Luca Ciabatta

Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data

Measuring precipitation intensity is not straightforward; and over many areas, ground observations are lacking and satellite observations are used to fill this gap. The most common way of retrieving rainfall is by addressing the problem “top-down” by inverting the atmospheric signals reflected or radiated by atmospheric hydrometeors. However, most applications are interested in how much water reaches the ground, a problem that is notoriously difficult to solve from a top-down perspective. In this study, a novel “bottom-up” approach is proposed that, by doing “hydrology backward,” uses variations in soil moisture (SM) sensed by microwave satellite sensors to infer preceding rainfall amounts. In other words, the soil is used as a natural rain gauge. Three different satellite SM data sets from the Advanced SCATterometer (ASCAT), the Advanced Microwave Scanning Radiometer (AMSR-E), and the Microwave Imaging Radiometer with Aperture Synthesis are used to obtain three new daily global rainfall products. The “First Guess Daily” product of the Global Precipitation Climatology Centre (GPCC) is employed as main benchmark in the validation period 2010–2011 for determining the continuous and categorical performance of the SM-derived rainfall products by considering the 5 day accumulated values. The real-time version of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis product, i.e., the TRMM-3B42RT, is adopted as a state-of-the-art satellite rainfall product. The SM-derived rainfall products show good Pearson correlation values (R) with the GPCC data set, mainly in areas where SM retrievals are found to be accurate. The global median R values (in the latitude band ±50°) are equal to 0.54, 0.28, and 0.31 for ASCAT-, AMSR-E-, and SMOS-derived products, respectively. For comparison, the median R for the TRMM-3B42RT product is equal to 0.53. Interestingly, the SM-derived products are found to outperform TRMM-3B42RT in terms of average global root-mean-square error statistics and in terms of detection of rainfall events. The regions for which the SM-derived products perform very well are Australia, Spain, South and North Africa, India, China, the Eastern part of South America, and the central part of the United States. The SM-derived products are found to estimate accurately the rainfall accumulated over a 5 day period, an aspect particularly important for their use for hydrological applications, and that address the difficulties of estimating light rainfall from TRMM-3B42RT.

Integration of Satellite Soil Moisture and Rainfall Observations over the Italian Territory

State-of-the-art rainfall products obtained by satellites are often the only way of measuring rainfall in remote areas of the world. However,it is well known that they may fail in properly reproducing the amountof precipitation reaching the ground, which is of paramount importance for hydrological applications. To address this issue, an integration between satellite rainfall and soil moisture SM products is proposed here by using an algorithm, SM2RAIN, which estimates rainfall from SM observations. A nudging scheme is used for integrating SM-derived and state-of-the-art rainfall products. Two satellite rainfall products are considered: H05 provided by EUMESAT and the real-time (3B42-RT) TMPA product provided by NASA. The rainfall dataset obtained through SM2RAIN, SM2RASC, considers SM retrievals from the Advanced Scatterometer (ASCAT). The rainfall datasets are compared with quality-checked daily rainfall observations throughout the Italian territory in the period 2010‐13. In the validation period 2012‐13, the integrated products show improved performances in terms of correlation with an increase in median values, for 5-day rainfall accumulations, of 26% (18%) when SM2RASC is integrated with the H05 (3B42-RT) product. Also, the median root-mean-square error of the integrated products is reduced by 18% and 17% with respect to H05 and 3B42RT, respectively. The integration of the products is found to improve the threat score for medium‐high rainfall accumulations. Since SM2RASC, H05, and 3B42-RT datasets are provided in near‐real time, their integration might provide more reliable rainfall products for operational applications, for example, for flood and landslide early warning systems.

Rainfall-runoff modelling by using SM2RAIN-derived and state-of-the-art satellite rainfall products over Italy

Abstract Satellite rainfall products (SRPs) are becoming more accurate with ever increasing spatial and temporal resolution. This evolution can be beneficial for hydrological applications, providing new sources of information and allowing to drive models in ungauged areas. Despite the large availability of rainfall satellite data, their use in rainfall-runoff modelling is still very scarce, most likely due to measurement issues (bias, accuracy) and the hydrological community acceptability of satellite products. In this study, the real-time version (3B42-RT) of Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis, TMPA, and a new SRP based on the application of SM2RAIN algorithm ( Brocca et al., 2014 ) to the ASCAT (Advanced SCATterometer) soil moisture product, SM2R ASC , are used to drive a lumped hydrologic model over four basins in Italy during the 4-year period 2010–2013. The need of the recalibration of model parameter values for each SRP is highlighted, being an important precondition for their suitable use in flood modelling. Results shows that SRPs provided, in most of the cases, performance scores only slightly lower than those obtained by using observed data with a reduction of Nash–Sutcliffe efficiency ( NS ) less than 30% when using SM2R ASC product while TMPA is characterized by a significant deterioration during the validation period 2012–2013. Moreover, the integration between observed and satellite rainfall data is investigated as well. Interestingly, the simple integration procedure here applied allows obtaining more accurate rainfall input datasets with respect to the use of ground observations only, for 3 out 4 basins. Indeed, discharge simulations improve when ground rainfall observations and SM2R ASC product are integrated, with an increase of NS between 2 and 42% for the 3 basins in Central and Northern Italy. Overall, the study highlights the feasibility of using SRPs in hydrological applications over the Mediterranean region with benefits in discharge simulations also in well gauged areas.

Rainfall estimation from in situ soil moisture observations at several sites in Europe: An evaluation of the SM2RAIN algorithm

Abstract Rain gauges, weather radars, satellite sensors and modelled data from weather centres are used operationally for estimating the spatial-temporal variability of rainfall. However, the associated uncertainties can be very high, especially in poorly equipped regions of the world. Very recently, an innovative method, named SM2RAIN, that uses soil moisture observations to infer rainfall, has been proposed by Brocca et al. (2013) with very promising results when applied with in situ and satellite-derived data. However, a thorough analysis of the physical consistency of the SM2RAIN algorithm has not been carried out yet. In this study, synthetic soil moisture data generated from a physically-based soil water balance model are employed to check the reliability of the assumptions made in the SM2RAIN algorithm. Next, high quality and multiyear in situ soil moisture observations, at different depths (5-30 cm), and rainfall for ten sites across Europe are used for testing the performance of the algorithm, its limitations and applicability range. SM2RAIN shows very high accuracy in the synthetic experiments with a correlation coefficient, R, between synthetically generated and simulated data, at daily time step, higher than 0.940 and an average Bias lower than 4%. When real datasets are used, the agreement between observed and simulated daily rainfall is slightly lower with average R-values equal to 0.87 and 0.85 in the calibration and validation periods, respectively. Overall, the performance is found to be better in humid temperate climates and for sensors installed vertically. Interestingly, algorithms of different complexity in the reproduction of the underlying hydrological processes provide similar results. The average contribution of surface runoff and evapotranspiration components amounts to less than 4% of the total rainfall, while the soil moisture variations (63%) and subsurface drainage (30%) terms provide a much higher contribution. Overall, the SM2RAIN algorithm is found to perform well both in the synthetic and real data experiments, thus offering a new and independent source of data for improving rainfall estimation, and consequently enhancing hydrological, meteorological and climatic studies.

Rainfall estimation by inverting SMOS soil moisture estimates: A comparison of different methods over Australia

Remote sensing of soil moisture has reached a level of maturity and accuracy for which the retrieved products can be used to improve hydrological and meteorological applications. In this study, the soil moisture product from the Soil Moisture and Ocean Salinity (SMOS) satellite is used for improving satellite rainfall estimates obtained from the Tropical Rainfall Measuring Mission multi-satellite precipitation analysis product (TMPA) using three different “bottom up” techniques: SM2RAIN, SMART and API-mod. The implementation of these techniques aims at improving the well-known “top down” rainfall estimate derived from TMPA products (version 7) available in near real time. Ground observations provided by the Australian Water Availability Project (AWAP) are considered as a separate validation dataset. The three algorithms are calibrated against the gauge-corrected TMPA re-analysis product, 3B42, and used for adjusting the TMPA real-time product, 3B42RT, using SMOS soil moisture data. The study area covers the entire Australian continent and the analysis period ranges from January 2010 to November 2013. Results show that all the SMOS-based rainfall products improve the performance of 3B42RT, even at daily time scale (differently from previous investigations). The major improvements are obtained in terms of estimation of accumulated rainfall with a reduction of the root mean square error of more than 25%. Also in terms of temporal dynamic (correlation) and rainfall detection (categorical scores) the SMOS-based products provide slightly better results with respect to 3B42RT, even though the relative performance between the methods is not always the same. The strengths and weaknesses of each algorithm and the spatial variability of their performances are identified in order to indicate the ways forward for this promising research activity. Results show that the integration of “bottom up” and “top down” approaches has the potential to improve the quality of near real-time rainfall estimates from remote sensing in the near future.

Exploiting Satellite-Based Surface Soil Moisture for Flood Forecasting in the Mediterranean Area: State Update Versus Rainfall Correction

Many satellite soil moisture products are today globally available in near real-time. These observations are of paramount importance for enhancing the understanding of the hydrological cycle and particularly useful for flood forecasting purposes. In recent decades, several studies assimilated satellite soil moisture observations into rainfall-runoff models to improve their flood forecasting skills. The rationale is that a better representation of the catchment states leads to a better stream flow estimation. By exploiting the strong physical connection between the soil moisture dynamic and rainfall, some recent studies demonstrated that satellite soil moisture observations can be also used for enhancing the quality of rainfall observations. Given that the quality of the rainfall is one of the main drivers of the hydrological model uncertainty, this begs the question—to what extent updating soil moisture states leads to better flood forecasting skills than correcting rainfall forcing? In this study, we try to answer this question by using rainfall-runoff observations from 10 catchments throughout the Mediterranean area and a continuous rainfall-runoff model—MISDc—forced with reanalysis- and satellite-based rainfall observations. Satellite soil moisture retrievals from the Advanced SCATterometer (ASCAT) are either assimilated into MISDc model via the Ensemble Kalman filter to update model states or, alternatively, used to correct rainfall observations derived from a reanalysis and a satellite-based product through the integration with soil moisture-based rainfall estimates. 4–9 years (depending on the catchment) of stream flow observations are organized into calibration and validation periods to test the two different schemes. Results show that the rainfall correction is favourable if the target is the predictions of high flows while for low flows there is a small advantage of the state correction scheme with respect to the rainfall correction. The improvements for high flows are particularly large when the quality of the rainfall is relatively poor with important implications for large-scale flood forecasting in the Mediterranean area.

Remote Sensing of Terrestrial Rainfall From Ku-Band Scatterometers

Rainfall is the most fundamental variable of the terrestrial hydrological cycle. However, in many regions of the world, ground observations are still very scarce or even missing. Recently, a bottom-up approach, named SM2RAIN, for terrestrial rainfall estimation from satellite soil moisture (SM) products was proposed and successfully applied to Cand L-band products from scatterometers and radiometers. Thanks to the multiple Ku-band scatterometers launched in the recent years and a number of new sensors expected in the near future, accurate rainfall estimation at subdaily time scale could be obtained. We present here a first attempt to estimate terrestrial rainfall from Ku-band scatterometers using SM2RAIN. To this end, backscattering data (sigma-0) collected in central Italy from the RapidScat instrument on board the International Space Station are compared with the Advanced SCATterometer (ASCAT, C-band) SM product and in situ observations for assessing its sensitivity to SM variations. Then, RapidScat sigma-0 is used for rainfall retrieval and compared with ground observations over a regular grid of 15-km spacing. The 8-month period from Nov 2014 to Jun 2015 is considered. Results show a very good agreement between ASCAT SM and RapidScat SM index with a median temporal correlation coefficient R of ~0.9 and a reasonable performance (R > 0.52) against in situ data. More interestingly, the performance of RapidScat in 1-day rainfall estimation is found to be satisfactory with median R-values equal to ~0.6. These promising results highlight the large potential of using the constellation of scatterometers for providing an accurate rainfall product with high spatial-temporal resolution.

Comparison of Different Satellite Rainfall Products Over the Italian Territory

In this study, a preliminary analysis of three satellite-derived rainfall products is carried out in order to evaluate their reliability and accuracy. Specifically, two state-of-art rainfall products are used: the PR-OBS-5 provided by EUMETSAT within the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) project and the 3B42-RT product from the Tropical Rainfall Measuring Mission (TRMM) Multiple Precipitation Analysis (TMPA). The third product is obtained by applying a new inversion method, called SM2RAIN, to satellite soil moisture data. In this latter approach, the soil is considered to be a “natural raingauge” that is employed for “measuring” rainfall. As benchmark, quality checked daily rainfall observations throughout the Italian territory for the period 2010–2011 are used. The comparison with ground observations is carried out in terms of correlation coefficients, R, and root mean square error, RMSE. The results show satisfactory R-values (and low RMSEs) between satellite and observed 5-day rainfall data with median R-values greater than 0.50. Moreover, by analyzing the error spatial patterns, and by considering the different temporal resolution of the products, the potential of integrating them in space and time is underlined as this can be expected to further improve the estimation of rainfall for hydrological applications over the Italian territory.

Use of Satellite Soil Moisture Products for the Operational Mitigation of Landslides Risk in Central Italy

Abstract The mitigation of landslide risk is needed for improving the resilience of our society to extreme events, particularly under climatic and global change conditions. The two main climatic drivers triggering shallow landslides can be identified in precipitation and soil moisture, as clearly recognized in the recent scientific literature. Surprisingly, the use of measurements of soil moisture is rarely employed in Early Warning systems for landslides prediction, and only a couple studies have considered satellite soil moisture observations in this context. This chapter aims at describing the Early Warning system for hydrogeological risk mitigation that is operating in Umbria Region (central Italy) since 2012, named PRESSCA, that fully exploit rainfall and soil moisture data for issuing warnings of possible landslides occurrence. Recently, PRESSCA system is being updated by incorporating in situ and satellite measurement of soil moisture for improving its reliability, robustness and accuracy. The preliminary analysis carried out for the optimal integration and use of satellite soil moisture data is described. Additionally, the added value of soil moisture observations with respect to the use of rainfall data only is shown.

Robust Assessment of an Operational Algorithm for the Retrieval of Soil Moisture From AMSR-E Data in Central Italy

In this study, the soil moisture content (SMC) derived from the AMSR-E acquisitions by using the “HydroAlgo” algorithm, which is based on artificial neural networks (ANN), is compared with simulated data obtained from the application of a soil water balance model (SWBM) in central Italy. All the overpasses available for the 9-year lifetime of AMSR-E have been considered for this comparison, which was carried out point by point over a grid of 91 nodes spaced at 0.1° × 0.1°, roughly corresponding to the Umbria region. HydroAlgo includes a disaggregation technique (smoothing filter-based intensity modulation), which allowed obtaining an SMC product with enhanced spatial resolution (0.1°) that is expected to be more suitable for hydrological applications. The main purpose of this study is to exploit the potential of AMSR-E sensors for hydrological studies, and in particular for SMC monitoring on a regional scale in heterogeneous landscapes typical of Mediterranean environment. Slightly different results were obtained using ascending or descending overpasses; however, the overall correlation coefficient between the SMC retrieved by HydroAlgo and the SMC simulated by SWBM was higher than 0.8 and the corresponding root mean square error was less than 0.055 m3/m3. Based on these successful results, HydroAlgo is going to be implemented for current multifrequency microwave radiometers (AMSR2) in order to obtain a high-resolution SMC product suitable to be assimilated into flood- and landslide-related modeling in central Italy.