# Water Quality Modelling, Monitoring, and Mitigation
## 3 Water Quality Models, Challenges, and Limitations  
Water quality modeling (WQM) is an important tool that aids environmentalists, policymakers, water resource planners, and managers in strategic water resource management.  
Figure 1: Commonly used WQI models worldwide [15].  
However, WQM represents a challenge in the scientific community due to several constraints and limitations. In general, WQ models are classified based on the types of receiving water, the complexity of the models, and the WQ parameters (e.g., nutrients, dissolved oxygen, biological oxygen demand, etc.) that the model can predict. Thus, WQM requires proper standardization, pollution hotspots, the identification of common features, and policy-relevant models. These models save labor costs, materials, and time [16] and help in effective pollution mitigation for the watershed. In the recent era, numerous models have been frequently used to simulate the water quality of freshwater bodies (streams, rivers, reservoirs, and lakes), estuaries, coastal waters, and marine ecosystems [17]. However, due to the different theories and algorithms applied in the models, their corresponding outputs are different and create huge differences in the results; thus, models could be useful and produce fruitful results when applied to solve particular environmental problems [18].  
Water quality (WQ) models are generally categorized into two categories: (i) physical and (ii) mathematical models [19]. Furthermore, they can be categorized according to the complexity of model simulation, i.e., 1D, 2D, and 3D; type of approach (conceptual, physical, or empirical); data requirements; types of pollutants; area of application (groundwater, catchment, lake, river, coastal waters, etc.); nature (stochastic or deterministic); and spatial analysis [20]. In recent decades, WQ models such as ANSWERS-2000, AquaChem, MIKE SHE, AGWA, GLEAMS/CREAMS, AQUATOX, APEX, EFDC, EPD-RIV1, BASINS, HSPP, KINEROS2, LSPC, NLEAP, PRMS, QUAL2K, QUAL2E, SWMM, SWAT, WARMF, WAM, WCS, and WASP7 have been frequently used to predict WQ worldwide [21]. Because of data requirements and availability as well as types of catchment problems, the simplest reliable models are dominant over complex models [22].  
WQ modeling is still challenging in the scientific domain due to the lack of expert handling of user, site, and/or regionally specific and parameter-specific information as well as inadequacies in model calibration and errors in data reporting. The uncertainty in WQM comes from various sources of errors, such as (i) parametric uncertainty, (ii) structural errors, and (iii) errors in the measurements of the input values and response uncertainty [23]. In developing countries (e.g., India and China), a uniform model standardization system has not been recognized, which limits the extensive utilization of those models for ecological and water management as a result of the lack of benchmarks and comparisons between different modeling outcomes [9; 10; 16]. Spatial variability is reported as a serious problem in catchment-scale WQM that generally acquires catchment behavior, representative site selection, and the integration of nonlinear biogeochemistry [24]. However, the complexity of models, the inadequate availability of data, and poor WQ data are other important limiting factors for WQM.