In the United States, urban population growth, improved living standards, limited development of new water supplies, and dwindling current water supplies are causing the demand for treated municipal water to exceed the supply. Although water used to irrigate the residential urban landscape will vary according to factors such as landscape type, management practices, and region, landscape irrigation can vary from 40% to 70% of household use of water. So, the efficient use of irrigation water in urban landscapes must be the primary focus of water conservation. In addition, plants in a typical residential landscape often are given more water than is required to maintain ecosystem services such as carbon regulation, climate control, and preservation of aesthetic appearance. This implies that improvements in the efficiency of landscape irrigation will yield significant water savings. Urban areas across the United States face different water supply and demand issues and a range of factors will affect how water is used in the urban landscape. The purpose of this review is to summarize how irrigation and water application technologies; landscape design and management strategies; the relationship among people, plants, and the urban landscape; the reuse of water resources; economic and noneconomic incentives; and policy and ordinances impact the efficient use of water in the urban landscape.

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Microbes have been identified as a major contaminant of water resources. Escherichia coli (E. coli) is a commonly used indicator organism. It is well recognized that the fate of E. coli in surface water systems is governed by multiple physical, chemical, and biological factors. The aim of this work is to provide insight into the physical, chemical, and biological factors along with their interactions that are critical in the estimation of E. coli loads in surface streams. There are various models to predict E. coli loads in streams, but they tend to be system or site specific or overly complex without enhancing our understanding of these factors. Hence, based on available data, a Bayesian Neural Network (BNN) is presented for estimating E. coli loads based on physical, chemical, and biological factors in streams. The BNN has the dual advantage of overcoming the absence of quality data (with regards to consistency in data) and determination of mechanistic model parameters by employing a probabilistic framework. This study evaluates whether the BNN model can be an effective alternative tool to mechanistic models for E. coli loads estimation in streams. For this purpose, a comparison with a traditional model (LOADEST, USGS) is conducted. The models are compared for estimated E. coli loads based on available water quality data in Plum Creek, Texas. All the model efficiency measures suggest that overall E. coli loads estimations by the BNN model are better than the E. coli loads estimations by the LOADEST model on all the three occasions (three-fold cross validation). Thirteen factors were used for estimating E. coli loads with the exhaustive feature selection technique, which indicated that six of thirteen factors are important for estimating E. coli loads. Physical factors included temperature and dissolved oxygen; chemical factors include phosphate and ammonia; biological factors include suspended solids and chlorophyll. The results highlight that the LOADEST model estimates E. coli loads better in the smaller ranges, whereas the BNN model estimates E. coli loads better in the higher ranges. Hence, the BNN model can be used to design targeted monitoring programs and implement regulatory standards through TMDL programs.