Search

The uses of clay pipes in irrigation water management are becoming popular, especially in arid and semi-arid soils. The study examined clay and zeolite materials for irrigation pipes, and this paper reported characteristic properties of these materials. Hydrometer, pychnometer and core sampler methods were for soil physical properties determination. Consistency tests using (Atterberg method) and analytical techniques (Scanning Electron Microscopy, SEM and Energy Dispersive Spectroscopy, EDS) for samples analyses. The results indicate the soil to contain 11 % sand, 34 % silt and 55 % clay fractions and texturally classified as clay with a particle density of 1.58 g/cm3 and bulk density 2.43 g/cm3 respectively. Addition of zeolite at 3:1 clay/zeolite mix ratio shows liquid limit (LL) and plastic (PL) values to decreases from 50.7% to 43.7% and 27.6% to 27.3% while plasticity index, (PI) change from 23.2 to 16.7 respectively. The shrinkage rate decreases from 11.67% for raw clay to 8.92 % for the treated sample. The EDS analysis shows both clay and zeolite samples to contain carbon, silica (SiO2) and alumina (Al2O3) as the major constituents with ferric oxide (Fe2O3), potassium oxide (K2O) and cobalt (Co) as the minor constituents. The major constituents contribute 89.26 and 94.4% while minor contribute 10.74 and 5.59 % in clay and zeolite samples. Modifying clay improved its workability, reduces cracking potential and absorption capacity and performance of porous clay pipes.

Water allocation is a key component of good water management in an irrigation system. Water imbalance between upstream and downstream has been a major issue at Tanjung-Karang Rice Irrigation Scheme Malaysia. This study applied the HEC-RAS model to estimate water for supply and analysed its allocation to the demand in the scheme. Water demand was computed based on water requirement, effective rainfall and seepage/percolation. Results of R2, NSE, PBIAS and RSR during the model calibration and validation were 0.66, 0.64, 0.94 and 0.60; and 0.65, 0.59, 1.77 and 0.64, respectively. The irrigation scheme was under-supplied from January-March and over-supplied from April-June during the off-season. While in the main-season was under-supplied from July-September and over-supplied from October-December. Similarly, the excess water during the off and main seasons for the period was 40.10 and 52.40 Mm3 respectively. Whiles the deficit water during the same seasons was 52.46 and 53.14 Mm3, respectively. This suggests providing an adequate storage facility, which could store excess water during low water demand and use it in the period of water shortage. The developed model could therefore assist in estimating the over/under-supply with respect to the demand thereby storing the excess for use during the period of high demand.&nbsp

Water productivity (WP) is a key indicator of agricultural water management, since it affects the quantity of water used for crop yield in various management scenarios. This study evaluated the WP of irrigated rice due to a changing climate in the Northwest Selangor Rice Irrigation Scheme (NSRIS) by using field experimental data and the FAO-AquaCrop Model. Pertinent soil, water, climate, and crop data were acquired by executing a field investigation during the off-season (dry season, January–April) and main season (wet season, July–October) in 2017. The AquaCrop 6.0 model was calibrated and validated using the measured data. A Climate-smart Decision Support System (CSDSS) with an ensemble of 10 Global Climate Models (GCMs) was used to downscale climate variables under RCP4.5, RCP6.0, and RCP8.5 emission scenarios during baseline (1976 to 2005) and future (2020 to 2099) periods. The AquaCrop model fairly predicted rice yields under field conditions with root-mean-square error (RMSE), mean absolute error (MAE), prediction error (PE) and index of agreement (d) between the observed and estimated yields of 0.173, 0.157, −0.31 to 5.4 and 0.78, respectively for the off-season; and 0.167, 0.127, −5.6 to 2.3 and 0.73, respectively for the main season. It predicted a 10% decrease in actual crop evapotranspiration (ETc) in both crop seasons in the future. The WP of rice based on total water input (WPIrr+RF), applied irrigation (WPIrr), and actual crop evapotranspiration (WPETc) will likely increase by 14–24%, 14–19%, and 17–29%, respectively under the three RCP emission scenarios in the off-season. The likely increase in WP for the corresponding base is 13–22%, 15–24%, and 14–25% in the main season. Various agronomic management options linked to WP will most likely become important in making crucial decisions to cope with the risk of impacts on climate change.

Unlike other micro-irrigation facilities like a drip, trickle, and sprinklers that emits water at regularly spaced intervals with predefined discharges, porous rubber pipes (soaker hose) has openings of variable sizes that become unevenly spaced with uneven distribution. The latter makes discharge to be variant along its lateral. Shorter sections are used under laboratory column experiments of soil wetting pattern studies and for this reason, laboratory experiments were conducted to evaluate the extent of emission rates variability on short sections of commercial Irrigation Soaker Hose, 16 mm diameter. Three sections of 10 cm length pipes were randomly selected from 15 no's cuts from different parts of the twenty meters length pipe bundle and used to investigate the extent of variability on emission rates characteristics under six different operating pressures. The result was achieved by collecting and measuring water emitted through the pipe sections at pre-determined pressures. The various discharges, coefficient of variation, and pressure-discharge curves of the section of the pipe then determined from the data. The result shows somewhat similar trends on the increase for water collected with an increase in pressures; however, when statistically compared, the discharges among the pipe sections vary. The values of Coefficient of Variation (CV) are less than 10 % as the values CV range from 0.92 % to 5.82 %, which is within a good category, according to ASAE Standard EP405.1 of 0-10%. The findings indicate that, despite variations among the investigated sections, it can use any part as a representative unit in the soil column experiments with reasonable accuracy.

Agricultural drought is crucial in understanding the relationship to crop production functions which can be monitored using satellite remote sensors. The aim of this research is to combine temperature vegetation dryness index (TVDI) and normalized difference water index (NDWI) classifications for identifying drought areas in Chuping, Malaysia which has regularly recorded high temperatures. TVDI and NDWI are assessed using three images of the dry spell period in March for the years 2015, 2016 and 2017. NDWI value representing water content in vegetation decreases numerically to −0.39, −0.37 and −0.36 for the year 2015, 2016 and 2017. Normalized difference vegetation indices (NDVI) values representing vegetation health status in the given area for images of years 2015 to 2017 decreases significantly (p ≤ 0.05) from 0.50 to 0.35 respectively. Overall, TVDI in the Chuping area showed agricultural drought with an average value of 0.46. However, Kilang Gula Chuping area in Chuping showed a significant increase in dryness for all of the three years assessed with an average value of 0.70. When both TVDI and NDWI were assessed, significant clustering of spots in Chuping, Perlis for all the 3 years was identified where geographical local regressions of 0.84, 0.70 and 0.70 for the years 2015, 2016 and 2017 was determined. Furthermore, Moran’s I values revealed that the research area had a high I value of 0.63, 0.30 and 0.23 with respective Z scores of 17.80, 8.63 and 6.77 for the years 2015, 2016 and 2017, indicating that the cluster relationship is significant in the 95–99 percent confidence interval. Using both indices alone was sufficient to understand the drier spots of Chuping over 3 years. The findings of this research will be of interest to local agriculture authorities, like plantation and meteorology departments to understand drier areas in the state to evaluate water deficits severity and cloud seeding points during drought.

Nitrogen loss from agricultural fields results in contamination of ground and surface water resources due to leaching and runoff, respectively. Nitrogen transport dynamics vary significantly among agricultural fields of different climates, especially in the tropical climate. This study intended to evaluate the rainfall impact on nitrogen distribution and losses under tropical rain-fed conditions. The study was carried out in a sweet corn field for two growing seasons at the Malaysian Agricultural Research and Development Institute (MARDI) research field. The HYDRUS-1D numerical model was used to simulate nitrogen transport dynamics in this study. The observed nitrogen concentrations were used for calibration and validation of the model. Total nitrogen input to sweet corn was 120 kg/ha for both seasons. Nitrogen losses through surface runoff and leaching were dominating pathways. Surface runoff accounted for 35.3% and 22.2% of total nitrogen input during the first and second seasons, respectively. The leaching loss at 60 cm depth accounted for 4.0% (first season) and 18.5% (second season). The crop N uptake was 37.5% and 24.9% during the first and second seasons, respectively. Nitrate was the dominant form of N uptake by the crop that accounted for 83.6% (first season) and 78.5% (second season). The HYDRUS-1D simulation results of nitrogen concentrations and fluxes were found in good agreement with observed data. The overall results of simulation justified the HYDRUS-1D for improved fertilizer use in the tropical climate.

Agro-hydrological water management frameworks help to integrate expected planned management and expedite regulation of water allocation for agricultural production. Low production is not only due to the variability of available water during crop growing seasons, but also poor water management decisions. The Tanjung Karang Rice Irrigation Scheme in Malaysia has yet to model agro-hydrological systems for effective water distribution under climate change impacts. A climate-smart agro-hydrological model was developed using Excel-based Visual Basic for Applications (VBA) for adaptive irrigation and wise water resource management towards water security under new climate change realities. Daily climate variables for baseline (1976&ndash, 2005) and future (2010&ndash, 2099) periods were extracted from 10 global climate models (GCMs) under three Representative Concentration Pathway scenarios (RCP4.5, RCP6.0, and RCP8.5). The projected available water for supply to the scheme would noticeably decrease during the dry season. The water demand in the scheme will differ greatly during the months in future dry seasons, and the increase in effective rainfall during the wet season will compensate for the high dry season water demand. No irrigation will therefore be needed in the months of May and June. In order to improve water distribution, simulated flows from the model could be incorporated with appropriate cropping patterns.

The prediction of nitrogen not only assists in monitoring the nitrogen concentration in streams but also helps in optimizing the usage of fertilizers in agricultural fields. A precise prediction model guarantees the delivering of better-quality water for human use, as the operations of various water treatment plants depend on the concentration of nitrogen in streams. Considering the stochastic nature and the various hydrological variables upon which nitrogen concentration depends, a predictive model should be efficient enough to account for all the complexities of nature in the prediction of nitrogen concentration. For two decades, artificial neural networks (ANNs) and other models (such as autoregressive integrated moving average (ARIMA) model, hybrid model, etc.), used for predicting different complex hydrological parameters, have proved efficient and accurate up to a certain extent. In this review paper, such prediction models, created for predicting nitrogen concentration, are critically analyzed, comparing their accuracy and input variables. Moreover, future research works aiming to predict nitrogen using advanced techniques and more reliable and appropriate input variables are also discussed.

In the past few decades, there has been a rapid growth in the concentration of nitrogenous compounds such as nitrate-nitrogen and ammonia-nitrogen in rivers, primarily due to increasing agricultural and industrial activities. These nitrogenous compounds are mainly responsible for eutrophication when present in river water, and for 'blue baby syndrome' when present in drinking water. High concentrations of these compounds in rivers may eventually lead to the closure of treatment plants. This study presents a training and a selection approach to develop an optimum artificial neural network model for predicting monthly average nitrate-N and monthly average ammonia-N. Several studies have predicted these compounds, but most of the proposed procedures do not involve testing various model architectures in order to achieve the optimum predicting model. Additionally, none of the models have been trained for hydrological conditions such as the case of Malaysia. This study presents models trained on the hydrological data from 1981 to 2017 for the Langat River in Selangor, Malaysia. The model architectures used for training are General Regression Neural Network (GRNN), Multilayer Neural Network and Radial Basis Function Neural Network (RBFNN). These models were trained for various combinations of internal parameters, input variables and model architectures. Post-training, the optimum performing model was selected based on the regression and error values and plot of predicted versus observed values. Optimum models provide promising results with a minimum overall regression value of 0.92.

Hydrological models are commonly used to quantify the hydrological impacts of climate change using general circulation model (GCM) simulations as input. However, application of the model results with respect to future changes in streamflow scenarios remains limited by the large uncertainties stemming from various sources. Therefore, this study aimed to explore uncertainties involved in climate change impact assessment in Hulu Langat Basin, Malaysia, and define the contribution of uncertainty sources to the final uncertainty level. Hydrological model parameters, GCMs, and emission scenario uncertainties were considered the main uncertainty contributors in local-scale impact studies. The equidistant quantile matching method is used to bias-correct simulations of 19 GCMs under two emission scenarios of RCP4.5 and RCP8.5. The Soil and Water Assessment Tool (SWAT) hydrological model is next run by the bias-corrected GCM data to generate a wide spectrum of future streamflow scenarios. Projected monthly streamflow pattern under RCP8.5 showed a different temporal pattern from the observed one. Hydrological model parameter uncertainty was proven to be a larger uncertainty contributor than emission scenario during baseline climate. GCM and emission scenario uncertainties escalated as progressed in time and GCM uncertainty showed larger increments. The monthly pattern of effect of each uncertainty source varied when comparing the two periods of 2030s and 2080s. Therefore, for a superior management of water resources, a study of climate change impacts and uncertainty sources on a smaller scale than the decadal or annual scales can be more informative to the decision makers.

Synthetic-aperture radar’s (SAR’s) capacity to resolve the cloud cover concerns encountered while gathering optical data has tremendous potential for soil moisture data retrieval using SAR data. It is possible to use SAR data to recover soil moisture because the backscatter coefficient is sensitive to both soil and vegetation by penetrating through the vegetation layer. This study investigated the feasibility of employing a SAR-derived radar vegetation index (RVI), the ratios of the backscatter coefficients using polarizations of HH/HV (RHH/HV) and HV/HH (RHH/HV) to an oil palm crops as vegetation indicators in the water cloud model (WCM) using phased-array L-band SAR-2 (PALSAR-2). These data were compared to the manual leaf area index (LAI) and a physical soil sampling method for computing soil moisture. The field data included the LAI input parameters and, more importantly, physical soil samples from which to calculate the soil moisture. The fieldwork was carried out in Chuping District, Perlis State, Malaysia. Corresponding PALSAR-2 data were collected on three observation dates in 2019: 17 January, 16 April, and 9 July. The results showed that the WCM modeled using the LAI under HV polarization demonstrated promising accuracy, with the root mean square error recorded as 0.033 m3/m3. This was comparable to the RVI and RHH/HV under HV polarization, which had accuracies of 0.031 and 0.049 m3/m3, respectively. The findings of this study suggest that SAR-based indicators, RHH/HV and RVI using PALSAR-2, can be used to reduce field-related input in the retrieval of soil moisture data using the WCM for oil palm crop.

AMD stands for Acid Mine Drainage. AMD results from the mining activities involved during mineral exploration. When minerals interact with each other in the presence of atmospheric oxygen and water AMD is the end product. AMD also accumulates from the weathering of sulphide minerals through the process of oxidation and hydrolysis. This case study was being carried out for the investigation of the occurrence of Acid Mine Drainage (AMD) and contamination of the heavy metals in the active and abandoned mining sites in Bentong, Pahang. This case study deals with the traces of heavy metal toxins found in the water bodies causing the discoloration of the water catchment, downstream of the Old Repas dam. This discoloration of water proved the occurrence of AMD which is a very bad pollutant. To get a better understanding of the situation, site reconnaissance and survey were carried out to examine those area closely that have been affected by AMD. Two different sampling points were recognised which had traces of AMD contamination. Water testing and sampling were to be carried on these particular sampling points. Four in-situ parameters and five heavy metals were selected for sampling. These parameters included PH of water, dissolved oxygen content. Total dissolved solids and Ammoniacal Nitrogen while the heavy metals included Zinc, Lead, Tin, Mercury and Arsenic. A total of four samples were collected on two different sampling points and on two different sampling days. Sampling was carried out, these parameters were measured and surface water with sediments were also collected and analysed for heavy metals. Based on the investigations done during this project, these sampling points had acidic PH values which indicate the occurrence of the Acid Mine Drainage, which had been a major find in this case study. During the Heavy Metal analysis concentration of Pb, Sn, Hg, As & Zn was observed to exceed the allowable standard limits for treated water by the Malaysian Ministry of Health. I concluded this with the believe that there was an urgent need for appropriate treatment of this site to prevent further environmental deterioration.

Spatial and temporal climatic variability influence on the productivity of agricultural watershed and irrigation systems. In a large irrigation system, the quantification and regulation of the flow at different locations of the channel is quite difficult manually, leading to a poor delivery of supply and demand. Water shortage is a crucial issue due to mismatch between available water and demand at intake point of Tanjung-Karang Irrigation Scheme. This study assessed the potential impacts of climate change on basin outflow for 2010&ndash, 2039, 2040&ndash, 2069, and 2070&ndash, 2099 to the baseline period (1976&ndash, 2005) and used it as input hydrograph to simulate river discharge. A Hydrologic Engineering Corps Hydrologic Modeling System (HEC-HMS) model driven by projections from ten global climate models (GCMs) with three scenarios (Representative Concentration Pathways (RCPs) 4.5, 6.0, and 8.5) used to simulate the outflow and the Hydrologic Engineering Centers River Analysis System (HEC-RAS) model applied for hydraulic modeling. The projected seasonal streamflow showed a decreasing trend for future periods. The average available irrigation supply for historical period is 15.97 m3/s, which would decrease by 12%, 18%, and 21% under RCPs 4.5, 6.0, and 8.5, respectively. Projected irrigation supply showed oversupply and undersupply to the required supply during the growing season. Simulated discharge could therefore be incorporated into cropping practices to boost the sustainable distribution of water under the new realities of climate change in the future.

Solute runoff and leaching are two direct pathways of nutrient pollution from paddy fields into water systems. Due to the dynamic nature of paddy fields, solute transport and transformation processes are complex and difficult to understand. Therefore, in this study, nitrogen (N) transport in flooded paddy rice fields with conventional irrigation (flooding irrigation) in the Tanjung Karang Rice Irrigation Scheme (TAKRIS), Sawah Sempadan, were observed and modelled using the Hydrus-1D numerical model during two consecutive rice growing seasons. Based on solute transport analysis results, it was observed that 50.3% to 48% of percolated N was accumulated in the top 40-cm soil layer, while 49.7% to 52% of leachate N was lost below the 40-cm soil layer (40&ndash, 100 cm) during the off and main seasons, respectively. About 85% of N leaching loss was in the form of NO3&minus, NO3&minus, was absorbed by rice roots within 0&ndash, 40 cm and the denitrified root zone, however, there was still a large quantity of NO3&minus, which remained below the root zone, which was quickly transported downward along with the leachate water. The NH4+ concentration in subsurface water was lower than the NO3&minus, concentration due to various processes that removed NH4+ from the topsoil layer (0&ndash, 40 cm), such as ammonium volatilisation, nitrification, and plant uptake. The total leaching loss of N was 34.9 and 27.9 kg/ha during the off and main seasons, respectively. The simulated and observed water flow and nutrient leaching were in a good agreement (R2 = 0.98, RMSE = 0.24). The results showed that Hydrus-1D successfully simulated the solute movement under different soil depths during the study period.

Potential impacts of climate change on the streamflow of the Bernam River Basin in Malaysia are assessed using ten Global Climate Models (GCMs) under three Representative Concentration Pathways (RCP4.5, RCP6.0 and RCP8.5). A graphical user interface was developed that integrates all of the common procedures of assessing climate change impacts, to generate high resolution climate variables (e.g., rainfall, temperature, etc.) at the local scale from large-scale climate models. These are linked in one executable module to generate future climate sequences that can be used as inputs to various models, including hydrological and crop models. The generated outputs were used as inputs to the SWAT hydrological model to simulate the hydrological processes. The evaluation results indicated that the model performed well for the watershed with a monthly R2, Nash–Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS) values of 0.67, 0.62 and −9.4 and 0.62, 0.61 and −4.2 for the calibration and validation periods, respectively. The multi-model projections show an increase in future temperature (tmax and tmin) in all respective scenarios, up to an average of 2.5 °C for under the worst-case scenario (RC8.5). Rainfall is also predicted to change with clear variations between the dry and wet season. Streamflow projections also followed rainfall pattern to a great extent with a distinct change between the dry and wet season possibly due to the increase in evapotranspiration in the watershed. In principle, the interface can be customized for the application to other watersheds by incorporating GCMs’ baseline data and their corresponding future data for those particular stations in the new watershed. Methodological limitations of the study are also discussed.

Drip irrigation system has become one of the most common irrigation systems especially in arid and semi-arid regions due to its advantages in saving water. One of the most essential considerations in designing these systems is the dimensions of the wetted soil volume under emitters. These dimensions are significant in choosing the proper emitter spacing along the laterals and the suitable distance between laterals. In this study, a modified empirical equations for estimating the horizontal and vertical extend of the wetted zone under surface emitters were suggested. Data from published papers includes different conditions of soil properties and emitter discharge were used in deriving the empirical model using the nonlinear regression. The developed model has high value for coefficient of determination, R2. The results from the developed model were compared with results of other empirical models derived by other researchers. Some statistical criteria were used to evaluate the model performance which are the mean error ME, root mean square error RMSE, and model efficiency EF. The results revealed that the modified model showed good performance in predicting the wetted zone dimensions and it can be used in design and management of drip irrigation systems.