In the present study we combined measurements of dissolved inorganic carbon (DIC) isotopes with a set of different geochemical and microbiological methods in order to get a comprehensive view of biogeochemical cycling and groundwater flow in two limestone aquifer assemblages. This allowed us to understand interactions and feedbacks between microbial communities, their carbon sources, and water chemistry.

We investigate the quantitative effect of neglected sensor position, small-scale heterogeneity, and lateral flow on soil hydraulic material properties. Thus, we analyze a fluctuating water table experiment in a 2-D architecture (ASSESS) with increasingly complex studies based on time domain reflectometry and hydraulic potential data. We found that 1-D studies may yield biased parameters and that estimating sensor positions as well as small-scale heterogeneity improves the model significantly.

We inspect the state-of-the-art of several land surface (LSMs) and hydrologic models (HMs) and show that most do not have consistent and realistic parameter fields for land surface geophysical properties. We propose to use the multiscale parameter regionalization (MPR) technique to solve, at least partly, the scaling problem in LSMs/HMs. A general model protocol is presented to describe how MPR can be applied to a specific model.

The paper explores a method for the assessment of the performance of global rainfall estimates without relying on ground-based observations. Thanks to this method, different global correlation maps are obtained (for the first time without relying on a benchmark dataset) for some of the most used globally available rainfall products. This is central for hydroclimatic studies within data-scarce regions, where ground observations are scarce to evaluate the relative quality of a rainfall product

This study extracted spatio-temporal patterns in the effects of LUCC and precipitation variability on sediment yield across the Loess Plateau during 1961–2011. The impacts of precipitation on sediment yield declined with time and the precipitation-sediment relationship showed a coherent spatial pattern. The sediment coefficient, representing the effect of LUCC, decreases linearly with fraction of area treated with erosion control measures and the slopes were highly variable among the catchments.

Bias correction of climate model outputs has become a standard procedure accompanying climate change impact studies. However, it introduces a new level of uncertainty in the modelling chain which remains relatively unexplored. In this work we present a new framework for the quantification and categorization of the effect of bias correction on global hydrological simulations and we derive information on the sensitivity and magnitude of the effect of GCM biases on runoff, at the global scale.

This study applied the exponential filter to produce an estimate of root-zone soil moisture at 20 to 25 cm depths. Four types of microwave, surface satellite soil moisture were used. The study focused on the continental United States, and in situ data were used from the International Soil Moisture Network for comparison. This study spans almost two decades (1997 to 2014). Root mean square error was close to 0.04, which is the baseline value for accuracy designated for many satellite missions.

It is critical to identify the origins of water in arid and semiarid regions for management and protection of the water resources. The D, ¹⁸O, ³H and ¹⁴C in water samples from the Badain Jaran Desert, China, were analyzed. The results show that groundwater supplies the lakes and originates from local precipitation and adjacent mountains. Negative d-excess values of water in the area were the result of evaporation. The ¹⁴C ages do not represent the residence time of local groundwater.

Time series of rainfall are developed in order to represent future climate conditions. These series can be used in design of, for example, drainage systems where future rainfall loads are important to account for. The climate projections are evaluated on a number of key statistical parameters of rainfall such as yearly and seasonal precipitation amounts, number of extreme events and rainfall intensities, specific duration, and return periods.

The goal of this paper is to improve the vegetation growth modelling in SWAT for tropical ecosystems. Therefore, we propose a straightforward but robust soil moisture index (SMI) – a quotient of rainfall (P) and reference evapotranspiration (ET_r) – to dynamically initiate a new growth cycle within a predefined period. Our results for the Mara Basin (Kenya/Tanzania) show that the simulated LAI corresponds well with the MODIS LAI for for evergreen forest, savanna grassland and shrubland.

In this study we statistically analyse the relationship between vegetation cover and components of total water storage. Splitting water storage into different components allows for a more comprehensive understanding of the temporal response of vegetation to changes in water storage. Generally, vegetation appears to be more sensitive to interannual changes in water storage than to shorter changes, though this varies in different land use types.

We monitor infiltration at multiple scales during managed aquifer recharge with desalinated seawater in an infiltration pond, while groundwater recharge is evaluated by simplified and numerical models. We found that pond-surface clogging is negated by the high-quality desalinated seawater or negligible compared to the low-permeability layers of the unsaturated zone. We show that a numerical model with a 1-D representative sediment profile is able to capture infiltration and recharge dynamics.

There are many global maps of hydrology and water resources, but none on necessary storage to smooth out discharge variability. This paper provides a methodology to create such a map, taking the Ganges–Brahmaputra–Meghna basin as an example. Necessary storage is calculated by a new method, intensity–duration–frequency curves of flood and drought (FDC–DDC). Necessary storage serves as a signature of hydrological variability and its geographical distribution provides new insights for hydrology.

Disaster managers would like to use seasonal forecasts to anticipate flooding months in advance. However, current seasonal forecasts give information on rainfall instead of flooding. Here, we find that the number of extreme events, rather than total rainfall, is most related to flooding in different regions of Africa. We recommend several forecast adjustments and research opportunities that would improve flood information at the seasonal timescale in different regions.

Previous studies have reported a systematic underestimation of debris flow occurrence thresholds, due to the use of sparse networks in non-stationary rain fields. We analysed high-resolution radar data to show that spatially aggregated estimates (e.g. satellite data) largely reduce this issue, in light of a reduced estimation variance. Our findings are transferable to other situations in which lower envelope curves are used to predict point-like events in the presence of non-stationary fields.

This study tests the phase and amplitude of GRACE TWS signals in the Upper Nile Basin from five commonly used gridded products (NASA's GRCTellus: CSR, JPL, GFZ; JPL-Mascons; GRGS) using in situ data and soil moisture from the Global Land Data Assimilation System. Resolution of changes in groundwater storage (ΔGWS) from GRACE is greatly constrained by the uncertain simulated soil moisture storage and the low amplitude in ΔGWS observed in deeply weathered crystalline rocks in the Upper Nile Basin.

In South Africa, the invasion of riparian forests by alien trees has the potential to affect the limited water resources. To justify alien clearing programs, hydrological benefits are required. Spatial upscaling of measured sapflows showed that an alien stand used 6 times more water per unit area than the indigenous stand. A gain in groundwater recharge and/or streamflow would be achieved if the alien species were removed from riparian forests and rehabilitated back to their natural state.

In this work, Van der Burgh's coefficient, K, is determined in the dry and wet seasons in a salt plug estuary to examine the contributions of tidal versus density-driven salt transport mechanisms. Gravitational circulation was entirely dominant over tidal dispersion in the wet season, whereas density-induced inverse and positive gravitational circulation facilitated an inverse and a positive estuarine circulation seaward and landward from the salt plug area during the dry season, respectively.

Rainfall is the primary trigger of landslides. However, the rainfall intensity is not uniform in space, which causes more landslides in the area of intense rainfall. The primary objective of this paper is to quantify spatial correlation characteristics of three landslide-triggering large storms in Hong Kong. The spatial maximum rolling rainfall is represented by a trend surface and a random field of residuals. The scales of fluctuation of the residuals are found between 5 km and 30 km.

The intense, prolonged Sahel drought has caused a widespread increase in surface runoff and surface waters like lakes or rivers, against all expectations. Using long-term observations and the Kineros2 hydrological model, we show that the runoff coefficient of the Agoufou watershed increased from ~ 0 to 5.5 % in 1950–2011. We attribute this phenomenon to a change in vegetation and soil surface properties, in response to the drought, rather than land–use change or rainfall regime intensification.

This paper presents for the first time the effects of aggregation errors on mean transit times and young fractions estimated using tritium concentrations. Such errors, due to heterogeneity in catchments, had previously been demonstrated for seasonal tracer cycles by Kirchner (2016a). We found that mean transit times derived from tritium are just as susceptible to aggregation bias as those from seasonal tracer cycles. Young fractions were found to be almost immune to aggregation bias.

ED2 is a terrestrial biosphere model (TBM) suited for investigating combined impacts of changes in climate, atmospheric CO₂, and land cover on the water cycle. In this study, we describe the integration of ED2 with a hydrological routing scheme. The resulting ED2+R model calculates the lateral propagation of surface and subsurface runoff resulting from the TBM and determines spatiotemporal patterns of river flows. We successfully evaluated the ED2+R model in the Tapajós, Brazilian Amazon.

The research deals with the problem how rainfall affects flow in a catchment. Until the present, one of the most popular solutions is to imagine the catchment as a cascade of tanks where the outflow from any tank is the supply to the next one. This idea is modified in the paper and the cascade is shown as a sequence of submerged tanks where each tank can be supplied from both sides. Performed tests show that in some cases this concept may be better than the classical cascade.

This study applies plot-scale and hill-slope knowledge to a process-based mesoscale model to improve the skill of distributed hydrological models to simulate the spatially and basin-integrated hydrological processes of complex ecosystems in the sub-arctic boreal forest. We developed a sub-grid parameterization method to parameterize the surface heterogeneity of interior Alaskan discontinuous permafrost watersheds.

The research presented here provides the first evaluation of the skill of a seasonal hydrological forecast for the UK. The forecast scheme combines rainfall forecasts from the Met Office GloSea5 forecast system with a national-scale hydrological model to provide estimates of river flows 1 to 3 months ahead. The skill in the combined model is assessed for different seasons and regions of Britain, and the analysis indicates that Autumn/Winter flows can be forecast with reasonable confidence.

The paper contributes a comparative analysis of the operational value of different forecast products for the agricultural sector. The underlying idea is that forecast accuracy per se might not be the most effective indicator of the goodness of a forecast but the behaviour of the farmers using the forecast should somehow also be taken into consideration and, correspondingly, the benefit generated by the forecast. This paper explores and validates this idea with a model-based analysis.

This work provides forecasts of water supply for the semi-arid Elqui River Valley, Chile, at periods prior to the October–January growing season. Forecasts are constructed provide water rights holders, whose allocations are subject to annual change, with an advanced indication of expected allocations. Forecasts, based on global and local indicators, are best suited to provide an initial indication of allocation category (above, near, or below normal) in May and are quantified in September.

River temperature (T_w) is important to fish populations, but one cannot monitor everywhere. Thus, models are used to predict T_w, sometimes in rivers with no data. To date, the accuracy of these predictions has not been determined. We found that models including landscape predictors (e.g. altitude, tree cover) could describe spatial patterns in T_w in other rivers better than those including air temperature. Such findings are critical for developing T_w models that have management application.

This paper aims at identifying Earth observation data sets that can help river basin managers detect drought conditions that may lead to impacts early enough to take mitigation actions. Six remote sensing products were assessed using two types of impact data as a benchmark: media records from a regional newspaper and crop yields. Precipitation, vegetation condition and evapotranspiration products showed the best results, offering early signs of impacts up to 6 months before the reported damages.

GNSS SNR data were obtained from an intensively cultivated wheat field in southwestern France. The data were used to retrieve soil moisture and vegetation characteristics during the growing period of wheat. Vegetation growth broke up the constant height assumption used in soil moisture retrieval algorithms. Soil moisture could not be retrieved after wheat tillering. A new algorithm based on a wavelet analysis was implemented and used to retrieve vegetation height.

The role that hydrology plays in controlling the interplay between dissolved organic carbon (DOC) and nitrogen in rivers of lowland, agricultural landscapes is poorly understood, yet important to assess given the potential changes to production and delivery of DOC and nitrate arising from climate change. We measured DOC and nitrate concentrations in river water of the lowland river Hampshire Avon (Wiltshire, southern UK), revealing significant seasonal variations in DOC : nitrate transport.

The roles of groundwater flow in the hydrological cycle within the alpine area characterized by permafrost or seasonal frost are poorly known. We investigated the role of permafrost in controlling groundwater flow and hydrological connections between glaciers and river. The recharge, flow path and discharge of permafrost groundwater at the study site were explored. Two mechanisms were proposed to explain the significantly seasonal variation in interaction between groundwater and surface water.

The work presents an original methodology for optimizing streamflow simulations with the distributed hydrological model GEM-Hydro. While minimizing the computational time required for automatic calibration, the approach allows us to end up with a spatially coherent and transferable parameter set. The GEM-Hydro model is useful because it allows simulation of all physical components of the hydrological cycle in every part of a domain. It proves to be competitive with other distributed models.

This study investigates the relationship between skill and value of ensemble seasonal streamflow forecasts. Using data from a modern forecasting system, we show that skilled forecasts are more likely to provide benefits for reservoirs operated to maintain a target water level rather than reservoirs operated to satisfy a target demand. We identify the primary causes for this behaviour and provide specific recommendations for assessing the value of forecasts for reservoirs with supply objectives.

Soil maximum available water content (MaxAWC) is a key parameter in land surface models. Being difficult to measure, this parameter is usually unavailable. A 15-year time series of satellite-derived observations of leaf area index (LAI) is used to retrieve MaxAWC for rainfed straw cereals over France. Disaggregated LAI is sequentially assimilated into the ISBA LSM. MaxAWC is estimated minimising LAI analyses increments. Annual maximum LAI observations correlate with the MaxAWC estimates.

The central part of the Himalayan Range presents tremendous heterogeneity in terms of topography and climatology, but the representation of hydro-climatic processes for Himalayan catchments is limited due to a lack of knowledge in such poorly instrumented environments. The proposed approach is to characterize the effect of altitude on precipitation by considering ensembles of acceptable altitudinal factors. Ensembles of acceptable values for the components of the water cycle are then provided.

We tested the usefulness of stream level class data for hydrological model calibration. Only two stream level classes, e.g. above or below a rock in the stream, were already informative, particularly when the boundary was chosen at a high stream level. There was hardly any improvement in model performance when using more than five stream level classes. These results suggest that model based streamflow time series can be obtained from citizen science based water level class data.

In this research, SWAT was used to model the upper Blue Nile Basin where comparisons between ground and CFSR data were done. Furthermore, this paper introduced the SWAT error index (SEI), an additional tool to measure the level of error of hydrological models. This work proposed an approach or methodology that can effectively be followed to create better and more efficient hydrological models.

Applications of data assimilation (DA) arise in many fields of geosciences, perhaps most importantly in weather forecasting and hydrology. We want to investigate the roles of data assimilation methods and land surface models (LSMs) in joint estimation of states and parameters in the assimilation experiments. We find that all DA methods can improve prediction of states, and that differences between DA methods were limited but that the differences between LSMs were much larger.

This research proposes a new method for determining efficient and effective synthetic initial conditions for an unsteady model using the Saint-Venant equations for a large river network. The proposed method increases the efficiency of initial condition convergence up to 3700 times. This can not only help the hydrology research move from empirically based to mechanistic-based models for large river networks but also provide a computationally effective approach for initial conditions for modelers.

This research investigates the rainwater dynamics in the snowpack under artificial rain-on-snow events. Deuterium-enriched water was sprayed on the isolated snowpack and rainwater was further identified in the runoff. We found that runoff from cold snowpack was created faster than from the ripe snowpack. Runoff from the cold snowpack also contained more rainwater compared to the ripe snowpack. These results are valuable for further snowpack runoff forecasting.