The flow of freshwater in rivers is commonly studied with computer programs known as hydrological models. An important component of those programs lies in the description of the river environment, such as the channel resistance to the flow, that is critical to accurately predict the river flow but is still not well known. Satellite data can be combined with models to enrich our knowledge of these features. Here, we show that the coming SWOT mission can help better know this channel resistance.

In the climate/hydrology network, each node represents a geographical location of climatological data, and links between nodes are set up based on their interaction or similar variability. Here, using network theory, we first generate a node-ranking measure and then prioritize the rain gauges to identify influential and expandable stations across Germany. To show the applicability of the proposed approach, we also compared the results with existing traditional and contemporary network measures.

Assessment of climate change impacts on hydrologic systems is critical for making adaptation strategies but subject to uncertainties from various sources. This study developed a framework to investigate such uncertainties from both hydrologic model components and climate forcings as well as associated parameterization. Results of this study reveal variable uncertainty compositions for different hydrological quantities and imply limited impact of hydrologic model parameter equifinality.

The complementary principle is an important methodology for estimating actual evaporation by using routinely observed meteorological variables. This review summaries its 56-year development, focusing on how related studies have shifted from adopting a symmetric linear complementary relationship to employing generalized nonlinear functions. We also compare the polynomial and sigmoid types of generalized complementary functions and discuss their future development.

For applications such as flood forecasting of urban- or town-scale distributed hydrological modeling, high-resolution quantitative precipitation estimation (QPE) with enough accuracy is the most important driving factor and thus the focus of this paper. Considering the fact that rain gauges are sparse but accurate and radar-based precipitation estimates are inaccurate but densely distributed, we are merging the two types of data intellectually to obtain accurate QPEs with high resolution.

We evaluate the soil moisture response in the humid tropics to El Niño during the three most recent super El Niño events. Our estimates are compared to in situ soil moisture estimates that span five continents. We find the strongest and most consistent soil moisture decreases in the Amazon and maritime southeastern Asia, while the most consistent increases occur over eastern Africa. Our results can be used to improve estimates of soil moisture in tropical ecohydrology models at multiple scales.

We proposed a novel many-objective simulation-optimization framework for conjunctive use of surface water and groundwater in Yanqi Basin, northwest China. The management model involving socioeconomic and environmental objectives was constructed to explore optimal water-use schemes. Three runoff scenarios were then specified to quantify the effect of runoff reduction related to climate change on water management. Results provide Pareto-optimal solutions for basin-scale water management.

Inflow forecasting plays an essential role in reservoir management and operation. To improve the accuracy of multistep-ahead daily inflow forecasting, the paper develops a new hybrid inflow forecast framework using ERA-Interim data. We find that the framework significantly enhances the accuracy of inflow forecasting at lead times of 4–10 d compared with widely used and mature methods. This research provides a reference for operational inflow forecasting in remote regions.

This paper developed a mathematically precise method to partition climate and catchment effects on streamflow. The method reveals that both the change magnitude and pathway (timing of change), not the magnitude alone, dictate the partition unless for a linear system. The method has wide relevance. For example, it suggests that the global warming effect of carbon emission is path dependent, and an optimal pathway would facilitate a higher global budget of carbon emission.

Rivers have been recognized as an active part of the carbon cycle where transformations are associated with CO₂ outgassing. To understand it, we propose a modeling approach with the biogeochemical model, pyNuts-Riverstrahler. We implemented it on the human-impacted Seine River. Sources of carbon to the river were characterized by field measurements in groundwater and in wastewater. Outgassing was the most important in streams, and peaks were simulated downstream of wastewater treatment effluent.

Due to increasing food demand and limited water resources, the quantification of the irrigation water productivity (IWP) is critical. Hydrological processes in irrigated areas differ in different watersheds owing to different irrigation–drainage activities, and this is more complex with shallow groundwater. Considering the complexity of the IWP, we developed a regional IWP model to simulate its spatial distribution; this informs irrigation managers on where they can improve IWP and save water.

Several models exist to track water through the atmosphere from its evaporation location to the next rain location. These models are typically driven by atmospheric wind and humidity data. Recently, a new version of these driving data sets has become available, with a higher spatial resolution of about 25 km. Here, we test the assumptions of these atmospheric moisture tracking models, given the high-resolution forcing data and find that the vertical mixing assumptions are the most important.

In this study, we use a combination of local- and global-sensitivity analyses to evaluate the relative importance of (1) the configuration of subsurface alluvial geology and (2) the hydraulic properties of geologic facies on recharge processes. Results show that there is a large variation of recharge rates possible in a typical alluvial aquifer system and that the configuration proportion of sand and gravel deposits in the subsurface have a large impact on recharge rates.

Wetropolis is a table-top demonstration model with extreme rainfall and flooding, including random rainfall, river flow, flood plains, an upland reservoir, a porous moor, and a city which can flood. It lets the viewer experience extreme rainfall and flood events in a physical model on reduced spatial and temporal scales with an event return period of 6.06 min rather than, say, 200 years. We disseminate its mathematical design and how it has been shown most prominently to over 500 flood victims.

We systematically compared 45 models for direct and indirect soil texture classification and soil particle size fraction interpolation based on 5 machine-learning models and 3 log-ratio transformation methods. Random forest showed powerful performance in both classification of imbalanced data and regression assessment. Extreme gradient boosting is more meaningful and computationally efficient when dealing with large data sets. The indirect classification and log-ratio methods are recommended.

The ERA5 reanalysis dataset is characterized by its high spatial (0.25) and temporal (hourly) resolutions and has therefore a large potential to drive environmental models in regions where the network of stations is deficient. ERA5 performance is evaluated on 3138 North American catchments. Results indicate that for hydrological modelling, ERA5 precipitation and temperature are just as good as observation all over North America, with the exception of the eastern half of the US.

Snow retained in the forest canopy (snow interception) drives spatial variability of the subcanopy snow accumulation. As such, accurately describing snow interception in models is of importance for various applications such as hydrological, weather, and climate predictions. We developed descriptions for the spatial mean and variability of snow interception. An independent evaluation demonstrated that the novel models can be applied in coarse land surface model grid cells.

Changes of stream water chemistry in response to discharge changes provide important insights into the storage and release of water from the catchment. Here we investigate the variability in concentration–discharge relationships among different solutes and hydrologic events and relate it to catchment conditions and dominant water sources.

Soil moisture plays an important role in hydrological modelling. However, most existing in situ observation networks rarely provide sufficient coverage to capture soil moisture variations. Clearly, there is a need to develop a systematic approach, so that with the minimal number of sensors the soil moisture information could be captured accurately. In this study, a simple and low-data requirement method is proposed (WRF, PCA, CA), which can provide very efficient soil moisture estimations.

Lake levels go up and down due to natural variability in the climate. But the effects of natural variability on lake levels can sometimes be confused for the influence of humans. Here we used long-term data from 200 globally distributed lakes and an advanced statistical approach to show that the effects of natural variability on lake levels can be disentangled from other effects leading to better estimates of long-term changes that may be partially caused by humans.

Interpolation methods accounting for elevation dependency from scattered gauges result in inaccurate inputs for snow-hydrological models. Altitudinal gradients of temperature and precipitation can be successfully inferred using an inverse snow-hydrological modelling approach. This approach can significantly improve the simulation of snow cover and streamflow dynamics through more parsimonious parametrization.

We could show that distributed soil moisture time series bear a considerable amount of information about dynamic changes in soil moisture. We developed a new method to describe spatial patterns and analyze their persistency. By combining uncertainty propagation with information theory, we were able to calculate the information content of spatial similarity with respect to measurement uncertainty. This does help to understand when and why the soil is drying in an organized manner.

The concept of time of concentration in the analysis of catchment responses dates back over 150 years. It is normally discussed in terms of the velocity of flow of a water particle from the furthest part of a catchment to the outlet. This is also the basis for the definition in the International Glossary of Hydrology, but this is in conflict with the way in which it is commonly used. This paper provides a clarification of the concept and its correct useage.

In recent years many methods have been developed for the statistical downscaling of climate model outputs. Each statistical downscaling method has strengths and limitations, but those are rarely evaluated. This paper illustrates an approach to evaluating the skill of statistical downscaling methods for the specific purpose of impact analysis in hydrology.

Rain gauges are unevenly spaced around the world with extremely low gauge density over places like Africa and South America. Here, water-related problems like floods, drought and famine are particularly severe and able to cause fatalities, migration and diseases. We have developed a rainfall dataset that exploits the synergies between rainfall and soil moisture to provide accurate rainfall observations which can be used to face these problems.

Global-scale hydrologic forecasts should account for attenuation through lakes and reservoirs. There is no consensus on the best approach to estimating this attenuation in large-spatial-scale hydrologic forecasts. This article investigates two existing parsimonious approaches to estimating reservoir outflows. We test each method at 60 reservoirs in the United States. We find that a method first developed in 2003 can provide a reasonable approximation of diurnal reservoir outflows.

High-resolution Weather Research and Forecasting model near-surface outputs from control and future periods were bias-corrected by downscaling outputs with respect to meteorological stations in Marmot Creek Research Basin, Canadian Rocky Mountains. A hydrological model simulation driven by the bias-corrected outputs showed declined seasonal peak snowpack, shorter snow-cover duration, higher evapotranspiration, and increased streamflow discharge in Marmot Creek for the warmer and wetter future.

To improve long-term sap flow measurements when using the heat ratio method, this study introduces a dynamic probe misalignment correction method. This work uses sap flow data from four Aleppo pines from April 2017 to December 2018 and shows how a classical probe correction approach declines in accuracy over time. Additionally, it is proposed that a new set of statistical information be recorded along with the sap flow readings to ensure the quality of the raw data.

Results show that coalitional strategy of blue and virtual water transfers can substantially save water and improve utilization efficiency without harming sectors' benefits and increasing ecological stresses. Under various polices, we use data-driven analysis to simulate hydrological and economic parameters, such as available water, crop import price, and water market price. Different water allocation and transfer results are obtained by adjusting hydrological and economic parameters.

The randomised Bartlett–Lewis (RBL) model is widely used to synthesise rainfall time series with realistic statistical features. However, it tended to underestimate rainfall extremes at sub-hourly and hourly timescales. In this paper, we revisit the derivation of equations that represent rainfall properties and compare statistical estimation methods that impact model calibration. These changes effectively improved the RBL model's capacity to reproduce sub-hourly and hourly rainfall extremes.

Existing approaches to quantify the emergence of climate change require several user choices that make these approaches less objective. We present an approach that uses a minimum number of choices and showcase its application in the extremely sensitive, permafrost-dominated region of eastern Siberia. Designed as a Python toolbox, it allows for incorporating climate model, reanalysis, and in situ data to make use of numerous existing data sources and reduce uncertainties in obtained estimates.

At subdaily resolution, rain intensity exhibits a strong variability in space and time due to the diversity of processes that produce rain (e.g., frontal storms, mesoscale convective systems and local convection). In this paper we explore a new method to simulate rain type time series conditional to meteorological covariates. Afterwards, we apply stochastic rain type simulation to the downscaling of precipitation of a regional climate model.

This study shifts from present discussions of the connectivity theory to the practical application of the connectivity concept for the analysis of runoff and sediment dynamics in Mediterranean dry slope systems. Overall, our results provide evidence for the feasibility of using the connectivity concept to understand how the spatial distribution of vegetation and micro-topography (including rills) interact with rainfall dynamics to generate spatially continuous runoff and sediment fluxes.