Energy Recovery in Pressurized Hydraulic Networks

Water Resources Management - This paper aims to evaluate the possibility of using non-utilized hydraulic energy in urban water distribution systems. For this purpose, the viability and possible...     

Demand Forecasting for Irrigation Water Distribution Systems

One of the main problems in the management of large water supply and distribution systems is the forecasting of daily demand in order to schedule pumping effort and minimize costs. This paper examines methodologies for consumer demand modeling and prediction in a real-time environment for an on-demand irrigation water distribution system. Approaches based on linear multiple regression, univariate time series models (exponential smoothing and ARIMA models), and computational neural networks (CNNs) are developed to predict the total daily volume demand. A set of templates is then applied to the daily demand to produce the diurnal demand profile. The models are established using actual data from an irrigation water distribution system in southern Spain. The input variables used in various CNN and multiple regression models are (1) water demands from previous days; (2) climatic data from previous days (maximum temperature, minimum temperature, average temperature, precipitation, relative humidity, wind speed, and sunshine duration); (3) crop data (surfaces and crop coefficients); and (4) water demands and climatic and crop data. In CNN models, the training method used is a standard back-propagation variation known as extended-delta-bar-delta. Different neural architectures are compared whose learning is carried out by controlling several threshold determination coefficients. The nonlinear CNN model approach is shown to provide a better prediction of daily water demand than linear multiple regression and univariate time series analysis. The best results were obtained when water demand and maximum temperature variables from the two previous days were used as input data.     

Heuristic Modelling of the Water Resources Management in the Guadalquivir River Basin, Southern Spain

A model comprising blocks of artificial neural networks (ANNs) combined in sequence was used to simulate the inflow and outflow in a water resources system under a shortage of water. We assessed the selection of appropriate input data using linear and non-linear cross-correlation functions and sensitivity analysis. The potential model inputs were flow, precipitation and temperature data from various gauging stations throughout the upper watershed of the ‘Guadiana Menor’ River (southern Spain), and the model considered various input time lags. The ANNs based on the selected inputs were effective relative to those with no relevant inputs, and produced more parsimonious models. We also investigated conceptual analogies inherent in the ANN models by analyzing the response profiles of the modelled variables (inflow and outflow) in relation to each of the selected input data. The results demonstrate that the neural approach approximated the behaviour of various components of the water resources system in terms of various hydrologic cycle processes and management rules. Our findings suggest that in dry periods a mean temperature increase of 1°C in low altitude locations of the region will result in a mean decrease of approximately 2% in the inflow to the water resources system, and a mean increase of approximately 12% in the outflow requirements for irrigation purposes.     

Regional Frequency Analysis of Droughts in Portugal

This study investigated the frequency of droughts for the period September 1910 to October 2004 in mainland Portugal, based on monthly precipitation data from 144 rain gauges distributed across the country. The drought events were characterized using the standardized precipitation index (SPI) applied to time scales of 1, 3, 6 and 12 consecutive months. Based on the SPI time scale series a regional frequency analysis of drought magnitudes was undertaken using two approaches: annual maximum series (AMS) and partial duration series (PDS). Three spatially defined regions (north, central and south) were identified by cluster analysis and analyzed for homogeneity. Maps of drought magnitude were developed using a kriging technique for several return periods. Similar uniform spatial patterns were found throughout the country using the AMS and PDS approaches. For several SPI time scales a comparison of the observed and estimated maximum magnitude (269-year empirical return period) showed that the AMS combined with the selected probability distribution models (Pearson type III, general Pareto and Kappa) provided better results than the PDS approach combined with the same models. A general and simplified characterization of drought duration revealed a relatively uniform pattern of droughts events across the country.     

Water Delivery System Planning Considering Irrigation Simultaneity

Time should be considered in carrying out the design and management of demand irrigation distribution systems. In this paper, a method to characterize the pumping flow in demand pressurized systems throughout the day and irrigation season is presented. This method considers the temporal evolution of water requirements during the irrigation season and water demand concentration in certain periods of the irrigation day due to different electrical energy charges. The model was established based on data from an actual water distribution network of an irrigation district in southern Spain. The results differed significantly from those obtained using approaches based on establishing a uniform working probability for the outlets of the water distribution network at all hours of the irrigation day, which underestimated the circulating flows or system capacity. The most probable pumping flow with uniform probability was 3.1 m3/s, a smaller value than those obtained in the off-peak and average energy tariff times (4 and 3.4 m3/s, respectively). The total energy head required at the booster pumping in each period of the irrigation season was simulated. 10,000 randomly chosen scenarios were simulated for each irrigation day and each energy tariff time. The heterogeneous vertical stratification between 50 and 103 m of the required piezometric head was obtained as a function of the demanded flow for the water distribution system. This paper includes a pump selection algorithm for recommending least cost or optimum pump combinations in the distribution network and to evaluate the system’s energy cost. The pump recommendations show that the optimal solution could have saved 41% of the pumping cost of the Fuente Palmera irrigation district.