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Evaluation of the effects of climate change on urban river flow has traditionally depended on design rainfall data due to the scarcity of other data sources. However, high-resolution precipitation data, both temporally and spatially, have become accessible from regional climate models. Despite this availability, few studies have tapped into such high-resolution data to analyze urban river flow systems. This study incorporates an event-based storm runoff and inundation simulation with data from a regional climate model. This model offers a spatial resolution of 5 km and a temporal resolution of 10 min for both current and projected future climate scenarios. Rainfall analysis reveals that the 20-year probability of an hourly rainfall of 75 mm, specific to Tokyo, decreases to around 13 years for the period 2016–2035 and further to approximately 8 years for 2076–2095. This trend indicates a growing frequency of heavy rainfall events. Moreover, flood discharge analysis shows an increased downstream flow during such rainfall events if reservoir capacities remain unchanged. Flood risk assessment underscores a significant threat: areas inundated to depths of 25 cm or more may increase by 1.25 times for 2016–2035 and by 1.55 times for the period 2076–2095 when compared to current conditions.

Accurate rainfall monitoring is critical for sustainable societies and yet challenging in many ways. Opportunistic monitoring using commercial microwave links (CMLs) in telecommunication networks is emerging as a powerful complement to conventional gauges and weather radar. However, CML data are often inaccessible or incomplete, which limits research and application. Here, we aim to reduce this barrier by openly sharing data at 10 s resolution with true coordinates from a pilot study involving 364 bi-directional CMLs in Gothenburg, Sweden. To enable further comparative analyses, we also share high-resolution data from 11 precipitation gauges and the Swedish operational weather radar composite in the area. This article presents an overview of the data, including the collection approach, descriptive statistics, and a case study of a high-intensity event. The results show that the data collection was very successful, providing near-complete time series for the CMLs (99.99 %), gauges (100 %), and radar (99.6 %) in the study period (June–August 2015). The bandwidth consumed during CML data collection was small, and hence, the telecommunication traffic was not significantly affected by the collection. The gauge records indicate that total rainfall was approximately 260 mm in the study period, with rainfall occurring in 6 % of each 15 min interval. One of the most intense events was observed on 28 July 2015, during which the Torslanda gauge recorded a peak of 1.1 mm min−1. The variability in the CML data generally followed the gauge dynamics very well. Here we illustrate this for 28 July, where a nearby CML recorded a drop in received signal level of about 27 dB at the time of the peak. The radar data showed a good distribution of reflectivities for mostly stratiform precipitation but also contained some values above 40 dBZ, which is commonly seen as an approximate threshold for convective precipitation. Clutter was also found and was mostly prevalent around low reflectivities of −15 dBZ. The data are accessible at https://doi.org/10.5281/zenodo.7107689 (Andersson et al., 2022). We believe this Open sharing of high-resolution data from Microwave links, Radar, and Gauges (OpenMRG) will facilitate research on microwave-based environmental monitoring using CMLs and support the development of multi-sensor merging algorithms.

It is well established that using kilometer scale grid resolution for simulations of weather systems in weather and climate models enhances their realism. This study explores heavy- and extreme-precipitation characteristics over the Nordic region generated by the regional climate model HARMONIE-Climate (HCLIM). Two model setups of HCLIM are used: ERA-Interim-driven HCLIM12 spanning over Europe at 12 km grid spacing with a convection parameterization scheme and HCLIM3 spanning over the Nordic region with 3 km grid spacing and explicitly resolved deep convection. The HCLIM simulations are evaluated against a unique and comprehensive set of gridded and in situ observation datasets for the warm season from April to September regarding their ability to reproduce sub-daily and daily heavy-precipitation statistics across the Nordic region. Both model setups are able to capture the daily heavy-precipitation characteristics in the analyzed region. At the sub-daily scale, HCLIM3 clearly improves the statistics of occurrence of the most intense heavy-precipitation events and the amplitude and timing of the diurnal cycle of these events compared to its forcing of HCLIM12. Extreme value analysis shows that HCLIM3 provides added value in capturing sub-daily return levels compared to HCLIM12, which fails to produce the most extreme events. The results indicate clear benefits of the convection-permitting model in simulating heavy and extreme precipitation in the present-day climate, therefore, offering a motivating way forward to investigate the climate change impacts in the region.

Short-duration high-intensity rainfall constitutes a major hydro-meteorological hazard, with impacts such as pluvial (urban) flooding and debris flow. There is a great demand in society for improved information on small-scale rainfall extremes, both in real time (e.g. for early warning) and historically (e.g. for post-flood analysis). Observing this type of events is notoriously difficult, because of their extreme small-scale space-time variability. However, owing to recent advances in weather radar technology as well as integration with ground-based sensors, observational products potentially applicable in this context are now available. In this paper we present a visualization prototype tailored for hydrological risk assessment by using sub-basins as spatial units, by allowing temporal aggregation over different durations (i.e. accumulation periods) and by expressing high rainfall intensities in terms of return period exceedance. The radar-based data is evaluated by comparison with gauge observations and the quality is deemed sufficient for the intended applications. Different stakeholders have shown great interest in the prototype, which is openly accessible online.

High-resolution precipitation observation based on signal attenuation in a Commercial Microwave Link (CML) network is an emerging technique that is becoming more and more used. Commonly, the raw data – line measurements from successive time steps – are mapped onto a grid to estimate precipitation fields with a full spatio-temporal coverage. Assuming the CML-estimated precipitation to be accurate, the attainable resolutions in time and space are primarily dependent on two factors: (i) the spatial distribution of the link network and (ii) the spatial correlation properties of the precipitation. Here we outline a pragmatic method for estimating the optimal resolution based on variogram analysis. The method is demonstrated using a CML network and a representative variogram in Stockholm, Sweden. Conceivable applications include preliminary investigations in cities considering starting CML-based precipitation observations.

Weather radar has become an invaluable tool for monitoring rainfall and studying its link to hydrological response. However, when it comes to accurately measuring small-scale rainfall extremes responsible for urban flooding, many challenges remain. The most important of them is that radar tends to underestimate rainfall compared to gauges. The hope is that by measuring at higher resolutions and making use of dual-polarization radar, these mismatches can be reduced. Each country has developed its own strategy for addressing this issue. However, since there is no common benchmark, improvements are hard to quantify objectively. This study sheds new light on current performances by conducting a multinational assessment of radar's ability to capture heavy rain events at scales of 5 min up to 2 h. The work is performed within the context of the joint experiment framework of project MUFFIN (Multiscale Urban Flood Forecasting), which aims at better understanding the link between rainfall and urban pluvial flooding across scales. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. The top 50 events in a 10-year database of radar data were used to quantify the overall agreement between radar and gauges as well as the bias affecting the peaks. Results show that the overall agreement in heavy rain is fair (correlation coefficient 0.7–0.9), with apparent multiplicative biases on the order of 1.2–1.8 (17  %–44  % underestimation). However, after taking into account the different sampling volumes of radar and gauges, actual biases could be as low as 10 %. Differences in sampling volumes between radar and gauges play an important role in explaining the bias but are hard to quantify precisely due to the many post-processing steps applied to radar. Despite being adjusted for bias by gauges, five out of six radar products still exhibited a clear conditional bias, with intensities of about 1 %–2 % per mmh−1. As a result, peak rainfall intensities were severely underestimated (factor 1.8–3.0 or 44 %–67 %). The most likely reason for this is the use of a fixed Z–R relationship when estimating rainfall rates (R) from reflectivity (Z), which fails to account for natural variations in raindrop size distribution with intensity. Based on our findings, the easiest way to mitigate the bias in times of heavy rain is to perform frequent (e.g., hourly) bias adjustments with the help of rain gauges, as demonstrated by the Dutch C-band product. An even more promising strategy that does not require any gauge adjustments is to estimate rainfall rates using a combination of reflectivity (Z) and differential phase shift (Kdp), as done in the Finnish OSAPOL product. Both approaches lead to approximately similar performances, with an average bias (at 10 min resolution) of about 30 % and a peak intensity bias of about 45 %.

Regional climate model simulations have routinely been applied to assess changes in precipitation extremes at daily time steps. However, shorter sub-daily extremes have not received as much attention. This is likely because of the limited availability of high temporal resolution data, both for observations and for model outputs. Here, summertime depth duration frequencies of a subset of the EURO-CORDEX 0.11∘ ensemble are evaluated with observations for several European countries for durations of 1 to 12 h. Most of the model simulations strongly underestimate 10-year depths for durations up to a few hours but perform better at longer durations. The spatial patterns over Germany are reproduced at least partly at a 12 h duration, but all models fail at shorter durations. Projected changes are assessed by relating relative depth changes to mean temperature changes. A strong relationship with temperature is found across different subregions of Europe, emission scenarios and future time periods. However, the scaling varies considerably between different combinations of global and regional climate models, with a spread in scaling of around 1–10 % K−1 at a 12 h duration and generally higher values at shorter durations.

Hydropower makes up nearly half of Sweden's electrical energy production. However, the distribution of the water resources is not aligned with demand, as most of the inflows to the reservoirs occur during the spring flood period. This means that carefully planned reservoir management is required to help redistribute water resources to ensure optimal production and accurate forecasts of the spring flood volume (SFV) is essential for this. The current operational SFV forecasts use a historical ensemble approach where the HBV model is forced with historical observations of precipitation and temperature. In this work we develop and test a multi-model prototype, building on previous work, and evaluate its ability to forecast the SFV in 84 sub-basins in northern Sweden. The hypothesis explored in this work is that a multi-model seasonal forecast system incorporating different modelling approaches is generally more skilful at forecasting the SFV in snow dominated regions than a forecast system that utilises only one approach. The testing is done using cross-validated hindcasts for the period 1981–2015 and the results are evaluated against both climatology and the current system to determine skill. Both the multi-model methods considered showed skill over the reference forecasts. The version that combined the historical modelling chain, dynamical modelling chain, and statistical modelling chain performed better than the other and was chosen for the prototype. The prototype was able to outperform the current operational system 57 % of the time on average and reduce the error in the SFV by ∼ 6 % across all sub-basins and forecast dates.

Hydropower is a major energy source in Sweden, and proper reservoir management prior to the spring-flood onset is crucial for optimal production. This requires accurate forecasts of the accumulated discharge in the spring-flood period (i.e. the spring-flood volume, SFV). Today's SFV forecasts are generated using a model-based climatological ensemble approach, where time series of precipitation and temperature from historical years are used to force a calibrated and initialized set-up of the HBV model. In this study, a number of new approaches to spring-flood forecasting that reflect the latest developments with respect to analysis and modelling on seasonal timescales are presented and evaluated. Three main approaches, represented by specific methods, are evaluated in SFV hindcasts for the Swedish river Vindelälven over a 10-year period with lead times between 0 and 4 months. In the first approach, historically analogue years with respect to the climate in the period preceding the spring flood are identified and used to compose a reduced ensemble. In the second, seasonal meteorological ensemble forecasts are used to drive the HBV model over the spring-flood period. In the third approach, statistical relationships between SFV and the large-sale atmospheric circulation are used to build forecast models. None of the new approaches consistently outperform the climatological ensemble approach, but for early forecasts improvements of up to 25 % are found. This potential is reasonably well realized in a multi-method system, which over all forecast dates reduced the error in SFV by ∼ 4 %. This improvement is limited but potentially significant for e.g. energy trading.

As the risk of a forest fire is largely influenced by weather, evaluating its tendency under a changing climate becomes important for management and decision making. Currently, biases in climate models make it difficult to realistically estimate the future climate and consequent impact on fire risk. A distribution-based scaling (DBS) approach was developed as a post-processing tool that intends to correct systematic biases in climate modelling outputs. In this study, we used two projections, one driven by historical reanalysis (ERA40) and one from a global climate model (ECHAM5) for future projection, both having been dynamically downscaled by a regional climate model (RCA3). The effects of the post-processing tool on relative humidity and wind speed were studied in addition to the primary variables precipitation and temperature. Finally, the Canadian Fire Weather Index system was used to evaluate the influence of changing meteorological conditions on the moisture content in fuel layers and the fire-spread risk. The forest fire risk results using DBS are proven to better reflect risk using observations than that using raw climate outputs. For future periods, southern Sweden is likely to have a higher fire risk than today, whereas northern Sweden will have a lower risk of forest fire.

The propagation of spatio-temporal errors in precipitation estimates to runoff errors in the output from the conceptual hydrological HBV model was investigated. The study region was the Gimån catchment in central Sweden, and the period year 2002. Five precipitation sources were considered: NWP model (H22), weather radar (RAD), precipitation gauges (PTH), and two versions of a mesoscale analysis system (M11, M22). To define the baseline estimates of precipitation and runoff, used to define seasonal precipitation and runoff biases, the mesoscale climate analysis M11 was used. The main precipitation biases were a systematic overestimation of precipitation by H22, in particular during winter and early spring, and a pronounced local overestimation by RAD during autumn, in the western part of the catchment. These overestimations in some cases exceeded 50% in terms of seasonal subcatchment relative accumulated volume bias, but generally the bias was within ±20%. The precipitation data from the different sources were used to drive the HBV model, set up and calibrated for two stations in Gimån, both for continuous simulation during 2002 and for forecasting of the spring flood peak. In summer, autumn and winter all sources agreed well. In spring H22 overestimated the accumulated runoff volume by ~50% and peak discharge by almost 100%, owing to both overestimated snow depth and precipitation during the spring flood. PTH overestimated spring runoff volumes by ~15% owing to overestimated winter precipitation. The results demonstrate how biases in precipitation estimates may exhibit a substantial space-time variability, and may further become either magnified or reduced when applied for hydrological purposes, depending on both temporal and spatial variations in the catchment. Thus, the uncertainty in precipitation estimates should preferably be specified as a function of both time and space.

The present paper develops linear regression models based on singular value decomposition (SVD) with the aim of downscaling atmospheric variables statistically to estimate average rainfall in the Chikugo River Basin, Kyushu Island, southern Japan, on a 12-hour basis. Models were designed to take only significantly correlated areas into account in the downscaling procedure. By using particularly precipitable water in combination with wind speeds at 850 hPa, correlation coefficients between observed and estimated precipitation exceeding 0.8 were reached. The correlations exhibited a seasonal variation with higher values during autumn and winter than during spring and summer. The SVD analysis preceding the model development highlighted three important features of the rainfall regime in southern Japan: (1) the so-called Bai-u front which is responsible for the majority of summer rainfall, (2) the strong circulation pattern associated with autumn rainfall, and (3) the strong influence of orographic lifting creating a pronounced east-west gradient across Kyushu Island. Results confirm the feasibility of establishing meaningful statistical relationships between atmospheric state and basin rainfall even at time scales of less than one day. Keywords: atmospheric downscaling, precipitation, rainfall, singular value decomposition, southern Japan

Rainfall data of high temporal resolution are required in a multitude of hydrological applications. In the present paper, a temporal rainfall disaggregation model is applied to convert daily time series into an hourly resolution. The model is based on the principles of random multiplicative cascade processes. Its parameters are dependent on (1) the volume and (2) the position in the rainfall sequence of the time interval with rainfall to be disaggregated. The aim is to compare parameters and performance of the model between two contrasting climates with different rainfall generating mechanisms, a semi-arid tropical (Brazil) and a temperate (United Kingdom) climate. In the range of time scales studied, the scale-invariant assumptions of the model are approximately equally well fulfilled for both climates. The model parameters differ distinctly between climates, reflecting the dominance of convective processes in the Brazilian rainfall and of advective processes associated with frontal passages in the British rainfall. In the British case, the parameters exhibit a slight seasonal variation consistent with the higher frequency of convection during summer. When applied for disaggregation, the model reproduces a range of hourly rainfall characteristics with a high accuracy in both climates. However, the overall model performance is somewhat better for the semi-arid tropical rainfall. In particular, extreme rainfall in the UK is overestimated whereas extreme rainfall in Brazil is well reproduced. Transferability of parameters in time is associated with larger uncertainty in the semi-arid climate due to its higher interannual variability and lower percentage of rainy intervals. For parameter transferability in space, no restrictions are found between the Brazilian stations whereas in the UK regional differences are more pronounced. The overall high accuracy of disaggregated data supports the potential usefulness of the model in hydrological applications. Keywords: Rainfall, temporal disaggregation, random cascade, scaling, semi-arid, temperate climate.

Sivakumar et al. (2000a), by employing the correlation dimension method, provided preliminary evidence of the existence of chaos in the monthly rainfall-runoff process at the Gota basin in Sweden. The present study verifies and supports the earlier results and strengthens such evidence. The study analyses the monthly rainfall, runoff and runoff coefficient series using the nonlinear prediction method, and the presence of chaos is investigated through an inverse approach, i.e. identifying chaos from the results of the prediction. The presence of an optimal embedding dimension (the embedding dimension with the best prediction accuracy) for each of the three series indicates the existence of chaos in the rainfall-runoff process, providing additional support to the results obtained using the correlation dimension method. The reasonably good predictions achieved, particularly for the runoff series, suggest that the dynamics of the rainfall-runoff process could be understood from a chaotic perspective. The predictions are also consistent with the correlation dimension results obtained in the earlier study, i.e. higher prediction accuracy for series with a lower dimension and vice-versa, so that the correlation dimension method can indeed be used as a preliminary indicator of chaos. However, the optimal embedding dimensions obtained from the prediction method are considerably less than the minimum dimensions essential to embed the attractor, as obtained by the correlation dimension method. A possible explanation for this could be the presence of noise in the series, since the effects of noise at higher embedding dimensions could be significantly greater than that at lower embedding dimensions. Keywords: Rainfall-runoff; runoff coefficient; chaos; phase-space; correlation dimension; nonlinear prediction; noise

The possibility of modelling the temporal structure of rainfall in southern Sweden by a simple cascade model is tested. The cascade model is based on exact conservation of rainfall volume and has a branching number of 2. The weights associated with one branching are 1 and 0 with probability P(1/0), 0 and 1 with P(0/1), and Wx/x, and 1 - Wx/x, 0 < Wx/x, < 1, with P(x/x), where Wx/x is associated with a theoretical probability distribution. Furthermore, the probabilities p are assumed to depend on two characteristics of the rainy time period (wet box) to be branched: rainfall volume and position in the rainfall sequence. In the first step, analyses of 2 years of 8-min data indicates that the model is applicable between approximately 1 hour and 1 week with approximately uniformly distributed Wx/x values. The probabilities P show a clear dependence on the box characteristics and a slight seasonal nonstationarity. In the second step, the model is used to disaggregate the time series from 17- to 1-hour resolution. The model-generated data reproduce well the ratio between rainy and nonrainy periods and the distribution of individual volumes. Event volumes, event durations, and dry period lengths are fairly well reproduced, but somewhat underestimated, as was the autocorrelation. From analyses of power spectrum and statistical moments the model preserves the scaling behaviour of the data. The results demonstrate the potential of scaling-based approaches in hydrological applications involving rainfall disaggregation.

The multifractal properties of a 2-year time series of 8-min rainfall intensity observations are investigated. The empirical probability distribution function suggests a hyperbolic intermittency with divergence of moment of order greater than 2. The power spectrum E(f) of the series obeys a power law form E(f)=f -0.66 in the range of scales 8 min to approximately 3 days. The variation of the average statistical moments with scale shows that the series is characterized by a multifractal behaviour between 8 min and approximately 11 days. The multifractal parameters associated with universality were estimated to be α=0.63 and C1=0,44 by using the Double Trace Moment, DTM, technique. The moment scaling functions obtained from the empirical values and the universal expression are in good agreement in the approximate range 1≤q≤3. Outside of this range, however, differences exist which may be related to either limitations of the data or an inexact estimation of the parameters by DTM. The evident multifractal nature of rainfall time series is encouraging since it may lead to new and improved ways of processing rainfall data used in hydrological calculations.