Sobri Harun

Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature

Climate change is believed to have significant impacts on the water basin and region, such as in a runoff and hydrological system. However, impact studies on the water basin and region are difficult, since general circulation models (GCMs), which are widely used to simulate future climate scenarios, do not provide reliable hours of daily series rainfall and temperature for hydrological modeling. There is a technique named as “downscaling techniques”, which can derive reliable hour of daily series rainfall and temperature due to climate scenarios from the GCMs output. In this study, statistical downscaling models are used to generate the possible future values of local meteorological variables such as rainfall and temperature in the selected stations in Peninsular of Malaysia. The models are: (1) statistical downscaling model (SDSM) that utilized the regression models and stochastic weather generators and (2) Long Ashton research station weather generator (LARS-WG) that only utilized the stochastic weather generators. The LARS-WG and SDSM models obviously are feasible methods to be used as tools in quantifying effects of climate change condition in a local scale. SDSM yields a better performance compared to LARS-WG, except SDSM is slightly underestimated for the wet and dry spell lengths. Although both models do not provide identical results, the time series generated by both methods indicate a general increasing trend in the mean daily temperature values. Meanwhile, the trend of the daily rainfall is not similar to each other, with SDSM giving a relatively higher change of annual rainfall compared to LARS-WG.

Particle swarm optimization feedforward neural network for modeling runoff

The rainfall-runoff relationship is one of the most complex hydrological phenomena. In recent years, hydrologists have successfully applied backpropagation neural network as a tool to model various nonlinear hydrological processes because of its ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. However, the backpropagation neural network convergence rate is relatively slow and solutions can be trapped at local minima. Hence, in this study, a new evolutionary algorithm, namely, particle swarm optimization is proposed to train the feedforward neural network. This particle swarm optimization feedforward neural network is applied to model the daily rainfall-runoff relationship in Sungai Bedup Basin, Sarawak, Malaysia. The model performance is measured using the coefficient of correlation and the Nash-Sutcliffe coefficient. The input data to the model are current rainfall, antecedent rainfall and antecedent runoff, while the output is current runoff. Particle swarm optimization feedforward neural network simulated the current runoff accurately with R = 0.872 and E2 = 0.775 for the training data set and R = 0.900 and E2= 0.807 for testing data set. Thus, it can be concluded that the particle swarm optimization feedforward neural network method can be successfully used to model the rainfall-runoff relationship in Bedup Basin and it could be to be applied to other basins.

Trends in rainfall and rainfall-related extremes in the east coast of peninsular Malaysia

The coastlines have been identified as the most vulnerable regions with respect to hydrological hazards as a result of climate change and variability. The east of peninsular Malaysia is not an exception for this, considering the evidence of heavy rainfall resulting in floods as an annual phenomenon and also water scarcity due to long dry spells in the region. This study examines recent trends in rainfall and rainfall- related extremes such as, maximum daily rainfall, number of rainy days, average rainfall intensity, heavy rainfall days, extreme rainfall days, and precipitation concentration index in the east coast of peninsular Malaysia. Recent 40 years (1971–2010) rainfall records from 54 stations along the east coast of peninsular Malaysia have been analyzed using the non-parametric Mann–Kendall test and the Sen’s slope method. The Monte Carlo simulation technique has been used to determine the field significance of the regional trends. The results showed that there was a substantial increase in the annual rainfall as well as the rainfall during the monsoon period. Also, there was an increase in the number of heavy rainfall days during the past four decades.

Spatial interpolation of climatic variables in a predominantly arid region with complex topography

The benefits of accurately interpolating spatial distribution patterns of precipitation and temperature are well recognized. However, precipitation and temperature patterns are difficult to understand in a region that has complex topography and poor meteorological information. In this study, geostatistical and deterministic interpolation methods are used to understand the best modeling approach for mapping precipitation and temperature in a data-scarce arid region of Pakistan, where elevation and climate vary widely within a short distance. Long-term climate data collected from 15 metrological stations distributed over the Balochistan province of Pakistan are used for this purpose. The performances of various deterministic and geostatistical methods are assessed by using root mean squared errors in interpolation. The results show a difference in accuracy among interpolation methods. Incorporation of elevation significantly improves the accuracy of the interpolation of climate variables. The study concluded that the most preferable models for reliable mapping of precipitation and temperature for such region are disjunctive and universal cokriging.

Assessment of groundwater potential zones in an arid region based on catastrophe theory

Evaluation of groundwater potential is a multi-criteria and multi-level comprehensive assessment system that needs judgment of decision makers in making decision. To avoid subjectivity or the preference of decision makers in the assessment, catastrophe theory based evaluation method is proposed in this study which calculates the importance of one criterion over other by its inner mechanism and thus, avoid subjectivity. The proposed method is applied for the assessment of groundwater potential zones in the arid region of lower Balochistan province of Pakistan. The groundwater is considered as a system with five sub-systems namely, geology, soil, drainage density, slope and rainfall. Seventeen sub-system indicators of groundwater potential are selected for modeling groundwater potential zone. The catastrophe theory is applied to derive the relative weights of indicators in predicting groundwater potential. Thematic maps of sub-systems are integrated within a geographical information system and the groundwater potential zones of the integrated layer are calculated by using the weights of indicators. The results are verified by existing number of tube wells operating in the study area. It has been found that the number of tube wells is more in the area where the groundwater potential is high. The study reveals that catastrophe theory is suitable for assessing groundwater potential.

Spatial assessment of groundwater over-exploitation in northwestern districts of Bangladesh

Groundwater demand in northwestern districts of Bangladesh is increasing rapidly with the growth of population and the expansion of irrigated agriculture. Development and management of groundwater resources are essential to supply the growing population with sufficient water for economic development as well as for the sustainable environment of the region. In the present study, groundwater recharge-abstraction balance method has been used for the spatial assessment of groundwater development potential. The abstraction of groundwater is estimated from irrigation and domestic water demands in the study area. The net recharge calculated from groundwater table fluctuation data, whereas the abstraction of groundwater is estimated from irrigation and domestic water demands in the study area. The study shows that out of twenty-six sub-districts, groundwater exploitation has reached to a critical condition in fourteen subdistricts. Development of surface water resources and water conservation are essential to reduce the stress on groundwater exploitation.

Impact of Direct Soil Moisture and Revised Soil Moisture Index Methods on Hydrologic Predictions in an Arid Climate

The soil and water assessment tool (SWAT) is a physically based model that is used extensively to simulate hydrologic processes in a wide range of climates around the world. SWAT uses spatial hydrometeorological data to simulate runoff through the computation of a retention curve number. The objective of the present study was to compare the performance of two approaches used for the calculation of curve numbers in SWAT, that is, the Revised Soil Moisture Index (SMI), which is based on previous meteorological conditions, and the Soil Moisture Condition II (SMCII), which is based on soil features for the prediction of flow. The results showed that the sensitive parameters for the SMI method are land-use and land-cover features. However, for the SMCII method, the soil and the channel are the sensitive parameters. The performances of the SMI and SMCII methods were analyzed using various indices. We concluded that the fair performance of the SMI method in an arid region may be due to the inherent characteristics of the method since it relies mostly on previous meteorological conditions and does not account for the soil features of the catchment.

Modeling Irrigation Water Demand in a Tropical Paddy Cultivated Area in the Context of Climate Change

AbstractIrrigation is the major user of total water use in most of the tropical countries located in Southeast Asia. Therefore, knowledge on future changes in irrigation demand in the context of cl...

Statistical Downscaling of Rainfall in an Arid Coastal Region: A Radial Basis Function Neural Network Approach

Downscaling Global Circulation Model (GCM) output is important in order to understand the present climate as well as future climate changes at local scale. In this study, Radial basis function (RBF) neural network was used to downscale the mean monthly rainfall in an arid coastal region located in Baluchistan province of Pakistan. The RBF model was used to downscale monthly rainfall from National Center for environmental prediction (NCEP) reanalysis dataset at four observation stations in the area. The potential predictors were selected using principal component analysis of NCEP variables at grid points located around the study area. Power transformation method was used to remove the bias in the prediction. The results showed that the RBF model was able to establish a good relation between NCEP predictors and local rainfall. The power transformation method was also found to perform well to correct errors in prediction. It can be concluded that RBF and power transformation methods are reliable and effective methods for downscaling rainfall in an arid coastal region.

Modeling daily stream flow using plant evapotranspiration method

In hydrological models, soil conservation services (SCS) are one of the most widely used procedures to calculate the curve number (CN) in rainfall run-off simulation. Recently, another new CN accounting procedure has been mentioned, namely the plant evapotranspiration (ET) method or simply known as the plant ET method. This method is embedded in the Soil and Water Assessment Tool (SWAT) model which has been developed for watersheds covered by shallow soils or soils with low storage characteristics. It uses antecedent climate and plant evapotranspiration for calculation of daily curve number. In this study, the same method had been used to simulate the daily stream flow for Roodan watershed located in the southern part of Iran. The watershed covers 10570 km2 and its climate is arid to semi-arid. The modeling process required data from digital elevation model (DEM), land use map, and soil map. It also required daily meteorological data which were collected from weather stations from 1988 to 2008. Other than that, the Sequential Uncertainty Fitting-2 (SUFI-2) algorithm was utilized for calibration and uncertainty analysis of daily stream flow. Criteria of modeling performance were determined through the Nash-Sutcliffe and coefficient of determination for calibration and validation. For calibration, the values were reported at 0.66 and 0.68 respectively and for validation; the values were 0.51 and 0.55. Moreover, percentiles of absolute error between observed and simulated data in calibration and validation period were calculated to be less than 21.78 and 6.37 (m3/s) for 95% of the data. The results were found to be satisfactory under the climatic conditions of the study area.