This study examines the temporal variations and spatial distributions of annual precipitation over Central Asia during the periods of 1901–2013, 1951–2013, and 1979–2013 using the latest version of Global Precipitation Climatology Centre (GPCC) full data reanalysis version 7 (GPCC V7) data set. The linear trend and multiperiods of the precipitation over the entire region and plain and mountainous area separately are analysed by linear least square method and ensemble empirical mode decomposition method. An overall increasing trend [0.66 mm (10 years)⁻¹] is found for the entire region during 1901–2013, which is smaller than that of 1951–2013. The regional annual precipitation exhibits multi‐decadal variations, with a sharp decline during 1901–1944, followed by an increase until 1980s, and a fluctuation thereafter. During 1979–2013, the mountainous area shows a greater increasing trend than the entire region. Furthermore, the regional annual precipitation has exhibited high‐frequency variations with 3‐year and 6‐year quasiperiods and a low‐frequency variation with 28‐year quasiperiods. In terms of the spatial distribution, increasing trend in the annual precipitation is found in Xinjiang and decreasing trends appear over the five countries of Central Asia during 1951–2013. Empirical orthogonal function results show that the mountainous area is the large variability centre of the annual precipitation. The dominant mode of interannual variability in Central Asia annual precipitation is related to El Niño‐Southern Oscillation, which explains about 17% of the interannual variance during 1951–2013. The results of this study describe the long‐term variation in the annual precipitation over Central Asia as well as its relationship with some key climate indices in great detail, which will benefit the understanding and the prediction of the climate variations in this region.

Suspended particulate matter (SPM) is one of the dominant water constituents in inland and coastal waters, and SPM concnetration (C_SPM) is a key parameter describing water quality. This study, using in-situ spectral and C_SPM measurements as well as Sentinel 2 Multispectral Imager (MSI) images, aimed to develop C_SPM retrieval models and further to estimate the C_SPM values of Poyang Lake, China. Sixty-eight in-situ hyperspectral measurements and relative spectral response function were applied to simulate Sentinel 2 MIS spectra. Thirty-four samples were used to calibrate and the left samples were used to validate C_SPM retrieval models, respectively. The developed models were then applied to two Sentinel 2 MSI images captured in wet and dry seasons, and the derived C_SPM values were compared with those derived from MODIS B1 (λ = 645 nm). Results showed that the Sentinel 2 MSI B4–B8b models achieved acceptable to high fitting accuracies, which explained 81–93% of the variation of C_SPM. The validation results also showed the reliability of these six models, and the estimated C_SPM explained 77–93% of the variation of measured C_SPM with the mean absolute percentage error (MAPE) ranging from 36.87% to 21.54%. Among those, a model based on B7 (λ = 783 nm) appeared to be the most accurate one. The Sentinel 2 MSI-derived C_SPM values were generally consistent in spatial distribution and magnitude with those derived from MODIS. The C_SPM derived from Sentinel 2 MSI B7 showed the highest consistency with MODIS on 15 August 2016, while the Sentinel 2 MSI B4 (λ = 665 nm) produced the highest consistency with MODIS on 2 April 2017. Overall, this study demonstrated the applicability of Sentinel 2 MSI for C_SPM retrieval in Poyang Lake, and the Sentinel 2 MSI B4 and B7 are recommended for low and high loadings of SPM, respectively.

Climate models use quantitative methods to simulate the interactions of the important drivers of climate system, to reveal the corresponding physical mechanisms, and to project the future climate dynamics among atmosphere, oceans, land surface and ice, such as regional climate models and global climate models. A comprehensive assessment of these climate models is important to identify their different overall performances, such as the accuracy of the simulated temperature and precipitation against the observed field. However, until now, the comprehensive performances of these models have not been quantified by a comprehensive index except the existed single statistical index, such as correlation coefficient (r), absolute error (AE), and the root‐mean‐square error (RMSE). To address this issue, therefore, in this study, a new comprehensive index Distance between Indices of Simulation and Observation (DISO) is developed to describe the overall performances of different models against the observed field quantitatively. This new index DISO is a merge of different statistical metrics including r, AE, and RMSE according to the distance between the simulated model and observed field in a three‐dimension space coordinate system. From the relationship between AE, RMSE, and RMS difference (RMSD) (i.e., standard deviation [SD] of bias time series), the new index also has the information of RMSD which is the statistical index in Taylor diagram. An example is applied objectively to display the applications of DISO and Taylor diagram in identifying the overall performances of different simulated models. Overall, with the strong physical characteristic of the distance in three dimensional space and the strict mathematical proof, the new comprehensive index DISO can convey the performances among different models. It can be applied in the comparison between different model data and in tracking changes in their performances.

Taylor diagram has been frequently used to evaluate climate or hydrology models or data. A Taylor diagram summarizes three frequently used metrics including correlation coefficient (CC), standard deviation (STD), and centred root mean square error (RMSE_c). Although these three metrics are relevant metrics for some applications, in some cases, additional indicators are needed, which calls for a new method. This study firstly addressed the short comments about the distance between indices of simulation and observation (DISO) described in a previous study. Secondly, the number of statistical metrics of DISO is extended from 3 to more than 3. The statistical metrics of the expanded DISO are more flexible than the Taylor diagram which uses the three fixed metrics. Thirdly, the current work compares the Taylor diagram with the expanded DISO in terms of their theoretical bases, revealing the advantage of DISO in terms of its flexibility in the selection of different types of metrics, and its suitability as an effective single metric to express a model's or dataset's overall quality. The power and flexibility of the expanded DISO are discussed.

Accuracy assessment for remote sensing classification is commonly based on using an error matrix, or confusion table, which needs reference, or ‘ground truthing’, data to support. When undertaking change detection using numerous multi-temporal images, it is often difficult to make the accuracy assessment by the ‘traditional’ method, which typically requires simultaneous collection of reference data. In this study, we propose a new approach by arguing change rationality with post-classification comparison. Multi-temporal Landsat TM images were classified for land use in an urban fringe area of Beijing, China and the post-classification comparison of these classified images shows change trajectories through the time series. These change trajectories were then analysed by assessing their rationality against a set of logical rules to separate cases of ‘real land use change’ and possible classification errors. The analysis results show that the overall accuracy for land use change in the urban fringe area was 86%, with a fuzziness of 7%. Although it is argued that the uncertainty still exists on classification accuracy assessed by this method, it nevertheless provides an alternative approach for more reasonable assessment when ideal simultaneous ‘ground truthing’ is not available.

The accuracies of gridded precipitation data sets are important for regional climate studies and hydrological models. In this study, the performances of Global Precipitation Climatology Centre (GPCC) V7, Climatic Research Unit (CRU) TS 3.22 and Willmott and Matsuura (WM) precipitation data sets were examined over central Asia by comparing them against observed precipitation records (OBS) from 586 meteorological stations during 1901–2010. The results show that all the three gridded data sets underestimated the observed precipitation at annual and monthly scales, especially in mountainous areas. Both GPCC and WM underestimated seasonal precipitation, especially for spring precipitation. Among the three gridded data sets, GPCC had the highest correlation and lowest bias compared with CRU and WM when against the OBS. WM had a higher correlation than that of CRU, and its bias was larger than that of CRU. In terms of the drought and heavy rainfall events, CRU had the best performance in capturing drought events, and GPCC was best at representing heavy rainfall events. These differences in the performances between the three gridded data sets were primarily induced by their different interpolation methods and the numbers of available meteorological stations used in the interpolations of the three gridded data sets. Therefore, compared to the other two data sets, GPCC is more suitable for studies of long‐term precipitation variations over central Asia.