Nur Shazwani Muhammad

Estimation the Physical Variables of Rainwater Harvesting System Using Integrated GIS-Based Remote Sensing Approach

Geographic Information System (GIS) are an intelligence technique skilled to extract, store, manage and display the spatial information for various applications of water resources management. Practically, arid and semi-arid environments suffer from several restrictions (e.g., lack of socio-economic and physical data, limited precipitation, and poor rain water management). In this research, Remote Sensing (RS) approach was integrated with GIS conducted to estimate the physical variables of reservoir system (i.e., elevation-area-volume curve). First and foremost, computing an accurate and reliable elevation-area-volume curve is a challenging task for the purpose of identifying the optimal depth, minimum surface area and maximum reservoir storage. Accordingly, a field study consisting of three constructed small earth dams were demonstrated the use of the geospatial approach in the western desert of Iraq, where the elevation-area-volume curve was extracted. The surface areas and the reservoir volumes that were obtained from field survey and spatial intelligence techniques were compared. A comprehensive analysis have been carried out for the evaluation purposes. The results indicate that the proposed approach efficiently applied with remarkable level of accuracy.

Optimization of area–volume–elevation curve using GIS–SRTM method for rainwater harvesting in arid areas

The major limitation in planning water harvesting is the lack of knowledge in the estimation of surface area and storage volume at any depth of dam reservoir. The area–volume–elevation (AVE) curve of a reservoir plays a key role in estimating the most suitable depth, optimum surface area and highest capacity of reservoir storage. The existing methods to estimate the AVE curve are costly and time-consuming and require laborious work. This study attempts to develop a method to optimize the AVE curve for earth dams, using the digital elevation model generated by the Shuttle Radar Topography Mission (SRTM) data, and integrate it with the geographic information system (GIS), known as the GIS–SRTM. The proposed method was tested using field data in the Western Desert of Iraq, which is an arid environment. Three constructed small earth dams were selected for this study. The AVE curves were extracted for Horan 2 (H2), Al-gara 2 (G2) and Al-gara 4 (G4) earth dams. Comprehensive analyses have been carried out to evaluate the performance of the AVE curves using the proposed GIS–SRTM method and the field data. From the comparison, the proposed GIS–SRTM method was able to produce reliable AVE curves with a relative error less than 20%. Additionally, the proposed method was less time-consuming and the AVE curves can be visualized immediately. The proposed GIS–SRTM method is relatively supportive in analyzing spatial data to select the optimal site for rainwater harvesting and prevent excessive evaporation losses.

Flood flow simulations and return period calculation for the Kota Tinggi watershed, Malaysia

The City of Kota Tinggi in Malaysia was under more than 5 m of water during the floods in December 2006 and January 2007. The watershed received between 280 and 530 mm of rain in 4 days. These extreme events occurred 3 weeks apart and caused extensive damages. The application of the fully distributed twodimensional model two-dimensional runoff, erosion and export (TREX) to simulate these events and the estimation of the return period of such extreme events are the main objectives of this study. The model performance was very good based on the relative percentage different (3.7%), percent BIAS (overestimated the volume by 1.5%) and Nash–Sutcliffe efficiency coefficient (0.8). The TREX model is successful in simulating extreme flood events. The December 2006 rainstorm event at Kota Tinggi is extremely rare, and this multiday rainstorm had an estimated return period greater than 2000 years. The analysis of the return period shows that multiday rainstorms occur more frequently than single-day events. In addition, we produce a flood threshold graph by integrating both the hydrological modelling process using TREX and the theoretical formulation of return period. The proposed flood threshold graph is useful in the estimation of the amount of accumulated rainfall from multiday rainfall that can cause flooding on a large watershed like Kota Tinggi.

Robust approach for optimal positioning and ranking potential rainwater harvesting structure (RWH): a case study of Iraq

Rainwater harvesting (RWH) structure is considered as the best solution to conserve water for arid regions. The selection of RWH location is based on several key determinants such as hydrology, environment, topography, and socio-economic. This study proposed a robust methodology to identify and select the location of RWH using geographical information systems (GIS) and remote sensing (RS) with multi-criteria decision techniques in areas where data are scarce. Several thematic maps were extracted such as vegetation cover, soil group, slope, land use, and digital elevation (DEM). The RWH sites were ranked based on four major indexes: evaporation, cost-benefit, sediment, and hydrological index. Sensitivity analysis shows that the variance inverse (VI) and rank order method (ROM) considered all indices that effect ranking as compared to the analytic hierarchy process (AHP) and fuzzy-AHP. Sensitivity analysis also proved that the proposed method is suitable to be used for RWH site selection in arid regions.

Estimation of Peak Discharges Using Flood Frequency Analysis and Hydrological Modeling System

This study presents the methodology to estimate peak discharges using flood frequency analysis (FFA) and hydrological modeling system (HMS). FFA method was performed using two distribution functions, i.e., Gumbel’s extreme value distribution (Gumbel’s) and lognormal (LN) distribution, while HMS was carried out using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) software. Long and reliable daily streamflow and rainfall data (i.e., 50 years) recorded at Sungai Rinching, Selangor, were used. Goodness-of-fit test indicates that the chi-square (χ2) values for Gumbel’s and lognormal distributions are 1.952 and 7.976, respectively, which are significantly less than the \({\upchi}_{\alpha ,v}^{2}\) value of 9.488. However, Gumbel’s is chosen to represent the distribution of annual maximum daily streamflow because the χ2 test value was much lesser as compared to lognormal distribution. The calibration and validation processes were carried out to test the suitability of HEC-HMS software to simulate the streamflow of Sungai Rinching. The relative percentage difference (RPD) estimated for peak discharges from these processes was between 3 and 6 %, and maximum lag times were only 1 h. These results indicate that the model performance is very good. The peak discharges for several average recurrence intervals (ARIs), i.e., 2, 5, 10, 20, 50, and 100 years estimated using FFA and HMS, were also compared. Results show that the peak discharges estimated using HMS for all ARIs (except 2 years) are comparable to the values given by FFA, and the given percentage differences are between 2.7 and 12.2 %. This finding indicates that HEC-HMS may be used as a tool in simulating the daily streamflow and estimate the annual daily maximum streamflow of several ARIs at Sungai Rinching.

Probability Structure and Return Period of Multiday Monsoon Rainfall

AbstractThe daily monsoon rainfall data recorded at Subang Airport, Malaysia, from 1960 to 2011 is examined in terms of probability structure for the estimation of extreme daily rainfall precipitation during the Northeast (NE) and Southwest (SW) Malaysian monsoons. The discrete autoregressive and moving average [DARMA(1,1)] model is preferable to the first-order Markov chain [DAR(1)] model. The conditional probabilities of t consecutive rainy days are time dependent. Nevertheless, a simple two-parameter gamma distribution appropriately fits the frequency distribution of multiday rainfall amounts. An algorithm is developed by combining the DARMA(1,1) and gamma models to estimate the return period of multiday rainfall. Extensive comparisons showed that the DARMA(1,1)-gamma model gives a reliable estimate of the return period of rainfall for both NE and SW monsoons at Subang Airport. Furthermore, values generated from the models enable the analysis of the frequency distribution of extreme rainfall events.

Multiday Rainfall Simulations for Malaysian Monsoons

This study examines the suitability of the discrete autoregressive and moving average [DARMA(1,1)] model to simulate the sequences of daily rainfall data in Malaysia. The daily monsoon rainfall data recorded at Subang Airport are used to test this modeling approach. The autocorrelation function and probability distributions of wet and dry run lengths estimated from the DARMA(1,1) model matched the sample values quite well. Both theoretical and sample autocorrelation functions slowly decay to zero at day 15. The theoretical probability distribution for two consecutive wet days estimated for the DARMA(1,1) is 0.1966, while the observed rainfall data give a probability of 0.2066. Additionally, the sum of squared errors for the DARMA(1,1) model were very small, i.e., 0.0015. Furthermore, two simulations were done, i.e., 100 samples of 9,600 days (simulation A) and a very long sequence of 1,000,000 days (simulation B). This was done to test the capability of DARMA(1,1) to model a long sequence of daily rainfall. The statistics examined in this study include the lag-1 autocorrelation coefficient (lag-1 ACF) and the maximum wet and dry run lengths. Generally, the statistics of generated rainfall for simulation A fall within two standard deviations from the sample. The diversions of these statistics were reasonable, considering the sample size used in this study. The estimated lag-1 ACF for simulation B was slightly lower than the sample. The maximum wet and dry run lengths were much higher than the observed data because of the different sample sizes. It is concluded that the DARMA(1,1) model is able to simulate the long sequences wet and dry days and preserving the statistics within reasonable accuracy.

Keynote: Analysis of Extreme Floods in Malaysia

This article reviews some of the recent advances in the analysis of extreme flood events in Malaysia. First, a detailed analysis of daily rainfall precipitation measurements leads to new understanding regarding Malaysian monsoons: the conditional probability of rainfall steadily increases as a function of the number of successive rainy days. The probability of multiday rainfall events has also been analyzed using stochastic models like DARMA(1,1) to demonstrate lower periods of returns of large precipitation amounts for rainfall events between 4 and 12 days. Advances in numerical modeling of surface runoff using the TREX model allowed improved simulations of large floods when considering rainfall amounts between the 2- and 100-year events and the PMP for extreme floods on both small to large watersheds in Malaysia. Examples on Lui, Semenyih, and Kota Tinggi have also been possible with GIS data at 30–90 m resolution. The recent floods of the Kota Tinggi and Muda River are also briefly discussed. Finally, a brief overview of the DID River Management Manual is also presented.

Hydrological Modeling in Malaysia

Hydrological modeling in Malaysia using two-dimensional fully distributed and physically based model is relatively new. Basic guidelines in choosing model dimensions (i.e., 1D, 1D–2D, 2D, and 3D) are discussed. The application of 3D model will give more accurate results if the groundwater movement is considered but requires more time to simulate and prepare input data. However, the 1D model is less accurate in representing the real topography of the study area, even though requires short time to simulate. This chapter also gives an overview about the hydrological modeling in Malaysia which ranges from 1D to 3D. Several researches on hydrology using different model were reviewed. The success of each model will be highlighted.