Landscape evolution models (LEMs) have the capability to characterize key aspects of geomorphological and hydrological processes. However, their usefulness is hindered by model equifinality and paucity of available calibration data. Estimating uncertainty in the parameter space and resultant model predictions is rarely achieved as this is computationally intensive and the uncertainties inherent in the observed data are large. Therefore, a limits-of-acceptability (LoA) uncertainty analysis approach was adopted in this study to assess the value of uncertain hydrological and geomorphic data. These were used to constrain simulations of catchment responses and to explore the parameter uncertainty in model predictions. We applied this approach to the River Derwent and Cocker catchments in the UK using a LEM CAESAR-Lisflood. Results show that the model was generally able to produce behavioural simulations within the uncertainty limits of the streamflow. Reliability metrics ranged from 24.4% to 41.2% and captured the high-magnitude low-frequency sediment events. Since different sets of behavioural simulations were found across different parts of the catchment, evaluating LEM performance, in quantifying and assessing both at-a-point behaviour and spatial catchment response, remains a challenge. Our results show that evaluating LEMs within uncertainty analyses framework while taking into account the varying quality of different observations constrains behavioural simulations and parameter distributions and is a step towards a full-ensemble uncertainty evaluation of such models. We believe that this approach will have benefits for reflecting uncertainties in flooding events where channel morphological changes are occurring and various diverse (and yet often sparse) data have been collected over such events.

Landscape evolution models (LEMs) have the capability to characterize key aspects of geomorphological and hydrological processes. However, their usefulness is hindered by model equifinality and paucity of available calibration data. Estimating uncertainty in the parameter space and resultant model predictions is rarely achieved as this is computationally intensive and the uncertainties inherent in the observed data are large. Therefore, a limits-of-acceptability (LoA) uncertainty analysis approach was adopted in this study to assess the value of uncertain hydrological and geomorphic data. These were used to constrain simulations of catchment responses and to explore the parameter uncertainty in model predictions. We applied this approach to the River Derwent and Cocker catchments in the UK using a LEM CAESAR-Lisflood. Results show that the model was generally able to produce behavioural simulations within the uncertainty limits of the streamflow. Reliability metrics ranged from 24.4% to 41.2% and captured the high-magnitude low-frequency sediment events. Since different sets of behavioural simulations were found across different parts of the catchment, evaluating LEM performance, in quantifying and assessing both at-a-point behaviour and spatial catchment response, remains a challenge. Our results show that evaluating LEMs within uncertainty analyses framework while taking into account the varying quality of different observations constrains behavioural simulations and parameter distributions and is a step towards a full-ensemble uncertainty evaluation of such models. We believe that this approach will have benefits for reflecting uncertainties in flooding events where channel morphological changes are occurring and various diverse (and yet often sparse) data have been collected over such events.

Water resources in semi‐arid regions like the Mediterranean Basin are highly vulnerable because of the high variability of weather systems. Additionally, climate change is altering the timing and pattern of water availability in a region where growing populations are placing extra demands on water supplies. Importantly, how reservoirs and dams have an influence on the amount of water resources available is poorly quantified. Therefore, we examine the impact of reservoirs on water resources together with the impact of climate change in a semi‐arid Mediterranean catchment. We simulated the Susurluk basin (23.779‐km2) using the Soil and Water Assessment Tool (SWAT) model. We generate results for with (RSV) and without reservoirs (WRSV) scenarios. We run simulations for current and future conditions using dynamically downscaled outputs of the MPI‐ESM‐MR general circulation model under two greenhouse gas relative concentration pathways (RCPs) in order to reveal the coupled effect of reservoir and climate impacts. Water resources were then converted to their usages – blue water (water in aquifers and rivers), green water storage (water in the soil) and green water flow (water losses by evaporation and transpiration). The results demonstrate that all water resources except green water flow are projected to decrease under all RCPs compared to the reference period, both long‐term and at seasonal scales. However, while water scarcity is expected in the future, reservoir storage is shown to be adequate to overcome this problem. Nevertheless, reservoirs reduce the availability of water, particularly in soil moisture stores, which increases the potential for drought by reducing streamflow. Furthermore, reservoirs cause water losses through evaporation from their open surfaces. We conclude that pressures to protect society from economic damage by building reservoirs have a strong impact on the fluxes of watersheds. This is additional to the effect of climate change on water resources.

Hydrology is still, and for good reasons, an inexact science, even if evolving hydrological understanding has provided a basis for improved water management for at least the last three millennia. The limitations of that understanding have, however, become much more apparent and important in the last century as the pressures of increasing populations, and the anthropogenic impacts on catchment forcing and responses, have intensified. At the same time, the sophistication of hydrological analyses and models has been developing rapidly, often driven more by the availability of computational power and geographical data sets than any real increases in understanding of hydrological processes. This sophistication has created an illusion of real progress but a case can be made that we are still rather muddling along, limited by the significant uncertainties in hydrological observations, knowledge of catchment characteristics and related gaps in conceptual understanding, particularly of the sub-surface. These knowledge gaps are illustrated by the fact that for many catchments we cannot close the water balance without significant uncertainty, uncertainty that is often neglected in evaluating models for practical applications.