Hydrology under change: an evaluation protocol to investigate how hydrological models deal with changing catchments

Thirel, Guillaume; Andréassian, Vazken; Perrin, Charles; Audouy, J.-N.; Berthet, Lionel; Edwards, Pamela J.; Folton, Nathalie; Furusho, Carina; Kuentz, Anna; Lerat, Julien; Lindström, Göran; Martin, E.; Mathevet, Thibault; Merz, Ralf; Parajka, J.; Ruelland, Denis; Vaze, Jai

doi:10.1080/02626667.2014.967248

Cited by 121 publications

(103 citation statements)

References 31 publications

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“…Time-variant hydrological model parameters have been reported in a few recent publications (Merz et al, 2011;Brigode et al, 2013;Jeremiah et al, 2013;Thirel et al, 2015;Westra et al, 2014;Patil and Stieglitz, 2015). For example, Ye et al (1997) and Paik et al (2005) mentioned the seasonal variations of hydrological model parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Westra et al (2014) proposed a strategy to cope with nonstationarity of hydrological model parameters, which were represented as a function of a time-varying covariate set before using an optimization algorithm for calibration. Previous studies provided two main methods to estimate the time-variant model parameters: (1) available historical records are divided into consecutive subsets, and parameters are calibrated separately for each subset using an optimization algorithm (Merz et al, 2011;Thirel et al, 2015); (2) a functional form of selected time-variant model parameters is constructed, and the parameters for the function are estimated using an optimization algorithm based on the entire historical record (Jeremiah et al, 2013;Westra et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Identification of hydrological model parameter variation using ensemble Kalman filter

Deng

Liu

Guo

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Hydrological model parameters play an important role in the ability of model prediction. In a stationary context, parameters of hydrological models are treated as constants; however, model parameters may vary with time under climate change and anthropogenic activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model (TWBM) by assimilating the runoff observations. Through a synthetic experiment, the proposed method is evaluated with time-invariant (i.e., constant) parameters and different types of parameter variations, including trend, abrupt change and periodicity. Various levels of observation uncertainty are designed to examine the performance of the EnKF. The results show that the EnKF can successfully capture the temporal variations of the model parameters. The application to the Wudinghe basin shows that the water storage capacity (SC) of the TWBM model has an apparent increasing trend during the period from 1958 to 2000. The identified temporal variation of SC is explained by land use and land cover changes due to soil and water conservation measures. In contrast, the application to the Tongtianhe basin shows that the estimated SC has no significant variation during the simulation period of 1982-2013, corresponding to the relatively stationary catchment properties. The evapotranspiration parameter (C) has temporal variations while no obvious change patterns exist. The proposed method provides an effective tool for quantifying the temporal variations of the model parameters, thereby improving the accuracy and reliability of model simulations and forecasts.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of hydrological model parameter variation using ensemble Kalman filter

Deng

Liu

Guo

et al. 2016

Hydrol. Earth Syst. Sci.

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“…Daily precipitation, temperature, potential evapotranspiration, and streamflow data were assembled by IRSTEA (http://non-stationarities.irstea.fr/data-2/data/) for 14 catchments around the world (Thirel et al, 2014). For one of the smallest watersheds, i.e.…”

Section: Methodsmentioning

confidence: 99%

How would peak rainfall intensity affect runoff predictions using conceptual water balance models?

2015

Proc. IAHS

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Abstract. Most hydrological models use continuous daily precipitation and potential evapotranspiration for streamflow estimation. With the projected increase in mean surface temperature, hydrological processes are set to intensify irrespective of the underlying changes to the mean precipitation. The effect of an increase in rainfall intensity on the long-term water balance is, however, not adequately accounted for in the commonly used hydrological models. This study follows from a previous comparative analysis of a non-stationary daily series of stream flow of a forested watershed (River Rimbaud) in the French Alps (area = 1.478 km 2 ) . Non-stationarity in the recorded stream flow occurred as a result of a severe wild fire in 1990. Two daily models (AWBM and SimHyd) were initially calibrated for each of three distinct phases in relation to the well documented land disturbance. At the daily and monthly time scales, both models performed satisfactorily with the Nash-Sutcliffe coefficient of efficiency (NSE) varying from 0.77 to 0.92. When aggregated to the annual time scale, both models underestimated the flow by about 22 % with a reduced NSE at about 0.71. Exploratory data analysis was undertaken to relate daily peak hourly rainfall intensity to the discrepancy between the observed and modelled daily runoff amount. Preliminary results show that the effect of peak hourly rainfall intensity on runoff prediction is insignificant, and model performance is unlikely to improve when peak daily precipitation is included. Trend analysis indicated that the large decrease of precipitation when daily precipitation amount exceeded 10-20 mm may have contributed greatly to the decrease in stream flow of this forested watershed.

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“…This cannot be achieved by following the routine water quality monitoring programmes, or relying on grab and/or continuous river sampling. Therefore, process-based hydrological and water quality models with varying levels of complexity, have been used to provide useful information on catchment responses in terms of nutrient loading, owing to land use activities, soil type, crop management and climatic conditions (Thirel et al, 2015). 45…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Hype Model for Simulation of Water and Nutrients in the Upper uMngeni River Catchment in South Africa

Namugize

Jewitt

Clark

et al. 2017

Preprint

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Abstract. Most studies considering water quality pollution in the upper reaches of the uMngeni Catchment have relied on the 10 physical grab sampling of water and the subsequent laboratory analysis of chemical determinants. However, this provides limited spatial and temporal information. Thus, the objectives of this study are to assess the capability of the Hydrological Predictions for the Environment (HYPE) model in simulating streamflow, dissolved inorganic nitrogen (DIN) and total phosphorus (TP), in the fast developing uMngeni Catchment in KwaZulu-Natal province, South Africa. The model was set up and calibrated, following a stepwise approach and then validated. Results indicated that the simulation of discharge is most 15 sensitive to the parameters related to evapotranspiration and the water-holding capacity of the soil, while DIN and TP are affected by plant uptake and initial pools of nutrients. DIN is also affected by denitrification. Runoff was captured well during the calibration (1989)(1990)(1991)(1992)(1993)(1994)(1995) and validation periods , with a Nash-Sutcliffe efficiency (NSE) greater than 0.0 in eight of the nine stations and a Pearson's correlation coefficient (r) of > 0.5 at all the sub-catchments. High streamflow events were represented well, low streamflows were over-simulated. The accumulative streamflows were over-predicted in the 20 downstream sub-catchments, with an absolute percentage of bias (PBIAS) of > 25 %. The transport and dynamics of DIN and TP vary differently and they are driven by hydrological and biochemical processes. The concentration of TP follows the pattern of the streamflow, whereas DIN shows an inconsistent variation. The values of DIN decrease from upstream to downstream, while the TP values increase from the headwaters to the outlet of the catchment. Agricultural activities were found to be the largest source of DIN, while the TP is mainly ascribed to the point sources of pollution. 25

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Hydrology under change: an evaluation protocol to investigate how hydrological models deal with changing catchments

Cited by 121 publications

References 31 publications

Identification of hydrological model parameter variation using ensemble Kalman filter

Identification of hydrological model parameter variation using ensemble Kalman filter

How would peak rainfall intensity affect runoff predictions using conceptual water balance models?

Assessment of the Hype Model for Simulation of Water and Nutrients in the Upper uMngeni River Catchment in South Africa

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