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The Sanjiang Plain of north-east China is presently the second largest freshwater marsh in China. The drainage and use of marshes for agricultural fields occurred in the past 50 years, resulting in the increase in cultivated land from about 2.9 × 108 m2 in 1893 to 4.57 × 1010 m2 in 1994. Under human disturbance in the past half century, the environment in Sanjiang Plain has had significant change. We hypothesised that environmental factors such as soil moisture, soil temperature, and soil N levels affect the rates of soil organic C mineralisation and the nature of the controls on microbial CO2 production to change with depth through the soil profile in the freshwater marsh in the Sanjiang Plain. In a series of experiments, we measured the influence of soil temperature, soil water content, and nitrogen additions on soil microbial CO2 production rates. The results showed that Q10 values (the factor by which the CO2 production rate increases when the temperature is increased by 10°C) significantly increased with soil depth through the soil profile (P < 0.05). The average Q10 values for the surface soils were 2.7 (0–0.2 m), significantly lower than that (average Q10 values 3.3) for the subsurface samples (0.2–0.6 m) (P < 0.05), indicating that C mineralisation rates were more sensitive to temperature in subsurface soil horizons than in surface horizons. The maximum respiration rate was measured at 60% water hold capacity for each sample. The quadratic equation function adequately describes the relationship between soil respiration and soil water content, and the R2 values were > 0.80. The sensitivity of microbial CO2 production rate response to soil water content for surface soils (0–0.2 m) was slightly lower than for subsurface soils (0.2–0.6 m). The responses of actual soil respiration rates to nitrogen fertilisation were different for surface and subsurface soils. In the surface soils (0–0.2 m), the addition of N caused a slight decreased in respiration rates compared with the control, whereas, in the subsurface soils (0.2–0.6 m), the addition of N tended to increase microbial CO2 production rates, and the addition of 10 µg N/g soil treatment caused twice the increase in C mineralisation rates of the control. Our results suggested that the responses of microbial CO2 production to changes in soil moisture, soil temperature, and soil N levels varied with soil depth through the profile, and subsurface soil organic C was more sensitive to temperature increase and nitrogen inputs in the freshwater marsh of the Sanjiang Plain.

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Extreme hydrometeorological events have far-reaching impacts on our daily life and may occur more frequently with rising global temperatures. The probability of the concurrence of these extreme events in the upper reaches of the river network is of particular importance for the lower reaches, which is referred to as the encounter probability of extreme events, and may have even stronger socio-economic impacts. In this study, the Rao River basin in China is selected as an example to explore the encounter probability and risk of future flood and drought based on the encounter probability model. The reference period was 1971–2000, and the future prediction periods were 2020–2049 and 2070–2099. The calibrated and validated statistical downscaling model (SDSM) was used to generate future daily precipitation and daily mean temperature. The calibrated and validated Xin’anjiang model was used to predict future daily mean streamflow in the basin. In addition, the encounter probability model was established using the joint distribution of occurrence dates and magnitudes of daily mean streamflow to investigate the encounter probabilities of flood and drought under future climate change. Results show that, for flood occurrence dates, the encounter probability during the flood season would decrease in the two future periods while the dates would generally be earlier. For flood magnitudes, the encounter probability of the two tributaries’ floods and the probability of flood at each tributary would decrease (e.g., the encounter probability with the same-frequency of 100-years would reduce by 53% to 95%), which indicates reduced risk of future major floods in the study area. For drought occurrence dates, the encounter probability during the non-flood season would decrease. For drought magnitudes, the encounter probability would decrease (e.g., the encounter probability with the same-frequency of 100-years would reduce by 18% to 33%), even though the probability of future drought at each tributary would increase. Such analyses provide important probabilistic information to help us prepare for the upcoming extreme events.