Automatic calibration of complex hydro-ecological models is an increasingly important issue which involves making decisions. One of the most relevant is the choice of the objective function, but its effects have been scarcely studied in complex models. We have used the SWAT model to assess the impact of the objective function for a multi-site (4 stations) and multi-variable (OrgP, OrgN, NO 3 − , PO 4 3− ) calibration of the Odense catchment (Denmark). Six calibration schemes were tested, varying the objective function and the nutrient fractions targeted. The best performance metrics (R 2 , NSE, PBIAS) were obtained when using NSE as objective function and targeting N-fractions and P-fractions separately. The scheme was validated in another SWAT set-up in northern Denmark. Although NSE is often questioned, we found it as an adequate objective function when addressing a multi-site and multi-variable calibration. Our findings may serve as guideline for hydro-ecological modellers, being useful to achieve watershed management goals.

The impact of the objective function in multi-site and multi-variable calibration of the SWAT model

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR029001

The abuse of popular performance metrics in hydrologic modeling

. This paper presents the Australian edition of the Catchment Attributes and
Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS (Australia) comprises data for 222 unregulated catchments, combining hydrometeorological
time series (streamflow and 18 climatic variables) with 134 attributes
related to geology, soil, topography, land cover, anthropogenic influence
and hydroclimatology. The CAMELS-AUS catchments have been monitored for
decades (more than 85 % have streamflow records longer than 40 years) and
are relatively free of large-scale changes, such as significant changes in
land use. Rating curve uncertainty estimates are provided for most (75 %)
of the catchments, and multiple atmospheric datasets are included, offering
insights into forcing uncertainty. This dataset allows users globally to
freely access catchment data drawn from Australia's unique hydroclimatology,
particularly notable for its large interannual variability. Combined with
arid catchment data from the CAMELS datasets for the USA and Chile,
CAMELS-AUS constitutes an unprecedented resource for the study of arid-zone
hydrology. CAMELS-AUS is freely downloadable from https://doi.org/10.1594/PANGAEA.921850 (Fowler et al., 2020a).

/pdf/camels-aus-hydrometeorological-time-series-and-landscape-wolclcbkwm.pdf

CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia

. Monthly to seasonal streamflow forecasts provide useful information for a
range of water resource management and planning applications. This work
focuses on improving such forecasts by considering the following two aspects:
(1) state updating to force the models to match observations from the start
of the forecast period, and (2) selection of a shorter calibration period
that is more representative of the forecast period, compared to a longer
calibration period traditionally used. The analysis is undertaken in the
context of using streamflow forecasts for environmental flow water management
of an open channel drainage network in southern Australia. Forecasts of
monthly streamflow are obtained using a conceptual rainfall–runoff model
combined with a post-processor error model for uncertainty analysis. This
model set-up is applied to two catchments, one with stronger evidence of
non-stationarity than the other. A range of metrics are used to assess
different aspects of predictive performance, including reliability,
sharpness, bias and accuracy. The results indicate that, for most scenarios
and metrics, state updating improves predictive performance for both observed
rainfall and forecast rainfall sources. Using the shorter calibration period
also improves predictive performance, particularly for the catchment with
stronger evidence of non-stationarity. The results highlight that a
traditional approach of using a long calibration period can degrade
predictive performance when there is evidence of non-stationarity. The
techniques presented can form the basis for operational monthly streamflow
forecasting systems and provide support for environmental decision-making.

/pdf/state-updating-and-calibration-period-selection-to-improve-3775gi104g.pdf

State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application

Influence of three common calibration metrics on the diagnosis of climate change impacts on water resources

Hydraulic performance and wave transmission through pile-rock breakwaters

Influential point detection diagnostics in the context of hydrological model calibration

A hybrid framework for quantifying the influence of data in hydrological model calibration.

Influence diagnostics are used to identify data points that have a disproportionate impact on model parameters, performance and/or predictions, providing valuable information for use in model calibration. Regression-theory influence diagnostics identify influential data by combining the leverage and the standardised residuals, and are computationally more efficient than case-deletion approaches. This study evaluates the performance of a range of regression-theory influence diagnostics on ten case studies with a variety of model structures and inference scenarios including: nonlinear model response, heteroscedastic residual errors, data uncertainty and Bayesian priors. A new technique is developed, generalised Cook's distance, that is able to accurately identify the same influential data as standard case deletion approaches (Spearman rank correlation: 0.93–1.00) at a fraction of the computational cost (

/pdf/a-generalised-approach-for-identifying-influential-data-in-3ygwj58isy.pdf

A generalised approach for identifying influential data in hydrological modelling

Across the field of hydrology practitioners apply a range of objective functions which are selected based upon the intended model application and suitability of the objective function assumptions to the data in question. Despite most objective functions providing fundamentally different calibration results there are currently limited methods for comparison of alternatives. Influence diagnostics quantify the impact of individual data points on model performance, parameters and predictions. The goal of this study is to use compare four commonly applied objective functions in hydrology using influence diagnostics to provide insights on how objective function selection changes the influence of individual data points on model calibration. The specific aims are to: 1) explore the impact on magnitude of influence of objective functions, 2) investigate similarities between influential points identified by objective functions and, 3) categorise flows that are influential under objective functions.

David P. Wright

Papers

Hydraulic performance and wave transmission through pile-rock breakwaters

Influential point detection diagnostics in the context of hydrological model calibration

A hybrid framework for quantifying the influence of data in hydrological model calibration.

A generalised approach for identifying influential data in hydrological modelling

The impact of objective function selection on the influence of individual data points