Perfiles de suelos

From my study I can draw five main lessons for nutrient modeling at the sub-basin scale. Lesson 1: The MARINA model can help to better understand causes of water pollution and explore solutions

The MARINA model has been developed to better understand trends in river inputs of nutrients to Chinese seas and their sources in a spatially explicit way. The most important difference between the MARINA model and the other models is that it quantifies the relative importance of human activities to coastal water pollution in a better way for China and by sub-basin (see Lesson 2 for sub-basins). This helps to identify the main causes of coastal water pollution by nutrients. For example, with this model we now better understand the impact of animal production and uncollected human waste on water quality in China (Chapters 4 and 5). The model is also useful to explore solutions to reduce coastal water pollution by 2050. I show this in Chapter 6 for China.

Lesson 2: Sub-basin scale modeling is useful support for effective nutrient management in large basins

My sub-basin scale modeling approach is meant for large basins such as the Yangtze, Pearl and Yellow. It identifies areas with human activities that pollute coastal waters with nutrients (see Lesson 1). This information can help to allocate nutrient management and thus effectively reduce coastal water pollution from large basins. This makes the sub-basin scale approach of the MARINA model a useful tool to support decision making (see also Section 7.2.3). I believe that the sub-basin scale modeling is also an appropriate alternative to, for example, basin or gridded scale modeling to address nutrient flows from large basins. This is because it provides the useful information with minimum efforts in terms of model inputs and computation time. In contrast, basin scale modeling does not account for the spatial variation in human activities within a basin. Meanwhile, gridded scale modeling requires more inputs and computation time, and may also need more simplifications and assumptions. This can increase uncertainties, particularly in data scarce regions. Another important feature of the sub-basin scale MARINA model is its transparency. This makes the model easy to understand by other users and thus to apply it to the other large basins.

185 Lesson 3: Preferred sizes of sub-basins depend on the research objectives and resource availability

What is a preferred or appropriate size of a sub-basin? I think part of this answer is related to the research objective and another part to the availability of resources such as time, finances and data. For MARINA, many model inputs were available at 0.5 latitude by 0.5 longitude grid. These gridded data were easily aggregated to sub-basins, making modeling of nutrients fluxes at the sub-basin scale possible. The MARINA sub-basins were delineated using the river network of the WBM model (STN-30 at 0.5 latitude by 0.5 longitude). The intention was to cover the primary tributaries of a large river. This resulted in 22 sub-basins of the Yellow, Yangtze and Pearl rivers with drainage areas ranging from 31 and 460 thousands km2_{. Classifying up-, middle- and downstream sub-}

basins allowed us to account for upstream and downstream relationships (see research objectives in Chapter 1). This delineation and classification was supported by literature (see Chapter 5). My research shows that my choices on the sizes and delineation of sub- basins provide the information needed to meet thesis’ research objectives (see Lessons 1 and 2).

Lesson 4: Basin scale approaches can serve as a basis for sub-basin scale modeling of nutrient fluxes

The MARINA model has been developed based on basin scale approaches from Global NEWS-2. Implementing these basin approaches for sub-basins proved to be a good choice for China. Some of the Global NEWS-2 approaches for animal manure and human waste were improved to include missing sources of nutrients in the studied rivers. Evaluation results of MARINA justify the choice of applying basin scale approaches for sub-basin modeling of nutrient fluxes. This is shown by the evaluation results of MARINA, indicating its better performance over Global NEWS-2 for China (Lesson 5, Section 7.2.5). This study can also serve as an example for other large basins in the world. However, it has to be noted that the improvements of Global NEWS-2 approaches for animal manure and human waste were specific for China. Other basins may require improvements in modeling approaches for the other specific sources (e.g., open defecation for the Bay of Bengal and Java Sea; Section 7.3.2).

Lesson 5: Building trust in nutrient models is more than just comparing modeled and measured data

Results of nutrient models are often validated against measurements. This type of validation depends largely on the quality and availability of such water quality data. When these data are scarce, model validation may not provide enough confidence in the model performance. However, such model validation is not the only option to build trust

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7.4 Conclusions of the PhD thesis

7.4.1 Main lessons for nutrient modeling

From my study I can draw five main lessons for nutrient modeling at the sub-basin scale. Lesson 1: The MARINA model can help to better understand causes of water pollution and explore solutions

The MARINA model has been developed to better understand trends in river inputs of nutrients to Chinese seas and their sources in a spatially explicit way. The most important difference between the MARINA model and the other models is that it quantifies the relative importance of human activities to coastal water pollution in a better way for China and by sub-basin (see Lesson 2 for sub-basins). This helps to identify the main causes of coastal water pollution by nutrients. For example, with this model we now better understand the impact of animal production and uncollected human waste on water quality in China (Chapters 4 and 5). The model is also useful to explore solutions to reduce coastal water pollution by 2050. I show this in Chapter 6 for China.

Lesson 2: Sub-basin scale modeling is useful support for effective nutrient management in large basins

My sub-basin scale modeling approach is meant for large basins such as the Yangtze, Pearl and Yellow. It identifies areas with human activities that pollute coastal waters with nutrients (see Lesson 1). This information can help to allocate nutrient management and thus effectively reduce coastal water pollution from large basins. This makes the sub-basin scale approach of the MARINA model a useful tool to support decision making (see also Section 7.2.3). I believe that the sub-basin scale modeling is also an appropriate alternative to, for example, basin or gridded scale modeling to address nutrient flows from large basins. This is because it provides the useful information with minimum efforts in terms of model inputs and computation time. In contrast, basin scale modeling does not account for the spatial variation in human activities within a basin. Meanwhile, gridded scale modeling requires more inputs and computation time, and may also need more simplifications and assumptions. This can increase uncertainties, particularly in data scarce regions. Another important feature of the sub-basin scale MARINA model is its transparency. This makes the model easy to understand by other users and thus to apply it to the other large basins.

185 Lesson 3: Preferred sizes of sub-basins depend on the research objectives and resource availability

What is a preferred or appropriate size of a sub-basin? I think part of this answer is related to the research objective and another part to the availability of resources such as time, finances and data. For MARINA, many model inputs were available at 0.5 latitude by 0.5 longitude grid. These gridded data were easily aggregated to sub-basins, making modeling of nutrients fluxes at the sub-basin scale possible. The MARINA sub-basins were delineated using the river network of the WBM model (STN-30 at 0.5 latitude by 0.5 longitude). The intention was to cover the primary tributaries of a large river. This resulted in 22 sub-basins of the Yellow, Yangtze and Pearl rivers with drainage areas ranging from 31 and 460 thousands km2_{. Classifying up-, middle- and downstream sub-}

basins allowed us to account for upstream and downstream relationships (see research objectives in Chapter 1). This delineation and classification was supported by literature (see Chapter 5). My research shows that my choices on the sizes and delineation of sub- basins provide the information needed to meet thesis’ research objectives (see Lessons 1 and 2).

Lesson 4: Basin scale approaches can serve as a basis for sub-basin scale modeling of nutrient fluxes

The MARINA model has been developed based on basin scale approaches from Global NEWS-2. Implementing these basin approaches for sub-basins proved to be a good choice for China. Some of the Global NEWS-2 approaches for animal manure and human waste were improved to include missing sources of nutrients in the studied rivers. Evaluation results of MARINA justify the choice of applying basin scale approaches for sub-basin modeling of nutrient fluxes. This is shown by the evaluation results of MARINA, indicating its better performance over Global NEWS-2 for China (Lesson 5, Section 7.2.5). This study can also serve as an example for other large basins in the world. However, it has to be noted that the improvements of Global NEWS-2 approaches for animal manure and human waste were specific for China. Other basins may require improvements in modeling approaches for the other specific sources (e.g., open defecation for the Bay of Bengal and Java Sea; Section 7.3.2).

Lesson 5: Building trust in nutrient models is more than just comparing modeled and measured data

Results of nutrient models are often validated against measurements. This type of validation depends largely on the quality and availability of such water quality data. When these data are scarce, model validation may not provide enough confidence in the model performance. However, such model validation is not the only option to build trust

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in nutrient models. In this PhD thesis, I explored several options (including validation based on empirical data), a combination of which can increase our trust in the use of models for nutrient analyses. I started with comparing modeled nutrient fluxes with available measurements as many studies do. Furthermore, I compared modeled trends in river exports of nutrients with available monitoring studies. Comparison of model inputs with an independent dataset, sensitivity analyses and expert knowledge helped to justify model inputs and parameters. Finally, I looked at previous modeling studies on river export of nutrients for China and compared them to MARINA results to get insights into differences and similarities. Results of all these options build adequate trust in using the MARINA model for its purpose. I believe that the proposed options for the MARINA model can also be useful for other studies to evaluate their nutrient models. This holds especially for studies modeling large basins for which water quality data are often limited.