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[en] An agro-climatic study was carried out in eastern Indian state of Bihar (middle Indo-Gangetic Plains) to identify optimum planting schedules and water availability of rainfed crops based on moisture availability index (MAI), i.e., the ratio of weekly assured rainfall and potential evapotranspiration (PET) for delineating safe growing period and crop production potential at micro-level in order to develop climate smart agricultural production system. For this purpose, historical weekly rainfall data for a period ranging from 30 to 55 years of 110 rain-gauge stations and normal weekly PET were employed. The assured weekly rainfall at different probability levels, viz. 25, 50, and 75%, was computed employing incomplete gamma distribution technique. The study revealed that at 50% probability (i.e., 50 out of 100 years), the sowing window of rainfed crops with MAI ≥ 0.33 ranged from 19 to 24 SMW (standard meteorological week) over different districts in Zone I (North west alluvial plains), 18 to 23 SMW in Zone II (North east alluvial plains), 23–24 SMW in Zone IIIA (Part of South Bihar alluvial plains), and 24–25 SMW in Zone IIIB (Part of South Bihar alluvial plains). The districts under Zone II recorded the earliest sowing week for starting sowing of rainfed crops, and the most delayed start of sowing was recorded in the districts under Zone IIIB at all probability levels. Kishanganj District recorded the highest duration of water availability followed by West Chamaparan District at all MAI and probability levels. In terms of longer length of water availability and higher values of MAI, Zone II appeared to be the most potential agroclimatic zone followed by Zone I and Zone IIIA. The Zone IIIB was adjudged as the least potential Zone in terms of shorter water availability period for rainfed crop production.
[en] Soil temperature, an important indicator of climate change, has rarely explored due to scarce observations, especially in the Tibetan Plateau (TP) area. In this study, changes observed in five meteorological variables obtained from the TP between 1960 and 2014 were investigated using two non-parametric methods, the modified Mann–Kendall test and Sen’s slope estimator method. Analysis of annual series from 1960 to 2014 has shown that surface (0 cm), shallow (5–20 cm), deep (40–320 cm) soil temperatures (ST), mean air temperature (AT), and precipitation (P) increased with rates of 0.47 °C/decade, 0.36 °C/decade, 0.36 °C/decade, 0.35 °C/decade, and 7.36 mm/decade, respectively, while maximum frozen soil depth (MFD) as well as snow cover depth (MSD) decreased with rates of 5.58 and 0.07 cm/decade. Trends were significant at 99 or 95% confidence level for the variables, with the exception of P and MSD. More impressive rate of the ST at each level than the AT indicates the clear response of soil to climate warming on a regional scale. Monthly changes observed in surface ST in the past decades were consistent with those of AT, indicating a central place of AT in the soil warming. In addition, with the exception of MFD, regional scale increasing trend of P as well as the decreasing MSD also shed light on the mechanisms driving soil trends. Significant negative-dominated correlation coefficients (α = 0.05) between ST and MSD indicate the decreasing MSD trends in TP were attributable to increasing ST, especially in surface layer. Owing to the frozen ground, the relationship between ST and P is complicated in the area. Higher P also induced higher ST, while the inhibition of freeze and thaw process on the ST in summer. With the increasing AT, P accompanied with the decreasing MFD, MSD should be the major factors induced the conspicuous soil warming of the TP in the past decades.
[en] Accurately estimating the surface fluxes of over the heterogeneous land surface in Tibetan Plateau will be helpful to advance the understanding of its influence on regional climate and hydrology. This paper presents a study on the spatial heterogeneity of land surface parameters in terms of the spatial variability and spatial structure of land surface parameters and the influence on surface fluxes over a typical land surface in Tibetan Plateau. The results suggest that the sensible heat fluxes (H) and latent heat fluxes (LE) in the study area in the rain and dry seasons show apparent spatial variabilities due to the spatial heterogeneity in the leaf area index (LAI) and land surface undulations. The relative frequency distribution of H and LE at the spatial resolution of 30 m suggests that the spatial variability of surface fluxes has a close relationship with the spatial heterogeneity of land surface temperature (LST) and LAI. The variogram analyses of LST, LAI, H, and LE in the study area in rain season indicate that the spatial structures of LST and LAI are different and the spatial structures of H and LE are strongly influenced by the spatial structures of LST and LAI in both rain and dry seasons. The optimal pixel sizes for LST, LAI, H, and LE in the study area are 506, 156, 500, and 225 m in the rain season. The optimal pixel sizes for LST, H, and LE in the study area are 165, 165, and 162 m in the dry season. An analysis of the relative frequency distributions (RFDs) of the LST, LAI, H, and LE at different spatial resolutions in the rain and dry seasons reveals that their values at the maximum relative frequency keep stable although their spatial variabilities become weak as the spatial resolution decreases. The averages of LST, LAI, H, and LE of different spatial resolutions of the study area in rain and dry seasons vary within small ranges, suggesting that the influence of spatial resolution on the averaged land surface parameters and surface fluxes in the study area is small. This work will be helpful for the accurate estimation of the surface fluxes over a large heterogeneous land surface for regional climate modeling in Tibetan Plateau.
[en] According to the statistics and collation of data on the main hydrological disasters that occurred in Urumqi from 1949 to 2015, the risk characteristics of the main hydrological disasters in the city are identified and analyzed to reveal the distribution of local hydrological hazard risk based on the comprehensive disaster risk assessment theory for historical disasters. The research findings are as follows: (1) the main hydrological disasters in Urumqi are caused by floods, snow, and droughts, among which floods have the greatest risk, while the risk of droughts is minimal. (2) There are various and complex types of flood disasters in Urumqi, which have frequent occurrences, a wide range of influence, and easily cause secondary disasters. Snow disasters are complex and diverse, with frequent occurrences. Moreover, there are varied and complex types of drought disasters in Urumqi, which have frequent occurrences. Drought disasters in Urumqi, which last for a long time, have enormous effects on agricultural and forestry crops, while the frequencies of frost and hail disasters in Urumqi are lower. (3) Historical hydrological disasters in Urumqi mainly occurred from May to August and November to March of the following year, which are high-frequency months for hydrological disasters. (4) Flood disasters in Urumqi occur every 1.59 years, on average, while snow disasters in Urumqi occur every 1.65 years, on average. Additionally, flood and snow disasters could easily occur in the next 15 years. (5) The hydrological disaster environment in Urumqi is mainly affected by local temperature, sunshine, elevation, topography, precipitation, plant resources, and the social economy.
[en] Degradation in drylands is a critically important global issue that threatens ecosystem and environmental in many ways. Researchers have tried to use remote sensing data and meteorological data to perform residual trend analysis and identify human-induced vegetation changes. However, complex interactions between vegetation and climate, soil units and topography have not yet been considered. Data used in the study included annual accumulated Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m normalized difference vegetation index (NDVI) from 2002 to 2013, accumulated rainfall from September to August, digital elevation model (DEM) and soil units. This paper presents linear mixed-effect (LME) modeling methods for the NDVI-rainfall relationship. We developed linear mixed-effects models that considered the random effects of sample points nested in soil units for nested two-level modeling and single-level modeling of soil units and sample points, respectively. Additionally, three functions, including the exponential function (exp), the power function (power), and the constant plus power function (CPP), were tested to remove heterogeneity, and an additional three correlation structures, including the first-order autoregressive structure [AR(1)], a combination of first-order autoregressive and moving average structures [ARMA(1,1)] and the compound symmetry structure (CS), were used to address the spatiotemporal correlations. It was concluded that the nested two-level model considering both heteroscedasticity with (CPP) and spatiotemporal correlation with [ARMA(1,1)] showed the best performance (AMR = 0.1881, RMSE = 0.2576, adj-R2 = 0.9593). Variations between soil units and sample points that may have an effect on the NDVI-rainfall relationship should be included in model structures, and linear mixed-effects modeling achieves this in an effective and accurate way.
[en] The extent to which statistical bias-adjusted outputs of two regional climate models alter the projected change signals for the mean (and extreme) rainfall and temperature over the Volta Basin is evaluated. The outputs from two regional climate models in the Coordinated Regional Climate Downscaling Experiment for Africa (CORDEX-Africa) are bias adjusted using the quantile mapping technique. Annual maxima rainfall and temperature with their 10- and 20-year return values for the present (1981–2010) and future (2051–2080) climates are estimated using extreme value analyses. Moderate extremes are evaluated using extreme indices (viz. percentile-based, duration-based, and intensity-based). Bias adjustment of the original (bias-unadjusted) models improves the reproduction of mean rainfall and temperature for the present climate. However, the bias-adjusted models poorly reproduce the 10- and 20-year return values for rainfall and maximum temperature whereas the extreme indices are reproduced satisfactorily for the present climate. Consequently, projected changes in rainfall and temperature extremes were weak. The bias adjustment results in the reduction of the change signals for the mean rainfall while the mean temperature signals are rather magnified. The projected changes for the original mean climate and extremes are not conserved after bias adjustment with the exception of duration-based extreme indices.
[en] Energy balance closure is the main indicator of the quality of energy flux measurements obtained by the eddy covariance technique. Many researchers use a simple linear regression model between energy balance components to evaluate closure. However, these studies typically fail to verify the appropriateness of the statistical assumptions of regression analysis, which can lead to erroneous conclusions if the model is not satisfactory. Thus, the aim of this study was to calibrate and validate simple and robust with bootstrap and cross-validation linear regression models to verify the efficiency of energy balance closure in the Amazon ecosystem. Measurements of net radiation and latent, sensible, and ground heat fluxes were made from January to December 2008 in a tropical rain forest area in South West Amazonia in an experimental site belonging to the network of flux towers of the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA). The results demonstrated that simple linear regression models are not appropriate for analyzing energy balance closure. However, robust linear regression models with bootstrap and cross-validation improved the fit to the data. Despite the better fit, there was an increase in energy balance closure residuals suggesting that the eddy covariance technique is underestimating the values of energy fluxes in Amazon forest areas more than what were reported in previous researches.
[en] Surface albedo is one of the essential climate variables as it influences the radiation budget and the energy balance. Because it is used in a variety of scientific fields, from local to global scale, spatially and temporally disaggregated albedo data are required, which can be derived from satellites. Satellite observations have led to directional-hemispherical (black-sky) and bi-hemispherical (white-sky) albedo products, but time series of high spatial resolution true (blue-sky) albedo estimations at global level are not available. Here, we exploit the capabilities of Google Earth Engine (GEE) for big data analysis to derive global snow-free land surface albedo estimations and trends at a 500-m scale, using satellite observations from 2000 to 2015. Our study reveals negative albedo trends mainly in Mediterranean, India, south-western Africa and Eastern Australia, whereas positive trends mainly in Ukraine, South Russia and Eastern Kazakhstan, Eastern Asia, Brazil, Central and Eastern Africa and Central Australia. The bulk of these trends can be attributed to rainfall, changes in agricultural practices and snow cover duration. Our study also confirms that at local scale, albedo changes are consistent with land cover/use changes that are driven by anthropogenic activities.
[en] Autumn rain of West China is a typical climate phenomenon, which is characterized by continuous rainy days and large rainfall amounts and exerts profound impacts on the economic society. Based on daily precipitation data from 524 observation stations for the period of 1961–2014, this article comprehensively examined secular changes in autumn rain of West China, including its amount, frequency, intensity, and associated extremes. The results generally show a significant reduction of rainfall amount and rainy days and a significant enhancement of mean rainfall intensity for the average of West China during autumn (September–October) since 1961. Meanwhile, decreasing trends are consistently observed in the maximum daily rainfall, the longest consecutive rainy days, the greatest consecutive rainfall amount, and the frequencies of the extreme daily rainfall, consecutive rainfall, and consecutive rainfall process. Further analysis indicates that the decreases of autumn rainfall and related extremes in West China are associated with the decreases in both water vapor content and atmospheric unstable stratification during the past decades. On the regional scale, some differences exist in the changes of autumn rainfall between the eastern and western parts of West China. Besides, it is found that the autumn rainy season tends to start later and terminate earlier particularly in eastern West China.
[en] The present study aims at the assessment of six satellite rainfall estimates (SREs) in Pakistan. For each assessed products, both real-time (RT) and post adjusted (Adj) versions are considered to highlight their potential benefits in the rainfall estimation at annual, monthly, and daily temporal scales. Three geomorphological climatic zones, i.e., plain, mountainous, and glacial are taken under considerations for the determination of relative potentials of these SREs over Pakistan at global and regional scales. All SREs, in general, have well captured the annual north-south rainfall decreasing patterns and rainfall amounts over the typical arid regions of the country. Regarding the zonal approach, the performance of all SREs has remained good over mountainous region comparative to arid regions. This poor performance in accurate rainfall estimation of all the six SREs over arid regions has made their use questionable in these regions. Over glacier region, all SREs have highly overestimated the rainfall. One possible cause of this overestimation may be due to the low surface temperature and radiation absorption over snow and ice cover, resulting in their misidentification with rainy clouds as daily false alarm ratio has increased from mountainous to glacial regions. Among RT products, CMORPH-RT is the most biased product. The Bias was almost removed on CMORPH-Adj thanks to the gauge adjustment. On a general way, all Adj versions outperformed their respective RT versions at all considered temporal scales and have confirmed the positive effects of gauge adjustment. CMORPH-Adj and TMPA-Adj have shown the best agreement with in situ data in terms of Bias, RMSE, and CC over the entire study area.