**1. Introduction**

Water is a scarce source in arid and semiarid areas where most of the countries face pressure due to limited opportunities to explore new water resources. This necessitates that all potential unutilized resources of water be used to increase agriculture production. The changes in surface and subsurface flows and land use conditions have direct affect on the downstream in the form of floods and/or water quality deterioration. Climate change and human interference could lead to significant spatio-temporal variations of water quantity, quality, and the associated ecological conditions besides affecting the related management systems [1]. Such complexities force researchers to develop more robust mathematical methods and tools to analyze the relevant information, simulate the related processes, assess the potential impacts/ risks, and generate sound decision alternatives. Spatially meaningful simulation of environ‐ mental flows and storages at the catchment scale is essential for predicting water quantity and quality, as well as operational management of the system [2]. There are numerous modeling wastewater efforts undertaken globally by different researchers (e.g., [1, 3–5]), the ultimate focus of which is mainly to mitigate sediment, contaminants, and non-point source nutrient; enhance water quality; and improve sustainability in agricultural production by increasing resilience. Unforeseen and undesirable consequences can result if biophysical and human systems are not examined together [6, 7]. Daloğlu et al. [4] presented a modeling framework that synthesizes social, economic, and ecological aspects of landscape change to evaluate how different agricultural policy and land tenure scenarios and land management preferences affect landscape pattern and downstream water quality. Wrede et al. [3] evaluated the performance of a fully distributed conceptual hydrologic model based on the Hydrologiska Byråns Vattenbalansavdelning (HBV) and Tracer Aided Catchment model-Distributed (TACD) model concepts in the Central Swedish lowlands. Nesmerak and Blazkova [8] employed a simple transfer function (SISO model) to describe the relationship between the daily total precipitation and the wastewater discharge at the inflow to the wastewater treatment plant (WWTP) for a large city. However, scientific quantifications were required on temporal and spatial scale to identify any feasible wastewater management solution rather than spot and one time sampling of effluents as reported by several studies (e.g., see [9–11]).

The development of a sufficient understanding on which to base decisions or make predictions often requires consideration of a multitude of data of different types and with varying levels of uncertainty [12]. Wastewater contains chemicals such as nitrogen, phosphorus and levels of dissolved oxygen, as well as others that may affect its composition and pH rating. Agricul‐ tural runoff, drainage, as well as inputs from municipal and industrial wastewater often degrade the quantity and quality of surface water bodies. There is a serious need for appro‐ priate water quality monitoring for future planning and management of clean water resources. The SWAT model offers distributed parameter and continuous time simulation, and flexible watershed configuration and with the adoption of GIS technology, a user-friendly and interactive decision support system can be developed for wastewater management. The primary focus of this chapter is to assess the spatio-temporal evolution of wastewater con‐ taminants through the modeling approach and identify management options to improve the watershed health and agro-environment. The findings of the study may support policy makers, researchers, and water managers to make more robust water policy and management options under the changing environment in the future.
