The relationship between initial soil moisture and floods is well studied in sloping areas, but not in lowland catchments, where the saturated zone, unsaturated zone and surface water are strongly coupled. The aim of this study was to determine the importance of initial groundwater depth (representing soil wetness) on flood peaks in lowland catchments and to examine if and how this affects the magnitude and timing of floods in the future. We used the rainfall-runoff model WALRUS to investigate the relation between initial groundwater depth (48 h before the peak), effective rainfall sum (over the 48 h before the peak) and the resulting peak discharge and peak volume in 12 lowland catchments, for 109 years of forcing in the current climate and four climate scenarios for both 2050 and 2085. We found that this relation is strong in these catchments, with a stronger dependence on initial groundwater depth in flatter catchments. When climate changes, less precipitation and more evapotranspiration are projected in summer, resulting in deeper groundwater in summer and autumn, reducing flood frequency and magnitude. More rain in autumn, winter and spring will lead to more severe floods in winter and spring only. Averaged over all catchments, scenarios and seasons, the effective rainfall sum is projected to increase with 1.5 % in 2050 and 5.6 % in 2085, while the initial groundwater depth increases with 0.7 % in 2050 and 0.3 % in 2085. This combination leads to more frequent and severe floods, with 1 % more floods and 3 % larger peak volumes in 2050 and 9 % more floods and 21 % larger peak volumes in 2085. Without the mitigating effect of the deeper initial groundwater tables, the higher rainfall sums would have led to more frequent and more severe floods in the future.

The erosion-safe mode (ESM) is a novel mitigation strategy that reduces rainfall-induced erosion damage by lowering the tip-speed of the turbine during precipitation events. The ESM requires accurate information about future expected rainfall for its control. In current research, it is debated what method or source should be used to this end. This study explores the effectiveness of driving the ESM using a state-of-the-art weather-radar-based probabilistic rainfall nowcast provided by the Royal Netherlands Meteorological Institute (KNMI). The performance of the nowcast is assessed for various lead times with an impingement-based damage model for three sample sites in the Netherlands and for two distinct ESM strategies. The results show that the quality of the nowcast degrades with increasing lead times, where the 5- and 15-minute lead times exhibit sufficiently good accuracy and response time for adjusting turbine speeds. Overall, the results highlight that the probabilistic information in the nowcast can be employed to improve the efficiency and viability of the ESM.

Nowcasting leverages real-time atmospheric conditions to forecast weather over short periods. State-of-the-art models, including PySTEPS, encounter difficulties in accurately forecasting extreme weather events because of their unpredictable distribution patterns. In this study, we design a physics-informed neural network to perform precipitation nowcasting using the precipitation and meteorological data from the Royal Netherlands Meteorological Institute (KNMI). This model draws inspiration from the novel Physics-Informed Discriminator GAN (PID-GAN) formulation, directly integrating physics-based supervision within the adversarial learning framework. The proposed model adopts a GAN structure, featuring a Vector Quantization Generative Adversarial Network (VQ-GAN) and a Transformer as the generator, with a temporal discriminator serving as the discriminator. Our findings demonstrate that the PID-GAN model outperforms numerical and SOTA deep generative models in terms of precipitation nowcasting downstream metrics.

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 a crucial component of seamless prediction systems.

To assess the potential of radar rainfall nowcasting for early warning, nowcasts for 659 events were used to construct discharge forecasts for 12 Dutch catchments. Four open-source nowcasting algorithms were tested: Rainymotion Sparse (RM-S), Rainymotion DenseRotation (RM-DR), Pysteps deterministic (PS-D), and probabilistic (PS-P) with 20 ensemble members. As benchmark, Eulerian Persistence (EP) and zero precipitation input (ZP) were used. For every 5-min step in the available nowcasts, a discharge forecast with a 12-hr forecast horizon was constructed. Simulations using the observed radar rainfall were used as reference. Rainfall and discharge forecast errors were found to increase with both increasing rainfall intensity and spatial variability. For the discharge forecasts, this relationship depends on the initial conditions, as the forecast error increases more quickly with rainfall intensity when the groundwater table is shallow. Overall, discharge forecasts using RM-DR, PS-D, and PS-P outperform the other methods. Threshold exceedance forecasts were assessed by using the maximum event discharge as threshold. Compared to benchmark ZP, an exceedance is, on average, forecast 223 (EP), 196 (RM-S), 213 (RM-DR), 119 (PS-D), and 143 min (PS-P) in advance. The EP results are counterbalanced by both a high false alarm ratio (FAR) and inconsistent forecasts. Contrarily, PS-D and PS-P produce lower FAR and inconsistency index values than all other methods. All methods advance short-term discharge forecasting compared to no rainfall forecasts at all, though all have shortcomings. As forecast rainfall volumes are a crucial factor in discharge forecasts, a future focus on improving this aspect in nowcasting is recommended.

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 hydrological model simulations. Discharge simulations using the QPE from CARROTS outperform those with the MFB-adjusted product for all but one basin. Moreover, the proposed factor derivation method is robust. It is hardly sensitive to leaving individual years out of the historical set and to the moving window length, given window sizes of more than a week.

Accurate and timely precipitation forecasts are crucial for early warning. Rainfall nowcasting, the process of statistically extrapolating recent rainfall observations, is increasingly used for short-term forecasting. Nowcasts are generally constructed with high-resolution radar observations. As a proof of concept, we construct nowcasts with country-wide rainfall maps estimated from signal level data of commercial microwave links (CMLs) for 12 summer days in the Netherlands. CML nowcasts compare well to radar rainfall nowcasts. Provided well-calibrated CML rainfall estimates are employed, CML nowcasts can outperform unadjusted real-time radar nowcasts for high rainfall rates, which are underestimated as compared to a reference. Care should be taken with the sensitivity of the advection field derivation to areas with low CML coverage and the inherent measurement scale of CML data, which can be larger than the application scale. We see potential for rainfall nowcasting with CML data, for example, in regions where weather radars are absent.

Radar rainfall nowcasting, the process of statistically extrapolating the most recent rainfall observation, is increasingly used for very short range rainfall forecasting (less than 6 hr ahead). We performed a large-sample analysis of 1,533 events, systematically selected for 4 event durations and 12 lowland catchments (6.5–957 km²), to determine the predictive skill of nowcasting. Four algorithms are tested and compared with Eulerian Persistence: Rainymotion Sparse, Rainymotion DenseRotation, Pysteps deterministic, and Pysteps probabilistic with 20 ensemble members. We focus on the dependency of nowcast skill on event duration, season, catchment size, and location. Maximum skillful lead times increase for longer event durations, due to the more persistent character of these events. For all four event durations, Pysteps deterministic attains the longest average decorrelation times, with 25 min for 1-hr durations, 40 min for 3 hr, 56 min for 6 hr, and 116 min for 24 hr. During winter, with more persistent stratiform precipitation, we find three times lower mean absolute errors than for convective summer precipitation. Higher skill is also found after spatially upscaling the forecast. Catchment location matters too: Given the prevailing storm movement, two times higher skillful lead times are found downwind than upwind toward the edge of the domain. In most cases, Pysteps algorithms outperform the Rainymotion benchmark algorithms. We speculate that most errors originate from growth and dissipation processes which are not or only partially (stochastically) accounted for.