Articles | Volume 21, issue 8
https://doi.org/10.5194/hess-21-3953-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/hess-21-3953-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Opinion article
| 
01 Aug 2017
Opinion article |
| 01 Aug 2017
HESS Opinions: The complementary merits of competing modelling philosophies in hydrology
Markus Hrachowitz
CORRESPONDING AUTHOR
Faculty of Civil Engineering and Geosciences, Delft University of
Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
Martyn P. Clark
National Center for Atmospheric Research, Boulder CO, 80301, USA
Related authors
Muhammad Ibrahim, Markus Hrachowitz, Miriam Coenders-Gerrits, and Ruud van der Ent
EGUsphere, https://doi.org/10.5194/egusphere-2026-4226, https://doi.org/10.5194/egusphere-2026-4226, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Root zone storage capacity is an important parameter in hydrological and land-surface models and is often estimated using the memory method rather than through model calibration. We evaluated the uncertainty associated with this widely used method across thousands of catchments worldwide. Our results show that uncertainty varies systematically with climate, while memory method estimates remain in close agreement with calibrated values, supporting the commonly used 20-year return period.
Julia M. Rudlang, Thiago V. M. do Nascimento, Ruud van der Ent, Fabrizio Fenicia, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 30, 4481–4508, https://doi.org/10.5194/hess-30-4481-2026, https://doi.org/10.5194/hess-30-4481-2026, 2026
Short summary
Short summary
River flow (streamflow) behaviour varies across Europe and is shaped by climate and landscape. Using data from more than 7000 European catchments, we identified 10 hydrological response types based on flow magnitude, timing, and seasonality. At the continental scale, streamflow behaviour is mainly driven by climate, but landscape features are equally or more influential in several regions. These results show that streamflow behaviour emerges from the combined effects of climate and landscape.
Hatice Türk, Christine Stumpp, Markus Hrachowitz, Peter Strauss, Günter Blöschl, and Michael Stockinger
Hydrol. Earth Syst. Sci., 30, 1053–1076, https://doi.org/10.5194/hess-30-1053-2026, https://doi.org/10.5194/hess-30-1053-2026, 2026
Short summary
Short summary
This study shows that streamflow isotope data in a catchment-scale isotope-based transport model were sufficient to isolate preferential flow in the unsaturated zone but insufficient to isolate the preferential release of young groundwater, as isotope signals were strongly damped by large passive groundwater storage. As a result, groundwater age-selection assumptions affected estimates of long transit times, making it difficult to quantify the fraction of stream water older than 100 days.
Thiago V. M. do Nascimento, Julia Rudlang, Sebastian Gnann, Jan Seibert, Markus Hrachowitz, and Fabrizio Fenicia
Hydrol. Earth Syst. Sci., 29, 7173–7200, https://doi.org/10.5194/hess-29-7173-2025, https://doi.org/10.5194/hess-29-7173-2025, 2025
Short summary
Short summary
We show that geological maps with varying levels of detail may influence the identification of geology–streamflow relationships across European catchments at multiple scales. At the large scale, controls varied between basins, with no map consistently superior. At the intermediate and small scales, however, higher geological detail consistently strengthened correlations, particularly for baseflow signatures, with the regional map highlighting controls more consistent with process understanding.
Nathalie Rombeek, Markus Hrachowitz, and Remko Uijlenhoet
Hydrol. Earth Syst. Sci., 29, 6715–6733, https://doi.org/10.5194/hess-29-6715-2025, https://doi.org/10.5194/hess-29-6715-2025, 2025
Short summary
Short summary
On 29 October 2024 Valencia (Spain) was struck by torrential rainfall, triggering devastating floods in this area. In this study, we quantify and describe the spatial and temporal structure of this rainfall event using personal weather stations (PWSs). These PWSs provide near real-time observations at a temporal resolution of ~5 min. This study shows the potential of PWSs for real-time rainfall monitoring and potentially flood early warning systems by complementing dedicated rain gauge networks.
Nathalie Rombeek, Markus Hrachowitz, Arjan Droste, and Remko Uijlenhoet
Hydrol. Earth Syst. Sci., 29, 4585–4606, https://doi.org/10.5194/hess-29-4585-2025, https://doi.org/10.5194/hess-29-4585-2025, 2025
Short summary
Short summary
Rain gauge networks from personal weather stations (PWSs) have a network density 100 times higher than dedicated rain gauge networks in the Netherlands. However, PWSs are prone to several sources of error, as they are generally not installed and maintained according to international guidelines. This study systematically quantifies and describes the uncertainties arising from PWS rainfall estimates. In particular, the focus is on the highest rainfall accumulations.
Hatice Türk, Christine Stumpp, Markus Hrachowitz, Karsten Schulz, Peter Strauss, Günter Blöschl, and Michael Stockinger
Hydrol. Earth Syst. Sci., 29, 3935–3956, https://doi.org/10.5194/hess-29-3935-2025, https://doi.org/10.5194/hess-29-3935-2025, 2025
Short summary
Short summary
Using advances in transit time estimation and tracer data, we tested if fast-flow transit times are controlled solely by soil moisture or if they are also controlled by precipitation intensity. We used soil-moisture-dependent and precipitation-intensity-conditional transfer functions. We showed that a significant portion of event water bypasses the soil matrix through fast flow paths (overland flow, tile drains, preferential-flow paths) in dry soil conditions for both low- and high-intensity precipitation.
Magali Ponds, Sarah Hanus, Harry Zekollari, Marie-Claire ten Veldhuis, Gerrit Schoups, Roland Kaitna, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 3545–3568, https://doi.org/10.5194/hess-29-3545-2025, https://doi.org/10.5194/hess-29-3545-2025, 2025
Short summary
Short summary
This research examines how future climate changes impact root zone storage, a key hydrological model parameter. Root zone storage – the soil water accessible to plants – adapts to climate but is often kept constant in models. We estimated climate-adapted storage in six Austrian Alps catchments. While storage increased, streamflow projections showed minimal change, which suggests that dynamic root zone representation is less critical in humid regions but warrants further study in arid areas.
Muhammad Ibrahim, Miriam Coenders-Gerrits, Ruud van der Ent, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 1703–1723, https://doi.org/10.5194/hess-29-1703-2025, https://doi.org/10.5194/hess-29-1703-2025, 2025
Short summary
Short summary
The quantification of precipitation into evaporation and runoff is vital for water resources management. The Budyko framework, based on aridity and evaporative indices of a catchment, can be an ideal tool for that. However, recent research highlights deviations of catchments from the expected evaporative index, casting doubt on its reliability. This study quantifies deviations of 2387 catchments, finding them minor and predictable. Integrating these into predictions upholds the framework's efficacy.
Wouter R. Berghuijs, Ross A. Woods, Bailey J. Anderson, Anna Luisa Hemshorn de Sánchez, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 1319–1333, https://doi.org/10.5194/hess-29-1319-2025, https://doi.org/10.5194/hess-29-1319-2025, 2025
Short summary
Short summary
Water balances of catchments will often strongly depend on their state in the recent past, but such memory effects may persist at annual timescales. We use global data sets to show that annual memory is typically absent in precipitation but strong in terrestrial water stores and also present in evaporation and streamflow (including low flows and floods). Our experiments show that hysteretic models provide behaviour that is consistent with these observed memory behaviours.
Jordy Salmon-Monviola, Ophélie Fovet, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 127–158, https://doi.org/10.5194/hess-29-127-2025, https://doi.org/10.5194/hess-29-127-2025, 2025
Short summary
Short summary
To increase the predictive power of hydrological models, it is necessary to improve their consistency, i.e. their physical realism, which is measured by the ability of the model to reproduce observed system dynamics. Using a model to represent the dynamics of water and nitrate and dissolved organic carbon concentrations in an agricultural catchment, we showed that using solute-concentration data for calibration is useful to improve the hydrological consistency of the model.
Nienke Tempel, Laurène Bouaziz, Riccardo Taormina, Ellis van Noppen, Jasper Stam, Eric Sprokkereef, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 28, 4577–4597, https://doi.org/10.5194/hess-28-4577-2024, https://doi.org/10.5194/hess-28-4577-2024, 2024
Short summary
Short summary
This study explores the impact of climatic variability on root zone water storage capacities and, thus, on hydrological predictions. Analysing data from 286 areas in Europe and the US, we found that, despite some variations in root zone storage capacity due to changing climatic conditions over multiple decades, these changes are generally minor and have a limited effect on water storage and river flow predictions.
Hongkai Gao, Markus Hrachowitz, Lan Wang-Erlandsson, Fabrizio Fenicia, Qiaojuan Xi, Jianyang Xia, Wei Shao, Ge Sun, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 28, 4477–4499, https://doi.org/10.5194/hess-28-4477-2024, https://doi.org/10.5194/hess-28-4477-2024, 2024
Short summary
Short summary
The concept of the root zone is widely used but lacks a precise definition. Its importance in Earth system science is not well elaborated upon. Here, we clarified its definition with several similar terms to bridge the multi-disciplinary gap. We underscore the key role of the root zone in the Earth system, which links the biosphere, hydrosphere, lithosphere, atmosphere, and anthroposphere. To better represent the root zone, we advocate for a paradigm shift towards ecosystem-centred modelling.
Siyuan Wang, Markus Hrachowitz, and Gerrit Schoups
Hydrol. Earth Syst. Sci., 28, 4011–4033, https://doi.org/10.5194/hess-28-4011-2024, https://doi.org/10.5194/hess-28-4011-2024, 2024
Short summary
Short summary
Root zone storage capacity (Sumax) changes significantly over multiple decades, reflecting vegetation adaptation to climatic variability. However, this temporal evolution of Sumax cannot explain long-term fluctuations in the partitioning of water fluxes as expressed by deviations ΔIE from the parametric Budyko curve over time with different climatic conditions, and it does not have any significant effects on shorter-term hydrological response characteristics of the upper Neckar catchment.
Fransje van Oorschot, Ruud J. van der Ent, Andrea Alessandri, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 28, 2313–2328, https://doi.org/10.5194/hess-28-2313-2024, https://doi.org/10.5194/hess-28-2313-2024, 2024
Short summary
Short summary
Vegetation plays a crucial role in regulating the water cycle by transporting water from the subsurface to the atmosphere via roots; this transport depends on the extent of the root system. In this study, we quantified the effect of irrigation on roots at a global scale. Our results emphasize the importance of accounting for irrigation in estimating the vegetation root extent, which is essential to adequately represent the water cycle in hydrological and climate models.
Fransje van Oorschot, Ruud J. van der Ent, Markus Hrachowitz, Emanuele Di Carlo, Franco Catalano, Souhail Boussetta, Gianpaolo Balsamo, and Andrea Alessandri
Earth Syst. Dynam., 14, 1239–1259, https://doi.org/10.5194/esd-14-1239-2023, https://doi.org/10.5194/esd-14-1239-2023, 2023
Short summary
Short summary
Vegetation largely controls land hydrology by transporting water from the subsurface to the atmosphere through roots and is highly variable in space and time. However, current land surface models have limitations in capturing this variability at a global scale, limiting accurate modeling of land hydrology. We found that satellite-based vegetation variability considerably improved modeled land hydrology and therefore has potential to improve climate predictions of, for example, droughts.
Siyuan Wang, Markus Hrachowitz, Gerrit Schoups, and Christine Stumpp
Hydrol. Earth Syst. Sci., 27, 3083–3114, https://doi.org/10.5194/hess-27-3083-2023, https://doi.org/10.5194/hess-27-3083-2023, 2023
Short summary
Short summary
This study shows that previously reported underestimations of water ages are most likely not due to the use of seasonally variable tracers. Rather, these underestimations can be largely attributed to the choices of model approaches which rely on assumptions not frequently met in catchment hydrology. We therefore strongly advocate avoiding the use of this model type in combination with seasonally variable tracers and instead adopting StorAge Selection (SAS)-based or comparable model formulations.
Pau Wiersma, Jerom Aerts, Harry Zekollari, Markus Hrachowitz, Niels Drost, Matthias Huss, Edwin H. Sutanudjaja, and Rolf Hut
Hydrol. Earth Syst. Sci., 26, 5971–5986, https://doi.org/10.5194/hess-26-5971-2022, https://doi.org/10.5194/hess-26-5971-2022, 2022
Short summary
Short summary
We test whether coupling a global glacier model (GloGEM) with a global hydrological model (PCR-GLOBWB 2) leads to a more realistic glacier representation and to improved basin runoff simulations across 25 large-scale basins. The coupling does lead to improved glacier representation, mainly by accounting for glacier flow and net glacier mass loss, and to improved basin runoff simulations, mostly in strongly glacier-influenced basins, which is where the coupling has the most impact.
Judith Uwihirwe, Alessia Riveros, Hellen Wanjala, Jaap Schellekens, Frederiek Sperna Weiland, Markus Hrachowitz, and Thom A. Bogaard
Nat. Hazards Earth Syst. Sci., 22, 3641–3661, https://doi.org/10.5194/nhess-22-3641-2022, https://doi.org/10.5194/nhess-22-3641-2022, 2022
Short summary
Short summary
This study compared gauge-based and satellite-based precipitation products. Similarly, satellite- and hydrological model-derived soil moisture was compared to in situ soil moisture and used in landslide hazard assessment and warning. The results reveal the cumulative 3 d rainfall from the NASA-GPM to be the most effective landslide trigger. The modelled antecedent soil moisture in the root zone was the most informative hydrological variable for landslide hazard assessment and warning in Rwanda.
Judith Uwihirwe, Markus Hrachowitz, and Thom Bogaard
Nat. Hazards Earth Syst. Sci., 22, 1723–1742, https://doi.org/10.5194/nhess-22-1723-2022, https://doi.org/10.5194/nhess-22-1723-2022, 2022
Short summary
Short summary
This research tested the value of regional groundwater level information to improve landslide predictions with empirical models based on the concept of threshold levels. In contrast to precipitation-based thresholds, the results indicated that relying on threshold models exclusively defined using hydrological variables such as groundwater levels can lead to improved landslide predictions due to their implicit consideration of long-term antecedent conditions until the day of landslide occurrence.
Elisa Ragno, Markus Hrachowitz, and Oswaldo Morales-Nápoles
Hydrol. Earth Syst. Sci., 26, 1695–1711, https://doi.org/10.5194/hess-26-1695-2022, https://doi.org/10.5194/hess-26-1695-2022, 2022
Short summary
Short summary
We explore the ability of non-parametric Bayesian networks to reproduce maximum daily discharge in a given month in a catchment when the remaining hydro-meteorological and catchment attributes are known. We show that a saturated network evaluated in an individual catchment can reproduce statistical characteristics of discharge in about ~ 40 % of the cases, while challenges remain when a saturated network considering all the catchments together is evaluated.
Laurène J. E. Bouaziz, Emma E. Aalbers, Albrecht H. Weerts, Mark Hegnauer, Hendrik Buiteveld, Rita Lammersen, Jasper Stam, Eric Sprokkereef, Hubert H. G. Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 26, 1295–1318, https://doi.org/10.5194/hess-26-1295-2022, https://doi.org/10.5194/hess-26-1295-2022, 2022
Short summary
Short summary
Assuming stationarity of hydrological systems is no longer appropriate when considering land use and climate change. We tested the sensitivity of hydrological predictions to changes in model parameters that reflect ecosystem adaptation to climate and potential land use change. We estimated a 34 % increase in the root zone storage parameter under +2 K global warming, resulting in up to 15 % less streamflow in autumn, due to 14 % higher summer evaporation, compared to a stationary system.
Muhammad Ibrahim, Markus Hrachowitz, Miriam Coenders-Gerrits, and Ruud van der Ent
EGUsphere, https://doi.org/10.5194/egusphere-2026-4226, https://doi.org/10.5194/egusphere-2026-4226, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Root zone storage capacity is an important parameter in hydrological and land-surface models and is often estimated using the memory method rather than through model calibration. We evaluated the uncertainty associated with this widely used method across thousands of catchments worldwide. Our results show that uncertainty varies systematically with climate, while memory method estimates remain in close agreement with calibrated values, supporting the commonly used 20-year return period.
Julia M. Rudlang, Thiago V. M. do Nascimento, Ruud van der Ent, Fabrizio Fenicia, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 30, 4481–4508, https://doi.org/10.5194/hess-30-4481-2026, https://doi.org/10.5194/hess-30-4481-2026, 2026
Short summary
Short summary
River flow (streamflow) behaviour varies across Europe and is shaped by climate and landscape. Using data from more than 7000 European catchments, we identified 10 hydrological response types based on flow magnitude, timing, and seasonality. At the continental scale, streamflow behaviour is mainly driven by climate, but landscape features are equally or more influential in several regions. These results show that streamflow behaviour emerges from the combined effects of climate and landscape.
Hatice Türk, Christine Stumpp, Markus Hrachowitz, Peter Strauss, Günter Blöschl, and Michael Stockinger
Hydrol. Earth Syst. Sci., 30, 1053–1076, https://doi.org/10.5194/hess-30-1053-2026, https://doi.org/10.5194/hess-30-1053-2026, 2026
Short summary
Short summary
This study shows that streamflow isotope data in a catchment-scale isotope-based transport model were sufficient to isolate preferential flow in the unsaturated zone but insufficient to isolate the preferential release of young groundwater, as isotope signals were strongly damped by large passive groundwater storage. As a result, groundwater age-selection assumptions affected estimates of long transit times, making it difficult to quantify the fraction of stream water older than 100 days.
Thiago V. M. do Nascimento, Julia Rudlang, Sebastian Gnann, Jan Seibert, Markus Hrachowitz, and Fabrizio Fenicia
Hydrol. Earth Syst. Sci., 29, 7173–7200, https://doi.org/10.5194/hess-29-7173-2025, https://doi.org/10.5194/hess-29-7173-2025, 2025
Short summary
Short summary
We show that geological maps with varying levels of detail may influence the identification of geology–streamflow relationships across European catchments at multiple scales. At the large scale, controls varied between basins, with no map consistently superior. At the intermediate and small scales, however, higher geological detail consistently strengthened correlations, particularly for baseflow signatures, with the regional map highlighting controls more consistent with process understanding.
Nathalie Rombeek, Markus Hrachowitz, and Remko Uijlenhoet
Hydrol. Earth Syst. Sci., 29, 6715–6733, https://doi.org/10.5194/hess-29-6715-2025, https://doi.org/10.5194/hess-29-6715-2025, 2025
Short summary
Short summary
On 29 October 2024 Valencia (Spain) was struck by torrential rainfall, triggering devastating floods in this area. In this study, we quantify and describe the spatial and temporal structure of this rainfall event using personal weather stations (PWSs). These PWSs provide near real-time observations at a temporal resolution of ~5 min. This study shows the potential of PWSs for real-time rainfall monitoring and potentially flood early warning systems by complementing dedicated rain gauge networks.
Nathalie Rombeek, Markus Hrachowitz, Arjan Droste, and Remko Uijlenhoet
Hydrol. Earth Syst. Sci., 29, 4585–4606, https://doi.org/10.5194/hess-29-4585-2025, https://doi.org/10.5194/hess-29-4585-2025, 2025
Short summary
Short summary
Rain gauge networks from personal weather stations (PWSs) have a network density 100 times higher than dedicated rain gauge networks in the Netherlands. However, PWSs are prone to several sources of error, as they are generally not installed and maintained according to international guidelines. This study systematically quantifies and describes the uncertainties arising from PWS rainfall estimates. In particular, the focus is on the highest rainfall accumulations.
Hatice Türk, Christine Stumpp, Markus Hrachowitz, Karsten Schulz, Peter Strauss, Günter Blöschl, and Michael Stockinger
Hydrol. Earth Syst. Sci., 29, 3935–3956, https://doi.org/10.5194/hess-29-3935-2025, https://doi.org/10.5194/hess-29-3935-2025, 2025
Short summary
Short summary
Using advances in transit time estimation and tracer data, we tested if fast-flow transit times are controlled solely by soil moisture or if they are also controlled by precipitation intensity. We used soil-moisture-dependent and precipitation-intensity-conditional transfer functions. We showed that a significant portion of event water bypasses the soil matrix through fast flow paths (overland flow, tile drains, preferential-flow paths) in dry soil conditions for both low- and high-intensity precipitation.
Magali Ponds, Sarah Hanus, Harry Zekollari, Marie-Claire ten Veldhuis, Gerrit Schoups, Roland Kaitna, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 3545–3568, https://doi.org/10.5194/hess-29-3545-2025, https://doi.org/10.5194/hess-29-3545-2025, 2025
Short summary
Short summary
This research examines how future climate changes impact root zone storage, a key hydrological model parameter. Root zone storage – the soil water accessible to plants – adapts to climate but is often kept constant in models. We estimated climate-adapted storage in six Austrian Alps catchments. While storage increased, streamflow projections showed minimal change, which suggests that dynamic root zone representation is less critical in humid regions but warrants further study in arid areas.
Muhammad Ibrahim, Miriam Coenders-Gerrits, Ruud van der Ent, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 1703–1723, https://doi.org/10.5194/hess-29-1703-2025, https://doi.org/10.5194/hess-29-1703-2025, 2025
Short summary
Short summary
The quantification of precipitation into evaporation and runoff is vital for water resources management. The Budyko framework, based on aridity and evaporative indices of a catchment, can be an ideal tool for that. However, recent research highlights deviations of catchments from the expected evaporative index, casting doubt on its reliability. This study quantifies deviations of 2387 catchments, finding them minor and predictable. Integrating these into predictions upholds the framework's efficacy.
Wouter R. Berghuijs, Ross A. Woods, Bailey J. Anderson, Anna Luisa Hemshorn de Sánchez, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 1319–1333, https://doi.org/10.5194/hess-29-1319-2025, https://doi.org/10.5194/hess-29-1319-2025, 2025
Short summary
Short summary
Water balances of catchments will often strongly depend on their state in the recent past, but such memory effects may persist at annual timescales. We use global data sets to show that annual memory is typically absent in precipitation but strong in terrestrial water stores and also present in evaporation and streamflow (including low flows and floods). Our experiments show that hysteretic models provide behaviour that is consistent with these observed memory behaviours.
Jordy Salmon-Monviola, Ophélie Fovet, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 127–158, https://doi.org/10.5194/hess-29-127-2025, https://doi.org/10.5194/hess-29-127-2025, 2025
Short summary
Short summary
To increase the predictive power of hydrological models, it is necessary to improve their consistency, i.e. their physical realism, which is measured by the ability of the model to reproduce observed system dynamics. Using a model to represent the dynamics of water and nitrate and dissolved organic carbon concentrations in an agricultural catchment, we showed that using solute-concentration data for calibration is useful to improve the hydrological consistency of the model.
Nienke Tempel, Laurène Bouaziz, Riccardo Taormina, Ellis van Noppen, Jasper Stam, Eric Sprokkereef, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 28, 4577–4597, https://doi.org/10.5194/hess-28-4577-2024, https://doi.org/10.5194/hess-28-4577-2024, 2024
Short summary
Short summary
This study explores the impact of climatic variability on root zone water storage capacities and, thus, on hydrological predictions. Analysing data from 286 areas in Europe and the US, we found that, despite some variations in root zone storage capacity due to changing climatic conditions over multiple decades, these changes are generally minor and have a limited effect on water storage and river flow predictions.
Hongkai Gao, Markus Hrachowitz, Lan Wang-Erlandsson, Fabrizio Fenicia, Qiaojuan Xi, Jianyang Xia, Wei Shao, Ge Sun, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 28, 4477–4499, https://doi.org/10.5194/hess-28-4477-2024, https://doi.org/10.5194/hess-28-4477-2024, 2024
Short summary
Short summary
The concept of the root zone is widely used but lacks a precise definition. Its importance in Earth system science is not well elaborated upon. Here, we clarified its definition with several similar terms to bridge the multi-disciplinary gap. We underscore the key role of the root zone in the Earth system, which links the biosphere, hydrosphere, lithosphere, atmosphere, and anthroposphere. To better represent the root zone, we advocate for a paradigm shift towards ecosystem-centred modelling.
Siyuan Wang, Markus Hrachowitz, and Gerrit Schoups
Hydrol. Earth Syst. Sci., 28, 4011–4033, https://doi.org/10.5194/hess-28-4011-2024, https://doi.org/10.5194/hess-28-4011-2024, 2024
Short summary
Short summary
Root zone storage capacity (Sumax) changes significantly over multiple decades, reflecting vegetation adaptation to climatic variability. However, this temporal evolution of Sumax cannot explain long-term fluctuations in the partitioning of water fluxes as expressed by deviations ΔIE from the parametric Budyko curve over time with different climatic conditions, and it does not have any significant effects on shorter-term hydrological response characteristics of the upper Neckar catchment.
Fransje van Oorschot, Ruud J. van der Ent, Andrea Alessandri, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 28, 2313–2328, https://doi.org/10.5194/hess-28-2313-2024, https://doi.org/10.5194/hess-28-2313-2024, 2024
Short summary
Short summary
Vegetation plays a crucial role in regulating the water cycle by transporting water from the subsurface to the atmosphere via roots; this transport depends on the extent of the root system. In this study, we quantified the effect of irrigation on roots at a global scale. Our results emphasize the importance of accounting for irrigation in estimating the vegetation root extent, which is essential to adequately represent the water cycle in hydrological and climate models.
Fransje van Oorschot, Ruud J. van der Ent, Markus Hrachowitz, Emanuele Di Carlo, Franco Catalano, Souhail Boussetta, Gianpaolo Balsamo, and Andrea Alessandri
Earth Syst. Dynam., 14, 1239–1259, https://doi.org/10.5194/esd-14-1239-2023, https://doi.org/10.5194/esd-14-1239-2023, 2023
Short summary
Short summary
Vegetation largely controls land hydrology by transporting water from the subsurface to the atmosphere through roots and is highly variable in space and time. However, current land surface models have limitations in capturing this variability at a global scale, limiting accurate modeling of land hydrology. We found that satellite-based vegetation variability considerably improved modeled land hydrology and therefore has potential to improve climate predictions of, for example, droughts.
Siyuan Wang, Markus Hrachowitz, Gerrit Schoups, and Christine Stumpp
Hydrol. Earth Syst. Sci., 27, 3083–3114, https://doi.org/10.5194/hess-27-3083-2023, https://doi.org/10.5194/hess-27-3083-2023, 2023
Short summary
Short summary
This study shows that previously reported underestimations of water ages are most likely not due to the use of seasonally variable tracers. Rather, these underestimations can be largely attributed to the choices of model approaches which rely on assumptions not frequently met in catchment hydrology. We therefore strongly advocate avoiding the use of this model type in combination with seasonally variable tracers and instead adopting StorAge Selection (SAS)-based or comparable model formulations.
Pau Wiersma, Jerom Aerts, Harry Zekollari, Markus Hrachowitz, Niels Drost, Matthias Huss, Edwin H. Sutanudjaja, and Rolf Hut
Hydrol. Earth Syst. Sci., 26, 5971–5986, https://doi.org/10.5194/hess-26-5971-2022, https://doi.org/10.5194/hess-26-5971-2022, 2022
Short summary
Short summary
We test whether coupling a global glacier model (GloGEM) with a global hydrological model (PCR-GLOBWB 2) leads to a more realistic glacier representation and to improved basin runoff simulations across 25 large-scale basins. The coupling does lead to improved glacier representation, mainly by accounting for glacier flow and net glacier mass loss, and to improved basin runoff simulations, mostly in strongly glacier-influenced basins, which is where the coupling has the most impact.
Judith Uwihirwe, Alessia Riveros, Hellen Wanjala, Jaap Schellekens, Frederiek Sperna Weiland, Markus Hrachowitz, and Thom A. Bogaard
Nat. Hazards Earth Syst. Sci., 22, 3641–3661, https://doi.org/10.5194/nhess-22-3641-2022, https://doi.org/10.5194/nhess-22-3641-2022, 2022
Short summary
Short summary
This study compared gauge-based and satellite-based precipitation products. Similarly, satellite- and hydrological model-derived soil moisture was compared to in situ soil moisture and used in landslide hazard assessment and warning. The results reveal the cumulative 3 d rainfall from the NASA-GPM to be the most effective landslide trigger. The modelled antecedent soil moisture in the root zone was the most informative hydrological variable for landslide hazard assessment and warning in Rwanda.
Judith Uwihirwe, Markus Hrachowitz, and Thom Bogaard
Nat. Hazards Earth Syst. Sci., 22, 1723–1742, https://doi.org/10.5194/nhess-22-1723-2022, https://doi.org/10.5194/nhess-22-1723-2022, 2022
Short summary
Short summary
This research tested the value of regional groundwater level information to improve landslide predictions with empirical models based on the concept of threshold levels. In contrast to precipitation-based thresholds, the results indicated that relying on threshold models exclusively defined using hydrological variables such as groundwater levels can lead to improved landslide predictions due to their implicit consideration of long-term antecedent conditions until the day of landslide occurrence.
Elisa Ragno, Markus Hrachowitz, and Oswaldo Morales-Nápoles
Hydrol. Earth Syst. Sci., 26, 1695–1711, https://doi.org/10.5194/hess-26-1695-2022, https://doi.org/10.5194/hess-26-1695-2022, 2022
Short summary
Short summary
We explore the ability of non-parametric Bayesian networks to reproduce maximum daily discharge in a given month in a catchment when the remaining hydro-meteorological and catchment attributes are known. We show that a saturated network evaluated in an individual catchment can reproduce statistical characteristics of discharge in about ~ 40 % of the cases, while challenges remain when a saturated network considering all the catchments together is evaluated.
Laurène J. E. Bouaziz, Emma E. Aalbers, Albrecht H. Weerts, Mark Hegnauer, Hendrik Buiteveld, Rita Lammersen, Jasper Stam, Eric Sprokkereef, Hubert H. G. Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 26, 1295–1318, https://doi.org/10.5194/hess-26-1295-2022, https://doi.org/10.5194/hess-26-1295-2022, 2022
Short summary
Short summary
Assuming stationarity of hydrological systems is no longer appropriate when considering land use and climate change. We tested the sensitivity of hydrological predictions to changes in model parameters that reflect ecosystem adaptation to climate and potential land use change. We estimated a 34 % increase in the root zone storage parameter under +2 K global warming, resulting in up to 15 % less streamflow in autumn, due to 14 % higher summer evaporation, compared to a stationary system.
Cited articles
Ajami, N. K., Gupta, H., Wagener, T., and Sorooshian, S.: Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system, J. Hydrol., 298, 112–135, 2004.
Andersen, H. and Hepburn, B.: Scientific Method, in: The Stanford Encyclopedia of Philosophy, edited by: Zalta, E. N., Stanford, 2016.
Andréassian, V. and Perrin, C.: On the ambiguous interpretation of the Turc-Budyko nondimensional graph, Water Resour. Res., 48, W10601, https://doi.org/10.1029/2012WR012532, 2012.
Andréassian, V., Perrin, C., Berthet, L., Le Moine, N., Lerat, J., Loumagne, C., Oudin, L., Mathevet, T., Ramos, M.-H., and Valéry, A.: HESS Opinions
Crash tests for a standardized evaluation of hydrological models, Hydrol. Earth Syst. Sci., 13, 1757–1764, https://doi.org/10.5194/hess-13-1757-2009, 2009.
Arnold, J. G., Srinivasan, R., Muttiah, R. S., and Williams, J. R.: Large area hydrologic modeling and assessment part I: Model development, J. Am. Water Resour. As., 34, 73–89, 1998.
Atkinson, S. E., Woods, R. A., and Sivapalan, M.: Climate and landscape controls on water balance model complexity over changing timescales, Water Resour. Res., 38, 1314, https://doi.org/10.1029/2002WR001487, 2002.
Bahremand, A.: HESS Opinions: Advocating process modeling and de-emphasizing parameter estimation, Hydrol. Earth Syst. Sci., 20, 1433–1445, https://doi.org/10.5194/hess-20-1433-2016, 2016.
Bearup, L. A., Maxwell, R. M., Clow, D. W., and McCray, J. E.: Hydrological effects of forest transpiration loss in bark beetle-impacted watersheds, Nature Climate Change, 4, 481–486, https://doi.org/10.1038/nclimate2198, 2014.
Bearup, L. A., Maxwell, R. M., and McCray, J. E.: Hillslope response to insect-induced land-cover change: an integrated model of end-member mixing, Ecohydrology, 9, 125–203, 2016.
Berghuijs, W. R., Sivapalan, M., Woods, R. A., and Savenije, H. H.: Patterns of similarity of seasonal water balances: A window into streamflow variability over a range of time scales, Water Resour. Res., 50, 5638–5661, 2014.
Berghuijs, W. R., Woods, R. A., Hutton, C. J., and Sivapalan, M.: Dominant flood generating mechanisms across the United States, Geophys. Res. Lett., 43, 4382–4390, 2016.
Bergström, S.: The HBV model – its structure and applications, SMHI Norrköping, Schweden, SMHI, SMHI Reports Hydrology, 4, 32 pp., 1992.
Beven, K.: Changing ideas in hydrology – the case of physically based models, J. Hydrol., 105, 157–172, 1989.
Beven, K.: Prophecy, reality and uncertainty in distributed hydrological modelling, Adv. Water Resour., 16, 41–51, 1993.
Beven, K.: Linking parameters across scales: subgrid parameterizations and scale dependent hydrological models, Hydrol. Process., 9, 507–525, 1995.
Beven, K. J.: Uniqueness of place and process representations in hydrological modelling, Hydrol. Earth Syst. Sci., 4, 203–213, https://doi.org/10.5194/hess-4-203-2000, 2000.
Beven, K.: How far can we go in distributed hydrological modelling?, Hydrol. Earth Syst. Sci., 5, 1–12, https://doi.org/10.5194/hess-5-1-2001, 2001.
Beven, K.: Searching for the Holy Grail of scientific hydrology: Qt = (S, R, Δt)A as closure, Hydrol. Earth Syst. Sci., 10, 609–618, https://doi.org/10.5194/hess-10-609-2006, 2006a.
Beven, K.: On undermining the science?, Hydrol. Process., 20, 3141–3146, 2006b.
Beven, K. J.: Preferential flows and travel time distributions: defining adequate hypothesis tests for hydrological process models, Hydrol. Process., 24, 1537–1547, 2010.
Beven, K.: So how much of your error is epistemic? Lessons from Japan and Italy, Hydrol. Process., 27, 1677–1680, 2013.
Beven, K. J. and Cloke, H. L.: Comment on
Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial waterby Eric F. Wood et al., Water Resour. Res., 48, W01801, https://doi.org/10.1029/2011WR010982, 2012.
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area model of basin hydrology, Hydrological Sciences Bulletin, 24, 43–70, 1979.
Beven, K. and Westerberg, I.: On red herrings and real herrings: disinformation and information in hydrological inference, Hydrol. Process., 25, 1676–1680, 2011.
Beven, K., Smith, P. J., and Wood, A.: On the colour and spin of epistemic error (and what we might do about it), Hydrol. Earth Syst. Sci., 15, 3123–3133, https://doi.org/10.5194/hess-15-3123-2011, 2011.
Bierkens, M. F. P., Bell, V. A., Burek, P., Chaney, N., Condon, L., David, C. H., de Roo, A., Döll, P., Drost, N., Famiglietti, J. S., Flörke, M., Gochis, D. J., Houser, P., Hut, R., Keune, J., Kollet, S., Maxwell, R., Reager, J. T., Samaniego, L., Sudicky, E., Sutanudjaja, E. H., van de Giesen, N., Winsemius, H., and Wood, E. F.: Hyperresolution global hydrological modelling: what is next?, Hydrol. Process., 29, 310–320, 2015.
Birkel, C., Dunn, S. M., Tetzlaff, D., and Soulsby, C.: Assessing the value of high-resolution isotope tracer data in the stepwise development of a lumped conceptual rainfall–runoff model, Hydrol. Process., 24, 2335–2348, 2010.
Blöschl, G.: Scaling in hydrology, Hydrol. Process., 15, 709–711, 2001.
Blöschl, G.: Runoff Prediction in Ungauged Basins: Synthesis across Processes, Places and Scales, Cambridge University Press, 2013.
Blöschl, G. and Sivapalan, M.: Scale issues in hydrological modelling: a review, Hydrol. Process., 9, 251–290, 1995.
Botter, G. and Rinaldo, A.: Scale effect on geomorphologic and kinematic dispersion, Water Resour. Res., 39, 1286, https://doi.org/10.1029/2003WR002154, 2003.
Boyle, D. P., Gupta, H. V., Sorooshian, S., Koren, V., Zhang, Z., and Smith, M.: Toward improved streamflow forecasts: Value of semidistributed modeling, Water Resour. Res., 37, 2749–2759, 2001.
Breuer, L., Huisman, J. A., Willems, P., Bormann, H., Bronstert, A., Croke, B. F. W., Frede, H.-G., Gräff, T., Hubrechts, L., Jakeman, A. J., Kite, G., Lanini, J., Leavesley, G., Lettenmaier, D. P., Lindström, G., Seibert, J., Sivapalan, M., and Viney, N. R.: Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) I: Model intercomparison with current land use, Adv. Water Resour., 32, 129–146, 2009.
Burnash, R. J. C.: The NWS River Forecast System – Catchment Modeling, in: Computer models of watershed hydrology, edited by: Singh, V. P., Water Resources Publications, Highlands Ranch, CO, 311–366, 1995.
Budyko, M. I.: Climate and Life, Academic, Orlando, 508 pp., 1974.
Butts, M. B., Payne, J. T., Kristensen, M., and Madsen, H.: An Evaluation of the impact of model structure and complexity on hydrologic modelling uncertainty for streamflow prediction, J. Hydrol., 298, 242–266, 2004.
Camporese, M., Paniconi, C., Putti, M., and Orlandini, S.: Surface-subsurface flow modeling with path-based runoff routing, boundary condition-based coupling, and assimilation of multisource observation data, Water Resour. Res., 46, W02512, https://doi.org/10.1029/2008WR007536, 2010.
Carrillo, G., Troch, P. A., Sivapalan, M., Wagener, T., Harman, C., and Sawicz, K.: Catchment classification: hydrological analysis of catchment behavior through process-based modeling along a climate gradient, Hydrol. Earth Syst. Sci., 15, 3411–3430, https://doi.org/10.5194/hess-15-3411-2011, 2011.
Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta, H. V., Wagener, T., and Hay, L. E.: Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models, Water Resour. Res., 44, W00B02, https://doi.org/10.1029/2007WR006735, 2008.
Clark, M. P., Kavetski, D., and Fenicia, F.: Pursuing the method of multiple working hypotheses for hydrological modeling, Water Resour. Res., 47, W09301, https://doi.org/10.1029/2010WR009827, 2011.
Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Brekke, L. D., Arnold, J. R., Gochis, D. J., and Rasmussen, R. M.: A unified approach for process-based hydrologic modeling: 1. Modeling concept, Water Resour. Res., 51, 2498–2514, 2015.
Clark, M. P., Schaefli, B., Schymanski, S. J., Samaniego, L., Luce, C. H., Jackson, B. M., Freer, J. E., Arnold, J. R., Moore, R. D., Istanbulluoglu, E., and Ceola, S.: Improving the theoretical underpinnings of process-based hydrologic models, Water Resour. Res., 52, 2350–2365, 2016.
Clark, M. P., Bierkens, M. F. P., Samaniego, L., Woods, R. A., Uijlenhoet, R., Bennett, K. E., Pauwels, V. R. N., Cai, X., Wood, A. W., and Peters-Lidard, C. D.: The evolution of process-based hydrologic models: historical challenges and the collective quest for physical realism, Hydrol. Earth Syst. Sci., 21, 3427–3440, https://doi.org/10.5194/hess-21-3427-2017, 2017.
Coopersmith, E., Yaeger, M. A., Ye, S., Cheng, L., and Sivapalan, M.: Exploring the physical controls of regional patterns of flow duration curves – Part 3: A catchment classification system based on regime curve indicators, Hydrol. Earth Syst. Sci., 16, 4467–4482, https://doi.org/10.5194/hess-16-4467-2012, 2012.
Coron, L., Andréassian, V., Perrin, C., Lerat, J., Vaze, J., Bourqui, M., and Hendrickx, F.: Crash testing hydrological models in contrasted climate conditions: An experiment on 216 Australian catchments, Water Resour. Res., 48, W05552, https://doi.org/10.1029/2011WR011721, 2012,
Coxon, G., Freer, J., Wagener, T., Odoni, N. A., and Clark, M.: Diagnostic evaluation of multiple hypotheses of hydrological behaviour in a limits-of-acceptability framework for 24 UK catchments, Hydrol. Process., 28, 6135–6150, 2014.
Coxon, G., Freer, J., Westerberg, I. K., Wagener, T., Woods, R., and Smith, P. J.: A novel framework for discharge uncertainty quantification applied to 500 UK gauging stations, Water Resour. Res., 51, 5531–5546, 2015.
Craig, J. R., Liu, G., and Soulis, E. D.: Runoff-infiltration partitioning using an upscaled Green-Ampt solution, Hydrol. Process., 24, 2328–2334, 2010.
Das, T., Bárdossy, A., Zehe, E., and He, Y.: Comparison of conceptual model performance using different representations of spatial variability, J. Hydrol., 356, 106–118, 2008.
de Boer-Euser, T., McMillan, H. K., Hrachowitz, M., Winsemius, H. C., and Savenije, H. H.: Influence of soil and climate on root zone storage capacity, Water Resour. Res., 52, 2009–2024, 2016.
Donohue, R. J., Roderick, M. L., and McVicar, T. R.: On the importance of including vegetation dynamics in Budyko's hydrological model, Hydrol. Earth Syst. Sci., 11, 983–995, https://doi.org/10.5194/hess-11-983-2007, 2007.
Dooge, J. C.: Looking for hydrologic laws, Water Resour. Res., 22, 46S–58S, https://doi.org/10.1029/WR022i09Sp0046S, 1986.
Eder, G., Sivapalan, M., and Nachtnebel, H. P.: Modelling water balances in an Alpine catchment through exploitation of emergent properties over changing time scales, Hydrol. Process., 17, 2125–2149, 2003.
Euser, T., Winsemius, H. C., Hrachowitz, M., Fenicia, F., Uhlenbrook, S., and Savenije, H. H. G.: A framework to assess the realism of model structures using hydrological signatures, Hydrol. Earth Syst. Sci., 17, 1893–1912, https://doi.org/10.5194/hess-17-1893-2013, 2013.
Euser, T., Hrachowitz, M., Winsemius, H. C., and Savenije, H. H. G.: The effect of forcing and landscape distribution on performance and consistency of model structures, Hydrol. Process., 29, 3727–3743, 2015.
Fatichi, S., Zeeman, M. J., Fuhrer, J., and Burlando, P.: Ecohydrological effects of management on subalpine grasslands: From local to catchment scale, Water Resour. Res., 50, 148–164, 2014.
Fatichi, S., Vivoni, E. R., Ogden, F. L., Ivanov, V. Y., Mirus, B., Gochis, D., Downer, C. W., Camporese, M., Davison, J. H., Ebel, B., Jones, N., Kim, J., Mascaro, G., Niswonger, R., Restrepo, P., Rigon, R., Shen, C., Sulis, M., and Tarboton, D.: An overview of current applications, challenges, and future trends in distributed process-based models in hydrology, J. Hydrol., 537, 45–60, 2016.
Fenicia, F., Savenije, H. H., Matgen, P., and Pfister, L.: Understanding catchment behavior through stepwise model concept improvement, Water Resour. Res., 44, W01402, https://doi.org/10.1029/2006WR005563, 2008a
Fenicia, F., McDonnell, J. J., and Savenije, H. H.: Learning from model improvement: On the contribution of complementary data to process understanding, Water Resour. Res., 44, W06419, https://doi.org/10.1029/2007WR006386, 2008b.
Fenicia, F., Wrede, S., Kavetski, D., Pfister, L., Hoffmann, L., Savenije, H. H., and McDonnell, J. J.: Assessing the impact of mixing assumptions on the estimation of streamwater mean residence time, Hydrol. Process., 24, 1730–1741, 2010.
Fenicia, F., Kavetski, D., and Savenije, H. H.: Elements of a flexible approach for conceptual hydrological modeling: 1. Motivation and theoretical development, Water Resour. Res., 47, W11510, https://doi.org/10.1029/2010WR010174, 2011.
Fenicia, F., Kavetski, D., Savenije, H. H., Clark, M. P., Schoups, G., Pfister, L., and Freer, J.: Catchment properties, function, and conceptual model representation: is there a correspondence?, Hydrol. Process., 28, 2451–2467, 2014.
Fenicia, F., Kavetski, D., Savenije, H. H., and Pfister, L.: From spatially variable streamflow to distributed hydrological models: Analysis of key modeling decisions, Water Resour. Res., 52, 954–989, https://doi.org/10.1002/2015WR017398, 2016.
Flúgel, W. A.: Delineating hydrological response units by geographical information system analyses for regional hydrological modelling using PRMS/MMS in the drainage basin of the River Bröl, Germany, Hydrol. Process., 9, 423–436, 1995.
Gao, H., Hrachowitz, M., Fenicia, F., Gharari, S., and Savenije, H. H. G.: Testing the realism of a topography-driven model (FLEX-Topo) in the nested catchments of the Upper Heihe, China, Hydrol. Earth Syst. Sci., 18, 1895–1915, https://doi.org/10.5194/hess-18-1895-2014, 2014a.
Gao, H., Hrachowitz, M., Schymanski, S. J., Fenicia, F., Sriwongsitanon, N., and Savenije, H. H. G.: Climate controls how ecosystems size the root zone storage capacity at catchment scale, Geophys. Res. Lett., 41, 7916–7923, 2014b.
Gao, H., Hrachowitz, M., Sriwongsitanon, N., Fenicia, F., Gharari, S., and Savenije, H. H. G.: Accounting for the influence of vegetation and landscape improves model transferability in a tropical savannah region, Water Resour. Res., 52, 7999–8022, 2016.
Garavaglia, F., Le Lay, M., Gottardi, F., Garçon, R., Gailhard, J., Paquet, E., and Mathevet, T.: Impact of model structure on flow simulation and hydrological realism: from lumped to semi-distributed approach, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-82, in review, 2017.
Gharari, S., Hrachowitz, M., Fenicia, F., and Savenije, H. H. G.: Hydrological landscape classification: investigating the performance of HAND based landscape classifications in a central European meso-scale catchment, Hydrol. Earth Syst. Sci., 15, 3275–3291, https://doi.org/10.5194/hess-15-3275-2011, 2011.
Gharari, S., Hrachowitz, M., Fenicia, F., and Savenije, H. H. G.: An approach to identify time consistent model parameters: sub-period calibration, Hydrol. Earth Syst. Sci., 17, 149–161, https://doi.org/10.5194/hess-17-149-2013, 2013.
Gharari, S., Hrachowitz, M., Fenicia, F., Gao, H., and Savenije, H. H. G.: Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration, Hydrol. Earth Syst. Sci., 18, 4839–4859, https://doi.org/10.5194/hess-18-4839-2014, 2014a.
Gharari, S., Shafiei, M., Hrachowitz, M., Kumar, R., Fenicia, F., Gupta, H. V., and Savenije, H. H. G.: A constraint-based search algorithm for parameter identification of environmental models, Hydrol. Earth Syst. Sci., 18, 4861–4870, https://doi.org/10.5194/hess-18-4861-2014, 2014b.
Grayson, R. and Blöschl, G.: Spatial Patterns in Catchment Hydrology Observations and Modelling, Cambridge University Press, Cambridge, 2001.
Gupta, H. V., Wagener, T., and Liu, Y.: Reconciling theory with observations: elements of a diagnostic approach to model evaluation, Hydrol. Process., 22, 3802–3813, 2008.
Gupta, H. V., Clark, M. P., Vrugt, J. A., Abramowitz, G., and Ye, M.: Towards a comprehensive assessment of model structural adequacy, Water Resour. Res., 48, W08301, https://doi.org/10.1029/2011WR011044, 2012.
Gupta, H. V., Perrin, C., Blöschl, G., Montanari, A., Kumar, R., Clark, M., and Andréassian, V.: Large-sample hydrology: a need to balance depth with breadth, Hydrol. Earth Syst. Sci., 18, 463–477, https://doi.org/10.5194/hess-18-463-2014, 2014.
Guse, B., Pfannerstill, M., Strauch, M., Reusser, D. E., Lüdtke, S., Volk, M., Gupta, H., and Fohrer, N.: On characterizing the temporal dominance patterns of model parameters and processes, Hydrol. Process., 30, 2255–2270, 2016.
Haghnegahdar, A., Tolson, B. A., Craig, J. R., and Paya, K. T.: Assessing the performance of a semi-distributed hydrological model under various watershed discretization schemes, Hydrol. Process., 29, 4018–4031, 2015.
Hrachowitz, M., Fovet, O., Ruiz, L., Euser, T., Gharari, S., Nijzink, R., Freer, J., Savenije, H. H. G., and Gascuel-Odoux, C.: Process consistency in models: The importance of system signatures, expert knowledge, and process complexity, Water Resour. Res., 50, 7445–7469, 2014.
Immerzeel, W. W. and Droogers, P.: Calibration of a distributed hydrological model based on satellite evapotranspiration, J. Hydrol., 349, 411–424, 2008.
Ivanov, V. Y., Fatichi, S., Jenerette, G. D., Espeleta, J. F., Troch, P. A., and Huxman, T. E.: Hysteresis of soil moisture spatial heterogeneity and the
homogenizingeffect of vegetation, Water Resour. Res., 46, W09521, https://doi.org/10.1029/2009WR008611, 2010.
Jakeman, A. J. and Hornberger, G. M.: How much complexity is warranted in a rainfall-runoff model?, Water Resour. Res., 29, 2637–2649, 1993.
Jencso, K. G. and McGlynn, B. L.: Hierarchical controls on runoff generation: Topographically driven hydrologic connectivity, geology, and vegetation, Water Resour. Res., 47, W11527, https://doi.org/10.1029/2011WR010666, 2011.
Jones, J. P., Sudicky, E. A., Brookfield, A. E., and Park, Y. J.: An assessment of the tracer-based approach to quantifying groundwater contributions to streamflow, Water Resour. Res., 42, W02407, https://doi.org/10.1029/2005WR004130, 2006.
Jothityangkoon, C., Sivapalan, M., and Farmer, D. L.: Process controls of water balance variability in a large semi-arid catchment: downward approach to hydrological model development, J. Hydrol., 254, 174–198, 2001.
Kauffeldt, A., Halldin, S., Rodhe, A., Xu, C.-Y., and Westerberg, I. K.: Disinformative data in large-scale hydrological modelling, Hydrol. Earth Syst. Sci., 17, 2845–2857, https://doi.org/10.5194/hess-17-2845-2013, 2013.
Kavetski, D. and Fenicia, F.: Elements of a flexible approach for conceptual hydrological modeling: 2. Application and experimental insights, Water Resour. Res., 47, W11511, https://doi.org/10.1029/2011WR010748, 2011.
Kelleher, C., McGlynn, B., and Wagener, T.: Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding, Hydrol. Earth Syst. Sci., 21, 3325–3352, https://doi.org/10.5194/hess-21-3325-2017, 2017.
Kirchner, J. W.: Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology, Water Resour. Res., 42, W03S04, https://doi.org/10.1029/2005WR004362, 2006.
Klemeš, V.: Conceptualization and scale in hydrology, J. Hydrol., 65, 1–23, 1983.
Klemeš, V.: Operational testing of hydrological simulation models, Hydrolog. Sci. J., 31, 13–24, 1986.
Knudsen, J., Thomsen, A., and Refsgaard, J. C.: WATBAL, Hydrol. Res., 17, 347–362, 1986.
Kollet, S. J. and Maxwell, R. M.: Integrated surface–groundwater flow modeling: A free-surface overland flow boundary condition in a parallel groundwater flow model, Adv. Water Resour., 29, 945–958, 2006.
Kollet, S. J. and Maxwell, R. M.: Demonstrating fractal scaling of baseflow residence time distributions using a fully-coupled groundwater and land surface model, Geophys. Res. Lett., 35, L07402, https://doi.org/10.1029/2008GL033215, 2008.
Kollet, S. J., Maxwell, R. M., Woodward, C. S., Smith, S., Vanderborght, J., Vereecken, H., and Simmer, C.: Proof of concept of regional scale hydrologic simulations at hydrologic resolution utilizing massively parallel computer resources, Water Resour. Res., 46, W04201, https://doi.org/10.1029/2009WR008730, 2010.
Kuchment, L. S., Romanov, P., Gelfan, A. N., and Demidov, V. N.: Use of satellite-derived data for characterization of snow cover and simulation of snowmelt runoff through a distributed physically based model of runoff generation, Hydrol. Earth Syst. Sci., 14, 339–350, https://doi.org/10.5194/hess-14-339-2010, 2010.
Kumar, M., Duffy, C. J., and Salvage, K. M.: A second order accurate, finite volume based, integrated hydrologic modeling (fihm) framework for simulation of surface and subsurface flow, Vadose Zone J., 8, 873–890, 2009.
Kumar, R., Samaniego, L., and Attinger, S.: The effects of spatial discretization and model parameterization on the prediction of extreme runoff characteristics, J. Hydrol., 392, 54–69, 2010.
Kumar, R., Samaniego, L., and Attinger, S.: Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations, Water Resour. Res., 49, 360–379, 2013.
Lamb, R. and Beven, K.: Using interactive recession curve analysis to specify a general catchment storage model, Hydrol. Earth Syst. Sci., 1, 101–113, https://doi.org/10.5194/hess-1-101-1997, 1997.
Lehmann, P., Hinz, C., McGrath, G., Tromp-van Meerveld, H. J., and McDonnell, J. J.: Rainfall threshold for hillslope outflow: an emergent property of flow pathway connectivity, Hydrol. Earth Syst. Sci., 11, 1047–1063, https://doi.org/10.5194/hess-11-1047-2007, 2007.
Li, H. Y., Sivapalan, M., Tian, F., and Harman, C.: Functional approach to exploring climatic and landscape controls of runoff generation: 1. Behavioral constraints on runoff volume, Water Resour. Res., 50, 9300–9322, 2014.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res.-Atmos., 99, 14415–14428, 1994.
Lindström, G., Pers, C., Rosberg, J., Strömqvist, J., and Arheimer, B.: Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales, Hydrol. Res., 41, 295–319, 2010.
Linsley, R. K.: Rainfall–runoff models: An overview, in: Proceedings of the International Symposium of Rainfall–Runoff Modelling, edited by: Singh, V. P., Water Resour. Publ., Littleton, Colorado, 3–22, 1982.
Lobligeois, F., Andréassian, V., Perrin, C., Tabary, P., and Loumagne, C.: When does higher spatial resolution rainfall information improve streamflow simulation? An evaluation using 3620 flood events, Hydrol. Earth Syst. Sci., 18, 575–594, https://doi.org/10.5194/hess-18-575-2014, 2014.
López López, P., Sutanudjaja, E. H., Schellekens, J., Sterk, G., and Bierkens, M. F. P.: Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products, Hydrol. Earth Syst. Sci., 21, 3125–3144, https://doi.org/10.5194/hess-21-3125-2017, 2017.
Loritz, R., Hassler, S. K., Jackisch, C., Allroggen, N., van Schaik, L., Wienhöfer, J., and Zehe, E.: Picturing and modeling catchments by representative hillslopes, Hydrol. Earth Syst. Sci., 21, 1225–1249, https://doi.org/10.5194/hess-21-1225-2017, 2017.
Lyon, S. W. and Troch, P. A.: Hillslope subsurface flow similarity: Real-world tests of the hillslope Péclet number, Water Resour. Res., 43, W07450, https://doi.org/10.1029/2006WR005323, 2007.
Martinez, G. F. and Gupta, H. V.: Hydrologic consistency as a basis for assessing complexity of monthly water balance models for the continental United States, Water Resour. Res., 47, W12540, https://doi.org/10.1029/2011WR011229, 2011.
Maxwell, R. M. and Kollet, S. J.: Interdependence of groundwater dynamics and land-energy feedbacks under climate change, Nat. Geosci., 1, 665–669, 2008.
Maxwell, R. M., Condon, L. E., and Kollet, S. J.: A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3, Geosci. Model Dev., 8, 923–937, https://doi.org/10.5194/gmd-8-923-2015, 2015.
Maxwell, R. M., Condon, L. E., Kollet, S. J., Maher, K., Haggerty, R., and Forrester, M. M.: The imprint of climate and geology on the residence times of groundwater, Geophys. Res. Lett., 43, 701–708, 2016.
McDonnell, J. J. and Beven, K.: Debates – The future of hydrological sciences: A (common) path forward? A call to action aimed at understanding velocities, celerities, and residence time distributions of the headwater hydrograph, Water Resour. Res., 50, 5342–5350, https://doi.org/10.1002/2013WR015141, 2014.
McDonnell, J. J., Sivapalan, M., Vaché, K., Dunn, S., Grant, G., Haggerty, R., Hinz, C., Hooper, R., Kirchner, J., Roderick, M. L., Selker, J., and Weiler, M.: Moving beyond heterogeneity and process complexity: A new vision for watershed hydrology, Water Resour. Res., 43, W07301, https://doi.org/10.1029/2006WR005467, 2007.
McGlynn, B. L., McDonnell, J. J., Seibert, J., and Kendall, C.: Scale effects on headwater catchment runoff timing, flow sources, and groundwater–streamflow relations, Water Resour. Res., 40, W07504, https://doi.org/10.1029/2003WR002494, 2004.
McMillan, H., Krueger, T., and Freer, J.: Benchmarking observational uncertainties for hydrology: rainfall, river discharge and water quality, Hydrol. Process., 26, 4078–4111, 2012.
McMillan, H., Gueguen, M., Grimon, E., Woods, R., Clark, M., and Rupp, D. E.: Spatial variability of Hydrol. Process. and model structure diagnostics in a 50 km2 catchment, Hydrol. Process., 28, 4896–4913, 2014.
McMillan, H. K. and Westerberg, I. K.: Rating curve estimation under epistemic uncertainty, Hydrol. Process., 29, 1873–1882, 2015.
Mendoza, P. A., Clark, M. P., Barlage, M., Rajagopalan, B., Samaniego, L., Abramowitz, G., and Gupta, H.: Are we unnecessarily constraining the agility of complex process-based models?, Water Resour. Res., 51, 716–728, 2015.
Nijzink, R. C., Samaniego, L., Mai, J., Kumar, R., Thober, S., Zink, M., Schäfer, D., Savenije, H. H. G., and Hrachowitz, M.: The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models, Hydrol. Earth Syst. Sci., 20, 1151–1176, https://doi.org/10.5194/hess-20-1151-2016, 2016a.
Nijzink, R., Hutton, C., Pechlivanidis, I., Capell, R., Arheimer, B., Freer, J., Han, D., Wagener, T., McGuire, K., Savenije, H., and Hrachowitz, M.: The evolution of root-zone moisture capacities after deforestation: a step towards hydrological predictions under change?, Hydrol. Earth Syst. Sci., 20, 4775–4799, https://doi.org/10.5194/hess-20-4775-2016, 2016b.
Ogden, F. L., Crouch, T. D., Stallard, R. F., and Hall, J. S.: Effect of land cover and use on dry season river runoff, runoff efficiency, and peak storm runoff in the seasonal tropics of Central Panama, Water Resour. Res., 49, 8443–8462, 2013.
Or, D., Lehmann, P., and Assouline, S.: Natural length scales define the range of applicability of the Richards equation for capillary flows, Water Resour. Res., 51, 7130–7144, 2015.
Paniconi, C. and Putti, M.: Physically based modeling in catchment hydrology at 50: Survey and outlook, Water Resour. Res., 51, 7090–7129, 2015.
Pappenberger, F. and Beven, K. J.: Ignorance is bliss: Or seven reasons not to use uncertainty analysis, Water Resour. Res., 42, W05302, https://doi.org/10.1029/2005WR004820, 2006.
Parajka, J. and Blöschl, G.: The value of MODIS snow cover data in validating and calibrating conceptual hydrologic models, J. Hydrol., 358, 240–258, 2008.
Perrin, C., Michel, C., and Andréassian, V.: Improvement of a parsimonious model for streamflow simulation, J. Hydrol., 279, 275–289, 2003.
Pfannerstill, M., Guse, B., and Fohrer, N.: Smart low flow signature metrics for an improved overall performance evaluation of hydrological models, J. Hydrol., 510, 447–458, 2014.
Piras, M., Mascaro, G., Deidda, R., and Vivoni, E. R.: Quantification of hydrologic impacts of climate change in a Mediterranean basin in Sardinia, Italy, through high-resolution simulations, Hydrol. Earth Syst. Sci., 18, 5201–5217, https://doi.org/10.5194/hess-18-5201-2014, 2014.
Pokhrel, P., Gupta, H. V., and Wagener, T.: A spatial regularization approach to parameter estimation for a distributed watershed model, Water Resour. Res., 44, W12419, https://doi.org/10.1029/2007WR006615, 2008.
Popper, K.: The Logic of Scientific Discovery, Routledge, New York, NY, 1959.
Qu, Y. and Duffy, C. J.: A semidiscrete finite volume formulation for multiprocess watershed simulation, Water Resour. Res., 43, W08419, https://doi.org/10.1029/2006WR005752, 2007.
Reed, S., Koren, V., Smith, M. B., Zhang, Z., Moreda, F., Seo, D., and DMIP Participants: Overall distributed model intercomparison project results, J. Hydrol., 298, 27–60, 2004.
Refsgaard, J. C. and Henriksen, H. J.: Modelling guidelines – terminology and guiding principles, Adv. Water Resour., 27, 71–82, 2004.
Refsgaard, J. C. and Storm, B.: Construction, calibration and validation of hydrological models, in: Distributed Hydrological Modelling, edited by: Abbott, M. B. and Refsgaard, J. C., Kluwer Academic Press, Dordrecht, Niederlande, 41–54, 1996.
Refsgaard, J. C., Van der Sluijs, J. P., Brown, J., and Van der Keur, P.: A framework for dealing with uncertainty due to model structure error, Adv. Water Resour., 29, 1586–1597, 2006.
Refsgaard, J. C., Storm, B., and Clausen, T.: Système Hydrologique Europèen (SHE): Review and perspectives after 30 years development in distributed physically-based hydrological modelling, Hydrol. Res., 41, 355–377, 2010.
Renard, B., Kavetski, D., Kuczera, G., Thyer, M., and Franks, S. W.: Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors. Water Resour. Res., 46, W05521, https://doi.org/10.1029/2009WR008328, 2010.
Renard, B., Kavetski, D., Leblois, E., Thyer, M., Kuczera, G., and Franks, S. W.: Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation, Water Resour. Res., 47, W11516, https://doi.org/10.1029/2011WR010643, 2011.
Rinaldo, A., Marani, A., and Rigon, R.: Geomorphological dispersion, Water Resour. Res., 27, 513–525, 1991.
Rinaldo, A., Botter, G., Bertuzzo, E., Uccelli, A., Settin, T., and Marani, M.: Transport at basin scales: 1. Theoretical framework, Hydrol. Earth Syst. Sci., 10, 19–29, https://doi.org/10.5194/hess-10-19-2006, 2006.
Robinson, J. S., Sivapalan, M., and Snell, J. D.: On the relative roles of hillslope processes, channel routing, and network geomorphology in the hydrologic response of natural catchments, Water Resour. Res., 31, 3089–3101, 1995.
Rockström, J., Falkenmark, M., Karlberg, L., Hoff, H., Rost, S., and Gerten, D.: Future water availability for global food production: the potential of green water for increasing resilience to global change, Water Resour. Res., 45, W00A12, https://doi.org/10.1029/2007WR006767, 2009.
Salmon, W. C.: The foundations of scientific inference, in: Pitt paperback, University of Pittsburgh Press, 28, 157 pp., 1967.
Samaniego, L., Kumar, R., and Attinger, S.: Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale, Water Resour. Res., 46, W02506, https://doi.org/10.1029/2008WR007327, 2010.
Savenije, H. H. G. and Hrachowitz, M.: HESS Opinions
Catchments as meta-organisms – a new blueprint for hydrological modelling, Hydrol. Earth Syst. Sci., 21, 1107–1116, https://doi.org/10.5194/hess-21-1107-2017, 2017.
Sawicz, K., Wagener, T., Sivapalan, M., Troch, P. A., and Carrillo, G.: Catchment classification: empirical analysis of hydrologic similarity based on catchment function in the eastern USA, Hydrol. Earth Syst. Sci., 15, 2895–2911, https://doi.org/10.5194/hess-15-2895-2011, 2011.
Schmocker-Fackel, P., Naef, F., and Scherrer, S.: Identifying runoff processes on the plot and catchment scale, Hydrol. Earth Syst. Sci., 11, 891–906, https://doi.org/10.5194/hess-11-891-2007, 2007.
Schulla, J. and Jasper, K.: Modellbeschreibung WaSiM-ETH, ETH Zürich, 1998.
Seibert, J.: Reliability of model predictions outside calibration conditions, Nord. Hydrol., 34, 477–492, 2003.
Seibert, J., Rodhe, A., and Bishop, K.: Simulating interactions between saturated and unsaturated storage in a conceptual runoff model, Hydrol. Process., 17, 379–390, 2003.
Shafii, M. and Tolson, B. A.: Optimizing hydrological consistency by incorporating hydrological signatures into model calibration objectives, Water Resour. Res., 51, 3796–3814, 2015.
Sherman, L. K.: Streamflow from rainfall by the unit graph method, Eng. News-Rec., 108, 501–505, 1932.
Shi, Y., Davis, K. J., Zhang, F., Duffy, C. J., and Yu, X.: Parameter estimation of a physically based land surface hydrologic model using the ensemble Kalman filter: A synthetic experiment, Water Resour. Res., 50, 706–724, 2014.
Šimůnek, J., van Genuchten, M. T., and Šejna, M.: Development and applications of the HYDRUS and STANMOD software packages and related codes, Vadose Zone J., 7, 587–600, 2008.
Sivakumar, B., Singh, V. P., Berndtsson, R., and Khan, S. K.: Catchment classification framework in hydrology: challenges and directions, J. Hydrol. Eng., 20, A4014002, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000837#sthash.iiOz484A.dpuf, 2013.
Sivapalan, M.: Pattern, process and function: Elements of a new unified theory of hydrologic at the catchment scale, in: Encyclopedia of Hydrological Sciences, edited by: Anderson, M. G., John Wiley & Sons Australia Ltd, UK, vol. 1, 193–220, 2005.
Sivapalan, M., Blöschl, G., Zhang, L., and Vertessy, R.: Downward approach to hydrological prediction, Hydrol. Process., 17, 2101–2111, 2003.
Sivapalan, M., Yaeger, M. A., Harman, C. J., Xu, X., and Troch, P. A.: Functional model of water balance variability at the catchment scale: 1. Evidence of hydrologic similarity and space-time symmetry, Water Resour. Res., 47, W02522, https://doi.org/10.1029/2010WR009568, 2011.
Smith, M. B., Koren, V., Zhang, Z., Zhang, Y., Reed, S. M., Cui, Z., Moreda, F., Cosgrove, B. A., Mizukami, N., and Anderson, E. A.: Results of the DMIP 2 Oklahoma experiments, J. Hydrol., 418–419, 17–48, 2012.
Snell, J. D. and Sivapalan, M.: On geomorphological dispersion in natural catchments and the geomorphological unit hydrograph, Water Resour. Res., 30, 2311–2323, 1994.
Son, K. and Sivapalan, M.: Improving model structure and reducing parameter uncertainty in conceptual water balance models through the use of auxiliary data, Water Resour. Res., 43, W01415, https://doi.org/10.1029/2006WR005032, 2007.
Spence, C., Guan, X. J., Phillips, R., Hedstrom, N., Granger, R., and Reid, B.: Storage dynamics and streamflow in a catchment with a variable contributing area, Hydrol. Process., 24, 2209–2221, 2010.
Sudicky, E. A., Jones, J. P., Park, Y. J., Brookfield, A. E., and Colautti, D.: Simulating complex flow and transport dynamics in an integrated surface-subsurface modeling framework, Geosci. J., 12, 107–122, 2008.
Sutanudjaja, E. H., Van Beek, L. P. H., De Jong, S. M., Van Geer, F. C., and Bierkens, M. F. P.: Calibrating a large-extent high-resolution coupled groundwater–land surface model using soil moisture and discharge data, Water Resour. Res., 50, 687–705, 2014.
Syed, T. H., Famiglietti, J. S., Rodell, M., Chen, J., and Wilson, C. R.: Analysis of terrestrial water storage changes from GRACE and GLDAS, Water Resour. Res., 44, W02433, https://doi.org/10.1029/2006WR005779, 2008.
Todini, E.: Rainfall–runoff modeling – Past, present and future, J. Hydrol., 100, 341–352, 1988.
Uhlenbrook, S., Roser, S., and Tilch, N.: Hydrological process representation at the meso-scale: the potential of a distributed, conceptual catchment model, J. Hydrol., 291, 278–296, 2004.
Uhlenbrook, S., Mohamed, Y., and Gragne, A. S.: Analyzing catchment behavior through catchment modeling in the Gilgel Abay, Upper Blue Nile River Basin, Ethiopia, Hydrol. Earth Syst. Sci., 14, 2153–2165, https://doi.org/10.5194/hess-14-2153-2010, 2010.
Vaché, K. B. and McDonnell, J. J.: A process-based rejectionist framework for evaluating catchment runoff model structure, Water Resour. Res., 42, W02409, https://doi.org/10.1029/2005WR004247, 2006.
Vansteenkiste, T., Tavakoli, M., Van Steenbergen, N., De Smedt, F., Batelaan, O., Pereira, F., and Willems, P.: Intercomparison of five lumped and distributed models for catchment runoff and extreme flow simulation, J. Hydrol., 511, 335–349, 2014.
Vinogradov, Y. B., Semenova, O. M., and Vinogradova, T. A.: An approach to the scaling problem in hydrological modelling: the deterministic modelling hydrological system, Hydrol. Process., 25, 1055–1073, 2011.
Vivoni, E. R., Rodriguez, J. C., and Watts, C. J.: On the spatiotemporal variability of soil moisture and evapotranspiration in a mountainous basin within the North American monsoon region, Water Resour. Res., 46, W02509, https://doi.org/10.1029/2009WR008240, 2010.
Wagener, T.: Evaluation of catchment models, Hydrol. Process., 17, 3375–3378, 2003.
Wagener, T. and Gupta, H. V.: Model identification for hydrological forecasting under uncertainty, Stoch. Env. Res. Risk. A., 19, 378–387, 2005.
Weiler, M. and McDonnell, J.: Virtual experiments: a new approach for improving process conceptualization in hillslope hydrology, J. Hydrol., 285, 3–18, 2004.
Westerberg, I. K. and McMillan, H. K.: Uncertainty in hydrological signatures, Hydrol. Earth Syst. Sci., 19, 3951–3968, https://doi.org/10.5194/hess-19-3951-2015, 2015.
Wigmosta, M. S., Vail, L. W., and Lettenmaier, D. P.: A distributed hydrology-vegetation model for complex terrain, Water Resour. Res., 30, 1665–1679, 1994.
Willems, P.: Parsimonious rainfall–runoff model construction supported by time series processing and validation of hydrological extremes – Part 1: Step-wise model-structure identification and calibration approach, J. Hydrol., 510, 578–590, 2014.
Willems, P., Mora, D., Vansteenkiste, T., Taye, M. T., and Van Steenbergen, N.: Parsimonious rainfall-runoff model construction supported by time series processing and validation of hydrological extremes – Part 2: Intercomparison of models and calibration approaches, J. Hydrol., 510, 591–609, 2014.
Winter, T. C.: The concept of hydrologic landscapes, J. Am. Water Resour. Assoc., 37, 335–349, 2001.
Winsemius, H. C., Savenije, H. H. G., and Bastiaanssen, W. G. M.: Constraining model parameters on remotely sensed evaporation: justification for distribution in ungauged basins?, Hydrol. Earth Syst. Sci., 12, 1403–1413, https://doi.org/10.5194/hess-12-1403-2008, 2008.
Wood, E. F., Sivapalan, M., Beven, K., and Band, L.: Effects of spatial variability and scale with implications to hydrologic modeling, J. Hydrol., 102, 29–47, 1988.
Wood, E. F., Sivapalan, M., and Beven, K.: Similarity and scale in catchment storm response, Rev. Geophys., 28, 1–18, 1990.
Xu, X., Li, J., and Tolson, B. A.: Progress in integrating remote sensing data and hydrologic modeling, Prog. Phys. Geog., 38, 464–498, 2014.
Yadav, M., Wagener, T., and Gupta, H.: Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins, Adv. Water Resour., 30, 1756–1774, 2007.
Ye, S., Yaeger, M., Coopersmith, E., Cheng, L., and Sivapalan, M.: Exploring the physical controls of regional patterns of flow duration curves – Part 2: Role of seasonality, the regime curve, and associated process controls, Hydrol. Earth Syst. Sci., 16, 4447–4465, https://doi.org/10.5194/hess-16-4447-2012, 2012.
Young, P.: Data-based mechanistic modelling of environmental, ecological, economic and engineering systems, Environ. Modell. Softw., 13, 105–122, 1998.
Young, P.: Top-down and data-based mechanistic modelling of rainfall–flow dynamics at the catchment scale, Hydrol. Process., 17, 2195–2217, 2003.
Zehe, E. (Referee): Interactive comment on
Opinion paper: How to make our models more physically-basedby H. H. G. Savenije and M. Hrachowitz, Hydrol. Earth Syst. Sci. Discuss., 4 pp., https://doi.org/10.5194/hess-2016-433-RC2, 2016.
Zehe, E. and Blöschl, G.: Predictability of hydrologic response at the plot and catchment scales: Role of initial conditions, Water Resour. Res., 40, W10202, https://doi.org/10.1029/2003WR002869, 2004.
Zehe, E., Maurer, T., Ihringer, J., and Plate, E.: Modelling water flow and mass transport in a Loess catchment, Phys. Chem. Earth Pt. B, 26, 487–507, 2001.
Zehe, E., Lee, H., and Sivapalan, M.: Dynamical process upscaling for deriving catchment scale state variables and constitutive relations for meso-scale process models, Hydrol. Earth Syst. Sci., 10, 981–996, https://doi.org/10.5194/hess-10-981-2006, 2006.
Zehe, E., Elsenbeer, H., Lindenmaier, F., Schulz, K., and Blöschl, G.: Patterns of predictability in hydrological threshold systems, Water Resour. Res., 43, W07434, https://doi.org/10.1029/2006WR005589, 2007.
Zehe, E., Ehret, U., Pfister, L., Blume, T., Schröder, B., Westhoff, M., Jackisch, C., Schymanski, S. J., Weiler, M., Schulz, K., Allroggen, N., Tronicke, J., van Schaik, L., Dietrich, P., Scherer, U., Eccard, J., Wulfmeyer, V., and Kleidon, A.: HESS Opinions: From response units to functional units: a thermodynamic reinterpretation of the HRU concept to link spatial organization and functioning of intermediate scale catchments, Hydrol. Earth Syst. Sci., 18, 4635–4655, https://doi.org/10.5194/hess-18-4635-2014, 2014.
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(1745 KB) - Full-text XML
- Corrigendum
-
Supplement
(117 KB) - BibTeX
- EndNote
Short summary
Physically based and conceptual models in hydrology are the two endpoints in the spectrum of modelling strategies, mostly differing in their degree of detail in resolving the model domain. Given the limitations both modelling strategies face, we believe that to achieve progress in hydrological modelling, a convergence of these methods is necessary. This would allow us to exploit the respective advantages of the bottom-up and top-down models while limiting their respective uncertainties.
Physically based and conceptual models in hydrology are the two endpoints in the spectrum of...
Special issue