Ravinesh C. Deo

Impacts of land use/land cover change on climate and future research priorities.

Several recommendations have been proposed for detecting land use and land cover change (LULCC) on the environment from, observed climatic records and to modeling to improve its understanding and its impacts on climate. Researchers need to detect LULCCs accurately at appropriate scales within a specified time period to better understand their impacts on climate and provide improved estimates of future climate. The US Climate Reference Network (USCRN) can be helpful in monitoring impacts of LULCC on near-surface atmospheric conditions, including temperature. The USCRN measures temperature, precipitation, solar radiation, and ground or skin temperature. It is recommended that the National Climatic Data Center (NCDC) and other climate monitoring agencies develop plans and seek funds to address any monitoring biases that are identified and for which detailed analyses have not been completed.

Modeling the impact of historical land cover change on Australia's regional climate

The Australian landscape has been transformed extensively since European settlement. However, the potential impact of historical land cover change (LCC) on regional climate has been a secondary consideration in the climate change projections. In this study, we analyzed data from a pair of ensembles (10 members each) for the period 1951–2003 to quantify changes in regional climate by comparing results from pre-European and modern-day land cover characteristics. The results of the sensitivity simulations showed the following: a statistically significant warming of the surface temperature, especially for summer in eastern Australia (0.4–2°C) and southwest Western Australia (0.4–0.8°C); a statistically significant decrease in summer rainfall in southeast Australia; and increased surface temperature in eastern regions during the 2002/2003 El Nino drought event. The simulated magnitude and pattern of change indicates that LCC has potentially been an important contributing factor to the observed changes in regional climate of Australia.

The influence of Reynolds number on a plane jet

The present study systematically investigates through experiments the influence of Reynolds number on a plane jet issuing from a radially contoured, rectangular slot nozzle of large aspect ratio. Detailed velocity measurements were performed for a jet exit Reynolds number spanning the range 1500≤Reh≤16 500, where Reh≡Ubh/υ with Ub as the momentum-averaged exit mean velocity, h as the slot height, and υ as the kinematic viscosity. Additional centerline measurements were also performed for jets from two different nozzles in the same facility to achieve Reh=57 500. All measurements were conducted using single hot-wire anemometry to an axial distance (x) of x≤160h. These measurements revealed a significant dependence of the exit and the downstream flows on Reh despite all exit velocity profiles closely approximating a “top-hat” shape. The effect of Reh on both the mean and turbulent fields is substantial for Reh<10 000 but becomes weaker with increasing Reh. The length of the jet’s potential core, initial pri...

Impact of historical land cover change on daily indices of climate extremes including droughts in eastern Australia

There is growing scientific evidence that anthropogenic land cover change (LCC) can produce a significant impact on regional climate. However, few studies have quantified this impact on climate extremes and droughts. In this study, we analysed daily data from a pair of ensemble simulations using the CSIRO AGCM for the period 1951–2003 to quantify the impact of LCC on selected daily indices of climate extremes in eastern Australia. The results showed: an increase in the number of dry and hot days, a decrease in daily rainfall intensity and wet day rainfall, and an increase in the decile‐based drought duration index for modified land cover conditions. These changes were statistically significant for all years, and especially pronounced during strong El Nino events. Therefore it appears that LCC has exacerbated climate extremes in eastern Australia, thus resulting in longer‐lasting and more severe droughts.

An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland

A predictive model for streamflow has practical implications for understanding the drought hydrology, environmental monitoring and agriculture, ecosystems and resource management. In this study, the state-or-art extreme learning machine (ELM) model was utilized to simulate the mean streamflow water level (QWL) for three hydrological sites in eastern Queensland (Gowrie Creek, Albert, and Mary River). The performance of the ELM model was benchmarked with the artificial neural network (ANN) model. The ELM model was a fast computational method using single-layer feedforward neural networks and randomly determined hidden neurons that learns the historical patterns embedded in the input variables. A set of nine predictors with the month (to consider the seasonality of QWL); rainfall; Southern Oscillation Index; Pacific Decadal Oscillation Index; ENSO Modoki Index; Indian Ocean Dipole Index; and Nino 3.0, Nino 3.4, and Nino 4.0 sea surface temperatures (SSTs) were utilized. A selection of variables was performed using cross correlation with QWL, yielding the best inputs defined by (month; P; Nino 3.0 SST; Nino 4.0 SST; Southern Oscillation Index (SOI); ENSO Modoki Index (EMI)) for Gowrie Creek, (month; P; SOI; Pacific Decadal Oscillation (PDO); Indian Ocean Dipole (IOD); EMI) for Albert River, and by (month; P; Nino 3.4 SST; Nino 4.0 SST; SOI; EMI) for Mary River site. A three-layer neuronal structure trialed with activation equations defined by sigmoid, logarithmic, tangent sigmoid, sine, hardlim, triangular, and radial basis was utilized, resulting in optimum ELM model with hard-limit function and architecture 6-106-1 (Gowrie Creek), 6-74-1 (Albert River), and 6-146-1 (Mary River). The alternative ELM and ANN models with two inputs (month and rainfall) and the ELM model with all nine inputs were also developed. The performance was evaluated using the mean absolute error (MAE), coefficient of determination (r2), Willmott’s Index (d), peak deviation (Pdv), and Nash–Sutcliffe coefficient (ENS). The results verified that the ELM model as more accurate than the ANN model. Inputting the best input variables improved the performance of both models where optimum ELM yielded R2 ≈ (0.964, 0.957, and 0.997), d ≈ (0.968, 0.982, and 0.986), and MAE ≈ (0.053, 0.023, and 0.079) for Gowrie Creek, Albert River, and Mary River, respectively, and optimum ANN model yielded smaller R2 and d and larger simulation errors. When all inputs were utilized, simulations were consistently worse with R2 (0.732, 0.859, and 0.932 (Gowrie Creek), d (0.802, 0.876, and 0.903 (Albert River), and MAE (0.144, 0.049, and 0.222 (Mary River) although they were relatively better than using the month and rainfall as inputs. Also, with the best input combinations, the frequency of simulation errors fell in the smallest error bracket. Therefore, it can be ascertained that the ELM model offered an efficient approach for the streamflow simulation and, therefore, can be explored for its practicality in hydrological modeling.

Characterization of turbulent jets from high-aspect-ratio rectangular nozzles

Turbulent free jets issuing from rectangular slots with various high aspect ratios (15–120) are characterized. The centerline mean and rms velocities are measured using hot-wire anemometry over a downstream distance of up to 160 slot heights at a slot-height-based Reynolds number of 10 000. Experimental results suggest that a rectangular jet with sufficiently high aspect ratio (>15) may be distinguished between three flow zones: an initial quasi-plane-jet zone, a transition zone, and a final quasi-axisymmetric-jet zone. In the quasi-plane-jet zone, the turbulent velocity field is statistically similar, but not identical, to those of a plane jet.

Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River

Abstract Accurate prediction of the chemical constituents in major river systems is a necessary task for water quality management, aquatic life well-being and the overall healthcare planning of river systems. In this study, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia. The predictive ability of the MLP-FFA model is assessed against the MLP-based model. To validate the proposed MLP-FFA model, monthly water quality data over a 10-year duration (2001–2010) for two different hydrological stations (1L04 and 1L05) provided by the Irrigation and Drainage Ministry of Malaysia are used to predict the biochemical oxygen demand (BOD) and dissolved oxygen (DO). The input variables are the chemical oxygen demand (COD), total phosphate (PO4), total solids, potassium (K), sodium (Na), chloride (Cl), electrical conductivity (EC), pH and ammonia nitrogen (NH4-N). The proposed hybrid model is then evaluated in accordance with statistical metrics such as the correlation coefficient (r), root-mean-square error, % root-mean-square error and Willmott’s index of agreement. Analysis of the results shows that MLP-FFA outperforms the equivalent MLP model. Also, in this research, the uncertainty of a MLP neural network model is analyzed in relation to the predictive ability of the MLP model. To assess the uncertainties within the MLP model, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals (d-factors) are selected. The effect of input variables on BOD and DO prediction is also investigated through sensitivity analysis. The obtained values bracketed by 95PPU show about 77.7%, 72.2% of data for BOD and 72.2%, 91.6% of data for DO related to the 1L04 and 1L05 stations, respectively. The d-factors have a value of 1.648, 2.269 for BOD and 1.892, 3.480 for DO related to the 1L04 and 1L05 stations, respectively. Based on the values in both stations for the 95PPU and d-factor, it is concluded that the neural network model has an acceptably low degree of uncertainty applied for BOD and DO simulations. The findings of this study can have important implications for error assessment in artificial intelligence-based predictive models applied for water resources management and the assessment of the overall health in major river systems.

Identifying separate impacts of climate and land use/cover change on hydrological processes in upper stream of Heihe River, Northwest China

&NA; Climate change and land use/cover change (LUCC) are two factors that produce major impacts on hydrological processes. Understanding and quantifying their respective influence is of great importance for water resources management and socioeconomic activities as well as policy and planning for sustainable development. In this study, the Soil and Water Assessment Tool (SWAT) was calibrated and validated in upper stream of the Heihe River in Northwest China. The reliability of the SWAT model was corroborated in terms of the Nash‐Sutcliffe efficiency (NSE), the correlation coefficient (R), and the relative bias error (BIAS). The findings proposed a new method employing statistical separation procedures using a physically based modeling system for identifying the individual impacts of climate change and LUCC on hydrology processes, in particular on the aspects of runoff and evapotranspiration (ET). The results confirmed that SWAT was a powerful and accurate model for diagnosis of a key challenge facing the Heihe River Basin. The model assessment metrics, NSE, R, and BIAS, in the data were 0.91%, 0.95%, and 1.14%, respectively, for the calibration period and 0.90%, 0.96%, and −0.15%, respectively, for the validation period. An assessment of climate change possibility showed that precipitation, runoff, and air temperature exhibited upward trends with a rate of 15.7 mm, 6.1 mm, and 0.38 °C per decade for the 1980 to 2010 period, respectively. Evaluation of LUCC showed that the changes in growth of vegetation, including forestland, grassland, and the shrub area have increased gradually while the barren area has decreased. The integrated effects of LUCC and climate change increased runoff and ET values by 3.2% and 6.6% of the total runoff and ET, respectively. Climate change outweighed the impact of LUCC, thus showing respective increases in runoff and ET of about 107.3% and 81.2% of the total changes. The LUCC influence appeared to be modest by comparison and showed about −7.3% and 18.8% changes relative to the totals, respectively. The increase in runoff caused by climate change factors is more than the offsetting decreases resulting from LUCC. The outcomes of this study show that the climate factors accounted for the notable effects more significantly than LUCC on hydrological processes in the upper stream of the Heihe River.

Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms

Long-term air temperature prediction is of major importance in a large number of applications, including climate-related studies, energy, agricultural, or medical. This paper examines the performance of two Machine Learning algorithms (Support Vector Regression (SVR) and Multi-layer Perceptron (MLP)) in a problem of monthly mean air temperature prediction, from the previous measured values in observational stations of Australia and New Zealand, and climate indices of importance in the region. The performance of the two considered algorithms is discussed in the paper and compared to alternative approaches. The results indicate that the SVR algorithm is able to obtain the best prediction performance among all the algorithms compared in the paper. Moreover, the results obtained have shown that the mean absolute error made by the two algorithms considered is significantly larger for the last 20 years than in the previous decades, in what can be interpreted as a change in the relationship among the prediction variables involved in the training of the algorithms.

Projection of heat wave mortality related to climate change in Korea

Heat waves associated with climate change are a significant future concern. Although deaths from heat disorders are a direct effect of heat wave incidences, only a few studies have addressed the causal factors between heat wave incidences and deaths from heat disorder. This study applies regression analysis to the time series data in order to deduce the causal factors that affect the number of deaths from heat disorders (NDHD) in Korea using observational dataset from 1994–2012. The duration of a heat wave and the age of the population are highly correlated with the magnitude of the NDHD. Based on this correlation we also analyze heat wave projections to the climate change scenarios produced using the Hadley Centre Global Environmental Model version 3 under the Representative Concentration Pathways (RCP 4.5 and RCP 8.5) and to the single aging population scenario till 2060. The magnitude of the NDHD is expected to elevate by approximately fivefold under the RCP4.5 and 7.2-fold under the RCP 8.5 scenarios compared to the current baseline value (≈23 people per summer). Of greater concern is that the steady death rate increase is expected to be intercepted by the more severe events in future compared to the present period. Under both RCP scenarios considered, the extreme cases are projected to eventuate around the 2050s with approximately 250 deaths. We find that in spite of the greenhouse gas policy proposed to meet reductions under the RCP 4.5 scenario; serious heat wave damage in terms of human mortality may still be unavoidable in Korea.