Comparison of Ensembles Projections of Rainfall from Four Bias Correction Methods over Nigeria

Application of relative importance metrics for CMIP6 models selection in projecting basin-scale rainfall over Johor River basin, Malaysia

The most recent set of General Circulation Models (GCMs) derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) was used in this work to analyse the spatiotemporal patterns of future rainfall distribution across the Johor River Basin (JRB) in Malaysia. A group of 23 GCMs were chosen for comparative assessment in simulating basin-scale rainfall based on daily rainfall from the historical period of the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS). The methodological novelty of this study lies in the application of relative importance metrics (RIM) to rank and select historical GCM simulations for reproducing rainfall at 109 CHIRPS grid points within the JRB. In order to choose the top GCMs, the rankings given by RIM were aggregated using the compromise programming index (CPI) and Jenks optimised classification (JOC). It was found that ACCESS-ESM1-5 and CMCC-ESM2 were ranked the highest in most of the grid. The final GCM was then bias-corrected using the linear scaling method before being ensemble based on the Bayesian model averaging (BMA) technique. The spatiotemporal assessment of the ensemble model for the different months over the near-future period 2021-2060 and far-future period 2061-2100 was compared with those under Shared Socioeconomic Pathways (SSPs), namely, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Heterogeneous changes in rainfall were projected across the JRB, with both increasing and decreasing trends. In the near-future and far-future scenarios, higher rainfall was projected for December, indicating an elevated risk of flooding during the end of the North East monsoon (NEM). Conversely, August showed a decreasing trend in rainfall, implying an increasing risk of severe drought. The findings of this study provide valuable insights for effective water resource management and climate change adaptation in the region.

Impacts of climate change on water footprint components of rainfed and irrigated wheat in a semi-arid environment

Climate change is one of the biggest environmental challenges that significantly impact water resources and the quantity and quality of agricultural products. Assessment of these impacts during the historical period and under future climate is essential for achieving a sustainable agricultural system in the face of climate change threats and water scarcity. In this research, we evaluated the yield and water footprint of rainfed and irrigated wheat during the historical period (1986-2015) and two future periods (2016 to 2055) in a semi-arid environment in Fars province, Iran. The future climate data was selected from the CanESM2 model outputs (bias-corrected and downscaled using the SDSM model) under the RCP4.5 scenario, and the yield projection was made using the AquaCrop model. Our result showed that for both irrigated and rainfed wheat, the yield significantly increases in southern parts of the study area in future climates, primarily because of an increase in effective precipitation. Other regions will experience a marginal yield decrease or no yield changes (in the case of irrigated wheat). Our assessments of the water footprint of wheat production showed a significant reduction in green and blue water footprints in the southern regions. In other regions, various patterns emerged for irrigated and rainfed wheat, but an overall increase was observed. The southern regions of the study area will be more suitable for wheat production owing to the higher yield and lower water footprint.

STORAGE (STOchastic RAinfall GEnerator): A User-Friendly Software for Generating Long and High-Resolution Rainfall Time Series

The MS Excel file with VBA (Visual Basic for Application) macros named STORAGE (STOchastic RAinfall GEnerator) is introduced herein. STORAGE is a temporal stochastic simulator aiming at generating long and high-resolution rainfall time series, and it is based on the implementation of a Neymann–Scott Rectangular Pulse (NSRP) model. STORAGE is characterized by two innovative aspects. First, its calibration (i.e., the parametric estimation, on the basis of available sample data, in order to better reproduce some rainfall features of interest) is carried out by using data series (annual maxima rainfall, annual and monthly cumulative rainfall, annual number of wet days) which are usually longer than observed high-resolution series (that are mainly adopted in literature for the calibration of other stochastic simulators but are usually very short or absent for many rain gauges). Second, the seasonality is modelled using series of goniometric functions. This approach makes STORAGE strongly parsimonious with respect to the use of monthly or seasonal sets for parameters. Applications for the rain gauge network in the Calabria region (southern Italy) are presented and discussed herein. The results show a good reproduction of the rainfall features which are mainly considered for usual hydrological purposes.

Responses of Hydrological Processes under Different Shared Socioeconomic Pathway Scenarios in the Huaihe River Basin, China

The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides more scenarios and reliable climate change results for improving the accuracy of future hydrological parameter change analysis. This study uses five CMIP6 global climate models (GCMs) to drive the variable infiltration capacity (VIC) model, and then simulates the hydrological response of the upper and middle Huaihe River Basin (UMHRB) under future shared socioeconomic pathway scenarios (SSPs). The results show that the five-GCM ensemble improves the simulation accuracy compared to a single model. The climate over the UMHRB likely becomes warmer. The general trend of future precipitation is projected to increase, and the increased rates are higher in spring and winter than in summer and autumn. Changes in annual evapotranspiration are basically consistent with precipitation, but seasonal evapotranspiration shows different changes (0–18%). The average annual runoff will increase in a wavelike manner, and the change patterns of runoff follow that of seasonal precipitation. Changes in soil moisture are not obvious, and the annual soil moisture increases slightly. In the intrayear process, soil moisture decreases slightly in autumn. The research results will enhance a more realistic understanding of the future hydrological response of the UMHRB and assist decision-makers in developing watershed flood risk-management measures and water and soil conservation plans.