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Remote sensing technology is an essential tool for tracking human-induced alterations on the water cycle, among which irrigation prevails. The possibility of obtaining detailed and accurate information on the actual irrigation extent through remote-sensing-based approaches is of paramount importance for water resources management. In this study, an update of the TSIMAP (Temporal-Stability-derived Irrigation MAPping) method, originally developed with satellite soil moisture as an input, is proposed. To demonstrate that the flexibility of the approach does not affect its main strength point (i.e., good accuracy in the face of high simplicity for users), a dual analysis relying on 1 km NDVI (Normalised Difference Vegetation Index) instead of soil moisture is carried out over the Ebro basin (Spain); data delivered by the Copernicus Global Land Service (CGLS) are used. First, results of this work are compared with outcomes from the method's original implementation obtained over a focus area (denominated "Ebro_CATAR") through satellite soil moisture. In the proposed configuration relying on NDVI, an overall accuracy (OA) up to 93% is found. Results highlight an increase in OA ranging from +2% to +6% depending on the validation strategy with respect to the TSIMAP implementation relying on soil moisture. Then, a basin-scale application is performed, providing performances still satisfactory (OA = 75%) notwithstanding a higher degree of heterogeneity. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach, STREAM – SaTellite-based Runoff Evaluation And Mapping – to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and total water storage anomalies (TWSAs). Within a very simple model structure, precipitation and soil moisture data are used to estimate the quick-flow river discharge component while TWSAs are used for obtaining its complementary part, i.e., the slow-flow river discharge component. The two are then added together to obtain river discharge estimates. The method is tested over the Mississippi River basin for the period 2003–2016 by using precipitation data from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), soil moisture data from the European Space Agency's Climate Change Initiative (ESA CCI), and total water storage data from the Gravity Recovery and Climate Experiment (GRACE). Despite the model simplicity, relatively high-performance scores are obtained in river discharge estimates, with a Kling–Gupta efficiency (KGE) index greater than 0.64 both at the basin outlet and over several inner stations used for model calibration, highlighting the high information content of satellite observations on surface processes. Potentially useful for multiple operational and scientific applications, from flood warning systems to the understanding of water cycle, the added value of the STREAM approach is twofold: (1) a simple modeling framework, potentially suitable for global runoff monitoring, at daily timescale when forced with satellite observations only, and (2) increased knowledge of natural processes and human activities as well as their interactions on the land. [ABSTRACT FROM AUTHOR]

Life on Earth vitally depends on the availability of water. Human pressure on freshwater resources is increasing, as is human exposure to weather-related extremes (droughts, storms, floods) caused by climate change. Understanding these changes is pivotal for developing mitigation and adaptation strategies. The Global Climate Observing System (GCOS) defines a suite of essential climate variables (ECVs), many related to the water cycle, required to systematically monitor Earth's climate system. Since long-term observations of these ECVs are derived from different observation techniques, platforms, instruments, and retrieval algorithms, they often lack the accuracy, completeness, and resolution, to consistently characterize water cycle variability at multiple spatial and temporal scales. Here, we review the capability of ground-based and remotely sensed observations of water cycle ECVs to consistently observe the hydrological cycle. We evaluate the relevant land, atmosphere, and ocean water storages and the fluxes between them, including anthropogenic water use. Particularly, we assess how well they close on multiple temporal and spatial scales. On this basis, we discuss gaps in observation systems and formulate guidelines for future water cycle observation strategies. We conclude that, while long-term water cycle monitoring has greatly advanced in the past, many observational gaps still need to be overcome to close the water budget and enable a comprehensive and consistent assessment across scales. Trends in water cycle components can only be observed with great uncertainty, mainly due to insufficient length and homogeneity. An advanced closure of the water cycle requires improved model–data synthesis capabilities, particularly at regional to local scales. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach, STREAM - SaTellite based Runoff Evaluation And Mapping - to derive daily river discharge and runoff estimates from satellite soil moisture, precipitation and terrestrial water storage anomalies observations. Within a very simple model structure, the first two variables (precipitation and soil moisture) are used to estimate the quick-flow river discharge component while the terrestrial water storage anomalies are used for obtaining its complementary part, i.e., the slow-flow river discharge component. The two are then summed up to obtain river discharge and runoff estimates. The method is tested over the Mississippi river basin for the period 2003-2016 by using Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) rainfall data, European Space Agency Climate Change Initiative (ESA CCI) soil moisture data and Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage data. Despite the model simplicity, relatively high-performance scores are obtained in river discharge simulations, with a Kling-Gupta efficiency index greater than 0.65 both at the outlet and over several inner stations used for model calibration highlighting the high information content of satellite observations on surface processes. Potentially useful for multiple operational and scientific applications (from flood warning systems to the understanding of water cycle), the added-value of the STREAM approach is twofold: 1) a simple modelling framework, potentially suitable for global runoff monitoring, at daily time scale when forced with satellite observations only, 2) increased knowledge on the natural processes, human activities and on their interactions on the land. [ABSTRACT FROM AUTHOR]

The Mediterranean region is one of the climate hotspots where the climate change impacts are both pronounced and documented. The HyMeX (Hydrometeorological Mediterranean eXperiment) aims to improve our understanding of the water cycle from the meteorological to climate scales. However, monitoring the water cycle with Earth observations (EO) is still a challenge: EO products are multiple, and their utility is degraded by large uncertainties and incoherences among the products. Over the Mediterranean region, these difficulties are exacerbated by the coastal/mountainous regions and the small size of the hydrological basins. Therefore, merging/integration techniques have been developed to reduce these issues. We introduce here an improved methodology that closes not only the terrestrial but also the atmospheric and ocean budgets. The new scheme allows us to impose a spatial and temporal multi-scale budget closure constraint. A new approach is also proposed to downscale the results from the basin to pixel scales (at the resolution of 0.25 ∘). The provided Mediterranean WC budget is, for the first time, based mostly on observations such as the GRACE water storage or the netflow at the Gibraltar Strait. The integrated dataset is in better agreement with in situ measurements, and we are now able to estimate the Bosporus Strait annual mean netflow. [ABSTRACT FROM AUTHOR]

The assessment of precipitation over land is extremely important for a number of scientific purposes related to the mitigation of natural hazards, climate modelling and prediction, famine and disease monitoring, to cite a few. Due to the difficulties and the cost to maintain ground monitoring networks, i.e., raingauges and meteorological radars, remote sensing is receiving more and more attention in the recent decade(s). However, the accuracy of satellite observations of rainfall should be assessed with ground information as it is affected by a number of factors (topography, vegetation density, land-sea interface). Calabria is a peninsular region in southern Italy characterized by complex topography, dense vegetation and a narrow North-South elongated shape, thus being a very challenging place for rainfall retrieval from remote sensing. In this study, we built a high-quality rainfall datasets from raingauges and meteorological radars for testing three remotely sensed rainfall products: 1) the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement product (IMERG), 2) the SM2RASC product obtained from the application of SM2RAIN (Soil Moisture TO RAIN) algorithm to the Advanced SCATterometer (ASCAT) derived satellite soil moisture data, and 3) a product derived from a simple combination of IMERG and SM2RASC. The assessment of the products is carried out at different rainfall time accumulation (e.g., from 0.5 to 24 h) for a ~2-year period from 10th March 2015, to 31st December 2016. Results show that IMERG has good performance at time resolutions higher than 6 h. At daily time scale, IMERG and SM2RASC show similar results with median correlations, R, ~0.60, and root mean square error, RMSE, ~7.6 mm/day (BIAS is −0.85 and +0.51 mm/day, respectively). The combined product outperforms the parent products (median R  > 0.70, RMSE<6.5 mm/day, BIAS -0.07 mm/day). Among the different factors affecting products quality, topographic complexity seems to play the more relevant role, particularly for SM2RASC but also for IMERG. Overall, this study shows that the investigated satellite-based products agree reasonably well with observations notwithstanding the challenging features of the region, and the combination of IMERG and SM2RASC provides a way to overcome their limitations and to produce a higher quality satellite rainfall product. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To promote the advancement of novel observation techniques that may lead to new sources of information to help better understand the hydrological cycle, the International Association of Hydrological Sciences (IAHS) established the Measurements and Observations in the XXI century (MOXXI) Working Group in July 2013. The group comprises a growing community of tech-enthusiastic hydrologists that design and develop their own sensing systems, adopt a multi-disciplinary perspective in tackling complex observations, often use low-cost equipment intended for other applications to build innovative sensors, or perform opportunistic measurements. This paper states the objectives of the group and reviews major advances carried out by MOXXI members toward the advancement of hydrological sciences. Challenges and opportunities are outlined to provide strategic guidance for advancement of measurement, and thus discovery. [ABSTRACT FROM AUTHOR]

Soil moisture is widely recognized as a key parameter in the mass and energy balance between the land surface and the atmosphere and, hence, the potential societal benefits of an accurate estimation of soil moisture are immense. Recently, scientific community is making great effort for addressing the estimation of soil moisture over large areas through in situ sensors, remote sensing and modelling approaches. The different techniques used for addressing the monitoring of soil moisture for hydrological applications are briefly reviewed here. Moreover, some examples in which in situ and satellite soil moisture data are successfully employed for improving hydrological monitoring and predictions (e.g., floods, landslides, precipitation and irrigation) are presented. Finally, the emerging applications, the open issues and the future opportunities given by the increased availability of soil moisture measurements are outlined. [ABSTRACT FROM AUTHOR]

As a natural feature of the Earth's weather system, rainfall is the main driver of the hydrological cycle. Rainfall plays an essential role in many applications including climate monitoring, extreme weather prediction and weather forecasting. On a global scale, ground-monitoring networks do not provide sufficient coverage and satellite rainfall products are often the only source of rainfall that guarantee a continuous temporal coverage. However, the indirect and the instantaneous nature of the measurement makes satellite rainfall products prone to errors (Kucera et al., 2013). Thanks to the strong connection between soil moisture and precipitation, capable to track accumulated precipitation estimates (rather than instantaneous), soil moisture can be successfully used to enhance the quality of satellite rainfall observations (Crow et al., 2011; Pellarin et al., 2013; Brocca et al. 2014). The SMOS+rainfall project of the European Space Agency (ESA), started in 2015 and concluded in 2017, has demonstrated the capability of the SMOS soil moisture product to enhance satellite rainfall information over land and has raised many interesting research questions related to the potential improvement that can be obtained by a combination of different soil moisture sensors. Here, we propose the use of a new near real time purely observational rainfall dataset derived from the combination of the Integrated Multi-Satellite Retrievals for GPM (IMERG early run) with multiple satellite rainfall products obtained from the inversion of the soil moisture retrievals derived from: 1) the Soil Moisture Active and Passive (SMAP) mission, 2) the Advanced Scatterometer (ASCAT) and 3) the Soil Moisture and Ocean Salinity (SMOS) mission via SM2RAIN (Brocca et al. 2014).The weighting method (Hobeichi et al. 2018) is based on a technique that provides an analytically optimal linear combination of rainfall products and accounts for both the performance differences and error covariance between the participating products. We examine the performance of the weighting approach in India, United States, Australia and Europe showing that the simultaneous use of soil moisture products is able to increase the quality of IMERG early run product and its performance for hydrological applications.Brocca et al., 2014. Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data. J. Geophy. Res. 119 (9), 5128–5141.Crow et al., 2011. Correcting rainfall using satellite-based surface soil moisture retrievals: the soil moisture analysis rainfall tool (SMART). Water Resour. Res. 47, W08521.Kucera et al. 2013. Precipitation from space: advancing earth system science. Bull. Am. Meteorol. Soc. 94, 365–375.Pellarin et al. 2013. A simple and effective method for correcting soil moisture and precipitation estimates using AMSR-E measurements. Remote Sens. Environ. 136, 28–36.Hobeichi et al. 2018. Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate, Hydrol. Earth Syst. Sci., 22, 1317-1336. [ABSTRACT FROM AUTHOR]

The Mediterranean region has been identified as one of the main climate change hotspots: its sensitivity to global change is high and its evolution remains uncertain. The region experiences many interactions and feedbacks at the oceanic, atmospheric, and hydrological levels, while facing high anthropogenic activities. Analysing the water cycle over the Mediterranean region is of major importance to environmental and socio-economic aspects. The satellite monitoring of the Mediterranean water cycle represents one of the key challenges for the hydrological community.The presentation will show recent results on using satellite soil moisture and precipitation products for hydrometeorological prediction in the Mediterranean region, and particularly for the prediction of floods and landslides. Specifically, we will show the comparison of multiple satellite precipitation products for predicting flood in 100+ basins over the Mediterranean Basin by also using different hydrological models. Moreover, the assessment of satellite precipitation products for predicting the occurrence of landslides in Italy is carried out. Among the investigated satellite products, we have firstly considered state-of-the-art products such as TMPA (TRMM Multisatellite Precipitation Analysis), GPM (Global Precipitation Measurement), and H SAF (EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management). Secondly, we have tested the innovative products using the SM2RAIN algorithm for rainfall retrieval from multiple satellite soil moisture products including ASCAT (Advanced SCATterometer), SMOS (Soil Moisture and Ocean Salinity mission) and SMAP (Soil Moisture Active and Passive mission). [ABSTRACT FROM AUTHOR]

Hydrological models have been largely developed in the last 40 years achieving great advances in our capability to simulate hydrological processes at high resolution in the horizontal and vertical domains. In principle, physically-based hydrological model are able to reproduce fluxes and states with high accuracy. However, in the real world, our models strongly depend on the parameterization and on the input data used. Uncertainties in the parameterization and in the inputs are translated into the model, and even highly accurate models, in terms of physical representation of processes, will totally fail due to error in the parameterization and the inputs.This well-known problem is much more important in locations and regions in which the human impact on the hydrological cycle is more pronounced. Specifically, irrigation is the larger consumer of water worldwide and, in some regions, irrigation during the summer season is by far the major component of the hydrological cycle (i.e., if compared with precipitation and evapotranspiration). In recent years, specific parameterizations for including the irrigation process have been added to hydrological and land surface models, but typically they rely on theoretical conditions (e.g., constant irrigation rates when soil moisture is below to certain values) that might not occur in the reality. Therefore, we strongly need irrigation observations for correctly simulate the hydrological cycle in these regions.Very recently, we developed an approach for quantifying irrigation water from space by using satellite soil moisture observations (Brocca et al., 2018). The approach has been found successful at some locations in Iran, Spain, United States and Morocco, and now is being improved by using high resolution soil moisture products from Sentinel-1 and from downscaling techniques. The obtained irrigation estimates are used as input into hydrological modelling and their impact in terms of evapotranspiration and runoff fluxes is assessed. Results will be shown in basins in Spain and Italy, and presented at the conference.REFERENCEBrocca, L., Tarpanelli, A., Filippucci, P., Dorigo, W., Zaussinger, F., Gruber, A., Fernández-Prieto, D. (2018). How much water is used for irrigation? A new approach exploiting coarse resolution satellite soil moisture products. International Journal of Applied Earth Observation and Geoinformation, 73C, 752-766, doi:10.1016/j.jag.2018.08.023. [ABSTRACT FROM AUTHOR]

Summary In addition to the soil saturated hydraulic conductivity, K s , the initial soil moisture content, θ i , is the quantity commonly incorporated in rainfall infiltration models for simulation of surface runoff hydrographs. Previous studies on the effect of the spatial heterogeneity of initial soil water content in the generation of surface runoff were generally not conclusive, and provided no guidance on designing networks for soil moisture measurements. In this study, the role of the spatial variability of θ i at the small watershed scale is examined through the use of a simulation model and measurements of θ i . The model combines two existing components of infiltration and surface runoff to model the flow discharge at the watershed outlet. The observed values of soil moisture in three experimental plots are combined to determine seven different distributions of θ i , each used to compute the hydrographs produced by four different rainfall patterns for two initial conditions classified as “dry” soil and “wet” soil. For rainfalls events typically associated with floods, the spatial variability of θ i at the watershed scale does not cause significant variations in surface runoff for initially dry or wet soils. Furthermore, when the main objective is to represent flood events a single ground point measurement of θ i in each area with the same land use may suffice to obtain adequate outflow hydrographs at the outlet. [ABSTRACT FROM AUTHOR]

Human-induced changes on the hydrological cycle nowadays rival the geophysical processesand their short-term impacts are often larger than the effects of climate change, even ifthey are not independent. Among the anthropogenic activities that alter the naturalhydrological cycle, the irrigation is the most impacting one. It is estimated thatover 70% of global freshwater is used for irrigation practices (Foley et al., 2011),with the major contributions of water consumption for this purpose coming fromNorth America and Eurasia (Zhou et al., 2016). Despite the important implicationsof irrigation on water scarcity and food production, data on irrigation practicesover large areas and for long periods are lacking, that is, often there is no reliableinformation about where irrigation practices occur and how much water is used forthem.This PhD project is aimed to evaluate the effects of the irrigation on the hydrological cycleover two pilot Mediterranean areas, the upper Tiber river basin in Italy and the Ebro riverbasin in Spain. The project is structured according to two main research lines: the first oneconsists in estimating the amount of water used for irrigation through the adapted SM2RAINmethod (Brocca et al., 2018), the second one consists in evaluating the irrigation effectsthrough SURFEX, the land surface model developed by Météo-France. In order to estimatethe amount of water used for irrigation, the adapted SM2RAIN method needs remotelysensed soil moisture data as input. Within this project, the method will be applied by usinghigh-resolution soil moisture products. Pilot tests carried out over the Urgell area in Spainshow promising results in terms of the capability to detect the irrigated areas of 1km resolution DISPATCH downscaled SMOS and SMAP surface soil moisturedata.According to the general plan of the project, the amount of water used for irrigation in thestudy areas will be estimated by applying the adapted SM2RAIN method withhigh-resolution soil moisture products. Then, this amount of water will be introduced with theatmospheric forcings in SURFEX simulations in order to represent a human-altered, real,scenario and to evaluate the effects of irrigation practices. The coupling with a riverrouting model will be useful to study the consequences of the irrigation on thestreamflow and to investigate from where water for irrigation is withdrawn (e.g.,dams).References:Brocca, L., Tarpanelli, A., Filippucci, P., Dorigo, W., Zaussinger, F., Gruber, A. and Fernandez-Prieto,D., 2018. How much water is used for irrigation? A new approach exploiting coarse resolution satellitesoil moisture products. Int. J. Appl. Earth Obs. Geoinformation. 73, 752-766.Foley, J.A., Ramankutty, N., Brauman, K.A., Cassidy, E.S., Gerber, J.S., Johnston, M., et al., 2011.Solutions for a cultivated planet. Nature. 478 (7369), 337–342.Zhou, T., Haddeland, I., Nijssen, B. and Lettenmaier, D. P., 2016. Human-induced changes in the globalwater cycle. Terrestrial Water Cycle and Climate Change: Natural and Human-Induced Impacts,Geophysical Monograph 221, First Edition. [ABSTRACT FROM AUTHOR]