Exploring hydrologically similar catchments in terms of the physical characteristics of upstream regions

Abstract Catchment classification strategies based on easily available physical characteristics are important for extrapolating hydrologic model parameters and improving hydrologic predictions in ungauged catchments. In this study, we conduct an experiment of catchment classification and explore the feasibility of characterizing hydrologically similar catchments using certain physical characteristics in upstream regions of the Huai River Basin. The similarity metrics of hydrologic response factors (high flow, low flow and average annual runoff) and physical factors (topography, shape, soil and vegetation) are fed into the K-means algorithm for catchment classification. All the catchments are classified into two classes regardless of the types of metrics used. By comparing the overlap coefficient (η) and Rand index (RI) between any two classification results, we found that the topography classification displays the highest concordance with the high flow classification (η = 79.2% and RI = 0.66) among all metrics. Including more metrics would not produce consistently better classification results. The optimal combination of metrics, with η = 87.5%, is the high flow metrics (Q10%, SFH and MAX90) with the topography metrics (AS and HI). The results indicate that the physical metrics adopted for hydrologic classification should be determined carefully in terms of specific hydrologic characteristics.

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On the selection of precipitation products for the regionalisation of hydrological model parameters

Hydrology and Earth System Sciences ◽

10.5194/hess-25-5805-2021 ◽

2021 ◽

Vol 25 (11) ◽

pp. 5805-5837

Author(s):

Oscar M. Baez-Villanueva ◽

Mauricio Zambrano-Bigiarini ◽

Pablo A. Mendoza ◽

Ian McNamara ◽

Hylke E. Beck ◽

...

Keyword(s):

Hydrological Model ◽

Model Performance ◽

Temporal Distribution ◽

Hydrological Regime ◽

Hydrologic Model ◽

Spatial Proximity ◽

Model Parameters ◽

Ungauged Catchments ◽

Water Balance Components ◽

Feature Similarity

Abstract. Over the past decades, novel parameter regionalisation techniques have been developed to predict streamflow in data-scarce regions. In this paper, we examined how the choice of gridded daily precipitation (P) products affects the relative performance of three well-known parameter regionalisation techniques (spatial proximity, feature similarity, and parameter regression) over 100 near-natural catchments with diverse hydrological regimes across Chile. We set up and calibrated a conceptual semi-distributed HBV-like hydrological model (TUWmodel) for each catchment, using four P products (CR2MET, RF-MEP, ERA5, and MSWEPv2.8). We assessed the ability of these regionalisation techniques to transfer the parameters of a rainfall-runoff model, implementing a leave-one-out cross-validation procedure for each P product. Despite differences in the spatio-temporal distribution of P, all products provided good performance during calibration (median Kling–Gupta efficiencies (KGE′s) > 0.77), two independent verification periods (median KGE′s >0.70 and 0.61, for near-normal and dry conditions, respectively), and regionalisation (median KGE′s for the best method ranging from 0.56 to 0.63). We show how model calibration is able to compensate, to some extent, differences between P forcings by adjusting model parameters and thus the water balance components. Overall, feature similarity provided the best results, followed by spatial proximity, while parameter regression resulted in the worst performance, reinforcing the importance of transferring complete model parameter sets to ungauged catchments. Our results suggest that (i) merging P products and ground-based measurements does not necessarily translate into an improved hydrologic model performance; (ii) the spatial resolution of P products does not substantially affect the regionalisation performance; (iii) a P product that provides the best individual model performance during calibration and verification does not necessarily yield the best performance in terms of parameter regionalisation; and (iv) the model parameters and the performance of regionalisation methods are affected by the hydrological regime, with the best results for spatial proximity and feature similarity obtained for rain-dominated catchments with a minor snowmelt component.

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Transferability of the Xin'anjiang model based on similarity in climate and geography

Water Science & Technology Water Supply ◽

10.2166/ws.2021.055 ◽

2021 ◽

Author(s):

Yue Liu ◽

Jian-yun Zhang ◽

Amgad Elmahdi ◽

Qin-li Yang ◽

Xiao-xiang Guan ◽

...

Keyword(s):

Eastern China ◽

Hydrologic Model ◽

Hydrological Modelling ◽

Model Parameters ◽

Hydrological Process ◽

Hydrologic Models ◽

Ungauged Catchments ◽

Model Based ◽

Hydrological Cycles ◽

Xin’Anjiang Model

Abstract Hydrological experiments are essential to understand the hydrological cycles and promoting the development of hydrologic models. Model parameter transfers provide a new way of doing hydrological forecasts and simulations in ungauged catchments. To study the transferability of model parameters for hydrological modelling and the influence of parameter transfers on hydrological simulations, the Xin'anjiang model (XAJ model), which is a lumped hydrologic model based on a saturation excess mechanism, and has been widely applied in different climate regions of the world, was applied to a low hilly catchment in eastern China, the Chengxi Experimental Watershed (CXEW). The suitability of the XAJ model was tested in the eastern branch catchment of CXEW and the calibrated model parameters of the eastern branch catchment were then transferred to the western branch catchment and the entire watershed of the CXEW. The results show that the XAJ model performs well for the calibrated eastern branch catchment at both daily and monthly scales on hydrological modelling with the NSEs over 0.6 and the REs less than 2.0%. Besides, the uncalibrated catchments of the western branch catchment and the entire watershed of the CSEW share similarities in climate (the precipitation) and geography (the soil texture and vegetation cover) with the calibrated catchment, the XAJmodel and the transferred model parameters can capture the main features of the hydrological processes in both uncalibrated catchments (western catchments and entire watershed). This transferability of the model is useful for a scarce data region to simulate the hydrological process and its forecasting.

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Estimation of parameters in a distributed precipitation-runoff model for Norway

Hydrology and Earth System Sciences ◽

10.5194/hess-7-304-2003 ◽

2003 ◽

Vol 7 (3) ◽

pp. 304-316 ◽

Cited By ~ 92

Author(s):

S. Beldring ◽

K. Engeland ◽

L. A. Roald ◽

N. R. Sælthun ◽

A. Voksø

Keyword(s):

Land Surface ◽

Distributed Model ◽

Small Scale ◽

Model Parameters ◽

Estimation Of Parameters ◽

Ungauged Catchments ◽

Time Step ◽

Landscape Characteristics ◽

Average Annual Runoff ◽

Scale Properties

Abstract. A distributed version of the HBV-model using 1 km2 grid cells and daily time step was used to simulate runoff from the entire land surface of Norway for the period 1961-1990. The model was sensitive to changes in small scale properties of the land surface and the climatic input data, through explicit representation of differences between model elements, and by implicit consideration of sub-grid variations in moisture status. A geographically transferable set of model parameters was determined by a multi-criteria calibration strategy, which simultaneously minimised the residuals between model simulated and observed runoff from 141 Norwegian catchments located in areas with different runoff regimes and landscape characteristics. Model discretisation units with identical landscape classification were assigned similar parameter values. Model performance was evaluated by simulating discharge from 43 independent catchments. Finally, a river routing procedure using a kinematic wave approximation to open channel flow was introduced in the model, and discharges from three additional catchments were calculated and compared with observations. The model was used to produce a map of average annual runoff for Norway for the period 1961-1990. Keywords: distributed model, multi-criteria calibration, global parameters, ungauged catchments.

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Multi-objective Optimization of Catchment Reforestation Robust to Uncertainty in Bayesian-Calibrated Watershed Model Parameters

10.5194/egusphere-egu21-6234 ◽

2021 ◽

Author(s):

Jared Smith ◽

Laurence Lin ◽

Julianne Quinn ◽

Lawrence Band

Keyword(s):

Computational Cost ◽

Bayesian Framework ◽

Hydrologic Model ◽

Low Flow ◽

Critical Parameters ◽

Model Parameters ◽

Calibration Data ◽

Baltimore County ◽

True Parameter ◽

Multi Objective

Urban land expansion is expected for our changing world, which unmitigated will result in increased flooding and nutrient exports that already wreak havoc on the wellbeing of coupled human-natural systems worldwide. Reforestation of urbanized catchments is one green infrastructure strategy to reduce stormwater volumes and nutrient exports. Reforestation designs must balance the benefits of flood flow reduction against the costs of implementation and the chance to exacerbate droughts via reduction in recharge that supplies low flows. Optimal locations and numbers of trees depend on the spatial distribution of runoff and streamflow in a catchment; however, calibration data are often only available at the catchment outlet. Equifinal model parameterizations for the outlet can result in uncertainty in the locations and magnitudes of streamflows across the catchment, which can lead to different optimal reforestation designs for different parameterizations.Multi-objective robust optimization (MORO) has been proposed to discover reforestation designs that are robust to such parametric model uncertainty. However, it has not been shown that this actually results in better decisions than optimizing to a single, most likely parameter set, which would be less computationally expensive. In this work, the utility of MORO is assessed by comparing reforestation designs optimized using these two approaches with reforestation designs optimized to a synthetic true set of hydrologic model parameters. The spatially-distributed RHESSys ecohydrological model is employed for this study of a suburban-forested catchment in Baltimore County, Maryland, USA. Calibration of the model&#8217;s critical parameters is completed using a Bayesian framework to estimate the joint posterior distribution of the parameters. The Bayesian framework estimates the probability that different parameterizations generated the synthetic streamflow data, allowing the MORO process to evaluate reforestation portfolios across a probability-weighted sample of parameter sets in search of solutions that are robust to this uncertainty.Reforestation portfolios are designed to minimize flooding, low flow intensity, and construction costs (number of trees). Comparing the Pareto front obtained from using MORO with the Pareto fronts obtained from optimizing to the estimated maximum a posteriori (MAP) parameter set and the synthetic true parameter set, we find that MORO solutions are closer to the synthetic solutions than are MAP solutions. This illustrates the value of considering parametric uncertainty in designing robust water systems despite the additional computational cost.

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A trading-space-for-time approach to probabilistic continuous streamflow predictions in a changing climate – accounting for changing watershed behavior

Hydrology and Earth System Sciences ◽

10.5194/hess-15-3591-2011 ◽

2011 ◽

Vol 15 (11) ◽

pp. 3591-3603 ◽

Cited By ~ 72

Author(s):

R. Singh ◽

T. Wagener ◽

K. van Werkhoven ◽

M. E. Mann ◽

R. Crane

Keyword(s):

Spatial Relationship ◽

Model Performance ◽

Climate Scenario ◽

Hydrologic Model ◽

Climatic Conditions ◽

Low Flow ◽

Parameter Estimates ◽

Model Parameters ◽

Watershed Model ◽

The Impact

Abstract. Projecting how future climatic change might impact streamflow is an important challenge for hydrologic science. The common approach to solve this problem is by forcing a hydrologic model, calibrated on historical data or using a priori parameter estimates, with future scenarios of precipitation and temperature. However, several recent studies suggest that the climatic regime of the calibration period is reflected in the resulting parameter estimates and model performance can be negatively impacted if the climate for which projections are made is significantly different from that during calibration. So how can we calibrate a hydrologic model for historically unobserved climatic conditions? To address this issue, we propose a new trading-space-for-time framework that utilizes the similarity between the predictions under change (PUC) and predictions in ungauged basins (PUB) problems. In this new framework we first regionalize climate dependent streamflow characteristics using 394 US watersheds. We then assume that this spatial relationship between climate and streamflow characteristics is similar to the one we would observe between climate and streamflow over long time periods at a single location. This assumption is what we refer to as trading-space-for-time. Therefore, we change the limits for extrapolation to future climatic situations from the restricted locally observed historical variability to the variability observed across all watersheds used to derive the regression relationships. A typical watershed model is subsequently calibrated (conditioned) on the predicted signatures for any future climate scenario to account for the impact of climate on model parameters within a Bayesian framework. As a result, we can obtain ensemble predictions of continuous streamflow at both gauged and ungauged locations. The new method is tested in five US watersheds located in historically different climates using synthetic climate scenarios generated by increasing mean temperature by up to 8 °C and changing mean precipitation by −30% to +40% from their historical values. Depending on the aridity of the watershed, streamflow projections using adjusted parameters became significantly different from those using historically calibrated parameters if precipitation change exceeded −10% or +20%. In general, the trading-space-for-time approach resulted in a stronger watershed response to climate change for both high and low flow conditions.

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Estimation of flow in ungauged catchments by coupling a hydrological model and neural networks: case study

Hydrology Research ◽

10.2166/nh.2011.157 ◽

2011 ◽

Vol 42 (5) ◽

pp. 386-400 ◽

Cited By ~ 9

Author(s):

A. H. Saliha ◽

S. B. Awulachew ◽

J. Cullmann ◽

Hans-B. Horlacher

Keyword(s):

Regional Model ◽

Homogeneous Group ◽

Low Flow ◽

Model Parameters ◽

Watershed Model ◽

Ungauged Catchments ◽

Kohonen Neural Network ◽

Catchment Characteristics ◽

Ungauged Basin ◽

Distributed Hydrological Models

The prediction of hydrological variables for ungauged basins is still a big challenge. Regionalization is the most widely used method to date, which relates parameters of watershed models to catchment characteristics. Relating catchment characteristics to watershed model parameters is too difficult for distributed hydrological models, due to the heterogeneous nature of catchments. A regional model was proposed by coupling a Kohonen neural network (KNN) and distributed Water Balance Simulation Model (WaSiM-ETH) to estimate flow in ungauged basin. KNN was used to delineate a hydrological homogeneous group based on predefined physical characteristics of catchments and WaSiM-ETH was applied to generate daily stream flow. Twenty-six subcatchments of the Blue Nile River basin, Ethiopia, were grouped into five hydrological homogenous groups, each with its own full set of optimized WaSiM-ETH parameters. In the regional model, the KNN assigned the ungauged catchment into one of the five hydrological homogenous groups. The whole set of optimized WaSiM parameters from the homogeneous group (which the ungauged river belongs to) were transferred to the ungauged river and WaSiM-ETH was used to compute the flow for this ungauged river. The regional model generally overestimated the low flow. In general, the results for validation subcatchments showed the regional model is satisfactory in transferring information from data-rich to data-poor catchments.

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Relative impacts of climate change and land cover change on streamflow using SWAT in the Clackamas River Watershed, USA

Journal of Water and Climate Change ◽

10.2166/wcc.2020.123 ◽

2020 ◽

Author(s):

Junjie Chen ◽

Heejun Chang

Keyword(s):

Climate Change ◽

Land Cover ◽

Land Cover Change ◽

Water Resource Management ◽

Assessment Tool ◽

Hydrologic Model ◽

Low Flow ◽

Model Parameters ◽

River Watershed ◽

Daily Streamflow

Abstract To understand the spatial–temporal pattern of climate and land cover (CLC) change effects on hydrology, we used three land cover change (LCC) coupled scenarios to estimate the changes in streamflow metrics in the Clackamas River Watershed in Oregon for the 2050s (2040–2069) and the 2080s (2070–2099). Coupled scenarios, which were split into individual and combined simulations such as climate change (CC), LCC, CLC change, and daily streamflow were simulated in the Soil and Water Assessment Tool. The interannual variability of streamflow was higher in the lower urbanized area than the upper forested region. The watershed runoff was projected to be more sensitive to CC than LCC. Under the CLC scenario, the top 10% peak flow and the 7-day low flow are expected to increase (2–19%) and decrease (+9 to −20 cm s), respectively, in both future periods. The center timing of runoff in the year is projected to shift 2–3 weeks earlier in response to warming temperature and more winter precipitation falling as rain. High streamflow variability in our findings suggests that uncertainties can stem from both climate models and hydrologic model parameters, calling for more adaptive water resource management in the watershed.

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The critical role of uncertainty in projections of hydrological extremes

Hydrology and Earth System Sciences ◽

10.5194/hess-21-4245-2017 ◽

2017 ◽

Vol 21 (8) ◽

pp. 4245-4258 ◽

Cited By ~ 12

Author(s):

Hadush K. Meresa ◽

Renata J. Romanowicz

Keyword(s):

Hydrological Model ◽

Climate Model ◽

Critical Role ◽

Resource Planning ◽

High Flow ◽

Low Flow ◽

Model Parameters ◽

Climate Projections ◽

Hydrological Extremes ◽

Distribution Fit

Abstract. This paper aims to quantify the uncertainty in projections of future hydrological extremes in the Biala Tarnowska River at Koszyce gauging station, south Poland. The approach followed is based on several climate projections obtained from the EURO-CORDEX initiative, raw and bias-corrected realizations of catchment precipitation, and flow simulations derived using multiple hydrological model parameter sets. The projections cover the 21st century. Three sources of uncertainty are considered: one related to climate projection ensemble spread, the second related to the uncertainty in hydrological model parameters and the third related to the error in fitting theoretical distribution models to annual extreme flow series. The uncertainty of projected extreme indices related to hydrological model parameters was conditioned on flow observations from the reference period using the generalized likelihood uncertainty estimation (GLUE) approach, with separate criteria for high- and low-flow extremes. Extreme (low and high) flow quantiles were estimated using the generalized extreme value (GEV) distribution at different return periods and were based on two different lengths of the flow time series. A sensitivity analysis based on the analysis of variance (ANOVA) shows that the uncertainty introduced by the hydrological model parameters can be larger than the climate model variability and the distribution fit uncertainty for the low-flow extremes whilst for the high-flow extremes higher uncertainty is observed from climate models than from hydrological parameter and distribution fit uncertainties. This implies that ignoring one of the three uncertainty sources may cause great risk to future hydrological extreme adaptations and water resource planning and management.

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A Model-Based Tool for Assessing the Impact of Land Use Change Scenarios on Flood Risk in Small-Scale River Systems—Part 1: Pre-Processing of Scenario Based Flood Characteristics for the Current State of Land Use

Hydrology ◽

10.3390/hydrology8030102 ◽

2021 ◽

Vol 8 (3) ◽

pp. 102

Author(s):

Frauke Kachholz ◽

Jens Tränckner

Keyword(s):

Land Use ◽

River Flow ◽

Land Use Changes ◽

Small Rivers ◽

Small Scale ◽

Model Parameters ◽

Ungauged Catchments ◽

Model Based ◽

Current State ◽

The Impact

Land use changes influence the water balance and often increase surface runoff. The resulting impacts on river flow, water level, and flood should be identified beforehand in the phase of spatial planning. In two consecutive papers, we develop a model-based decision support system for quantifying the hydrological and stream hydraulic impacts of land use changes. Part 1 presents the semi-automatic set-up of physically based hydrological and hydraulic models on the basis of geodata analysis for the current state. Appropriate hydrological model parameters for ungauged catchments are derived by a transfer from a calibrated model. In the regarded lowland river basins, parameters of surface and groundwater inflow turned out to be particularly important. While the calibration delivers very good to good model results for flow (Evol =2.4%, R = 0.84, NSE = 0.84), the model performance is good to satisfactory (Evol = −9.6%, R = 0.88, NSE = 0.59) in a different river system parametrized with the transfer procedure. After transferring the concept to a larger area with various small rivers, the current state is analyzed by running simulations based on statistical rainfall scenarios. Results include watercourse section-specific capacities and excess volumes in case of flooding. The developed approach can relatively quickly generate physically reliable and spatially high-resolution results. Part 2 builds on the data generated in part 1 and presents the subsequent approach to assess hydrologic/hydrodynamic impacts of potential land use changes.

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Unraveling the time-dependent relevance of input model uncertainties for a lumped hydrologic model of a pre-alpine karst system

Hydrogeology Journal ◽

10.1007/s10040-021-02377-1 ◽

2021 ◽

Author(s):

Daniel Bittner ◽

Beatrice Richieri ◽

Gabriele Chiogna

Keyword(s):

Groundwater Recharge ◽

Temporal Variations ◽

Hydrologic Model ◽

Time Dependent ◽

Model Parameters ◽

Lumped Parameter ◽

Hydrodynamic Processes ◽

Input Model ◽

Karst Model ◽

Input Uncertainties

AbstractUncertainties in hydrologic model outputs can arise for many reasons such as structural, parametric and input uncertainty. Identification of the sources of uncertainties and the quantification of their impacts on model results are important to appropriately reproduce hydrodynamic processes in karst aquifers and to support decision-making. The present study investigates the time-dependent relevance of model input uncertainties, defined as the conceptual uncertainties affecting the representation and parameterization of processes relevant for groundwater recharge, i.e. interception, evapotranspiration and snow dynamic, on the lumped karst model LuKARS. A total of nine different models are applied, three to compute interception (DVWK, Gash and Liu), three to compute evapotranspiration (Thornthwaite, Hamon and Oudin) and three to compute snow processes (Martinec, Girons Lopez and Magnusson). All the input model combinations are tested for the case study of the Kerschbaum spring in Austria. The model parameters are kept constant for all combinations. While parametric uncertainties computed for the same model in previous studies do not show pronounced temporal variations, the results of the present work show that input uncertainties are seasonally varying. Moreover, the input uncertainties of evapotranspiration and snowmelt are higher than the interception uncertainties. The results show that the importance of a specific process for groundwater recharge can be estimated from the respective input uncertainties. These findings have practical implications as they can guide researchers to obtain relevant field data to improve the representation of different processes in lumped parameter models and to support model calibration.

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