As global water resources decline and demand increases due to population growth and climate change, innovative rainwater storage systems (IRSSs) have become crucial. This review examines the potential of IRSSs to sustainably address rainwater challenges by analyzing key factors that influence their success. Drawing on research from Scopus and Google Scholar, it evaluates IRSSs in both urban and rural settings across different countries and regions, focusing on their contribution to Sustainable Development Goal (SDG) 6. This review highlights how social, environmental, economic, and policy factors affect the success of IRSS compared to traditional systems common in developing nations. IRSSs can outperform traditional methods in sustainability, encouraging their adoption. However, there is a significant gap in policy integration that needs to be addressed for successful implementation. Further research is needed to better understand the contributing factors and their role in achieving sustainability. Integrating rainwater harvesting into national water policies could offer valuable guidance for policymakers and water resource managers in addressing issues like urban floods, water scarcity, and related social and environmental challenges in developing countries.

Extreme climate change events are major causes of devastating impacts on socioeconomic well-being and ecosystem damage. Therefore, understanding the performance of appropriate climate models representing local climate characteristics is critical for future projections. Thus, this study analyses the performance of 24 GCMs from the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and 6) and their multi-model ensembles in simulating climate variables including average rainfall, maximum (Tmax), and minimum (Tmin) temperatures at annual and seasonal scales over the Chungcheong region of South Korea from 1975 to 2015. A trend analysis was conducted to estimate the future trends in climate variables in the 2060s (2021–2060) and 2080s (2061–2100). Inverse distance weighting and quantile delta mapping were applied to bias-correct the GCM data. Further, six major evaluating indices comprising temporal and spatial performance assessments were used, after which a comprehensive GCM ranking was applied. The results showed that CMIP6 models performed better in simulating rainfall, Tmax, and Tmin at both temporal and spatial scales. For CMIP5, the top three performing models were GISS, ACCESS1-3, and MRI-CGCM3 for rain; CanESM2, GISS, and MPI-ESM-L-R for Tmax; and GFDL, MRI-CGCM3, and CanESM2 for Tmin. However, the top three performing models in the CMIP6 were MRI-ESM2-0, BCC_CSM, and GFDL for rain; MIROC6, BCC_CSM, and MRI-ESM2-0 for Tmax, and GFDL, MPI_ESM_HR, and MRI-ESM2-0 for Tmin. The multi-model ensembles (an average of the top three GCMs) performed better in simulating rain and Tmin for both CMIP5 and CMIP6 compared with multi-model ensembles (an average of all the GCMs), which only performed slightly better in simulating Tmax. The trend analysis of future projection indicates an increase in rain, Tmax, and Tmin; however, with distinct changes under similar radiative forcing levels in both CMIP5 and CMIP6 models. The projections under RCP4.5 and RCP8.5 increase more than the SSP2-4.5 and SSP5-8.5 scenarios for most climate conditions but are more pronounced, especially for rain, under RCP8.5 than SSP5-8.5 in the far future (2080s). This study provides insightful findings on selecting appropriate GCMs to generate reliable climate projections for local climate conditions in the Chungcheong region of South Korea.

Technological development and climate change dictate farming practices, which can directly affect irrigation water requirement and supply. In this article, the water supply reliability (WSR) of 62 major Korean agricultural reservoirs was comprehensively evaluated for varying climate and farming practices. Field surveys identified the recent divergence from standard rice farming practices and a 45-year daily weather data set (1973–2017) was examined to understand the phenomenon of climate change. Effective rainfall increments mitigated the imminent surges in rice irrigation water requirements driven by warming-led accelerated crop evapotranspiration rates; therefore, climate change marginally influenced the WSR of selected reservoirs. The transplanting period and associated water consumption were the primary deviations from standard rice farming practices. A significantly prolonged transplanting period seriously compromised the WSR of agricultural reservoirs and the maximum number of unsafe reservoirs was detected for a 24-day increase in the transplanting period. A watershed/irrigated area ratio of less than 2.5 was the lower threshold below which all the reservoirs had unsafe WSR regardless of the climate change and/or farming practices. Recent variations in farming practices were the primary cause of reservoir failure in maintaining the WSR.

This article analyzed the wheat yield variabilities and water footprints under projected future climate based on wheat yield sensitivity to past (1980–2017) trends of maximum temperature (T_max), minimum temperature (T_min), solar radiation (R_n) and rainfall (P) for the semi-arid condition of Punjab, Pakistan. The past and projected future trends of the climate variables were identical featuring prominent T_min rise than T_max accompanied by R_n and P declines. Based on the past influences and the projected future trends of the climate variables, T_min was the principal driver of wheat yield decline followed by the T_max, P and R_n. Wheat evapotranspiration and net irrigation water requirement declined due to the influence of both decrease of growing season length and R_n for the first half of 21st-century, and this trend reversed during the second half of 21st-century. These phenomena were caused by different degree of climate warming between the two-time slices. Due to future wheat yield decline and a gradual rise in green water scarcity the blue and grey water footprints rise consistently particularly during 2nd-time slices. CO₂ enrichment showed limited mitigation potential of nullifying the warming-induced threats on future wheat yield and water footprints.

Conceptualizing the implications of climate change for crop evapotranspiration (ET_c) and subsequent net irrigation water requirement (NIWR) is critical to sustaining Pakistan’s agriculture and food security. In this article, future ET_c, NIWR, and design water requirements (DWR) were projected for the rice–wheat system of Punjab, Pakistan. Consistently increasing temperatures signify an impending hotter transition in the future thermal regime, accompanied by a substantial increase in monsoon rainfall. Future climate warming accelerated ET_c and NIWR, which were compensated by 2–5 and 1–2 additional irrigations during the rice and wheat seasons, respectively. Future rice and wheat required 13–18 and 2–5 irrigations per season, respectively. Effective rainfall increments did not compensate for the warming-driven higher ET_c and NIWR because of uneven and erratic rainfall distribution. Rainfall occurrence and the duration of peak irrigation demand were mismatched, resulting in surplus rainwater availability during the future rice season. The results suggest that DWR for 5- and 10-year return period droughts during the baseline period (965 and 1000 mm, respectively) should be revised to accommodate the additional 100–200 mm of irrigation water per season; otherwise, the study area will face an acute water shortage in the future.

This study investigates the impact of climate variability on wheat production in Algeria’s semi-arid interior plains from 2014 to 2024, aiming to curb the challenges of rainfed wheat cultivation, optimize irrigation, and improve water productivity. The Soil–Water–Atmosphere–Plant (SWAP) model-driven approach refined irrigation scheduling to mitigate climate-induced losses and improve resource efficiency. Using historical climate data, soil properties, and wheat growth observations from the experimental farm of the Technical Institute for Field Crops, the SWAP model was calibrated and validated using one-factor-at-a-time sensitivity analysis, achieving a coefficient of determination (R²) of 0.93 and a Normalized Root Mean Squared Error (NRMSE) of 17.75. Two drought-based irrigation indices, Soil Moisture Drought Index (SMDI) and Crop Water Stress Index (CWSI), guided adaptive irrigation strategies, showing a significant reduction in crop failure during drought periods. Results revealed a strong link between rainfall variability and wheat yield. Adopting a 9-day irrigation interval could increase water productivity to 18.91 kg ha⁻¹ mm⁻¹, enhancing yield stability under varying climatic conditions. The SMDI approach maintained soil moisture during extreme drought, while CWSI optimized water use in normal and wet years. This study integrates SMDI and CWSI into a validated irrigation framework, offering data-driven strategies to enhance wheat production resilience. Findings support sustainable water management and provide practical insights for policymakers and farmers to refine irrigation planning and climate adaptation, contributing to long-term agricultural sustainability.