Testing a 1-D analytical salt intrusion model and its predictive equations in Malaysian estuaries

Little is known about the salt intrusion behaviour in Malaysian estuaries. Study of salt intrusion generally requires large amounts of data, especially if 2-D or 3-D numerical models are used; thus, in data-poor environments, 1-D analytical models are more appropriate. A fully analytical 1-D salt intrusion model, which is simple to implement and requires minimal data, was tested in six previously unsurveyed Malaysian estuaries (Kurau, Perak, Bernam, Selangor, Muar and Endau). The required data can be collected during a single day of observations. Site measurements were conducted during the dry season (June–August 2012 and February–March 2013) near spring tide. Data on cross-sections (by echo-sounding), water levels (by pressure loggers) and salinity (by moving boat) were collected as model input. A good fit was demonstrated between the simulated and observed salinity distribution for all six estuaries. Additionally, the two calibration parameters (the Van der Burgh coefficient and the boundary condition for the dispersion) were compared with the existing predictive equations. Since gauging stations were only present in some nested catchments in the drainage basins, the river discharge had to be up-scaled to represent the total discharge contribution of the catchments. However, the correspondence between the calibration coefficients and the predictive equations was good, particularly in view of the uncertainty in the river discharge data used. This confirms that the predictive salt intrusion model is valid for the cases studied in Malaysia. The model provides a reliable, predictive tool, which the water authority of Malaysia can use for making decisions on water abstraction or dredging

Selecting a conceptual hydrological model using Bayes' factors computed with Replica Exchange Hamiltonian Monte Carlo and Thermodynamic Integration

We develop a method for computing Bayes’ factors of conceptual rainfall-runoff models based on thermodynamic integration, gradient-based replica-exchange Markov Chain Monte Carlo algorithms and modern differentiable programming languages. We apply our approach to the problem of choosing from a set of conceptual bucket-type models with increasing dynamical complexity calibrated against both synthetically generated and real runoff data from Magela Creek, Australia. We show that using the proposed methodology the Bayes factor can be used to select a parsimonious model and can be computed robustly in a few hours on modern computing hardware. We introduce formal posterior predictive checks for the selected model. The prior calibrated posterior predictive p-value, which also tests for prior data conflict, is used for the posterior predictive checks. Prior data conflict is when the prior favours parameter values that are less likely given the data.Preprint15. Life on lan

The evolution of root-zone moisture capacities after deforestation : a step towards hydrological predictions under change?

The core component of many hydrological systems, the moisture storage capacity available to vegetation, is impossible to observe directly at the catchment scale and is typically treated as a calibration parameter or obtained from a priori available soil characteristics combined with estimates of rooting depth. Often this parameter is considered to remain constant in time. Using long-term data (30–40 years) from three experimental catchments that underwent significant land cover change, we tested the hypotheses that: (1) the root-zone storage capacity significantly changes after deforestation, (2) changes in the root-zone storage capacity can to a large extent explain post-treatment changes to the hydrological regimes and that (3) a time-dynamic formulation of the root-zone storage can improve the performance of a hydrological model.A recently introduced method to estimate catchment-scale root-zone storage capacities based on climate data (i.e. observed rainfall and an estimate of transpiration) was used to reproduce the temporal evolution of root-zone storage capacity under change. Briefly, the maximum deficit that arises from the difference between cumulative daily precipitation and transpiration can be considered as a proxy for root-zone storage capacity. This value was compared to the value obtained from four different conceptual hydrological models that were calibrated for consecutive 2-year windows.It was found that water-balance-derived root-zone storage capacities were similar to the values obtained from calibration of the hydrological models. A sharp decline in root-zone storage capacity was observed after deforestation, followed by a gradual recovery, for two of the three catchments. Trend analysis suggested hydrological recovery periods between 5 and 13 years after deforestation. In a proof-of-concept analysis, one of the hydrological models was adapted to allow dynamically changing root-zone storage capacities, following the observed changes due to deforestation. Although the overall performance of the modified model did not considerably change, in 51 % of all the evaluated hydrological signatures, considering all three catchments, improvements were observed when adding a time-variant representation of the root-zone storage to the model.In summary, it is shown that root-zone moisture storage capacities can be highly affected by deforestation and climatic influences and that a simple method exclusively based on climate data can not only provide robust, catchment-scale estimates of this critical parameter, but also reflect its time-dynamic behaviour after deforestation

Where should hydrology go? An early-career perspective on the next IAHS Scientific Decade: 2023–2032

This paper shares an early-career perspective on potential themes for the upcoming International Association of Hydrological Sciences (IAHS) Scientific Decade (SD). This opinion paper synthesizes six discussion sessions in western Europe identifying three themes that all offer a different perspective on the hydrological threats the world faces and could serve to direct the broader hydrological community: “Tipping points and thresholds in hydrology,” “Intensification of the water cycle,” and “Water services under pressure.” Additionally, four trends were distinguished concerning the way in which hydrological research is conducted: big data, bridging science and practice, open science, and inter- and multidisciplinarity. These themes and trends will provide valuable input for future discussions on the theme for the next IAHS SD. We encourage other early-career scientists to voice their opinion by organizing their own discussion sessions and commenting on this paper to make this initiative grow from a regional initiative to a global movement