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Research on the landscape level dynamics of land cover and an understanding of the drivers responsible for these changes are useful for formulations of sustainable land management strategies and policy decisions. The pace and magnitude of the LCCs in Ethiopia are threatening the natural ecosystem and creating vulnerability to environmental hazards. This study used a combination of remotely sensed data, field interviews, and observations to examine the dynamics and identify the driving forces of LCC from 1986 to 2020 in the Muger sub-basin, Ethiopia. Multi-temporal satellite images were classified using supervised and unsupervised methods. Information from focus group discussions and the variable ranking method were used to explain the drivers of the observed changes. The study results showed a decline in forest cover from 11.80% in 1986 to 5.90% in 2020, which indicates a loss of 49.90% of the forest cover that existed in 1986. Bare land declined from 1.15% in 1986 to 0.57% in 2020. Similarly, grazing land declined from 1.52% in 1986 to 0.41% in 2020. Wetland also declined from 1.93% in 1986 to 0.31% in 2020. Conversely, during the same period, the proportions of cultivated land, built-up areas, shrubland, and water bodies have risen from 68.86% to 70.44%, from 0.03% to 1.53%, from 14.39% to 20.27%, and from 0.34% to 0.56%, respectively. Population growth and poor agricultural practices were identified as the main causes of LCC in the sub-basin. Therefore, the findings of this study suggest that implementation of comprehensive and integrated basin management policies and strategies will be indispensable to managing the risks and ensuring sustainable development in the larger Abay basin.

Despite the rapid economic and population growth, the risks related to the current dynamics of land use and land cover (LULC) have attracted a lot of attention in Ethiopia. Therefore, a complete investigation of past and future LULC changes is essential for sustainable water resources and land-use planning and management. Since the 1980s, LULC change has been detected in the upper stream of the Awash River basin. The main purpose of this research was to investigate the current dynamics of LULC and use the combined application of the cellular automata and the Markov chain (CA–Markov) model to simulate the year 2038 LULC in the future; key informant interviews, household surveys, focus group discussions, and field observations were used to assess the consequences and drivers of LULC changes in the upstream Awash basin (USAB). This research highlighted the importance of remote sensing (RS) and geographic information system (GIS) techniques for analyzing the LULC changes in the USAB. Multi-temporal cloud-free Landsat images of three sequential data sets for the periods (1984, 2000, and 2019) were employed to classify based on supervised classification and map LULC changes. Satellite imagery enhancement techniques were performed to improve and visualize the image for interpretation. ArcGIS10.4 and IDRISI software was used for LULC classification, data processing, and analyses. Based on Landsat 5 TM-GLS 1984, Landsat 7 ETM-GLS 2000, and Landsat 8 2019 OLI-TIRS, the supervised maximum likelihood image classification method was used to map the LULC dynamics. Landsat images from 1984, 2000, and 2019 were classified to simulate possible LULC in 2019 and 2038. The result reveals that the maximum area is covered by agricultural land and shrubland. It showed, to the areal extent, a substantial increase in agricultural land and urbanization and a decrease in shrubland, forest, grassland, and water. The LULC dynamics showed that those larger change rates were observed from forest and shrubland to agricultural areas. The results of the study show the radical changes in LULC during 1984–2019; the main reasons for this were agricultural expansion and urbanization. From 1984 to 2019, agriculture increased by 62%, urban area increased by 570.5%, and forest decreased by 88.7%. In the same year, the area of shrubland decreased by 68.6%, the area of water decreased by 65.5%, and the area of grassland decreased by 57.7%. In view of the greater increase in agricultural land and urbanization, as well as the decrease in shrubland, it means that the LULC of the region has changed. This research provides valuable information for water resources managers and land-use planners to make changes in the improvement of future LULC policies and development of sub-basin management strategies in the context of sustainable water resources and land-use planning and management.

Elevated atmospheric temperature and CO2 concentration ([CO2]) can strongly affect yield, CH4 and N2O emissions in rice paddies. However, understanding of their response to combined temperature and [CO2] increases under field conditions is still limited. To study this issue further, an open-top chamber (OTC) platform was set up to simulate two levels of atmospheric temperature (ambient and 2 °C elevated) and two levels of [CO2] (ambient and 60 ppm elevated) during two rotations of double rice, including twelve independent OTCs and three un-chambered open field sites as trial plots. The experimental site is located in Hubei Province, Central China, which has a subtropical monsoon climate. In all four rice seasons, elevated CO2 induced an increment in grain yield by 11.4–19.7%, while also enhancing CH4 and N2O emissions by 19.8–52.6% and 102.4–140.0%, respectively, compared with ambient temperature and [CO2]. Elevated temperature enhanced CH4 emissions by 4.4–36.0%, and decreased N2O emissions by 1.5–10.5% in three rice seasons. Elevated temperature had different effects on grain yield, with a reduction of 1.0–3.2% in early rice and an increase of 6.3–9.2% in late rice. When elevated temperature and [CO2] were combined, there was a positive interaction that further enhanced CH4 emissions and yield of late rice, and an offsetting effect on N2O emissions and yield of early rice. Over the 2-year experiment, warming and CO2 enrichment caused an increase of 9.3–44.2% in greenhouse gas intensity, suggesting that more greenhouse gas emissions will be emitted to produce a unit mass of rice under projected global warming. These findings provide initial data for the estimation of CH4 and N2O emissions under elevated temperature and [CO2] in double rice cropping system, and stress the need for effective management practices that promote rice yield while reducing greenhouse gas emissions.

Effectiveness of N might be modified in rice cultivation under future climate change with elevated atmospheric CO

Changes in atmospheric CO2 concentration ([CO2]) and temperature have impacts on leaf photosynthesis (Anet) and stomatal conductance (Gs) of rice, which strongly affect dry matter productivity. Understanding of the acclimation responses of Anet and Gs under elevated [CO2] and/or temperature is limited. A field experiment of double rice (early rice-late rice rotation) was conducted using open-top chambers from 2013 to 2016 in Hubei Province, Central China, with two levels of [CO2] (ambient, ambient + 60 μmol mol−1) and two levels of temperature (ambient, ambient + 2°C). Averaging across the four-year experiment, elevated [CO2] and elevated temperature increased Anet by 2.4 %–9.4 % and 2.4 %–10.7 %, respectively at pre-heading stages. A positive interaction was observed between elevated [CO2] and temperature in early rice which further increased Anet, while the interaction was not additive in late rice. Elevated [CO2] caused lower Gs and partly offset the stimulation of elevated temperature on Gs at tillering and jointing. At post-heading stages, photosynthetic acclimation to elevated [CO2] was observed as the stimulation of Anet was not continued. Elevated temperature decreased Anet by 1.7 %–16.6 % and further accelerated photosynthetic down-regulation by elevated [CO2]. Warming reduced Gs at milking and maturity in early rice because of high ambient temperature, which resulted in stomatal limitation to photosynthesis, but its effects on Gs in late rice were positive because the ambient temperature was low. Generally, Gs acclimated to warming and CO2 enrichment in parallel with Anet when there were no environmental constraints. Earlier leaf senescence, decreased leaf SPAD, lower seed-setting rate or larger sink capacity caused by elevated [CO2] and/or temperature might directly or indirectly explain the down-regulation on photosynthesis after heading. Our results show that warming alters the acclimation of leaf photosynthesis and stomatal conductance to CO2 enrichment and the combined effects differ between growth stages, and indicate that co-elevation of [CO2] and temperature may increase dry matter productivity in the Chinese double rice cropping system.

Elevated atmospheric CO2 concentration ([CO2]) and temperature can strongly affect rice (Oryza sativa L.) production. However, the impact of warming is variable depending on ambient temperature and cropping system. Little is known about the effect of elevated temperature and its interaction with elevated [CO2] in the Chinese double rice cropping system. To study this issue, field experiments were conducted using open-top chambers (OTC) with varying [CO2] (ambient, ambient+60 μmol mol−1) and varying temperature (ambient, ambient+2 °C) for 4 rotations of double rice from 2013 to 2016. Elevated [CO2] significantly increased grain yield in both early rice and late rice. This could be attributed to greater aboveground biomass, higher panicle number (m-2), spikelet number (per panicle) and 1000-grain weight. Elevated temperature decreased yield in early rice (except for 2016) due to decreases in % filled grains and 1000-grain weight. Elevated temperature increased yield in late rice due to increases in aboveground biomass, number of panicles and spikelets, although % filled grains decreased. We found that warming had positive effects on tiller development and dry matter accumulation during the pre-heading period both in early and late rice, which contributed to yield formation. When elevated temperature and [CO2] were combined, yield changed by −1.9–23.7% in early rice compared with ambient condition as warming partly offset the benefits of CO2 enrichment, while yield increased by 14.3–18.8% in late rice due to a synergy between warming and CO2 enrichment. The benefits of CO2 enrichment for fertile spikelets (m-2) and aboveground biomass could reduce or eliminate the negative impacts of warming. However, there were no significant interactions between elevated temperature and [CO2] in most rice seasons. Our findings indicate that warming by 2 °C is not detrimental to yield formation under CO2 enrichment, and suggest that grain yield may not decrease under future climate change in the Chinese double rice cropping system.

The uptake of nitrogen (N) and phosphorus (P) by plants in riparian zones can significantly decrease the water pollution risk. Moreover, the vegetation area in riparian zone can be impacted by raising of water level and afforestation. As the largest reservoir in North China, the Miyun Reservoir is affected by the South-to-North Water Transfer (SNWT) and large-scale afforestation. However, few efficient technology frameworks that can be used to assess the effects of similar anthropogenic projections on N and P uptake by plants at riparian zone catchment scale have been reported. Therefore, this study proposed a framework including an ecological simulation tool coupled with multi-source data and scenario setting methods to identify the effects of these two projects on the uptake of N and P by plants in Miyun Reservoir riparian zone from April to September in 2015. The results show that the total N and P uptake by plants in Miyun Reservoir riparian zone are 1214.18 t and 148.66 t in growing seasons. After afforestation, the N (P) removal will increase by 2.56 (2.17) times in the impacted area (below 160 m in elevation). When the water level rises to 150 m in elevation, the joint effects of afforestation and SNWT will increase the total N and P removals by 851.18 t and 83.33 t. This implies that the afforestation can offset the negative effect on N (P) removal caused by SNWT. Overall, this study can provide useful scientific reference for the design and effective management of the riparian zone.

The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Final Run (IMERGF) product has now been upgraded to Version 4 (V4), which has been available since March 2017. Therefore, it is desirable to evaluate the characteristic differences between the V4 and the previous V3 products. A comprehensive performance evaluation of the errors of the successive V3 and V4 IMERGF products is performed with a comparison of the China daily Precipitation Analysis Products (CPAP) from March 2014 to February 2015. The version 6 Global Satellite Mapping of Precipitation (GSMaP) research product (which is another Global Precipitation Measurement (GPM) based precipitation product) is also used as a comparison in this study. Overall, the IMERGF-V4 product does not exhibit the anticipated improvement for China compared to the IMERGF-V3 product. An analysis of the metrics of annual daily average precipitation over China for the IMERGF-V3 and IMERGF-V4 products indicates a decrease of the relative bias (RB) from 3.70% to −7.18%, a decrease of the correlation coefficient (CC) from 0.91 to 0.89, an increase of the fractional standard error (FSE) from 0.49 to 0.56, and an increase of the root-mean-square error (RMSE) from 0.63 mm to 0.72 mm. Compared to the IMERGF-V3 product, the IMERGF-V4 product exhibits a significant underestimation of precipitation in the Qinghai-Tibetan plateau with a much lower RB of −60.91% (−58.19%, −65.30%, and −63.74%) based on the annual (summer, autumn, and winter) daily average precipitation and an even worse performance during winter (−72.33% of RB). In comparison, the GSMaP product outperforms the IMERGF-V3 and IMERGF-V4 products and has the smallest RMSE (0.47 mm/day), highest CC (0.95), lowest FSE (0.37), and best performance of the RB (−2.39%) in terms of annual daily precipitation over China. However, the GSMaP product underestimates the precipitation more than the IMERGF-V3 product for the arid XJ region.

The Vegetation Photosynthesis Model (VPM) was used to simulate the gross primary productivities (GPP) of the alpine meadow ecosystem in the northern Tibet Plateau at three different spatial resolutions of 0.5 km, 1.5 km and 2.5 km, respectively. The linear relationships between enhanced vegetation indices (EVI) and GPP, with higher correlative coefficients, were better than those between normalized difference vegetation indices (NDVI) and GPP at the three resolutions. VPM could well simulate the seasonal changes and inter-annual variations of GPP, with similar trends at the three resolutions. There were significant differences (P < 0.0001) among the three modeled GPP with the three resolutions. Therefore, the modeled GPP at high resolution could not be directly extrapolated to low resolution, and vice versa. The contribution levels of different model parameters, including photosynthetically active radiation (PAR), air temperature (Ta), NDVI, EVI and land surface water indices (LSWI), to modeled GPP could vary with spatial resolution based on multiple stepwise linear regression analysis. This indicated that it was important to choose parameters properly and consider their effects on modeled GPP.

Mapping and monitoring cropland areas and distributions from remote sensing data could provide early warning information of threats to the global and regional food security. Winter wheat is traditionally the most cultivated food crop in China, and the Huang-Huai-Hai (HHH) plain is an important winter wheat production base. Due to a long latitudinal distance, the same winter wheat growth stage delays from the north to the south of the plain. Influenced by the monsoon climate, isolines of the winter wheat phenology are oriented in a northeast–southwest direction, which is similar to that of the temperature distribution. In this paper, Moderate Resolution Imaging Spectroradiometer-enhanced vegetation index (EVI) was applied to estimate the winter wheat planting information on the HHH plain, using a model built according to seasonal change of the winter wheat EVI. The result shows that the average accuracy of the estimation was 75.4% with a standard deviation of 26.1%, when the impacts from the phenology delay and the monsoon climate were not considered. When winter wheat phenology delay was considered with and without the influences from the monsoon climate, the accuracy was 93.2% with a standard deviation of 6.1% and 84.7% with a standard deviation of 11.0%, respectively. The accuracy increased evidently. Therefore, both the phenology delay and monsoon climate impacts should be taken into consideration when estimating the winter wheat planting information in a large monsoon climate region.

The objective of this research was to evaluate investment as a robust adaptation strategy in response to projected climate change in China. Four scenarios combining climate change and investment have been simulated in the model, which was established by adopting the standard approach of modern optimal economic growth theory; it also includes two discount factors from the climate sector (i.e., flood damage from climate variability and climate change). Relations between the flood prevention infrastructure and flood damage to the agricultural and the nonagricultural sectors were established from historical data and applied to estimate the benefit of investment to mitigate flood damage from climate variability. By assuming that the marginal adaptation costs to flood damage from projected climate change is the same as those from current climate variability, these relations were applied to estimate the benefit of investing in the mitigation of flood damage due to climate change. Tests were made to verify the model’s validity when the marginal adaptation cost for flood damage due to climate change was increased by 10%, 20%, and 30%, respectively. The conclusion is that optimized investment, taking climate change into consideration, effectively reduces the damage due to climate change and promotes the capacity to mitigate flood damage due to climate variability. Moreover, consumption and production increases regardless of whether climate change occurs.

The objective of this research is to evaluate investment as a robust adaptation strategy in response to projected climate change in China. Four scenarios combining climate change and investment have been simulated in the model which is established adopting the standard approach of modern optimal economic growth theory and includes two discount factors from the climate sector, i. e., flood damage from climate variability and climate change. Relationships between flood sectors are established from historical data and applied to estimate the benefit of investment to mitigate flood damage from climate variability. By assuming that the marginal adaptation costs to flood damage from projected climate change is the same as those from current climate variability, these relationships are also applied to estimate the benefit of investment in mitigating flood damage from climate change. The conclusion is that optimized investment, taking into climate change consideration will effectively reduce the damage from climate change and promote the capacity to mitigate flood damage from climate variability and that consumption and production increase no matter whether climate change occurs or not.

In order to assess vulnerability to climate change, not only the degree of climate change impact but also the adaptive capacity of the region with regard to the impact needs to be considered. To achieve such an assessment, a nationalscale impact assessment model is being developed. This paper introduces two case studies related to water problems in China. One of them is a scenario for an assessment of future water withdrawal and consumption in China taking into consideration alternative future socio-economic development patterns. This indicates that the adoption of appropriate policies and efficient technologies can ensure that future water consumption is maintained at the current level, even though water withdrawal will increase under all socio-economic scenarios, which reflects the expected increase in industrial withdrawal. In the other case study, an evaluation is made of the adaptation policies in China for mitigating current flood damage due to climate variability as well as the future increase in flood damage due to climate change is evaluated. Investment in infrastructure for flood prevention in accordance with the expected future climate change can also reduce current flood damage and is found to be robust and low-regret adaptation strategy.