Unpacking some of the linkages between uncertainties in observational data and the simulation of different hydrological processes using the Pitman model in the data scarce Zambezi River basin.

Abstract

The main objective of this study was to use an uncertainty version of a widely used monthly time step, semi-distributed model (the Pitman model) to explore the equifinalities in the way in which the main hydrological processes are simulated and any identifiable linkages with uncertainties in the available observational data. The study area is the Zambezi River basin and 18 gauged sub-basins have been included in the analyses. Unfortunately, it is not generally possible to quantify some of the observational uncertainties in such a data scarce area and mostly we are limited to identifying where these data are clearly deficient (i.e. erroneous or non-representative). The overall conclusion is that the equifinalities in the model are hugely dominant in terms of the uncertainties in the relative occurrence of different runoff generating processes, although water use uncertainties in the semi-arid parts of the basin can contribute to these uncertainties. The identification of landscape features that suggest the occurrence of saturation excess surface runoff provides some information to constrain the model. Improved independent estimates of groundwater recharge is also identified as a key source of observational data that would help a great deal in constraining the model parameter space and therefore reducing some of the model equifinality.Unpacking some of the linkages between uncertainties in observational data and the simulation of different hydrological processes using the Pitman model in the data scarce Zambezi River basin.

Impacts of observational uncertainty on analysis and modelling of hydrological processes: Preface

“…In contrast to the focus on well‐monitored local‐scale catchments in Hankin et al (2021), Hughes and Farinosi (2021) consider observational uncertainties and their impacts on hydrological modelling and process understanding in a data scarce region. They focus on observational uncertainties from multiple data sources (evaporation, soil moisture, water use, rainfall, streamflow and groundwater recharge) and assess their role in identifying the relevant contribution of different hydrological processes.…”

“…They focus on observational uncertainties from multiple data sources (evaporation, soil moisture, water use, rainfall, streamflow and groundwater recharge) and assess their role in identifying the relevant contribution of different hydrological processes. While quantitative estimates of observational uncertainties are rarely available in data‐scarce regions, Hughes and Farinosi (2021) demonstrate other techniques to assess observational uncertainties such as comparing multiple datasets of the same observation or assessing the consistency and completeness of the dataset. They conclude that while model equifinalities still dominate in terms of identifying the relative occurrence of different runoff‐generating processes, observational uncertainties are still a key issue and that there is not enough data to resolve equifinalities in their model.…”

“…While the previous papers in this special issue have typically focused on a single source of uncertainty, three of the contributions have taken a broader outlook focusing on multiple sources of observational uncertainty. These papers provide valuable guidance on evaluating observational uncertainties in both data rich (Hankin et al, 2021) and data poor (Hughes & Farinosi, 2021) regions, alongside an outlook and future directions for assessing observational data and model uncertainties (Beven, 2021a).…”

“…In contrast to the focus on well-monitored local-scale catchments in Hankin et al (2021), Hughes and Farinosi (2021) ). Looking to the future, Beven (2021a) advocates that there should be more interaction between observational and computational hydrologists to better define critical observations that could help us to distinguish between model formulations and/or parameterisations.…”

“…Data assimilation techniques such as Kalman or Particle filtering require data uncertainty information to enable them to correctly balance the information content of model predictions and new data observations (Ocio et al, 2017; Smith et al, 2008). Even when uncertainty analysis is not explicitly required for a model analysis, it brings a deeper appreciation of the strengths and limitations of our data, and of subsequent inference based on that data (Hughes & Farinosi, 2021; Juston et al, 2013).…”