The low hydrologic resilience of Asian Water Tower basins to adverse climatic changes
, 103996. Publisher's VersionAbstract
Climate change has a significant impact on the runoff of basins in cold, dry areas. The quantification of regional ecohydrological responses to climate change such as warming and drought is essential for establishing proper water resource management schemes. We propose a simple and novel method based on the Budyko framework to evaluate the hydrologic resilience of 16 basins that conform the Asian Water Tower in the Tibetan Plateau (TP). Our method defines two metrics within the Budyko domain – tolerance (ψ) and plasticity (φ) – that characterize the hydrologic resilience of a basin. Based on an ecohydrological point of view, a basin is considered hydrologically resilient if ψ and φ are both greater than 1 or its φ is negative and ψ is greater than 1. Our results show that ψ varies between 0.27 and 0.74, with an average value of 0.45 and φ varies between 2 and 16.33, with an average value of 6.90, for 14 out of the 16 basins. Only two basins – Taohe and Datonghe – had negative φ (-11.67 and -8.11, respectively) and ψ greater than 1 (2.26 and 19.58, respectively), suggesting that these two are the only basins with a hydrologic resilience to climatic warming/drying in the TP. Within the non-resilient basins, we found vegetation to play a key role in the level of tolerance and plasticity indicating that basins with a larger vegetation cover display a lower capability to adapt to adverse climatic changes. Following these results, we call for afforestation efforts to be carefully considered in cold, dry areas. The proposed method and conclusions drawn by this study may help predict the hydrologic responses to future adverse climatic conditions.
Worldwide continuous gap-filled MODIS land surface temperature dataset
. Scientific Data 2021
74 - 74. Publisher's VersionAbstract
Satellite land surface temperature (LST) is vital for climatological and environmental studies. However, LST datasets are not continuous in time and space mainly due to cloud cover. Here we combine LST with Climate Forecast System Version 2 (CFSv2) modeled temperatures to derive a continuous gap filled global LST dataset at a spatial resolution of 1 km. Temporal Fourier analysis is used to derive the seasonality (climatology) on a pixel-by-pixel basis, for LST and CFSv2 temperatures. Gaps are filled by adding the CFSv2 temperature anomaly to climatological LST. The accuracy is evaluated in nine regions across the globe using cloud-free LST (mean values: R2 = 0.93, Root Mean Square Error (RMSE) = 2.7 °C, Mean Absolute Error (MAE) = 2.1 °C). The provided dataset contains day, night, and daily mean LST for the Eastern Mediterranean. We provide a Google Earth Engine code and a web app that generates gap filled LST in any part of the world, alongside a pixel-based evaluation of the data in terms of MAE, RMSE and Pearson’s r.
Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series
, 142844. Publisher's VersionAbstract
Fire risk mapping – mapping the probability of fire occurrence and spread – is essential for pre-fire management as well as for efficient firefighting efforts. Most fire risk maps are generated using static information on variables such as topography, vegetation density, and fuel instantaneous wetness. Satellites are often used to provide such information. However, long-term vegetation dynamics and the cumulative dryness status of the woody vegetation, which may affect fire occurrence and spread, are rarely considered in fire risk mapping. Here, we investigate the impact of two satellite-derived metrics that represent long-term vegetation status and dynamics on fire risk mapping – the long-term mean normalized difference vegetation index (NDVI) of the woody vegetation (NDVIW) and its trend (NDVIT). NDVIW represents the mean woody density at the grid cell, while NDVIT is the 5-year trend of the woody NDVI representing the long-term dryness status of the vegetation. To produce these metrics, we decompose time-series of satellite-derived NDVI following a method adjusted for Mediterranean woodlands and forests. We tested whether these metrics improve fire risk mapping using three machine learning (ML) algorithms (Logistic Regression, Random Forest, and XGBoost). We chose the 2007 wildfires in Greece for the analysis. Our results indicate that XGBoost, which accounts for variable interactions and non-linear effects, was the ML model that produced the best results. NDVIW improved the model performance, while NDVIT was significant only when NDVIW was high. This NDVIW–NDVIT interaction means that the long-term dryness effect is meaningful only in places of dense woody vegetation. The proposed method can produce more accurate fire risk maps than conventional methods and can supply important dynamic information that may be used in fire behavior models.