Comment on hess-2022-421

<strong class="journal-contentHeaderColor">Abstract.</strong> There is an urgent demand for assessments of climate change impact on the hydrological cycle at high spatial<br />resolution. In particular, the impacts on shallow groundwater levels, which can lead to both flooding and drought, have<br />major implications for agriculture, adaptation and urban planning. Predicting such hydrological impacts is typically<br />performed using physically-based hydrological models. However, such models are computationally expensive, especially at<br />high spatial resolutions. This study is based on the Danish national groundwater model, setup as a distributed, integrated surface-subsurface model at<br />500 m horizontal resolution. Recently, a version at a higher resolution of 100 m was created; amongst others, to better<br />represent the uppermost groundwater table and to meet end user demands for water management and climate adaptation. The<br />increase in resolution of the hydrological model, however, also increases computational bottleneck. To evaluate climate<br />change impact, a large ensemble of climate models was run with the 500 m hydrological model, while performing the same<br />ensemble run with the 100 m resolution nation-wide model was deemed infeasible. The desired outputs at 100 m resolution<br />were produced by developing a novel, hybrid downscaling method based on machine learning. Hydrological models for five subcatchments, covering around 9 % of Denmark and selected to represent a range of<br />hydrogeological settings, were run at 100 m resolution with forcings from a reduced ensemble of climate models. Random<br />Forest algorithms were established using the simulated climate change impacts (future &ndash; present) on water table depth at 100<br />m resolution from those submodels as training data. The trained downscaling algorithms then were applied to create nation-wide maps of climate change-induced impacts on the<br />shallow groundwater table at 100 m resolution. These downscaled maps were successfully validated against results from a<br />validation submodel at 100 m resolution excluded from training the algorithms, and compared to the impact signals from the<br />500 m hydrological model across Denmark. The suggested downscaling algorithm also opens for the spatial downscaling of other model outputs. It has the potential for<br />further applications where, for example, computational limitations inhibit running distributed hydrological models at fine<br />resolutions.

There is an urgent demand for assessments of climate change impact on the hydrological cycle at high spatialresolution. In particular, the impacts on shallow groundwater levels, which can lead to both flooding and drought, havemajor implications for agriculture, adaptation and urban planning. Predicting such hydrological impacts is typicallyperformed using physically-based hydrological models. However, such models are computationally expensive, especially athigh spatial resolutions. This study is based on the Danish national groundwater model, setup as a distributed, integrated surface-subsurface model at500 m horizontal resolution. Recently, a version at a higher resolution of 100 m was created; amongst others, to betterrepresent the uppermost groundwater table and to meet end user demands for water management and climate adaptation. Theincrease in resolution of the hydrological model, however, also increases computational bottleneck. To evaluate climatechange impact, a large ensemble of climate models was run with the 500 m hydrological model, while performing the sameensemble run with the 100 m resolution nation-wide model was deemed infeasible. The desired outputs at 100 m resolutionwere produced by developing a novel, hybrid downscaling method based on machine learning. Hydrological models for five subcatchments, covering around 9 % of Denmark and selected to represent a range ofhydrogeological settings, were run at 100 m resolution with forcings from a reduced ensemble of climate models. RandomForest algorithms were established using the simulated climate change impacts (future – present) on water table depth at 100m resolution from those submodels as training data. The trained downscaling algorithms then were applied to create nation-wide maps of climate change-induced impacts on theshallow groundwater table at 100 m resolution. These downscaled maps were successfully validated against results from avalidation submodel at 100 m resolution excluded from training the algorithms, and compared to the impact signals from the500 m hydrological model across Denmark. The suggested downscaling algorithm also opens for the spatial downscaling of other model outputs. It has the potential forfurther applications where, for example, computational limitations inhibit running distributed hydrological models at fineresolutions.

&amp;lt;div&amp;gt;&amp;lt;span&amp;gt;Snow as a major part of the cryosphere is an important component of Earth&amp;amp;#8217;s hydrological cycle and energy balance. Understanding the microstructural, macrophysical, thermal and optical properties of the snowpack is essential for integration into numerical models and there is a great need for accurate snow data at different spatial and temporal resolutions to address the challenges of changing snow conditions.&amp;lt;/span&amp;gt;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;lt;span&amp;gt;Physical snow properties are currently determined by traditional ground-based measurements as well as remote sensing, over a range of temporal and spatial scales, following considerable developments in instrument technology over recent years.&amp;amp;#160;&amp;lt;/span&amp;gt;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;lt;span&amp;gt;Data assimilation (DA)&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt; methods are widely used&amp;lt;/span&amp;gt; &amp;lt;span&amp;gt;to combine data from different observations&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt; with numerical model using uncertainties &amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;of observed and modeled variables&amp;amp;#160; to produce an optimal estimate. DA provides a reliable improvement of the initial states of the numerical model and a benefit for hydrological and snow model forecasts. &amp;lt;/span&amp;gt;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;#160;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;lt;span&amp;gt;European efforts to harmonize approaches for validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation techniques were coordinated by the European Cooperation in Science and Technology (COST) Action ES1404 &amp;amp;#8220;HarmoSnow&amp;amp;#8221;, entitled, &amp;amp;#8220;A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction&amp;amp;#8221; (2014-2018) .&amp;lt;/span&amp;gt;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;lt;span&amp;gt;One of the key objectives of the action was &amp;amp;#8220;Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models, and show its benefit for weather and hydrological forecasting as well as other applications.&amp;amp;#8221;&amp;amp;#160;&amp;lt;/span&amp;gt;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;lt;span&amp;gt;One key result from COST HarmoSnow is a better knowledge about the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. The main parts of this knowledge are retrieved from a COST HarmoSnow survey exploring the common practices on the use of snow observations in different modeling environments. We will show results from the survey and their implications towards standardized and improved usage of snow observations in various data assimilation applications.&amp;lt;/span&amp;gt;&amp;lt;/div&amp;gt;

[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAU

The rapid development of free and open-access hydrological models and coupling framework tools continues to present more opportunities for coupled model development for improved assessment of floodplain hydrology. In this study, we set up an Upper Zambezi hydrological model and a fully spatially hydrological-hydrodynamic coupled model for the Barotse Floodplain using GLOFRIM (GLObally applicable computational FRamework for Integrated hydrological–hydrodynamic Modelling). The hydrological and hydrodynamic models used are WFLOW and LISFLOOD-FP, respectively. The simulated flows generated by the wflow model for the upstream gauge stations before the Barotse Floodplain were quite similar and closely matched the observed flow as indicated by the evaluation statistics; Chavuma, nse = 0.738; kge = 0.738; pbias = 2.561 and RSR = 0.511; Watopa, nse = 0.684; kge = 0.816; pbias = 10.577 and RSR = 0.557; and Lukulu, nse = 0.736; kge = 0.795; pbias = 10.437 and RSR = 0.509. However, even though the wflow hydrological model was able to simulate the upstream hydrology very well, the results at the floodplain outlet gauge stations did not quite match the observed monthly flows at Senanga gauge station as indicated by the evaluation statistics: nse = 0.132; kge = 0.509; pbias = 37.740 and RSR = 0.9233. This is mainly because the representation of both floodplain channel hydrodynamics and vertical hydrological processes is necessary to correctly capture floodplain dynamics. Thus, the need for an approach that saves as a basis for developing fully spatially distributed coupled hydrodynamic and hydraulic models’ assessments for groundwater dependent tropical floodplains such as the Barotse floodplain, in closing the gap between hydrology and hydrodynamics in floodplain assessments. A fully coupled model has the potential to be used in implementing adaptive wetland management strategies for water resources allocation, environmental flow (eflows), flood control, land use and climate change impact assessments. Doi: 10.28991/HEF-2022-03-02-09 Full Text: PDF

Climate change impact assessments form the basis for the development of suitable climate change adaptation strategies. For this purpose, ensembles consisting of stepwise coupled models are generally used [emission scenario → global circulation model → downscaling approach (DA) → bias correction → impact model (hydrological model)], in which every item is affected by considerable uncertainty. The aim of the current study is (1) to analyse the uncertainty related to the choice of the DA as well as the hydrological model and its parameterization and (2) to evaluate the vulnerability of the studied catchment, a subcatchment of the highly anthropogenically impacted Spree River catchment, to hydrological change. Four different DAs are used to drive four different model configurations of two conceptually different hydrological models (Water Balance Simulation Model developed at ETH Zürich and HBV‐light). In total, 452 simulations are carried out. The results show that all simulations compute an increase in air temperature and potential evapotranspiration. For precipitation, runoff and actual evapotranspiration, opposing trends are computed depending on the DA used to drive the hydrological models. Overall, the largest source of uncertainty can be attributed to the choice of the DA, especially regarding whether it is statistical or dynamical. The choice of the hydrological model and its parameterization is of less importance when long‐term mean annual changes are compared. The large bandwidth at the end of the modelling chain may exacerbate the formulation of suitable climate change adaption strategies on the regional scale. Copyright © 2013 John Wiley &amp; Sons, Ltd.

&amp;lt;p&amp;gt;Climate change (CC) already have widespread and significant impacts in Europe, which is expected to increase in the future. Groundwater plays a vital role for the land phase of the freshwater cycle and have the capability of buffering or enhancing the impact from extreme climate events causing droughts or floods, depending on the subsurface properties and the status of the system (dry/wet) prior to the climate event. Understanding and taking the hydrogeology into account is therefore essential in the assessment of climate change impacts.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The Geological Survey Organisations (GSOs) in Europe compile the necessary data and knowledge of the groundwater systems across Europe. The overall vision of the project &amp;amp;#8220;Tools for Assessment of ClimaTe change ImpacT on Groundwater and Adaptation Strategies &amp;amp;#8211; TACTIC&amp;amp;#8221; is to enhance the utilisation of these data and knowledge of the subsurface system in CC impact assessments, and the identification and analyses of potential adaptation strategies. To reach this vision, the objective of TACTIC is to contribute to the development of coherent and transparent assessments of CC impacts on groundwater and surface water, supporting improved EU policy making, and providing decision support for stakeholders and decision makers. To accomplish this, an infra-structure among European Geological Survey Organisations are developed in TACTIC to foster advancement and harmonisation of CC assessments, made up by: 1) The TACTIC Toolbox, consisting of relevant tools and methods for CC impact assessments, 2) TACTIC guidelines that will guide GSOs and other relevant stakeholders on the selection of appropriate tools and their use for producing comparable results, 3) The European Geological Data Infrastructure (EGDI) where data, reports and open-access papers will be stored &amp;amp;#160;and made freely available &amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The project is centred around 40 pilot studies covering a variety of CC challenges as well as different hydrogeological settings and different management systems found in Europe. The pilot activities are coordinated centrally in the project, to ensure that assessments, to the extent possible, are harmonised and can be compared across pilots. Synthesizing the experiences and results from the pilots will enable the development of a guideline and future roadmap, with the aim of 1) encouraging more GSOs to contribute in CC impact assessments 2) providing guidance to make the learning curve less steep and 3)ensuring that new assessments are comparable with assessments conducted in TACTIC.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;TACTIC is part of the Horizon 2020 ERA-NET on Applied Geoscience (GeoERA) and together with the three other GeoERA groundwater projects, TACTIC will provide new and important data for further development of the European Geological Data Infrastructure (EGDI) with publicly available data enabling the development of EU-wide decision support systems for sustainable management of subsurface resources in a changing climate.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 731166.&amp;lt;/p&amp;gt;

Uncertainty plays a key role in hydrological modeling and forecasting, which can have tremendous environmental, economic, and social impacts. Therefore, it is crucial to comprehend the nature of this uncertainty and identify its scope and effects in a way that enhances hydrological modeling and forecasting. During recent decades, hydrological researchers investigated several approaches for reducing inherent uncertainty considering the limitations of sensor measurement, calibration, parameter setting, model conceptualization, and validation. Nevertheless, the scope and diversity of applications and methodologies, sometimes brought from other disciplines, call for an extensive review of the state-of-the-art in this field in a way that promotes a holistic view of the proposed concepts and provides textbook-like guidelines to hydrology researchers and the community. This paper contributes to this goal where a systematic review of the last decade's research (2010 onward) is carried out. It aims to synthesize the theories and tools for uncertainty reduction in surface hydrological forecasting, providing insights into the limitations of the current state-of-the-art and laying down foundations for future research. A special focus on remote sensing and multi-criteria-based approaches has been considered. In addition, the paper reviews the current state of uncertainty ontology in hydrological studies and provides new categorizations of the reviewed techniques. Finally, a set of freely accessible remotely sensed data and tools useful for uncertainty handling and hydrological forecasting are reviewed and pointed out.

&amp;lt;p&amp;gt;Daily streamflow data are crucial for various scientific and operational water resources applications, such as climate change impact assessment, flood forecasting, and catchment classification, among others. Streamflow is typically estimated through the implementation of hydrological models, which rely on parameters to represent hypotheses about the dominant processes in a catchment. In most cases, these parameters cannot be measured at the scales relevant for model applications and are therefore estimated through model calibration. Because most streams worldwide remain ungauged, novel parameter regionalisation techniques have been developed to predict daily streamflow over ungauged catchments. These regionalisation techniques transfer calibrated model parameters from gauged to ungauged catchments. To this end, an accurate spatio-temporal representation of crucial meteorological variables such as precipitation is essential, and therefore, most regionalisation studies have been conducted over regions with a dense network of meteorological stations. However, the characterisation of precipitation over data-scarce areas is challenging and might be subject to large uncertainties when only ground-based measurements are used. Despite that few daily regionalisation studies have used gridded precipitation products, there is no precise evaluation on how the selection of a particular precipitation product can affect the performance of the existing regionalisation techniques. Therefore, this work aims to analyse how the choice of gridded daily precipitation products affects the relative performance of three well-known parameter regionalisation techniques (spatial proximity, feature similarity, and parameter regression) over 100 near-natural catchments with diverse hydrological regimes across Chile. For this purpose, we calibrated a conceptual semi-distributed HBV-like hydrological model (TUWmodel) for each catchment, using four precipitation products (CR2MET, RF-MEP, ERA5, and MSWEPv2.8). We assessed the ability of these regionalisation techniques to transfer the parameters of a rainfall-runoff model, implementing a leave-one-out cross-validation procedure for each precipitation product. Despite differences in the spatio-temporal distribution of precipitation, all products provided good performance during calibration (median KGE's &amp;gt; 0.77), two independent verification periods (median KGE's &amp;gt; 0.70 and 0.61, for near normal and dry conditions, respectively), and regionalisation (median KGE's for the best method ranging from 0.56 to 0.63). We show how model calibration can compensate, to some extent, differences between precipitation forcings by adjusting model parameters and thus the water balance components. Feature similarity provided the best results, followed by spatial proximity, while parameter regression resulted in the worst performance, reinforcing the importance of transferring complete model parameter sets to ungauged catchments. Our results suggest that: i) merging precipitation products and ground-based measurements does not necessarily translate into an improved hydrological model performance; ii) a precipitation product that provides the best individual model performance during calibration and verification does not necessarily yield the best performance in terms of parameter regionalisation; iii) the spatial resolution of the precipitation products does not substantially affect the regionalisation performance; and iv) the model parameters and the performance of regionalisation methods are affected by the hydrological regime, with the best results for spatial proximity and feature similarity obtained for rain-dominated catchments with a minor snowmelt component.&amp;lt;/p&amp;gt;

Climatic changes have altered hydrological and climatic parameters worldwide, and climate projections suggest that such alterations will continue. In order to maintain the sustainable development and acquire the knowledge of water availability, climatic projection must be coupled with hydrological models. In this study, Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models output were integrated with a calibrated hydrological model, Soil and Water Assessment Tools (SWAT) to evaluate the potential effect of climate change on green and blue water over Upper Narmada river Basin (UNB). Therefore, top three representative climate models (MIROC5, CNRM-CM5 and MPI-ESM-LR) from 24 CMIP5 climate models were selected for hydrological modelling. Selected representative climate model outputs were bias corrected by distribution mapping to remove systematic bias correction. Multi-site model calibration approaches indicated Nash Sutcliffe Efficiency (NSE) and Coefficient of Determination (R2) as 0.77 and 0.76 for calibration (1978–1995), and 0.73 and 0.70 for validation (1996–2005), respectively. Calibrated model was run for baseline period (1970–2000) and three futuristic period P1 (2011–2040), P2 (2041–2070) and P3 (2071–2100) under Representative Concentration Pathways (RCPs) 4.5 and 8.5 scenarios. Results indicated annual precipitation decreasing under RCP4.5 and RCP8.5 scenarios changes in green and blue water varying from 16.22 to −14.10% (CNRM,P3) under RCP4.5 and from 38.25 to −22.57% under RCP8.5 with reference to baseline scenario. This study established the sensitivity of UNB to future climatic changes employing projections from CMIP5 climate models and exhibited an approach that applied multiple climate model outputs to estimate potential change over the river basin.

Hydrological models are commonly used to quantify the hydrological impacts of climate change using general circulation model (GCM) simulations as input. However, application of the model results with respect to future changes in streamflow scenarios remains limited by the large uncertainties stemming from various sources. Therefore, this study aimed to explore uncertainties involved in climate change impact assessment in Hulu Langat Basin, Malaysia, and define the contribution of uncertainty sources to the final uncertainty level. Hydrological model parameters, GCMs, and emission scenario uncertainties were considered the main uncertainty contributors in local-scale impact studies. The equidistant quantile matching method is used to bias-correct simulations of 19 GCMs under two emission scenarios of RCP4.5 and RCP8.5. The Soil and Water Assessment Tool (SWAT) hydrological model is next run by the bias-corrected GCM data to generate a wide spectrum of future streamflow scenarios. Projected monthly streamflow pattern under RCP8.5 showed a different temporal pattern from the observed one. Hydrological model parameter uncertainty was proven to be a larger uncertainty contributor than emission scenario during baseline climate. GCM and emission scenario uncertainties escalated as progressed in time and GCM uncertainty showed larger increments. The monthly pattern of effect of each uncertainty source varied when comparing the two periods of 2030s and 2080s. Therefore, for a superior management of water resources, a study of climate change impacts and uncertainty sources on a smaller scale than the decadal or annual scales can be more informative to the decision makers.

Bias correction methods for regional climate model simulations considering the distributional parametric uncertainty underlying the observations

Seasonal hydrological forecasts are of critical importance for the effective management of water resources, particularly in complex and vulnerable basins such as the Mediterranean area which have both a deficitary water regimen and a high anthropogenic pressure. Nevertheless, inaccuracies in meteorological inputs can propagate through hydrological models, amplifying uncertainties in flow predictions. It is imperative to rectify these forecasts, whether meteorological or hydrological, to enhance prediction reliability and provide robust data for informed decision-making. This study proposes an advanced framework for seasonal hydrological forecasting that integrates raw and corrected weather forecasts with distributed hydrological modeling and sophisticated post-processing techniques to enhance flow prediction accuracy in the J&amp;#250;car River in Spain as a representative case study of the Mediterranean area.The catchment hydrological model was implemented using the model TETIS v9.1 wich was calibrated and validated using observed records from 1981 to 2019 at seven control points. To do this, a split sample test was conducted using the period 2009&amp;#8211;2019 for calibration, and the rest of the time series for validation (1981&amp;#8211;2008). The process involved refining parameter maps to ensure good or acceptable performance at all control points. Meteorological data were sourced from the W5E5 dataset, downscaled to a 0.09&amp;#176; resolution using ERA5-Land. This improved the spatial and temporal resolution of the hydrological model. Once we have established an acceptable hydrological model,, the seasonal forecast hindcasts were evaluated using meteorological inputs from global forecasting systems, including ECMWF-SEAS5, CMCC_SPSv35, DWD_GCFS21, and MeteoFrance System8. To address uncertainties in the meteorological forecasts and their propagation to hydrological outputs, two complementary correction strategies were implemented. First, artificial intelligence(fuzzy logic) was applied to correct meteorological inputs before integration into the hydrological model, assuming errors originate solely from meteorological data and treating the hydrological model as a &amp;#8220;perfect&amp;#8221; simulator. Second, a hydrological error model was developed to identify and adjust discrepancies between simulated and observed flows, addressing systematic biases and errors in the hydrological simulation.The results demonstrated that forecasts based on corrected meteorological inputs exhibited significant accuracy improvements compared to those using unprocessed inputs. The hydrological error model further enhanced prediction reliability by mitigating systematic biases. These findings underscore the effectiveness of combining meteorological forecasts with AI-driven corrections to address uncertainties, thereby improving the robustness of seasonal hydrological predictions. This study highlights the potential for integrating advanced correction techniques into seasonal hydrological forecasting frameworks, offering a replicable methodology for other basins with similar complexities. The proposed framework enhances both the reliability and applicability of forecasts, ensuring their relevance for effective decision-making in complex hydrological systems as the Mediterranean area. The improved accuracy of these forecasts provides a sound scientific support for adaptive water resource management, particularly in the face of increasing climatic variability and environmental changes.Acknowledgments: This study was funded by the Colombian Ministry of Science, Technology, and Innovation (MINCIENCIAS) through the Call for Doctorates Abroad 885-2; by the Valencian Regional Government through the WATER4CAST 2.0 (CIPROM/2023/5) research project; and Spanish Ministry of Science and Innovation through the research project TETISPREDICT (PID2022-141631OB-I00).&amp;#160;

This study is an attempt to quantify the impact of climate change on the hydrology of Armur watershed in Godavari river basin, India. A GIS-based semi-distributed hydrological model, soil and water assessment tool (SWAT) has been employed to estimate the water balance components on the basis of unique combinations of slope, soil and land cover classes for the base line (1961–1990) and future climate scenarios (2071–2100). Sensitivity analysis of the model has been performed to identify the most critical parameters of the watershed. Average monthly calibration (1987–1994) and validation (1995–2000) have been performed using the observed discharge data. Coefficient of determination (R^{2}), Nash–Sutcliffe efficiency (ENS) and root mean square error (RMSE) were used to evaluate the model performance. Calibrated SWAT setup has been used to evaluate the changes in water balance components of future projection over the study area. HadRM3, a regional climatic data, have been used as input of the hydrological model for climate change impact studies. In results, it was found that changes in average annual temperature (+3.25 °C), average annual rainfall (+28 %), evapotranspiration (28 %) and water yield (49 %) increased for GHG scenarios with respect to the base line scenario.

&amp;lt;p&amp;gt;Rainfall plays a critical role in the hydrological cycle, being the main downward forcing. It is well known that rainfall exhibits large variability in space and time due to the storm dynamics and its interaction with the topography. It is a difficult task to reconstruct the rainfall over an area accurately. Rainfall is usually collected through rain gauges, disdrometers, and weather radars. Rain gauges and disdrometers provide quite accurate measurements of rainfall on the ground, but at a single site, while weather radars provide an indication of rainfall field variability in space, even if their use is restricted to plain areas.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Recently, unconventional observations have been considered for the monitoring of rainfall. These consist in signal attenuation measurements induced by rain on a mesh of point-to-point commercial microwave links (CML). These data, integrated with the ones collected by a network of conventional rain gauges, can provide further information about rainfall dynamics leading to improvements in hydrological modelling, which requires accurate description of the rainfall field.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The work we are going to describe is part of MOPRAM (MOnitoring Precipitation through a Network of RAdio links at Microwaves), a scientific project funded by Fondazione Cariplo (see also the EGU abstract of Nebuloni et al., 2020). Here we use rainfall data, obtained both from a rain gauge network and from signal attenuation measurements, into a hydrological model in order to evaluate the improvement in the hydrological modelling due to a better description of the rainfall field. We consider a semi-distributed rainfall-runoff model and we apply it to the Mallero catchment (Western Rhaetian Alps, Northern Italy), with the outlet located in Sondrio. This catchment is equipped with 13 microwave links and a network of 13 rain gauges.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Firstly, we implement and test the Rain field Reconstruction Algorithm (RRA), which retrieves the 2D rainfall field from CML data through a tomographic inversion technique, developed by D&amp;amp;#8217;Amico et al., 2016. By RRA we generate synthetic rainfall maps from attenuation data measured by 13 links located in the Mallero basin, for a few historical events in the period 2016-2019. To improve the accuracy of rainfall field reconstruction, we also integrate the reconstructed maps with on ground data from 13 rain gauges. These maps are used as input to the hydrological rainfall-runoff model. Finally, we compare the observed discharge with the calculated one using the hydrological model and different rainfall inputs.&amp;lt;/p&amp;gt;