We present the Satellite Vegetation Index Time Series model for detecting historical floods in ungauged hyperarid regions (SatVITS-Flood). SatVITS-Flood is based on observations that floods are the primary cause of local vegetation expansion in hyperarid regions. To detect such expansion, we used two time series metrics: (1) trend change detection from the Breaks For Additive Season and Trend (BFAST-trend) and (2) a newly developed seasonal change metric based on Temporal Fourier Analysis (TFA) and the growing-season integral anomaly (TFA-GSIanom). The two metrics complement each other by capturing changes in perennial species following extreme, rare floods and ephemeral vegetation changes following more frequent floods. Metrics were derived from the time series of the normalized difference vegetation index (NDVI), the modified soil-adjusted vegetation index (MSAVI), and the normalized difference water index (NDWI), acquired from MODIS, Landsat, and AVHRR. The timing of the change was compared with the date of the flood and the magnitude of change with its volume and duration. We tested SatVITS-Flood in three regions on different continents with 40 years long, systematic, reliable gauge data. Our results indicate that SatVITS-Flood can predict flood occurrence with an accuracy of 78% and precision of 67% (Recall=0.69 and F1=0.68; p<0.01), and the flood volume and duration with NSE of 0.79 (RMSE=15.4 Mm3 event–1), and R2 of 0.69 (RMSE=5.7 days), respectively. SatVITS-Flood proved useful for detecting historical floods and may provide valuable long-term hydrological information in poorly-documented areas, which can help understand the impacts of climate change on the hydrology of hyperarid regions.

Water volume estimates of shallow desert lakes are the basis for water balance calculations, important both for water resource management and paleohydrology/climatology. Water volumes are typically inferred from bathymetry mapping; however, being shallow, ephemeral and remote, bathymetric surveys are scarce in such lakes. We propose a new, remote-sensing based, method to derive the bathymetry of such lakes using the relation between water occurrence, during >30-yr of optical satellite data, and accurate elevation measurements from the new Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). We demonstrate our method at three locations where we map bathymetries with ~0.3 m error. This method complements other remotely sensed, bathymetry-mapping methods as it can be applied to: (a) complex lake systems with sub-basins, (b) remote lakes with no in-situ records, and (c) flooded lakes. The proposed method can be easily implemented in other shallow lakes as it builds on publically accessible global data sets.

Heavy precipitation events (HPEs) can lead to deadly and costly natural disasters and are critical to the hydrological budget in regions where rainfall variability is high and water resources depend on individual storms. Thus, reliable projections of such events in the future are needed. To provide high-resolution projections under the RCP8.5 scenario for HPEs at the end of the 21st century and to understand the changes in sub-hourly to daily rainfall patterns, weather research and forecasting (WRF) model simulations of 41 historic HPEs in the eastern Mediterranean are compared with “pseudo global warming” simulations of the same events. This paper presents the changes in rainfall patterns in future storms, decomposed into storms’ mean conditional rain rate, duration, and area. A major decrease in rainfall accumulation (-30% averaged across events) is found throughout future HPEs. This decrease results from a substantial reduction of the rain area of storms (-40%) and occurs despite an increase in the mean conditional rain intensity (+15%). The duration of the HPEs decreases (-9%) in future simulations. Regionally maximal 10-min rain rates increase (+22%), whereas over most of the region, long-duration rain rates decrease. The consistency of results across events, driven by varying synoptic conditions, suggests that these changes have low sensitivity to the specific large-scale flow during the events. Future HPEs in the eastern Mediterranean will therefore likely be drier and more spatiotemporally concentrated, with substantial implications on hydrological outcomes of storms.

Projections of extreme precipitation based on modern climate models suffer from large uncertainties. Specifically, unresolved physics and natural variability limit the ability of climate models to provide actionable information on impacts and risks at the regional, watershed and city scales relevant for practical applications. Here we show that the interaction of precipitating systems with local features can constrain the statistical description of extreme precipitation. These observational constraints can be used to project local extremes of low yearly exceedance probability (e.g., 100-year events) using synoptic-scale information from climate models, which is generally represented more accurately than the local-scales, and without requiring climate models to explicitly resolve extremes. The novel approach offers a path for improving the predictability of local statistics of extremes in a changing climate, independent of pending improvements in climate models at regional and local scales.