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IntroductionTOPMODEL (Beven & Kirkby, 1979) continues to be one of the most widely applied hydrological models in both research and practice (Beven et al., 2021). It assumes that, independent of their specific spatial locations, some parts of the catchment will manifest similar tendencies to become, and remain, saturated during a storm. Based on a catchment-specific Topographic Index (TI) of similarity, these areas are grouped together to form Hydrologically Similar Units (HSUs), for which the numerical computations are performed when estimating a catchment's runoff response to rainfall. This grouping has enabled the dramatic reduction in runtimes associated with TOPMODEL, compared to fully spatially distributed models such as those of Freeze and Harlan (1969), Loague (2010), and Gao et al. (2015.Though fast, TOPMODEL is a lumped model in that only a single value of catchment average subsurface storage is updated in each timestep, from which individual HSU subsurface storage values are back-calculated according to the deviation of their TI value from the catchment average TI value (Beven, 2011, p. 211); hence, it can be construed as a quasi single-store (equally a pseudo multi-store) model. Implicit in such application of a time-invariant TI is the assumption (a) that the transients of water-table between HSUs across the catchment are fast enough, that in each timestep water table can be approximated by a steady-state configuration. At the same time the (quasi) single-store representation leads to assumption (b) that during rainfall there is always downslope flow at each and every point in the catchment, equal to the recharge rate from all of the upslope areas draining to that point (i.e., there is always downslope connectivity everywhere, all the way to the stream network) (Beven et al., 2021). Both assumptions (a) and (b) are clearly approximations.Dynamic-TOPMODEL of Beven and Freer (2001) relaxed both of the above assumptions by allowing subsurface storage of individual HSUs to vary locally and independently of both the catchment average storage and TI, by incorporating a time-dependent kinematic wave solution to the subsurface flow. However, since its introduction 20 years ago, and despite significantly improving catchment representation, the original steady-state version has remained the preferred choice (albeit sometimes with modifications/improvements) (
IntroductionTOPMODEL (Beven & Kirkby, 1979) continues to be one of the most widely applied hydrological models in both research and practice (Beven et al., 2021). It assumes that, independent of their specific spatial locations, some parts of the catchment will manifest similar tendencies to become, and remain, saturated during a storm. Based on a catchment-specific Topographic Index (TI) of similarity, these areas are grouped together to form Hydrologically Similar Units (HSUs), for which the numerical computations are performed when estimating a catchment's runoff response to rainfall. This grouping has enabled the dramatic reduction in runtimes associated with TOPMODEL, compared to fully spatially distributed models such as those of Freeze and Harlan (1969), Loague (2010), and Gao et al. (2015.Though fast, TOPMODEL is a lumped model in that only a single value of catchment average subsurface storage is updated in each timestep, from which individual HSU subsurface storage values are back-calculated according to the deviation of their TI value from the catchment average TI value (Beven, 2011, p. 211); hence, it can be construed as a quasi single-store (equally a pseudo multi-store) model. Implicit in such application of a time-invariant TI is the assumption (a) that the transients of water-table between HSUs across the catchment are fast enough, that in each timestep water table can be approximated by a steady-state configuration. At the same time the (quasi) single-store representation leads to assumption (b) that during rainfall there is always downslope flow at each and every point in the catchment, equal to the recharge rate from all of the upslope areas draining to that point (i.e., there is always downslope connectivity everywhere, all the way to the stream network) (Beven et al., 2021). Both assumptions (a) and (b) are clearly approximations.Dynamic-TOPMODEL of Beven and Freer (2001) relaxed both of the above assumptions by allowing subsurface storage of individual HSUs to vary locally and independently of both the catchment average storage and TI, by incorporating a time-dependent kinematic wave solution to the subsurface flow. However, since its introduction 20 years ago, and despite significantly improving catchment representation, the original steady-state version has remained the preferred choice (albeit sometimes with modifications/improvements) (
Field‐scale experiments have shown the Natural Flood Management (NFM) potential of peatland restoration. The likelihoods of effectiveness are yet unknown at scales and storms large enough to impact human lives. Using GMD‐TOPMODEL, we upscale a rare Before‐After‐Control‐Intervention empirical data set to a 25 km2 catchment with >600 properties at flood‐risk, and test storms of up to a 1,000‐year return period (RP). Under these scales/storms, we find that it is not necessary (nor feasible) to delay the outlet flow‐peak to meaningfully attenuate it. Enhancing catchment “kinematic” storage, for example, through restoration, can be sufficient to reduce flow magnitudes without detectable changes to peak‐flow timing. NFM benefit increases exponentially with restoration area size under smaller storms, but linearly under larger storms. At RP ≤ 100 years, longer‐lasting frontal‐type storms are more challenging to defend against via NFM, but at RP > 100 years shorter‐duration convectional‐type events become more challenging. In the order of 1,000–10 years storms: (a) revegetating the bare‐peat areas in 15% of the catchment is 31%–61% likely to reduce peak‐flows by >5%; (b) revegetating & damming the erosion gullies in ∼20% of the catchment is 42%–71% likely to reduce peak‐flows by >5%; (c) Growth of Sphagnum in the dammed gullies of ∼20% and ∼40% of the catchment increase the likelihoods of >5% peak reductions to 65%–86% and 90%–98%, respectively. The numerical evidence of significant NFM benefit due to Sphagnum re‐establishment is an important finding, because it shows that meaningful flood‐risk mitigation in headwater catchments under scales/storms relevant to communities at risk can be delivered alongside other ecosystem benefits of Sphagnum re‐establishment.
TOPMODEL has been widely employed in hydrology research, undergoing continuous modifications to broaden its practical applicability and enhance its simulation accuracy. To encompass spatial discretization, diffusion‐wave characteristics, depth‐dependent flow velocity, and flux estimation in the unsaturated zone, a generalized dynamic TOPMODEL is developed by introducing a greater number of physical parameters. The present study aims to evaluate the optimal combination of these parameters within the dynamic TOPMODEL framework using machine learning techniques to improve the accuracy of runoff predictions and bolster the model's reliability. An innovative training method is suggested to elevate the model's performance by integrating the Long Short‐Term Memory (LSTM) algorithm and a topological classification, which relies on the evolving spatial distribution of runoff conditions during floods. The research findings show that the proposed methodology achieves the lowest mean relative error (MRE) at 0.106, the highest Pearson correlation coefficient (PC) at 0.938, and the highest coefficient of determination (R2) at 0.906 among the three dynamic TOPMODEL types adopted in this study. The effective implementation of a case study in a river basin showcases the feasibility of the proposed method in conjunction with dynamic TOPMODEL and underscores the importance of employing the suggested training procedure.
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