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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. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Flood warnings in quickly responding catchments are still challenging, as the timing and location of the rainfall forecast should be as accurate as possible. Radar rainfall nowcasting, a technique to statistically extrapolate the most recent rainfall observations, can potentially improve the rainfall forecasts up to several hours ahead. To evaluate the benefits and possible pitfalls of radar rainfall nowcasting for hydrological forecasting, we tested four different nowcasting algorithms for 659 rainfall events in the Netherlands. We used these nowcasts for the construction of discharge forecasts for 12 Dutch lowland catchments. Based on the evaluation of the large sample of discharge forecasts for these catchments, we conclude that all nowcasting methods advance short‐term discharge forecasting compared to no rainfall forecasts at all. Nevertheless, all techniques have shortcomings. Rainfall and discharge forecast errors increase with increasing rainfall intensity and spatial variability, though discharge forecast errors also strongly depend on the initial catchment wetness. Moreover, forecast rainfall volumes have shown to be a crucial factor in the quality of the discharge forecast. Hence, rainfall nowcasting can be a valuable addition to hydrological forecasting systems. Yet we recommend a future focus on improving area‐averaged rainfall volumes in nowcasting algorithms to further advance hydrological forecasting. Key Points: The potential of nowcasting for discharge forecasting was evaluated with 659 rain events spread over 12 lowland catchmentsDischarge forecast errors depend on initial catchment wetness and increase with both rainfall intensity and spatial variabilityDischarge exceedance thresholds can be on average forecast between 119 and 223 min earlier than with no rainfall forecast at all [ABSTRACT FROM AUTHOR]

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 5 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. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Capturing the amount, timing and location of rainfall are of key importance for water management and the agricultural sector. Nowcasting of rainfall, a computational approach where the most recent rainfall observations are spatially extrapolated, is increasingly used for short‐term rainfall forecasts. The required rainfall observations normally originate from weather radars, but it has previously been demonstrated that rainfall can also be estimated from rain‐induced signal attenuation from the commercial microwave links (CMLs) in cellular telecommunication networks, which are used worldwide. In this study, we use such estimates to construct rainfall nowcasts for 12 summer events in the Netherlands, which we compare to nowcasts using operational radars. The results demonstrate that there is potential for using rainfall estimates from CML data to nowcast rainfall up to a few hours ahead, for example, in (low‐ and middle‐income) regions where high‐resolution rainfall observations or weather radars are absent. Key Points: Probabilistic rainfall nowcasts are constructed with rainfall estimates from commercial microwave linksThe nowcasts are compared to radar rainfall nowcasts for 12 summer daysNowcast skill depends on link network density and is generally comparable to that of radar rainfall nowcasts [ABSTRACT FROM AUTHOR]

For operational water management in lowlands and polders (for instance, in the Netherlands), lowland hydrological models are used for flow prediction, often as an input for a real‐time control system to steer water with pumps and weirs to keep water levels within acceptable bounds. Therefore, proper initialization of these models is essential. The ensemble Kalman filter (EnKF) has been widely used due to its relative simplicity and robustness, while the unscented Kalman filter (UKF) has received little attention in the operational context. Here, we test both UKF and EnKF using a lowland lumped hydrological model. The results of a reforecast experiment in an operational context using an hourly time step show that when using nine ensemble members, both filters can improve the accuracy of the forecast by updating the state of a lumped hydrological model (Wageningen Lowland Runoff Simulator, WALRUS) based on the observed discharge, while UKF has achieved better performance than EnKF. Additionally, we show that an increase in the ensemble members does not necessarily mean a significant increase in performance. WALRUS model with either UKF or EnKF could be considered for hydrological forecasting for supporting water management of polders and lowlands, with UKF being the computationally leaner option. Key Points: The ensemble Kalman filter and unscented Kalman filter are utilized to improve forecast skill of a hydrological model by state estimationBoth approaches have improved the forecast performance, with unscented Kalman filter being the computationally leaner optionThe unscented Kalman filter can be considered as a new and effective option to assimilate observations into low‐dimensional models [ABSTRACT FROM AUTHOR]

Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters. [ABSTRACT FROM AUTHOR]

Abstract: Deltaic land inundated by storm surges may reform by sedimentation from natural or human-induced river diversions. This is a well-known trigger mechanism for creation of new channels in coastal plains and deltas, which may develop into main channels and lead to abandonment of older (avulsion), particularly in the downstream parts of deltas that host tidal rivers. These new channels develop as part of deltaic splay complexes that heal initial diversion scars and fill up flooded basins at a certain pace. We study a case with excellent historical and geological data of a diversion of the river Rhine following catastrophic inundations (1421–1424AD) into medieval reclaimed land. Numerical modelling of deltaic splay and channel development is combined with reconstructions from historical maps and geological data. This yields detailed insight in pacing of splay sedimentation and changing hydrodynamics in the channel upstream of the diversion in the two centuries following the inundation. The equivalent of the full sand budget of the river Rhine was effectively trapped in the developing splay. The tidal-avulsion splay evolution on aspects is similar to that of fluvial crevassing into flood basins documented for settings lacking ‘downstream’ tidal control. The typical small-scale delta-lobe avulsion cycles: mouth bar formation, backward sedimentation, upstream avulsion, channel progradation and mouth bar formation are reproduced in the splay-modelling. The pacing of splay development, however, is relatively fast due to the presence of tides and the water depth in the receiving basin. The diversion had a strong upstream impact, in particular on water levels in the feeding river channel at stages of peak flow. For two centuries levels were significantly raised, because bifurcation-imposed reduced transport capacity and associated sedimentation at the diversion site increased hydraulic roughness and hampered flow. These findings have implications regarding flood mitigation for presently-planned lower-delta engineered diversions. Furthermore, they elucidate the differences between upstream fluvial avulsion and downstream tidal-river avulsion that are important to recognise if we want to understand how deltaic distributary-networks are maintained. [Copyright &y& Elsevier]

The Hennisdijk fluvial system in the central Rhine-Meuse delta is an abandoned Rhine distributary that was active on a wide floodplain from 3800 to 3000 yearsBP. Cross-sectional geometry, lithological characteristics and planform patterns of the channel-belt deposits indicate lateral migration of the Hennisdijk palaeochannel. Channel-belt deposits are around 10 m thick and 200–400 m wide. A gravelly facies near the base of the channel-belt deposits represents channel-lag and lower point-bar deposits. The axis of the channel belt is dominated by a sandy facies (medium and coarse sand), showing an overall fining upward trend with multiple cycles. This facies is interpreted as lower and middle point-bar deposits. The sandy facies is capped by a muddy facies, which is 1–2 m thick near the axis of the channel belt and thickens to 5–6 m along the margins. It laterally interfingers with the sandy facies that occurs near the channel-belt axis, but it has sharp, erosive outer contacts marking the edges of the channel belt. The muddy facies comprises inclined heterolithic stratification (IHS) (fine/medium sand–mud couplets) in its upper part. The relatively thin muddy facies with IHS that occurs near the channel-belt axis is interpreted as upper point-bar deposits with lateral accretion surfaces, formed under marine influence. Along the margins of the channel belt the muddy facies consists of thick, fairly homogeneous, successions of mud with variable sand content, and fine sand. Based on facies geometry and position, this part of the muddy facies is interpreted as counterpoint deposits, formed along the upstream limb of the concave bank of a channel bend. Counterpoint accretion seems to have been associated with the confined nature of the channel belt, which was the result of low stream power (4·5–7·8 W m−2, based on reconstructions of palaeodischarge and channel slope) and cohesive bank material, i.e. clayey floodbasin deposits with intercalated peat beds occurring next to the channel belt. In the literature, counterpoint accretion is mostly reported from alluvial valleys, where meandering is confined by limited floodplain width, whereas muddy lateral accretion surfaces are commonly reported from much wider marine-influenced floodplains. The present study shows juxtaposition of both forms of muddy channel deposits in a low-energy, wide coastal plain setting, where preservation potential is considerable. [ABSTRACT FROM AUTHOR]

The Rhine–Meuse system in the west-central Netherlands is a continental-scale fluvial system bordered by an extremely wide continental shelf. Consequently, late Quaternary eustatic sea-level changes have resulted in dramatic shoreline displacements, by as much as 800 km. In addition, changes in climate have been severe, given the latitudinal and palaeogeographic setting of the Rhine–Meuse system. We investigated the relative importance of these allogenic controls on fluvial aggradation and incision during the last two glacial–interglacial cycles. We used optical dating of quartz from∼30 samples in a cross-section perpendicular to the palaeoflow direction, allowing us to correlate periods of aggradation and incision with independent records of sea-level change, climate change and glacio-isostatic crustal movements. We found the long-term aggradation rate to be∼8 cm kyr−1, a value similar to previous estimates of tectonic subsidence rates in the study area. Several excursions from this long-term aggradation trend could be identified for the last glacial–interglacial cycle. Dry climatic conditions with relatively high sediment supply induced aggradation during oxygen-isotope stages (OIS) 4 and 3. Build-up of a glacio-isostatic forebulge during OIS 2 is a likely cause of incision around the Last Glacial Maximum, followed by an aggradation phase during forebulge collapse. Sea-level highstands during OIS 5 have likely resulted in the aggradation of coastal prisms, but only minor, basal estuarine deposits have been preserved because these coastal prisms were prone to erosion during ensuing sea-level falls. Overall, the sedimentary record is dominated by strata formed during time intervals when the study area was completely unaffected by sea-level control, and our evidence shows that the falling-stage systems tract has the highest preservation potential. Our study highlights the importance of considering the complex interplay of both upstream and downstream controls to obtain a comprehensive understanding of the evolution of basin-margin successions. [ABSTRACT FROM AUTHOR]

This ex post facto prospective study investigated whether symptoms of posttraumatic stress disorder (PTSD) are associated with increased physical health problems, while controlling for base-rates of symptoms and individual differences in neuroticism. Dutch army soldiers completed standardized questionnaires before they were deployed to Iraq (n = 479), and about 5 months (n = 382; 80%) and 15 months (n = 331; 69%) after their return home. PTSD-symptoms were evaluated by questionnaires and clinical interviews. The results showed that, on average, participants with PTSD at 5 months reported increased physical problems after deployment. PTSD-symptoms at 5 months predicted coexisting physical symptoms, after controlling for demographic variables, military factors, injury sustained on deployment, war-zone exposure on deployment, baseline physical problems, baseline PTSD-symptoms, and neuroticism. PTSD-symptoms at 5 months also predicted physical problems at 15 months, while controlling for physical symptoms at 5 months. The results suggest that posttraumatic stress contributes to physical symptoms. Clinicians are advised to be attentive to PTSD when individuals present with physical symptoms, and to pay attention to physical symptoms in patients diagnosed with PTSD. [ABSTRACT FROM AUTHOR]

Accurate and timely hydrological forecasts highly depend on the meteorological input. Current numerical weather predictions (NWP) do not have sufficiently high spatial and temporal resolutions for adequate use for short lead times (<6 hours) in fast responding lowland and polder catchments. Radar nowcasting seems to be a solution and an increasing number of nowcasting algorithms is becoming available. However, best practice for the use and choice of these algorithms within operational forecasting systems is not available yet. In this study, we performed an analysis in time over 12 catchments in the Netherlands, by making use of state-of-the-art nowcasting algorithms (pySTEPS and SBMcast), benchmarking systems (RainyMotion) and the local NWP (HARMONIE). We focussed on the rainfall predictability of different nowcasting algorithms and the choice for simple advection, deterministic or probabilistic nowcasting. We quantified this as a function of lead time, rainfall type, initial conditions, catchment size and catchment location with regard to the radar rainfall estimates. [ABSTRACT FROM AUTHOR]

Hydrology in lowlands is affected by processes such as groundwater-unsaturated zone coupling, wetness-dependent flow routes, groundwater-surface water feedbacks, and seepage and surface water supply. For operational water management in lowlands and polders (for instance in the Netherlands), models as WALRUS and SOBEK-RR are used for flow prediction, often as an input to real-time control algorithms to steer water with pumps and weirs to keep water levels within acceptable bounds. Therefore, good initialization of these models is important. Here, we test both Ensemble (EnKF) and Unscented (UKF) Kalman filter using low-dimensional lumped hydrological catchment models. EnKF has received much attention in the field of hydrology due to its relative simplicity and robustness when addressing the nonlinear filtering problem. UKF has its origin in transforming the Gaussian random variables (GRV) for nonlinear estimation in a deterministic way and has received little attention in the context of state estimation of conceptual hydrological models. We conducted a reforecast experiment for a period of ten years using an hourly time step. The experiments were conducted within Delft-FEWS using OpenDA-SOBEK. The results show that both UKF and EnKF can improve the accuracy of the discharge forecast by assimilating the discharge. The setup and methods are suitable for implementation in an operational context. Key words: Ensemble Kalman Filter; Unscented Kalman Filter; State Estimation; Lowland Catchments; Conceptual Hydrological model; Wageningen Lowland Runoff Simulator [ABSTRACT FROM AUTHOR]