Modelling the Hydrological Effects of Woodland Planting on Infiltration and Peak Discharge Using HEC-HMS

Woodland planting is gaining momentum as a potential method of natural flood management (NFM), due to its ability to break up soil and increase infiltration and water storage. In this study, a 2.2 km2 area in Warwickshire, England, planted with woodland every year from 2006 to 2012, was sampled using a Mini Disk infiltrometer (MDI). Infiltration measurements were taken from 10 and 200 cm away from the trees, from November 2019 to August 2021. Two individual hydrological models were built using the US Hydraulic Engineering Center Hydrological Modelling System (HEC-HMS), to model the effects of infiltration change on peak flows from the site throughout the summer and winter. The models were calibrated and validated using empirical data; the Nash and Sutcliffe Efficiency (NSE) was used as an indicator of accuracy. Results from this study show that woodland planting reduced peak flow intensity compared to impermeable land cover by an average of 6%, 2%, and 1% for 6-h, 24-h, and 96-h winter storms, respectively, and 48%, 18%, and 3% for 6-h, 24-h, and 96-h summer storms, respectively. However, grassland simulations show the greatest reduction in peak flows, being 32%, 21%, and 10%, lower than woodland for 6-, 24-, and 96-h winter storms, respectively, and 6%, 3%, and 0.5% lower than woodland for 6-, 24-, and 96-h summer storms, respectively.

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Rainfall Events as Paths of a Stochastic Process: Problems of Design Storm Analysis

Water Science & Technology ◽

10.2166/wst.1984.0184 ◽

1984 ◽

Vol 16 (8-9) ◽

pp. 131-138 ◽

Cited By ~ 2

Author(s):

Johannes Brummer

Keyword(s):

Stochastic Process ◽

Peak Flow ◽

Rainfall Events ◽

Peak Flows ◽

Design Storm ◽

Adequate Evaluation ◽

Rainfall Patterns

Problems in the construction of design storms are expressed in mathematical terms. Introduced here is a concept for approximating natural peak flow values by means of the distribution of typical rainfall patterns. A comparison demonstrates the quality of this concept and the competency of some well-known design storms for the adequate evaluation of peak flows.

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Uncertainty Assessment of Urban Hydrological Modelling from a Multiple Objective Perspective

Water ◽

10.3390/w12051393 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1393 ◽

Cited By ~ 2

Author(s):

Bo Pang ◽

Shulan Shi ◽

Gang Zhao ◽

Rong Shi ◽

Dingzhi Peng ◽

...

Keyword(s):

Flood Management ◽

Uncertainty Assessment ◽

Multiple Objective ◽

Hydrological Models ◽

Validation Period ◽

Urban Flood ◽

Calibration Period ◽

Storm Water Management Model ◽

Glue Method ◽

Generalized Likelihood Uncertainty Estimation

The uncertainty assessment of urban hydrological models is important for understanding the reliability of the simulated results. To satisfy the demand for urban flood management, we assessed the uncertainty of urban hydrological models from a multiple-objective perspective. A multiple-criteria decision analysis method, namely, the Generalized Likelihood Uncertainty Estimation-Technique for Order Preference by Similarity to Ideal Solution (GLUE-TOPSIS) was proposed, wherein TOPSIS was adopted to measure the likelihood within the GLUE framework. Four criteria describing different urban stormwater characteristics were combined to test the acceptability of the parameter sets. The TOPSIS was used to calculate the aggregate employed in the calculation of the aggregate likelihood value. The proposed method was implemented in the Storm Water Management Model (SWMM), which was applied to the Dahongmen catchment in Beijing, China. The SWMM model was calibrated and validated based on the three and two flood events respectively downstream of the Dahongmen catchment. The results showed that the GLUE-TOPSIS provided a more precise uncertainty boundary compared with the single-objective GLUE method. The band widths were reduced by 7.30 m3/s in the calibration period, and by 7.56 m3/s in the validation period. The coverages increased by 20.3% in the calibration period, and by 3.2% in the validation period. The median estimates improved, with an increase of the Nash–Sutcliffe efficiency coefficients by 1.6% in the calibration period, and by 10.0% in the validation period. We conclude that the proposed GLUE-TOPSIS is a valid approach to assess the uncertainty of urban hydrological model from a multiple objective perspective, thereby improving the reliability of model results in urban catchment.

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Stepwise improvement of hydrological models using satellite-based evaporation and total water storage estimations

10.5194/egusphere-egu21-796 ◽

2021 ◽

Author(s):

Markus Hrachowitz ◽

Petra Hulsman ◽

Hubert Savenije

Keyword(s):

Model Calibration ◽

Hydrological Model ◽

Model Development ◽

Water Storage ◽

Spatiotemporal Variability ◽

Model Structure ◽

Hydrological Models ◽

Total Water ◽

Spatiotemporal Model ◽

Multiple Variables

<p>Hydrological models are often calibrated with respect to flow observations at the basin outlet. As a result, flow predictions may seem reliable but this is not necessarily the case for the spatiotemporal variability of system-internal processes, especially in large river basins. Satellite observations contain valuable information not only for poorly gauged basins with limited ground observations and spatiotemporal model calibration, but also for stepwise model development. This study explored the value of satellite observations to improve our understanding of hydrological processes through stepwise model structure adaption and to calibrate models both temporally and spatially. More specifically, satellite-based evaporation and total water storage anomaly observations were used to diagnose model deficiencies and to subsequently improve the hydrological model structure and the selection of feasible parameter sets. A distributed, process based hydrological model was developed for the Luangwa river basin in Zambia and calibrated with respect to discharge as benchmark. This model was modified stepwise by testing five alternative hypotheses related to the process of upwelling groundwater in wetlands, which was assumed to be negligible in the benchmark model, and the spatial discretization of the groundwater reservoir. Each model hypothesis was calibrated with respect to 1) discharge and 2) multiple variables simultaneously including discharge and the spatiotemporal variability in the evaporation and total water storage anomalies. The benchmark model calibrated with respect to discharge reproduced this variable well, as also the basin-averaged evaporation and total water storage anomalies. However, the evaporation in wetland dominated areas and the spatial variability in the evaporation and total water storage anomalies were poorly modelled. The model improved the most when introducing upwelling groundwater flow from a distributed groundwater reservoir and calibrating it with respect to multiple variables simultaneously. This study showed satellite-based evaporation and total water storage anomaly observations provide valuable information for improved understanding of hydrological processes through stepwise model development and spatiotemporal model calibration.</p>

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The bias in GRACE estimates of continental water storage variations

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-3-3557-2006 ◽

2006 ◽

Vol 3 (6) ◽

pp. 3557-3594 ◽

Cited By ~ 5

Author(s):

R. Klees ◽

E. A. Zapreeva ◽

H. C. Winsemius ◽

H. H. G. Savenije

Keyword(s):

River Basin ◽

Southern Africa ◽

A Priori ◽

Water Storage ◽

Target Area ◽

Hydrological Models ◽

A Priori Information ◽

Basin Scale ◽

Priori Information ◽

Gravity Field Models

Abstract. The estimation of terrestrial water storage variations at river basin scale is among the best documented applications of the GRACE (Gravity and Climate Experiment) satellite gravity mission. In particular, it is expected that GRACE closes the water balance at river basin scale and allows the verification, improvement and modeling of the related hydrological processes by combining GRACE amplitude estimates with hydrological models' output and in-situ data. When computing monthly mean storage variations from GRACE gravity field models, spatial filtering is mandatory to reduce GRACE errors, but at the same time yields biased amplitude estimates. The objective of this paper is three-fold. Firstly, we want to compute and analyze amplitude and time behaviour of the bias in GRACE estimates of monthly mean water storage variations for several target areas in Southern Africa. In particular, we want to know the relation between bias and the choice of the filter correlation length, the size of the target area, and the amplitude of mass variations inside and outside the target area. Secondly, we want to know to what extent the bias can be corrected for using a priori information about mass variations. Thirdly, we want to quantify errors in the estimated bias due to uncertainties in the a priori information about mass variations that are used to compute the bias. The target areas are located in Southern Africa around the Zambezi river basin. The latest release of monthly GRACE gravity field models have been used for the period from January 2003 until March 2006. An accurate and properly calibrated regional hydrological model has been developed for this area and its surroundings and provides the necessary a priori information about mass variations inside and outside the target areas. The main conclusion of the study is that spatial smoothing significantly biases GRACE estimates of the amplitude of annual and monthly mean water storage variations. For most of the practical applications, the bias will be positive, which implies that GRACE underestimates the amplitudes. The bias is mainly determined by the filter correlation length; in the case of 1000 km smoothing, which is shown to be an appropriate choice for the target areas, the annual bias attains values up to 50% of the annual storage; the monthly bias is even larger with a maximum value of 75% of the monthly storage. A priori information about mass variations can provide reasonably accurate estimates of the bias, which significantly improves the quality of GRACE water storage amplitudes. For the target areas in Southern Africa, we show that after bias correction, GRACE annual amplitudes differ between 0 and 30 mm from the output of a regional hydrological model, which is between 0% and 25% of the storage. Annual phase shifts are small, not exceeding 0.25 months, i.e. 7.5 deg. Our analysis suggests that bias correction of GRACE water storage amplitudes is indispensable if GRACE is used to calibrate hydrological models.

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The importance of vegetation to understand terrestrial water storage variations

10.5194/hess-2021-394 ◽

2021 ◽

Author(s):

Tina Trautmann ◽

Sujan Koirala ◽

Nuno Carvalhais ◽

Andreas Güntner ◽

Martin Jung

Keyword(s):

Soil Moisture ◽

Large Scale ◽

Water Storage ◽

Model Parameters ◽

Intermediate Water ◽

Model Structure ◽

Hydrological Models ◽

Terrestrial Water Storage ◽

Terrestrial Water

Abstract. So far, various studies aimed at decomposing the integrated terrestrial water storage variations observed by satellite gravimetry (GRACE, GRACE-FO) with the help of large-scale hydrological models. While the results of the storage decomposition depend on model structure, little attention has been given to the impact of the way how vegetation is represented in these models. Although vegetation structure and activity represent the crucial link between water, carbon and energy cycles, their representation in large-scale hydrological models remains a major source of uncertainty. At the same time, the increasing availability and quality of Earth observation-based vegetation data provide valuable information with good prospects for improving model simulations and gaining better insights into the role of vegetation within the global water cycle. In this study, we use observation-based vegetation information such as vegetation indices and rooting depths for spatializing the parameters of a simple global hydrological model to define infiltration, root water uptake and transpiration processes. The parameters are further constrained by considering observations of terrestrial water storage anomalies (TWS), soil moisture, evapotranspiration (ET) and gridded runoff (Q) estimates in a multi-criteria calibration approach. We assess the implications of including vegetation on the simulation results, with a particular focus on the partitioning between water storage components. To isolate the effect of vegetation, we compare a model experiment with vegetation parameters varying in space and time to a baseline experiment in which all parameters are calibrated as static, globally uniform values. Both experiments show good overall performance, but including vegetation data led to even better performance and more physically plausible parameter values. Largest improvements regarding TWS and ET were seen in supply-limited (semi-arid) regions and in the tropics, whereas Q simulations improve mainly in northern latitudes. While the total fluxes and storages are similar, accounting for vegetation substantially changes the contributions of snow and different soil water storage components to the TWS variations, with the dominance of an intermediate water pool that interacts with the fast plant accessible soil moisture and the delayed water storage. The findings indicate the important role of deeper moisture storages as well as groundwater-soil moisture-vegetation interactions as a key to understanding TWS variations. We highlight the need for further observations to identify the adequate model structure rather than only model parameters for a reasonable representation and interpretation of vegetation-water interactions.

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Data assimilation of GRACE terrestrial water storage estimates into a regional hydrological model of the Rhine River basin

Hydrology and Earth System Sciences ◽

10.5194/hess-19-2079-2015 ◽

2015 ◽

Vol 19 (4) ◽

pp. 2079-2100 ◽

Cited By ~ 52

Author(s):

N. Tangdamrongsub ◽

S. C. Steele-Dunne ◽

B. C. Gunter ◽

P. G. Ditmar ◽

A. H. Weerts

Keyword(s):

Correlation Coefficient ◽

River Basin ◽

Hydrological Cycle ◽

Water Storage ◽

Model Parameters ◽

Hydrological Models ◽

Rhine River ◽

Terrestrial Water Storage ◽

Grace Data ◽

Terrestrial Water

Abstract. The ability to estimate terrestrial water storage (TWS) realistically is essential for understanding past hydrological events and predicting future changes in the hydrological cycle. Inadequacies in model physics, uncertainty in model land parameters, and uncertainties in meteorological data commonly limit the accuracy of hydrological models in simulating TWS. In an effort to improve model performance, this study investigated the benefits of assimilating TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) data into the OpenStreams wflow_hbv model using an ensemble Kalman filter (EnKF) approach. The study area chosen was the Rhine River basin, which has both well-calibrated model parameters and high-quality forcing data that were used for experimentation and comparison. Four different case studies were examined which were designed to evaluate different levels of forcing data quality and resolution including those typical of other less well-monitored river basins. The results were validated using in situ groundwater (GW) and stream gauge data. The analysis showed a noticeable improvement in GW estimates when GRACE data were assimilated, with a best-case improvement of correlation coefficient from 0.31 to 0.53 and root mean square error (RMSE) from 8.4 to 5.4 cm compared to the reference (ensemble open-loop) case. For the data-sparse case, the best-case GW estimates increased the correlation coefficient from 0.46 to 0.61 and decreased the RMSE by 35%. For the average improvement of GW estimates (for all four cases), the correlation coefficient increases from 0.6 to 0.7 and the RMSE was reduced by 15%. Only a slight overall improvement was observed in streamflow estimates when GRACE data were assimilated. Further analysis suggested that this is likely due to sporadic short-term, but sizeable, errors in the forcing data and the lack of sufficient constraints on the soil moisture component. Overall, the results highlight the benefit of assimilating GRACE data into hydrological models, particularly in data-sparse regions, while also providing insight on future refinements of the methodology.

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Possibility of Using Selected Rainfall-Runoff Models for Determining the Design Hydrograph in Mountainous Catchments: A Case Study in Poland

Water ◽

10.3390/w12051450 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1450 ◽

Cited By ~ 3

Author(s):

Dariusz Młyński ◽

Andrzej Wałęga ◽

Leszek Książek ◽

Jacek Florek ◽

Andrea Petroselli

Keyword(s):

Catchment Area ◽

Peak Flow ◽

Rainfall Runoff ◽

Peak Flows ◽

Mountainous Catchment ◽

Mountainous Catchments ◽

Relative Errors ◽

Runoff Hydrographs

The aim of the study was to analyze the possibility of using selected rainfall-runoff models to determine the design hydrograph and the related peak flow in a mountainous catchment. The basis for the study was the observed series of hydrometeorological data for the Grajcarek catchment area (Poland) for the years 1981–2014. The analysis was carried out in the following stages: verification of hydrometeorological data; determination of the design rainfall; and determination of runoff hydrographs with the following rainfall-runoff models: Snyder, NRCS-UH, and EBA4SUB. The conducted research allowed the conclusion that the EBA4SUB model may be an alternative to other models in determining the design hydrograph in ungauged mountainous catchments. This is evidenced by the lower values of relative errors in the estimation of peak flows with an assumed frequency for the EBA4SUB model, as compared to Snyder and NRCS-UH.

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Predicting ocean waves along the US east coast during energetic winter storms: sensitivity to whitecapping parameterizations

Ocean Science ◽

10.5194/os-15-691-2019 ◽

2019 ◽

Vol 15 (3) ◽

pp. 691-715 ◽

Cited By ~ 3

Author(s):

Mohammad Nabi Allahdadi ◽

Ruoying He ◽

Vincent S. Neary

Keyword(s):

East Coast ◽

Ocean Waves ◽

Source Terms ◽

Frequency Spectra ◽

Winter Storms ◽

Wave Parameters ◽

The Us ◽

Wind Input ◽

Simulation Results ◽

Us East Coast

Abstract. The performance of two methods for quantifying whitecapping dissipation incorporated in the Simulating Waves Nearshore (SWAN) wave model is evaluated for waves generated along and off the US east coast under energetic winter storms with a predominantly westerly wind. Parameterizing the whitecapping effect can be done using the Komen-type schemes, which are based on mean spectral parameters, or the saturation-based (SB) approach of van der Westhuysen (2007), which is based on local wave parameters and the saturation level concept of the wave spectrum (we use “Komen” and “Westhuysen” to denote these two approaches). Observations of wave parameters and frequency spectra at four National Data Buoy Center (NDBC) buoys are used to evaluate simulation results. Model–data comparisons show that when using the default parameters in SWAN, both Komen and Westhuysen methods underestimate wave height. Simulations of mean wave period using the Komen method agree with observations, but those using the Westhuysen method are substantially lower. Examination of source terms shows that the Westhuysen method underestimates the total energy transferred into the wave action equations, especially in the lower frequency bands that contain higher spectral energy. Several causes for this underestimation are identified. The primary reason is the difference between the wave growth conditions along the east coast during winter storms and the conditions used for the original whitecapping formula calibration. In addition, some deficiencies in simulation results are caused along the coast by the “slanting fetch” effect that adds low-frequency components to the 2-D wave spectra. These components cannot be simulated partly or entirely by available source terms (wind input, whitecapping, and quadruplet) in models and their interaction. Further, the effect of boundary layer instability that is not considered in the Komen and Westhuysen whitecapping wind input formulas may cause additional underestimation.

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Spatio-Temporal Evaluation of Water Storage Trends from Hydrological Models over Australia Using GRACE Mascon Solutions

Remote Sensing ◽

10.3390/rs12213578 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3578

Author(s):

Xinchun Yang ◽

Siyuan Tian ◽

Wei Feng ◽

Jiangjun Ran ◽

Wei You ◽

...

Keyword(s):

Land Surface ◽

Model Development ◽

Water Storage ◽

Validation Dataset ◽

Surface Model ◽

Hydrological Models ◽

Infiltration Capacity ◽

Australian Water ◽

Community Land Model ◽

Spatio Temporal

The Gravity Recovery and Climate Experiment (GRACE) data have been extensively used to evaluate the total terrestrial water storage anomalies (TWSA) from hydrological models. However, which individual water storage components (i.e., soil moisture storage anomalies (SMSA) or groundwater water storage anomalies (GWSA)) cause the discrepancies in TWSA between GRACE and hydrological models have not been thoroughly investigated or quantified. In this study, we applied GRACE mass concentration block (mascon) solutions to evaluate the spatio-temporal TWSA trends (2003–2014) from seven prevailing hydrological models (i.e., Noah-3.6, Catchment Land Surface Model (CLSM-F2.5), Variable Infiltration Capacity macroscale model (VIC-4.1.2), Water—Global Assessment and Prognosis (WaterGAP-2.2d), PCRaster Global Water Balance (PCR-GLOBWB-2), Community Land Model (CLM-4.5), and Australian Water Resources Assessment Landscape model (AWRA-L v6)) in Australia and, more importantly, identified which individual water storage components lead to the differences in TWSA trends between GRACE and hydrological models. The results showed that all of the hydrological models employed in this study, except for CLM-4.5 model, underestimated the GRACE-derived TWSA trends. These underestimations can be divided into three categories: (1) ignoring GWSA, e.g., Noah-3.6 and VIC-4.1.2 models; (2) underrating both SMSA and GWSA, e.g., CLSM-F2.5, WaterGAP-2.2d, and PCR-GLOBWB-2 models; (3) deficiently modeling GWSA, e.g., AWRA-L v6 model. In comparison, CLM-4.5 model yielded the best agreement with GRACE but overstated the GRACE-derived TWSA trends due to the overestimation of GWSA. Our results underscore that GRACE mascon solutions can be used as a valuable and efficient validation dataset to evaluate the spatio-temporal performance of hydrological models. Confirming which individual water storage components result in the discrepancies in TWSA between GRACE and hydrological models can better assist in further hydrological model development.

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Performance, environmental and cost comparisons of onsite detention (OSD) and onsite retention (OSR) in re-developed residential catchments

Water Science & Technology ◽

10.2166/wst.1999.0079 ◽

1999 ◽

Vol 39 (2) ◽

pp. 33-41 ◽

Cited By ~ 3

Author(s):

Peter Scott ◽

Ruben Santos ◽

John R. Argue

Keyword(s):

Peak Flow ◽

Storm Runoff ◽

Environmental Benefits ◽

Flood Wave ◽

Environmental Aspects ◽

Urban Flood ◽

Peak Flows ◽

Temporary Storage ◽

Urban Catchments ◽

Global Peak

On-site detention (OSD) of storm runoff decreases catchment peak flows through the routing effect of temporary storage; on-site retention (OSR) achieves the same objective by abstracting part of the urban flood wave and passing the retained water to disposal on site. The investigation explored both strategies applied to a set of hypothetical present/re-developed urban catchments ranging in size from 14 ha to 210 ha. Comparisons were made on the basis of site storage required (SSR) to achieve the same global peak flow reductions, environmental aspects and cost. OSR practice was shown to out-perform OSD generally in medium-large catchments with respect to SSR and, hence, cost. The retention option also has clear environmental benefits that fall beyond the scope normally ascribed to OSD practice. The paper cautions against use of OSR in unsuitable circumstances.

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