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The water resources of the Chari River basin, contributing more than 90% of the water to one of the largest lakes in Africa, known as Lake Chad, are highly vulnerable to natural and anthropogenic changes. Therefore, the changes in water resources were predicted for the next 20 years (i.e., 2016-35) by using the harmonic regression model (HRM), one of the most sophisticated time series methods, and also projected under representative concentration pathways (RCPs) by using the multimodel approach for the periods 2021-50, 2051-80, and 2081-2100, with respect to the baseline period (1971-2001). The Tropical Rainfall Measuring Mission (TRMM), Climatic Research Unit (CRU), and dynamically downscaled climatic data were used in the analysis of the present study. The results showed that under MME-RCP2.6 (multimodel ensemble of RCMs), low flow (average of low-flow months, December-July), high flow (August-November), and annual flow were projected to decrease in the future. In contrast, under MME-RCP4.5 and MME-RCP8.5, high and annual flows were projected to increase in all three time horizons, while low flow will decrease except in 2021-50 under MME-RCP8.5. In the next two decades, the HRM showed decrease in all type of flows (low, high, and annual), very similar to the results under MME-RCP2.6 for the same period. In contrast, almost all flows are expected to increase underMME-RCP4.5 andMME-RCP8.5 in the next two decades. On the whole, the flows are expected to decrease under the HRM and RCP2.6 but to increase under RCP4.5 and RCP8.5. [ABSTRACT FROM AUTHOR]

The drought episodes in the second half of the 20th century have profoundly modified the state of Lake Chad and investigation of its variations is necessary under the new circumstances. Multiple remote sensing observations were used in this paper to study its variation in the recent 25 years. Unlike previous studies, only the southern pool of Lake Chad (SPLC) was selected as our study area, because it is the only permanent open water area after the serious lake recession in 1973–1975. Four satellite altimetry products were used for water level retrieval and 904 Landsat TM/ETM+ images were used for lake surface area extraction. Based on the water level (L) and surface area (A) retrieved (with coinciding dates), linear regression method was used to retrieve the SPLC’s L-A curve, which was then integrated to estimate water volume variations (DV). The results show that the SPLC has been in a relatively stable phase, with a slight increasing trend from 1992 to 2016. On annual average scale, the increase rate of water level, surface area and water volume is 0.5 cm year-1, 0.14 km2 year-1 and 0.007 km3 year-1, respectively. As for the intra-annual variations of the SPLC, the seasonal variation amplitude of water level, lake area and water volume is 1.38 m, 38.08 km2 and 2.00 km3, respectively. The scatterplots between precipitation and DV indicate that there is a time lag of about one to two months in the response of water volume variations to precipitation, which makes it possible for us to predict DV. The water balance of the SPLC is significantly different from that of the entire Lake Chad. While evaporation accounts for 96% of the lake’s total water losses, only 16% of the SPLC’s losses are consumed by evaporation, with the other 84% offset by outflow. [ABSTRACT FROM AUTHOR]

Using National Oceanographic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Climatic Research Unit (CRU) climate datasets, we analyzed interannual trends in the growing-season Normalized Difference Vegetation Index (NDVI) in China from 1982 to 2011, as well as the effects of climatic variables and human activities on vegetation variation. Growing-season (period between the onset and end of plant growth) NDVI significantly increased (p < 0.01) on a national scale and showed positive trends in 52.76% of the study area. A multiple regression model was used to investigate the response of vegetation to climatic factors during recent and previous time intervals. The interactions between growing-season NDVI and climatic variables were more complex than expected, and a lag existed between climatic factors and their effects on NDVI. The regression residuals were used to show that over 6% of the study area experienced significantly human-induced vegetation variations (p < 0.05). These regions were mostly located in densely populated, reclaimed agriculture, afforestation, and conservation areas. Similar conclusions were drawn based on land-use change over the study period. [ABSTRACT FROM AUTHOR]

The knowledge of water storage variations in ungauged lakes is of fundamental importance to understanding the water balance on the Tibetan Plateau. In this paper, a simple framework was presented to monitor the fluctuation of inland water bodies by the combination of satellite altimetry measurements and optical satellite imagery without any in situ measurements. The fluctuation of water level, surface area, and water storage variations in Lake Qinghai were estimated to demonstrate this framework. Water levels retrieved from ICESat (Ice, Cloud, and Elevation Satellite) elevation data and lake surface area derived from MODIS (Moderate Resolution Imaging Spectroradiometer) product were fitted by linear regression during the period from 2003 to 2009 when the overpass time for both of them was coincident. Based on this relationship, the time series of water levels from 1999 to 2002 were extended by using the water surface area extracted from Landsat TM/ETM+ images as inputs, and finally the variations of water volume in Lake Qinghai were estimated from 1999 to 2009. The overall errors of water levels retrieved by the simple method in our work were comparable with other globally available test results with r = 0.93, MAE = 0.07 m, and RMSE = 0.09 m. The annual average rate of increase was 0.11 m/yr, which was very close to the results obtained from in situ measurements. High accuracy was obtained in the estimation of surface areas. The MAE and RMSE were only 6 km2, and 8 km2, respectively, which were even lower than the MAE and RMAE of surface area extracted from Landsat TM images. The estimated water volume variations effectively captured the trend of annual variation of Lake Qinghai. Good agreement was achieved between the estimated and measured water volume variations with MAE = 0.4 billion m3, and RMSE = 0.5 billion m3, which only account for 0.7% of the total water volume of Lake Qinghai. This study demonstrates that it is feasible to monitor comprehensively the fluctuation of large water bodies based entirely on remote sensing data. [ABSTRACT FROM AUTHOR]