A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling

Without a doubt the first step in any water resources management is the rainfall–runoff modeling over the watershed. However considering high stochastic property of the process, many models are being still developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently Artificial Neural Network (ANN) as a non-linear inter-extrapolator is extensively used by hydrologists for rainfall–runoff modeling as well as other fields of hydrology. In the current research, the wavelet analysis was linked to the ANN concept for modeling Ligvanchai watershed rainfall–runoff process at Tabriz, Iran. For this purpose the main time series of two variables, rainfall and runoff, were decomposed to some multi-frequently time series by wavelet theory, then these time series were imposed as input data to the ANN to predict the runoff discharge 1 day ahead. The obtained results show the proposed model can predict both short and long term runoff discharges because of using multi-scale time series of rainfall and runoff data as the ANN input layer.     

Policy options for promoting urban–rural cooperation in water management: a review

As most cities share their water basins with rural areas, an efficient and environmentally sensible urban water management system needs to consider the interplay between urban and rural water uses. This article analyses the three main challenges at this interface: increased competition for water resources (scarcity); flood management (abundance); and freshwater quality conservation (pollution). The merit of potential policy options is discussed from an economics perspective, taking examples from successful experiences in cities around the world. The benefits and challenges arising from enhanced coordination and integration between urban and rural water management are highlighted.     

Public–private partnerships for water in Asia: a review of two decades of experience

This article reviews the development of public–private partnerships (PPP) in water services in Asia over the last two decades and situates the Asian experience within the wider global context. Through a comparative analysis of PPP data from two different sources, the article highlights the critical issue of how ‘private’ is defined in understanding the extent of PPP in Asia, due to the important role played by enterprises under mixed public and private ownership. The article identifies cyclical patterns of development of PPP in the water sector across countries and the use of hybrid contractual and institutional arrangements for PPPs.     

The Accuracy and Reliability of Traditional Surface Flow Type Mapping: Is it Time for a New Method of Characterizing Physical River Habitat?

Surface flow types (SFTs) are advocated as ecologically relevant hydraulic units, often mapped visually from the bankside to characterize rapidly the physical habitat of rivers. SFT mapping is simple, non‐invasive and cost‐efficient. However, it is also qualitative, subjective and plagued by difficulties in recording accurately the spatial extent of SFT units. Quantitative validation of the underlying physical habitat parameters is often lacking and does not consistently differentiate between SFTs. Here, we investigate explicitly the accuracy, reliability and statistical separability of traditionally mapped SFTs as indicators of physical habitat, using independent, hydraulic and topographic data collected during three surveys of a c. 50 m reach of the River Arrow, Warwickshire, England. We also explore the potential of a novel remote sensing approach, comprising a small unmanned aerial system (sUAS) and structure‐from‐motion photogrammetry (SfM), as an alternative method of physical habitat characterization. Our key findings indicate that SFT mapping accuracy is highly variable, with overall mapping accuracy not exceeding 74%. Results from analysis of similarity tests found that strong differences did not exist between all SFT pairs. This leads us to question the suitability of SFTs for characterizing physical habitat for river science and management applications. In contrast, the sUAS–SfM approach provided high resolution, spatially continuous, spatially explicit, quantitative measurements of water depth and point cloud roughness at the microscale (spatial scales ≤1 m). Such data are acquired rapidly, inexpensively and provide new opportunities for examining the heterogeneity of physical habitat over a range of spatial and temporal scales. Whilst continued refinement of the sUAS–SfM approach is required, we propose that this method offers an opportunity to move away from broad, mesoscale classifications of physical habitat (spatial scales 10–100 m) and towards continuous, quantitative measurements of the continuum of hydraulic and geomorphic conditions, which actually exists at the microscale. Copyright © 2016 John Wiley & Sons, Ltd.