Projecting the impact of climate change and elevated CO2 concentration on rice irrigation water requirement in China

Climate change and elevated CO2 concentrations significantly affect rice growth and water consumption. Understanding the specific impacts of climate change and elevated CO2 concentrations on rice physiological phenology, crop water demand (ETC), and irrigation water requirement (IR) is of great significance for the sustainable utilization of water resources and food security. This is particularly true in China, the world's largest rice producer. In this study, with the help of two rice phenological models, the modified Penman-Monteith equation, and the paddy water balance model, we project the changes in rice phenological period, ETC, and IR in four main rice-producing regions of China in the period 2015-2100 based on the 11 GCM outputs. The results show that the rice growing period is shortened in most rice-producing regions, except for the parts of the middle and lower reaches of the Yangtze River. Meanwhile, the trend of ETC and IR of rice varies slightly among regions in the future scenario, with almost all regions decreasing yearly except for the middle and lower reaches of the Yangtze River, where the trend is increasing. The progressively increasing atmospheric CO2 concentration has a "fertilization effect" on the crop, which can reduce the water requirements of rice. In the SSP585 scenario, the " CO2 fertilization effect" can reduce up to 8.87 × 108 m3 of ETC and 6.94 × 108 m3 of IR in the middle and lower reaches of the Yangtze River in the period of 2090s. This study provides beneficial references to understand the response of rice ETC and IR to future climate change and CO2 concentration elevation in China and highlights that the simulation in terms of crop irrigation must account for the "CO2 fertilization effect".

Spatiotemporal variation of irrigation water requirements for grain crops under climate change in Northwest China

Clarifying the spatiotemporal variation of crop irrigation water requirement (IWR) under the background of climate change is an essential basis for water resource management, determining the irrigation quota and adjusting the planting structure. Using 61 years of climate data from 205 stations in Northwest China, this study investigated the spatiotemporal variations of climatic factors and IWR during the growth period of five main grain crops (spring wheat, winter wheat, spring maize, summer maize, and rice) and explored the dominant climatic driving factors of IWR variation. Results showed that (1) the IWR of grain crops showed distinct differences. Rice was the highest water consumption crop (mean of 753.78 mm), and summer maize was the lowest (mean of 452.90 mm). (2) The variation trends and average values of IWR of different grain crops have spatial heterogeneity across Northwest China. For most crops, high values and increasing trends of IWR were mainly located in eastern Xinjiang, western Gansu, and western Inner Mongolia. (3) T_max (maximum temperature), T_min (minimum temperature), and P_eff (effective precipitation) showed an increasing trend during the growth period of each grain crop, while U₁₀ (wind speed at 10 m height), SD (solar radiation), and RH (relative humidity) presented decreasing trends. (4) SD, T_max, and U₁₀ promoted the increase of grain crops' IWR, while P_eff and RH inhibited it. The impacts of climatic factors on the grain crop IWR differed among different regions. P_eff was the most influential factor to the IWR of all grain crops in most areas. Therefore, under the premise of a significant increase in T and uncertain precipitation mode in the future, it is urgent to take effective water-saving measures according to the irrigation needs of the region. To cope with the adverse impact of climate change on the sustainable development of agriculture in the northwest dry area, to ensure regional and national food security.

Simulation of regional irrigation requirement with SWAT in different agro-climatic zones driven by observed climate and two reanalysis datasets

Irrigation water is one of the most substantial water uses worldwide. Thus, global simulation studies about water availability and demand typically include irrigation. Nowadays, regional scale is of major interest for water resources management but irrigation lacks attention in many catchment modelling studies. This study evaluated the performance of the agro-hydrological model SWAT (Soil and Water Assessment Tool) for simulating streamflow, evapotranspiration and irrigation in four catchments of different agro-climatic zones at meso-scale (Baitarani/India: Subtropical monsoon; Ilmenau/Germany: Humid; Itata/Chile: Mediterranean; Thubon/Vietnam: Tropical). The models were calibrated well with Kling-Gupta Efficiency (KGE) varying from 0.74-0.89 and percentage bias (PBIAS) from 5.66-6.43%. The simulated irrigation is higher when irrigation is triggered by soil-water deficit compared to plant-water stress. The simulated irrigation scheduling scenarios showed that a significant amount of water can be saved by applying deficit irrigation (25-48%) with a small reduction in annual average crop yield (0-3.3%) in all climatic zones. Many catchments with a high share of irrigated agriculture are located in developing countries with a low availability of input data. For that reason, the application of uncorrected and bias-corrected National Centers for Environmental Prediction (NCEP) and ERA-interim (ERA) reanalysis data was evaluated for all model scenarios. The simulated streamflow under bias-corrected climate variables is close to the observed streamflow with ERA performing better than NCEP. However, the deviation in simulated irrigation between observed and reanalysis climate varies from -25.5-45.3%, whereas the relative irrigation water savings by deficit irrigation could be shown by all climate input data. The overall variability in simulated irrigation requirement depends mainly on the climate input data. Studies about irrigation requirement in data scarce areas must address this in particular when using reanalysis data.