scholarly journals The significance of spatial variability of rainfall on simulated runoff: an evaluation based on the Upper Lee catchment, UK

2016 ◽  
Vol 48 (4) ◽  
pp. 1118-1130 ◽  
Author(s):  
I. G. Pechlivanidis ◽  
N. McIntyre ◽  
H. S. Wheater
Keyword(s):  
Spatial Variability ◽  
Distributed Model ◽  
Model Parameters ◽  
Rainfall Time Series ◽  
Catchment Scale ◽  
Rainfall Events ◽  
Spatial Properties ◽  
Antecedent Conditions ◽  
Parameter Uncertainty Analysis ◽  
The Impact

The significance of spatial variability of rainfall on runoff is explored as a function of catchment scale and type, and antecedent conditions via the continuous time, semi-distributed probability distributed model (PDM) hydrological model applied to the Upper Lee catchment, UK. The impact of catchment scale and type is assessed using 11 nested catchments, and further assessed by artificially changing the catchment characteristics and translating these to model parameters (MPs) with uncertainty using model regionalisation. Dry and wet antecedent conditions are represented by ‘warming up’ the model under different rainfall time series. Synthetic rainfall events are introduced to directly relate the change in simulated runoff to the spatial variability of rainfall. Results show that runoff volume and peak are more sensitive to the spatial rainfall for more impermeable catchments; however, this sensitivity is significantly undermined under wet antecedent conditions. Although there is indication that the impact of spatial rainfall on runoff varies as a function of catchment scale, the variability of antecedent conditions between the synthetic catchments seems to mask this significance. Parameter uncertainty analysis highlights the importance of accurately representing the spatial variability of the catchment properties and their translation to MPs when investigating the effects of spatial properties of rainfall on runoff.

scholarly journals Hydrological appraisal of operational weather radar rainfall estimates in the context of different modelling structures

2013 ◽  
Vol 10 (8) ◽  
pp. 10495-10534
Author(s):  
D. Zhu ◽  
Y. Xuan ◽  
I. Cluckie
Keyword(s):  
Hydrological Model ◽  
Scale Effects ◽  
Model Performance ◽  
Extreme Rainfall ◽  
Distributed Model ◽  
Catchment Scale ◽  
Radar Rainfall ◽  
Rainfall Events ◽  
The Impact ◽  
Rainfall Estimates

Abstract. Radar rainfall estimates have become increasingly available for hydrological modellers over recent years, especially for flood forecasting and warning over poorly gauged catchments. However, the impact of using radar rainfall as compared with conventional raingauge inputs, with respect to various hydrological model structures, remains unclear and yet to be addressed. In the study presented by this paper, we analysed the flow simulations of the Upper Medway catchment of Southeast England using the UK NIMROD radar rainfall estimates using three hydrological models based upon three very different structures, e.g. a physically based distributed MIKE SHE model, a lumped conceptual model PDM and an event-based unit hydrograph model PRTF. We focused on the sensitivity of simulations in relation to the storm types and various rainfall intensities. The uncertainty in radar-rainfall estimates, scale effects and extreme rainfall were examined in order to quantify the performance of the radar. We found that radar rainfall estimates were lower than raingauge measurements in high rainfall rates; the resolutions of radar rainfall data had insignificant impact at this catchment scale in the case of evenly distributed rainfall events but was obvious otherwise for high-intensity, localised rainfall events with great spatial heterogeneity. As to hydrological model performance, the distributed model had consistent reliable and good performance on peak simulation with all the rainfall types tested in this study.

scholarly journals Hydrological appraisal of operational weather radar rainfall estimates in the context of different modelling structures

2014 ◽  
Vol 18 (1) ◽  
pp. 257-272 ◽  
Author(s):  
D. Zhu ◽  
Y. Xuan ◽  
I. Cluckie
Keyword(s):  
Hydrological Model ◽  
Scale Effects ◽  
Model Performance ◽  
Extreme Rainfall ◽  
Distributed Model ◽  
Catchment Scale ◽  
Radar Rainfall ◽  
Rainfall Events ◽  
The Impact ◽  
Rainfall Estimates

Abstract. Radar rainfall estimates have become increasingly available for hydrological modellers over recent years, especially for flood forecasting and warning over poorly gauged catchments. However, the impact of using radar rainfall as compared with conventional raingauge inputs, with respect to various hydrological model structures, remains unclear and yet to be addressed. In the study presented by this paper, we analysed the flow simulations of the upper Medway catchment of southeast England using the UK NIMROD radar rainfall estimates, using three hydrological models based upon three very different structures (e.g. a physically based distributed MIKE SHE model, a lumped conceptual model PDM and an event-based unit hydrograph model PRTF). We focused on the sensitivity of simulations in relation to the storm types and various rainfall intensities. The uncertainty in radar rainfall estimates, scale effects and extreme rainfall were examined in order to quantify the performance of the radar. We found that radar rainfall estimates were lower than raingauge measurements in high rainfall rates; the resolutions of radar rainfall data had insignificant impact at this catchment scale in the case of evenly distributed rainfall events but was obvious otherwise for high-intensity, localised rainfall events with great spatial heterogeneity. As to hydrological model performance, the distributed model had consistent reliable and good performance on peak simulation with all the rainfall types tested in this study.

scholarly journals Assessment of spatial uncertainty of heavy rainfall at catchment scale using a dense gauge network

2019 ◽  
Vol 23 (7) ◽  
pp. 2863-2875 ◽  
Author(s):  
Sungmin O ◽  
Ulrich Foelsche
Keyword(s):  
Remote Sensing ◽  
Heavy Rainfall ◽  
Spatial Scales ◽  
Horizontal Resolution ◽  
Spatial Uncertainty ◽  
Catchment Scale ◽  
Rainfall Events ◽  
Areal Rainfall ◽  
The Impact ◽  
Heavy Rainfall Events

Abstract. Hydrology and remote-sensing communities have made use of dense rain-gauge networks for studying rainfall uncertainty and variability. However, in most regions, these dense networks are only available at small spatial scales (e.g., within remote-sensing subpixel areas) and over short periods of time. Just a few studies have applied a similar approach, i.e., employing dense gauge networks to catchment-scale areas, which limits the verification of their results in other regions. Using 10-year rainfall measurements from a network of 150 rain gauges, WegenerNet (WEGN), we assess the spatial uncertainty in observed heavy rainfall events. The WEGN network is located in southeastern Austria over an area of 20 km × 15 km with moderate orography. First, the spatial variability in rainfall in the region was characterized using a correlogram at daily and sub-daily scales. Differences in the spatial structure of rainfall events between warm and cold seasons are apparent, and we selected heavy rainfall events, the upper 10 % of wettest days during the warm season, for further analyses because of their high potential for causing hazards. Secondly, we investigated the uncertainty in estimating mean areal rainfall arising from a limited gauge density. The average number of gauges required to obtain areal rainfall with errors less than a certain threshold (≤20 % normalized root-mean-square error – RMSE – is considered here) tends to increase, roughly following a power law as the timescale decreases, while the errors can be significantly reduced by establishing regularly distributed gauges. Lastly, the impact of spatial aggregation on extreme rainfall was examined, using gridded rainfall data with various horizontal grid spacings. The spatial-scale dependence was clearly observed at high intensity thresholds and high temporal resolutions; e.g., the 5 min extreme intensity increases by 44 % for the 99.9th and by 25 % for the 99th percentile, with increasing horizontal resolution from 0.1 to 0.01∘. Quantitative uncertainty information from this study can guide both data users and producers to estimate uncertainty in their own observational datasets, consequently leading to the sensible use of the data in relevant applications. Our findings could be transferred to midlatitude regions with moderate topography, but only to a limited extent, given that regional factors that can affect rainfall type and process are not explicitly considered in the study.

A look inside the Panola trenched hillslope - initial results of a tracer test

2020 ◽  
Author(s):  
Ilja van Meerveld ◽  
Jan Seibert
Keyword(s):  
Spatial Variability ◽  
Subsurface Flow ◽  
Tracer Test ◽  
Tracer Transport ◽  
Rainfall Events ◽  
Bedrock Topography ◽  
Antecedent Conditions ◽  
Storm Flow ◽  
Flow Pathways ◽  
Initial Results

<p>Trenched hilsllope studies are logistically challenging but have provided valuable information on hillslope hydrological processes. For example, they have shown that subsurface stormflow can respond very quickly to rainfall and that subsurface storm flow often varies in a non-linear and threshold-like way with total rainfall or antecedent conditions. They have also highlighted the high spatial variability in subsurface stormflow due to surface or bedrock topography or spatial variability in soil and bedrock characteristics. However, still less is known about mixing and flow velocities along hillslopes.</p><p>Here we present the initial results of a tracer test at the Panola trenched hillslope in Georgia, USA. We applied chloride to the surface of the lower half of the hillslope and bromide as a line source. We measured the concentrations in subsurface flow at 2-m sections of the trench face and for two macropores during a five-month period that included two large rainfall events that caused subsurface flow, and several sprinkling experiments on parts of the hillslope. We used 20 lysimeter pairs and more than 50 wells and piezometers across the hillslope to track the transport of the tracer through the soil to the trench. The results highlight the variability in flow pathways, the considerable difference between celerity and velocity, as well as the fast tracer transport through the weathered bedrock</p>

Modeling global anthropogenic erosion in the Holocene

The Holocene ◽  
2018 ◽  
Vol 29 (3) ◽  
pp. 367-379 ◽  
Author(s):  
Zhengang Wang ◽  
Kristof Van Oost
Keyword(s):  
Land Use ◽  
Spatial Variability ◽  
Natural Vegetation ◽  
Catchment Scale ◽  
Land Use Scenario ◽  
Soil Production ◽  
Wide Range ◽  
Spatially Distributed ◽  
Agricultural Use ◽  
The Impact

A large proportion of natural vegetation has been converted to agricultural use, and this typically accelerates erosion by one to two orders of magnitude. Quantification of this accelerated erosion is important to understand the impact of human activities on soil ecosystem service given that soil erosion induces soil degradation and changes in soil organic carbon (SOC) stocks. Until now, few studies have evaluated the accumulated impact of agricultural erosion, since the start of agriculture (ca. 6000 BC), on the soils system and the carbon cycle. In this study, we mainly focused on the enhanced water erosion by conversion of natural vegetation to crops, while wind erosion on the cropland is not assessed. We first evaluated and constrained existing anthropogenic land cover change (ALCC) scenarios by comparing observed cumulative erosion for the agricultural period under a wide range of global agro-ecological conditions with model simulations. An optimized land-use scenario that makes the best fit between the simulation and the observation was derived in the model calibration. We further applied a spatially distributed erosion model, which was modified based on Revised Universal Soil Loss Equation (RUSLE), under the optimized land-use scenario across globe to estimate the total anthropogenic cumulative erosion and characterize their spatial variability. Simulations suggest that conversion from natural vegetation to cropland has caused a global cumulative agricultural erosion of 27,187 ± 9030 Pg for the period of agriculture. This results in an average cumulative sediment mobilization of 1829 ± 613 kg m−2 on croplands, corresponding to a soil truncation of ca. 1.34 ± 0.45 m. Regions of early civilization, particularly with high cropland fractions such as South Asia, Southeast Asia, and Central America have higher area-averaged anthropogenic erosion than other regions. This results in spatial variability in soil truncation rates because of erosion, which would further affect the soil production rate. Our study shows that observations of long-term anthropogenic erosion at the catchment scale can be used to constrain the reconstructed land-use scenarios.

An analysis of the impact of spatial variability in rainfall on runoff and sediment predictions from a distributed model

2012 ◽  
Vol 26 (21) ◽  
pp. 3263-3280 ◽  
Author(s):  
Russell Adams ◽  
Andrew W. Western ◽  
Alan W. Seed
Keyword(s):  
Spatial Variability ◽  
Distributed Model ◽  
The Impact

scholarly journals When does higher spatial resolution rainfall information improve streamflow simulation? An evaluation on 3620 flood events

2013 ◽  
Vol 10 (10) ◽  
pp. 12485-12536 ◽  
Author(s):  
F. Lobligeois ◽  
V. Andréassian ◽  
C. Perrin ◽  
P. Tabary ◽  
C. Loumagne
Keyword(s):  
Spatial Variability ◽  
Hydrological Model ◽  
Distributed Model ◽  
Large Set ◽  
Runoff Generation ◽  
Lumped Model ◽  
Climate Conditions ◽  
Key Factor ◽  
The Impact ◽  
Streamflow Simulation

Abstract. Precipitation is the key factor controlling the high-frequency hydrological response in catchments, and streamflow simulation is thus dependent on the way rainfall is represented in the hydrological model. A characteristic that distinguishes distributed from lumped models is the ability to explicitly represent the spatial variability of precipitation. Although the literature on this topic is abundant, the results are contrasted and sometimes contradictory. This paper investigates the impact of spatial rainfall on runoff generation to better understand the conditions where higher-resolution rainfall information improves streamflow simulations. In this study, we used the rainfall reanalysis developed by Météo-France over the whole French territory at 1 km and 1 h resolution over a 10 yr period. A hydrological model was applied in the lumped mode (a single spatial unit) and in the semi-distributed mode using three unit sizes of sub-catchments. The model was evaluated against observed streamflow data using split-sample tests on a large set of 181 French catchments representing a variety of size and climate conditions. The results were analyzed by catchment classes and types of rainfall events based on the spatial variability of precipitation. The evaluation clearly showed different behaviors. The lumped model performed as well as the semi-distributed model in western France where catchments are under oceanic climate conditions with quite spatially uniform precipitation fields. In contrast, higher resolution in precipitation inputs significantly improved the simulated streamflow dynamics and accuracy in southern France (Cévennes and Mediterranean regions) for catchments in which precipitation fields were identified to be highly variable in space. In all regions, natural variability allows for contradictory examples to be found, showing that analyzing a large number of events over varied catchments is warranted.

scholarly journals Quantifying the impact of land cover changes on hydrological responses in India

2021 ◽  
Author(s):  
Shaini Naha ◽  
Miguel A. Rico-Ramirez ◽  
Rafael Rosolem
Keyword(s):  
Land Use ◽  
Land Cover ◽  
River Basin ◽  
Distributed Model ◽  
Model Parameters ◽  
Area Index ◽  
Mahanadi River ◽  
Extreme Flows ◽  
Hydrological Responses ◽  
The Impact

Abstract. The objective of this study is to assess the impacts of Land Use Land Cover change on the hydrological responses of the Mahanadi river basin, a large river basin in India. Commonly, such assessments are accomplished by using distributed hydrological models in conjunction with different land use scenarios. However, these models through their complex interactions among the model parameters to generate hydrological processes, can introduce significant uncertainties to the hydrological projections. Therefore, we seek to further understand the uncertainties associated with model parameterization in those simulated hydrological responses due to different land cover scenarios. We performed a sensitivity-guided model calibration of a physically semi-distributed model, the Variable Infiltration Capacity (VIC) within a Monte Carlo Framework to generate behavioural models for subcatchments of the Mahanadi river basin. These behavioural models are then used in conjunction with historical and future land cover scenarios from the recently released, Land use Harmonisation (LUH2) to generate hydrological predictions and related uncertainties from behavioural model parameterisation. The LUH2 dataset indicates a noticeable increase in the cropland (23.3 % cover) at the expense of forest (22.65 % cover) by the end of year 2100 compared to the baseline year, 2005. As a response, simulation results indicate a median percent increase in the extreme flows (defined as the 95th percentile or higher river flow magnitude) and mean annual flows in the range of 1.8 to 11.3 % across the subcatchments. The direct conversion of forested areas to agriculture (on the order of 30,000 km2) reduces the Leaf Area Index and which subsequently reduces the Evapotranspiration (ET) and increases surface runoff. Further, the range of behavioural hydrological predictions indicated variation in the magnitudes of extreme flows simulated for the different land cover scenarios, for instance uncertainty in far future scenario ranges from 17 to 210 cumecs across subcatchments. This study indicates that the recurrent flood events occurring in the Mahanadi river basin might be influenced by the changes in LULC at the catchment scale and suggests that model parameterisation represents an uncertainty, which should be accounted for in the land-use change impact assessment.

scholarly journals Examining the impacts of precipitation isotope input (<i>δ</i><sup>18</sup>O<sub>ppt</sub>) on distributed, tracer-aided hydrological modelling

2017 ◽  
Vol 21 (5) ◽  
pp. 2595-2614 ◽  
Author(s):  
Carly J. Delavau ◽  
Tricia Stadnyk ◽  
Tegan Holmes
Keyword(s):  
High Latitude ◽  
Hydrologic Model ◽  
Data Availability ◽  
Model Parameters ◽  
Rainfall Events ◽  
Modelling Uncertainty ◽  
Process Representation ◽  
Model Parameterization ◽  
Process Understanding ◽  
The Impact

Abstract. Tracer-aided hydrological models are becoming increasingly popular tools as they assist with process understanding and source separation, which facilitates model calibration and diagnosis of model uncertainty (Tetzlaff et al., 2015; Klaus and McDonnell, 2013). Data availability in high-latitude regions, however, proves to be a major challenge associated with this type of application (Tetzlaff et al., 2015). Models require a time series of isotopes in precipitation (δ18Oppt) to drive simulations, and throughout much of the world – particularly in sparsely populated high-latitude regions – these data are not widely available. Here we investigate the impact that choice of precipitation isotope product (δ18Oppt) has on simulations of streamflow, δ18O in streamflow (δ18OSF), resulting hydrograph separations, and model parameters. In a high-latitude, data-sparse, seasonal basin (Fort Simpson, NWT, Canada), we assess three precipitation isotope products of different spatial and temporal resolutions (i.e. semi-annual static, seasonal KPN43, and daily bias-corrected REMOiso), and apply them to force the isoWATFLOOD tracer-aided hydrologic model. Total simulated streamflow is not significantly impacted by choice of δ18Oppt product; however, simulated isotopes in streamflow (δ18OSF) and the internal apportionment of water (driven by model parameterization) are impacted. The highest-resolution product (REMOiso) was distinct from the two lower-resolution products (KPN43 and static), but could not be verified as correct due to a lack of daily δ18Oppt observations. The resolution of δ18Oppt impacts model parameterization and seasonal hydrograph separations, producing notable differences among simulations following large snowmelt and rainfall events when event compositions differ significantly from δ18OSF. Capturing and preserving the spatial variability in δ18Oppt using distributed tracer-aided models is important because this variability impacts model parameterization. We achieve an understanding of tracer-aided modelling and its application in high-latitude regions with limited δ18Oppt observations, and the value such models have in defining modelling uncertainty. In this study, application of a tracer-aided model is able to identify simulations with improved internal process representation, reinforcing the fact that tracer-aided modelling approaches assist with resolving hydrograph component contributions and work towards diagnosing equifinality.

scholarly journals Estimation of the Effect of Changes in Forest Associations on Runoff Processes in Basins: Case Study in the Hron and Topľa River Basins

2016 ◽  
Vol 24 (3) ◽  
pp. 1-7 ◽  
Author(s):  
Peter Rončák ◽  
Kamila Hlavčová ◽  
Tamara Látková
Keyword(s):  
Land Use ◽  
Global Climate ◽  
River Basins ◽  
Distributed Model ◽  
Model Parameters ◽  
Climate Data ◽  
Global Climate Changes ◽  
Wetspa Model ◽  
Runoff Regime ◽  
The Impact

Abstract Distributed rainfall-runoff model simulations are often used to evaluate the impact of changes on the generation of runoff. These models have the advantage of reflecting the effects of land use on spatially distributed model parameters. The article deals with changes in forest associations as a result of global climate changes. In this article the WetSpa model was used for estimating the impact of forest changes on the runoff regime in the Hron and Topla river basins, with an emphasis on the parameterization of the land cover properties in the runoff simulations. The parameters of the model were estimated using climate data and three digital map layers: a land-use map, soil map and digital elevation model. This work contains two land use change scenarios of forest associations and also two scenarios of global climate change. Both types of scenarios of changes were prepared, and the runoff under the new conditions was simulated.

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