EVALUACIÓN DE FAUNA ASOCIADA AL MANGLAR

IV. METODOLOGÍA

3. EVALUACIÓN DE FAUNA ASOCIADA AL MANGLAR

o The spatial and temporal level of detail for local or inter-annual water pollution problems

In Section 7.2.3, I showed how the sub-basin scale is useful to study large basins. On the other hand, this scale seems too coarse for local water pollution problems. For example, cities like Beijing, Shanghai and Shenzhen are densely populated with a lot of human waste, creating local pollution. These cities are not geographically specified in the model,

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the performance of the MARINA model was evaluated. Results of this evaluation provided enough confidence in using the model for sub-basin analyses of nutrient fluxes in China (see Section 7.2.5 on the model evaluation).

o Improved modeling of pollution by manure and human waste

MARINA is the first river export model that includes direct discharges of animal manure to rivers and uncollected human waste at the sub-basin scale simultaneously (Table 7.3). MARINA accounts for many other diffuse and point sources of nutrients in Chinese rivers. Table 7.2 lists these sources. Other river export models like IMAGE-GNM and Global NEWS-2 also account for many of these sources, but ignore direct inputs of manure to rivers which are the largest source of nutrients in many Chinese rivers (Table 7.2). Direct inputs of manure are modeled by NUFER for all of China, but this is not a river export model and cannot address impacts of this source on coastal water quality. The same holds for uncollected human waste in NUFER. IMAGE-GNM accounts for the impact of urban human waste on river export of nutrients, but not at the sub-basin scale and not for the different nutrient forms and future years yet.

Direct discharges of manure to rivers seem to be unique for China (see Section 7.3.2 for the comparison with other studies). These direct discharges of manure to rivers have happened because animal production has been industrializing since the 1990s with rather poor manure management. Before the 1990s, traditional farms were dominant where most animal manure was recycled on land. Today, industrial farms dominate meat production. This holds especially true for pork and poultry production. For example, in 2010, 40-80% of the pigs and poultry in China were grown in industrial farms. Considerable amounts of manure in industrial farms are directly lost to nearby water systems. Some manure can be collected and composted, but it is hardly applied on land (Chadwick et al. 2015). One of the reasons is the location of industrial farms. Industrial farms are often constructed in semi-urban areas to meet high food demand. However, these semi-urban areas are often far from cropland. Consequently, this increases the cost for manure transportation and the demand for labor. Another reason is outdated techniques for manure application (Bai et al. 2015; Chadwick et al. 2015; Ma et al. 2013b). Thus, arable farmers prefer synthetic fertilizers because of their lower prices and lower labor demand (e.g., Ju et al. 2005; Li et al. 2013). However, limited knowledge on when and how much to apply, for both manure and synthetic fertilizers, has led to inefficient use of nutrients in crop production (e.g., Ju et al. 2009).

Another source of nutrients in rivers is uncollected human waste, an input that is ignored in Global NEWS-2, but important for China. Particularly in rural areas, people lack sewage connections (WHO/UNICEF 2014). Therefore, uncollected human waste

167 may enter rivers via leaching or runoff from soils (diffuse sources) or be directly discharged as untreated influents (point sources). In Chapter 5, I showed the importance of uncollected human waste in water pollution especially for the past when most of the Chinese population was without sewage connections.

o Coupling with NUFER

Another important feature of MARINA is that it is coupled with NUFER (Table 7.3). I did this by using information from NUFER to improve modeling of animal manure and of human waste from the rural population (Section 7.2.2). Although NUFER is a food chain model for China, it has comprehensive information on nutrient management in agriculture at the scale ranging from the county (Wang et al. in preparation), to the province (Ma et al. 2012), and to the country (Ma et al. 2010). In addition, this information relies largely on local sources (see Section 7.2.1). Thus, for nutrient management aspects in Chinese agriculture NUFER has advantages over, for example, global river export models that are often based on national assessment reports like the FAO to generate gridded information for model inputs (e.g., Bouwman et al. 2009). o Updated reservoir information

Another strong point of MARINA is the updated information for reservoirs (Section 7.2.2, Table 7.3). This is an update of Global NEWS-2 (Fekete et al. 2010; Vörösmarty et al. 2003). MARINA uses the recent GRanD database (Lehner et al. 2011b). This database is more complete than the information in Global NEWS-2. For example, GRanD provides information for around hundred dams in China and their associated reservoirs having water discharges larger than or equal to 0.5 km3_{. The number for dammed reservoirs is}

80 in Global NEWS-2 (Fekete et al. 2010; Vörösmarty et al. 2003). o Transparency

Finally, I consider the MARINA model transparent (Table 7.3). This makes the model easy to understand by other users. Thus, the model has the potential to be applied to other large river basins in the world to analyze causes of water pollution and solutions (see Section 7.5 on future outlook).

7.2.4 Weaknesses of the MARINA approach

o The spatial and temporal level of detail for local or inter-annual water pollution problems

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but they are part of the downstream sub-basins (see Figures 5.1 and 5.2 for locations of the sub-basins). Thus, the current version of MARINA cannot provide city-oriented information. Models such as SWAT and AGNPS are, perhaps, better tools for in-depth analyses of such local areas, but these models are more developed for rural areas (Tables 7.1-7.3). IMAGE-GNM can also be a good tool because it covers both rural and urban areas at the grid scale. Nevertheless, I believe that MARINA can also be useful here: (i) it accounts for urban and rural population and (ii) sub-basin information from MARINA can serve as a first step in identifying areas that contribute largely to coastal water pollution. This information can also indicate where monitoring of water quality is more needed. Results of such monitoring can be used to reduce uncertainties in the model.

The MARINA model uses the ICEP approach to quantify the potential for coastal eutrophication (Garnier et al. 2010). However, this indicator is based on annual totals of riverine nutrient fluxes (Tables 7.1-7.3). Annual values are useful to analyze trends in nutrient fluxes, but may be less sufficient to analyze eutrophication problems such as blooms of harmful algae. This is because algal blooms often last for certain periods within a year: days, months or season (Davis et al. 2009; Tang et al. 2006a). This depend on various factors such as light, oxygen, nutrients and the presence of algal consumers in aquatic systems (Conley et al. 2009; Paerl et al. 2001). N and P are among the controlling factors for development of algal blooms, which are often undesirable (Conley et al. 2009). An example is the shift from siliceous diatoms to non-siliceous, often harmful algae like N2-fixing cyanobacteria. This happens when aquatic systems have too

much N or P in relation to Si (N:P:Si ratio). This limits the growth of diatoms and gives a chance for the development of non-diatom algae (Garnier et al. 2010). The Redfield ratio of N:P:Si (16:1:20) is often used as an indication of the diatom requirements for growth. This Redfield ratio is the basis of the ICEP indicator (Garnier et al. 2010). This is an advantage of ICEP.

Furthermore, ICEP can be a useful indicator, but we must interpret it with care. ICEP values above zero should be interpreted as an indication that the potential for harmful algae is high. Negative ICEP indicates a rather low potential for algal blooms. However, it does not mean that events of harmful algae do not occur even though the annual total river export of nutrients is low compared to Si levels. An important advantage of ICEP is its transparency. Thus, it is easy to use this indicator. In fact, ICEP has been applied in various studies to analyze coastal eutrophication problems (e.g., Dupas et al. 2015; Kroeze et al. 2013; Sattar et al. 2014; Strokal & Kroeze 2013).

169 o Missing local nutrient sources

The MARINA model includes sources of nutrients in rivers that are important for China (Section 7.2.3, Table 7.3). Nevertheless, there are still some additional local sources of nutrients that the model ignored. These are, for example, aquaculture, industry and direct atmospheric N deposition on seas. Aquaculture and industry are modeled by IMAGE-GNM for TN and TP (Tables 7.1-7.3). In general, this model indicates the minor contribution of these sources to the total nutrient loads in surface waters globally (Beusen et al. 2015a). However, in China, the demand for fish products has been increasing (Biao & Kaijin 2007; Cao et al. 2007). Cities have been developing rapidly along with industries (Maimaitiming et al. 2013). Thus, local pollution may occur, such as polluted fish ponds from aquaculture and lakes in cities. In addition, direct N deposition on the sea was found to be an important polluter for specific coastal areas according to an experimental study of Xu et al. (2015). Thus, I realize that these local sources of water pollution are important to consider in local assessments. However, I believe that including them in the MARINA model will not change the major conclusions for China (see Section 7.4).

o Missing reservoir retentions

The impact of reservoirs on dissolved inorganic N and P export is considered in MARINA. However, this is not done for dissolved organic N and P. To my knowledge none of the selected models for China (Tables 7.1-7.3) account for dissolved organic N and P retentions in reservoirs. A few studies for the Yangtze addressed the side-effects of the largest dam on nutrient flows from up- to downstream (Sun et al. 2013a; Sun et al. 2013b), but mainly for TP and DIN. Global NEWS-2 does it only for dissolved inorganic N and P and IMAGE-GNM for TN and TP (Tables 7.1-7.3). I followed the approach of Global NEWS-2 when I developed MARINA for China. I, however, realize that this weakness may lead to an overestimation of river export of dissolved organic N and P, but it will not affect largely the conclusions of the study on pollution causes (see Section 7.2.5 for the model evaluation). Furthermore, the model evaluation results are promising. This provides a confidence in using the model for China (Section 7.2.5).

o Steady state approach

The MARINA model takes a steady state approach for nutrient cycling in terrestrial and aquatic systems, following Global NEWS-2 (Tables 7.1-7.3). Other models like IMAGE- GNM and SWAT take a dynamic approach. Both approaches have pros and cons. Cleary, a dynamic approach better represents processes of N and P in soils and in rivers. In IMAGE-GNM the spiraling approach is better at accounting for N and P retentions in rivers (Beusen et al. 2015a). The steady state approach is, however, much more

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169 o Missing local nutrient sources

o Missing reservoir retentions

o Steady state approach

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transparent and flexible with modeling the source attribution for multiple nutrient forms. Furthermore, a model with a steady state approach is generally easier and faster to run. I realize that ignoring dynamics / spiraling in MARINA for China may introduce uncertainties in the model results. This may hold especially for future projections to account for time-delay responses in management options as concluded by Strokal and de Vries (2012) for world rivers. Nevertheless, my aim was to have a transparent model for other users, so that it can be easily applied to other world basins. And my choice is justified by the model evaluation results (Section 7.2.5).

o Calibration

The MARINA model is based largely on model parameters from Global NEWS-2 (Mayorga et al. 2010). A few of them are calibrated at the global scale. I did not re-calibrate Global NEWS-2 parameters because I did not have enough water quality data. One may argue that it is not appropriate to apply a calibrated parameter from a global model to China. However, the evaluation results show that the MARINA model performs better than the Global NEWS-2 model for China. This builds trust in the MARINA model (see discussion in Section 7.2.5). An interesting future exercise could be to have an uncalibrated, process-based version of MARINA as IMAGE-GNM is (Tables 7.1-7.3). Comparing the results of both approaches may help better understand uncertainties in both approaches to model nutrient export by rivers.

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