Perspectives of Hydrologic Modeling in Agricultural Research

*Miha Curk and Matjaž Glavan*

### **Abstract**

For decades agricultural research was done in the field or laboratories, but with the rise of computer science, hydrologic modeling became another essential tool for environmental impact studies. Many types of models can be used, each with its strengths and weaknesses in terms of accuracy, speed, and amount of input data needed. Models can be used on different scales and simulate very different processes. Based on a literature review, APEX (Agricultural Policy Extender) and SWAT (Soil and Water Assessment Tool) models are the most popular for environmental research in agronomy. An important share of modeling work in agronomic studies is focused on pollution research, mainly nutrient and pesticide leaching and soil erosion processes. Other topics include simulating the effects of irrigation and other agricultural practices and studying the impact of extreme weather events and climate change. When working with model results, it is crucial to be mindful of inevitable uncertainties and consider them during interpretation. Modeling is gaining importance in agronomic research in Slovenia, with many studies done in the recent decade and more underway.

**Keywords:** hydrologic modeling, agriculture, agronomy, model applications, agricultural pollution mitigation, model uncertainty

#### **1. Introduction**

For decades, agricultural research was predominantly done in the field or laboratories. Such in situ research results are usually exact, but experiments are timeconsuming. Due to different natural conditions, their validity is usually limited to the small area under study. For example, a study on crop yield in a specific area and under a specific agricultural management only applies to such conditions, and for different conditions, a new experiment needs to be devised. Even when results are visible and relatively easy to measure (like crop yield), the spatial differences prevent us from extrapolating them over a large area without some degree of error. This error only gets larger when research goals include measuring more complicated phenomena like pollution.

With strict limitations in environmental policy (Water Framework Directive (WFD) (Directive 2000/60 EC) in EU; Clean Water Act in the USA), an important branch of agricultural research is focused on mitigating pollution from agricultural activities. Nitrate leaching, sediment erosion, ammonia emissions, or pesticide pollution are hard to measure reliably over larger areas. Natural processes like plant growth also take time, making it hard to conduct conventional field trials to study alternative fertilization or pesticide application methods. There is another way to

estimate the impact of different practices, which involves modeling. Modeling did not render the field studies useless because their results are indispensable as input and validation data.

Several decades ago, with the rise of computer science, different mathematical model approaches were developed to simulate parts of the natural system. Hydrologic modeling in the 1970s quickly became a trendy way of studying the physical processes behind water and nutrient cycling in soil, plants, and whole ecosystems. By coupling several of the more focused models (plant growth, nutrient and water cycle, etc.), more complex models were developed, enabling fast (relative to in situ research) estimations of outcomes of different climate scenarios, land-use changes, etc.

Hydrologic models are not intuitively connected to agriculture, as their use is far more prevalent in other fields. An article on 'Brief history of agricultural systems modeling' by Jones et al. [1], for example, does not even mention them. Despite that, hydrologic modeling is an essential tool in an increasingly important field of agricultural research, the environmental impact studies. The use of hydrologic models enabled fast advances in understanding pollutant movement in different ecosystems, making pollution mitigation strategies easier to evaluate.

This chapter will discuss different hydrologic models used in agricultural research, their strengths and weaknesses, their potential for agricultural pollution mitigation, and the uncertainty associated with model results. Lastly, we will look into practical applications and present some case studies from Slovenia.

#### **2. Hydrologic models – an overview**

Science and research in some fields depend strongly on modeling these days, and the world would probably be different if this tool were not available to us. Hundreds of models were developed over several decades, some simple and some very complex. Each model usually has its particular purpose, though some are quite elaborate and enable the user to model several extensive systems processes simultaneously. In an excellent paper about the evolution of hydrologic models, Clark et al. [2] discussed the challenges of designing hydrologic models that are as close to physical realism as possible while still keeping them simple enough and practical. The authors summarized that there were many noteworthy advances in their development in the last years, as improvements in representations of hydrologic processes by mathematical functions, parameter estimation, and optimizing computing resources by justifiable model simplifications. Some of the main goals for the future they mention are improvements of the basic hydrologic processes understanding, of parallel processing, of cooperation between different model developers in order to find the best methods, of the model analysis methods in order to minimize uncertainty, but also enhancement of the developer-field scientist interaction to promote usability and most importantly improvement and clarification of the construction of the models themselves, to enable more specific add-ons and better modularity.

Hydrologic models are divided into several different categories, depending on how they are structured and represent spatial processes [3]. Based on the structure, models are divided into empirical, conceptual, and physical; based on spatial distribution into lumped, semi-distributed, and distributed. Hydrologic models used in agriculture are almost exclusively either conceptual or physical and semidistributed or distributed. Empirical and lumped models are not practical for such applications because the former models are very exact and depend heavily on large amounts of measured input data, and the latter disregard the spatial variability inside the modeled area. The difference between conceptual and physical models is

**29**

**Table 1.**

*weaknesses.*

*Perspectives of Hydrologic Modeling in Agricultural Research*

that conceptual models consist of simplified equations representing water storage in the catchment, and physical models are based on physical laws and equations

Consequently, the latter are more difficult to calibrate and require many parameters, but the former rarely consider spatial variability within the catchment and are better to use in large catchments with limited data and computational times. On the other hand, distributed models are the ones where the modeled area is divided into smaller cells by a grid of specific size, and semi-distributed ones divide it into specific shapes that represent essential features inside the area. Consequently, the former models are data-intense with long computational times, but the latter risk loss of spatial resolution as the sub-catchments get larger [3]. As mentioned before, there are plenty of models a researcher can consider for his work. Malone et al. [4] discussed the parameterization guidelines and considerations and mentioned at least 15 different models. Google Scholar search was performed to assess the popularity of some of the mentioned models in the agricultural context, with a query: "model acronym" model AND (agronomy OR agriculture OR farm). The number of hits is written in brackets after each model acronym: Watershed Analysis Risk Management Framework (WARMF) (400), HYDRUS (8900), European Hydrological System Model (MIKE-SHE)(3900), DRAINMOD (2500), Soil and Water Assessment Tool (SWAT)(45,100), Environmental Policy Integrated Climate and Agricultural Policy/Environmental Extender (EPIC/APEX) (178,000/172,000), Root Zone Water Quality Model (RZWQM)(2000), Better Assessment Science Integrating Point and Nonpoint Sources (BASINS)(1000), Hydrological Simulation Program – FORTRAN (HSPF)(6000). The EPIC/APEX models are the most used in agricultural context with over 170,000 hits, followed by SWAT with 45,000 hits based on the search results. Other models achieved less than 10,000 hits and seemed far less popular. EPIC/APEX, SWAT, and MIKE-SHE models are presented in more detail in **Table 1**, based on a comparison study by

According to the study [5], SWAT and MIKE-SHE were recognized as very well-performing models (in terms of river discharge), and SWAT was considered the better of the two when simulating processes in agricultural catchments. On the other hand, APEX was recognized as perfect for scenario assessment on farm scale, due to its many options in management practices (different irrigation types, drainage, buffer strips, terraces, fertilization management, etc.), but also economic

**Strengths Weaknesses**

Only for small-scale watersheds or farms

Simplified spatial distribution

Long computational times, data-intensive

of many agricultural management scenarios

Shorter computational times due to lumping of spatial units in the form of hydrological response units

processor for calibration and

analysis of results

(HRUs)

*DOI: http://dx.doi.org/10.5772/intechopen.95179*

based on measured hydrologic responses.

Golmohammadi et al. [5].

**Structure Spatial** 

SWAT Physical Semi-

**distribution**

distributed

MIKE-SHE Physical Distributed Built-in graphics and post-

*Comparison of three models commonly used in agricultural research with model type, strengths, and* 

EPIC-APEX Physical Distributed Suitable for simulation

**Model acronym** *Hydrology*

and validation data.

changes, etc.

estimate the impact of different practices, which involves modeling. Modeling did not render the field studies useless because their results are indispensable as input

Several decades ago, with the rise of computer science, different mathematical

Hydrologic models are not intuitively connected to agriculture, as their use is far more prevalent in other fields. An article on 'Brief history of agricultural systems modeling' by Jones et al. [1], for example, does not even mention them. Despite that, hydrologic modeling is an essential tool in an increasingly important field of agricultural research, the environmental impact studies. The use of hydrologic models enabled fast advances in understanding pollutant movement in different

model approaches were developed to simulate parts of the natural system. Hydrologic modeling in the 1970s quickly became a trendy way of studying the physical processes behind water and nutrient cycling in soil, plants, and whole ecosystems. By coupling several of the more focused models (plant growth, nutrient and water cycle, etc.), more complex models were developed, enabling fast (relative to in situ research) estimations of outcomes of different climate scenarios, land-use

ecosystems, making pollution mitigation strategies easier to evaluate.

into practical applications and present some case studies from Slovenia.

themselves, to enable more specific add-ons and better modularity.

Hydrologic models are divided into several different categories, depending on how they are structured and represent spatial processes [3]. Based on the structure, models are divided into empirical, conceptual, and physical; based on spatial distribution into lumped, semi-distributed, and distributed. Hydrologic models used in agriculture are almost exclusively either conceptual or physical and semidistributed or distributed. Empirical and lumped models are not practical for such applications because the former models are very exact and depend heavily on large amounts of measured input data, and the latter disregard the spatial variability inside the modeled area. The difference between conceptual and physical models is

**2. Hydrologic models – an overview**

This chapter will discuss different hydrologic models used in agricultural research, their strengths and weaknesses, their potential for agricultural pollution mitigation, and the uncertainty associated with model results. Lastly, we will look

Science and research in some fields depend strongly on modeling these days, and the world would probably be different if this tool were not available to us. Hundreds of models were developed over several decades, some simple and some very complex. Each model usually has its particular purpose, though some are quite elaborate and enable the user to model several extensive systems processes simultaneously. In an excellent paper about the evolution of hydrologic models, Clark et al. [2] discussed the challenges of designing hydrologic models that are as close to physical realism as possible while still keeping them simple enough and practical. The authors summarized that there were many noteworthy advances in their development in the last years, as improvements in representations of hydrologic processes by mathematical functions, parameter estimation, and optimizing computing resources by justifiable model simplifications. Some of the main goals for the future they mention are improvements of the basic hydrologic processes understanding, of parallel processing, of cooperation between different model developers in order to find the best methods, of the model analysis methods in order to minimize uncertainty, but also enhancement of the developer-field scientist interaction to promote usability and most importantly improvement and clarification of the construction of the models

**28**

that conceptual models consist of simplified equations representing water storage in the catchment, and physical models are based on physical laws and equations based on measured hydrologic responses.

Consequently, the latter are more difficult to calibrate and require many parameters, but the former rarely consider spatial variability within the catchment and are better to use in large catchments with limited data and computational times. On the other hand, distributed models are the ones where the modeled area is divided into smaller cells by a grid of specific size, and semi-distributed ones divide it into specific shapes that represent essential features inside the area. Consequently, the former models are data-intense with long computational times, but the latter risk loss of spatial resolution as the sub-catchments get larger [3].

As mentioned before, there are plenty of models a researcher can consider for his work. Malone et al. [4] discussed the parameterization guidelines and considerations and mentioned at least 15 different models. Google Scholar search was performed to assess the popularity of some of the mentioned models in the agricultural context, with a query: "model acronym" model AND (agronomy OR agriculture OR farm). The number of hits is written in brackets after each model acronym: Watershed Analysis Risk Management Framework (WARMF) (400), HYDRUS (8900), European Hydrological System Model (MIKE-SHE)(3900), DRAINMOD (2500), Soil and Water Assessment Tool (SWAT)(45,100), Environmental Policy Integrated Climate and Agricultural Policy/Environmental Extender (EPIC/APEX) (178,000/172,000), Root Zone Water Quality Model (RZWQM)(2000), Better Assessment Science Integrating Point and Nonpoint Sources (BASINS)(1000), Hydrological Simulation Program – FORTRAN (HSPF)(6000). The EPIC/APEX models are the most used in agricultural context with over 170,000 hits, followed by SWAT with 45,000 hits based on the search results. Other models achieved less than 10,000 hits and seemed far less popular. EPIC/APEX, SWAT, and MIKE-SHE models are presented in more detail in **Table 1**, based on a comparison study by Golmohammadi et al. [5].

According to the study [5], SWAT and MIKE-SHE were recognized as very well-performing models (in terms of river discharge), and SWAT was considered the better of the two when simulating processes in agricultural catchments. On the other hand, APEX was recognized as perfect for scenario assessment on farm scale, due to its many options in management practices (different irrigation types, drainage, buffer strips, terraces, fertilization management, etc.), but also economic


#### **Table 1.**

*Comparison of three models commonly used in agricultural research with model type, strengths, and weaknesses.*

analysis of measures. Management practices options are also available in SWAT, but it is usually considered better for larger scale watersheds. However, the authors conclude that no single model is superior under all conditions and that the model's performances are very site-specific. In light of agricultural research, the EPIC/APEX model is most useful in small catchments with lots of known data. MIKE-SHE is most useful for large areas when computational time is not a constraint, and data is plentiful. SWAT seems to be somewhere in between – allowing short computational times even in large watersheds but enabling the reasonably accurate agricultural management simulation. Another advantage of SWAT is its modularity – it is easy to link it to other more specific models.

Therefore, models are the most appropriate and cost-effective method for assessing different agricultural management strategies and their impact on the environment. Outputs can be calculated daily, monthly, and yearly and can be used to study the long-term effects of climate change adaptation and short term influence on crop yields, state of the soil, etc.
