54 research outputs found
Consistency of satellite-based precipitation products in space and over time compared with gauge observations and snow- hydrological modelling in the Lake Titicaca region
This paper proposes a protocol to assess the space–time consistency of 12
satellite-based precipitation products (SPPs) according to various
indicators, including (i)Â direct comparison of SPPs with 72 precipitation
gauges; (ii)Â sensitivity of streamflow modelling to SPPs at the outlet of
four basins; and (iii)Â the sensitivity of distributed snow models to SPPs
using a MODIS snow product as reference in an unmonitored mountainous area.
The protocol was applied successively to four different time windows
(2000–2004, 2004–2008, 2008–2012 and 2000–2012) to account for the
space–time variability of the SPPs and to a large dataset composed of
12 SPPs (CMORPH–RAW v.1, CMORPH–CRT v.1, CMORPH–BLD v.1, CHIRP v.2, CHIRPS
v.2, GSMaP v.6, MSWEP v.2.1, PERSIANN, PERSIANN–CDR, TMPA–RT v.7, TMPA–Adj
v.7 and SM2Rain–CCI v.2), an unprecedented comparison. The aim of using
different space scales and timescales and indicators was to evaluate whether
the efficiency of SPPs varies with the method of assessment, time window and
location. Results revealed very high discrepancies between SPPs. Compared to
precipitation gauge observations, some SPPs (CMORPH–RAW v.1, CMORPH–CRT
v.1, GSMaP v.6, PERSIANN, and TMPA–RT v.7) are unable to estimate regional
precipitation, whereas the others (CHIRP v.2, CHIRPS v.2, CMORPH–BLD v.1,
MSWEP v.2.1, PERSIANN–CDR, and TMPA–Adj v.7) produce a realistic
representation despite recurrent spatial limitation over regions with
contrasted emissivity, temperature and orography. In 9 out of 10 of the cases
studied, streamflow was more realistically simulated when SPPs were used as
forcing precipitation data rather than precipitation derived from the
available precipitation gauge networks, whereas the SPP's ability to
reproduce the duration of MODIS-based snow cover resulted in poorer
simulations than simulation using available precipitation gauges.
Interestingly, the potential of the SPPs varied significantly when they were
used to reproduce gauge precipitation estimates, streamflow observations or
snow cover duration and depending on the time window considered. SPPs thus
produce space–time errors that cannot be assessed when a single indicator
and/or time window is used, underlining the importance of carefully
considering their space–time consistency before using them for
hydro-climatic studies. Among all the SPPs assessed, MSWEP v.2.1 showed the
highest space–time accuracy and consistency in reproducing gauge
precipitation estimates, streamflow and snow cover duration.</p
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Global lake responses to climate change
Climate change is one of the most severe threats to global lake ecosystems. Lake surface conditions, such as ice cover, surface temperature, evaporation and water level, respond dramatically to this threat, as observed in recent decades. In this Review, we discuss physical lake variables and their responses to climate change. Decreases in winter ice cover and increases in lake surface temperature modify lake mixing regimes and accelerate lake evaporation. Where not balanced by increased mean precipitation or inflow, higher evaporation rates will favour a decrease in lake level and surface water extent. Together with increases in extreme-precipitation events, these lake responses will impact lake ecosystems, changing water quantity and quality, food provisioning, recreational opportunities and transportation. Future research opportunities, including enhanced observation of lake variables from space (particularly for small water bodies), improved in situ lake monitoring and the development of advanced modelling techniques to predict lake processes, will improve our global understanding of lake responses to a changing climate
Synthesis, Structural Characterization, and Reactivity of (8-Methoxynaphthyl)hydridogermanium Triflates and Iodides
Towards the Improvement of Soil Salinity Mapping in a Data-Scarce Context Using Sentinel-2 Images in Machine-Learning Models
International audienceSeveral recent studies have evidenced the relevance of machine-learning for soil salinity mapping using Sentinel-2 reflectance as input data and field soil salinity measurement (i.e., Electrical Conductivity-EC) as the target. As soil EC monitoring is costly and time consuming, most learning databases used for training/validation rely on a limited number of soil samples, which can affect the model consistency. Based on the low soil salinity variation at the Sentinel-2 pixel resolution, this study proposes to increase the learning database’s number of observations by assigning the EC value obtained on the sampled pixel to the eight neighboring pixels. The method allowed extending the original learning database made up of 97 field EC measurements (OD) to an enhanced learning database made up of 691 observations (ED). Two classification machine-learning models (i.e., Random Forest-RF and Support Vector Machine-SVM) were trained with both OD and ED to assess the efficiency of the proposed method by comparing the models’ outcomes with EC observations not used in the models´ training. The use of ED led to a significant increase in both models’ consistency with the overall accuracy of the RF (SVM) model increasing from 0.25 (0.26) when using the OD to 0.77 (0.55) when using ED. This corresponds to an improvement of approximately 208% and 111%, respectively. Besides the improved accuracy reached with the ED database, the results showed that the RF model provided better soil salinity estimations than the SVM model and that feature selection (i.e., Variance Inflation Factor-VIF and/or Genetic Algorithm-GA) increase both models´ reliability, with GA being the most efficient. This study highlights the potential of machine-learning and Sentinel-2 image combination for soil salinity monitoring in a data-scarce context, and shows the importance of both model and features selection for an optimum machine-learning set-up
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