. These algorithms assign burned pixels from the MCD64 products using a moving window whose size is defined by spatial and temporal parameters. They are created as monthly or yearly slices of the entire globe, and they can be subsetted. These products are extremely valuable for global scale studies. But when we look at how those products delineate known fire events we see a consistent problem in that they both seem to over-segment events in ways that appear unrealistic. This inconsistent event delineation is not problematic for coarse-scale or regional estimates of burned area or fire seasonality, but can lead to unrealistic estimates for number of fire events and event-level characteristics like fire size and spread rate. In Fig. 1 we illustrate this with an example of the 2013 Rim Fire in California, United States, which was unmistakably a single event that burned about 90,000 ha over the course of three months. Figure 2 illustrates how the day-to-day progression of the Rim Fire was a steady progression from a single ignition in late August. Table 1 shows how the differences in event delineation propagate to calculations of burned area and number of events. In the GFA, the Rim Fire is delineated as one large event of 804.5 km2, and 13 additional events totaling 88.7 km2. in GWIS it is delineated as one event of 878 km2 and 47 additional events totalling 20 km2. With FIRED, there is one event of 892 km2 and 2 single pixel events totalling less than one km2. One cause for potential differences is how one defines a “fire event”. Large fires often have multiple ignition sources. The Global Fire Atlas algorithm and others19, for example, search for local minima to identify various ignition locations that may begin as small patches, only to later form a large complex and in the end described with a single fire perimeter. The choice of outside sources for optimizing the spatial-temporal parameters, the method of optimization, and the intent of the final product’s meaning (defining events as single ignition patches vs contiguous burned area) all lead to different outcomes in the final events that are delineated. Another likely source of this discrepancy is that GWIS and GFA are calibrated to create a single global product. Because different geographical areas have different types of fire regimes, they have fires that grow at different rates and to different sizes, and occur in greater or fewer frequencies, and so the spatial and temporal parameters that work well for defining a fire event in one area may result in over- or under-segmentation in other areas. Here, we decided upon an approach of creating many regional products across the globe, rather than one product for everywhere on earth.Fig. 1Comparison of global fire event products performance for the 2013 Rim Fire (a). In the FIRED product (b), the Rim fire was classified as one very large event with two single pixel events. The Global Fire Atlas (GFA, c) and Global Wildfire Information System (GWIS, d) each delineated a very large event, with 13 and 47 smaller events, respectively.Full size imageFig. 2The two primary outputs FIREDpy provides are a daily- and event-level product. Panel a shows the default single event polygon. In b, each day has a separate polygon, with associated statistics generated, within each event. Panel c shows the daily perimeters derived from the airborne infrared by the incident management team for comparison.Full size imageTable 1 Rim fire comparison.Full size tableBesides the ease of access and use, the advantage of the FIRED product lies in the user’s ability to use the open-source software, FIREDpy, to tailor the spatial and temporal parameters of the moving window algorithm in order to realistically delineate events for their region of interest. In Fig. 3, we illustrate this by comparing the three products for a pair of small fires in Florida. In this case, the FIRED product that was created with a larger moving window (5 pixels and 11 days) over-aggregated the events, but it only required one line of code at command line to recreate the product with a smaller moving window (1 pixel and 5 days) to get more realistic results.Fig. 3Product comparison for two small events in Florida, the Moonshine Bay and Sour Orange fires (outlined) that both ignited in February of 2007 and were delineated by MTBS. In b the firedpy product that was optimized for the entire United States with a moving window of 5 pixels, 11 days resulted in aggregation of the two fires delineated by MTBS, but also several smaller fires nearby. In b, it was re-ran with a window of one pixel and five days, for a more realistic result. Delineations by the Global Fire Atlas (c) and the Global Wildfire Information System (d) are shown for comparison.Full size imageHere, we present a collection of regionally-tailored fire perimeter datasets for every country in the world with significant fire activity20, which we created with the open source algorithm, FIREDpy21. Each dataset is either a single country or a broader region, depending on the data volume. These datasets differ from other similar efforts14,15 in that each dataset created by FIREDpy is a single file containing a collection of polygons that is generated for the entire time series, rather than monthly or yearly aggregations with a global extent. Furthermore, we have generated the data products at a spatial extent land managers and ecologists would typically use to do regional-scale research, and we adjusted the spatial and temporal parameters for each country to yield realistic event delineations. We also made every effort to ensure that download sizes are reasonable (  More

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Case study areaThe studied catchment (2 621 km2) is located in the central-western part of Poland, and constitutes a part of the Oder River basin. The Wełna River (118 km) discharges to the Warta River near the town of Oborniki18, with an average flow rate of 8.1 m3s−1 (1980–2019) in this profile19. The natural conditions in this catchment favour the development of intensive agriculture, which covers almost 72% of this area (1888 km2) and contributes to the high consumption of mineral fertilizers20. Forest areas cover another 22% of this catchment (589 km2), while urbanised ones only 4% (93 km2) (Fig. 1). The Wełna River catchment is inhabited by approx. 230,000 people, of which only approx. 74% is served by wastewater treatment facilities21.Figure 1Localisation of the Wełna River catchment with its land use forms and nutrient sources. This figure was created using ArcGIS 10.2.1 for Desktop available at https://www.esri.com/en-us/home. Licence granted to Institute of Meteorology and Water Management.Full size imageInput dataBoth the mass-balance method and the modelling method require a similar amount and type of input data (Supplementary Table S1). Basic information on the Wełna River daily flow rates and nutrient concentrations in the closing profile of the catchment (Oborniki) has been obtained from the state monitoring services (Institute of Meteorology and Water Management—National Research Institute—IMGW-PIB13 and State Environmental Monitoring22—SEM) (Supplementary Table S1). They have formed the basis for the estimation of the share of individual sources in the mass-balance method, as well as for the calibration of the Macromodel DNS/SWAT in the modelling method. Other data, such as maps of elevation, river network and soil maps, as well as meteorological data, necessary for the development of an accurate representation of the studied catchment area on the Macromodel DNS/SWAT digital platform, were also obtained from state repositories. Data on the land use comes from the Corine Land Cover8, while detailed information on nutrient sources has been obtained mostly from the Local Data Bank of statistical information. The utilisation of the collected database has been presented in Fig. 2, and described in the following text. The comparison of the results for nutrient loads from both method was based on the year 2017, which was characterised by the maximum amount of monitoring data for both flows (365 measurements) (IMGW-PIB) and total nitrogen (TN) and total phosphorus (TP) (12 measurements–SEM). The average air temperature in 2017 in Poland was 1.5 °C higher than the long-term average (1971–2000) and was over 10 °C which resulted from the warm autumn and the end of the year. The time of the snow cover presence was shorter than the long-term data, and the rest of the year was classified as thermally normal.Figure 2Methodology diagram with relevant chapters marked in grey ovals (green—steps and data used for Mass Balance method, blue—steps data used for Modelling method, green/blue—steps and data used for both methods).Full size imageIn terms of precipitation, 2017 was assessed as wet, similarly due to rainy autumn and summer. In the Wełna River catchment area, the annual rainfall was about 770 mm, however high variability of precipitation conditions in particular months, from extremely wet to very dry, should be noted23. Therefore hydrologically, 2017 was considered normal with the flows only slightly lower than the long-term average.Mass-balance methodThe first method used for the quantification of sources and loads in the studied area was the static mass-balance method. It is widely used by the Polish administration authorities responsible for water management17. This method is based on the assumption that the sum of the nutrient loads in the river’s closing profile (selected based on access to the monitoring data) and its retention in the catchment equals the emission of nutrients in a given time. Such assumption allows the apportioning of the river loads among identified sources and the estimation of their contribution to the total loads, based on known or assumed values of their retention.River load calculationThe total load of nutrients discharged from the catchment was calculated using the daily flow rate and nutrient concentrations in the closing profile of the catchment area (Oborniki, Fig. 1) from the SEM (Supplementary Table S1). The daily river load was calculated using the following Eq. 5:$${L}_{river}=0.0864sum_{t=1}^{n=365}{({Q}_{t}cdot {C}_{t})}_{t}$$
(1)
where: Lriver is the annual load [kga-1], n is the number of days, t is the consecutive day, Ct is the concentration [mg L-1], Qt is the mean daily flow rate [Ls-1], and 0.0864 is the unit conversion.Due to the fact that the flow rate is measured daily and nutrient concentrations only 12 times a year, the linear interpolation method5 was used to obtain the daily concentration values:$${x}_{k}={x}_{a}+kcdot frac{{x}_{b}-{x}_{a}}{n+1}$$
(2)
where: xk is the interpolated concentration value, xa is the first of the two measured concentration values between which the concentrations are interpolated, xb is the second of the two measured concentration values between which the concentrations are interpolated, k is the consecutive number of missing value and n is number of missing values.Source apportionmentFor the mass-balance method, data on nutrient loads for source apportionment (emission inventory) was collected in order to proceed with further calculations. The calculations were performed for 2017, due to the availability of river monitoring data and the nutrient sources were divided into 7 categories, based on the HELCOM guidelines5: municipal (MWS) and industrial (INS) point sources, municipal diffuse sources (SCS), forestry (MFS), agriculture (AGS), natural background (NBS) and atmospheric deposition (ATS). The category of “unknown sources” (UKS) was taken into account, in order to include possible discrepancies in nutrients load apportionment, and to cover eventual differences between calculated river load and inventoried emission.The MWS loads were calculated on the basis of the number of inhabitants served by the wastewater treatment plants (WWTPs)21. In the Wełna River catchment, 151 771 inhabitants were served by the 12 WWTPs covered by the National Wastewater Treatment Program (NWTP)24, which provides information on the total discharge volume from each facility. For 5 of these plants, information on influent/effluent nutrient concentrations was also available, allowing the direct calculation of discharged loads. For the remaining seven facilities, the loads were calculated on the basis of the mean influent concentration information, available for the WWTPs covered by the NWTP (80 mgL−1 and 12 mgL−1 for TN and TP, respectively), and approximated nutrient reduction level in non-biological WWTPs. This reduction level, based on data from the NWTP, was set at 65% for TN and 35% for TP24. Another 19 350 inhabitants of this catchment were connected to the small WWTPs, not included in the NWTP. This part of the MWS load was calculated using the mean daily wastewater outflow (0.12 m3day−1 per person), the same mean nutrient concentrations and reduction levels as presented above. Additionally, the remaining 25% of the catchment’s population (58,000) is not connected to any WWTP and uses septic tanks and other types of individual wastewater treatment systems. The load from this source was expressed as SCS, and calculated using unit loads set on 11 gday−1 per person and 1.6 gday-1 per person for TN and TP, respectively17. The industrial nutrient input information (INS) was gathered directly from the Statistics Poland office database21.The AGS load was calculated using nitrate and phosphate concentrations in shallow groundwater (90 cm below the ground surface), from 22 sampling points located on agricultural areas in the Wełna River catchment17. Concentrations were recalculated to TN and TP respectively and averaged. Thus, the obtained mean concentrations were 8.25 mgL−1 of TN and 1.92 mgL−1 of TP. Subsequently, load values were calculated by multiplying these concentrations by the outflow from agricultural areas, calculated as a share of the total catchment outflow respective to the agricultural use of the area. The calculated loads were multiplied by coefficients reflecting the share of monitored outflow (groundwaters and tile drainage) from the agricultural runoff (1.11 for TN and 4.17 for TP)25. Subsequently, the natural background (NBS) was subtracted from the AGS load.The load corresponding to NBS was calculated using the total catchment outflow and nutrient concentration values reflecting conditions in undisturbed areas of pre-human activity, set as 0.15 mgL−1 and 0.02 mgL−1, for TN and TP respectively17. The MFS load was also calculated in a similar way, using nutrient concentrations set to represent forest catchment as 0.31 mgL−1 and 0.038 mgL−117 and the outflow calculated as the share of the total catchment area, respective for the catchment part covered by forest. Also in this case, the NBS load has been subtracted. As for the ATS load, data on pollutant deposition into the ground from precipitation was taken from the SEM network26. This data was based on precipitation and its chemistry measurements taken from 22 monitoring stations covering the entire territory of Poland. The total load from the point and diffuse sources was calculated by adding the loads mentioned above. The eventual difference between the river load (“River load calculation” Section) and inventoried emission (“Mass-balance method” Section) accounted for the other sources (UKS).Load apportionmentThe contribution of each source to the calculated river load was calculated based on a simplified equation modified from HELCOM5:$${L}_{river}=DP+LOD-RP-RD$$
(3)
where: Lriver is the river load [kga−1], DP is the load from point sources (MWS and INS) [kga−1], LOD is the load from diffuse sources (SCS, ATS, MFS, AGS and, NBS) [kga−1], RP is the point source retention [kga−1] and RD is the diffuse source retention [kga−1].In the adopted mass-balance method, it is assumed that nutrient load from the point sources (DP) is introduced directly into the river bed phase, while load from the diffuse sources (LOD) is discharged into both phases of the catchment, land and river bed ones. In both phases, self-purification processes are taking place, resulting in the reduction of nutrient loads on the way from the source to the catchment closing profile. However, due to the limited amount of data, the self-purification processes in the river have been omitted, therefore the point source retention (RP) equalled 0 kga−1. Subsequently, the diffuse source retention (RD) has been estimated on the basis of the difference between each nutrient load of the river (Lriver) and the point sources (LOD). The remaining river load has been then attributed proportionally to the contribution of the particular diffuse sources to the total source apportionment (emission inventory).Modelling methodThe digital platform, the Macromodel DNS with the SWAT module27,28,29,30,31,32, was used for comparison for the nutrient balancing method described in “Mass-balance method” Section. This advanced dynamic tool tracks nitrogen and phosphorus migration paths in the river basin taking into account their spatial and temporal variability. For this purpose, it takes into account a very extensive input database, similar to that used in the mass balance method (Supplementary Table S1). Natural and anthropogenic processes that affect the transport and transformation of nutrients, are also part of this platform. The SWAT module (version 2012) is a tool which operates in the spatial information system (GIS) and is fully integrated with it. Using the digital elevation model (DEM), the SWAT module divided the entire analysed Wełna River catchment into a total of 225 sub-catchments of an average area of 11.5 km2. The subsequent use of data on land use (forests, agriculture and urbanised areas) and the types of soils (31 classes) allowed the authors to identify a total of 2824 hydrological response units (HRUs), homogeneous in terms of vegetation, soil and topography33. Afterwards, a simulation of soil water content, evapotranspiration, surface runoff, primary and underground flows was carried out in accordance with the water balance Eq. (4), which represents the basis for the quantitative component and the HRU.$${SW}_{t}={SW}_{0}+sum_{i=1}^{t}({R}_{day}-{Q}_{surf}-{E}_{a}-{W}_{seep}-{Q}_{gw})$$
(4)
where: SWt is the final soil water content (mm H2O), SW0 is the initial soil water content (mm H2O), Rday is the amount of precipitation (mm H2O), Qsurf is amount of surface runoff (mm H2O), Ea is the amount of evapotranspiration (mm H2O), Wseep is the amount of water entering the vadose zone from the soil profile (mm H2O), Qgw is the amount of return flow (mm H2O).The model directs all runoff flows generated by each HRU through the channel network, thus simulating a catchment. The water balance equation also represents a basis for the simulation, transport and transformation of nutrients required for the quantitative component of the model. This tool models forms of nitrogen, organic and inorganic , different forms of phosphorus in soil34, as well as organic nitrogen and phosphorus forms associated with plant residues, microbial biomass and soil humus35,36,37,38. Final results of simulations are produced by the SWAT model as all the forms of nitrogen and phosphorus (in kilograms of N and P per a time unit, respectively) are then summed up to give TN and TP values. To verify that the model properly predicts TN and TP values its results are calibrated with the TN and TP values resulting from SEM, as described in Sect. 2.4.1. Moreover, the particular forms of nitrogen and phosphorus have also been compared with the modelling results (Supplementary Table S4). A detailed overview of the migration and transformation pathways of nitrogen and phosphorus forms in the catchment has been presented in the Supplementary Information (Sect. S1), while mathematical description of these processes is included in the theoretical documentation of the SWAT model39.Similarly, as in the case of the mass-balance method, diffuse sources of nutrients from agriculture (AGS), forestry (MFS) or urban areas (URB) in SWAT were simulated in the land phase of the catchment. In the land phase, the model simulates both the infiltration of nutrients into the soil (fertilization, plant biomass, precipitation) and their removal from it (volatilization, denitrification, erosion, surface runoff). Additionally, changes in the distribution of nutrients in the soil (uptake by plants) and the low mobility of phosphorus itself are also taken into account39,40,41.Pollutants from municipal and industrial point sources (MWS, INS) are introduced directly into the river bed phase. The exception here is the nutrient load from municipal diffuse sources (SCS) which, reduced as a result of the self-purification processes taking place in the land phase, is also treated in the model as point sources. The SCS nutrient load mainly derives from leaking or illegally emptied septic tanks. For this purpose, septic tanks have been divided into three types: leaky, partially illegally emptied, and sealed septic tanks, legally emptied. The loads from the legally emptied tanks are included in the effluents from WWTPs reported in the catchment. While for the remaining types, their loads are calculated using factors depending on their effectiveness in nutrient removal (15 – 50%). The final nutrient load derived from these types of facilities is then calculated, taking into consideration the number of inhabitants using the different types of septic tanks and the average chemical composition of wastewater21.The load of nutrients from the atmospheric deposition (ATS) affects both land and river phases due to the presence of two deposition mechanisms in the SWAT module, i.e., wet and dry deposition. The model also allows for the determination of nutrient loads generated as a result of natural processes of nitrogen and phosphorus transformation and transport in the soil, with the omission of all anthropogenic pressure—natural background (NBS).Calibration, verification and validationThe SWAT module for the Wełna River has been calibrated, verified and validated using the SWAT-CUP software42. For the quantitative component (water circulation in the catchment), the implemented daily flow data (source: IMGW-PIB) for the period of 18 years (2001–2018) came from the water gauge stations on the Wełna River (Pruśce and Kowanówko) and its tributary (the Flinta River-Ryczywół) (Fig. 1). The qualitative component (nitrogen and phosphorus concentration in the catchment) was gathered from the SEM stations localised at the Wełna River (Oborniki and Rogoźno) (Fig. 1) and covered a period of 13 years (2005–2018). Three statistical measures, coefficient of determination (R2)43, percent bias (PBIAS)44, and Kling-Gupta efficiency (KGE)45, have been used to indicate the Wełna River model performance (Supplementary Tables S2 and S3). In terms of the quantitative component, the calibration and verification coefficients R2, KGE and PBIAS classified the model performance generally as good and very good for the main river (Wełna), and satisfactory and good for its tributary (Flinta). During the validation procedure, all coefficient values rated the model performance for daily flow simulations as very good. In terms of qualitative components, the model performance for TN simulations can be considered as very good or good, according to the all-applied coefficients. Lower model performance, mostly satisfactory, was observed for TP mainly due to the variability of phosphorus temporal distribution patterns (Supplementary Table S2). The entire process was described in detail in Orlińska-Woźniak et al46.Variant scenariosIn order to determine the contribution of individual sources to the total load of nutrients in the profile closing the analysed catchment, a final simulation of the model was used and subjected to calibration, verification and validation procedures, and called the baseline scenario (A0). Subsequent so-called variant scenarios (A1–A5), i.e. model simulations, were developed. In variant scenarios the values of selected parameters were changed in relation to the A0 scenario. This was used both in the river bed phase for point sources (A1) and for individual diffuse sources (A2–5), thus imitating surface changes for particular types of land use in the land phase of the catchment (Fig. 3).Figure 3Variant analysis diagram for assessment of nutrient loads (L) for particular modelling scenarios and sources described in the text: MWS, INS, SCS—point sources, URB—urban, AGS—agricultural, MFS—forest.Full size imageIn the A1 scenario, all parameterized and aggregated point sources (MWS, INS, SCS) for each relevant sub-basin (LMWS,INS,SCS), were removed from the model to calculate their contribution to the total nutrient loads in the closing profile of the studied catchment (LA1).In the next two scenarios (A2 and A3), concerning urban and agricultural land use, their surface areas (5 663 ha and 192 917 ha, respectively) were successively replaced by the forest land use. This procedure was based on the assumption that the forest is the primary type of land use for this catchment area47. In order to completely eliminate the influence of these areas, the nutrient loads from the relevant surface area occupied by forest land use were subtracted, in order to estimate the contribution of urban and agricultural land (LURB and LAGS, respectively).The change in land use from urbanised and agricultural areas to forest areas increased their percentage of the catchment area to almost 100%, thus the original image of the catchment area and the nutrient load at its mouth. On this basis, in scenario A4, the nutrient load from forests LMFS, which currently occupy only 20% of the catchment area (A0), flowing to the closing profile, was calculated from the proportion.The A5 scenario is the difference between the nutrient load from the A0 scenario and the sum of the remaining loads from the subsequent variant scenarios (A1–A4). In this way, both the natural background (NBS) and atmospheric deposition (ATS) were taken into account. More