Abstract:The Nanliujiang catchment is one of major rice production bases of South China. Irrigation districts play an important role in rice production which requires a large quantity of water. There are potential risks on future climate change in response to rice production, agricultural irrigation water use and pollution control locally. The SWAT model was used to quantify the yield and water footprint (WF) of rice in this catchment. A combined method of automatic and manual sub-basin delineation was used for the model setup in this work to reflect the differences between irrigation districts in yield and water use of rice. We validated our simulations against observed leaf area index, biomass and yield of rice, evapotranspiration and runoff. The outputs of three GCMs (GFDL-ESM2M, IPSL-CM5A-LR and HadGEM2-ES) under three RCPs (RCP2.6, 4.5, 8.5) were fed to the SWAT model. The results showed that: (a) the SWAT model is an ideal tool to simulate rice development as well as hydrology; (b) there would be increases in rice yield ranged from +1.4 to +10.6% under climate projections of GFDL-ESM2M and IPSL-CM5A-LR but slight decreases ranged from −3.5 to −0.8% under that of HadGEM2-ES; (c) the yield and WFs of rice displayed clear differences in the catchment, with a characteristic that high in the south and low in the north, mainly due to the differences in climatic conditions, soil quality and fertilization amount; (d) there would be a decrease by 45.5% in blue WF with an increase by 88.1% in green WF, which could provide favorable conditions to enlarge irrigated areas and take technical measures for improving green water use efficiency of irrigation districts; (e) a clear rise in future grey WF would present enormous challenges for the protection of water resources and environmental pollution control in this catchment. So it should be to improved nutrient management strategies for the agricultural non-point source pollution control in irrigation districts, especially for the Hongchaojiang and Hepu irrigation districts.

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The statistical characteristics of precipitation play important roles not only in flood and drought risk assessments but also in water resource management. This paper implements a statistical analysis to study the spatial and temporal variability in precipitation in the upper reaches of the Hongshui River basin (UHRB), southwestern China, by analysing time series of daily precipitation from 18 weather stations during the period of 1959 to 2015. To detect precipitation concentrations and the associated patterns, three indices, the precipitation concentration index (PCI), precipitation concentration degree (PCD), and precipitation concentration period (PCP), were used. The relationships between the precipitation concentration indices (PCI, PCD, and PCP) and geographic variables (latitude, longitude, and elevation), large-scale atmospheric circulation indices, and summer monsoon indices were investigated to identify specific dependencies and spatial patterns in the precipitation distribution and concentration. The results show that high PCI values were mainly observed in the northeastern portion of the basin, whereas low PCI values were mainly detected in the southwest. The Mann-Kendall test results demonstrate that the majority of the UHRB is characterized by nonsignificant trends in the PCI, PCD, and PCP from 1959 to 2015. The PCP results reveal that rainfall in the UHRB mainly occurs in summer months, and the rainy season arrives earlier in the eastern UHRB than in the western UHRB. Additionally, the PCD results indicate that the rainfall in the western UHRB is more dispersed throughout the year than that in the eastern UHRB. Compared with other geographical factors, longitude is the most important variable that governs the spatial distribution and variations in annual precipitation and the precipitation concentration indices. Due to a combination of topography, the Indian subtropical high, and monsoon weakening, precipitation may be more concentrated in one period, especially in the eastern part of the basin, which increases the risk of drought.

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In the current human-influenced era, drought is initiated by natural and human drivers, and human activities are as integral to drought as meteorological factors. In large irrigated agricultural regions with high levels of human intervention, where the natural farmland soil moisture has usually been changed significantly by high-frequency irrigation, the actual severity of agricultural drought is distorted in traditional drought indices. In this work, an agricultural drought index that considering irrigation processes based on the Palmer drought severity index (IrrPDSI) was developed to interpret the real agricultural drought conditions in irrigated regions, with a case study in the Haihe River Basin in northeast China. The water balance model in the original PDSI was revised by an auto-irrigation threshold method combined with a local irrigation schedule. The auto-irrigation setting of the index was used by taking irrigation quotas during specific growth stages of specific crops (wheat-corn) into consideration. A series of weekly comparative analyses are as follows: (1) The soil moisture analyses showed that soil moisture values calculated by the modified water balance model were close to the real values; (2) The statistical analyses indicated that most of the stations in the study area based on IrrPDSI had nearly normal distributed values; (3) The time series and spatial analyses showed that the results of the IrrPDSI-reported dry-wet evaluation were more consistent with documented real conditions. All the results revealed that IrrPDSI performed well when used to assess agricultural drought. This work has direct significance for agricultural drought management in large irrigated areas heavily disturbed by human activity.