High-resolution precipitation data, needed for many applications in hydrology, are typically rare. Such data can be simulated from daily precipitation with stochastic disaggregation. In this work, multiplicative random cascades are used to disaggregate time series of 40 min precipitation from daily precipitation for 81 Swiss stations. We show that very relevant statistics of precipitation are obtained when precipitation asymmetry is accounted for in a continuous way in the cascade generator.

Hydrological models often overlook vegetation's response to CO₂ and climate, impairing their ability to forecast impacts on evapotranspiration and water resources. To address this, we suggest involving two model variants: (1) the standard method and (2) a modified approach (proposed here) based on the Priestley–Taylor equation (PT-MA). While not universally applicable, a dual approach helps consider uncertainties related to vegetation responses to climate change, enhancing model representation.

Accurate wetland inventories are critical to monitor and protect wetlands. However, in many areas a large proportion of wetlands are unmapped because they are hard to detect in imagery. We developed a machine learning approach using spatially mapped variables of wetland indicators (i.e., vegetation, hydrology, soils), including novel multi-scale topographic indicators, to predict wetland probability. Our approach can be adapted to diverse landscapes to improve wetland detection.

We present a method to collect water vapor samples into bags in the field without an in-field analyser, followed by isotope analysis in the lab. This new method resolves even fine-scaled natural isotope variations. It combines low-cost and lightweight components for maximum spatial and temporal flexibility regarding environmental setups. Hence, it allows for sampling even in terrains that are rather difficult to access, enabling future extended isotope datasets in soil sciences and ecohydrology.

Intensity–duration–frequency (IDF) curves describe the likelihood of extreme rainfall and are used in hydrology and engineering, for example, for flood forecasting and water management. We develop a model to estimate IDF curves from daily meteorological observations, which are more widely available than the observations on finer timescales (minutes to hours) that are needed for IDF calculations. The method is applied to all data at once, making it efficient and robust to individual errors.

Mathematical models are key to the study of environmental processes in rivers. Such models often require information on river morphology from geographic information system (GIS) software, which hinders the use of replicable workflows. Here I present rivnet, an R package for simple, robust, GIS-free extraction and analysis of river networks. The package is designed so as to require minimal user input and is oriented towards ecohydrological, ecological and biogeochemical modeling.

The GRACE (Gravity Recovery And Climate Experiment) satellite mission enabled the quantification of water mass redistributions from 2002 to 2017. The analysis of GRACE satellite data shows here that slow changes in terrestrial water storage occurring over a few years to a decade are severely underestimated by global hydrological models. Several sources of errors may explain such biases, likely including the inaccurate representation of groundwater storage changes.

Riverbeds are densely populated with microorganisms which catalyze ecologically relevant processes. To study this complex zone, we tested pore-water extraction with microfilter tubes. The method was found to be suitable for the measurement of dissolved solutes but less so for gases. The pumping rate during sample extraction strongly influenced gas analyses in the samples. The combination with an optical oxygen sensor and a temperature monitoring system was found to be highly valuable.

With the aim of improving the understanding of seasonal variations in water stable isotopes and catchment hydrological processes, we compared the temporal variations of precipitation and river water isotopes with the hydrometeorological factors in the Xiangjiang River over 13 years. Results showed that the changes in river water isotopes can be variables that reflect the seasonal variations in local environments and extreme events and may show implications for paleoclimate reconstruction.

To solve the issue of sparsity of field-observed runoff data, we propose a methodology that leverages taxi GPS data to support hydrological parameter calibration for road networks. Novel to this study is that a new kind of data source, namely floating car data, is introduced to tackle the ungauged catchment problem, providing alternative flooding early warning supports for cities that have little runoff data but rich taxi data.

In deltas, saltwater sometimes intrudes far inland and causes problems with freshwater availability. We created a model to forecast salt concentrations at a critical location in the Rhine–Meuse delta in the Netherlands. It requires a rather small number of data to make a prediction and runs fast. It predicts the occurrence of salt concentration peaks well but underestimates the highest peaks. Its speed gives water managers more time to reduce the problems caused by salt intrusion.