The A-Luoi district in Thua Thien Hue province of Vietnam is under extreme pressure from natural and anthropogenic factors. The area is ungauged and suffering from data scarcity. To evaluate the water resources availability and water management, we used Soil and Water Assessment Tools (SWAT). A multi-approach technique was used to calibrate the hydrological model. The model was calibrated in three time scales: daily, monthly and yearly by river discharge, actual evapotranspiration (ETa) and crop yield, respectively. The model was calibrated with Nash-Sutcliffe and R² coefficients greater than 0.7, in daily and monthly scales, respectively. In the yearly scale, the crop yield inside the model was calibrated and validated with Root Mean Square Error (RMSE) less than 2.4 ton/ha. The water resource components were mapped temporally and spatially. The outcomes showed that the highest mean monthly surface runoff, 323 to 369 mm, between September and November, resulted in extreme soil erosion and sedimentation. The monthly average of actual evapotranspiration was the highest in May and lowest in December. Furthermore, installing “Best Management Practices” (BMPs) reduced surface runoff in agricultural lands. However, using event-based hydrological and hydraulic models in the prediction and simulation of flooding events is recommended in further studies.

Oil pollution in the world’s oceans is predominantly caused by operational discharges from tankers (predominant oil dumped during cleaning operations). These discharges result in over 2 million tons of oil which are introduced annually comparable to one full-tanker disaster per week. Land-based sources such as urban waste and industrial discharges, which compass the ocean via rivers, are also a major contamination factor. The Caspian Sea is the largest inland water body on Earth without a direct connection to the world’s oceans, which makes it a unique water reservoir. This fact makes the Caspian Sea particularly vulnerable and subject to human influence, for example, pollution from shipping activities and oil transports by ships. The research study shows results from a monitoring campaign in northern and central parts of the Caspian Sea (Kazakhstan sector) for the period from 2005 to 2012 (April–October), based on visual interpretation of radar images from ASAR ENVISAT. Radar data (529 radar images) such as ASAR_WSM_1P and ASAR_IM_1P were obtained from the archives of the European Space Agency. In 529 ASAR ENVISAT images, 160 images were selected, on which 329 oil spills were detected. Examples of individual slicks were analyzed by visual image interpretation. The total surface of contamination for the entire study period was estimated by 822.66 km². The average size of the oil spills ranged from 1.09 to 10.62 km², in different years. The monitoring of oil spills over the Kazakhstan sector of the Caspian Sea could show that it is possible to state with certainty that the main source of oil pollution is shipping (confirmed by analysis of slicks form and clusters along shipping routes) and the most contaminated area is its southern part, where there is more intensive navigation in the direction of Aktau–Baku (Azerbaijan), Aktau–Turkmenbashi (Turkmenistan), and Aktau–Neka (Iran). In the northern part of the Caspian Sea, it was determined that a low number of spills could have been caused by fishing and marine activities in the development area of the Kashagan oil field. The result is based on comparing the contamination map and the map of navigation, as well as through an analysis of the geometry of the slicks. Monitoring results largely coincided with the results of work carried out in the region, and at different times for some of its sites. These findings based on the radar data showed advantages of ASAR ENVISAT satellite monitoring for the control of oil pollution on the sea surface.

Long-term global datasets of the Leaf Area Index (LAI) are important for monitoring global vegetation dynamics and are an important input for Earth system models (ESM). The comparison of long-term datasets is based on two recently available datasets both derived from AVHRR (Advanced Very High Resolution Radiometer) time series. The LAI3g dataset is developed from the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) from AVHRR sensors and best-quality MODIS LAI data. The second long-term LAI dataset is based on the 8-km spatial resolution GIMMS-AVHRR data (Goettingen GIS & Remote Sensing, GGRS dataset). The GGRS-LAI product uses a satellite-based LAI. This algorithm uses a three-dimensional physical radiative transfer model, which establishes the relationship between LAI, vegetation fractional cover and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation. The model incorporates a number of site-/region-specific parameters, including the vegetation architecture variables, such as leaf angle distribution, clumping index and light extinction coefficient. For the application of the model to Kazakhstan, the vegetation architecture variables were computed at the local (pixel) level based on extensive field surveys of the biophysical properties of vegetation in representative grassland areas of Kazakhstan. As a main result of our study, we could summarize that the differences between both products are most pronounced at the start and the end of the growing season. During the spring and autumn months, the LAI difference maps showed a considerable difference of LAI GGRS and LAI3g. LAI3g is characterized by a considerably earlier start and a later finish to the growing season than LAI GGRS. Moreover, LAI3g showed LAI > 0 during the winter months when any green vegetation is absent in all land covers of Kazakhstan. A direct cause for this could be a too high base level of the LAI3g during the leafless phase.

A Dzud is a climate event in the Mongolian that causes serious environmental and economic damage. Although a natural phenomenon, its effects can be exacerbated by human activities such as livestock overgrazing and inadequate fodder, resulting in mass deaths of the livestock in the spring following a severe winter. This article is based on the analysis of various Dzud events (2000, 2001, 2002, and 2010) and their specific effect on the vegetation condition by analyzing Normalized Difference Vegetation Index NDVI in the Gobi regions of Mongolia. Our evaluation methods utilize the seasonal aridity index, time series of MODIS NDVI and data from livestock statistics. Heavy snowfall is one of the limiting factors for animal productivity and socioeconomic development in Mongolia. Based on the findings, steppe areas have the highest degree of vulnerability of climate, with the potential decline of growth grassland being stronger for humid areas. When there are high snowy winters, there is a 10 to a 20-day earlier peak of NDVI values as well as an increase in vegetation growth. Additionally, grazing pressure (caused by high livestock loss) played a minor role in plant growth. We found that during the dry winter conditions of a black Dzud, low soil moisture, and high evapotranspiration, the vegetation growth phase begins later due to water deficiency, leading to a lower peak in growth. During the year 2009/2010, a white Dzud occurred in the presence of a thick snow layer, which acted as a water reservoir. The effect of livestock loss and the reduction of grazing pressure played a minor part in vegetation recovery after different types of Dzud events in Mongolia.