Reservoir volume optimization and performance evaluation of rooftop catchment systems in arid regions: A case study of Birjand, Iran

This study evaluated the performance of rooftop catchment systems in securing non-potable water supply in Birjand, located in an arid area in southeastern Iran. The rooftop catchment systems at seven study sites of different residential buildings were simulated for dry, normal, and wet water years, using 31-year rainfall records. The trial and error approach and mass diagram method were employed to optimize the volume of reservoirs in five different operation scenarios. Results showed that, during the dry water year from 2000 to 2001, for reservoirs with volumes of 200-20000 L, the proportion of days that could be secured for non-portable water supply was on average computed to be 16.4%-32.6% across all study sites. During the normal water year from 2009 to 2010 and the wet water year from 1995 to 1996, for reservoirs with volumes of 200-20000 L, the proportions were 20.8%-69.6% and 26.8%-80.3%, respectively. Therefore, a rooftop catchment system showed a high potential to meet a significant portion of non-potable water demand in the Birjand climatic region. Reservoir volume optimization using the mass diagram method produced results consistent with those obtained with the trial and error approach, except at sites #1, #2, and #5. At these sites, the trial and error approach performed better than the mass diagram method due to relatively high water consumption. It is concluded that the rooftop catchment system is applicable under the same climatic conditions as the study area, and it can be used as a drought mitigation strategy as well.

Validation of CA-Markov for Simulation of Land Use and Cover Change in the Langat Basin, Malaysia

Validity of CA-Markov in land use and cover change simulation was investigated at the Langat Basin, Selangor, Malaysia. CA-Markov validation was performed using validation metrics, allocation disagreement, quantity disagreement, and figure of merit in a three-dimensional space. The figure of merit, quantity error, and allocation error for total landscape simulation using the 1990-1997 calibration data were 5.62%, 3.53%, and 6.13%, respectively. CA-Markov showed a poor performance for land use and cover change simulation due to uncertainties in the source data, the model, and future land use and cover change processes in the study area.

Incorporation of GIS Based Program into Hydraulic Model for Water Level Modeling on River Basin

Water resources management usually requires that hydraulic, ecological, and hydrological models be linked. The Hy- drologic Engineering Center River Analysis System (HEC-RAS) hydraulic model and the Hydrologic Engineering Center Geospatial River Analysis System (HEC-GEORAS), imitates flow and water profiles in the Neka river basin’s downstream flood plain. Hydrograph phases studied during the flood seasons of 1986-1999 and from 2002-2004 were used to calibrate and verify the hydraulic model respectively. Simulations of peak flood stages and hydrographs’ evaluations are congruent with studies and observations, with the former showing mean square errors between 4.8 - 10 cm. HECRAS calculations and forecast flood water levels. Nash-Sutcliffe effectiveness (CR3) is more than 0.92 along with elevated levels of water which were created with some effectiveness (CR5) of 0.94 for the validation period. The coupled two models show good performance in the water level modeling.

Comparison between Multi-Layer Perceptron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed

Prediction of highly non-linear behavior of suspended sediment flow in rivers has prime importance in environmental studies and watershed management. In this study, the predictive performance of two Artificial Neural Networks (ANNs), namely Radial Basis Function (RBF) and Multi-Layer Perceptron (MLP) were compared. Time series data of daily suspended sediment discharge and water discharge at the Langat River, Malaysia were used for training and testing the networks. Mean Square Error (MSE), Normalized Mean Square Error (NMSE) and correlation coefficient (r) were used for performance evaluation of the models. Using the testing data set, both models produced a similar level of robustness in sediment load simulation. The MLP network model showed a slightly better output than the RBF network model in predicting suspended sediment discharge, especially in the training process. However, both ANNs showed a weak robustness in estimating large magnitudes of sediment load.