scholarly journals Large-scale runoff generation – parsimonious parameterisation using high-resolution topography

2010 ◽  
Vol 7 (5) ◽  
pp. 6613-6646
Author(s):  
L. Gong ◽  
S. Halldin ◽  
C.-Y. Xu
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Storage Capacity ◽  
Hydrological Models ◽  
Runoff Generation ◽  
Model Framework ◽  
Topographic Index ◽  
Topographic Analysis ◽  
Generation Algorithm ◽  
Water Storage Capacity

Abstract. World water resources have primarily been analysed by global-scale hydrological models in the last decades. Runoff generation in many of these models are based on process formulations developed at catchments scales. The division between slow runoff (baseflow) and fast runoff is primarily governed by slope and spatial distribution of effective water storage capacity, both acting a very small scales. Many hydrological models, e.g. VIC, account for the spatial storage variability in terms of statistical distributions; such models are generally proven to perform well. The statistical approaches, however, use the same runoff-generation parameters everywhere in a basin. The TOPMODEL concept, on the other hand, links the effective maximum storage capacity with real-world topography. Recent availability of global high-quality, high-resolution topographic data makes TOPMODEL attractive as a basis for a physically-based runoff-generation algorithm at large scales, even if its assumptions are not valid in flat terrain or for deep groundwater systems. We present a new runoff-generation algorithm for large-scale hydrology based on TOPMODEL concepts intended to overcome these problems. The TRG (topography-derived runoff generation) algorithm relaxes the TOPMODEL equilibrium assumption so baseflow generation is not tied to topography. TGR only uses the topographic index to distribute average storage to each topographic index class. The maximum storage capacity is proportional to the range of topographic index and is scaled by one parameter. The distribution of storage capacity within large-scale grid cells is obtained numerically through topographic analysis. The new topography-derived distribution function is then inserted into a runoff-generation framework similar VIC's. Different basin parts are parameterised by different storage capacities, and different shapes of the storage-distribution curves depend on their topographic characteristics. The TRG algorithm is driven by the HydroSHEDS dataset with a resolution of 3'' (around 90 m at the equator). The TRG algorithm was validated against the VIC algorithm in a common model framework in 3 river basins in different climates. The TRG algorithm performed equally well or marginally better than the VIC algorithm with one less parameter to be calibrated. The TRG algorithm also lacked equifinality problems and offered a realistic spatial pattern for runoff generation and evaporation.

scholarly journals Large-scale runoff generation – parsimonious parameterisation using high-resolution topography

2011 ◽  
Vol 15 (8) ◽  
pp. 2481-2494 ◽  
Author(s):  
L. Gong ◽  
S. Halldin ◽  
C.-Y. Xu
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Storage Capacity ◽  
Hydrological Models ◽  
Runoff Generation ◽  
Model Framework ◽  
Topographic Index ◽  
Topographic Analysis ◽  
Generation Algorithm ◽  
Water Storage Capacity

Abstract. World water resources have primarily been analysed by global-scale hydrological models in the last decades. Runoff generation in many of these models are based on process formulations developed at catchments scales. The division between slow runoff (baseflow) and fast runoff is primarily governed by slope and spatial distribution of effective water storage capacity, both acting at very small scales. Many hydrological models, e.g. VIC, account for the spatial storage variability in terms of statistical distributions; such models are generally proven to perform well. The statistical approaches, however, use the same runoff-generation parameters everywhere in a basin. The TOPMODEL concept, on the other hand, links the effective maximum storage capacity with real-world topography. Recent availability of global high-quality, high-resolution topographic data makes TOPMODEL attractive as a basis for a physically-based runoff-generation algorithm at large scales, even if its assumptions are not valid in flat terrain or for deep groundwater systems. We present a new runoff-generation algorithm for large-scale hydrology based on TOPMODEL concepts intended to overcome these problems. The TRG (topography-derived runoff generation) algorithm relaxes the TOPMODEL equilibrium assumption so baseflow generation is not tied to topography. TRG only uses the topographic index to distribute average storage to each topographic index class. The maximum storage capacity is proportional to the range of topographic index and is scaled by one parameter. The distribution of storage capacity within large-scale grid cells is obtained numerically through topographic analysis. The new topography-derived distribution function is then inserted into a runoff-generation framework similar VIC's. Different basin parts are parameterised by different storage capacities, and different shapes of the storage-distribution curves depend on their topographic characteristics. The TRG algorithm is driven by the HydroSHEDS dataset with a resolution of 3" (around 90 m at the equator). The TRG algorithm was validated against the VIC algorithm in a common model framework in 3 river basins in different climates. The TRG algorithm performed equally well or marginally better than the VIC algorithm with one less parameter to be calibrated. The TRG algorithm also lacked equifinality problems and offered a realistic spatial pattern for runoff generation and evaporation.

scholarly journals Derivation of the Spatial Distribution of Free Water Storage Capacity Based on Topographic Index

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1407
Author(s):  
Bingxing Tong ◽  
Zhijia Li ◽  
Cheng Yao ◽  
Jingfeng Wang ◽  
Yingchun Huang
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Land Surface ◽  
Storage Capacity ◽  
Water Storage ◽  
Free Water ◽  
Flood Events ◽  
Runoff Generation ◽  
Topographic Index ◽  
Water Storage Capacity

Free water storage capacity, an important characteristic of land surface related to runoff process, has a significant influence on runoff generation and separation. It is thus necessary to derive reasonable spatial distribution of free water storage capacity for rainfall-runoff simulation, especially in distributed modeling. In this paper, a topographic index based approach is proposed for the derivation of free water storage capacity spatial distribution. The topographic index, which can be obtained from digital elevation model (DEM), are used to establish a functional relationship with free water storage capacity in the proposed approach. In this case, the spatial variability of free water storage capacity can be directly estimated from the characteristics of watershed topography. This approach was tested at two medium sized watersheds, including Changhua and Chenhe, with the drainage areas of 905 km2 and 1395 km2, respectively. The results show that locations with larger values of free water storage capacity generally correspond to locations with higher topographic index values, such as riparian region. The estimated spatial distribution of free water storage capacity is also used in a distributed, grid-based Xinanjiang model to simulate 10 flood events for Chenhe Watershed and 17 flood events for Changhua Watershed. Our analysis indicates that the proposed approach based on topographic index can produce reasonable spatial variability of free water storage capacity and is more suitable for flood simulation.

scholarly journals A high-resolution global dataset of topographic index values for use in large-scale hydrological modelling

2014 ◽  
Vol 11 (6) ◽  
pp. 6139-6166 ◽  
Author(s):  
T. R. Marthews ◽  
S. J. Dadson ◽  
B. Lehner ◽  
S. Abele ◽  
N. Gedney
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Global Scale ◽  
Surface Fluxes ◽  
Data Sets ◽  
Topographic Index ◽  
Surface Water Flow ◽  
Land Surface Water ◽  
Compound Topographic Index

Abstract. Modelling land surface water flow is of critical importance for simulating land-surface fluxes, predicting runoff and water table dynamics and for many other applications of Land Surface Models. Many approaches are based on the popular hydrology model TOPMODEL, and the most important parameter of this model is the well-knowntopographic index. Here we present new, high-resolution parameter maps of the topographic index for all ice-free land pixels calculated from hydrologically-conditioned HydroSHEDS data sets using the GA2 algorithm. At 15 arcsec resolution, these layers are 4× finer than the resolution of the previously best-available topographic index layers, the Compound Topographic Index of HYDRO1k (CTI). In terms of the largest river catchments occurring on each continent, we found that in comparison to our revised values, CTI values were up to 20% higher in e.g. the Amazon. We found the highest catchment means were for the Murray-Darling and Nelson-Saskatchewan rather than for the Amazon and St. Lawrence as found from the CTI. We believe these new index layers represent the most robust existing global-scale topographic index values and hope that they will be widely used in land surface modelling applications in the future.

scholarly journals The Influence of a Prolonged Meteorological Drought on the Catchment Water Storage Capacity: A Hydrological Model Perspective

2020 ◽  
Author(s):  
Zhengke Pan ◽  
Pan Liu ◽  
Chongyu Xu ◽  
Lei Cheng ◽  
Jing Tian ◽  
...  
Keyword(s):  
Change Point ◽  
Storage Capacity ◽  
Critical Role ◽  
Time Lag ◽  
Water Storage ◽  
Meteorological Drought ◽  
Change Point Analysis ◽  
Runoff Generation ◽  
Broadleaf Forest ◽  
Water Storage Capacity

Abstract. Understanding the propagation process of prolonged meteorological droughts (i.e., decade) helps solve the problem of increasing water scarcity around the world. Historical literature studied the propagation between different drought types (e.g., from meteorological to hydrological drought) with mainly statistical approaches, however, little attention has been paid to the causality between the meteorological drought with potential changes in the Catchment Water Storage Capacity (CWSC) where the latter plays a critical role in catchment response behavior to former. This study used the temporal variation in the estimated value of a model parameter that denotes the CWSC in its model structure to reflect the potential changes in real CWSC. The most likely Change points of the CWSC were determined based on the Bayesian Change point analysis. Also, the possible association and linkage between the shift in the CWSC and the time-lag of the catchment (i.e., time-lag between the occurrence of the drought with the Change point) with multiple catchment properties and climate characteristics have been studied. Catchments from southeastern Australia were used as a study area to verify the effectiveness of the proposed approach. Results indicated that (1) in 62.7 % of the catchments, the sustained drought causes significant shifts in the CWSC. The shift led to the opposite response in two subsets of catchments, i.e., 48.2 % of catchments had lower runoff generation rates for a given rainfall while 14.5 % of catchments had higher runoff generation rate. (2) Catchments with larger elevation and slope, lower forest coverage of Evergreen Broadleaf Forest are more likely to have increase in the CWSC during a chronic drought while smaller catchments with lower elevation, lower coverage of the Evergreen Broadleaf Forest are more likely to have a decrease in the CWSC. (3) The changed catchments were not equally susceptible to the pressure due to persistent meteorological drought. Catchments with a lower proportion of Evergreen Broadleaf Forest usually have longer time-lag and are more resilient. This study improves our understanding of possible changes in CWSC induced by a prolonged meteorological drought, which will help improve our ability to simulate the hydrological system.

A hydrogeomorphic perspective on emergent topographic properties at landscape equilibrium

2021 ◽  
Author(s):  
David G. Litwin ◽  
Ciaran J. Harman ◽  
Gregory E. Tucker ◽  
Katherine R. Barnhart
Keyword(s):  
Overland Flow ◽  
Landscape Evolution ◽  
Subsurface Flow ◽  
Shallow Groundwater ◽  
Runoff Generation ◽  
Topographic Wetness Index ◽  
Topographic Analysis ◽  
Water Storage Capacity ◽  
Input Parameters

<p>Geomorphic properties of watersheds influence runoff generation, which drives landscape evolution over long timescales. Despite this strong process feedback, our understanding of how runoff generation affects long-term catchment evolution remains rudimentary. In most humid landscapes, storm runoff arises from shallow subsurface flow and from precipitation on saturated areas. Catchment geomorphology drives these runoff mechanisms, as landscape relief generates hydraulic gradients from hillslopes to streams, and regolith thickness and permeability affect flow partitioning and water storage capacity. However, there are few studies of how runoff coupled to dynamic shallow groundwater affects landscape form. In this study, we present a new groundwater-landscape evolution model and introduce a nondimensional framework to explore how subsurface-mediated runoff generation affects long-term catchment evolution. The model solves hydraulic groundwater equations to predict the water table location given prescribed recharge. Water in excess of the subsurface capacity for transport becomes overland flow, which may detach and transport sediment, affecting the landscape form that in turn affects runoff generation. We show that (1) two input parameters fully describe the possible steady state landscapes that coevolve under steady recharge, (2) subsurface flow capacity exerts a fundamental control on hillslope length and relief of these landscapes, and (3) three topographic metrics derived from the governing equations, steepness index, Laplacian curvature, and topographic wetness index, form a natural basis for evaluating the resulting coevolved landscapes. We derive a theoretical relationship using these metrics that allows us to recover the key model input parameters (including subsurface transmissivity) from topographic analysis of the landscape. These results open possibilities for topographic analysis of humid upland landscapes that could inform quantitative understanding of hydrological processes at the landscape scale.</p>

scholarly journals The influence of a prolonged meteorological drought on catchment water storage capacity: a hydrological-model perspective

2020 ◽  
Vol 24 (9) ◽  
pp. 4369-4387
Author(s):  
Zhengke Pan ◽  
Pan Liu ◽  
Chong-Yu Xu ◽  
Lei Cheng ◽  
Jing Tian ◽  
...  
Keyword(s):  
Change Point ◽  
Hydrological Model ◽  
Storage Capacity ◽  
Time Lag ◽  
Water Storage ◽  
Meteorological Drought ◽  
Model Parameters ◽  
Runoff Generation ◽  
Broadleaf Forest ◽  
Water Storage Capacity

Abstract. Understanding the propagation of prolonged meteorological drought helps solve the problem of intensified water scarcity around the world. Most of the existing literature studied the propagation of drought from one type to another (e.g., from meteorological to hydrological drought) with statistical approaches; there remains difficulty in revealing the causality between meteorological drought and potential changes in the catchment water storage capacity (CWSC). This study aims to identify the response of the CWSC to the meteorological drought by examining the changes of hydrological-model parameters after drought events. Firstly, the temporal variation of a model parameter that denotes that the CWSC is estimated to reflect the potential changes in the real CWSC. Next, the change points of the CWSC parameter were determined based on the Bayesian change point analysis. Finally, the possible association and linkage between the shift in the CWSC and the time lag of the catchment (i.e., time lag between the onset of the drought and the change point) with multiple catchment properties and climate characteristics were identified. A total of 83 catchments from southeastern Australia were selected as the study areas. Results indicated that (1) significant shifts in the CWSC can be observed in 62.7 % of the catchments, which can be divided into two subgroups with the opposite response, i.e., 48.2 % of catchments had lower runoff generation rates, while 14.5 % of catchments had higher runoff generation rate; (2) the increase in the CWSC during a chronic drought can be observed in smaller catchments with lower elevation, slope and forest coverage of evergreen broadleaf forest, while the decrease in the CWSC can be observed in larger catchments with higher elevation and larger coverage of evergreen broadleaf forest; (3) catchments with a lower proportion of evergreen broadleaf forest usually have a longer time lag and are more resilient. This study improves our understanding of possible changes in the CWSC induced by a prolonged meteorological drought, which will help improve our ability to simulate the hydrological system under climate change.

scholarly journals A hydrography upscaling method for scale invariant parametrization of distributed hydrological models

2020 ◽  
Author(s):  
Dirk Eilander ◽  
Willem van Verseveld ◽  
Dai Yamazaki ◽  
Albrecht Weerts ◽  
Hessel C. Winsemius ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Flow Direction ◽  
Hydrological Models ◽  
Scale Invariant ◽  
Distributed Hydrological Models ◽  
Relationship Of ◽  
Improved Accuracy ◽  
Basin Boundaries ◽  
Made In

Abstract. Distributed hydrological models rely on hydrography data such as flow direction, river length, slope and width. For large-scale applications, many of these models still rely on a few flow-direction datasets, which are often manually derived. We propose the Iterative Hydrography Upscaling (IHU) method to upscale high-resolution flow direction data to the typically coarser resolutions of distributed hydrological models. The IHU aims to preserve the upstream-downstream relationship of river structure, including basin boundaries, river meanders and confluences, in the D8 format, which is commonly used to describe river networks in models. Additionally, it derives sub-grid river attributes such as drainage area, river length, slope and width. We derived the multi-resolution MERIT Hydro IHU dataset at resolutions of 30 arcsec (~1 km), 5 arcmin (~10 km) and 15 arcmin (~30 km) by applying IHU to the recently published 3 arcsec MERIT Hydro data. Results indicate improved accuracy of IHU at all resolutions studied compared to other often applied methods. Furthermore, we show that using IHU-derived hydrography data minimizes the errors made in timing and magnitude of simulated peak discharge throughout the Rhine basin compared to simulations at the native data resolutions. As the method is fully automated, it can be applied to other high-resolution hydrography datasets to increase the accuracy and enhance the uptake of new datasets in distributed hydrological models in the future.

scholarly journals High-resolution meteorological forcing data for hydrological modelling and climate change impact analysis in the Mackenzie River Basin

2020 ◽  
Vol 12 (1) ◽  
pp. 629-645 ◽  
Author(s):  
Zilefac Elvis Asong ◽  
Mohamed Ezzat Elshamy ◽  
Daniel Princz ◽  
Howard Simon Wheater ◽  
John Willard Pomeroy ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Impact Assessment ◽  
River Basin ◽  
Climate Change Impact ◽  
Large Scale ◽  
Historical Record ◽  
Hydrological Models ◽  
Climate Change Impact Assessment ◽  
Change Impact

Abstract. Cold region hydrology is very sensitive to the impacts of climate warming. Impacts of warming over recent decades in western Canada include glacier retreat, permafrost thaw, and changing patterns of precipitation, with an increased proportion of winter precipitation falling as rainfall and shorter durations of snow cover, as well as consequent changes in flow regimes. Future warming is expected to continue along these lines. Physically realistic and sophisticated hydrological models driven by reliable climate forcing can provide the capability to assess hydrological responses to climate change. However, the provision of reliable forcing data remains problematic, particularly in data-sparse regions. Hydrological processes in cold regions involve complex phase changes and so are very sensitive to small biases in the driving meteorology, particularly in temperature and precipitation, including precipitation phase. Cold regions often have sparse surface observations, particularly at high elevations that generate a large amount of runoff. This paper aims to provide an improved set of forcing data for large-scale hydrological models for climate change impact assessment. The best available gridded data in Canada are from the high-resolution forecasts of the Global Environmental Multiscale (GEM) atmospheric model and outputs of the Canadian Precipitation Analysis (CaPA), but these datasets have a short historical record. The EU WATCH ERA-Interim reanalysis (WFDEI) has a longer historical record but has often been found to be biased relative to observations over Canada. The aim of this study, therefore, is to blend the strengths of both datasets (GEM-CaPA and WFDEI) to produce a less-biased long-record product (WFDEI-GEM-CaPA) for hydrological modelling and climate change impact assessment over the Mackenzie River Basin. First, a multivariate generalization of the quantile mapping technique was implemented to bias-correct WFDEI against GEM-CaPA at 3 h ×0.125∘ resolution during the 2005–2016 overlap period, followed by a hindcast of WFDEI-GEM-CaPA from 1979. The derived WFDEI-GEM-CaPA data are validated against station observations as a preliminary step to assess their added value. This product is then used to bias-correct climate projections from the Canadian Centre for Climate Modelling and Analysis Canadian Regional Climate Model (CanRCM4) between 1950 and 2100 under RCP8.5, and an analysis of the datasets shows that the biases in the original WFDEI product have been removed and the climate change signals in CanRCM4 are preserved. The resulting bias-corrected datasets are a consistent set of historical and climate projection data suitable for large-scale modelling and future climate scenario analysis. The final historical product (WFDEI-GEM-CaPA, 1979–2016) is freely available at the Federated Research Data Repository at https://doi.org/10.20383/101.0111 (Asong et al., 2018), while the original and corrected CanRCM4 data are available at https://doi.org/10.20383/101.0162 (Asong et al., 2019).

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