Catchment Functioning Under Prolonged Drought Stress: Tracer‐Aided Ecohydrological Modeling in an Intensively Managed Agricultural Catchment

Yang, Xiaoqiang; Tetzlaff, Doerthe; Soulsby, Chris; Smith, Aaron; Borchardt, Dietrich

doi:10.1029/2020wr029094

Cited by 18 publications

(36 citation statements)

References 87 publications

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“…Third, including a moderate drought (the 2017 event) in the calibration did not lead to an improvement in Q and ET simulation during a severe drought (the 2022 event), with mean KGE = 0.18 for Q across the study sub-catchments, and mean r = -0.11 and nRMSE = 1.85 for ET across the croplands during 2022. Yang et al (2021) reported that an ecohydrological model better simulated Q in an experimental German catchment during the 2018-2019 drought when including it in the calibration period. However, here we proved that calibrating during a moderate drought was not sufficient to improve model transferability to a different and more severe drought.…”

Section: Main Findings and Implicationsmentioning

confidence: 99%

“…Multivariable calibration may be helpful to improve model internal consistency (Dembélé et al, 2020;Duethmann et al, 2022), also during low-flow periods (Rakovec et al, 2016a) and droughts (Yang et al, 2021). Yang et al (2021) for example showed that including tracer data in the calibration of an ecohydrological model increased model internal consistency during the 2018-2019 drought in Central Europe. Here we calibrated the model against Q data only (Section 2.4.2).…”

Section: Future Workmentioning

confidence: 99%

“…Distributed process-based hydrological models allow spatial estimates of hydrological fluxes and states (Fatichi et al, 2016), even at large scales and hyper-resolutions (< 1 km, Bierkens et al (2015)). Such models are increasingly being used for assessments of climate change impacts on droughts (Van Huijgevoort et al, 2014;Cammalleri et al, 2020b;Dembélé et al, 2022), drought monitoring (Cammalleri et al, 2017(Cammalleri et al, , 2020aSaha et al, 2021), forecasting (Trambauer et al, 2015;Van Hateren et al, 2019;Sutanto et al, 2020), and drought studies in general (Mastrotheodoros et al, 2020;Yang et al, 2021;Rakovec et al, 2022). However, some studies revealed reduced model performances when simulating streamflow droughts (Kumar et al, 2022) and their generating processes (Van Loon et al, 2012;Avanzi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Such issues in the transferability of model parameterizations (hereinafter model transferability) can pose challenges in correctly simulating Q during droughts (Saft et al, 2016). Some studies suggested that including dry periods in the calibration improves Q simulation during droughts (Li et al, 2012;Yang et al, 2021), but results in this regard are still inconclusive (Avanzi et al, 2020). Further, previous studies revealed that model deterioration in Q simulation during droughts may be related to poor simulation of actual evapotranspiration (ET, Avanzi et al (2020)) or Terrestrial Water Storage Anomalies (TWSA, Westra et al (2014); Fowler et al (2020)).…”

Section: Introductionmentioning

confidence: 99%

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Parameter transferability of a distributed hydrological model to droughts

Bruno

Duethmann

Avanzi

et al. 2022

Preprint

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Abstract. Hydrological models often have issues in simulating streamflow (Q) during droughts, because of hard-to-capture feedback mechanisms across precipitation deficit, actual evapotranspiration (ET), and terrestrial water storage anomalies (TWSA). To gain more insights into these performance drops and move toward more robust hydrological models in the anthropogenic era, we evaluated Q, ET, and TWSA simulations during droughts of different severity and their sensitivity to the climatic conditions of the calibration period. We used the distributed hydrological model Continuum over the heavily human-affected Po river basin (northern Italy, period 2010–2022) and independent ground- and remote sensing-based datasets of Q, ET, and TWSA as benchmarks. Across the 38 study sub-catchments, Continuum simulated Q comparably well during wet years (2014 and 2020) and moderate droughts (2012 and 2017) with mean KGE = 0.59±0.32 during wet years and = 0.55±0.25 during moderate droughts. The model simulated well Q for the outlet section of the basin also for the severe 2022 drought (KGE = 0.82). However, performances for 2022 declined across the other sub-catchments (mean KGE = 0.18±0.69, meaning the model still preserved some skill over a climatological mean). The model properly represented seasonality of Q, ET, and TWSA over the basin, as well as a declining trend in TWSA. We explained the performance drops in 2022 with an increased uncertainty in ET anomalies, in particular in human-affected croplands. Calibrating during a moderate drought (2017) did not improve model performances during the severe 2022 drought (mean KGE = 0.18±0.63), pointing to the fairly unique conditions of this period in terms of hydrological processes and human interference on the hydrological cycle. By highlighting increased uncertainty of hydrological models specifically during severe droughts which are expected to increase in frequency, these findings provide relevant guidelines for assessments of model robustness in a changing climate and so for informing water management, disaster risk reduction, and climate change adaptation strategies.

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Section: Main Findings and Implicationsmentioning

confidence: 99%

Section: Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parameter transferability of a distributed hydrological model to droughts

Bruno

Duethmann

Avanzi

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Quantifying catchment-scale water cycling over longer periods is important as wet and dry precipitation cycles can adversely affect blue (groundwater and discharge) and green (evapotranspiration) water fluxes and storage dynamics (Orth & Destouni, 2018;Yang, Tetzlaff, Soulsby, Smith, & Borchardt, 2021). Spatially distributed modelling approaches are, in many circumstances, essential for spatio-temporal evaluation of the non-stationarity of flow paths and storages within catchments (Fatichi et al, 2016).…”

Section: Descriptionmentioning

confidence: 99%