Tag: Climate Change Downscaling Techniques

Abstract

This research examines the suitability of four land surface schemes (LSSs) in Weather Research and Forecasting (WRF) model over different West Africa’s (WA) climatological zones, namely Guinea, Savanna, and Sahel. The four LSSs include Noah, Noah (Multiparameterization) MP, Noah MP with the groundwater option (Noah GW), and Community Land Model Version 4 (CLM4). A 3-month simulation was carried out with each LSS during July–September 2012. Temperature and dew point temperature (Dpt) were evaluated using the ERA5 dataset, while precipitation was evaluated using the TRMM product. Based on the three variables, CLM4 is most suitable for the Guinea zone; Noah, Noah MP, and CLM4 perform equally well for Savanna; and Noah MP is most suitable for the Sahel zone. In general, and over all zones, Noah MP is most suitable for precipitation simulation, while CLM4 is the best for dew point temperature as Noah MP and Noah GW are equally suitable for 2 m temperature. Noah GW overestimates surface moisture, altering surface fluxes, increasing evaporative fraction, and increasing convective activities (especially in semi-arid areas), which led to a significant bias in temperature and precipitation. Also, over the Savanna and Sahel zone, a strong African Easterly Jet (AEJ) with a weak Tropical Easterly Jet (TEJ) in Noah led to lower rainfall. In contrast, weak AEJ with strong TEJ in Noah MP and Noah GW caused higher rainfall. Further studies would be to evaluate the sensitivity of each LSS under different initial conditions, and their land-atmosphere interactions strength over the zones.

Abstract

Central Asia has a dry climate, scarce water resources, extremely fragile ecosystems, and frequent extreme precipitation events. Using the data of 22 global climate models in the CMIP6 plan, the trend of the extreme precipitation index under four Shared Socioeconomic Pathways (SSPs) was estimated by calculating eight extreme precipitation indices in Central Asia and optimizing the best multi-model set using the Taylor evaluation and comprehensive score. The results showed that in Central Asia, the CMIP6 mode and multi-mode collection can reasonably reproduce the regional differences of various severe precipitation indices. However, these results only performed well for consecutive dry days (CDD) and annual total precipitation (PRCPTOT), but poorly for the replication of extreme high- and low-value regions. We found that the simulation effect of the multi-mode ensemble results was better than that of a single mode, and that CMIP6 can roughly depict the evolving characteristics of extreme precipitation events. However, the CMIP6 data performed poorly in terms of spatial divergence ability characteristics. According to the estimated results, mountainous regions have experienced considerable changes, and a significant increase in the range of change was observed for severe precipitation (consecutive wet days (CWD), single day maximum precipitation (Rx1day), and PRCPTOT) in wet and dry regions during the twenty-first century. Simultaneously, the humidification trend accelerated after 2050, and four shared socioeconomic paths showed similar trends; however, the extreme precipitation rate was higher under the high forcing path. Consecutive dry days (CDD) in Central Asia decreased by 90% under SSP5-8.5 relative to SSP1-2.6, whereas CWD, Rx1day, and PRCPTOT increased by 20%, 150%, and 118%, respectively.

Abstract

Future climate prediction at a local scale is one of the pressing challenges affecting water management-related mitigation plans. The rice irrigation demands are always related to the climate of the area. This study presents possible changes in the monthly rice irrigation demand patterns under future climate scenarios in the Kerian Irrigation Scheme, Malaysia. An ensemble of five Global Climate Models under three Shared Socioeconomic Pathways (SSPs) (SSP1-2.6, SSP2-4.5, and SSP5-8.5) was employed to help project irrigation demand from 2021 to 2080. The study compared the future projections with the baseline period (1985–2014) and revealed that future irrigation demand changes for two planting periods range between − 1.0 to 0.1% and − 5.3 to − 2.6% during the dry season (February–July) and wet season (August–January), respectively. A significant decrease in irrigation water demand was predicted in September and October for each SSP scenario due to increased rainfall during the wet season, with SSP5-8.5 being the most prominent. Although the temperature and reference evapotranspiratopn (ET_o) were predicted to increase, mainly during the near future (2021–2050) rather than the far future (2051–2080), the increase in predicted monthly rainfall successfully copes with the risk of the possible high demand for irrigation supply. Climate change potentially alters the future monthly irrigation water demand pattern, resulting in challenges to water resource management. Predicting the impacts of rice irrigation water demand under the potential future climate change is crucial for Bukit Merah Reservoir to help establish appropriate operational policies for irrigation release for its sustainability.

Abstract

Karst resources are sensitive to environmental changes, especially climate change. In this research, monthly data (years 1994–2020) were collected for five karstic springs, namely Famaseb (Sp1), Faresban (Sp2), Ghalebaroodab (Sp3), Giyan (Sp4), and Gonbadkabood (Sp5), located in the Nahavand plain, west of Iran. Data mining models such as KNN, SVM, and M5tree were used to simulate the discharge of springs. The results obtained, based on two statistical indices, correlation coefficient (r) and normalized root mean square error (nRMSE), the M5tree model was more accurate than the other models and was selected to simulate the discharge of the springs. The r-value was equal to 0.736, and nRMSE was 0.113. In the later stage, discharge of the springs was projected for four time periods: 2021–2040, 2041–2060, 2061–2080, and 2081–2100 under two scenarios, RCP8.5 and RCP4.5. The results showed that in the monthly survey, the ratio of the variation in the discharge of the springs compared to the historical period in the Sp1 and Sp2 springs had the most variations. It was found that in November, there was an average increase of + 250% in the investigated periods. Also, the highest decrease compared to the historical period of these two springs was observed during May, which were about − 37 and − 70%, respectively. In springs Sp3, Sp4, and Sp5, like the previous two springs, the most fluctuations occurred in November and May, respectively.

Graphical abstract

Abstract

Site-specific crop management is based on the postulate of varying soil and crop requirements in a field. Therefore, a field is separated into homogenous management zones, using available data to adapt management practices environment to maximize productivity and profitability while reducing environmental impacts. Due to advancing sensor technologies, crop growth and yield data on more minor scales are common, but soil data often needs to be more appropriate. Crop growth models have shown promise as a decision support tool for site-specific farming. The Decision Support System for Agrotechnology Transfer (DSSAT) is a widely used point-based model. To overcome the problem of inappropriate soil input data problem, this study introduces an external plug-in program called Soil Profile Optimizer (SPO), which uses the current DSSAT v4.8 to calibrate soil profile parameters on a site-specific level. Developed as an inverse modelling approach, the SPO can calibrate selected soil profile parameters by targeting available in-season plant data. Root Mean Square Error (RMSE) and normalized RMSE as error minimization criteria are used. The SPO was tested and evaluated by comparing different simulation scenarios in a case study of a 3-yr field trial with maize. The scenario with optimized soil profiles, conducted with the SPO, resulted in an R² of 0.76 between simulated and observed yield and led to significant improvements compared to the scenario conducted with field scale soil profile information (R² 0.03). The SPO showed promise in using spatial plant measurements to estimate management zone scale soil parameters required for the DSSAT model.

Abstract

Conventional convolutional neural networks (CNN) are deficient in the rock type recognition due to large convolutional kernels and numerous network parameters necessitated for recognition of complex images. The advanced convolutional neural network, Visual Geometry Group-16 (VGG16) model, which is based on multiple small convolutional kernels and fully connected layers, attains higher classification accuracy, yet is limited by low computational efficiency. In this paper, we propose a novel approach that integrates the advanced VGG16 with the Principal Component Analysis (PCA), a dimensionality reduction technique. This integration, referred to as the PCA-VGG16 model, aims to enhance the computational efficiency of automatic rock type identification. A dataset comprising 3000 images of six rock types: limestone, shale, dolomite, quartzite, marble, and granite, is assembled for training and testing of the PCA-VGG16 model. The feasibility of the PCA-VGG16 model for classification prediction is demonstrated through evaluation metrics including accuracy, loss value, and F1-score. A comparative analysis with the CNN and VGG16 models reveals that the proposed PCA-VGG16 model exhibits superior classification accuracy and reduced training durations, making a very promising advancement in the field. Furthermore, an in-depth analysis is conducted to understand the impact of dataset size and key hyperparameters (such as epochs and batch size) on the classification accuracy of the PCA-VGG16 model. The findings indicate that a minimum dataset of 1500 sample images is necessary to achieve a classification accuracy above 90%. For optimal model performance, a division of training, validation, and test sets approximately at 6:2:2, along with two epochs and a batch size of 128, is recommended in this study.