Future Predictions of Rainfall and Temperature Using GCM and ANN for Arid Regions: A Case Study for the Qassim Region, Saudi Arabia

Developing an efficient climate forecasting model for the spatiotemporal climate dynamics estimation and the prediction that fits the variable topography feature of the upper Blue Nile basin, Ethiopia

The spatiotemporal climate estimation and prediction are challenging and demanding for variable topography feature areas. Spatially, the upper Blue Nile basin (UBNB) is unsatisfactory owing to complex topographical features and the lack of accurate climate prediction and estimation models. Yet accurate information and reliable seasonal climate dynamics estimation and forecasting are essential in the region for controlling reservoir operation and flooding prevention. However, there is a lack of reports regarding the accuracy and predictability of the climate in the UBNB region. Therefore, this article aims to improve the Artificial Neural Network (ANN) model by adopting the Impulse Response Function (IRF) and comparing it to the Regional Climate Model (RCM) and European Centre of Medium-range Weather Forecast (ECMWF) models for spatiotemporal climate dynamics estimation and forecasting, which are reliant on the UBNB topography features. Different atmospheric parameter data are investigated from reanalysis models. A fast Fourier transform is applied to remove the redundancy of the data and avoid the computational cost. The RCM and ECMWF models are used to test the performance of ANN model prediction skills. The IRF model was applied to enhance the ANN model's climate prediction performance. A 12-month spatial variation of precipitation is analyzed. The ANN model showed a satisfactory prediction performance, better than the RCM and ECMWF models by 20 %. After increasing the ANN model performance by IRF, the prediction errors are reduced by 10.2 % for precipitation and by 7.9 % for temperature. Based on the model results, the temperature has increased over the past 40 years and is expected to continue for the coming three decades (30 years). In contrast, precipitation over the past 40 years has decreased, and a slight increment will be expected in the next eight years, from 2024 to 2029. Therefore, this model should be practiced across Ethiopia and the Globe for accurate prediction of climate patterns. Hence, clear awareness should be created for the local community by providing a scientific remedy for future climate conditions to reduce production risk.

Future climate projections using the LARS-WG6 downscaling model over Upper Indus Basin, Pakistan

This study investigates the projections of precipitation and temperature at the local scale in the Upper Indus Basin (UIB) in Pakistan using six Regional Climate Models (RCMs) from CORDEX under two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). For twenty-four stations spread across the study area, the Long Ashton Research Station Weather Generator, version six (LARS-WG6), was used to downscale the daily data from the six different RCMs for maximum temperature (T_max), minimum temperature (T_min), and precipitation (pr) at a spatial resolution of 0.44°. Investigations were made to predict changes in mean annual values of T_max, T_min, and precipitation during two future periods, i.e., the mid-century (2041-2070) and end-century (2071-2100). The model results from statistical and graphical comparison validated that the LARS-WG6 can simulate the temperature and the precipitation in the UIB. Each of the six RCMs and their ensemble revealed a continuously increased temperature projection in the basin; nevertheless, there is variation in projected magnitude across RCMs and between RCPs. The rise in average T_max and T_min was more significant under RCP 8.5 than RCP 4.5, possibly due to unmitigated greenhouse gas emissions (GHGs). The precipitation projections follow the non-uniform trend, i.e., not all RCMs agree on whether the precipitation will increase or decrease in the basin, and no orderly variations were detected during any future periods under any RCP. However, an overall increase in precipitation is projected by the ensemble of RCMs.

Trend analysis of temperature data using innovative polygon trend analysis and modeling by gene expression programming

Presenting temperature data using recently introduced innovative polygon trend analysis (IPTA) can improve our understanding of the effects of climate change. This method was applied to analyze temperature trends at six stations in Turkey: Istanbul (17,064), Ankara (17,131), Bursa (17,116), Iznik (17,661), Gemilik (17,663), and Sakarya (17,069). At station 17,064, there was an increasing trend in temperature data for seven months, while only one month showed a decreasing trend, and the remainder presented no trend. For station 17,131, there was a decreasing trend for two months, an increasing trend for five months, and no trend for the remaining months. At station 17,116, an increasing trend was present for nine months, with a decreasing trend for two months and only one month indicating no trend. An increasing trend over seven months was noted at station 17,661, while two and three months showed a decreasing and no trend, respectively. For station 17,663, there was an increasing trend for nine months, one month showed no trend, and two months presented a decreasing trend. At station 17,069, five, four, and three months showed increasing, decreasing, and no trends, respectively. The gene expression programming (GEP) model was tested to predict the short-term monthly average temperature for this dataset. The proposed GEP model presented good prediction results for all selected stations by tracing the relationship with a coefficient of determination (R-Sq) ≥ 0.90. Trend analysis by IPTA can help understand temperature trends better, aiding future decision-making, and the GEP model can effectively predict short-term values.

Evaluating Permeable Clay Brick Pavement for Pollutant Removal from Varying Strength Stormwaters in Arid Regions

Permeable pavement is a low impact development technology for stormwater (SW) runoff control and pollutant removal. The strength of SW depends on land use of the catchment, e.g., semi-urban vs. industrial. The performance (in terms of pollutants removal) of permeable clay bricks (PCB) has not been adequately assessed for SW of varying strengths. For using the permeable clay bricks as a pavement surface layer, the present research investigates its pollutant removal capacity through SW infiltration using a laboratory setup. SW samples of two different strengths, i.e., high polluted stormwater (HPSW) and less polluted stormwater (LPSW), were tested for a pavement system consisting of the clay brick layer on top of a coarse gravel support layer. The tests were performed at a rainfall intensity of 12.5 mm/h (for a 10-year return period in Buraidah, Qassim) to evaluate the suitability of PCB for the arid and semi-arid regions. The experiments revealed that PCB became fully saturated and achieved a steady-state outflow condition after 10 min of rainfall. Irrespective of contamination level, the pollutant removal efficiency was found to be similar for both HPSW and LPSW. High TSS (>98%) and turbidity (>99%) removals were achieved for both strengths, while BOD5 (78.4%) and COD (76.1%) removals were moderate. Poor to moderate nutrient removal, 30.5% and 39.1% for total nitrogen (TN) and 34.7% and 31.3% for total phosphorus (TP), respectively for HPSW and LPSW, indicates an adsorptive removal of nutrients in the system. Heavy metal removal efficiency ranged from 6.7% to 94%, with higher removals archived for Fe, Mn, Se, and Pb. The study provides insights into the role of PCB as a surface layer in the permeable pavement for pollutant removal. The study also establishes the guidelines for the optimal permeable pavement design to deal with SW of varying contamination levels. Permeable clay bricks showed the potential to be used as a sustainable LID technology for arid regions.

Development of Rice Bran Mixed Porous Clay Bricks for Permeable Pavements: A Sustainable LID Technique for Arid Regions

Permeable pavement provides sustainable solutions for urban stormwater management. In this research, the potential of rice bran mixed porous clay bricks were evaluated for permeable pavements. Physical, mechanical and hydrological properties along with stormwater treatment capabilities of the brick samples were assessed. The study found that ratio of rice bran and clay soil has significant impacts on the properties of the produced bricks. Water adsorption and porosity increased with increasing rice bran ratio. Compressive strength of brick samples decreased from 29.6 MPa to 6.9 MPa when the ratio of rice bran was increased from 0% to 20%. The permeability coefficient increased from 4 × 10−4 to 1.39 × 10−2 mm/s with the increase in rice bran from 0% to 30%. The preamble clay bricks were efficient to remove turbidity, total suspended solids (TSS), five days’ biochemical oxygen demand (BOD5), and heavy metals (Mn, Cu, and Zn) from stormwater to meet the World Health Organization (WHO) standard for wastewater reuse application. The bricks with ≤10% of rice bran achieved the American Society for Testing and Materials (ASTM) standard of the desire compressive strength and permeability coefficient for pedestrian and light traffic pavements. The porous bricks prepared in this study can be used to construct permeable pavements and would be a sustainable low impact developments technique for stormwater management in urban areas.

Risk Factors Impacting the Project Value Created by Green Buildings in Saudi Arabia

Green buildings are playing a pivotal role in sustainable urban development around the world, including Saudi Arabia. Green buildings subject to various sources of risk that influence the potential outcomes of the investments or services added in their design. The present study developed a structured framework to examine various risks that may lead to green buildings’ value destruction in Saudi Arabia. The framework initiates with identification of 66 potential risk factors from reported literature. A questionnaire compiling a list of identified risk factors was hand-delivered to 300 practitioners (managers, engineers, and architects) having knowledge of value engineering in the construction industry, and an overall response rate of 29.7% was achieved. Subsequently, descriptive statistics ranked the risk factors based on scores given by the respondents. The principal component analysis extracted 16 components, based on the likelihood of risk factors impacting the value created by green building design. Finally, the factor analysis grouped the 35 most significant risk factors in 5 clusters—i.e., 8 in functional risk, 13 in financial risk, 3 in operational risk, 3 in environmental risk, and 8 in management risk cluster. The study enhances the understanding of the importance of the risk factors’ impact on value creation. Based on the results, the value management (or engineering) teams and the top-level management can identify, manage, and control the risk factors that have a significant impact on the project value created by green building design.

Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series?

Weather forecasting, especially that of extreme climatic events, has gained considerable attention among researchers due to their impacts on natural ecosystems and human life. The applicability of artificial neural networks (ANNs) in non-linear process forecasting has significantly contributed to hydro-climatology. The efficiency of neural network functions depends on the network structure and parameters. This study proposed a new approach to forecasting a one-day-ahead maximum temperature time series for South Korea to discuss the relationship between network specifications and performance by employing various scenarios for the number of parameters and hidden layers in the ANN model. Specifically, a different number of trainable parameters (i.e., the total number of weights and bias) and distinctive numbers of hidden layers were compared for system-performance effects. If the parameter sizes were too large, the root mean square error (RMSE) would be generally increased, and the model’s ability was impaired. Besides, too many hidden layers would reduce the system prediction if the number of parameters was high. The number of parameters and hidden layers affected the performance of ANN models for time series forecasting competitively. The result showed that the five-hidden layer model with 49 parameters produced the smallest RMSE at most South Korean stations.

Evaluating the Impacts of Pumping on Aquifer Depletion in Arid Regions Using MODFLOW, ANFIS and ANN

In arid regions, the groundwater drawdown consistently increases, and even for a constant pumping rate, long-term predictions remain a challenge. The present research applies the modular three-dimensional finite-difference groundwater flow (MODFLOW) model to a unique aquifer facing challenges of undefined boundary conditions. Artificial neural networks (ANN) and adaptive neuro fuzzy inference systems (ANFIS) have also been investigated for predicting groundwater levels in the aquifer. A framework is developed for evaluating the impact of various scenarios of groundwater pumping on aquifer depletion. A new code in MATLAB was written for predictions of aquifer depletion using ANN/ANFIS. The geotechnical, meteorological, and hydrological data, including discharge and groundwater levels from 1980 to 2018 for wells in Qassim, were collected from the ministry concerned. The Nash–Sutcliffe efficiency and mean square error examined the performance of the models. The study found that the existing pumping rates can result in an alarming drawdown of 105 m in the next 50 years. Appropriate water conservation strategies for maintaining the existing pumping rate can reduce the impact on aquifer depletion by 33%.

Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment

Accurate forecast of hydrological data such as precipitation is critical in order to provide useful information for water resources management, playing a key role in different sectors. Traditional forecasting methods present many limitations due to the high-stochastic property of precipitation and its strong variability in time and space: not identifying non-linear dynamics or not solving the instability of local weather situations. In this work, several alternative models based on the combination of wavelet analysis (multiscalar decomposition) with artificial neural networks have been developed and evaluated at sixteen locations in Southern Spain (semiarid region of Andalusia), representative of different climatic and geographical conditions. Based on the capability of wavelets to describe non-linear signals, ten wavelet neural network models (WNN) have been applied to predict monthly precipitation by using short-term thermo-pluviometric time series. Overall, the forecasting results show differences between the ten models, although an effective performance (i.e., correlation coefficients ranged from 0.76 to 0.90 and Root Mean Square Error values ranged from 6.79 to 29.82 mm) was obtained at each of the locations assessed. The most appropriate input variables to obtain the best forecasts are analyzed, according to the geo-climatic characteristics of the sixteen sites studied.

Projecting Wet Season Rainfall Extremes Using Regional Climate Models Ensemble and the Advanced Delta Change Model: Impact on the Streamflow Peaks in Mkurumudzi Catchment, Kenya

Each year, many African countries experience natural hazards such as floods and, because of their low adaptative capabilities, they hardly have the means to face the consequences, and therefore suffer huge economic losses. Extreme rainfall plays a key role in the occurrence of these hazards. Therefore, climate projection studies should focus more on extremes in order to provide a wider range of future scenarios of extremes which can aid policy decision making in African societies. Some researchers have attempted to analyze climate extremes through indices reflecting extremes in climate variables such as rainfall. However, it is difficult to assess impacts on streamflow based on these indices alone, as most hydrological models require daily data as inputs. Others have analyzed climate projections through general circulation models (GCMs) but have found their resolution too coarse for regional studies. Dynamic downscaling using regional climate models (RCMs) seem to address the limitation of GCMs, although RCMs might still lack accuracy due to the fact that they also contain biases that need to be eliminated. Given these limitations, the current study combined both dynamic and statistical downscaling methods to correct biases and improve the reproduction of high extremes by the models. This study’s aim was to analyze extreme high flows under the projection of extreme wet rainfall for the horizon of 2041 of a Kenyan South Coast catchment. The advanced delta change (ADC) method was applied on observed data (1982–2005), control (1982–2005) and near future (2018–2041) from an ensemble mean of multiple regional climate models (RCMs). The created future daily rainfall time series was introduced in the HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) hydrological model and the generated future flow were compared to the baseline flow at the gaging station 3KD06, where the observed flow was available. The findings suggested that in the study area, the RCMs, bias corrected by the ADC method, projected an increase in rainfall wet extremes in the first rainy season of the year MAMJ (March–April–May–June) and a decrease in the second rainy season OND (October–November–December). The changes in rainfall extremes, induced a similar change pattern in streamflow extremes at the gaging station 3KD06, meaning that an increase/decrease in rainfall extremes generated an increase/decrease in the streamflow extremes. Due to lack of long-term good quality data, the researchers decided to perform a frequency analysis for up to a 50 year return period in order to assess the changes induced by the ADC method. After getting a longer data series, further analysis could be done to forecast the maximum flow to up to 1000 years, which could serve as design flow for different infrastructure.

Groundwater Nitrate Contamination Integrated Modeling for Climate and Water Resources Scenarios: The Case of Lake Karla Over-Exploited Aquifer

Groundwater quantity and quality degradation by agricultural practices is recorded as one of the most critical issues worldwide. This is explained by the fact that groundwater is an important component of the hydrological cycle, since it is a source of natural enrichment for rivers, lakes, and wetlands and constitutes the main source of potable water. The need of aquifers simulation, taking into account water resources components at watershed level, is imperative for the choice of appropriate restoration management practices. An integrated water resources modeling approach, using hydrological modeling tools, is presented for assessing the nitrate fate and transport on an over-exploited aquifer with intensive and extensive agricultural activity under various operational strategies and future climate change scenarios. The results indicate that climate change affects nitrates concentration in groundwater, which is likely to be increased due to the depletion of the groundwater table and the decrease of groundwater enrichment in the future water balance. Application of operational agricultural management practices with the construction and use of water storage infrastructure tend to compensate the groundwater resources degradation due to climate change impacts.