A multiplexed pumping system (MPS) for obtaining continuous water quality data at multiple locations was previously reported. The existing design was not practical for sampling water in volume-limited applications such as small mesocosms or porewater sampling. This paper discusses the design and performance of a small-volume MPS and illustrates two applications, showing spatial variability in replicate in situ mesocosms and short-circuiting in a woodchip bioreactor using porewater sampling.

One common approach to cope with floods is the implementation of structural flood protection measures, such as levees. Numerous scholars have problematized this approach and shown that increasing levels of flood protection can generate a false sense of security and attract more people to the risky areas. We briefly review the literature on this topic and then propose a research agenda to explore the unintended consequences of structural flood protection.

Recently, deep learning (DL) has emerged as a revolutionary tool for transforming industries and scientific disciplines. We argue that DL can offer a complementary avenue toward advancing hydrology. New methods are being developed to interpret the knowledge learned by deep networks. We argue that open competitions, integrating DL and process-based models, more data sharing, data collection from citizen scientists, and improved education will be needed to incubate advances in hydrology.

Decision-making for flood risk management is often the collective effort of professionals within government, NGOs, private practice, and advocacy groups. Our research investigates differences among flood experts within Tompkins County, New York (USA). We explore how they differ in their perceptions of flooding risk, desired project outcomes, and knowledge. We observe substantial differences among experts, and recommend formally acknowledging these perceptions when engaging in flood management.

This paper addresses questions related to the adoption of stochastic methods in hydrogeology, looking at factors such as environmental regulations, financial incentives, higher education, and the collective feedback loop involving these factors. We show that stochastic hydrogeology's blind spot is in focusing on risk while ignoring uncertainty, to the detriment of its potential clients. The imbalance between the treatments of risk and uncertainty is shown to be common to multiple disciplines.

Predicting meteorological droughts more than 2 months in advance became difficult due to low predictability, leading to weak skill for hydrological droughts in wet seasons. Hydrological drought forecasts showed skills up to 3–6 lead months due to the memory of initial hydrologic conditions in dry seasons. Human activities have increased hydrological predictability during wet seasons in the MHRB. This fills gaps in understanding drought and predictability predictions in endorheic and arid basins.

A data assimilation scheme was developed to improve snow water equivalent (SWE) simulations by updating meteorological forcings and snowpack states using passive microwave satellite observations. A chain of models was first calibrated to simulate satellite observations over northeastern Canada. The assimilation was then validated over 12 stations where daily SWE measurements were acquired during 4 winters (2012–2016). The overall SWE bias is reduced by 68 % compared to original SWE simulations.

This work observes that the result of streamflow simulation is often biased, especially with regards to extreme events, and proposes a novel technique to reduce this bias. By using parallel simulations of relative streamflow timing (sequencing) and the distribution of streamflow (magnitude), severe biases can be mitigated. Reducing this bias allows for improved utility of streamflow simulation for water resources management.

Predicting floods requires first knowing the amount of water in the valleys, which is complicated because we cannot know for sure how much water there is in the soil. We created a unique system that combines the best methods to estimate these conditions accurately based on the observed water flow in the rivers and on detailed simulations of the valleys. Comparisons with popular methods show that our system can produce realistic predictions efficiently, even for very detailed river networks.

Phosphorus (P) is important to global food security. Thus it is concerning that natural P reserves are predicted to deplete within the century. Here we explore the potential of P recovery from wastewater (WW) at global scale. We identify high production and demand sites to determine optimal market prices and trade flows. We show that 20 % of the agricultural demand can be met, yet only 4 % can be met economically. Nonetheless, this recovery stimulates circular economic development in WW treatment.

The goal of this article is to first provide an overview of the available PERSIANN precipitation retrieval algorithms and their differences. We evaluate the products over CONUS at different spatial and temporal scales using CPC data. Daily scale is the finest temporal scale used for the evaluation over CONUS. We provide a comparison of the available products at a quasi-global scale. We highlight the strengths and limitations of the PERSIANN products.

CAMELS-CL provides a catchment dataset in Chile, including 516 catchment boundaries, hydro-meteorological time series, and 70 catchment attributes quantifying catchments' climatic, hydrological, topographic, geological, land cover and anthropic intervention features. By using CAMELS-CL, we characterise hydro-climatic regional variations, assess precipitation and potential evapotranspiration uncertainties, and analyse human intervention impacts on catchment response.

We show event- and pre-event-water volumes as fractions of precipitation, rather than discharge, to provide an alternative and more insightful approach to study catchment hydrological processes. For this, we analyze 24 storm events using high-frequency measurements of stable water isotopes in stream water and precipitation at a pre-Alpine catchment. Antecedent wetness and storm characteristics are dominant controls on event-water discharge and pre-event-water mobilization from storage.

The hydrological functioning of western African headwater wetlands remains poorly understood, despite their potential for small-scale farming and their role in streamflow production. We found that land cover changes significantly affect water budgets, and pedo-geological features control dry season baseflow. These are the results of virtual experiments with a physically based critical zone model evaluated against streamflow, evapotranspiration, soil moisture, water table and water storage data.

One of the main objectives of remote sensing methodology is to downscale soil moisture to improve irrigation management. The DISPATCH algorithm is able to measure soil moisture at 1 km resolution using SMOS and MODIS data. In this work DISPATCH has been evaluated with soil moisture sensors, under heterogeneous conditions where local irrigation is applied. Results show that DISPATCH is not sensitive when local irrigation is applied even at low resolution.

In this paper we follow a user-based approach to examine operational drought management decisions and how the role of information on them can be assessed. The approach combines a stakeholder consultation and a decision model representing the interrelated decisions of the irrigation association and farmers. The decision model was extended to include information on snow cover from satellite. This contributed to better decisions in the simulation and ultimately higher benefits for the farmers.

We propose a method for unsupervised classification of the space–time–intensity structure of weather radar images. The resulting classes are interpreted as rain types, i.e. pools of rain fields with homogeneous statistical properties. Rain types can in turn be used to define stationary periods for further stochastic rainfall modelling. The application of rain typing to real data indicates that non-stationarity can be significant within meteorological seasons, and even within a single storm.

Extreme weather, like heavy rainstorms and droughts, will become more frequent under climate change, jeopardizing water availability. We show that climate change in a large Mediterranean catchment leads to a redistribution of water from soil to reservoirs and to increased crop stress. Furthermore, increased soil erosion threatens the storage capacity of reservoirs. We conclude that climate change may affect rainfed and irrigated agriculture potential and threatens overall water security.

The paper analyzes the effect of two input data (DEMs and the combination of soil and land use data) with different resolution and complexity on the uncertainty of model outputs (the predictions of streamflow and saturated areas) and parameter uncertainty using SWAT-HS. Results showed that DEM resolution has significant effect on the spatial pattern of saturated areas and using complex soil and land use data may not necessarily improve model performance or reduce model uncertainty.

Inundation models are useful for hazard management and prevention. They are traditionally based on hydraulic partial differential equations (with satisfying results but large data and computational requirements). This study presents a simplified approach combining reach-scale geometric properties with steady uniform flow equations. The model shows promising results overall, although difficulties persist in the most complex urbanised reaches.

We demonstrate how thermal infrared images can be used for mapping the appearance and disappearance of water at the surface. The use of thermal infrared images allows for mapping this appearance and disappearance for various temporal and spatial resolutions, and the images can be understood intuitively. We explain the necessary steps in detail, from image acquisition to final processing, by relying on image examples and experience from an 18-month mapping campaign.

In this paper, we propose a novel data-driven approach for rainfall–runoff modelling, using the long short-term memory (LSTM) network, a special type of recurrent neural network. We show in three different experiments that this network is able to learn to predict the discharge purely from meteorological input parameters (such as precipitation or temperature) as accurately as (or better than) the well-established Sacramento Soil Moisture Accounting model, coupled with the Snow-17 snow model.

We investigated the wetland system classified as a threatened ecological community and found that organic-rich soil close to surfaces retains significant moisture necessary for ecosystems. At the base of the swamp an identified sand layer allows relatively rapid drainage and lateral groundwater interaction. Evaporation estimated from stable water isotopes from sediments indicated that groundwater contribution to the swamp is significant in dry periods, supporting ecosystems when water is scarce.

We want to estimate how much water will be available in a river basin (runoff) at the end of the 21st century. Climate models alone are considered unsuitable for this task due to biases in representing the present-day climate. We show that the output from these models can be corrected using a simple mathematical framework. This approach narrows the range of future runoff projections for the Yellow river in China by 34 %. It serves as a quick tool for updating projections from climate models.

Soil erosion is a global problem and models identify vulnerable areas for management. One such model is the Revised Universal Soil Loss Equation. We review its different sub-factors and compile studies and equations that modified it for local conditions. The limitations of RUSLE include its data requirements and exclusion of gullying and landslides. Future directions include accounting for these erosion types. This paper serves as a reference for others working with RUSLE and related approaches.

Model bias is commonly seen in discharge simulation by hydrological or land surface models. This study tested an approach with the Budyko hypothesis to retrospect the estimated discharge bias to different bias sources including the atmospheric variables and model structure. Results indicate that the bias is most likely caused by the forcing variables, and the forcing bias should firstly be assessed and reduced in order to perform pertinent analysis of the regional water cycle.

Public water supply systems (PWSSs) are important infrastructure susceptible to contamination and physical disruption. In general, PWSSs are analyzed using hydrodynamic models, which requires detailed supply infrastructure information. In this paper, we have shown that stable isotope mixing models can also provide useful information on PWSSs. The method developed here can be useful in studying decentralized PWSSs, validating hydrodynamic models and solving water right issues.