Assessing Environmentally Sensitive Land to Desertification Using MEDALUS Method in Mongolia

Desertification is a global phenomenon caused by various processes, including climate change, vegetation processes, and human activities. The need to combat desertification is increasing in many countries. A reasonable assessment of the vulnerability or sensitivity of land cover to desertification at national scales is crucial to formulate appropriate strategies or policies for combating it. The main purpose of this work was to quantitatively assess the sensitivity of land cover to desertification in Mongolia using the MEDALUS approach. The MEDALUS method is a widely known technique for assessing desertification in the Mediterranean area. In this study, the method was adjusted to be applied to Mongolia, while the numerical methods of the MEDALUS remained the same. The modified MEDALUS method used nine factors from 2003 and 2008 to quantify the sensitivity of land to desertification. As a result, our study resulted in the calculation and spatial distribution of the Environmental Sensitive Area Index (ESAI), produced throughout Mongolia. In 2003, the middle region of the southern Mongolia had the highest sensitivity to desertification, while sensitivity in 2008 increased in the western area. Mongolia’s area with the highest ESAI range increased approximately five times, indicating rapid desertification occurring throughout Mongolia from 2003 to 2008

Integrating Satellite Rainfall Estimates with Hydrological Water Balance Model: Rainfall-Runoff Modeling in Awash River Basin, Ethiopia

Hydrologic models play an indispensable role in managing the scarce water resources of a region, and in developing countries, the availability and distribution of data are challenging. This research aimed to integrate and compare the satellite rainfall products, namely, Tropical Rainfall Measuring Mission (TRMM 3B43v7) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), with a GR2M hydrological water balance model over a diversified terrain of the Awash River Basin in Ethiopia. Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination (R2), and root mean square error (RMSE) and Pearson correlation coefficient (PCC) were used to evaluate the satellite rainfall products and hydrologic model performances of the basin. The satellite rainfall estimations of both products showed a higher PCC (above 0.86) with areal observed rainfall in the Uplands, the Western highlands, and the Lower sub-basins. However, it was weakly associated in the Upper valley and the Eastern catchments of the basin ranging from 0.45 to 0.65. The findings of the assimilated satellite rainfall products with the GR2M model exhibited that 80% of the calibrated and 60% of the validated watersheds in a basin had lower magnitude of PBIAS (<±10), which resulted in better accuracy in flow simulation. The poor performance with higher PBIAS (≥±25) of the GR2M model was observed only in the Melka Kuntire (TRMM 3B43v7 and PERSIANN-CDR), Mojo (PERSIANN-CDR), Metehara (in all rainfall data sets), and Kessem (TRMM 3B43v7) watersheds. Therefore, integrating these satellite rainfall data, particularly in the data-scarce basin, with hydrological data, generally appeared to be useful. However, validation with the ground observed data is required for effective water resources planning and management in a basin. Furthermore, it is recommended to make bias corrections for watersheds with poorlyww performing satellite rainfall products of higher PBIAS before assimilating with the hydrologic model

Classification of Global Land Development Phases by Forest and GDP Changes for Appropriate Land Management in the Mid-Latitude

To implement appropriate land management strategies, it is essential to identify past and current land cover and land use conditions. In addition, an assessment of land development phases (LDPs) in a human-dominated landscape coupled with an analysis of the water-food-ecosystem (WFE) nexus can deepen our understanding of sustainable land management. In this study, we proposed the concept of land development phases (LDPs) by forest and GDP changes using previously-applied theoretical and empirical approaches. The positive relationship between GDP growth and forest stock changes was used to analyze the timing of forest stock changes as five-year averages, which were aggregated over 20 years to classify LDPs. In addition, forest area changes compared with GDP and GDP per capita changes were analyzed to identify LDPs. Based on two conceptual approaches, we suggested global land into three LDPs: degradation, restoration and sustainability. Using this approach, most of Europe, North America and northeast Asia were classified as sustainability phases, while Africa and Central Asia in the Mid-Latitude region appeared to have degradation or restoration phases. The LDPs described could be improved with further incorporation of solid data analysis and clear standards, but even at this stage, these LDP classifications suggest points for implementing appropriate land management. In addition, indices from comparative analysis of the LDPs with the WFE nexus can be connected with socio-economic global indices, such as the Global Hunger Index, the Food Production Index and the Climate Change Performance Index. The LDPs have the potential to facilitate appropriate land management strategies through integrating WFE nexus and ecosystem services; we propose future research that uses this integration for the Mid-Latitude region and worldwide

Blue-green water resource availability dynamics in the upper Awash basin, central Ethiopia: implications for agricultural water scarcity assessment

Quantifying and characterizing the spatial distribution of freshwater availability and water scarcity plays an indispensable role in managing water resources in a basin. This study aimed at quantifying green and blue water resource availability using an eco-hydrological model under different land use land cover conditions between 2000–2010 and 2020 in the upper Awash basin, central Ethiopia. Further, the agricultural water scarcity is assessed for dominantly cultivated crops in the basin. The freshwater components such as the green water (GW) flow (∼1041–1240 mm/yr), blue water (BW) flow (277–304 mm/yr), and GW storage (809-872 mm/yr) were observed to be high in the western highlands compared to the central and eastern parts of the basin. The results of GW scarcity indices show low to moderate scarcity for rainfed crops, and moderate to significant BW scarcity for irrigated sugarcane. Integrating GW potential to reduce BW scarcity in the basin is thus crucial

Long-term trend of and correlation between vegetation greenness and climate variables in Asia based on satellite data

Satellite data has been used to ascertain trends and correlations between climate change and vegetation greenness in Asia. Our study utilized 33-year (1982–2014) AVHRR-GIMMS (Advanced Very High Resolution Radiometer–Global Inventory Modelling and Mapping Studies) NDVI3g and CRU TS (Climatic Research Unit Time Series) climate variable (temperature, rainfall, and potential evapotranspiration) time series. First, we estimated the overall trends for vegetation greenness and climate variables and analyzed trends during summer (April–October), winter (November–March), and the entire year. Second, we carried out correlation and regression analyses to detect correlations between vegetation greenness and climate variables. Our study revealed an increasing trend (0.05–0.28) in temperature in northeastern India (bordering Bhutan), Southeast Bhutan, Yunnan Province of China, Northern Myanmar, Central Cambodia, northern Laos, southern Vietnam, eastern Iran, southern Afghanistan, and southern Pakistan. However, a decreasing trend in temperature (0.00 to −0.04) was noted for specific areas in southern Asia including Central Myanmar and northwestern Thailand and the Guangxi, Southern Gansu, and Shandong provinces of China. The results also indicated an increasing trend for evapotranspiration and air temperature accompanied by a decreasing trend for vegetation greenness and rainfall. Increases in both the mean annual signal and annual cycle occurred in the forest, herbaceous, and cropland areas of India, Northwest China, and eastern Kazakhstan. The temperature was found to be the main driver of the changing vegetation greenness in Kazakhstan, northern Mongolia, Northeast and Central China, North Korea, South Korea, and northern Japan, showing an indirect relationship (R = 0.84–0.96). • Temperature is the main climatic variable affecting vegetation greenness. • A downward trend in vegetation greenness was observed during summer (April–October). • Temperature showed an upward trend across many areas of Asia during the study period. • In winter, rainfall showed downward and upward trends in different parts of Asia. Method name: Temporal trend analysis, Statistical analysis, Keywords: Vegetation greenness, Precipitation, Evaporation, Temperature, Correlation, Tren

Effects of Forest and Agriculture Land Covers on Organic Carbon Flux Mediated through Precipitation

Carbon stored on land is discharged into rivers through water flow, which is an important mechanism for energy transfer from land to river ecosystems. The goal of this study was to identify the relationship between land cover and carbon flux mediated through precipitation. In order to clarify the general relationship, research was conducted on a range of national scales. Eighty-two watershed samples from an area where the urban land cover area was less than 10% and with a water-quality measurement point at an outlet were delineated. Carbon flux and soil organic carbon of the watershed was estimated using the Soil and Water Assessment Tool model, Forest Biomass and Dead Organic Matter Carbon model, and other data. Finally, the data were analyzed to determine the relationship between soil organic carbon and carbon flux. As a result, it was concluded that the carbon flux of the watershed increased with increasing area of the watershed. Under the same area condition, it was revealed that the greater the forest soil organic carbon, the less the carbon flux released from the watershed. Through this study, it was observed that as the above-ground biomass of forest increased, the carbon flux from watershed to river outlet decreased logarithmically

Restoration Plan for Degraded Forest in The Democratic People’s Republic of Korea Considering Suitable Tree Species and Spatial Distribution

The ecosystem across the Democratic People’s Republic of Korea (DPRK) is threatened by deforestation. However, there is very little attention being given to government efforts for afforestation and rehabilitation plan. The most significant barriers to addressing this problem are technique limitations, availability of information, and lack of a stepwise forest management plan. This study identifies spatially suitable tree species, and establishes a stepwise restoration plan to support decision making for restoring degraded forest in the DPRK throughout a suitable restoration map. First off, target species were chosen from reference data, and spatial distribution maps for each tree species were prepared based on social needs as well as natural conditions in the DPRK. The suitable restoration map was calculated by two priorities in a weighting method; suitable priority, and distributional clustering level. Finally, the 23 afforestation species were selected for the suitable restoration map, including 11 coniferous and 12 deciduous tree species. We introduced a stepwise afforestation/restoration plan of degraded forest in the DPRK; general (long-term), detailed (medium-term), implementation (short-term) plans. Maps with different spatial resolutions were prepared for each of the plans. A restoration map with 12.5 km spatial resolution can be used for the general plan at the national level, and maps with 5 km and 1 km spatial resolutions can be used for detailed plan at the local level and implementation plan at the site level, respectively

Development of earth observational diagnostic drought prediction model for regional error calibration: A case study on agricultural drought in Kyrgyzstan

Drought is a natural disaster that occurs globally and is a main trigger of secondary environmental and socio-economic damages, such as food insecurity, land degradation, and sand-dust storms. As climate change is being accelerated by human activities and environmental changes, both the severity and uncertainties of drought are increasing. In this study, a diagnostic drought prediction model (DDPM) was developed to reduce the uncertainties caused by environmental diversity at the regional level in Kyrgyzstan, by predicting drought with meteorological forecasts and satellite image diagnosis. The DDPM starts with applying a prognostic drought prediction model (PDPM) to 1) estimate future agricultural drought by explaining its relationship with the standardized precipitation index (SPI), an accumulated precipitation anomaly, and 2) compensate for regional variances, which were not reflected sufficiently in the PDPM, by taking advantage of preciseness in the time-series vegetation condition index (VCI), a satellite-based index representing land surface conditions. Comparing the prediction results with the monitored VCI from June to August, it was found that the DDPM outperformed the PDPM, which exploits only meteorological data, in both spatiotemporal and spatial accuracy. In particular, for June to August, respectively, the results of the DDPM (coefficient of determination [R2] = 0.27, 0.36, and 0.4; root mean squared error [RMSE] = 0.16, 0.13, and 0.13) were more effective in explaining the spatial details of drought severity on a regional scale than those of the PDPM (R2 = 0.09, 0.10, and 0.11; RMSE = 0.17, 0.15, and 0.16). The DDPM revealed the possibility of advanced drought assessment by integrating the earth observation big data comprising meteorological and satellite data. In particular, the advantage of data fusion is expected to be maximized in areas with high land surface heterogeneity or sparse weather stations by providing observational feedback to the PDPM. This research is anticipated to support policymakers and technical officials in establishing effective policies, action plans, and disaster early warning systems to reduce disaster risk and prevent environmental and socio-economic damage

Applicability Analysis of Vegetation Condition and Dryness for Sand and Dust Storm (SDS) Risk Reduction in SDS Source and Receptor Region

Central Asian countries, which are included the Mid-Latitude Region (MLR), need to develop regional adaptive strategies for reducing Sand and Dust Storm (SDS)-induced negative damages based on adequate information and data. To overcome current limitation about data and assessment approaches in this region, the macroscale verified methodologies were required. Therefore, this study analyzed environmental conditions based on the SDS impacts and regional differences of SDS sources and receptors to support regional SDS adaptation plans. This study aims to identify environmental conditions based on the phased SDS impact and regional differences of SDS source and receptor to support regional adaptation plans in MLR. The Normalized Difference Vegetation Index (NDVI), Aridity Index (AI), and SDS frequency were calculated based on satellite images and observed meteorological data. The relationship among SDS frequency, vegetation, and dryness was determined by performing statistical analysis. In order to reflect phased SDS impact and regional differences, SDS frequency was classified into five classes, and representative study areas were selected by dividing source and receptor in Central Asia and East Asia. The spatial analysis was performed to characterize the effect of phased SDS impact and regional distribution differences pattern of NDVI and AI. The result revealed that vegetation condition was negatively correlated with the SDS frequency, while dryness and the SDS frequency were positively correlated. In particular, the range of dryness and vegetation was related to the SDS frequency class and regional difference based on spatial analysis. Overall, the Aral Sea and the Caspian Sea can be considered as an active source of SDS in Central Asia, and the regions were likely to expand into potential SDS risk areas compared to East Asia. This study presents the possibility of potential SDS risk area using continuously monitored vegetation and dryness index, and aids in decision-making which prioritizes vegetation restoration to prevent SDS damages with the macrolevel approach in the MLR perspective

Subcellular distribution of PCBP1 protein during EV71 infection.

<p>(A) RD cells were mock-infected or infected with EV71 at an MOI of 5 for 6 and 12 h. Cells were probed with rabbit anti-PCBP1 antibody and mouse anti-dsRNA antibody, stained with DAPI, examined by confocal microscopy (Fluoview FV1000; Olympus). PCBP1 (red), dsRNA (green), DAPI (blue). Bar = 20 µm. (B) RD cells were mock-infected or infected with EV71 at an MOI of 5 for 6 and 12 h. Cells were probed with mouse anti-PCBP1 antibody and rabbit anti-EV71 3C antibody, stained with DAPI and examined by confocal microscopy. PCBP1 (red), EV71 3C (green), DAPI (blue). Bar = 10 µm. (C) RD cells were infected with or without EV71 at an MOI of 5 for 12 h. Whole cell lysates, cytoplasm, and nucleus extractions were prepared from mock-infected and EV71-infected cells, respectively. Endogenous proteins were detected by western blot analysis using antibody to PCBP1 or β-actin. The blot is a representative of three independent experiments with similar results. The bar graph showed change of PCBP1 distribution based on three independent experiments with similar results (**, <i>p</i><0.01).</p