The use of nature-based solutions (NbS) to address the risks posed by hydro-meteorological hazards have not yet become part of the mainstream policy response, and one of the main reasons cited for this, is the lack of evidence that they can effectively reduce disaster risk. This paper addresses this issue, by providing model-based evidence from five European case studies which demonstrate the effectiveness of five different NbS in reducing the magnitude of the hazard and thus risk, in present-day and possible future climates. In OAL-Austria, the hazard is a deep-seated landslide, and the NbS analysed is afforestation. Modelling results show that in today's climate and a landcover scenario of mature forest, a reduction in landslide velocity of 27.6 % could be achieved. In OAL-Germany, the hazard is river flooding and the NbS analysed is managed grazing with removal of woody vegetation. Modelling results show that the NbS could potentially reduce maximum flood water depth in the near-future (2031–2060) and far-future (2070–2099), by 0.036 m and 0.155 m, respectively. In OAL-Greece, the hazard is river flooding, and the NbS is upscaled natural storage reservoirs. Modelling results show that in a possible future climate the upscaled NbS show most potential in reducing the total flooded area by up to 1.26 km². In OAL-Ireland, the hazard is surface and river flooding, and the NbS is green roofs. Results from a modelled upscaling analysis under two different climate scenarios show that both maximum flood water depth, and total flooded area were able to be reduced. In OAL-UK, the hazard is shallow landslides, and the NbS is high-density planting of two different tree species. Modelling results under two different climate scenarios show that both tree species were able to improve slope stability, and that this increased over time as the NbS matured. The significance of these results is discussed within the context of the performance of the NbS over time, to different magnitude events, impact with stakeholders in engendering wider support for the adoption of the NbS in the OALs, and the uncertainty in the modelling analyses.

Nature-based solutions (NBS) for hydro-meteorological risks (HMRs) reduction and management are becoming increasingly popular, but challenges such as the lack of well-recognised standard methodologies to evaluate their performance and upscale their implementation remain. We systematically evaluate the current state-of-the art on the models and tools that are utilised for the optimum allocation, design and efficiency evaluation of NBS for five HMRs (flooding, droughts, heatwaves, landslides, and storm surges and coastal erosion). We found that methods to assess the complex issue of NBS efficiency and cost-benefits analysis are still in the development stage and they have only been implemented through the methodologies developed for other purposes such as fluid dynamics models in micro and catchment scale contexts. Of the reviewed numerical models and tools MIKE-SHE, SWMM (for floods), ParFlow-TREES, ACRU, SIMGRO (for droughts), WRF, ENVI-met (for heatwaves), FUNWAVE-TVD, BROOK90 (for landslides), TELEMAC and ADCIRC (for storm surges) are more flexible to evaluate the performance and effectiveness of specific NBS such as wetlands, ponds, trees, parks, grass, green roof/walls, tree roots, vegetations, coral reefs, mangroves, sea grasses, oyster reefs, sea salt marshes, sandy beaches and dunes. We conclude that the models and tools that are capable of assessing the multiple benefits, particularly the performance and cost-effectiveness of NBS for HMR reduction and management are not readily available. Thus, our synthesis of modelling methods can facilitate their selection that can maximise opportunities and refute the current political hesitation of NBS deployment compared with grey solutions for HMR management but also for the provision of a wide range of social and economic co-benefits. However, there is still a need for bespoke modelling tools that can holistically assess the various components of NBS from an HMR reduction and management perspective. Such tools can facilitate impact assessment modelling under different NBS scenarios to build a solid evidence base for upscaling and replicating the implementation of NBS.

To bring to fruition the capability of nature-based solutions (NBS) in mitigating hydro-meteorological risks (HMRs) and facilitate their widespread uptake require a consolidated knowledge-base related to their monitoring methods, efficiency, functioning and the ecosystem services they provide. We attempt to fill this knowledge gap by reviewing and compiling the existing scientific literature on methods, including ground-based measurements (e.g. gauging stations, wireless sensor network) and remote sensing observations (e.g. from topographic LiDAR, multispectral and radar sensors) that have been used and/or can be relevant to monitor the performance of NBS against five HMRs: floods, droughts, heatwaves, landslides, and storm surges and coastal erosion. These can allow the mapping of the risks and impacts of the specific hydro-meteorological events. We found that the selection and application of monitoring methods mostly rely on the particular NBS being monitored, resource availability (e.g. time, budget, space) and type of HMRs. No standalone method currently exists that can allow monitoring the performance of NBS in its broadest view. However, equipments, tools and technologies developed for other purposes, such as for ground-based measurements and atmospheric observations, can be applied to accurately monitor the performance of NBS to mitigate HMRs. We also focused on the capabilities of passive and active remote sensing, pointing out their associated opportunities and difficulties for NBS monitoring application. We conclude that the advancement in airborne and satellite-based remote sensing technology has signified a leap in the systematic monitoring of NBS performance, as well as provided a robust way for the spatial and temporal comparison of NBS intervention versus its absence. This improved performance measurement can support the evaluation of existing uncertainty and scepticism in selecting NBS over the artificially built concrete structures or grey approaches by addressing the questions of performance precariousness. Remote sensing technical developments, however, take time to shift toward a state of operational readiness for monitoring the progress of NBS in place (e.g. green NBS growth rate, their changes and effectiveness through time). More research is required to develop a holistic approach, which could routinely and continually monitor the performance of NBS over a large scale of intervention. This performance evaluation could increase the ecological and socio-economic benefits of NBS, and also create high levels of their acceptance and confidence by overcoming potential scepticism of NBS implementations.

This study on a large irrigation scheme in Morocco addressed a two-fold question: (a) does the local water management authority adapt water releases to atmospheric water demand ET₀-P? and (b) does crop actual evapotranspiration respond to inter- and intra-annual variability in water releases? We have evaluated the inter-annual variability of ET₀-P during the period 1992-2017 and compared its anomalies (i.e., deviations from average) with anomalies in annual water release. Overall, it appeared that anomalies in water release were consistent with anomalies in ET₀-P. The actual evapotranspiration (ETa) was estimated using a time series of multi-spectral satellite image data by applying the Surface Energy Balance (SEBAL) algorithm in a dry, wet, and reference year. We have determined the quartiles of the distribution of the ET₀-P time series to identify these three years. The dry year was 2015-2016, the wet year was 2014-2015, and the reference (median of ET₀-P) was 2013-2014. Finally, we compared seasonal and annual anomalies in ET₀-P, ETa and release, W_d of irrigation water. In the period 1992-2017, the relative anomalies in ET₀-P and W_d were similar only in 2000-2001 and 2015-2016. The analysis of anomalies in water inflow and stocks confirmed the response in increased W_d following wet years with higher inflow and replenishment of the reservoir. The response of crop water use to the irrigation water supply was evaluated by comparing anomalies in the ratio of actual to maximum ET, i.e., ET_a/ET_c with anomalies in W_d. As regards the Sidi Bennour, Faregh, and Gharbia districts, the relative anomalies in ET_a/ET_c and W_d were consistent, i.e., they had the same sign and comparable magnitude. Overall, the study shows that water delivery W_d responds to inter-annual variability in the pre-irrigation season water inflows into the reservoirs, rather than in ET₀-P. On the other hand, actual crop water use, i.e., ET_a/ET_c, does respond to inter- and intra-annual variability in W_d. This evidence suggests that there is scope for adaptive water management based on a flexible adaptation of water release to inter- and intra-annual variability in water demand.

Small reservoirs play an important role in mining, industries, and agriculture, but storage levels or stage changes are very dynamic. Accurate and up-to-date maps of surface water storage and distribution are invaluable for informing decisions relating to water security, flood monitoring, and water resources management. Satellite remote sensing is an effective way of monitoring the dynamics of surface waterbodies over large areas. The European Space Agency (ESA) has recently launched constellations of Sentinel-1 (S1) and Sentinel-2 (S2) satellites carrying C-band synthetic aperture radar (SAR) and a multispectral imaging radiometer, respectively. The constellations improve global coverage of remotely sensed imagery and enable the development of near real-time operational products. This unprecedented data availability leads to an urgent need for the application of fully automatic, feasible, and accurate retrieval methods for mapping and monitoring waterbodies. The mapping of waterbodies can take advantage of the synthesis of SAR and multispectral remote sensing data in order to increase classification accuracy. This study compares automatic thresholding to machine learning, when applied to delineate waterbodies with diverse spectral and spatial characteristics. Automatic thresholding was applied to near-concurrent normalized difference water index (NDWI) (generated from S2 optical imagery) and VH backscatter features (generated from S1 SAR data). Machine learning was applied to a comprehensive set of features derived from S1 and S2 data. During our field surveys, we observed that the waterbodies visited had different sizes and varying levels of turbidity, sedimentation, and eutrophication. Five machine learning algorithms (MLAs), namely decision tree (DT), k-nearest neighbour (k-NN), random forest (RF), and two implementations of the support vector machine (SVM) were considered. Several experiments were carried out to better understand the complexities involved in mapping spectrally and spatially complex waterbodies. It was found that the combination of multispectral indices with SAR data is highly beneficial for classifying complex waterbodies and that the proposed thresholding approach classified waterbodies with an overall classification accuracy of 89.3%. However, the varying concentrations of suspended sediments (turbidity), dissolved particles, and aquatic plants negatively affected the classification accuracies of the proposed method, whereas the MLAs (SVM in particular) were less sensitive to such variations. The main disadvantage of using MLAs for operational waterbody mapping is the requirement for suitable training samples, representing both water and non-water land covers. The dynamic nature of reservoirs (many reservoirs are depleted at least once a year) makes the re-use of training data unfeasible. The study found that aggregating (combining) the thresholding results of two SAR and multispectral features, namely the S1 VH polarisation and the S2 NDWI, respectively, provided better overall accuracies than when thresholding was applied to any of the individual features considered. The accuracies of this dual thresholding technique were comparable to those of machine learning and may thus offer a viable solution for automatic mapping of waterbodies.

Adaptation to climate change is a major challenge facing the agricultural sector worldwide. Olive (Olea europaea L.) is a global, high value crop currently cultivated in 28 countries worldwide. Global data to assess the vulnerability of the crop to climate variability are scarce, and in some notable cases, such the United Nations Food and Agricutlure Organization database (FAO, 2006), qualitative assessments rather than quantitative indicators are provided. The aim of this study is to demonstrate a new approach to help overcome these constraints toward a globally applicable method to assess the adaptability of olive cultivars. The adaptability of 11 cultivars, widely used in 11 countries worldwide, was studied using a new generic approach based on the evaluation of soil hydrological regime against cultivar-specific hydrological requirements. The approach requires local data, notably on soil hydrological properties, but it is easily transferable to other countries and regions. We applied an agrohydrological model in 60 soil units to determine hydrological indicators both in a reference (1961–1990) and a future (2021–2050) climate case. We compared indicators with cultivar-specific requirements to achieve the target yield; requirements were established using experimental yield response curves. We estimated the probability of adaptation, i.e., the probability that a given cultivar attains the target yield, and we used it to evaluate the cultivar potential distribution in the study area. At the locations where soil hydrological conditions were favorable, the probabilities of adaptation of the cultivars were high in both climate cases. The results show that the area with suitable conditions for the target yield (area of adaptability) decreased under future climate for all the cultivars, with higher reduction for Frantoio and Maiatica and smaller reduction for Itrana, Nocellara, Ascolana, and Kalamata. These cultivars are currently grown in Argentina, United States (US), Australia, France, Greece, and Italy. Our results indicate also that these cultivars require higher available soil water to attain the target yield, i.e., we may expect similar vulnerability in other parts of the world. Based on these findings, we provide some specific recommendations for enrichment of global databases and for further developments of our approach, to increase its potential for global application.

Remote sensing imagery to monitor global biophysical dynamics requires the availability of thermal infrared data at high temporal and spatial resolution because of the rapid development of crops during the growing season and the fragmentation of most agricultural landscapes. Conversely, no single sensor meets these combined requirements. Data fusion approaches offer an alternative to exploit observations from multiple sensors, providing data sets with better properties. A novel spatio-temporal data fusion model based on constrained algorithms denoted as multisensor multiresolution technique (MMT) was developed and applied to generate TIR synthetic image data at both temporal and spatial high resolution. Firstly, an adaptive radiance model is applied based on spectral unmixing analysis of. TIR radiance data at TOA (top of atmosphere) collected by MODIS daily 1-km and Landsat - TIRS 16-day sampled at 30-m resolution are used to generate synthetic daily radiance images at TOA at 30-m spatial resolution. The next step consists of unmixing the 30 m (now lower resolution) images using the information about their pixel land-cover composition from co-registered images at higher spatial resolution. In our case study, TIR synthesized data were unmixed to the Sentinel 2 MSI with 10 m resolution. The constrained unmixing preserves all the available radiometric information of the 30 m images and involves the optimization of the number of landcover classes and the size of the moving window for spatial unmixing. Results are still being evaluated, with particular attention for the quality of the data streams required to apply our approach.

In the context of climate change strategies are needed towards sustainable agricultural production. The aim of this study is to identify crop adaptation options to face the expected changes in water availability by exploiting the existing intra-specific biodiversity of the tomato crop and accounting for irrigation management and the hydrological properties of soils. The biophysical dimension of crop adaptation is therefore addressed. A study is presented examining an irrigated district in southern Italy. Using as a climatic reference the period 1961–90 and as a future climate the period 2021–2050, a soil water availability indicator was determined by a soil water balance model, at optimal irrigation and at different deficit irrigation strategies, in 23 soil units. For five tomato cultivars, hydrological requirements were determined by means of yield response functions to soil water availability. Cultivar-specific hydrological requirements were evaluated against the soil water availability indicator to determine probabilities of adaptation of each cultivar. These cultivars are not currently being grown in the study area so their potential spatial distribution in the study area was estimated. For instance, with 60% of optimal irrigation, two cultivars were assessed as having probabilities of crop adaptation larger than 0.89, in 90% and 62% of the area. In the future climate, with limited water resources, a proper choice and combination of cultivars, irrigation strategies and soils would allow to maintain the current production system in a large part of the study area.

The Caprivi basin in Namibia has been affected by severe flooding in recent years resulting in deaths, displacements and destruction of infrastructure. The negative consequences of these floods have emphasized the need for timely, accurate and objective information about the extent and location of affected areas. Due to the high temporal variability of flood events, Earth Observation (EO) data at high revisit frequency is preferred for accurate flood monitoring. Currently, EO data has either high temporal or coarse spatial resolution. Accurate methodologies for the estimation and monitoring of flooding extent using coarse spatial resolution optical image data are needed in order to capture spatial details in heterogeneous areas such as Caprivi. The objective of this work was the retrieval of the fractional abundance of water (γ_w) by applying a new spectral indices-based unmixing algorithm to Medium Resolution Imaging Spectrometer Full Resolution (MERIS FR) data using a minimum number of spectral bands. These images are technically similar to the OLCI image data acquired by the Sentinel-3 satellite, which are to be systematically provided in the near future. The normalized difference wetness index (NDWI) was applied to delineate the water surface and combined with normalized difference vegetation index (NDVI) to account for emergent vegetation within the water bodies. The challenge to map flooded areas by applying spectral unmixing is the estimation of spectral endmembers, i.e., pure spectra of land cover features. In our study, we developed and applied a new unmixing method based on the use of an ensemble of spectral endmembers to capture and take into account spectral variability within each endmember. In our case study, forty realizations of the spectral endmembers gave a stable frequency distribution of γ_w. Quality of the flood map derived from the Envisat MERIS (MERIS) data was assessed against high (30 m) spatial resolution Landsat Thematic Mapper (TM) images on two different dates (17 April 2008 and 22 May 2009) during which floods occurred. The findings show that both the spatial and the frequency distribution of the γ_w extracted from the MERIS data were in good agreement with the high-resolution TM retrievals. The use of conventional linear unmixing, instead, applied using the entire available spectra for each image, resulted in relatively large differences between TM and MERIS retrievals.

Satellite observations of the terrestrial biosphere cover a period of time sufficiently extended to allow the calculation of a reliable climatology. The latter is particularly relevant for studies of vegetation response to climate variability. Observations from space of the land surface are hampered by clouds at shorter wavelength and affected by water in the atmosphere in the microwave range. Both polar orbiting and geostationary satellites have a revisit frequency high enough to allow for some redundancy relative to the processes being observed, so that time series where a fraction of observations are removed and the resulting gaps filled are still very useful to monitor land surface processes. Two examples illustrate this concept in two different spectral regions: Thermal Infrared (TIR) and observations of land surface temperature to study the thermal behavior of the land surface in response to weather and climate and 37 GHz observations of the polarization difference in brightness temperature to retrieve the fractional abundance of watersaturated soil. Three applications of time series of land surface temperature are presented: (a) monitoring of spectral thermal admittance of the land surface; (b) estimation and mapping of air temperature and (c) monitoring of thermal load to assess the risk of forest fires. Two methods were applied to identify and remove anomalous observations (outliers) and to fill the resulting gaps: Harmonic ANalysis of Time Series (HANTS) and theMultichannel Singular Spectrum Analysis (M-SSA). The HANTS algorithm has been widely used to reconstruct time series of Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Land Surface Temperature (LST) as well as the polarization difference brightness temperature (PDBT) during the past 20 years to remove random noise or eliminate cloud/snow contamination. The M-SSA, an advanced methodology for time series analysis, was utilized to reconstruct gap-free LST time series using both the spatial and the temporal information content in the data set.