Modeling Climate Change Effects on Rice Yield and Soil Carbon under Variable Water and Nutrient Management

Soil organic carbon (SOC) conservation in agricultural soils is vital for sustainable agricultural production and climate change mitigation. To project changes of SOC and rice yield under different water and carbon management in future climates, based on a two-year (2015 and 2016) field test in Kunshan, China, the Denitrification Decomposition (DNDC) model was modified and validated and the soil moisture module of DNDC was improved to realize the simulation under conditions of water-saving irrigation. Four climate models under four representative concentration pathways (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5), which were integrated from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), were ensembled by the Bayesian Model Averaging (BMA) method. The results showed that the modified DNDC model can effectively simulate changes in SOC, dissolved organic carbon (DOC), and rice yield under different irrigation and fertilizer management systems. The normalized root mean squared errors of the SOC and DOC were 3.45–17.59% and 8.79–13.93%, respectively. The model efficiency coefficients of SOC and DOC were close to 1. The climate scenarios had a great impact on rice yield, whereas the impact on SOC was less than that of agricultural management measures on SOC. The average rice yields of all the RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 scenarios in the 2090s decreased by 18.41%, 38.59%, 65.11%, and 65.62%, respectively, compared with those in the 2020s. The long-term effect of irrigation on the SOC content of paddy fields was minimal. The SOC of the paddy fields treated with conventional fertilizer decreased initially and then remained unchanged, while the other treatments increased obviously with time. The rice yields of all the treatments decreased with time. Compared with traditional management, controlled irrigation with straw returning clearly increased the SOC and rice yields of paddy fields. Thus, this water and carbon management system is recommended for paddy fields.

Analysis of Extreme Temperature Events over the Iberian Peninsula during the 21st Century Using Dynamic Climate Projections Chosen Using Max-Stable Processes

Due to climate change, the Iberian Peninsula is suffering an increasing trend of extreme temperature events. The main objective of this study is to analyse this trends in frequency, duration and intensity of warm events and heat waves. The datasets used are 14 different regionalized dynamic climate projections. We choose the projections that present a spatial dependence similar to that of observed data. The spatial dependence is calculated by adjusting the data to max-stable processes. The observed data belong to the SPAIN02 grid for the period 1961–2000. We apply the Mann-Kendall test and the Theil-Sen estimator to calculate model trends in the future period (2011–2099). We have studied future extreme temperature events using two different definitions. One varying the threshold for each period and the other keeping it constant. The results show that the variability of maximum temperatures is decreasing for the western region of the Peninsula, while the Mediterranean area will see an increase in this variability. There will be an increase in the frequency of warm events for the southwestern corner of the Peninsula. Also, maximum temperatures will be higher in this area at the end of the century. However, in the Mediterranean region the warm events will last longer. Heat waves will be more frequent throughout the territory and more lasting in the Mediterranean area. We also found that studying extreme events using a varying threshold allows these events to be studied from the point of view of the variability of maximum temperatures, while if the study is carried out maintaining the threshold constant the results will be more direct.

Changes in Precipitation Extremes over the Source Region of the Yellow River and Its Relationship with Teleconnection Patterns

Precipitation extremes and their underlying causes are important processes to understand to plan appropriate adaptation measures. This paper presents an analysis of the spatiotemporal variability and trend of precipitation extremes in the important source region of the Yellow River and explores the connection to global teleconnection patterns and the 850-mb vector wind. Six indices for precipitation extremes were computed and analyzed for assessment of a changing regional climate. Results showed that these indices have a strong gradient from the northwest to the southeast part for the period 1961–2015, due to the great influence from the south-easterly summer monsoon flow. However, no statistically significant trends were found for the defined indices at the majority of stations, and their spatial distribution are noticed by irregularly mixed positive and negative changes except for the maximum number of consecutive wet days (CWD). Singular value decomposition analysis revealed that the precipitation extreme indices—including annual total precipitation when daily precipitation >95th percentile (R95p), annual count of days with daily precipitation ≥10 mm (R10mm), annual maximum consecutive 5-day precipitation (R5d), total precipitation divided by the number of wet days (SDII), and CWD—are negatively related to the El Nino-Southern Oscillation (NINO 3.4) in the first mode, and the maximum number of consecutive dry days (CDD) is positively related to the Scandinavian pattern in the second mode at 0.05 significance level. The 850-mb vector wind analysis showed that the southwestern monsoon originating from the Indian Ocean brings sufficient moisture to this region. Furthermore, the anti-cyclone in the western part of the North Pacific plays a significant role in the transport of moisture to the source region of the Yellow River. The links between precipitation extremes and teleconnection patterns explored in this study are important for better prediction and preparedness of climatic extremes.

An Agricultural Drought Index for Assessing Droughts Using a Water Balance Method: A Case Study in Jilin Province, Northeast China

Drought, which causes the economic, social, and environmental losses, also threatens food security worldwide. In this study, we developed a vegetation-soil water deficit (VSWD) method to better assess agricultural droughts. The VSWD method considers precipitation, potential evapotranspiration (PET) and soil moisture. The soil moisture from different soil layers was compared with the in situ drought indices to select the appropriate depths for calculating soil moisture during growing seasons. The VSWD method and other indices for assessing the agricultural droughts, i.e., Scaled Drought Condition Index (SDCI), Vegetation Health Index (VHI) and Temperature Vegetation Dryness Index (TVDI), were compared with the in situ and multi-scales of Standardized Precipitation Evapotranspiration Index (SPEIs). The results show that the VSWD method has better performance than SDCI, VHI, and TVDI. Based on the drought events collected from field sampling, it is found that the VSWD method can better distinguish the severities of agricultural droughts than other indices mentioned here. Moreover, the performances of VSWD, SPEIs, SDCI and VHI in the major historical drought events recorded in the study area show that VSWD has generated the most sensible results than others. However, the limitation of the VSWD method is also discussed.

Observed Key Surface Parameters for Characterizing Land–Atmospheric Interactions in the Northern Marginal Zone of the Taklimakan Desert, China

An observational data set of the year 2010 at a site in the northern marginal zone of the Taklimakan Desert (TD) was used to analyse the key surface parameters in land–atmospheric interactions in the desert climate of northwest China. We found that the surface albedo (α) and emissivity (ε) were 0.27 and 0.91, respectively, which were consistent with the values obtained based on observations in the hinterland of the TD as well as being similar to the dry parts of the Great Basin desert in North America, where they were comparable to the α and ε values retrieved from remote sensing products. Peak frequency value of z0m was 5.858 × 10−3 m, which was similar to the Mojave Desert, Peruvian desert, Sonoran Desert, HEIFE (Heihe region) Desert, and Badain Jaran Desert. The peak frequency value of z0h was 1.965 × 10−4 m, which was different from those obtained in the hinterland of the TD. The average annual value of excess resistance to heat transfer (kB−1) was 2.5, which was different from those obtained in the HEIFE Gobi and desert, but they were similar to those determined for the Qinghai–Tibetan Plateau and HAPEX-Sahel. Both z0m and z0h varied less diurnally but notably seasonally, and kB−1 exhibited weak diurnal and seasonal variations. We also found that z0m was strongly influenced by the local wind direction. There were many undulating sand dunes in the prevailing wind and opposite to the prevailing wind, which were consistent with the directions of the peak z0m value. The mean values calculated over 24 h for Cd and Ch were 6.34 × 10−3 and 5.96 × 10−3, respectively, which were larger than in the Gobi area, hinterland of the TD and semiarid areas, but similar to HEIFE desert. Under the normal prevailing (NNE–ESE) wind, the mean bulk transfer coefficient Cd and Ch were of the same order of magnitude as expected based on similarity theory. Using the data obtained under different wind directions, we determined the relationships between Cd, Ch, the wind speed U, and stability parameter z/L, and the results were different. Cd and Ch decreased rapidly as the wind speed dropped below 3.0 m s−1 and their minimum values reached around 1–2 m s−1. It should also be noted that the ε values estimated using the sensible heat flux (H) were better compared with those produced using other estimation methods.

Temporal-spatial variability in correlations of drought and flood during recent 500years in Inner Mongolia, China

Drought and flood are two frequent natural disasters in the world. Identification of temporal-spatial variability and its scale-dependence for drought-flood correlation can help to predict, hence to alleviate the losses. The correlations of drought with flood at 1, 3, 5, 10, 50 and 100-year scales at moving window of 30years during recent 500years were analyzed by spatial statistics based on geographical information system in Inner Mongolia, China. The results demonstrated that the frequency and intensity of two disasters increased generally during last 500years. The drought-flood correlations indicated a rhythm of alternate positive and negative relation with unspecific timescale, enlarged by prolonging of time scale from 1year to 5years, for either frequency or intensity. A lag effect, flood always occurred at the 4 or 5year following drought was observed. Located in the climatic marginal zone, eastern study region was strongly affected by the East Asia monsoon, showed a weak drought-flood correlation; western study region was controlled by continental climate, showed a significant drought-flood correlation. These correlations were also affected by periodic ENSO disturbance and mountain terrains. With the climatic changes, the drought-flood correlations also change in recent decades. These long-range correlations at various temporal-spatial scales provide a theoretical basis for the scientific prediction of drought and flood outbreaks, for a long term as well as a short wavelet.

Multi-model ensemble simulation and projection in the climate change in the Mekong River Basin. Part I: temperature

This paper evaluates the performance of the Coupled Model Intercomparison Project phase 5 (CMIP5) in simulating annual and decadal temperature in the Mekong River Basin from 1950 to 2005. By use of Bayesian multi-model averaging method, the future projection of temperature variation under different scenarios are also analyzed. The results show, the performances of climate model are more accurate in space than time, the model can catch the warming characteristics in the Mekong river Basin, but the accuracy of simulation is not good enough. Bayesian multi-model averaging method can improve the annual and decadal temperature simulation when compared to a single result. The projected temperature in Mekong River will increase by 0.88 °C/100 year, 2.15 °C/100 year and 4.96 °C/100 year for the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively, over the twenty-first century. The findings will be beneficial for local people and policy-maker to formulate regional strategies against the potential menaces of warming scenarios.

Inferring the relative resilience of alternative states

Ecological systems may occur in alternative states that differ in ecological structures, functions and processes. Resilience is the measure of disturbance an ecological system can absorb before changing states. However, how the intrinsic structures and processes of systems that characterize their states affects their resilience remains unclear. We analyzed time series of phytoplankton communities at three sites in a floodplain in central Spain to assess the dominant frequencies or “temporal scales” in community dynamics and compared the patterns between a wet and a dry alternative state. The identified frequencies and cross-scale structures are expected to arise from positive feedbacks that are thought to reinforce processes in alternative states of ecological systems and regulate emergent phenomena such as resilience. Our analyses show a higher species richness and diversity but lower evenness in the dry state. Time series modeling revealed a decrease in the importance of short-term variability in the communities, suggesting that community dynamics slowed down in the dry relative to the wet state. The number of temporal scales at which community dynamics manifested, and the explanatory power of time series models, was lower in the dry state. The higher diversity, reduced number of temporal scales and the lower explanatory power of time series models suggest that species dynamics tended to be more stochastic in the dry state. From a resilience perspective our results highlight a paradox: increasing species richness may not necessarily enhance resilience. The loss of cross-scale structure (i.e. the lower number of temporal scales) in community dynamics across sites suggests that resilience erodes during drought. Phytoplankton communities in the dry state are therefore likely less resilient than in the wet state. Our case study demonstrates the potential of time series modeling to assess attributes that mediate resilience. The approach is useful for assessing resilience of alternative states across ecological and other complex systems.