Comparison of radar and gauge precipitation data in watershed models across varying spatial and temporal scales

Precipitation is a key control on watershed hydrologic modelling output, with errors in rainfall propagating through subsequent stages of water quantity and quality analysis. Most watershed models incorporate precipitation data from rain gauges; higher‐resolution data sources are available, but they are associated with greater computational requirements and expertise. Here, we investigate whether the Multisensor Precipitation Estimator (MPE or Stage IV Next‐Generation Radar) data improve the accuracy of streamflow simulations using the Soil and Water Assessment Tool (SWAT), compared with rain gauge data. Simulated flows from 2002 to 2010 at five timesteps were compared with observed flows for four nested subwatersheds of the Neuse River basin in North Carolina (21‐, 203‐, 2979‐, and 10 100‐km² watershed area), using a multi‐objective function, informal likelihood‐weighted calibration approach. Across watersheds and timesteps, total gauge precipitation was greater than radar precipitation, but radar data showed a conditional bias of higher rainfall estimates during large events (>25–50 mm/day). Model parameterization differed between calibrations with the two datasets, despite the fact that all watershed characteristics were the same across simulation scenarios. This underscores the importance of linking calibration parameters to realistic processes. SWAT simulations with both datasets underestimated median and low flows, whereas radar‐based simulations were more accurate than gauge‐based simulations for high flows. At coarser timesteps, differences were less pronounced. Our results suggest that modelling efforts in watersheds with poor rain gauge coverage can be improved with MPE radar data, especially at short timesteps. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.     

Hydrological modelling uncertainty analysis for different flow quantiles: a case study in two hydro-geographically different watersheds

This study focuses on analysis of hydrological model parameter uncertainty at varying sub-basin spatial scales. It was found that the variation in sub-basin spatial scale had little influence on the entire flow simulations. However, the different sub-basin spatial scales had a significant impact on the reproduction of the flow quantiles. The coarser sub-basin spatial scale provided a better coverage of most prediction uncertainty in observations. However, the finer sub-basin spatial scale produced the best single simulation output closer to the observations. In general, the optimal sub-basin spatial scales (ratio to the entire watershed size) in the two test watersheds were found to be in the ranges 14–19% and 2–4% for good simulation of high and low flows, respectively. It is therefore worthwhile to put more effort into reproducing different flow quantiles by investigating an appropriate sub-basin spatial scale.     

Effects of spatial aggregation of soil spatial information on watershed hydrological modelling

Many researchers have examined the impact of detailed soil spatial information on hydrological modelling due to the fact that such information serves as important input to hydrological modelling, yet is difficult and expensive to obtain. Most research has focused on the effects at single scales; however, the effects in the context of spatial aggregation across different scales are largely missing. This paper examines such effects by comparing the simulated runoffs across scales from watershed models based on two different levels of soil spatial information: the 10‐m‐resolution soil data derived from the Soil‐Land Inference Model (SoLIM) and the 1:24000 scale Soil Survey Geographic (SSURGO) database in the United States. The study was conducted at three different spatial scales: two at different watershed size levels (referred to as full watershed and sub‐basin, respectively) and one at the model minimum simulation unit level. A fully distributed hydrologic model (WetSpa) and a semi‐distributed model (SWAT) were used to assess the effects. The results show that at the minimum simulation unit level the differences in simulated runoff are large, but the differences gradually decrease as the spatial scale of the simulation units increases. For sub‐basins larger than 10 km² in the study area, stream flows simulated by spatially detailed SoLIM soil data do not significantly vary from those by SSURGO. The effects of spatial scale are shown to correlate with aggregation effect of the watershed routing process. The unique findings of this paper provide an important and unified perspective on the different views reported in the literature concerning how spatial detail of soil data affects watershed modelling. Different views result from different scales at which those studies were conducted. In addition, the findings offer a potentially useful basis for selecting details of soil spatial information appropriate for watershed modelling at a given scale. Copyright © 2011 John Wiley & Sons, Ltd.     

How does DEM resolution affect microtopographic characteristics,hydrologic connectivity,and modelling of hydrologic processes?

The resolution of a digital elevation model (DEM) is a crucial factor in watershed hydrologic and environmental modelling. DEM resolution can cause significant variability in the representation of surface topography, which further affects quantification of hydrologic connectivity and simulation of hydrologic processes. The objective of this study is to examine the effects of DEM resolution on (1) surface microtopographic characteristics, (2) hydrologic connectivity, and (3) the spatial and temporal variations of hydrologic processes. A puddle‐to‐puddle modelling system was utilized for surface delineation and modelling of the puddle‐to‐puddle overland flow dynamics, surface runoff, infiltration, and unsaturated flow for nine DEM resolution scenarios of a field plot surface. Comparisons of the nine modelling scenarios demonstrated that coarser DEM resolutions tended to eliminate topographic features, reduce surface depression storage, and strengthen hydrologic connectivity and surface runoff. We found that reduction in maximum depression storage and maximum ponding area was as high as 97.56% and 76.36%, respectively, as the DEM grid size increased from 2 to 80 cm. The paired t‐test and fractal analysis demonstrated the existence of a threshold DEM resolution (10 cm for the field plot), within which the DEM‐based hydrologic modelling was effective and acceptable. The effects of DEM resolution were further evaluated for a larger surface in the Prairie Pothole Region subjected to observed rainfall events. It was found that simulations based on coarser resolution DEMs (>10 m) tended to overestimate ponded areas and underestimate runoff discharge peaks. The simulated peak discharge from the Prairie Pothole Region surface reduced by approximately 50% as the DEM resolution changed from 2 to 90 m. Fractal analysis results elucidated scale dependency of hydrologic and topographic processes. In particular, scale analysis highlighted a unique constant–threshold–power relationship between DEM scale and topographic and hydrologic parameters/variables. Not only does this finding allow one to identify threshold DEM but also further develop functional relationships for scaling to achieve valid topographic characterization as well as effective and efficient hydrologic modelling. Copyright © 2016 John Wiley & Sons, Ltd.     

Multi‐site evaluation of hydrology component of SWAT in the coastal plain of southwest Georgia

Simulation of watershed scale hydrologic and water quality processes is important for watershed assessments. Proper characterization of the accuracy of these simulations, particularly in cases with limited observed data, is critical. The Soil & Water Assessment Tool (SWAT) is frequently used for watershed scale simulation. The accuracy of the model was assessed by extrapolating calibration results from a well studied Coastal Plain watershed in Southwest Georgia, USA, to watersheds within the same geographic region without further calibration. SWAT was calibrated and validated on a 16.7‐km² subwatershed within the Little River Experimental Watershed by varying six model parameters. The optimized parameter set was then applied to a watershed of similar land use and soils, a smaller watershed with different land use and soils and three larger watersheds within the same drainage system without further calibration. Simulation results with percent bias (PB) ±15% ≤ PB < ±25% and Nash–Sutcliffe efficiency (NSE) 0.50 < NSE ≤ 0.65 were considered to be satisfactory, whereas those with PB < ±10% and 0.75 < NSE ≤ 1.00 were considered very good. With these criteria, simulation results for the five non‐calibration watersheds were satisfactory to very good. Differences across watersheds were attributed to differences in soils, land use, and surficial aquifer characteristics. These results indicate that SWAT can be a useful tool for predicting streamflow for ungauged watersheds with similar physical characteristics to the calibration watershed studied here and provide an indication of the accuracy of hydrologic simulations for ungauged watersheds. Copyright © 2012 John Wiley & Sons, Ltd.     

Two‐dimensional continuous simulation of spatiotemporally varied hydrological processes using the time‐area method

Distributed, continuous hydrologic models promote better understanding of hydrology and enable integrated hydrologic analyses by providing a more detailed picture of water transport processes across the varying landscape. However, such models are not widely used in routine modelling practices, due in part to the extensive data input requirements, computational demands, and complexity of routing algorithms. We developed a two‐dimensional continuous hydrologic model, HYSTAR, using a time‐area method within a grid‐based spatial data model with the goal of providing an alternative way to simulate spatiotemporally varied watershed‐scale hydrologic processes. The model calculates the direct runoff hydrograph by coupling a time‐area routing scheme with a dynamic rainfall excess sub‐model implemented here using a modified curve number method with an hourly time step, explicitly considering downstream ‘reinfiltration’ of routed surface runoff. Soil moisture content is determined at each time interval based on a water balance equation, and overland and channel runoff is routed on time‐area maps, representing spatial variation in hydraulic characteristics for each time interval in a storm event. Simulating runoff hydrographs does not depend on unit hydrograph theory or on solution of the Saint Venant equation, yet retains the simplicity of a unit hydrograph approach and the capability of explicitly simulating two‐dimensional flow routing. The model provided acceptable performance in predicting daily and monthly runoff for a 6‐year period for a watershed in Virginia (USA) using readily available geographic information about the watershed landscape. Spatial and temporal variability in simulated effective runoff depth and time area maps dynamically show the areas of the watershed contributing to the direct runoff hydrograph at the outlet over time, consistent with the variable source area overland flow generation mechanism. The model offers a way to simulate watershed processes and runoff hydrographs using the time‐area method, providing a simple, efficient, and sound framework that explicitly represents mechanisms of spatially and temporally varied hydrologic processes. Copyright © 2015 John Wiley & Sons, Ltd.     

On the effects of triangulated terrain resolution on distributed hydrologic model response

Distributed hydrologic models based on triangulated irregular networks (TIN) provide a means for computational efficiency in small to large‐scale watershed modelling through an adaptive, multiple resolution representation of complex basin topography. Despite previous research with TIN‐based hydrology models, the effect of triangulated terrain resolution on basin hydrologic response has received surprisingly little attention. Evaluating the impact of adaptive gridding on hydrologic response is important for determining the level of detail required in a terrain model. In this study, we address the spatial sensitivity of the TIN‐based Real‐time Integrated Basin Simulator (tRIBS) in order to assess the variability in the basin‐averaged and distributed hydrologic response (water balance, runoff mechanisms, surface saturation, groundwater dynamics) with respect to changes in topographic resolution. Prior to hydrologic simulations, we describe the generation of TIN models that effectively capture topographic and hydrographic variability from grid digital elevation models. In addition, we discuss the sampling methods and performance metrics utilized in the spatial aggregation of triangulated terrain models. For a 64 km² catchment in northeastern Oklahoma, we conduct a multiple resolution validation experiment by utilizing the tRIBS model over a wide range of spatial aggregation levels. Hydrologic performance is assessed as a function of the terrain resolution, with the variability in basin response attributed to variations in the coupled surface–subsurface dynamics. In particular, resolving the near‐stream, variable source area is found to be a key determinant of model behaviour as it controls the dynamic saturation pattern and its effect on rainfall partitioning. A relationship between the hydrologic sensitivity to resolution and the spatial aggregation of terrain attributes is presented as an effective means for selecting the model resolution. Finally, the study highlights the important effects of terrain resolution on distributed hydrologic model response and provides insight into the multiple resolution calibration and validation of TIN‐based hydrology models. Copyright © 2005 John Wiley & Sons, Ltd.     

Development of a soil moisture‐based distributed hydrologic model for determining hydrologically based critical source areas

A simple grid cell‐based distributed hydrologic model was developed to provide spatial information on hydrologic components for determining hydrologically based critical source areas. The model represents the critical process (soil moisture variation) to run‐off generation accounting for both local and global water balance. In this way, it simulates both infiltration excess run‐off and saturation excess run‐off. The model was tested by multisite and multivariable evaluation on the 50‐km² Little River Experimental Watershed I in Georgia, U.S. and 2 smaller nested subwatersheds. Water balance, hydrograph, and soil moisture were simulated and compared to observed data. For streamflow calibration, the daily Nash‐Sutcliffe coefficient was 0.78 at the watershed outlet and 0.56 and 0.75 at the 2 nested subwatersheds. For the validation period, the Nash‐Sutcliffe coefficients were 0.79 at the watershed outlet and 0.85 and 0.83 at the 2 subwatersheds. The per cent bias was less than 15% for all sites. For soil moisture, the model also predicted the rising and declining trends at 4 of the 5 measurement sites. The spatial distribution of surface run‐off simulated by the model was mainly controlled by local characteristics (precipitation, soil properties, and land cover) on dry days and by global watershed characteristics (relative position within the watershed and hydrologic connectivity) on wet days when saturation excess run‐off was simulated. The spatial details of run‐off generation and travel time along flow paths provided by the model are helpful for watershed managers to further identify critical source areas of non‐point source pollution and develop best management practices.     

Internal catchment process simulation in a snow‐dominated basin: performance evaluation with spatiotemporally variable runoff generation and groundwater dynamics

Hydrologic models have increasingly been used in forest hydrology to overcome the limitations of paired watershed experiments, where vegetative recovery and natural variability obscure the inferences and conclusions that can be drawn from such studies. Models are also plagued by uncertainty, however, and parameter equifinality is a common concern. Physically‐based, spatially‐distributed hydrologic models must therefore be tested with high‐quality experimental data describing a multitude of concurrent internal catchment processes under a range of hydrologic regimes. This study takes a novel approach by not only examining the ability of a pre‐calibrated model to realistically simulate watershed outlet flows over a four year period, but a multitude of spatially‐extensive, internal catchment process observations not previously evaluated, including: continuous groundwater dynamics, instantaneous stream and road network flows, and accumulation and melt period spatial snow distributions. Many hydrologic model evaluations are only on the comparison of predicted and observed discharge at a catchment outlet and remain in the ‘infant stage’ in terms of model testing. This study, on the other hand, tests the internal spatial predictions of a distributed model with a range of field observations over a wide range of hydroclimatic conditions. Nash‐Sutcliffe model efficiency was improved over prior evaluations due to continuing efforts in improving the quality of meteorological data collection. Road and stream network flows were generally well simulated for a range of hydrologic conditions, and snowpack spatial distributions were well simulated for one of two years examined. The spatial variability of groundwater dynamics was effectively simulated, except at locations where strong stream–groundwater interactions exist. Model simulations overall were quite successful in realistically simulating the spatiotemporal variability of internal catchment processes in the watershed, but the premature onset of simulated snowmelt for one of the simulation years has prompted further work in model development. Copyright © 2011 John Wiley & Sons, Ltd.     

Effects of reduced land cover detail on hydrological model response

Problems related to scale continue to be at the forefront of research in hydrology. Past research into issues of scale has focused mainly on digital elevation model grid size, the appropriate number and size of sub‐areas for subdividing a watershed, parameter transferability between watersheds and appropriate scales for linking hydrological and general circulation models. Much less attention has been given to the effects of scale on the representation of land cover and hydrological model response. Recent studies with respect to changes in land cover and hydrologic response have tended to focus on the issue of land cover maturity and the conversion of land through agricultural and forestry practices. The focus of this study is to examine the impact of the level of detail at which land cover is represented in modelling the hydrological response of Wolf Creek Basin in northwest Canada. A grid‐based land cover map with a spatial resolution of 30 m is coarsened or smoothed using several common grid‐based methods of aggregating categorical data, including: pixel thinning, modal smoothing and modal aggregation. A majority rule method based on polygons is also applied to the 30 m base cover. The SLURP hydrologic model is calibrated for the base cover and used as a reference for comparing simulations for the coarsened or ‘generalized’ land cover maps. Results of the simulations are compared to examine the sensitivity of hydrologic response to generalized land cover information. Comparisons of the SLURP model runs for Wolf Creek suggest that reducing the level of detail of land cover information generally has a limited effect on hydrologic response at the outlet. However, results for averages of water balance components across the basin suggest that the local variability of hydrologic response is affected in general. Copyright © 2007 John Wiley & Sons, Ltd.     

On the role of spatial resolution on snow estimates using a process‐based snow model across a range of climatology and elevation

Hydrological processes in mountainous settings depend on snow distribution, whose prediction accuracy is a function of model spatial scale. Although model accuracy is expected to improve with finer spatial resolution, an increase in resolution comes with modelling costs related to increased computational time and greater input data and parameter information. This computational and data collection expense is still a limiting factor for many large watersheds. Thus, this work's main objective is to question which physical processes lead to loss in model accuracy with regard to input spatial resolution under different climatic conditions and elevation ranges. To address this objective, a spatially distributed snow model, iSnobal, was run with inputs distributed at 50‐m—our benchmark for comparison—and 100‐m resolutions and with aggregated (averaged from the fine to the large resolution) inputs from the 50‐m model to 100‐, 250‐, 500‐, and 750‐m resolution for wet, average, and dry years over the Upper Boise River Basin (6,963 km²), which spans four elevation bands: rain dominated, rain–snow transition, and snow dominated below treeline and above treeline. Residuals, defined as differences between values quantified with high resolution (>50 m) models minus the benchmark model (50 m), of simulated snow‐covered area (SCA) and snow water equivalent (SWE) were generally slight in the aggregated scenarios. This was due to transferring the effects of topography on meteorological variables from the 50‐m model to the coarser scales through aggregation. Residuals in SCA and SWE in the distributed 100‐m simulation were greater than those of the aggregated 750 m. Topographic features such as slope and aspect were simplified, and their gradient was reduced due to coarsening the topography from the 50‐ to 100‐m resolution. Therefore, solar radiation was overestimated, and snow drifting was modified and caused substantial SCA and SWE underestimation in the distributed 100‐m model relative to the 50‐m model. Large residuals were observed in the wet year and at the highest elevation band when and where snow mass was large. These results support that model accuracy is substantially reduced with model scales coarser than 50 m.     

HydroBlocks: a field‐scale resolving land surface model for application over continental extents

Land surface spatial heterogeneity plays a significant role in the water, energy, and carbon cycles over a range of temporal and spatial scales. Until now, the representation of this spatial heterogeneity in land surface models has been limited to over simplistic schemes because of computation and environmental data limitations. This study introduces HydroBlocks – a novel land surface model that represents field‐scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). HydroBlocks is a coupling between the Noah‐MP land surface model and the Dynamic TOPMODEL hydrologic model. The HRUs are defined by clustering proxies of the drivers of spatial heterogeneity using high‐resolution land data. The clustering mechanism allows for each HRU's results to be mapped out in space, facilitating field‐scale application and validation. The Little Washita watershed in the USA is used to assess HydroBlocks' performance and added benefit from traditional land surface models. A comparison between the semi‐distributed and fully distributed versions of the model suggests that using 1000 HRUs is sufficient to accurately approximate the fully distributed solution. A preliminary evaluation of model performance using available in situ soil moisture observations suggests that HydroBlocks is generally able to reproduce the observed spatial and temporal dynamics of soil moisture. Model performance deficiencies can be primarily attributed to parameter uncertainty. HydroBlocks' ability to explicitly resolve field‐scale spatial heterogeneity while only requiring an increase in computation of one to two orders of magnitude when compared with existing land surface models is encouraging – ensemble field‐scale land surface modelling over continental extents is now possible. Copyright © 2016 John Wiley & Sons, Ltd.     

Evaluating hydrologic effects of spatial and temporal patterns of forest canopy change using numerical modelling

Although hydrologic responses to land cover changes are often studied using a paired watershed approach, it is not feasible to assess the hydrological effects of many different patterns of land cover alteration by empirical studies alone. An alternative is to use well validated, spatially explicit, physically based numerical models to estimate watershed storage and flux dynamics. The objectives of this study were to assess the sensitivity of watershed flow regimes to several spatial and temporal patterns of forest harvest and recovery in a snow‐dominated mountain watershed. The Distributed Hydrology Soil‐Vegetation Model (DHSVM) was parameterized using 1998–2007 climate data for the 28‐km² Mica Creek Experimental Watershed (MCEW), a headwater catchment in the inland Pacific Northwest. The modelling experiment indicated that clear‐cutting the entire watershed would increase runoff volume by 79% and 5^th percentile flows by 68%. Hydrologic recovery resulting from forest regeneration after clear‐cut harvesting is expected to take up to 25 years to return to baseline conditions, and 50 years to fully recover to preharvest conditions. A more realistic harvesting scenario where the watershed was gradually harvested in a series of clear‐cut blocks allowing for subsequent regeneration to occur was also assessed. This approach reduced the magnitude of hydrologic alteration. Analysis of several other scenarios, defined by aspect, elevation, and distance to the stream network, revealed that flow regime was more sensitive to the amount of alteration rather than pattern and landscape position of disturbance. Copyright © 2015 John Wiley & Sons, Ltd.     

How do hydrologic modeling decisions affect the portrayal of climate change impacts?

End users face a range of subjective decisions when evaluating climate change impacts on hydrology, but the importance of these decisions is rarely assessed. In this paper, we evaluate the implications of hydrologic modelling choices on projected changes in the annual water balance, monthly simulated processes, and signature measures (i.e. metrics that quantify characteristics of the hydrologic catchment response) under a future climate scenario. To this end, we compare hydrologic changes computed with four different model structures – whose parameters have been obtained using a common calibration strategy – with hydrologic changes computed with a single model structure and parameter sets from multiple options for different calibration decisions (objective function, local optima, and calibration forcing dataset). Results show that both model structure selection and the parameter estimation strategy affect the direction and magnitude of projected changes in the annual water balance, and that the relative effects of these decisions are basin dependent. The analysis of monthly changes illustrates that parameter estimation strategies can provide similar or larger uncertainties in simulations of some hydrologic processes when compared with uncertainties coming from model choice. We found that the relative effects of modelling decisions on projected changes in catchment behaviour depend on the signature measure analysed. Furthermore, parameter sets with similar performance, but located in different regions of the parameter space, provide very different projections for future catchment behaviour. More generally, the results obtained in this study prompt the need to incorporate parametric uncertainty in multi‐model frameworks to avoid an over‐confident portrayal of climate change impacts. Copyright © 2015 John Wiley & Sons, Ltd.     

Evaluation of dynamically downscaled reanalysis precipitation data for hydrological application

Skilful and reliable precipitation data are essential for seasonal hydrologic forecasting and generation of hydrological data. Although output from dynamic downscaling methods is used for hydrological application, the existence of systematic errors in dynamically downscaled data adversely affects the skill of hydrologic forecasting. This study evaluates the precipitation data derived by dynamically downscaling the global atmospheric reanalysis data by propagating them through three hydrological models. Hydrological models are calibrated for 28 watersheds located across the southeastern United States that is minimally affected by human intervention. Calibrated hydrological models are forced with five different types of datasets: global atmospheric reanalysis (National Centers for Environmental Prediction/Department of Energy Global Reanalysis and European Centre for Medium‐Range Weather Forecasts 40‐year Reanalysis) at their native resolution; dynamically downscaled global atmospheric reanalysis at 10‐km grid resolution; stochastically generated data from weather generator; bias‐corrected dynamically downscaled; and bias‐corrected global reanalysis. The reanalysis products are considered as surrogates for large‐scale observations. Our study indicates that over the 28 watersheds in the southeastern United States, the simulated hydrological response to the bias‐corrected dynamically downscaled data is superior to the other four meteorological datasets. In comparison with synthetically generated meteorological forcing (from weather generator), the dynamically downscaled data from global atmospheric reanalysis result in more realistic hydrological simulations. Therefore, we conclude that dynamical downscaling of global reanalysis, which offers data for sufficient number of years (in this case 22 years), although resource intensive, is relatively more useful than other sources of meteorological data with comparable period in simulating realistic hydrological response at watershed scales. Copyright © 2013 John Wiley & Sons, Ltd.     

When does spatial resolution become spurious in probabilistic flood inundation predictions?

Advances in remote sensing have enabled hydraulic models to run at fine scale resolutions, producing precise flood inundation predictions. However, running models at finer resolutions increase their computational expense, reducing the feasibility of running the multiple model realizations required to undertake uncertainty analysis. Furthermore, it is possible that precision gained by running fine scale models is smoothed out when treating models probabilistically. The aim of this paper is to determine the level of spatial complexity that is required when making probabilistic flood inundation predictions. The Imera basin, Sicily is used as a case study to assess how changing the spatial resolution of the hydraulic model LISFLOOD‐FP impacts on the skill of conditional probabilistic flood inundation maps given model parameter and boundary condition uncertainties. We find that model performance deteriorates at resolutions coarser than 50 m. This is predominantly caused by changes in flow pathways at coarser resolutions which lead to non‐stationarity in the optimum model parameters at different spatial resolutions. However, although it is still possible to produce probabilistic flood maps that contain a coherent outline of the flood extent at coarser resolutions, the reliability of these maps deteriorates at resolutions coarser than 100 m. Additionally, although the rejection of non‐behavioural models reduces the uncertainty in probabilistic flood maps the reliability of these maps is also reduced. Models with resolutions finer than 50 m offer little gain in performance yet are more than an order of magnitude computationally expensive which can become infeasible when undertaking probabilistic analysis. Furthermore, we show that using deterministic, high‐resolution flood maps can lead to a spurious precision that would be misleading and not representative of the overall uncertainties that are inherent in making inundation predictions. Copyright © 2015 The Authors Hydrological Processes Published by John Wiley & Sons Ltd.     

Using high‐resolution isotope data and alternative calibration strategies for a tracer‐aided runoff model in a nested catchment

Testing hydrological models over different spatio‐temporal scales is important for both evaluating diagnostics and aiding process understanding. High‐frequency (6‐hr) stable isotope sampling of rainfall and runoff was undertaken during 3‐week periods in summer and winter within 12 months of daily sampling in a 3.2‐km² catchment in the Scottish Highlands. This was used to calibrate and test a tracer‐aided model to assess the (a) information content of high‐resolution data, (b) effect of different calibration strategies on simulations and inferred processes, and (c) model transferability to <1‐km² subcatchment. The 6‐hourly data were successfully incorporated without loss of model performance, improving the temporal resolution of the modelling, and making it more relevant to the time dynamics of the isotope and hydrometric response. However, this added little new information due to old‐water dominance and riparian mixing in this peatland catchment. Time variant results, from differential split sample testing, highlighted the importance of calibrating to a wide range of hydrological conditions. This also provided insights into the nonstationarity of catchment mixing processes, in relation to storage and water ages, which varied markedly depending on the calibration period. Application to the nested subcatchment produced equivalent parameterization and performance, highlighting similarity in dominant processes. The study highlighted the utility of high‐resolution data in combination with tracer‐aided models, applied at multiple spatial scales, as learning tools to enhance process understanding and evaluation of model behaviour across nonstationary conditions. This helps reveal more fully the catchment response in terms of the different mechanistic controls on both wave celerites and particle velocities.     

Evaluation of distributed BMPs in an urban watershed—High resolution modeling for stormwater management

The urban environment modifies the hydrologic cycle resulting in increased runoff rates, volumes, and peak flows. Green infrastructure, which uses best management practices (BMPs), is a natural system approach used to mitigate the impacts of urbanization onto stormwater runoff. Patterns of stormwater runoff from urban environments are complex, and it is unclear how efficiently green infrastructure will improve the urban water cycle. These challenges arise from issues of scale, the merits of BMPs depend on changes to small‐scale hydrologic processes aggregated up from the neighborhood to the urban watershed. Here, we use a hyper‐resolution (1 m), physically based hydrologic model of the urban hydrologic cycle with explicit inclusion of the built environment. This model represents the changes to hydrology at the BMP scale (~1 m) and represents each individual BMP explicitly to represent response over the urban watershed. Our study varies both the percentage of BMP emplacement and their spatial location for storm events of increasing intensity in an urban watershed. We develop a metric of effectiveness that indicates a nonlinear relationship that is seen between percent BMP emplacement and storm intensity. Results indicate that BMP effectiveness varies with spatial location and that type and emplacement within the urban watershed may be more important than overall percent.     

PESERA‐PEAT: a fluvial erosion model for blanket peatlands

In peatlands, fluvial erosion can lead to a dramatic decline in hydrological function, major changes in the net carbon balance and loss of biodiversity. Climate and land management change are thought to be important influences on rates of peat erosion. However, sediment production in peatlands is different to that of other soils and no models of erosion specifically for peatlands currently exist. Hence, forecasting the influence of future climate or spatially‐distributed management interventions on peat erosion is difficult. The PESERA‐GRID model was substantially modified in this study to include dominant blanket peat erosion processes. In the resulting fluvial erosion model, PESERA‐PEAT, freeze–thaw and desiccation processes were accounted for by a novel sediment supply index as key features of erosion. Land management practices were parameterized for their influence on vegetation cover, biomass and soil moisture condition. PESERA‐PEAT was numerically evaluated using available field data from four blanket peat‐covered catchments with different erosion conditions and management intensity. PESERA‐PEAT was found to be robust in modelling fluvial erosion in blanket peat. A sensitivity analysis of PESERA‐PEAT showed that modelled sediment yield was more sensitive to vegetation cover than other tested factors such as precipitation, temperature, drainage density and ditch/gully depth. Two versions of PESERA‐PEAT, equilibrium and time‐series, produced similar results under the same environmental conditions, facilitating the use of the model at different scales. The equilibrium model is suitable for assessing the high‐resolution spatial variability of average monthly peat erosion over the study period across large areas (national or global assessments), while the time‐series model is appropriate for investigating continuous monthly peat erosion throughout study periods across smaller areas or large regions using a coarser‐spatial resolution. PESERA‐PEAT will therefore support future investigations into the impact of climate change and management options on blanket peat erosion at various spatial and temporal scales. Copyright © 2016 John Wiley & Sons, Ltd.     

Interactions between subgrid‐scale resolution,feature representation and grid‐scale resolution in flood inundation modelling

Numerical modelling of flood inundation over large and complex floodplains often requires mesh resolutions coarser than the structural features (e.g. buildings) that are known to influence the inundation process. Recent research has shown that this mismatch is not well represented by conventional roughness treatments, but that finer‐scale features can be represented through porosity‐based subgrid‐scale treatments. This paper develops this work by testing the interactions between feature representation, subgrid‐scale resolution and mesh resolution. It uses as the basis for this testing a 2D diffusion‐based flood inundation model which is applied to a 2004 flood event in a topologically complex upland floodplain in northern England. This study formulated simulations with different grid mesh resolution and subgrid mesh ratio. The sensitivity of the model to mesh resolution and roughness specification was investigated. Model validation and verification suggest that the subgrid treatment with higher subgrid mesh ratio can give much improved predictions of flood propagation, in particular, in terms of the predicted water depth. This study also highlighted the limitation of using at‐a‐point in time inundation extent for validation of flood models of this type. Copyright © 2010 John Wiley & Sons, Ltd.