Springer Science and Business Media LLC

The southeastern section of Iran, especially the province of Khuzestan, experience severe air pollution levels, such as high values of surface dust mass concentration (SDMC). The province lacks accurate and well-placed ground observational stations, therefore the only viable approach for evaluating SDMC is via remote sensing. In this study, meteorological, hydrological and geological data on 11 input variables from Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2), global precipitation measurement (GPM) and Global Land Data Assimilation System (GLDAS) for the year 2018 are used for prediction of the SDMC values, also obtained from another MERRA-2 mission. For real-time prediction, Pearson’s correlation coefficient (PCC) analysis shows that wind-related variables—surface wind speed, surface aerodynamic conductance and surface pressure—are those with the highest correlation with SDMC. Using the gradient boosting regression (GBR) algorithm, these three variables can simulate SDMC with good accuracy $$(R^{2} = 0.76,\;NSE = 0.76, \;N{\text{-}}RMSE = 0.48 \;and\;N{\text{-}}MAE = 0.34)$$ . Furthermore, near-future SDMC forecasting down to 8 days prior of SMDC occurrence is also carried out. A sequential forward feature selection of the input variables, based on PCC, is used for four lead times and results show that surface pressure and heat flux govern near-future predictions. With $$R^{2} = 0.46$$ and $$N{\text{-}}RMSE = 0.74$$ , GBR shows good potential for forecasting SDMC 8 days in advance. Real-time and near-future simulation results generally show that robust SDMC prediction can be obtained using exclusively remote sensing data, without ground-based observations.

This paper investigates the dynamics of the time-series of water temperature of the Skokomish River (2019–2020) at hourly time scale by employing well-known nonlinear methods of chaotic data analysis including average mutual information, false nearest neighbors, correlation exponent, and local divergence rates. The delay time and the embedding dimension were calculated as 1400 and 9, respectively. The results indicated that the thermal regime in this river is chaotic due to the correlation dimension (1.38) and the positive largest Lyapunov exponent (0.045). Furthermore, complex networks have been applied to study the periodicity of thermal time-series throughout a year. A special algorithm is then used to find the so-called communities of the nodes. The algorithm found three communities which have been called Cold, Intermediate, and Warm. The temperatures in these three communities are, respectively, in the intervals (0.8, 5.8), (5.8, 11.63), and (11.63, 15.8). This analysis indicates that highest variations in water temperature occur between warm and cold seasons, and complex networks are highly capable to analyze hydrothermal fluctuations and classify their time-series.

For the offline segmentation of long hydrometeological time series, a new algorithm which combines the dynamic programming with the recently introduced remaining cost concept of branch-and-bound approach is developed. The algorithm is called modified dynamic programming (mDP) and segments the time series based on the first-order statistical moment. Experiments are performed to test the algorithm on both real world and artificial time series comprising of hundreds or even thousands of terms. The experiments show that the mDP algorithm produces accurate segmentations in much shorter time than previously proposed segmentation algorithms.

Water resources provide the foundation for human development and environmental sustainability. Water shortage occurs more or less in some regions, which usually causes sluggish economic activities, degraded ecology, and even conflicts and disputes over water use sectors. Game theory can better reflect the behaviors of involved stakeholders and has been increasingly employed in water resources management. This paper presents a framework for the allocation of river basin water in a cooperative way. The proposed framework applies the TOPSIS model combined with the entropy weight to determine stakeholders’ initial water share, reallocating water and net benefit by using four solution concepts for crisp and fuzzy games. Finally, the Fallback bargaining model was employed to achieve unanimous agreement over the four solution concepts. The framework was demonstrated with an application to the Dongjiang River Basin, South China. The results showed that, overall, the whole basin gained more total benefits when the players participated in fuzzy coalitions rather than in crisp coalitions, and $$\left\{ {NHS_{Fuzzy} \,and\, SV_{Crisp} } \right\}$$ could better distribute the total benefit of the whole basin to each player. This study tested the effectiveness of this framework for the water allocation decision-making in the context of water management in river basins. The results provide technical support for water right trade among the stakeholders at basin scale and have the potential to relieve water use conflicts of the entire basin.

Five downscaling techniques, namely the statistical downscaling model, the automated statistical downscaling method, the change factor (CF) method, the advanced CF method, the Weather generator (LarsWG5) method, are applied to the upstream basin of the Huaihe River. Changes in regional climate scenarios and hydrology variables are compared in future periods to investigate the uncertainty associated with the downscaling techniques. Paired-sample T test is applied to evaluation the significant of the difference of the means between the observed data and the downscaled data in the future. The Xinanjiang rainfall–runoff model is employed to simulate the rainfall–runoff relation. The results demonstrate that the downscaling techniques utilized herein predict an increased tendency in the future. The increases range of maximum temperature (Tmax) is between 3.7 and 4.7 °C until the time period of 2070–2099 (2080s). While, the increases range of minimum temperature (Tmin) is between 2.8 and 4.9 °C until 2080s. The research presented herein determined that there is an increase predicted for the peaks over threshold (discussed in the paper) and a decrease predicted for the peaks below the threshold (discussed in the paper) in the future, which illustrates that the temperature would rise gradually in the future. Precipitation changes are not as obvious as temperatures changes and tend to be influence by the season. Most downscaling techniques predict increases, and others indict decreases. The annual mean precipitation range changes between 3.2 and 53.3 %, and moreover, these changes vary from season to season.

We use spatial point pattern methods to analyse gorilla nest site data, and to enhance our understanding of the nesting behaviour of the Gorilla gorilla diehli in the Kagwene Sanctuary, Cameroon. Data were split into different seasons and different gorilla groups to better understand gorilla nesting behaviour at these different scales. Gorilla nest site distribution was found to be inhomogeneous and clustered, as a result of the inhomogeneity in the distribution of the environmental factors (such as elevation, slope, vegetation and aspect), and because of the interaction between nest sites. The proposed models reflected therefore a combination of the effect of environmental factors and interaction between nest sites. Predictions from these models showed that there is less space available for gorilla nest site location in the dry season than in the rainy season. It also showed that the Minor gorilla group has a bigger niche than the Major group, suggesting a nesting disadvantage in the larger size group. We also found that nest site locations of Major gorilla groups attract Minor groups, and vice versa.

 Many plotting position formulae have been proposed for the past few decades. These formulae are derived or obtained under some specific assumption of probability distribution. Because in practice the data are often plotted in order to determine its probability distribution, it causes difficulty and confusion in selecting the plotting position formula. The objective of this study is to find a plotting position formula which is distribution free. In this study, the plotting position formulae corresponding to the order statistic mean, mode and median are investigated. The order statistic mean, mode and median values are determined by numerical integration and differentiation, and the corresponding plotting position formulae are obtained by regression analysis. The results indicate that both the plotting position formulae for the order statistic mean and mode vary with the distribution of data, but the plotting position formula for the order statistic median is distribution free. The distribution free plotting position formula for the order statistic median is proposed in this study as (i−0.326)/(n+0.348).

Salinization threatens the viability of water resources and is common in many important inland freshwater lakes worldwide, especially in arid and semi-arid areas. Bosten Lake is a typical inland freshwater lake that has evolved into a subsaline lake and is located in the arid region of Northwest China. The water resources of Bosten Lake are important for supplying regional drinking water and agricultural irrigation and for economic development. In this study, changes in salinity with time and space were analyzed in Bosten Lake. Overall, the salinity increased from 0.39 g/L in 1958 to 1.87 g/L in 1987, reaching its highest value in 1987. After 1987, the salinity decreased to 1.17 g/L in 2003 and increased to 1.45 g/L in 2010. Increased salinity adversely affects aquatic lake systems, regional eco-environments and water resource use, and has become a serious environmental problem in Bosten Lake. Thus, the causes of increasing salinity are discussed in this paper. Overall, the influences of climate variations and human activities resulted in the salinization of the lake. Understanding the salinization processes in Bosten Lake can be useful for implementing actions that improve water quality and water resource use in the lake.