Aimrun Wayayok

Stochastic modelling of seasonal and yearly rainfalls with low-frequency variability

Stochastic rainfall models are important for many hydrological applications due to their appealing ability to simulate synthetic series that resemble the statistical characteristics of the observed series for a location of interest. However, an important limitation of stochastic rainfall models is their inability to preserve the low-frequency variability of rainfall. Accordingly, this study presents a simple yet efficient stochastic rainfall model for a tropical area that attempts to incorporate seasonal and inter-annual variabilities in simulations. The performance of the proposed stochastic rainfall model, the tropical climate rainfall generator (TCRG), was compared with a stochastic multivariable weather generator (MV-WG) in various aspects. Both models were applied on 17 rainfall stations at the Kelantan River Basin, Malaysia, with tropical climate. The validations were carried out on seasonal (monsoon and inter-monsoon) and annual basis. The third-order Markov chain of the TCRG was found to perform better in simulating the rainfall occurrence and preserving the low-frequency variability of the wet spells. The log-normal distribution of the TCRG was consistently better in modelling the rainfall amounts. Both models tend to underestimate the skewness and kurtosis coefficient of the rainfall. The spectral correction approach adopted in the TCRG successfully preserved the seasonal and inter-annual variabilities of rainfall amounts, whereas the MV-WG tends to underestimate the variability bias of rainfall amounts. Overall, the TCRG performed reasonably well in the Kelantan River Basin, as it can represent the key statistics of rainfall occurrence and amounts successfully, as well as the low-frequency variability.

Generation of a stochastic precipitation model for the tropical climate

A tropical country like Malaysia is characterized by intense localized precipitation with temperatures remaining relatively constant throughout the year. A stochastic modeling of precipitation in the flood-prone Kelantan River Basin is particularly challenging due to the high intermittency of precipitation events of the northeast monsoons. There is an urgent need to have long series of precipitation in modeling the hydrological responses. A single-site stochastic precipitation model that includes precipitation occurrence and an intensity model was developed, calibrated, and validated for the Kelantan River Basin. The simulation process was carried out separately for each station without considering the spatial correlation of precipitation. The Markov chains up to the fifth-order and six distributions were considered. The daily precipitation data of 17 rainfall stations for the study period of 1954–2013 were selected. The results suggested that second- and third-order Markov chains were suitable for simulating monthly and yearly precipitation occurrences, respectively. The fifth-order Markov chain resulted in overestimation of precipitation occurrences. For the mean, distribution, and standard deviation of precipitation amounts, the exponential, gamma, log-normal, skew normal, mixed exponential, and generalized Pareto distributions performed superiorly. However, for the extremes of precipitation, the exponential and log-normal distributions were better while the skew normal and generalized Pareto distributions tend to show underestimations. The log-normal distribution was chosen as the best distribution to simulate precipitation amounts. Overall, the stochastic precipitation model developed is considered a convenient tool to simulate the characteristics of precipitation in the Kelantan River Basin.

Downscaling daily precipitation and temperatures over the Langat River Basin in Malaysia: A comparison of two statistical downscaling approaches

Increasing greenhouse gas concentrations can cause future changes in the climate system that have a major impact on the hydrologic cycle. To realize and predict future climate parameters, the Atmosphere-Ocean Global Climate Models (AOGCMs) are common employed tools to predict the future changes in climate parameters. The statistical downscaling methods have been applied as a practical tool to bridge the spatial difference between grid-box scale and sub-grid box scale. This paper investigates the capability of Statistical Downscaling Model (SDSM) and Artificial Neural Network (ANN) with different complexities in downscaling and projecting climate variables in the tropical Langat River Basin. These two statistical downscaling models have been calibrated, validated and used to project the possible future scenarios (2030s and 2080s) of meteorological variables, which are the maximum and minimum temperatures as well as precipitation using the CGCM3.1 under A2 emission scenario. The statistical validation of generated precipitation as well as maximum and minimum temperatures on a daily scale illustrated that the SDSM is more accurate than the ANN with different learning rules. On the other hand, the SDSM showed more capability to catch the wet-spell and dry-spell lengths than the ANN model. The calibrated models show higher accuracy in simulating the maximum and minimum temperatures in comparison with the capture of the variability of precipitation. The trend analysis test of generated time series by the SDSM indicates an increasing trend by the 2030s and 2080s at most of the stations. Key words: Statistical downscaling, multiple linear regression, nonlinear regression, artificial neural network, tropical area, Malaysia.

Preference Index-Based Allocation of Optimized Cropping Area at the Mirpurkhas Subdivision: Jamrao Irrigation Scheme in Sindh, Pakistan

AbstractOptimal utilization of limited water resources is important due to the continuously increasing demand for it. This study was aimed at devising a model and a preference index for allocating limited land and water resources at the Mirpurkhas subdivision of the Jamrao irrigation scheme in Sindh, Pakistan. The deterministic linear programming (DLP) model formulated was set to the minimum and maximum area constraints and applied for different scenarios (at levels of 100, 70, 60, and 50% of existing water supply), in an effort to find the optimum conditions of land area allocation. Emphasis was given to the cultivation of vegetable, banana, and oilseed crops during times of shortages of water. Upon optimization with the DLP model, the proposed preference index was found to be an effective guide in equitable distribution among the competing command areas through the resulting competitive and conflict-free allocating of the resources. The model cum index that was developed is proposed to be adopted while ...

Investigation of Salinity Consequences Resulting from Drainage Systems Using Numerical Models

AbstractLand drainage practices represent a suitable technique that can assist managing the water table and control salinity levels. Until the last few decades, mainly the positive aspects of drain...

Monitoring spatial and temporal variations of the rice backscatter coefficient (σ0) at different phenological stages in Sungai Burong and Sawah Sempadan, Kuala Selangor.

Monitoring rice growth and yield estimation using optical remote sensing data constitutes a big challenge largely due to cloud conditions that are typical of tropical regions. Using Radar remote sensing data helps because it overcomes the cloud issue and distinguishes the behaviour of the radar backscattering of rice crops specifically. This study indicated the temporal change of rice backscatter (σ°) at two different growth stages using HH polarimetric Radarsat-2. The aims of this study are: (1) to identify crop with different life spans based on the backscatter coefficients values from a single polarisation for understanding the backscatter characteristic of rice over the entire growth cycle, and (2) to understand the advantages and limitations using the RADARSAT-2, C band with HH polarisation. The values of backscattering coefficients have been related to the Malaysia rice crop calendar to get the information of the growth status. The result shows strong backscatter coefficient values on the 21st of May that referred to the reproductive-maturity of rice in the Sawah Sempadan area, and out of season for the Sungai Burong area. While for the August 1st imagery, the result shows weak backscatter values which refers to early vegetative and vegetative-reproductive. The values of backscattering coefficient are found to be much less for early vegetation compare to mature rice crop. In this paper, we have also performed a classification of a rice field using Landsat 8 OLI.

Comparative Study on the Performance of Rice Seedlings Raised by Single Seedling Nursery Tray Method and Conventional System

Rising of rice seedlings is among the important factors responsible for better growth and development of rice plants as well as increasing the grain yield. Conventional method of raising rice seedlings requires larger space, time (24 hours seed soaking and 34 hours in jute bag up to sprouting) and labour intensive procedure, which limits the production capacity of rice seedlings. Transplanted seedlings raised by newly developed single seedling nursery tray have not been compared with the conventional system in the field so far. The objective of this study was to evaluate the average number of tillers per hill at 60-days after transplanting (DAT). The experimental design was one treatment [newly developed single seedling nursery tray (T1)] and three replications. The age of the seedlings raised by the newly developed single seedling nursery tray at the time of transplantation was 8 days. Plants were randomly selected and number of tillers were counted and recorded at 60 DAT from each treatment plot for further analysis. Average number of tillers (32.27 tillers/hill) in the case of the conventional system at 60 DAT using 8 days old seedlings was collected from previously published data. One sample T test was used to analyse the data using SPSS statistical analysis software (version 21) at 95% Confidence Levelof the Difference. The result of the analysis showed significant difference between them, with larger average number of tillers in T1 (68.56 tillers/hill) than the existing average number of tillers. The study depicted that using the newly developed single seedling nursery tray is one of the options to increase the number of tillers in SRI farming in order to increase the number of effective tillers, number of panicles, straw yield and grain yield of rice plants.

Using SPOT-5 images in rice farming for detecting BPH (Brown Plant Hopper)

Infestation of rice plant-hopper such as Brown Plant Hopper (BPH) (Nilaparvata lugens) is one of the most notable risk in rice yield in tropical areas especially in Asia. In order to use visible and infrared images to detect stress in rice production caused by BPH infestation, several remote sensing techniques have been developed. Initial recognition of pest infestation by means of remote sensing will spreads, for precision farming practice. To address this issue, detection of sheath blight in rice farming was examined by using SPOT-5 images. Specific image indices such as Normalized decrease food production costs, limit environmental hazards, and enhance natural pest control before the problem Normalized Difference Vegetation Index (NDVI), Standard difference indices (SDI) and Ratio Vegetation Index (RVI) were used for analyses using ENVI 4.8 and SPSS software. Results showed that all the indices to recognize infected plants are significant at α = 0.01. Examination of the association between the disease indices indicated that band 3 (near infrared) and band 4 (mid infrared) have a relatively high correlation. The selected indices declared better association for detecting healthy plants from diseased ones. Consequently, these sorts of indices especially NDVI could be valued as indicators for developing techniques for detecting the sheath blight of rice by using remote sensing. This infers that they are useful for crop disease detection but the spectral resolution is probably not sufficient to distinguish plants with light infections (low severity level). Using the index as an indicator can clarify the threshold for zoning the outbreaks. Quick assessment information is very useful in precision farming to practice site specific management such as pesticide application.