Explore the vast range of titles available.

Few studies attempt to measure changes to discharge hydrographs during floods resulting from nature-based Solutions (NbS) for risk mitigation. The Q-NFM project in the UK has sought to measure and compare such changes for a wide range of NbS pilots applied to managed grasslands and woodlands. Also measured were underlying shifts in key hydrological processes leading to flood hydrograph changes of enhanced evaporation, hillslope-, channel- and floodplain-storage, and infiltration. How well particular NbS pilots changed these processes to reduce flood hydrographs was found to depend on the attributes of the NbS features and scheme. This learning is presented for the first time to highlight, with supporting evidence, seven potential criteria to help practitioners of flood risk management to improve existing and future designs of NbS for more effective flood mitigation within temperate grassland and woodland environments.

Studying the centennial or millennial timescale response of large rivers to changing patterns in precipitation, discharge, flood intensity and recurrence, and associated sediment erosion is critical for understanding long‐term fluvial geomorphic adjustment to climate. Long hydrographs, maintaining reliable Flow Duration Curves (FDCs), are a fundamental input for such simulations; however, recorded discharge series rarely span more than a few decades. The absence of robust methodologies for generating representative long‐term hydrographs, especially those incorporating coarse temporal resolution or lacking continuous simulations, is therefore a fundamental challenge for climate resilience. We present a novel approach for constructing multi‐century hydrographs that successfully conserve the statistical, especially frequency analysis, and stochastic characteristics of observed hydrographs. This approach integrates a powerful combination of a weather generator with a fine disaggregation technique and a continuous rainfall‐runoff transformation model. We tested our approach to generate a statistically representative 300‐year hydrograph on the Ninnescah River Basin in Kansas, using a satellite precipitation data set to address the considerable gaps in the available hourly observed data sets. This approach emphasizes the similarities of FDCs between the observed and generated hydrographs, exhibiting a reasonably acceptable range of average absolute deviation between 6% and 18%. We extended this methodology to create projected high‐resolution hydrographs based on a range of climate change scenarios. The projected outcomes present pronounced increases in the FDCs compared to the current condition, especially for more distant futures, which necessitates more efficient adaptation strategies. This approach represents a paradigm shift in long‐term hydrologic modeling. Plain Language Summary River hydrographs are key inputs for understanding long term Earth surface processes. Due to the limited lengths of observational streamflow records, various techniques were previously developed with limited capabilities to generate representative long hydrographs. Through a novel integrated approach, we are able to construct robust high‐resolution hydrographs on multi‐century timescales, based on developing a linkage between hydroclimatic forces and watershed characteristics within a stochastic framework. We used this methodology to generate a 300‐year high‐resolution hydrograph with satisfactory correlation with the observed FDC. Due to the stochastic background of this framework, the deviation between the observed and generated FDCs was estimated to fall within a reasonable range of 6% and 18%. This framework was extended to provide hourly runoff projections for several future climatic models. Median projections for the near‐term period 2040–2069 demonstrated less deviation from reference data set compared to those for the more distant future 2070–2099. This study represents a scientific shift for long‐term simulations through re‐constructing past, simulating present, or projecting future hydrographs. Key Points Introducing a novel framework designed to generate statistically robust hydrographs on multi‐century timescales for long‐term simulations Integrating a weather generator and a disaggregation technique within a rainfall runoff model to achieve high‐temporal resolution hydrographs Utilizing multiple climate models to evaluate the impacts of climate change on hourly runoff responses

As a genre of physics-informed machine learning, differentiable process-based hydrologic models (abbreviated as δ or delta models) with regionalized deep-network-based parameterization pipelines were recently shown to provide daily streamflow prediction performance closely approaching that of state-of-the-art long short-term memory (LSTM) deep networks. Meanwhile, δ models provide a full suite of diagnostic physical variables and guaranteed mass conservation. Here, we ran experiments to test (1) their ability to extrapolate to regions far from streamflow gauges and (2) their ability to make credible predictions of long-term (decadal-scale) change trends. We evaluated the models based on daily hydrograph metrics (Nash–Sutcliffe model efficiency coefficient, etc.) and predicted decadal streamflow trends. For prediction in ungauged basins (PUB; randomly sampled ungauged basins representing spatial interpolation), δ models either approached or surpassed the performance of LSTM in daily hydrograph metrics, depending on the meteorological forcing data used. They presented a comparable trend performance to LSTM for annual mean flow and high flow but worse trends for low flow. For prediction in ungauged regions (PUR; regional holdout test representing spatial extrapolation in a highly data-sparse scenario), δ models surpassed LSTM in daily hydrograph metrics, and their advantages in mean and high flow trends became prominent. In addition, an untrained variable, evapotranspiration, retained good seasonality even for extrapolated cases. The δ models' deep-network-based parameterization pipeline produced parameter fields that maintain remarkably stable spatial patterns even in highly data-scarce scenarios, which explains their robustness. Combined with their interpretability and ability to assimilate multi-source observations, the δ models are strong candidates for regional and global-scale hydrologic simulations and climate change impact assessment.

Ephemeral streams in dry environments can convey high intensity sediment‐laden flash floods. The study of these events is quite difficult due to remote locations with limited accessibility, unexpected events, rough weather conditions and safety concerns. Hence combined data of rainfall and flood hydrograph is rather scarce in the world, and null in Patagonia‐South America. The Experimental Basin Arroyo Sagmata (total drained area of 129 km2), located in the Lower Valley of the Chubut River has been equipped with 4 rain gauges, and 4 time‐lapse cameras in four reaches of the arroyos. Data from a 4‐year monitoring provides valuable insights about the hydrological response of basins in the drylands of Patagonia, such as the time of response, the shape of hydrographs and its relation with the hyetographs. Four hydrograph styles have been observed: single‐peak (S), multi‐peaks (M), flat‐top (F) and compound (C) peaks. Rainfall styles have also been classified into single, multiple and compound. Soil processes operate reducing the number of peaks (M‐type hyetographs are transformed into S‐ or M‐type hydrographs). The rain structure is also important, especially when the high intensity occurs in the beginning of the event. Channel connectivity pattern and disconnections explain the observation of M‐type hydrograph and F‐type hydrograph, respectively. Key Points Flash floods are rare events that account for only 4% of the total water volume received by the basin Four hydrograph shapes have been identified: single‐peak, multiple‐peaks, compound and flat‐top The hydrograph diversity depends on the rainfall temporal structure and its spatial distribution, soil, hillslope and channel processes

Gravel‐bed rivers widen and narrow as bar‐push and bank‐pull wax and wane through individual floods, yet over decades the channel often holds near constant width, evidence of coupling between inner‐bank deposition and outer‐bank erosion. Because large, event‐scale data sets are scarce and most field rivers have mixed grain‐size beds, the physics of this coupling remains uncertain. Here we use a unique Outdoor Experimental River Facility (OERF) with sediment recirculation to investigate this coupling in 50 m‐long, 3 m‐wide sine‐generated gravel‐bed channel using four identical seven‐stage flood hydrographs (129‐hr total duration). In an unvegetated experimental gravel‐bed channel with erodible banks (50% gravel; median size of 16 mm), twenty‐nine drone photogrammetry surveys (i.e., 2mm$2\\ \\mathrm{m}\\mathrm{m}$Digital Elevation Models) and bedload samples were collected to determine a novel thalweg‐centered volumetric framework and analysis that partitions every survey into inner‐bank and outer‐bank contributions. Flood peaks, though only 3% of the experiment duration, produced 83% of the 52% increase in planform area relative to the initial condition: centroid migration reached 1.3 m (27% of width), with the widening varying from one bend to the other. At peaks, bank‐pull dominated 50% of events and bar‐push 40%, whereas during rising and falling limbs symmetric widening prevailed (41%) with bar‐push still active (31%). Local context modulated these stage effects: under identical forcing, one bend damped toward a medium‐stability balance that approached a steady but non‐zero bar‐push/bank‐pull imbalance, a mid‐reach bend reached high stability with near‐balanced inner and outer bank volumetric changes, and an outlet‐proximal bend diverged into low‐stability widening. We conclude that in gravel channels the bar‐push/bank‐pull is both stage‐dependent and bend‐specific; short peaks set the morphodynamic trajectory, but sub‐bankfull limbs are responsible for most of the in‐channel geomorphic work.

Intense floods represent a challenge to risk management. While they are multivariate in their nature, they are often studied in practice from univariate perspectives. Classical frequency analyses, which establish a relation between the peak flow or volume and the frequency of exceedance, may lead to improper risk estimations and mitigations. Therefore, it is necessary to study floods as multivariate stochastic events having mutually correlated characteristics, such as peak flood flow, corresponding volume and duration. The joint distribution properties of these characteristics play an important role in the assessment of flood risk and reservoir safety evaluation. In addition, the study of flood hydrographs is useful because of the inherent dependencies among their practice-relevant characteristics present on-site and in the regional records. This study aims to provide risk analysts with a consistent multivariate probabilistic framework using a copula-based approach. The framework respects and describes the dependence structures among the flood peaks, volumes, and durations of observed and synthetic control flood hydrographs. The seasonality of flood generation is respected by separate analyses of floods in the summer and winter seasons. A control flood hydrograph is understood as a theoretical/synthetic discharge hydrograph, which is determined by the flood peak with the chosen probability of exceedance, the corresponding volume, and the time duration with the corresponding probability. The framework comprises five steps: 1. Separation of the observed hydrographs, 2. Analysis of the flood characteristics and their dependence, 3. Modelling the marginal distributions, 4. A copula-based approach for modelling joint distributions of the flood peaks, volumes and durations, 5. Construction of synthetic flood hydrographs. The flood risk assessment and reservoir safety evaluation are described by hydrograph analyses and the conditional joint probabilities of the exceedance of the flood volume and duration conditioned on flood peak. The proposed multivariate probabilistic framework was tested and demonstrated based on data from two contrasting catchments in Slovakia. Based on the findings, the study affirms that the trivariate copula-based approach is a practical option for assessing flood risks and for reservoir safety.

This research work significantly advances hydrological modelling in Greece by introducing Geomorphological Unit Hydrographs (GUH) tailored for ungauged basins. These GUHs, specifically adapted to the region’s intricate geomorphology and limited data resources, provide essential insights into hydrological behaviour. Utilising an innovative approach that integrates the time-area diagram method with Python’s ArcPy module, this paper examines the geomorphological metrics of 70 drainage basins and their relationships with hydrograph characteristics. Empirical relationships, expressed through simple forms like second-order polynomials and linear equations, are established and validated across 30 basins. System analysis reveals the efficacy of the 0.1–2.0 channel velocity range, supported by positive Nash–Sutcliffe Efficiency (NSE) values. Visual representations, including histograms, elucidate the intricate connections between hydrograph attributes and geomorphological parameters. Regression analysis produces predictive equations, demonstrating strong performance with models such as T-Polynomial for Q max and Cc-Linear for other hydrograph attributes, and their precision is confirmed through validation regression analysis, enhancing forecasting across various drainage basins.

Runoff data is crucial for development of water resources. Runoff data is however rarely available for ungauged catchments, especially in developing countries. Geomorphological instantaneous unit hydrographs (GIUH) models can be used for predicting runoff in poorly gauged catchments, but a challenge with these models is estimating the dynamic velocity parameter. In this study, three GIUH models were developed based on estimation of flow velocity using calibration of Manning’s n (GIUH-cal), peak discharge (GIUH-pq) and 30-min rain intensity (GIUH-I30). The objectives of this study were to (a) assess suitability of a GIUH model for simulating runoff in Gule catchment, northern Ethiopia and (b) evaluate performance of three velocity parameter estimation methods in simulating runoff using GIUH models. Runoff hydrographs of the GIUH models matched well with observed hygrographs for most rain events. The GIUH-cal model had the best performance, 18 out of 20 rain events resulting in Nash–Sutcliffe model efficiency (NSE) values of 0.53 to 0.95. The GIUH-pq and GIUH-I30 models performed satisfactorily with 12 of the 20 rain events resulting in NSE values greater than 0.50. Overall, the GIUH models underestimated peak discharge compared to observed data. The GIUH models were moderately sensitive to changes in flow velocity. Peak discharge and time to peak discharge were highly sensitive to changes in flow velocity. The developed GIUH models could be used for simulating flood hydrographs of the Gule catchment. Particularly, the GIUH-I30 model will be very useful for estimating direct surface runoff in the absence of streamflow data.

SCS (Soil Conservation Service) synthetic unit hydrograph technique was used to estimate the storm runoff overflows entering the Housha tunnel. The flood hydrographs were derived under different storm event durations for the ungauged Husha Catchment area. Geomorphological and hydrological parameters of the watershed were extracted using GIS measurement tools. The data identified various parameters (time to peak, time of base, and peak flow) of the synthetic unit hydrograph. The peak discharge (Qp in m3 s−1 cm−1) of rainfall excess for different time durations (5, 10, 20, 30, 60, 120, 180, 360, and 720 minutes) was estimated. The results revealed that the peak discharge (Qp) decreased with the increase in time of rainfall excess. The maximum peak discharge (27.5 in m3 s−1 cm−1) was reached 84 minutes after the beginning of the rainfall storm for 5 minutes of rainfall excess whereas the minimum peak discharge (5.2 in m3 s−1 cm−1) was reached after 7 hours and 22 minutes for 12 hours of rainfall excess. Geographic information system (GIS) data-based SCS synthetic unit hydrograph model was verified by comparing the simulated runoff with the estimated runoff from measured rainfall data of the watershed.

A classical approach to understanding hydrological behavior is the unit hydrograph and its many variants, but these often assume linearity (runoff response is proportional to effective precipitation), stationarity (runoff response to a given unit of rainfall is identical, regardless of when it falls), and spatial homogeneity (runoff response depends only on spatially averaged precipitation). In the real world, by contrast, runoff response is typically nonlinear, nonstationary, and spatially heterogeneous. Quantifying this nonlinearity, nonstationarity, and spatial heterogeneity is essential to unraveling the mechanisms and subsurface properties controlling hydrological behavior. Here, I present proof-of-concept demonstrations illustrating how nonlinear, nonstationary, and spatially heterogeneous rainfall–runoff behavior can be quantified, directly from data, using ensemble rainfall–runoff analysis (ERRA), a data-driven, model-independent method for quantifying rainfall–runoff relationships across a spectrum of time lags. I show how ERRA uses nonlinear deconvolution to quantify how catchments' runoff responses vary with precipitation intensity and to estimate their precipitation-weighted runoff response distributions. I further illustrate how ERRA combines nonlinear deconvolution with de-mixing techniques to reveal how runoff response depends jointly on precipitation intensity and nonstationary ambient conditions, including antecedent wetness and vapor pressure deficit. I demonstrate how ERRA's de-mixing techniques can be used to quantify spatially heterogeneous runoff responses in different parts of a catchment, even if those subcatchments are not separately gauged. I also illustrate how ERRA's broken-stick deconvolution capabilities can be used to quantify multiscale runoff responses that combine hydrograph peaks lasting for hours and recessions lasting for weeks, well beyond the average spacing between storms. ERRA can unscramble these multiple effects on runoff response even if they are overprinted on each other through time and even if they are corrupted by autoregressive moving average (ARMA) noise. Results from this approach may be informative for catchment characterization, process understanding, and model–data comparisons; they may also lead to a better understanding of storage dynamics and landscape-scale connectivity. An R script is provided to perform the necessary calculations, including uncertainty analysis.