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 ( $\mathrm{\Delta }V$ ). 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 $\mathrm{\Delta }V$ 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 $\mathrm{\Delta }V$ . 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.

Recent evidence of regional climate change associated with the intensification of human activities has led hydrologists to study a flood regime in a non-stationarity context. This study utilized a Bayesian framework with informed priors on shape parameter for a generalized extreme value (GEV) model for the estimation of design flood quantiles for “at site analysis” in a changing environment, and discussed its implications for flood management in the Kabul River basin (KRB), Pakistan. Initially, 29 study sites in the KRB were used to evaluate the annual maximum flood regime by applying the Mann–Kendall test. Stationary (without trend) and a non-stationary (with trend) Bayesian models for flood frequency estimation were used, and their results were compared using the corresponding flood frequency curves (FFCs), along with their uncertainty bounds. The results of trend analysis revealed significant positive trends for 27.6% of the gauges, and 10% showed significant negative trends at the significance level of 0.05. In addition to these, 6.9% of the gauges also represented significant positive trends at the significance level of 0.1, while the remaining stations displayed insignificant trends. The non-stationary Bayesian model was found to be reliable for study sites possessing a statistically significant trend at the significance level of 0.05, while the stationary Bayesian model overestimated or underestimated the flood hazard for these sites. Therefore, it is vital to consider the presence of non-stationarity for sustainable flood management under a changing environment in the KRB, which has a rich history of flooding. Furthermore, this study also states a regional shape parameter value of 0.26 for the KRB, which can be further used as an informed prior on shape parameter if the study site under consideration possesses the flood type “flash”. The synchronized appearance of a significant increase and decrease of trends within very close gauge stations is worth paying attention to. The present study, which considers non-stationarity in the flood regime, will provide a reference for hydrologists, water resource managers, planners, and decision makers.

In this study, a new parameterization scheme of evaporative fraction (EF) was developed from the contextual information of remotely sensed radiative surface temperature ( $T_s$ ) and vegetation index (VI). In the traditional $T_s-VI$ triangle methods, the Priestley-Taylor parameter $\mathrm{\varnothing }$ of each pixel was interpolated for each VI interval; in our proposed new parameterization scheme (NPS), it was performed for each isopiestic line of soil surface moisture. Specifically, $\mathrm{\varnothing }$ of mixed pixels was determined as the weighted-average value of bare soil $\mathrm{\varnothing }$ and full-cover vegetation $\mathrm{\varnothing }$ . The maximum $T_s$ of bare soil ( $T_{smax}$ ) is the sole parameter needed as the constraint of the dry edge. This has not only bypassed the task involved in the determination of the maximum $T_s$ of fully vegetated surface ( $T_{cmax}$ ), but also made it possible to reduce the reliance of the $T_s-VI$ triangle methods on the determination of the dry edge. Ground-based measurements taken during 21 days in 2004 were used to validate the EF retrievals. Results show that the accuracy achieved by the NPS is comparable to that achieved by the traditional $T_s-VI$ triangle methods. Therefore, the simplicity of the proposed new parameterization scheme does not compromise its accuracy in monitoring EF.

Autumn phenology plays a crucial role in shaping the capacity for carbon sequestration. However, understories, a vital yet often neglected ecosystem component, have complicated autumn phenology prediction. We address the challenge of monitoring understory phenological dynamics by using a UVL4 trail camera and selecting appropriate deriving processes and vegetation indices (VIs). We found the understory photoperiod was on average 1.88 h shorter than the canopy’s, while the understory temperature was 2.11 °C higher than the canopy’s open-air temperature. The maximum temperature inside the understories was on average 1.37 °C lower than in open-air conditions. Specifically, the 60% quantile of the daily VI in July and the 15% quantile in November effectively captured the prolonged minimum and the minimum in the VI time series when applying logistic modeling. The excess green vegetation index (ExG) outperformed other VIs in estimating understory greenness change. The cold degree days model (CDD) and low-temperature and photoperiod multiplicative model (TPM) revealed that senescence progressed from the upper crown downwards, causing over 13 days of lag in the understory. These findings offer a new perspective on quantifying autumn phenology in subtropical forests and provide insights into asynchronous changes in vertical microclimatic gradients in Earth system and vegetation models.

Municipal water withdrawal (MWW) information is of great significance for water supply planning, including water supply pipeline networks planning, optimization and management. Currently most MWW data are reported as spatially aggregated over large-area survey regions or even lack of data, which is unable to meet the growing demand for spatially detailed data in many applications. In this paper, six different models are constructed and evaluated in estimating global MWW using aggregated MWW data and gridded raster covariates. Among the models, the artificial neural network-based indirect model (NNM) shows the best accuracy with higher R2 and lower NMAE and NRMSE in different spatial scales. The estimates achieved from the NNM model are consistent with census and survey data, and outperforms the existing global gridded MWW dataset. At last, the NNM model is applied to mapping global gridded MWW for the year 2015 at 0.1 × 0.1° resolution. The proposed method can be applied to a wider aggregated output learning problem and the high-resolution global gridded MWW data can be used in hydrological models and water resources management.