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Estuarine salt intrusion causes problems with freshwater availability in many deltas. Water managers require timely and accurate forecasts to be able to mitigate and adapt to salt intrusion. Data-driven models derived with machine learning are ideally suited for this, as they can mimic complex non-linear systems and are computationally efficient. We set up a long short-term memory (LSTM) model to forecast salt intrusion in the Rhine-Meuse delta, the Netherlands. Inputs for this model are chloride concentrations, water levels, discharges and wind speed, measured at nine locations. It forecasts daily minimum, mean and maximum chloride concentrations up to 7 d ahead at Krimpen aan den IJssel, an important location for freshwater provision. The model forecasts baseline concentrations and peak timing well but peak height is underestimated, a problem that becomes worse with increasing lead time. Between lead times of 1 and 7 d, forecast precision declines from 0.9 to 0.7 and forecast recall declines from 0.7 to 0.5 on average. Given these results, we aim to extend the model to other locations in the delta. We expect that a similar setup can work in other deltas, especially those with a similar or simpler channel network., Rivers, Ports, Waterways and Dredging Engineering

Raw and processed data used to build an LSTM model for salt intrusion at Krimpen aan den IJssel in the Rhine-Meuse delta.

Estuarine salt intrusion causes problems with freshwater availability in many deltas. Water managers require timely and accurate forecasts to be able to mitigate and adapt to salt intrusion. Data-driven models derived with machine learning are ideally suited for this, as they can mimic complex non-linear systems and are computationally efficient. We set up a long short-term memory (LSTM) model to forecast salt intrusion in the Rhine–Meuse delta, the Netherlands. Inputs for this model are chloride concentrations, water levels, discharges and wind speed, measured at nine locations. It forecasts daily minimum, mean and maximum chloride concentrations up to 7 d ahead at Krimpen aan den IJssel, an important location for freshwater provision. The model forecasts baseline concentrations and peak timing well but peak height is underestimated, a problem that becomes worse with increasing lead time. Between lead times of 1 and 7 d, forecast precision declines from 0.9 to 0.7 and forecast recall declines from 0.7 to 0.5 on average. Given these results, we aim to extend the model to other locations in the delta. We expect that a similar setup can work in other deltas, especially those with a similar or simpler channel network.

Flash flood early warning requires accurate rainfall forecasts with a high spatial and temporal resolution. As the first few hours ahead are already not sufficiently well captured by the rainfall forecasts of numerical weather prediction (NWP) models, radar rainfall nowcasting can provide an alternative. Because this observation-based method quickly loses skill after the first 2 hr of the forecast, it needs to be combined with NWP forecasts to extend the skillful lead time of short-term rainfall forecasts, which should increase decision-making times. We implemented an adaptive scale-dependent ensemble blending method in the open-source pysteps library, based on the Short-Term Ensemble Prediction System scheme. In this implementation, the extrapolation (ensemble) nowcast, (ensemble) NWP, and noise components are combined with skill-dependent weights that vary per spatial scale level. To constrain the (dis)appearance of rain in the ensemble members to regions around the rainy areas, we have developed a Lagrangian blended probability matching scheme and incremental masking strategy. We describe the implementation details and evaluate the method using three heavy and extreme (July 2021) rainfall events in four Belgian and Dutch catchments. We benchmark the results of the 48-member blended forecasts against the Belgian NWP forecast, a 48-member nowcast, and a simple 48-member linear blending approach. Both on the radar domain and catchment scale, the introduced blending approach predominantly performs similarly or better than only nowcasting (in terms of event-averaged continuous ranked probability score and critical success index values) and adds value compared with NWP for the first hours of the forecast, although the difference, particularly with the linear blending method, reduces when we focus on catchment-average cumulative rainfall sums instead of instantaneous rainfall rates. By properly combining observations and NWP forecasts, blending methods such as these are

Die Onlinehändlerbefragung 2023 (Kapitel 2 bis 5) wurde zum sechsten Mal in Folge durchgeführt und es beteiligten sich 598 Onlineshops im Bereich Business-to-Consumer (B2C; 82 Prozent), Business-to-Business (B2B; 45 Prozent) und Herstellershops (D2C; 14 Prozent). Dabei können Händler sowohl im B2B, als auch im B2C oder D2C gleichzeitig operieren. Neben 441 Schweizer Onlinehändlern (74 Prozent) nahmen 136 österreichische Onlinehändler (23 Prozent) an der repräsentativen Onlinebefragung teil. In den Jahren 2020 und 2021 boomte der Onlinehandel aufgrund der Corona-Krise, die Einkäufe verschoben von offline zu online. Bei fast der Hälfte der Onlineshops geht das Online-Wachstum, meist abgeschwächt, weiter und ist der Online-Umsatz im Jahr 2022 weitergewachsen. Bei der anderen Hälfte hingegen ist der Online-Umsatz im Jahr 2022 im Vorjahresvergleich (leicht) zurück-gegangen und es ist wieder "business as usual" eingekehrt. Neun von zehn Händler geben an, dass «der Corona-E-Commerce-Boom vorbei ist». Zwei Drittel stimmen zu, dass die Konkurrenz online zugenommen hat und daher der Umsatz zurückgegangen ist. Zudem bestätigen fast alle Händler, dass sich die Konsumentenstimmung seit 2022 verschlechterte, u.a. wegen dem Ukraine-Krieg. Drei Viertel glauben, dass die Kaufkraft ihrer Kunden und Kundinnen wegen der Inflation abnahm und dass diese ihr Geld anderswo bzw. anderswie ausgeben, z.B. für Reisen. Weiter stimmt ein Viertel voll und die Hälfte eher zu, dass die Kundschaft wieder verstärkt stationär einkauft und auch deshalb der Umsatz im Onlineshop sank. Erstmals werden in dieser Studienreihe die Anwendungen der Künstlichen Intelligenz (KI) im E-Commerce genauer untersucht. Am häufigsten wird die KI – insbesondere ChatGPT – für die Content-Erstellung im Onlineshop eigesetzt, meist für die Erstellung von Texten, vereinzelt auch für Fotos und Grafiken. Am zweithäufigsten genannt werden automatisierte Produkt- und Angebotsempfehlungen. Auch die Personalisierung von Inhalten, A

Flash flood early warning requires accurate rainfall forecasts with a high spatial and temporal resolution. As the first few hours ahead are already not sufficiently well captured by the rainfall forecasts of numerical weather prediction (NWP) models, radar rainfall nowcasting can provide an alternative. Because this observation-based method quickly loses skill after the first 2 hr of the forecast, it needs to be combined with NWP forecasts to extend the skillful lead time of short-term rainfall forecasts, which should increase decision-making times. We implemented an adaptive scale-dependent ensemble blending method in the open-source pysteps library, based on the Short-Term Ensemble Prediction System scheme. In this implementation, the extrapolation (ensemble) nowcast, (ensemble) NWP, and noise components are combined with skill-dependent weights that vary per spatial scale level. To constrain the (dis)appearance of rain in the ensemble members to regions around the rainy areas, we have developed a Lagrangian blended probability matching scheme and incremental masking strategy. We describe the implementation details and evaluate the method using three heavy and extreme (July 2021) rainfall events in four Belgian and Dutch catchments. We benchmark the results of the 48-member blended forecasts against the Belgian NWP forecast, a 48-member nowcast, and a simple 48-member linear blending approach. Both on the radar domain and catchment scale, the introduced blending approach predominantly performs similarly or better than only nowcasting (in terms of event-averaged continuous ranked probability score and critical success index values) and adds value compared with NWP for the first hours of the forecast, although the difference, particularly with the linear blending method, reduces when we focus on catchment-average cumulative rainfall sums instead of instantaneous rainfall rates. By properly combining observations and NWP forecasts, blending methods such as these, Water Resources

This paper presents the results of a survey, conducted in 2020, to gauge the current standing of digital analytics in Germany, Switzerland and Austria. The findings highlight the increasing maturity level, benefits, challenges and future developments in the field. The research confirms that digital analytics supports the analysis and optimisation of digital marketing campaigns, user experience, search engine marketing and data-driven decisions. For many of the companies analysed, the most important challenges were reported to be data quality, lack of skills and data culture; however, maturity level, capabilities, agility and professionalism in digital analytics are steadily increasing, and artificial intelligence is enjoying many new and different applications within the fields of sales and marketing. These findings suggest that companies in German-speaking countries should focus on improving data quality and data culture.

Real-time radar quantitative precipitation estimations (QPEs) generally show significant biases from the true rainfall amount. Despite the abundant number of adjustment methods, the absence of a timely reporting high-density rain gauge network limits the use of these methods. This especially holds for more advanced geostatistical and Bayesian methods that can also correct the radar QPE in space. As an alternative, we present CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a gridded climatological QPE correction product, which corrects the radar QPE both in space and time. The publicly available CARROTS factors are based on a historical set of 10 years of 5-min radar and reference rainfall data from KNMI, which makes CARROTS independent of real-time rain gauge availability. We tested the CARROTS factors on the resulting corrected radar QPE and subsequent discharge simulations for 12 Dutch catchments. We regarded the mean field bias (MFB) adjustment method as benchmark in this study. This real-time adjustment method determines spatially uniform adjustment factors based on the 32 automatic rain gauges of KNMI and is operationally used in the Netherlands. The CARROTS factors show clear spatial and temporal patterns. From December through March, the factors are higher than in other seasons, which is likely a result of sampling above the melting layer during these months. Compared to the unadjusted radar QPE, both adjustment methods significantly improve the estimated rainfall sums, but annual rainfall sums from CARROTS outperform the MFB-adjusted QPE for catchments in the south and east of the Netherlands. In these regions, the MFB-adjusted QPE still underestimates the rainfall amounts. Differences in the rainfall estimations are amplified in the discharge simulations, where CARROTS outperforms the simulations with the MFB-adjusted product for all but one basin. Concluding, CARROTS can be a benchmark for QPE adjustment method develop, Real-time kwantitatieve neerslagschattingen (QPE) op basisvan radarproducten hebben over het algemeen significante afwijkingen ten opzichte van de werkelijke neerslaghoeveelheden aan de grond. Hoewel er veel correctiemethoden beschikbaar zijn, zijn de huidige methoden afhankelijk van een dicht regenmeternetwerk dat tijdig metingen levert. Als de dichtheid van het netwerk onvoldoende is, zijn geavanceerde correctiemethoden die de radar QPE ook ruimtelijk corrigeren, niet bruikbaar. Als alternatief presenteren we CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting): een gegridde, klimatologische QPE-correctie die zowel in de tijd als in de ruimte kan corrigeren. De publiekelijk beschikbare correctiefactoren zijn gebaseerd op 10 jaar (2009 – 2018) aan historische 5-min radar QPE en referentiedata van het KNMI, wat de methode onafhankelijk maakt van de real-time regenmeterbeschikbaarheid. We hebben CARROTS getest met betrekking tot de gecorrigeerde neerslagschattingen die er uit volgen en de daarmee uitgevoerde afvoersimulaties voor twaalf stroomgebieden in Nederland. Hierbij hebben we de mean field bias (MFB) correctiemethode als referentie gebruikt. De MFB-methode wordt operationeel gebruikt door het KNMI en leidt ruimtelijk uniforme correctiefactoren af in real time, gebruik makend van de 32 automatische regenmeters van KNMI. De CARROTS-factoren hebben een duidelijk ruimtelijk en temporeel patroon met hogere factoren verder van de radars af en hogere factoren van december tot en met maart dan in de overige seizoenen. Dat laatste lijkt een gevolg te zijn van radarobservaties boven de smeltlaag gedurende deze maanden. Vergeleken met de ongecorrigeerde radar QPE verbeteren beide correctiemethoden de neerslagschattingen aanzienlijk. De jaarsommen met CARROTS liggen echter wel dichter bij de referentie dan die van de MFB-methode voor Zuid- en Oost-Nederland, omdat het MFB-gecorrigeerde product hier de neerslagvolumes onderschat. Deze versch

Although shallow ((Formula presented.) 1.5 m) soil water storage has been extensively studied, the significance of deeper unsaturated zone water storage to flow generation is poorly documented. However, a limited but growing body of empirical work shows that the weathered bedrock vadose zone, not soil, stores the majority of plant available water in many seasonally dry and semi-arid landscapes. Moreover, this storage dynamic mediates recharge to hillslope groundwater systems that generate stream discharge and support ecologically significant baseflows. Explicit representations of bedrock vadose zone processes are rarely incorporated into runoff models, due in part to a paucity of observations that can constrain simulations. Here, we develop a simple representation of the weathered bedrock vadose zone that is guided by in situ field observations. We incorporate this representation into a rainfall-runoff model, and calibrate it on streamflow alone, on rock moisture (i.e., weathered bedrock vadose zone moisture) alone, and on both using the concept of Pareto optimality. We find that the model is capable of accurately simultaneously simulating dynamics in rock moisture and streamflow, in terms of Kling-Gupta Efficiency, when using Pareto optimal parameter sets. Calibration on streamflow alone, however, is insufficient to accurately simulate rock moisture dynamics. We further find that the posterior distributions of some model parameters are sensitive to choice of calibration scenario. The posterior distribution of high-performing model parameters resulting from the streamflow only calibration scenario include physically unrealistic values that are not yielded by the rock moisture only or Pareto calibration strategies. These results suggest that the accuracy of some model results can be increased and parameter uncertainty decreased via incorporation of rock moisture data in calibration, without sacrificing streamflow simulation quality. Emerging recognition of the global

The Swiss Online Retailer Survey 2022 is the fifth online retail survey the first author has conducted. A total of 625 online retailers participated in it, including business-to-consumer (88%), business-to-business (43%), and direct-to-consumer (13%) companies. In addition to 496 Swiss retailers, 109 Austrian online retailers also took part in the representative survey. After the boom in e-commerce in 2020 and 2021 due to the corona pandemic, normality has returned for many retailers. A large proportion of the online retailers surveyed mentioned that the lifting of the Coronavirus measures had no impact on their online business. Some report that they have seen a slight to strong decline in online sales and fewer online orders compared to the previous year. The study confirmed that online retailers are very broadly positioned in distribution and often sell through multiple sales channels such as online shops, physical stores, face-to-face sales and over-the-phone sales. Digital sales channels such as digital marketplaces (e.g., Digitec Galaxus and Amazon) and social commerce (e.g., Facebook marketplace and Facebook and Instagram shops) continue to grow. The results show that omnichannel retailers often offer both a broader range (with more product categories) and a deeper assortment (with more products in one category) online than in their physical stores. In the 2022 survey, the authors examined the success factors in e-commerce were examined for the first time: most frequently cited was the quality of the products or services offered, followed by their exclusivity. The analyses show that large, professional, and successful online shop operators are much more customer-oriented than the small and unsuccessful ones, and that they attach greater importance to user-friendliness, i.e., an optimal user experience in the online shop. A/B and multivariate tests are therefore used much more frequently by the successful and larger online shops than by the smaller ones. In dig

Die Onlinehändlerbefragung 2022 wurde zum fünften Mal in Folge durchgeführt und es beteiligten sich 625 Onlineshops im Bereich Business-to-Consumer (88 Prozent), Business-to-Business (43 Prozent) und Herstellershops (13 Prozent). Neben 496 Schweizer Onlinehändlern nahmen 109 österreichische Onlinehändler an der repräsentativen Onlinebefragung teil. Nachdem der Onlinehandel in den Jahren 2020 und 2021 aufgrund der Corona-Krise boomte, ist bei vielen Händlern wieder Normalität eingekehrt. Ein Grossteil der befragten Onlinehändler erwähnte, dass die Aufhebung der Corona-Massnahmen keine Auswirkungen auf das Onlinegeschäft hatten. Ein Viertel berichten, dass sie im Vorjahresvergleich einen leichten Umsatzrückgang und weniger Bestellungen verzeichneten. Die Studie bestätigte, dass die Onlinehändler im Vertrieb sehr breit aufgestellt sind und häufig über mehrere Verkaufskanäle wie Onlineshops, Ladengeschäfte, persönlicher Verkauf vor Ort und über Telefon verkaufen. Digitale Vertriebskanäle wie digitale Marktplätze (z. B. Digitec Galaxus und Amazon) und Social Commerce (v. a. Facebook Shops und Instagram Shops) gewinnen an Popularität. Die Ergebnisse zeigen, dass Omnichannel-Händler online häufig ein breiteres Sortiment (mit mehr Produktkategorien) und auch tieferes Sortiment (mit mehr Produkten einer Kategorie) anbieten als der stationäre Handel. Erstmals wurden die Erfolgsfaktoren im E-Commerce genauer untersucht: Die Qualität der angebotenen Produkte oder Dienstleistungen wurde mit Abstand am häufigsten genannt, gefolgt von deren Exklusivität. Die Analysen zeigen, dass grosse und professionell aufgestellte Onlineshop-Betreiber nach Eigenaussage häufig kundenorientierter sind als die kleinen, und dass sie der Benutzerfreundlichkeit, sprich einer optimalen User Experience im Onlineshop, eine höhere Bedeutung zumessen. A/B- und multivariate Tests werden daher von den erfolgreichen und grösseren Onlineshops viel häufiger eingesetzt als von den kleinen. Sie optimieren im Dig

The presence of significant biases in real-time radar quantitative precipitation estimations (QPEs) limits its use in hydrometeorological forecasting systems. Here, we introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors, which vary per grid cell and day of the year. The factors are based on a historical set of 10 years of 5ĝ€¯min radar and reference rainfall data for the Netherlands. CARROTS is both operationally available and independent of real-time rain gauge availability and can thereby provide an alternative to current QPE adjustment practice. In addition, it can be used as benchmark for QPE algorithm development. We tested this method on the resulting rainfall estimates and discharge simulations for 12 Dutch catchments and polders. We validated the results against the operational mean field bias (MFB)-adjusted rainfall estimates and a reference dataset. This reference consists of the radar QPE, that combines an hourly MFB adjustment and a daily spatial adjustment using observations from 32 automatic and 319 manual rain gauges. Only the automatic gauges of this network are available in real time for the MFB adjustment. The resulting climatological correction factors show clear spatial and temporal patterns. Factors are higher away from the radars and higher from December through March than in other seasons, which is likely a result of sampling above the melting layer during the winter months. The MFB-adjusted QPE outperforms the CARROTS-corrected QPE when the country-average rainfall estimates are compared to the reference. However, annual rainfall sums from CARROTS are comparable to the reference and outperform the MFB-adjusted rainfall estimates for catchments away from the radars, where the MFB-adjusted QPE generally underestimates the rainfall amounts. This difference is absent for catchments closer to the radars. QPE underestimations are amplified when used in the hydrolog, Water Resources

Streamflow is often the only variable used to evaluate hydrological models. In a previous international comparison study, eight research groups followed an identical protocol to calibrate 12 hydrological models using observed streamflow of catchments within the Meuse basin. In the current study, we quantify the differences in five states and fluxes of these 12 process-based models with similar streamflow performance, in a systematic and comprehensive way. Next, we assess model behavior plausibility by ranking the models for a set of criteria using streamflow and remote-sensing data of evaporation, snow cover, soil moisture and total storage anomalies. We found substantial dissimilarities between models for annual interception and seasonal evaporation rates, the annual number of days with water stored as snow, the mean annual maximum snow storage and the size of the root-zone storage capacity. These differences in internal process representation imply that these models cannot all simultaneously be close to reality. Modeled annual evaporation rates are consistent with Global Land Evaporation Amsterdam Model (GLEAM) estimates. However, there is a large uncertainty in modeled and remote-sensing annual interception. Substantial differences are also found between Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled number of days with snow storage. Models with relatively small root-zone storage capacities and without root water uptake reduction under dry conditions tend to have an empty root-zone storage for several days each summer, while this is not suggested by remote-sensing data of evaporation, soil moisture and vegetation indices. On the other hand, models with relatively large root-zone storage capacities tend to overestimate very dry total storage anomalies of the Gravity Recovery and Climate Experiment (GRACE). None of the models is systematically consistent with the information available from all different (remote-sensing) data sources. Yet we di, Water Resources

The data are the hydrological model results of 12 models calibrated to the Ourthe catchment at Tabreux in the Belgian Ardennes. It provides hourly modeled streamflow, states and fluxes results for a selection of feasible parameter sets applied to 5 catchments (Ourthe at Tabreux, Ourthe Orientale at Mabompré, Ourthe Occidentale at Ortho, Semois at Membre-Pont, Lesse at Gendron). The models were calibrated by several institutes and universities working on the Meuse basin and gathering at the Meuse International Symposium in Liège. The data were used for the study Behind the scenes of streamflow performance by Bouaziz et al., 2021, HESS., The data are the hydrological model results of 12 models calibrated to the Ourthe catchment at Tabreux in the Belgian Ardennes. It provides hourly modeled streamflow, states and fluxes results for a selection of feasible parameter sets applied to 5 catchments (Ourthe at Tabreux, Ourthe Orientale at Mabompré, Ourthe Occidentale at Ortho, Semois at Membre-Pont, Lesse at Gendron). The models were calibrated by several institutes and universities working on the Meuse basin and gathering at the Meuse International Symposium in Liège. The data were used for the study Behind the scenes of streamflow performance by Bouaziz et al., 2021, HESS.

This repository contains the radar rainfall nowcasts for 1,533 rainfall events spread over 12 basins in the Netherlands, from Imhoff et al. (2020). The basins, a combination of freely draining catchments and polder areas, are: Aa, Beemster, Delfland, Dwarsdiep, Grote Waterleiding, Hupsel, Linde, Luntersebeek, Regge, Reusel, Rijnland and Roggelsebeek. More catchment information can be found in figure 1 in Imhoff et al. (2020). The nowcasts were run with the radar composite of the Royal Netherlands Meteorological Institute for the period 2008 – 2018. Subsequently, the nowcasts were clipped to the extent of one of the basins and saved.

This dataset contains gridded adjustment factors for correction of the Quantitative Precipitation Estimations (QPE) of the two operational C-band weather radars operated by the Royal Netherlands Meteorological Institute (KNMI). The factors are based on the CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting) method, described in Imhoff et al. (2021). The factors are available for every yearday (temporal resolution of one day) and are based on ten years (2009 - 2018) of radar and reference rainfall data, as distributed by KNMI. For the derivation of the factors, both the operational radar QPE (https://doi.org/10.4121/uuid:05a7abc4-8f74-43f4-b8b1-7ed7f5629a01) and a reference rainfall dataset of KNMI (https://dataplatform.knmi.nl/catalog/datasets/index.html?x-dataset=rad_nl25_rac_mfbs_em_5min&&x-dataset-version=2.0) are used. The reference is not available in real time, but becomes available with a one to two month delay and was therefore available for this climatological factor derivation. The derivation method was as follows per grid cell in the radar domain (Imhoff et al., 2021): 1. For every day in the period 2009--2018, an accumulation took place of all 5-min rainfall sums (of both the unadjusted radar QPE and the reference) within a moving window of 15 days prior to and 15 days after the day of interest. 2. For every yearday, the accumulations (per day) from the previous step were averaged over the ten years. 3. Gridded climatological adjustment factors (Fclim) were calculated per yearday as: Fclim(i,j) = RA(i,j) / RU(i,j). In this equation, RA(i,j) is the reference rainfall sum for the ten years and RU(i,j) the operationally available unadjusted radar QPE sum, based on the previous two steps, at grid cell (i, j). For more details about the method, see Imhoff et al. (2021). For more information about the reference dataset, which consists of the radar QPE spatially adjusted with observations from 31 automatic and 325 manual rain ga

Streamflow is often the only variable used to evaluate hydrological models. In a previous international comparison study, eight research groups followed an identical protocol to calibrate 12 hydrological models using observed streamflow of catchments within the Meuse basin. In the current study, we quantify the differences in five states and fluxes of these 12 process-based models with similar streamflow performance, in a systematic and comprehensive way. Next, we assess model behavior plausibility by ranking the models for a set of criteria using streamflow and remote-sensing data of evaporation, snow cover, soil moisture and total storage anomalies. We found substantial dissimilarities between models for annual interception and seasonal evaporation rates, the annual number of days with water stored as snow, the mean annual maximum snow storage and the size of the root-zone storage capacity. These differences in internal process representation imply that these models cannot all simultaneously be close to reality. Modeled annual evaporation rates are consistent with Global Land Evaporation Amsterdam Model (GLEAM) estimates. However, there is a large uncertainty in modeled and remote-sensing annual interception. Substantial differences are also found between Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled number of days with snow storage. Models with relatively small root-zone storage capacities and without root water uptake reduction under dry conditions tend to have an empty root-zone storage for several days each summer, while this is not suggested by remote-sensing data of evaporation, soil moisture and vegetation indices. On the other hand, models with relatively large root-zone storage capacities tend to overestimate very dry total storage anomalies of the Gravity Recovery and Climate Experiment (GRACE). None of the models is systematically consistent with the information available from all different (remote-sensing) data sources. Yet we did n

Following the rise of R as a scientific programming language, the increasing requirement for more transferable research and the growth of data availability in hydrology, R packages containing hydrological models are becoming more and more available as an open-source resource to hydrologists. Corresponding to the core of the hydrological studies workflow, their value is increasingly meaningful regarding the reliability of methods and results. Despite package and model distinctiveness, no study has ever provided a comparison of R packages for conceptual rainfall-runoff modelling from a user perspective by contrasting their philosophy, model characteristics and ease of use. We have selected eight packages based on our ability to consistently run their models on simple hydrology modelling examples. We have uniformly analysed the exact structure of seven of the hydrological models integrated into these R packages in terms of conceptual storages and fluxes, spatial discretisation, data requirements and output provided. The analysis showed that very different modelling choices are associated with these packages, which emphasises various hydrological concepts. These specificities are not always sufficiently well explained by the package documentation. Therefore a synthesis of the package functionalities was performed from a user perspective. This synthesis helps to inform the selection of which packages could/should be used depending on the problem at hand. In this regard, the technical features, documentation, R implementations and computational times were investigated. Moreover, by providing a framework for package comparison, this study is a step forward towards supporting more transferable and reusable methods and results for hydrological modelling in R.

The presence of significant biases in real-time radar quantitative precipitation estimations (QPEs) limits its use in hydrometeorological forecasting systems. Here, we introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors, which vary per grid cell and day of the year. The factors are based on a historical set of 10 years of 5ĝ€¯min radar and reference rainfall data for the Netherlands. CARROTS is both operationally available and independent of real-time rain gauge availability and can thereby provide an alternative to current QPE adjustment practice. In addition, it can be used as benchmark for QPE algorithm development. We tested this method on the resulting rainfall estimates and discharge simulations for 12 Dutch catchments and polders. We validated the results against the operational mean field bias (MFB)-adjusted rainfall estimates and a reference dataset. This reference consists of the radar QPE, that combines an hourly MFB adjustment and a daily spatial adjustment using observations from 32 automatic and 319 manual rain gauges. Only the automatic gauges of this network are available in real time for the MFB adjustment. The resulting climatological correction factors show clear spatial and temporal patterns. Factors are higher away from the radars and higher from December through March than in other seasons, which is likely a result of sampling above the melting layer during the winter months. The MFB-adjusted QPE outperforms the CARROTS-corrected QPE when the country-average rainfall estimates are compared to the reference. However, annual rainfall sums from CARROTS are comparable to the reference and outperform the MFB-adjusted rainfall estimates for catchments away from the radars, where the MFB-adjusted QPE generally underestimates the rainfall amounts. This difference is absent for catchments closer to the radars. QPE underestimations are amplified when used in the hydrologica