Integration of Satellite Precipitation Data and Deep Learning for Improving Flash Flood Simulation in a Poor-Gauged Mountainous Catchment

Satellite remote sensing precipitation is useful for many hydrological and meteorological applications such as rainfall-runoff forecasting. However, most studies have focused on the use of satellite precipitation on daily, monthly, or larger time scales. This study focused on flash flood simulation using satellite precipitation products (IMERG) on an hourly scale in a poorly gauged mountainous catchment in southwestern China. Deep learning (long short-term memory, LSTM) was used, merging satellite precipitation and gauge observations, and the merged precipitation data were used as inputs for flood simulation based on the HEC-HMS model, compared with the gauged precipitation data and original IMERG data. The results showed that the application of original IMERG data used directly in the HEC-HMS hydrological model had much lower accuracy than that of gauged data and merged data. The simulation using the merged precipitation in HEC-HMS exhibited much better performances than gauged data. The mean NSE improved from 0.84 to 0.87 for calibration and 0.80 to 0.84 for verification, while the lower NSE improved from 0.81 to 0.84 for calibration and 0.73 to 0.86 for verification, which showed that accuracy and robustness were both significantly improved. Results of this study indicate the advances of remote sensing precipitation with deep learning for flash flood forecasting in mountainous regions. It is likely that more significant improvements can be made in flash flood forecasting by employing multi-source remote sensing products and deep learning merging methods considering the impact of complex terrain.

A spatial fingerprint of land-water linkage of biodiversity uncovered by remote sensing and environmental DNA

Aquatic and terrestrial ecosystems are tightly connected via spatial flows of organisms and resources. Such land-water linkages integrate biodiversity across ecosystems and suggest a spatial association of aquatic and terrestrial biodiversity. However, knowledge about this spatial extent is limited. By combining satellite remote sensing (RS) and environmental DNA (eDNA) extraction from river water across a 740-km2 mountainous catchment, we identify a characteristic spatial land-water fingerprint. Specifically, we find a spatial association of riverine eDNA diversity with RS spectral diversity of terrestrial ecosystems upstream, peaking at a 400 m distance yet still detectable up to a 3.3 km radius. Our findings testify that biodiversity patterns in rivers can be linked to the functional diversity of surrounding terrestrial ecosystems and provide a dominant scale at which these linkages are strongest. Such spatially explicit information is necessary for a functional understanding of land-water linkages and provides a reference scale for adequate conservation and landscape management decisions.

Linking Large-scale Circulation Descriptors to Precipitation Variability in the Northern French Alps

<p>This work analyses the link between Western Europe large-scale circulation and precipitation variability in the Northern French Alps from 1950 to 2017. We consider simple descriptors characterizing the daily 500hPa geopotential height fields. They are the Maximum Pressure Difference - representing the range of geopotential heights over Western Europe -, and the singularity - representing the mean distance between a geopotential shape and its closest analogs, i.e. the way this geopotential shape is reproduced in the climatology. These descriptors are compared to the occurrence of different atmospheric influences - Atlantic, Mediterranean, Northeast, Anticyclonic - and to the leading mode of large-scale circulation variability over Europe - the North Atlantic Oscillation (NAO) - for explaining precipitation variability in the Isère River catchment from one day to 10 years. We show that the Maximum Pressure Difference and the singularity of geopotential shapes explain a significant part of precipitation variability in the Northern French Alps from 10 days to 10 years, especially in winter (correlation values of 0.7). These descriptors provide much better performance than NAO and the same performance as the occurrence of the Atlantic influence, which is the best performing atmospheric influence. This means that simple characteristics of large-scale circulation - that are easy to implement - provide as much information as weather pattern classification to explain precipitation variability over a medium size mountainous catchment. Furthermore, we show that NAO does not drive the pressure gradient in a domain spreading from the Iberic Peninsula to Southern Great Britain and weakly explains precipitation variability in the Northern French Alps.</p>

Distribution and Potential Availability of As, Metals and P in Sediments from a Riverine Reservoir in a Rural Mountainous Catchment (NE Portugal)

A geochemical investigation was carried out on the bottom sediments of a riverine reservoir, located in a mountainous rural region (NE Portugal), with the aim of evaluating the contents of As, metals and P and their potential availability. The elements contents were detected in the following ranges (µg g−1): As (18–64); Cr (32–128); Cu (39–93); Ni (18–80); Pb (49–160); Zn (207–334); P (1705–2681). The reducible fraction is the most significant in the retention of the elements. Based on their potential relative mobility, the detected metals could be classed as follows: Zn > As, Pb > Cu > Cr, Ni. The results on geochemical partitioning were revealed to be important when the Sediment Quality Guidelines (SQGs) were considered. Arsenic, Cr, Ni, Pb and Zn showed total contents exceeding the values of Probable Effect Level (PEL), but only As occurred in the most potentially available form; Cr and Ni can be considered relatively unavailable, since these are mainly associated with the residual phase. Locally, oxygen depletion could release P into the water column due to the higher concentrations in Fe-P and CDB-P fractions. The potential availability of As, metals and P in sediments indicates that the quality of sediments accumulated in small reservoirs should be considered in management policies.