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• Hydrological digital twins (HDT) are essential tools to understand the hydrological cycle especially in a changing climate. • We present, test, and validate an open-source framework to implement HDT in the large anthropized alpine Adige catchment. • The HDT is calibrated/validated on a variety of hydrological processes and data sources providing satisfactory performances. Understanding and simulating the hydrological cycle, especially in a context of climate change, is crucial for quantitative water risk assessment and basin management. The hydrological cycle is complex as it is a combination of non-linear natural processes and anthropogenic influences that alter landforms and water flows. Human-induced changes of relevance, including changes in land uses, construction of dams and artificial reservoirs, and diversion of the river course, lead to changes in water flows throughout the basin. These should be explicitly accounted for a realistic representation of the anthropogenically altered hydrological cycle. Such a realistic representation of the hydrological cycle is a necessary input for the water risk assessment in a particular region. In this paper, we present a hydrological digital twin (HDT) model of a large anthropized alpine basin: the Adige basin located in the northeast of Italy. Most catchments model often overlook land-uses changes over time and forget to model reservoir operation and their influence over time on water flow. Yet, for example, the Adige basin has>30 reservoirs affecting the water flow. We therefore use the GEOframe modeling framework to demonstrate the ability to create a hydrological twin model accounting for these anthropogenic changes. Specifically, we model each component of the water cycle over 39 years (1980–2018) at daily timescale through calibration of the Adige HDT with a multi-site approach using discharge data of 33 stations, based on a high-resolution (1 km) temperature and precipitation dataset and a calculated crop potential evapotranspiration (PETc) dataset, which accounts for human-induced change of the land cover over time. The modeling system also includes the simulation of artificial reservoirs and dams by the dynamically zoned target release (DZTR) reservoir model. The Adige HDT is assessed/validated/compared through a variety of hydrological processes (i.e., river and reservoir discharges, PETc and actual evapotranspiration, snow, and soil moisture) and data sources (i.e., observations and remote sensing data). Overall, the HDT reproduces well the measured discharge in space and time with a Kling Gupta Efficiency (KGE) above 0.7 (0.8) for 30 (23) of the 33 gauge-stations. For 7 artificial reservoirs with available data, the reservoir turbinated discharges are successfully reproduced with an average KGE of 0.92. A comparison between modeled and MODIS remote sensing snow data showed an average error of < 10% across the entire basin; the model also presented a good spatio-temporal agreement both with GLEAMS potential (and actual evapotranspiration) with an average KGE of 0.63 (0.60) and a high-level of correlation (0.5 on average) with the ASCAT satellite retrieved soil moisture. The findings of this paper demonstrate the potential of the open-source, component-based, GEOframe system to build a HDT, to provide a reliable and long term (39 years) estimation of all the water cycle components in a complex anthropized river basin at high spatial resolution. Spatially detailed HDT models results of this type can be used to inform basin-wise adaptation policy decisions and better water management practices in a time of changing climate. [ABSTRACT FROM AUTHOR]

[Display omitted] • Five discharge datasets have median correlation coefficients > 0.6 with the observations. • Overestimation of monthly mean flow is more common than underestimation. • GRFR showed the best estimate at around 40% of the locations. • None of the models provides good estimates for rivers and streams in arid regions. High-resolution global flow discharge datasets are critical to understand the terrestrial water cycle and river ecosystem services. Recently, a number of global hydrological models have been developed to capture global river flow dynamics at locally relevant scales. However, the relative accuracy of these simulated flow discharges is largely unknown. In this paper, using the largest observed streamflow datasets from > 26,000 gauging stations, we evaluated the performance of monthly and sub-monthly flow variation (1980–2014) generated from five major global hydrological models, Catchment-based Macro-scale Floodplain model (CaMa-Flood), Global Flood Awareness System (GloFAS-reanalysis), Global Reach-level Flood Reanalysis (GRFR), PCRaster GLOBal Water Balance model (PCR-GLOBWB), and Water Global Assessment and Prognosis (WaterGAP). Significant spatial heterogeneity in model performance was identified. We found that estimates by all five discharge datasets have a relatively high correlation with the observations (medians > 0.6). But none of them showed adequate correlations between simulated and observed flows for systems in Southwest or Central U.S.A., South Africa, or South Australia. Across all models, overestimation of monthly mean flow was more common than underestimation and high flows at a sub-monthly scale were more accurately characterized than low flows. Performance of all models was poor in Africa and Oceania. No single model performed universally best at all gauging stations, but GRFR showed the best estimate at around 40 % of the locations. None of the models provided satisfying estimates for rivers and streams in arid regions. We also found a significant negative effect of flow regulation on the predictive accuracy of all models. Our study highlights the importance of thorough evaluation of global discharge datasets before their local and regional application and provides guidance for future enhancement of global hydrological models. [ABSTRACT FROM AUTHOR]

[Display omitted] • Ten multi-model combination methods (MMCMs) are created from 29 models in 50 basins. • MMCMs improve some performance indicators, especially the Granger-Ramanathan method. • MMCMs do not improve performance in reproducing distributions of hydrological signatures. • Application with series of targeted signatures does not improve MMCM performance. • MMCM performance is improved by selecting more robust candidate models for ensembles. Multi-model combination (averaging) methods (MMCMs) are used to improve the accuracy of hydrological (precipitation-runoff) outputs in simulation or forecasting/prediction modes. In this paper, we examined if the application of MMCMs can improve model performance in reproducing distributions of hydrological signatures, such as annual maxima or minima of varying durations. To this end, ten MMCMs were applied to 29 bucket-type models to simulate runoff in 50 high-latitude catchments. The MMCMs were evaluated by comparing the resulting simulated flows to the reference (i.e., best-performing) individual model, considering various commonly used performance indicators, as well as model performance in reproducing the distributions of signatures. Additionally, we analysed whether (1) the selection of the candidate models, or (2) targeting specific signatures, such as annual maxima or minima, can improve performance of the model combinations. The results suggest that the application of MMCMs can improve accuracy of runoff simulations in terms of traditional performance indicators, but fails to improve performance in reproducing the distributions of signatures. Neither excluding poor-performing models nor applying the MMCMs with the targeted signatures, improves this aspect of model performance. These findings clearly reveal the need for further research aiming at enhancing model performance in reproducing the distributions of hydrological signatures, which is essential for climate-change impact studies. [ABSTRACT FROM AUTHOR]

• Guidelines for the use of snow data combined with runoff to calibrate models. • Improvement of the snow simulations without impairing runoff performance. • The improvement in the model consistency is not accompanied by lower equifinality. • Using snow data does not improve model robustness to climate variability. This paper compares different calibration strategies for using snow data combined with streamflow records to constrain model optimisation in mountain catchments. In particular, it assesses to what extent the use of snow observations makes it possible to improve the consistency, identifiability and robustness of the calibrated parameters. To answer this question, a semi-distributed snow and ice model was used on top of a rainfall-runoff model using the SAFRAN meteorological reanalysis as input dataset on several catchments in the Alps and Pyrenees. Model calibration and control were based on streamflow observations, remotely-sensed snow-covered areas and in-situ snow water equivalent (SWE) measurements. The snow and rainfall-runoff parameters were calibrated sequentially and simultaneously against objective functions integrating different combinations of runoff, snow cover and SWE criteria. Statistical assessment of model performances in independent evaluation periods showed that sequential calibration of the snow parameters gives too much weight to snow compared to runoff. Instead, incorporating snow data in the simultaneous calibration of the parameters improves snow simulations without impairing runoff performance. This can be achieved by limiting the weight of the snow criteria to 25% in the objective function. Although local SWE measurements were found to be more useful than satellite observations for identifying more consistent parameters, it is advisable to include both in the calibration process through a compromise objective function. However, the improved model consistency was not accompanied by a significant reduction in equifinality and optimisation times. On the other hand, improving internal consistency made it possible to reduce the interdependence between the parameters of the snow model and those of the rainfall-runoff model. This also made it possible to identify the least sensitive snow parameters in order to fix them at general values without impairing model performance while reducing equifinality with a more parsimonious model. Finally, there was no evidence that using snow observations in the calibration process improves model robustness with respect to climate variability. [ABSTRACT FROM AUTHOR]

• A framework to evaluate stochastic rainfall models with a quantitative scale. • 'Good' modelled rainfall does not guarantee 'good' modelled streamflow. • Streamflow-based evaluation is more holistic than rainfall-based evaluation. • Component-wise evaluation can misrepresent ultimate model performance. Stochastic rainfall models are important tools for evaluating hydrological risks such as flooding and drought because of their ability to randomly generate alternative plausible climatic timeseries. The stochastic generation of climatic timeseries is not an end in itself, since they are typically applied to a catchment to determine the performance of water-related infrastructure systems, such as reservoirs or flood-control measures. This methodology typically involves a train of models to determine the end-of-system impact, yet the evaluation of stochastically generated rainfall timeseries is usually a stand-alone procedure focused on metrics directly related to the stochastic generator. This paper demonstrates discrepancies in this approach by evaluating two, daily-timestep, stochastic rainfall models in terms of rainfall metrics and their subsequently generated flow metrics after rainfall-runoff transformation. The two models are a Markov-based model and a latent-variable model, where each model is calibrated and evaluated showing 'overall good' performance. Stochastically generated timeseries, alongside observed rainfall timeseries are inputted to a calibrated catchment model (GR4J) to derive daily flow timeseries. Whereas the rainfall metrics typically showed 'good' performance, streamflow-based metrics are not necessarily 'good'. The procedure is repeated for 277 stations from Australia and 106 stations from the United States of America. Depending on the strictness of the flow-based comparison and region analysed, using the Markov-based model 12–26% of sites were classified as 'poor' performing, and 1%-9% of sites were classified as 'poor' using the latent-variable model. The results demonstrate that catchment-based performance of flow metrics is more holistic since it magnifies features of the rainfall not otherwise visible to rainfall-based evaluation. [ABSTRACT FROM AUTHOR]

Summary Paired catchment approach probably provides the most robust method to detect the effects of land-use change on catchments’ flow characteristics. This approach is limited by the availability of two neighbor catchments with and without land-use change under similar climate conditions. This paper uses a hydrological model to detect the hydrological change caused by urbanization. This study describes (1) use a statistical method to evaluate change detection relative to variation of land use change, (2) simulation of non-urban condition for the urban catchment with an alternative approach, to this aim stream flow series of urban catchments have been reconstructed from the period that urbanization had not taken place yet, and (3) the model validation with observed data. This paper intends to compare the flow changes detected by two different approaches: a regional statistical approach (the paired-catchment approach) and a conceptual modeling approach (the residual approach) on the particular case of urbanized catchments. To investigate the sensitivity of the results to the settings of both approaches, the comparison is made on a relatively large number of 43 catchments located in the United States, with relatively large gradients in terms of geomorphology and hydroclimatic characteristics. Results show that the two approaches are generally in relative good agreement in terms of detection and quantification of changes for the three flow characteristics analyzed (mean annual flow, high and low flow characteristics). Besides, it is found that the impact of urbanization on the catchment’s hydrologic response is difficult to generalize: the proportion of nonsignificant trends, significantly increasing decreasing trends are on the same order of magnitude, even if an increase in urban areas generally has a greater impact on mean flows and high flows than on low flows. [ABSTRACT FROM AUTHOR]

Summary The effects of climate change on water resources have been studied extensively throughout the world through the use of hydrologic models coupled with General Circulation Model (GCM) output or climate sensitivity scenarios. This paper examines the effects of hydrologic model parameterization uncertainty or equifinality, where multiple unique hydrologic model parameter sets can result in adequate calibration metrics, on hydrologic projections from downscaled GCMs for three snowmelt-dependent watersheds (upper reaches of the Clearwater, Gunnison, and Sacramento River watersheds) in the western United States. The hydrologic model used in this study is the Soil and Water Assessment Tool (SWAT) and is calibrated for discharge at the watershed outlet in each watershed. Despite achieving similar calibration metrics, a majority of hydrologic projections of average annual streamflow during the 2080s were statistically different, with differences in magnitude and direction (increase or decrease) compared to historical annual streamflows. At the average monthly time-scale, a majority of the hydrologic projections varied in peak streamflow timing, peak streamflow magnitude, summer streamflows, as well as overall increases or decreases compared to the historical monthly streamflows. Snowmelt projections from the SWAT model also widely varied, both in depth and snowmelt peak timing, for all watersheds. Since a large portion of the runoff-producing regions in the western United States is snowmelt-dependent, this has large implications for the prediction of the amount and timing of streamflow in the coming century. This paper shows that hydrologic model parameterizations that give similar adequate calibration metrics can lead to statistically significant differences in hydrologic projections under climate change. Therefore, researchers and water resource managers should account for this uncertainty by assembling ensemble projections from both multiple parameter sets and GCMs. [ABSTRACT FROM AUTHOR]

• Improving M2S streamflow forecasting for rainfall-dominated basins. • Hydrologic EnKF data assimilation utilizing streamflow observations. • EnKF is more influential in improving correlation coefficient. Among different sources of uncertainty in hydrologic modeling (i.e., model structure, parameter estimation, input data, etc.), consecutive error reduction of model initial conditions can prevent a model from drifting away from reality and consequently improving model estimates. Most approaches that evaluated the correction of initial conditions through data assimilation (DA) have focused on improving hydrologic model simulations (i.e., under observed forcings) rather than evaluating the model performance in a forecasting context. This paper investigates the utility of Ensemble Kalman Filter (EnKF) data assimilation in which available observed streamflow is exploited to update state variables of a conceptual water balance model for forecasting monthly streamflow over 340 rainfall-dominated river basins across the contiguous United States (CONUS). Our results demonstrate that after EnKF application, streamflow simulation skill improves in terms of both Relative Root Mean Square Error (R-RMSE) and correlation coefficient (CC) for almost 90% of the selected river basins. Evaluating the model performance under different flow conditions shows that EnKF has stronger positive effect on monthly low flow predictions comparing to monthly high flows particularly during the summer season. The utility of EnKF is also assessed in the context of 1-month ahead streamflow forecasting. Due to the updated model initial conditions, streamflow forecasts are improved throughout the year even though the skill in hydrologic forecasts is predominantly dependent on the accuracy of precipitation forecasts. [ABSTRACT FROM AUTHOR]

• Three models were used to cover model structure uncertainty for each catchment. • AET is larger in the karst-dominated than the adjacent nonkarst-dominated catchment. • AET of karst-dominated catchment is more sensitive to the change in climate. • The karst-dominated catchment will experience much drier hydrological projections. Karst systems are well-known for their special features, such as epikarst zone and quick flow, compared to other hydrological systems. However, few studies explored the difference in hydrological responses to climate change between karst and nonkarst catchments. In this paper, two adjacent catchments dominated by karst and nonkarst in Southwest China (subtropical monsoon climate) were selected to investigate their different hydrological behaviors under future climates. To account for potential model structure uncertainties, three different karst hydrological models and three traditional catchment models were used to simulate these two catchments. The results demonstrate that different models yield comparable streamflow simulations within each catchment, allowing for the focus to be on the impact of climate change and catchment hydrological characteristics. The karst-dominated catchment is modeled to have larger actual evapotranspiration (AET) than the nonkarst-dominated catchment under similar climate conditions, with the AET of the former being more sensitive to changes in precipitation and potential evapotranspiration. These differences are primarily due to the presence of the epikarst in the unsaturated zone of the karst system, which can store more infiltration rainwater for evapotranspiration. Both catchments are expected to experience a wetter projection in the wet season and a drier projection in the dry season in the future. However, the karst-dominated catchment will face a drier climate with a significant increase in precipitation in just two months (July and August). Additionally, because of a higher sensitivity of monthly AET to climate change, the karst-dominated catchment will experience a greater monthly discharge reduction in the first two wet months (April and May), a relatively lower discharge increase in other wet months, and a lower monthly discharge in dry months, while the monthly discharge of the nonkarst-dominated catchment increases in the wet season and decrease in the dry season. Furthermore, the karst-dominated catchment will have a much lower annual discharge than the nonkarst-dominated catchment, particularly during extremely dry years in the future period due to its larger AET consumption. These all suggest that the karst-dominated catchment will be more vulnerable to droughts. Overall, due to the drier climate and the different hydrological backgrounds of the karst-dominated catchment, it is expected to have much drier hydrological conditions in the future compared to the nonkarst-dominated catchment. [ABSTRACT FROM AUTHOR]

Summary: Catchment hydrological models are widely used in climate change impact studies. The considered climate scenarios often involve increases in rainfall intensities. These increases might go beyond the range of historical events used in model calibration and validation. The hydrological impact results of climate change consequently are under these circumstances highly uncertain. This paper presents a method to test the validity of hydrological models for that type of impact analysis. The method is based on the evaluation of peak flow increases due to different levels of rainfall increases. The method is demonstrated, based upon the results of three conceptual rainfall–runoff models (NAM, PDM, VHM) for three catchments in Belgium. It is shown how the performance of the model in predicting impacts of rainfall increases can be integrated in the calibration process and how this method can increase the reliability of climate change impact results on peak flows. The paper addresses the importance of the relation between the soil moisture content and the overland flow coefficient, as it controls the generation of peak flows in the considered rainfall–runoff models. [ABSTRACT FROM AUTHOR]

Highlights • Hydrological signatures about dynamics derived from distributed water level and soil moisture. • Information relevant to document impact of land use, rainfall type, soil moisture. • Information useful to diagnose simulated processes in distributed hydrological models. • Information useful to prioritize future model improvements or parameters estimation. • Qualitative information not sufficient to improve simulated water volume. Abstract This paper emphasizes the importance of integrating outlet discharge and observed internal variables in the evaluation of distributed hydrological models outputs. It proposes a general methodology for a diagnostic evaluation of a complex distributed hydrological model, based on discharge data at the outlet and additional distributed information such as water level and surface soil moisture data. The proposed methodology is illustrated using the PUMMA model in the Mercier sub-catchment (6.6 km2). Model parameters are specified according to field data and a previous study performed in a neighbouring catchment (Jankowfsky et al., 2014), without calibration. The distributed water level and soil moisture network of sensors were useful in the model evaluation process. Thus, model parameters are specified either using in situ information or results from previous studies. A stepwise approach is used for model evaluation. It includes standard water balance assessment as well as comparison of observed and simulated outlet discharge, whether on annual or event timescales. Soil moisture sensors are used to assess the ability of the model to simulate seasonal water storage dynamics based on a normalized index. The water level sensors network is used on two timescales: on a seasonal timescale, sensors network is used to assess the model's ability to simulate intermittency; whereas on event timescales, sensors network is used in determining the model's ability to reproduce observed reaction as well as response times. Event timescales do also focus on the correlation between hydrological response and either rainfall event or antecedent soil moisture variables. Results show that the non-calibrated model is quite effective at capturing water flow and soil water-storage dynamics, but it fails to reproduce observed runoff volume during events. There is strong indication of a deficiency in the characterization of catchment storage and upstream flowpath description. The soil water content and a network of water level sensors provide interesting information about soil moisture and river flow dynamics. They however fail to provide quantitative information about catchment storage. This study opens interesting perspectives for the evaluation of distributed hydrological models using hydrological signatures. Furthermore, it highlights the requirement of quantitative as well as qualitative signatures for improving such models. [ABSTRACT FROM AUTHOR]

Highlights • 3DNet-Catch provides balanced model complexity and adaptability to local conditions. • 3DNet-Catch includes the interception, snow, soil, and runoff and channel routing. • A comprehensive evaluation of model parameterisation and performance is conducted. • Most model parameters are physically meaningful, well-identifiable and uncorrelated. • Consistently good performance despite input data from sparse meteorological network. Abstract Hydrologic models are important for effective water resources management. They vary in complexity from parsimonious, spatially lumped, to physically-based, fully distributed models, which are generally expected to outperform the former. Wide applications of complex models are limited due to high data and computational demands. Therefore, a new approach based on well-balanced model complexity is needed to obtain reasonable simulation results with low data requirements. This paper presents a novel 3DNet-Catch hydrologic model, developed to represent key processes in sloped catchments under a temperate climate with modest data requirements. 3DNet-Catch includes runoff simulations within computational units by employing the interception, snow and soil routines, as well as runoff and channel routing. The soil routine, which is the key model feature, combines the SCS-CN method, an analytically integrated nonlinear outflow equation and the Brooks-Corey relation for unsaturated conductivity in an innovative manner. To advance runoff routing in 3DNet-Catch, an approach for analytical integration of the linear and nonlinear outflow equations is implemented. Most model parameters are physically meaningful, thus facilitating model calibration. The model structure can be adjusted according to soil and groundwater flow data, and it can include hydraulic structures, thereby providing adaptability to local conditions. A comprehensive hydrologic evaluation framework is established and conducted to examine whether 3DNet-Catch is adequately parameterised and can accurately reproduce catchment hydrologic response. The model parameterisation is evaluated by sensitivity, identifiability and correlation analyses. Model efficiency is quantified in terms of performance measures, hydrologic signatures and plausibility of the simulated hydrological processes. The results show high sensitivity of the hydrologic variables and performance measures to the model parameters, particularly to those of the soil routine. The parameters are uncorrelated and generally well identifiable. The model performs equally well in the calibration and evaluation periods. High efficiency in the hydrological signatures related to the soil routine indicates its robustness. The results, therefore, suggest that 3DNet-Catch is a comprehensively parameterised, versatile hydrologic model. It realistically reproduces observed hydrographs with modest data requirements, thus being appropriate for both engineering applications and investigative catchment dynamics studies. [ABSTRACT FROM AUTHOR]

Summary: The objective of this paper is to present the development and application of a grid based hydrologic model using an object oriented framework within geographic information systems (GIS). The proposed model is called Storage Released based Distributed Hydrologic Model (STORE DHM). Development of STORE DHM is a part of a broader objective to develop a modular hydrologic modeling system within GIS. In this paper, the conceptual framework of STORE DHM including its application to three watersheds in Indiana is presented. The model is tested in the study watersheds by first calibrating it against observed flow hydrograph for a single event, and then verifying it for three additional storm events using both point (gauged) and continuous (radar) rainfall data. Results show that SOTRE DHM is able to predict runoff hydrographs for different types of events in terms of storm duration, peak flow magnitude and time-to-peak. The average Nash–Sutcliffe coefficient is greater than 0.8 for all runoff hydrograph during validation, and the errors in peak flow and time-to-peak predictions are within 5% and 15%, respectively. In addition, STORE DHM output is compared with outputs from two hydrologic models including Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and time variant Spatially Distributed Direct Hydrograph travel time method (SDDH). Results from these comparisons show that STORE DHM outperforms both HEC-HMS and SDDH in terms of overall hydrograph shape and flow magnitude. Although the results from application of STORE DHM are encouraging, the work presented in this paper is just an initial step towards developing a comprehensive tightly coupled GIS modeling framework for hydrology. [ABSTRACT FROM AUTHOR]

Summary: This paper analyses the relationship between the Saturated Area Ratio (SAR) and the Relative Water Content (RWC) within a hydrological catchment. The SAR–RWC relationship is fundamental in lumped modelling in correctly representing the dominant physical processes. According to the simulations provided by a distributed model (TOPKAPI) on several catchments, the SAR–RWC relationship is affected by hysteresis, which is also supported by observations and numerical simulations by other researchers. Due to the strong linearity, this relationship is difficult to derive analytically. In this paper we propose a physical interpretation of the hysteresis and present a methodology to model it. By incorporating this mechanism in a lumped model, we demonstrate improvement in discharge reproduction as evaluated by comparative streamflow simulations. [Copyright &y& Elsevier]

Summary: This work examines the possibility of using a surface hydrology model in a karst environment. In these kinds of environments, deterministic models such as SWAT, and the majority of surface hydrology models, are unable to compute the added contribution due to subsurface circulation. This can result in a substantial under-estimation of the river flows. Therefore such models are generally not applied by researchers to complex watersheds. Often the simulation of spring discharges is in itself very complex, making it even more difficult to simulate river discharge in karst environments, where the flow contribution coming from karst conduits cannot be directly measured. The present paper investigates the advantages of applying Wavelet Analysis (WA) to this field. The statistical method of WA has been recently applied in hydrogeology for interpreting spring discharges, and in hydrology to describe the variability of streamflows, thus providing insights into their dynamical link with the dominant modes of climate variability. However it has not yet been directly used to support the understanding of river flow components in karst environments. What is more, the present work proposes using WA not merely to assist in the analysis of experimental series, but also as a tool for interpreting the data series generated by the modelling process. In this paper we shall describe how WA can potentially be applied to help define the nature and behaviour of the karst contribution to river flows, thereby improving the future performance of surface hydrological modelling. [Copyright &y& Elsevier]

• A new hydrological-water quality model for humid and semi-humid catchments in China is presented. • Both horizontal and vertical heterogeneity are considered in the hydrological process and the water quality process modelling. • Model uncertainty and the sensitivity of model parameters are investigated. Non-point source (NPS) pollutants may cause water quality deterioration and eutrophication, posing a significant threat to aquatic systems. Understanding and modelling surface and sub-surface hydrological processes and nutrient cycles are therefore essential for water resources management and pollution control. This paper presents a new hydrological-water quality model for application in humid and semi-humid catchments in China, considering both hydrological processes, and nutrient transport and transformation. The model divides the soil into three layers, and each layer is implemented with individual computational procedures for soil wetness and nutrient process. Nutrient transport is driven by hydrological processes and follows the same pathways as water flow in the model: surface runoff, infiltration, and outflow from individual soil layers. River channels are described separately with routines to account for the turnover of nutrients. Model parameters are selected according to literature/open-sourced data or estimated from soil texture or land use types. The model is applied to simulate the hydrological processes and phosphorus transport in the Tongyang River Basin in China and the model performance is confirmed by comparing the predicted discharge and total phosphorus concentrations with measured data at the catchment outlets of Tonghe River and Yanghe River over 3 years. Uncertainty analysis has been further carried out using the GLUE method to demonstrate sensitivity of the simulation results to the selection of model parameters. After model calibration, the predicted results are found to compare well with field observations in terms of flow discharge and total phosphorus concentration. From sensitivity analysis, it is found that the recession coefficient of channel system (CS) and the pollutant recession coefficient of surface runoff (p KS) are the most influential hydrological and water quality parameters that control the arrival time and duration of flood peak and nutrient concentration peak, respectively. [ABSTRACT FROM AUTHOR]

Hydrological and non-point source pollution (H/NPS) predictions in ungagged basins have become the key problem for watershed studies, especially for those large-scale catchments. However, few studies have explored the comprehensive impacts of rainfall data scarcity on H/NPS predictions. This study focused on: 1) the effects of rainfall spatial scarcity (by removing 11%–67% of stations based on their locations) on the H/NPS results; and 2) the impacts of rainfall temporal scarcity (10%–60% data scarcity in time series); and 3) the development of a new evaluation method that incorporates information entropy. A case study was undertaken using the Soil and Water Assessment Tool (SWAT) in a typical watershed in China. The results of this study highlighted the importance of critical-site rainfall stations that often showed greater influences and cross-tributary impacts on the H/NPS simulations. Higher missing rates above a certain threshold as well as missing locations during the wet periods resulted in poorer simulation results. Compared to traditional indicators, information entropy could serve as a good substitute because it reflects the distribution of spatial variability and the development of temporal heterogeneity. This paper reports important implications for the application of Distributed Hydrological Models and Semi-distributed Hydrological Models, as well as for the optimal design of rainfall gauges among large basins. [ABSTRACT FROM AUTHOR]

Low flow conditions are governed by short-to-medium term weather conditions or long term climate conditions. This prompts the question: given climate scenarios, is it possible to assess future extreme low flow conditions from climate data indices (CDIs)? Or should we rely on the conventional approach of using outputs of climate models as inputs to a hydrological model? Several CDIs were computed using 42 climate scenarios over the years 1961–2100 for two watersheds located in Québec, Canada. The relationship between the CDIs and hydrological data indices (HDIs; 7- and 30-day low flows for two hydrological seasons) were examined through correlation analysis to identify the indices governing low flows. Results of the Mann-Kendall test, with a modification for autocorrelated data, clearly identified trends. A partial correlation analysis allowed attributing the observed trends in HDIs to trends in specific CDIs. Furthermore, results showed that, even during the spatial validation process, the methodological framework was able to assess trends in low flow series from: (i) trends in the effective drought index (EDI) computed from rainfall plus snowmelt minus PET amounts over ten to twelve months of the hydrological snow cover season or (ii) the cumulative difference between rainfall and potential evapotranspiration over five months of the snow free season. For 80% of the climate scenarios, trends in HDIs were successfully attributed to trends in CDIs. Overall, this paper introduces an efficient methodological framework to assess future trends in low flows given climate scenarios. The outcome may prove useful to municipalities concerned with source water management under changing climate conditions. [ABSTRACT FROM AUTHOR]

Recent research has found that hydrologic modeling over decadal time periods often requires time variant model parameters. Most prior work has focused on assessing time variance in model parameters conceptualizing watershed features and functions. In this paper, we assess whether adding a time variant scalar to potential evapotranspiration (PET) can be used in place of time variant parameters. Using the HBV hydrologic model and four different simple but common PET methods (Hamon, Priestly-Taylor, Oudin, and Hargreaves), we simulated 60+ years of daily discharge on four rivers in New York state. Allowing all ten model parameters to vary in time achieved good model fits in terms of daily NSE and long-term water balance. However, allowing single model parameters to vary in time – including a scalar on PET – achieved nearly equivalent model fits across PET methods. Overall, varying a PET scalar in time is likely more physically consistent with known biophysical controls on PET as compared to varying parameters conceptualizing innate watershed properties related to soil properties such as wilting point and field capacity. This work suggests that the seeming need for time variance in innate watershed parameters may be due to overly simple evapotranspiration formulations that do not account for all factors controlling evapotranspiration over long time periods. [ABSTRACT FROM AUTHOR]

This paper examines the multi-scale streamflow variability responses to precipitation over 16 headwater catchments in the Pearl River basin, South China. The long-term daily streamflow data (1952–2000), obtained using a macro-scale hydrological model, the Variable Infiltration Capacity (VIC) model, and a routing scheme, are studied. Temporal features of streamflow variability at 10 different timescales, ranging from 6 days to 8.4 years, are revealed with the Haar wavelet transform. The principal component analysis (PCA) is performed to categorize the headwater catchments with the coherent modes of multi-scale wavelet spectra. The results indicate that three distinct modes, with different variability distributions at small timescales and seasonal scales, can explain 95% of the streamflow variability. A large majority of the catchments (i.e. 12 out of 16) exhibit consistent mode feature on multi-scale variability throughout three sub-periods (1952–1968, 1969–1984, and 1985–2000). The multi-scale streamflow variability responses to precipitation are identified to be associated with the regional flood and drought tendency over the headwater catchments in southern China. [ABSTRACT FROM AUTHOR]

• The objectivity verification experiment of the dynamic system response curve (DSRC) method was designed based on multiple hydrological models. • The experiment in Sunshui River and Shiquan catchments presented that the use of DSRC-corrected rainfall series of each hydrological model showed improved simulation accuracy for other hydrological models. • The hypothesis that the DSRC method is objective is not rejected based on the results of the experiment. Error correction methods are widely used to improve the performance of streamflow modeling. The Dynamic system response curve (DSRC) is a newly developed error correction method that utilizes error feedback correction to obtain updated variables and thus enhances model accuracy. While the DSRC method is simple and practical, its objectivity has not been systematically demonstrated. To address this issue, the paper proposes an experiment to verify its objectivity. First, three hydrological models (Xinanjiang (XAJ), Hydrologiska Byråns Vattenavdelning (HBV) and vertically hybrid yielding (VHY)) were calibrated and three corrected areal mean rainfall series were obtained by using the DSRC-based models (XAJ-DSRC, HBV-DSRC and VHY-DSRC). The corrected rainfall series from one hydrological model was then used as input to other hydrological models, and its performance was compared to those of using the original rainfall series by five performance measures (Nash-Sutcliffe efficiency coefficient (NSE), root mean squared error (RMSE), percent bias (PBIAS), mean absolute error (MAE) and correlation coefficient (R)). Finally, this objectivity verification experiment was applied to different model combinations for further demonstration. Case studies of the Sunshui River and Shiquan catchments in China showed that when the DSRC-corrected rainfall series from either hydrological model was applied to the original or other hydrological models, an increase in NSE and R values and a decrease in RMSE and MAE values were observed. The absolute values of PBIAS were within 10%. Accordingly, the hypothesis that the DSRC method is objective is not rejected based on the results of the experiment. [ABSTRACT FROM AUTHOR]

Conceptual models often suffer from the over-parameterization problem due to limited available data for the calibration. This leads to the problem of parameter nonuniqueness and equifinality, which may bring much uncertainty of the simulation result. How to find out the appropriate model structure supported by the available data to simulate the catchment is still a big challenge in the hydrological research. In this paper, we adopt a multi-model framework to identify the dominant hydrological process and appropriate model structure of a karst spring, located in Guilin city, China. For this catchment, the spring discharge is the only available data for the model calibration. This framework starts with a relative complex conceptual model according to the perception of the catchment and then this complex is simplified into several different models by gradually removing the model component. The multi-objective approach is used to compare the performance of these different models and the regional sensitivity analysis (RSA) is used to investigate the parameter identifiability. The results show this karst spring is mainly controlled by two different hydrological processes and one of the processes is threshold-driven which is consistent with the fieldwork investigation. However, the appropriate model structure to simulate the discharge of this spring is much simpler than the actual aquifer structure and hydrological processes understanding from the fieldwork investigation. A simple linear reservoir with two different outlets is enough to simulate this spring discharge. The detail runoff process in the catchment is not needed in the conceptual model to simulate the spring discharge. More complex model should need more other additional data to avoid serious deterioration of model predictions. [ABSTRACT FROM AUTHOR]

Basin response to precipitation variability by terrestrial hydrological processes may be manifested at different timescales and vary from one basin to another. A quantitative grasp of natural variability modulation is desirable for characterizing basin tendency or vulnerability on floods and droughts; quantification is not always straightforward, however. Using a macro-scale hydrological model, the Variable Infiltration Capacity (VIC) model, the hydrological time series (runoff, soil moisture, and evapotranspiration) were obtained for the 10 sub-basins over the Pearl River basin in South China for the period 1952–2000. A continuous wavelet transform was used to identify the characteristics of the temporal scales of basin-featured extreme events and related multiple-scale variabilities. By use of wavelet coherence analysis and rank correlation method, the cross correlations of monthly anomalies of hydrological processes were examined. The results showed that the runoff correlated most with, and the evapotranspiration had less response to, the precipitation variability. The analyzed results revealed that the attenuation effect of runoff in response to precipitation variability was mainly within the timescale of 2 yr in the Pearl River basin. The soil moisture variability can extend precipitation variation along with timescales. With understanding of the variability features of the terrestrial hydrological processes, this paper showed one geomorphic index, a basin form factor (FF), can represent the wavelet-based indices for high- and low-frequency responses to precipitation variability. It was found that, for a sub-basin with a higher FF value, precipitation variability can be represented more by runoff variability at less than 2-yr timescales; such a sub-basin is apt to have serious floods. With a lower FF value, a sub-basin has longer memory of soil moisture, leading to severe droughts during dry periods. This finding would be valuable to evaluate the tendency on occurrence of floods and droughts in the Pearl River basin, a humid region, which may be transferable to other basins in the globe. [ABSTRACT FROM AUTHOR]

Summary Flash-floods are among the most devastating hazards in the Mediterranean. A major subset of damage and casualties caused by flooding is related to road submersion. Distributed hydrological nowcasting can be used for road flooding monitoring. This requires rainfall–runoff simulations at a high space and time resolution. Distributed hydrological models, such as the ISBA-TOP coupled system used in this study, are designed to simulate discharges for any cross-section of a river but they are generally calibrated for certain outlets and give deteriorated results for the sub-catchment outlets. The paper first analyses ISBA-TOP discharge simulations in the French Mediterranean region for target points different from the outlets used for calibration. The sensitivity of the model to its governing factors is examined to highlight the validity of results obtained for ungauged river sections compared with those obtained for the main gauged outlets. The use of improved model inputs is found beneficial for sub-catchments simulation. The calibration procedure however provides the parameters’ values for the main outlets only and these choices influence the simulations for ungauged catchments or sub-catchments. As a result, a new version of ISBA-TOP system without any parameter to calibrate is used to produce diagnostics relevant for quantifying the risk of road submersion. A first diagnostic is the simulated runoff spatial distribution, it provides a useful information about areas with a high risk of submersion. Then an indicator of the flood severity is given by simulated discharges presented with respect to return periods. The latter has to be used together with information about the vulnerability of road-river cross-sections. [ABSTRACT FROM AUTHOR]

• A new method of dividing datasets based on PSO is proposed. • BNN incorporates Langevin dynamics in the parameter update step (LDBNN). • Integration of LDBNN, CEEMDAN, VMD and the proposed method yield the hybrid model. • The new data dividing method performs better than the traditional dividing method. • The hybrid model yields excellent point and interval predictions. Obtaining accurate point estimates and reliable interval prediction results for rainfall and runoff series is important to aid in water resource decision-making and planning management in a changing environment. In this paper, we propose a two-stage hybrid model (P-CVEE-LDBNN). The model uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods for data preprocessing and a Bayesian neural network (LDBNN) based on Langevin dynamics as a prediction model. Then, a new dataset partitioning method based on a particle swarm optimization algorithm, which is different from the traditional dataset partitioning method, is applied. A one-stage hybrid model (T-CEE-LDBNN) that integrates the CEEMDAN method and the traditional method of partitioning datasets, a one-stage hybrid model (P-CEE-LDBNN) that integrates the CEEMDAN method and the new method of partitioning datasets, and the two-stage hybrid P-CVEE-LDBNN model were compared. These models were applied to monthly runoff and monthly precipitation series from seven hydrological and meteorological stations in the Yellow River basin. The mean absolute error (MAE), absolute root mean squared error (RMSE), Nash–Sutcliffe efficiency (NSE), and correlation coefficient (R) were used to evaluate the predictive ability of the models. The containment ratio (CR), the average bandwidth (IW), and the average asymmetry degree (S) were used to evaluate the interval prediction results of the models. The results show that (1) the subset obtained with the new method of data partitioning considering statistical properties is more favorable for model prediction. (2) The second decomposition approach based on VMD is a reliable method that can significantly improve the prediction accuracy of the final model. (3) Compared with traditional neural networks that can only obtain deterministic point prediction results, Bayesian methods can provide intervals with prediction results, making the results of the two-stage hybrid P-CVEE-LDBNN model highly reliable. [ABSTRACT FROM AUTHOR]

Summary Period analysis is of great significance for understanding various hydrologic processes and predicting the future hydrological regime of a watershed or region. Hence, many period analysis methods including fast Fourier transform (FFT), maximum entropy spectral analysis (MESA) and wavelet analysis (WA) have been developed to study this issue. However, due to the complex components of hydrologic series and the limitations of these conventional methods, the problem is still difficult to be solved. In this paper, the moving-window correlation analysis method (MWCA) has been proposed for analyzing the periodic component of hydrologic series, which includes construction of periodic processes, significant test of periods and time frequency analysis. Three commonly used methods (FFT, MESA and WA) and MWCA are employed to investigate the periods of synthetic series and observed hydrologic series, respectively. The results show that FFT, MESA and WA are not always as good as expected when detecting periods of a time series. By contrast, MWCA has better application effects, which could identify the true periods of time series, extract the reliable periodic components, find the active time ranges of periodic components and resist the disturbance of noises. In conclusion, MWCA is suitable to identify the true periods of hydrologic time series. [ABSTRACT FROM AUTHOR]

Summary In the past decades significant progress has been made in the fitting of hydrologic models to data. Most of this work has focused on simple, CPU-efficient, lumped hydrologic models using discharge, water table depth, soil moisture, or tracer data from relatively small river basins. In this paper, we focus on large-scale hydrologic modeling and analyze the effect of parameter and rainfall data uncertainty on simulated discharge dynamics with the global hydrologic model PCR-GLOBWB. We use three rainfall data products; the CFSR reanalysis, the ERA-Interim reanalysis, and a combined ERA-40 reanalysis and CRU dataset. Parameter uncertainty is derived from Latin Hypercube Sampling (LHS) using monthly discharge data from five of the largest river systems in the world. Our results demonstrate that the default parameterization of PCR-GLOBWB, derived from global datasets, can be improved by calibrating the model against monthly discharge observations. Yet, it is difficult to find a single parameterization of PCR-GLOBWB that works well for all of the five river basins considered herein and shows consistent performance during both the calibration and evaluation period. Still there may be possibilities for regionalization based on catchment similarities. Our simulations illustrate that parameter uncertainty constitutes only a minor part of predictive uncertainty. Thus, the apparent dichotomy between simulations of global-scale hydrologic behavior and actual data cannot be resolved by simply increasing the model complexity of PCR-GLOBWB and resolving sub-grid processes. Instead, it would be more productive to improve the characterization of global rainfall amounts at spatial resolutions of 0.5° and smaller. [ABSTRACT FROM AUTHOR]

Summary Forecast reliability and accuracy is a prerequisite for successful hydrological applications. This aim may be attained by using data assimilation techniques such as the popular Ensemble Kalman filter (EnKF). Despite its recognized capacity to enhance forecasting by creating a new set of initial conditions, implementation tests have been mostly carried out with a single model and few catchments leading to case specific conclusions. This paper performs an extensive testing to assess ensemble bias and reliability on 20 conceptual lumped models and 38 catchments in the Province of Québec with perfect meteorological forecast forcing. The study confirms that EnKF is a powerful tool for short range forecasting but also that it requires a more subtle setting than it is frequently recommended. The success of the updating procedure depends to a great extent on the specification of the hyper-parameters. In the implementation of the EnKF, the identification of the hyper-parameters is very unintuitive if the model error is not explicitly accounted for and best estimates of forcing and observation error lead to overconfident forecasts. It is shown that performance are also related to the choice of updated state variables and that all states variables should not systematically be updated. Additionally, the improvement over the open loop scheme depends on the watershed and hydrological model structure, as some models exhibit a poor compatibility with EnKF updating. Thus, it is not possible to conclude in detail on a single ideal manner to identify an optimal implementation; conclusions drawn from a unique event, catchment, or model are likely to be misleading since transferring hyper-parameters from a case to another may be hazardous. Finally, achieving reliability and bias jointly is a daunting challenge as the optimization of one score is done at the cost of the other. [ABSTRACT FROM AUTHOR]

Summary Streamflow stations are typical hydrometric stations that serve as the basic components of hydrological monitoring networks. To effectively obtain information for predicting the magnitude and frequency of future floods and droughts, an optimal configuration of streamflow stations should be established to maximize the sensing capability with limited resources. In this paper, we propose a method to site streamflow stations in space to maximize the total area for streamflow monitoring. Considering the special regulations for deploying streamflow stations, a modified maximal covering location problem (MCLP) model is introduced. The effective coverage range of a streamflow station is determined based on the minimum density required and the site-specific terrain slope. The candidate sites are assumed to be continuously distributed along a river, and the river network is abstracted as a series of line-based river segments. The covering priority for each segment can be determined by the river length, the drainage area, or the level of flooding risk. The hydrometric network intersection point set (HNIPS) is proposed to identify finite candidate sites along a river. By narrowing the continuous search space to a discrete point set, this siting problem is solved using the MCLP-based model and HNIPS. The Jinsha River Basin is selected as a study area to test the proposed streamflow station siting method. Results show that the proposed method is effective in prescribing the optimal configuration of streamflow stations and the model solution achieves better coverage than that of the real-world deployment. The applicability of the proposed optimal siting method using HNIPS is analyzed. The criteria for candidate site selection and impacts of different weighting schemes applied to river segments are also discussed. [ABSTRACT FROM AUTHOR]

• The threshold of the ground observation network has been identified. • A new bias correction method of SPPs for limited number and eccentrically located network. • Well-utilization of SPPs for gauge deployment. • Hydrological simulation in the ungauged basin has been solved to some extent. Precipitation is a crucial input for hydrological models to achieve various purposes such as water resource management and flood forecasting. However, precipitation stations are usually limited in number and eccentric in location. Although satellite precipitation products (SPPs) have improved remarkably, their application to data-sparse areas for flow simulation has significant limitations due to insufficient ground observations for bias correction. This paper proposes an integrated methodology from precipitation to flow simulation in a data-scarce basin by studying the impact of a precipitation gauge network on average areal precipitation (AAP) estimation, SPPs correction, and resultant hydrological simulation efficiency. To this end, seventeen observation network patterns were configured from a data-sufficient basin, the Fuji River Basin (FRB), Japan. Four bias correction methods were conducted for six SPPs under seventeen configurations of gauge network. A total of 264 AAP and 346 discharge results were comprehensively evaluated in combination with various gauge network configurations, bias correction methods, and hydrological simulation schemes. We found that, on bias correction methods, the proposed dynamic use of a statistical bias correction method (SDBC) can be the most effective, significantly improving the accuracy of SPPs even with a small number of eccentrically set observation stations. This study also revealed that four precipitation stations in the FRB (around 900 km2/station) could work as well as networks with more than ten or twenty stations for AAP estimation and flow simulation at the outlet. In addition, it is confirmed that the hydrological model could be well-calibrated individually by different precipitation inputs to fit the precipitation pattern. Those findings provide a robust basis for the use of SPPs in many poorly gauged basins. Also, the results on the configuration of networks provide scientific suggestions for properly deploying ground observation networks in ungauged basins for the effective use of SPPs. [ABSTRACT FROM AUTHOR]

• A stochastic hydrological modeling system (SHMS) is proposed for improving streamflow prediction. • Interactions of error parameters have significant contributions to model performance. • The SHMS improves upon the EnKF through a more accurate convergence of model parameters. • The SHMS improves upon the conditional vine copula model for probabilistic streamflow prediction. • Temporal dynamics of parameter sensitivity are examined using the time-varying sensitivity analysis. Streamflow prediction plays a crucial role in water resources systems planning and the mitigation of hydrological extremes such as floods and droughts. Since a variety of uncertainties exist in streamflow prediction, it is necessary to enhance our efforts to robustly address uncertainties and their interactions for improving the reliability of streamflow prediction. This paper presents a stochastic hydrological modeling system (SHMS) for improving daily streamflow prediction by explicitly addressing uncertainties in error and model parameters as well as in forcing data and model outputs. Specifically, the SHMS merges the strengths of the ensemble Kalman filter and the particle filter algorithms for improving the effectiveness and robustness of daily streamflow assimilation. Factorial analysis of variance and variance-based global sensitivity analysis are performed to reveal parameter interactions affecting predictive performance and temporal dynamics of parameter sensitivities, maximizing the accuracy of streamflow prediction. The SHMS has been applied to the Guadalupe River basin located in Texas of the United States to demonstrate feasibility and applicability. Our findings indicate that the SHMS improves upon the well-known ensemble Kalman filter for sequential estimation of hydrological model parameters through a more rapid and accurate convergence of model parameters in streamflow simulation. The SHMS also demonstrates a higher level of skill in streamflow prediction compared to the conditional vine copula model. The proposed SHMS can be applied straightforwardly to other river basins for probabilistic hydrological prediction. [ABSTRACT FROM AUTHOR]

Summary Yanhe watershed, as a typical and experimental district of Soil and Water Conservation District, has long been plagued by soil erosion due to severe human disturbances. Exploring the relationship between watershed landscape pattern and hydrological processes can find effective ways to solve soil erosion problems. At first, with remote sensing and GIS (Geographic Information System) technology and based on SWAT model, this paper analyzed and simulated ecological hydrological processes in Yanhe watershed. It is on subbasin scale that the runoff and sediment yields were simulated monthly in Yanhe watershed using SWAT model. Secondly, it quantified landscape pattern with landscape indices. The seven landscape indices at the landscape level were selected with principal component factor analysis, including Disjunct Core Area Density (DCAD), Radius of gyration (GYRATE_SD), Patch Cohesion Index (COHESION), Shannon’s diversity index (SHEI), Total Core Area (TCA), Perimeter-Area Fractal dimension index (PAFRAC), Interspersion and Juxtaposition Index (IJI), etc. Thirdly, a new composite landscape index was constructed on the basis of eco-hydrological processes, which was closely related to soil erosion. The results are as follows: (1) Coupled analysis on the relationship of landscape indices and annual runoff as well as annual sediment yields in each subbasin, the correlation coefficient of seven selected landscape indices and runoff is very small, no passing all significant tests. But the correlation between sediment yields and the indices except for TCA and IJI is significant, and the absolute value of the correlation coefficient is between 0.3 and 0.5. (2) According to the “source-sink” theory of soil erosion, Slope-HRUs landscape index (SHLI) was built and can reflect the relationship between landscape pattern and soil erosion processes to a certain extent. The coupling relationship between Slope-HRUs landscape index (SHLI) and annual sediment yields in each subbasin is very clear, and correlation coefficient is −0.6, which is significantly negatively correlated. [ABSTRACT FROM AUTHOR]

Summary In water resources modeling, meteorological data scarcity can be compensated by various global data sets, but those data sets can differ tremendously. In the literature, hydrological models of differing complexity are proposed for estimating the water resources of semi-arid catchments, and also to evaluate rainfall data sets. The goal of this paper is to provide a joint analysis of modeling uncertainty due to different input data and increasing model complexity. Impacts of mutually concealed uncertainties on model performance and model outputs are exemplified in two data sparse semi-arid catchments in Ethiopia. We applied a semi-distributed and a fully distributed hydrological model, having different levels of complexity. Three different satellite-based rainfall data sets and two temperature products were used as model inputs. The semi-distributed model demonstrated good validation performances, while the fully distributed model was more sensitive to data uncertainties. The application of TRMM version 6 completely failed and the high-resolution CMORPH precipitation estimate outperformed TRMM version 7. In contrast, the use of high-resolution temperature data did not improve the model results. The large differences in remotely sensed input data were buffered inside the hydrological models. Therefore, data set evaluations regarding only the simulated hydrographs were less meaningful. In contrast, the investigation of parameter evolution and distributed outputs’ variability appeared to be a valuable tool to uncover the interdependencies of data and model uncertainties. We suggest this procedure to be applied by default in water resources estimations that are affected by data scarcity, but especially when data sets are evaluated using hydrological models. Our case study demonstrates that estimations of groundwater recharge and actual evapotranspiration vary largely, depending on the applied data sets and models. The joint analysis reveals large interdependencies between data and model evaluations. It shows that traditional studies focusing only on one part of uncertainty, either the input uncertainty or the uncertainty arising from the choice of model structure are limited in their explanatory power of the modeling performance, particularly in poorly gauged regions. [ABSTRACT FROM AUTHOR]

Summary Given a record of daily rainfall over a network of gauges, this paper describes a method of linking the Gauge Wetness Ratio (GWR) on a given day to the joint distribution of the parameters of the anisotropic correlogram defining the spatial statistics of simulated radar-rainfall fields. We generate a large number of Gaussian random fields by sampling from the correlogram parameters conditioned on the GWR and then conditionally merge these fields to the gauge observations transformed into the Gaussian domain. Availability of such a tool allows better spatially distributed hydrological modelling, because good quality ensemble spatial information is required for such work, as it yields uncertainty of the fields so generated. To achieve these ends, correlograms of many Gaussianised daily accumulations of radar images were developed using the Fast Fourier Transform to generate their sample power spectra. Empirical correlograms were fitted using a 2D exponential distribution to yield the 3 key parameters of the correlogram: the range, the anisotropy ratio and the direction of the major axis. It was found that the range follows a Gamma distribution while the anisotropy parameters follow a Loglogistic one; a t5 copula was adequate to capture the bivariate negative dependence structure between the range and ratio. The Radar Wetted Area Ratio (RWAR) drives the parameters of the correlogram, and its link with GWR is modelled by a transition probability matrix. We take each of the generated Gaussian random fields and conditionally merge it with Gaussianised rainfall values at the gauge locations using Ordinary Kriging. The method produces realistic simulated radar images, on a grid chosen to suit the data, which match the gauge observations at their locations. Ensemble simulations of 1000 samples were used to derive the median and the inter-quartile range of the fields; these were found to narrow near the control gauge locations, as expected, emphasising the value of high density gauge networks. Ongoing research is looking towards integration of the presented methodology with a stochastic daily rainfall generator for useful spatial rainfall simulation over catchments with gauged records. [ABSTRACT FROM AUTHOR]

Summary There exists a widespread recognition that land use and management change should affect catchment response to precipitation (and therefore stream flow characteristics). Previous studies have shown that this affect exists at a local scale, but there is a paucity of evidence that local scale effects aggregate to detectable impacts within downstream catchments. This paper describes a novel wavelet-based analysis of hydrological model residuals to examine the effect of land-use change on the catchment hydrology across four UK catchments (the Kird, Lod, Coalburn and Wye), of which all but the Wye experienced significant changes in their land use. The HySim rainfall–runoff model was calibrated against observed long term flow series assuming a static land hydrology to allow for the effects of climatic variability. Deviations of model fit were assessed in relation to changes to catchment land hydrology. The wavelet transform was used to decompose both simulated and observed flows into different scale components and to assess changes in catchment hydrology across different temporal scales; model residuals and model performance were tested for significant changes in wavelet variance and wavelet correlation respectively. Significant changes in wavelet variance and wavelet correlation corresponded with changes in catchment land use for two of the three catchments that experience land use change, in the third there were significant changes in wavelet variation that may have resulted from land-use change, but no changes in wavelet correlation. The control catchment showed no significant features of variances and no significant change in wavelet correlation across the scales. This new approach holds great potential for separating the influences of land use and management change on catchment stream flow based on frequency scale. [ABSTRACT FROM AUTHOR]

Summary Distributed hydrological models are useful tools for process understanding and water management, especially in peri-urban catchments where the landscape heterogeneity is large, caused by a patchwork of natural and urbanized areas. This paper presents the Peri-Urban Model for landscape MAnagement (PUMMA) built within the LIQUID® modeling framework, specifically designed to study the hydrology of peri-urban catchments. It combines rural and urban hydrological models, and is used for process understanding. The originality of PUMMA is to follow a fully object-oriented approach, for both model mesh building and process representation. Urban areas, represented by cadastral units and rural areas divided in Hydrological Response Units are thus modeled with different interacting process modules. This provides a detailed representation of the runoff generation on natural and impervious areas. Furthermore, the exchange between process modules facilitates the simulation of subsurface and overland flow, as well as groundwater drainage by sewer pipes. Several drainage networks can coexist and interact (e.g. via storm water overflow devices) and water can be stored in retention basins, which allows the modeling of complex suburban drainage systems with multiple outlets. The model is then applied to the Chaudanne catchment (2.7 km 2 ), located in the suburbs of Lyon, France. The uncalibrated model results show the importance of surface runoff from impervious areas for summer events and flow contributions from rural zones for winter events. Furthermore, the model reveals that the retention capacity of the Chaudanne catchment is larger than for classical urban catchments due to the peri-urban character of the catchment. [ABSTRACT FROM AUTHOR]

Summary The coordinated operation of reservoirs in large-scale river basins has great potential to improve flood mitigation. However, this requires large scale hydrological models to translate the effect of reservoir operation to downstream points of interest, in a quality sufficient for the iterative development of optimized operation strategies. And, of course, it requires reservoirs large enough to make a noticeable impact. In this paper, we present and discuss several methods dealing with these prerequisites for reservoir operation using the example of three major floods in the Bavarian Danube basin (45,000 km 2 ) and nine reservoirs therein: We start by presenting an approach for multi-criteria evaluation of model performance during floods, including aspects of local sensitivity to simulation quality. Then we investigate the potential of joint hydrologic-2d-hydrodynamic modeling to improve model performance. Based on this, we evaluate upper limits of reservoir impact under idealized conditions (perfect knowledge of future rainfall) with two methods: Detailed simulations and statistical analysis of the reservoirs’ specific retention volume. Finally, we investigate to what degree reservoir operation strategies optimized for local (downstream vicinity to the reservoir) and regional (at the Danube) points of interest are compatible. With respect to model evaluation, we found that the consideration of local sensitivities to simulation quality added valuable information not included in the other evaluation criteria (Nash–Sutcliffe efficiency and Peak timing). With respect to the second question, adding hydrodynamic models to the model chain did, contrary to our expectations, not improve simulations, despite the fact that under idealized conditions (using observed instead of simulated lateral inflow) the hydrodynamic models clearly outperformed the routing schemes of the hydrological models. Apparently, the advantages of hydrodynamic models could not be fully exploited when fed by output from hydrological models afflicted with systematic errors in volume and timing. This effect could potentially be reduced by joint calibration of the hydrological–hydrodynamic model chain. Finally, based on the combination of the simulation-based and statistical impact assessment, we identified one reservoir potentially useful for coordinated, regional flood mitigation for the Danube. While this finding is specific to our test basin, the more interesting and generally valid finding is that operation strategies optimized for local and regional flood mitigation are not necessarily mutually exclusive, sometimes they are identical, sometimes they can, due to temporal offsets, be pursued simultaneously. [ABSTRACT FROM AUTHOR]

Summary Spatial variability of climate can negatively affect catchment streamflow predictions if it is not explicitly accounted for in hydrologic models. In this paper, we examine the changes in streamflow predictability when a hydrologic model is run with spatially variable (distributed) meteorological inputs instead of spatially uniform (lumped) meteorological inputs. Both lumped and distributed versions of the EXP-HYDRO model are implemented at 41 meso-scale (500–5000 km 2 ) catchments in the Pacific Northwest region of USA. We use two complementary metrics of long-term spatial climate variability, moisture homogeneity index ( I M ) and temperature variability index ( I TV ), to analyze the performance improvement with distributed model. Results show that the distributed model performs better than the lumped model in 38 out of 41 catchments, and noticeably better (>10% improvement) in 13 catchments. Furthermore, spatial variability of moisture distribution alone is insufficient to explain the observed patterns of model performance improvement. For catchments with low moisture homogeneity ( I M < 80%), I M is a better predictor of model performance improvement than I TV ; whereas for catchments with high moisture homogeneity ( I M > 80%), I TV is a better predictor of performance improvement than I M . Based on the results, we conclude that: (1) catchments that have low homogeneity of moisture distribution are the obvious candidates for using spatially distributed meteorological inputs, and (2) catchments with a homogeneous moisture distribution benefit from spatially distributed meteorological inputs if they also have high spatial variability of precipitation phase (rain vs. snow). [ABSTRACT FROM AUTHOR]

• Two multi-model integrated error correction models based on Bayesian model averaging (BMA) and dynamic system response curve (DSRC) were proposed. • The developed error correction methods performed better than most DSRC-based method and standard BMA method. • The case study in Sunshui River catchment indicated that the BMA-DSRC to be the most effective method. • The uncertainty of BMA, DSRC-BMA and BMA-DSRC were assessed, and the BMA-DSRC exhibited the best performance. Error correction methods play an important role in improving the reliability and accuracy of hydrological modeling. The Dynamic system response curve (DSRC) is a novel and effective error correction method, but it may have the problem of over-correction. Therefore, two multi-model integrated error correction models based on DSRC and Bayesian model averaging (BMA) were proposed in this paper, namely DSRC-BMA and BMA-DSRC. The Sunshui River catchment is selected for a case study. First, three hydrological models including Xinanjiang model (XAJ), Hydrologiska Fyrans Vattenbalans modell (HBV) and vertically hybrid yield model (VHY) were employed. Then, a standard BMA model and three DSRC-based models were constructed separately. Finally, two multi-model integrated error correction models (DSRC-BMA and BMA-DSRC) were applied. The performance of these nine models was compared by Nash-Sutcliffe Efficiency coefficient (NSE), root mean squared error (RMSE) and percent bias (PBIAS). Results showed that the DSRC-based models presented better results than the standard BMA method and most DSRC-based models. Moreover, the uncertainty in BMA, DSRC-BMA and BMA-DSRC models were assessed. The 90% confidence interval of the BMA-DSRC model had high containing ratio values and low average relative bandwidth. Overall, the proposed multi-model integrated error correction methods are effective and can be applicable in improving streamflow modeling. [ABSTRACT FROM AUTHOR]

• An integrated postprocessing framework for hydrometeorological ensemble is developed. • The canonical event considered observed precipitation improves the forecast accuracy. • The streamflow simulation can benefit from the seamless hydrologic prediction. • The Bayesian method improves the forecast effect than a single hydrological model. Flood forecasting is a challenging task with major concerns that include forecast uncertainty and lead time. This paper aims to comprehensively address the data correction issue for hydrological models by developing an integrated postprocessing framework for hydrometeorological ensemble forecasts based on three types of ensemble precipitation forecasts (ECMWF, GEFS, and CFSv2). This framework mainly comprises the Ensemble Preprocessor (EPP) and the Bayesian model averaging (BMA) scheme: (1) the EPP eliminates various deviations from the ensemble precipitation forecasts, with the canonical event model extracting useful predictive information and extending the lead time by linking the forecasts seamlessly, and (2) the BMA scheme develops more skillful and reliable probabilistic hydrological forecasts from ensemble streamflow forecasts generated by three hydrological models (XAJ, VIC and DTVGM) driven by the postprocessed ensemble precipitation forecasts. The Ganjiang River Basin was selected as a case study to examine the capacity and efficiency of our developed framework. We found that the performance of the integrated postprocessing of hydrometeorological ensemble forecasts benefitted from a reasonable design of canonical events, especially when the observed precipitation was factored into the canonical events. Streamflow forecasts with increased lead times and accuracy were obtained by employing postprocessed precipitation forecasts instead of raw forecasts as the input for the hydrological models. We also found that the BMA scheme further improved the forecast effect by generating more skillful and reliable forecasts than a single hydrological model, which was verified by a series of statistical indices and uncertainty analysis. Our results underscore the importance of considering both input and output for hydrological models in hydrological forecasting. [ABSTRACT FROM AUTHOR]

• We present a systematic top-down approach to develop a distributed perceptual model. • The approach is indicated for the regional scale where fieldwork is impractical. • The approach is demonstrated using 26 gauged subcatchments in the Moselle catchment. • The model captures dominant climate and landscape controls on streamflow variability. • The approach uses commonly available data and can be applied elsewhere. Perceptual catchment models are a key first step towards hydrologic synthesis and provide the process foundation of predictive models. However, their development is usually limited to the headwater catchment scale where field work can be done. Here we develop a perceptual model approach for a regional scale catchment. We use a systematic "top-down" approach based on an expert-driven interpretation of the streamflow responses of nested subcatchments. This approach complements the traditional "bottom-up" perceptual model development based on fieldwork observations, which is impractical at the regional scale due to measurement limitations. As an example to illustrate our approach and demonstrate its feasibility, we use the 27,100 km2 Moselle catchment, with 26 gauged subcatchments. Our perceptual model of the Moselle catchment presents a description of dominant processes that we consider a-posteriori reasonable, but not a-priori obvious: where precipitation, much more than evaporation or groundwater exchange controls the spatial variability of average streamflow, lithology influences the partitioning between baseflow and quickflow, and topography and land use control hydrograph lag times. A striking feature of our analysis is the rather minor role that vegetation and soil appear to play in streamflow spatial variability at the scale of our inquiry. We use our perceptual model to build a distributed yet parsimonious hydrological model in Part 2 of this paper series. Although the perceptual model is specific for the Moselle, we hope that the perceptual model approach used here can be helpful to others working in large catchments in different hydroclimates, where other factors will dominate. [ABSTRACT FROM AUTHOR]

• We present a systematic approach to distributed hydrological model development. • The approach is based on controlled model comparisons within a top-down framework. • A perceptual model is used to guide the controlled model comparison experiment. • Key streamflow signatures are captured by a 12 parameters distributed model. • The model development process favors the contribution of the experimentalist. Regional scale distributed conceptual models are typically developed with a bottom-up approach, which is process-inclusive but prone to over-parameterization. Here we demonstrate a proof of concept top-down approach for distributed conceptual model development, intended to emphasize dominant streamflow generating processes and to fulfill the principle of model parsimony. A key challenge in applying the top-down approach to distributed model development is devising a model comparison experiment that is both informative and limited to a few model alternatives. Here, we show how such model comparisons can be informed by a perceptual model of key processes that control streamflow response variability at the regional scale. We demonstrate our approach for the 27,100 km2 Moselle catchment, using the perceptual model developed in Part 1 of this two-part paper. We develop 5 distributed model structures for simulating daily streamflow at 26 subcatchments, and validate them on subcatchments that are not used during the calibration process. Our model comparisons illustrate how the spatial distribution of precipitation, lithology and topography affect the simulation of key signatures of streamflow response variability in the Moselle catchment, providing a basis to justify model decisions. Our analyses show how a minimally parameterized distributed model, with 12 calibration parameters, matches signatures of streamflow average (r = 0.96), baseflow index (r = 0.86), and hydrograph lag time (correct at 22 out of 26 subcatchments). Our proposed top-down approach contributes to improving distributed model development strategies, and can be used to develop parsimonious process based regional models elsewhere. [ABSTRACT FROM AUTHOR]

Summary: Understanding how changes in individual land use types influence the dynamics of streamflow and sediment yield would greatly improve the predictability of the hydrological consequences of land use changes and could thus help stakeholders to make better decisions. Multivariate statistics are commonly used to compare individual land use types to control the dynamics of streamflow or sediment yields. However, one issue with the use of conventional statistical methods to address relationships between land use types and streamflow or sediment yield is multicollinearity. In this study, an integrated approach involving hydrological modelling and partial least squares regression (PLSR) was used to quantify the contributions of changes in individual land use types to changes in streamflow and sediment yield. In a case study, hydrological modelling was conducted using land use maps from four time periods (1978, 1987, 1999, and 2007) for the Upper Du watershed (8973km2) in China using the Soil and Water Assessment Tool (SWAT). Changes in streamflow and sediment yield across the two simulations conducted using the land use maps from 2007 to 1978 were found to be related to land use changes according to a PLSR, which was used to quantify the effect of this influence at the sub-basin scale. The major land use changes that affected streamflow in the studied catchment areas were related to changes in the farmland, forest and urban areas between 1978 and 2007; the corresponding regression coefficients were 0.232, −0.147 and 1.256, respectively, and the Variable Influence on Projection (VIP) was greater than 1. The dominant first-order factors affecting the changes in sediment yield in our study were: farmland (the VIP and regression coefficient were 1.762 and 14.343, respectively) and forest (the VIP and regression coefficient were 1.517 and −7.746, respectively). The PLSR methodology presented in this paper is beneficial and novel, as it partially eliminates the co-dependency of the variables and facilitates a more unbiased view of the contribution of the changes in individual land use types to changes in streamflow and sediment yield. This practicable and simple approach could be applied to a variety of other watersheds for which time-sequenced digital land use maps are available. [Copyright &y& Elsevier]

Summary: In this paper we proposed: (1) an algorithm of glacier melt, sublimation/evaporation, accumulation, mass balance and retreat; (2) a dynamic Hydrological Response Unit approach for incorporating the algorithm into the Soil and Water Assessment Tool (SWAT) model; and (3) simulated the transient glacier retreat and its impacts on streamflow at basin scale. Application of the enhanced SWAT model in the Manas River Basin (MRB) in the Tianshan Mountains in northwest China, shows that the approach is viable as evidenced by a Nash–Sutcliff efficiency of 0.65 and a percent bias of −3.7% for daily streamflow and water balance, respectively. The results indicate that the glacier area decreased by 11% during the simulation period from 1961 to 1999, which is within the range of records from other glaciers. On average, glacier melt contributed 25% to streamflow, although glacier area accounts for only 14% of the catchment drainage area in the MRB. Glacier melt was positively correlated to temperature change (R 2 =0.70, statistical significance P <0.001) and negatively correlated to precipitation (R 2 =0.20, statistical significance P <0.005). The results indicate that glacier melt was more sensitive to temperature change than to precipitation change, implying that modeling the effects of climate change with increasing temperatures and decreasing precipitation should be further studied. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the uncertainty of hydrological predictions due to rainfall-runoff model parameters in the context of climate change impact studies. Two sources of uncertainty were considered: (i) the dependence of the optimal parameter set on the climate characteristics of the calibration period and (ii) the use of several posterior parameter sets over a given calibration period. The first source of uncertainty often refers to the lack of model robustness, while the second one refers to parameter uncertainty estimation based on Bayesian inference. Two rainfall-runoff models were tested on 89 catchments in northern and central France. The two sources of uncertainty were assessed in the past observed period and in future climate conditions. The results show that, given the evaluation approach followed here, the lack of robustness was the major source of variability in streamflow projections in future climate conditions for the two models tested. The hydrological projections generated by an ensemble of posterior parameter sets are close to those associated with the optimal set. Therefore, it seems that greater effort should be invested in improving the robustness of models for climate change impact studies, especially by developing more suitable model structures and proposing calibration procedures that increase their robustness. [ABSTRACT FROM AUTHOR]

Abstract: Rainfall is regarded as the most important input for the hydrology and nonpoint source (H/NPS) models and uncertainty related to rainfall is generally recognized as a major challenge in watershed modeling. In this paper, we focus on the impact of spatial rainfall variability on H/NPS modeling of a large watershed. The uncertainty introduced by spatial rainfall variability was determined using a number of commonly-used interpolation methods: (1) the Centroid method; (2) the Thiessen Polygon method; (3) the Inverse Distance Weighted (IDW) method; (4) the Dis-Kriging method; and (5) the Co-Kriging method. The Soil and Water Assessment tool (SWAT) was used to quantify the effect of rainfall spatial variability on watershed H/NPS modeling of the Daning watershed in China. Results indicated that these interpolation methods could contribute significant uncertainty in spatial rainfall variability and the carry-magnify effect caused even larger uncertainty in the H/NPS modeling. This uncertainty was magnified from hydrology modeling (stream flow) into NPS modeling (sediment, TP, organic nitrogen (N) and dissolved N). This study further suggested that H/NPS prediction uncertainty relating to spatial rainfall variability was scale-dependent due to the averaging effect of spatial heterogeneity. From a practical point of view, a global interpolation method, such as IDW and Kriging, as well as elevation data derived from a digital elevation model (DEM), should be included into the H/NPS models for reliable predictions in larger watersheds. [Copyright &y& Elsevier]

Summary: This paper compares the water budgets of two adjacent micro-catchments covered by mature (MAT) and 20-year-old secondary (SEC) lower montane cloud forests, respectively, in central Veracruz, Mexico over a 2-year period. Rainfall (P) and streamflow (Q) were measured continuously, whereas dry canopy evaporation (transpiration E t), wet canopy evaporation (rainfall interception I), and cloud water interception (CWI) were quantified using a combination of field measurements and modeling. Mean annual P was 3467mm, of which typically 80% fell during the wet season (May–October). Fog interception occurred exclusively during the dry season (November–April), and was ⩽2% of annual P for both forests. Rainfall interception loss was dominated by post-event evaporation of intercepted water rather than by within-event evaporation. Therefore, the higher overall I (i.e. including CWI) by the MAT (16% of P vs. 8% for the SEC) reflects a higher canopy storage capacity, related in turn to higher leaf area index and greater epiphyte biomass. Annual E t totals derived from sapflow measurements were nearly equal for the MAT and SEC (∼790mm each). Total annual water yield calculated as P minus (E t + I) was somewhat higher for the SEC (2441mm) than for the MAT (2077mm), and mainly reflects the difference in I. Mean annual Q was also higher for the SEC (1527mm) than for the MAT (1338mm), and consisted mostly of baseflow (∼90%). Baseflow recession rates were nearly equal between the two forests, as were stormflow coefficients (4% and 5% for MAT and SEC, respectively). The very low runoff response to rainfall is attributed to the high infiltration and water retention capacities of the volcanic soils throughout the ∼2m deep profile. The water budget results indicate that ∼875 and 700mmyear−1 leave the SEC and MAT as deep groundwater leakage, which is considered plausible given the fractured geology in the study area. It is concluded that 20years of natural regeneration following cloud forest disturbance in central-eastern Mexico is capable of producing near-original hydrological behavior. [Copyright &y& Elsevier]

Summary: This paper describes the application of a spatially distributed hydrologic model (WetSpa) Water and Energy Transfer between Soil, Plants and Atmosphere, for the second phase of the Distributed Model Intercomparison Project (DMIP2) study. The model implementation is based on 30-m spatial resolution and 1h time-step for all basins and interior watersheds involved in the DMIP study. Rainfall inputs are derived from Next Generation Radar (NEXRAD). The distributed model parameters are based on an extensive database of watershed characteristics available for the region, including digital maps of topography, soil type, and landuse. The model is calibrated and validated on part of the river flow records for each basin and applied to the smaller interior watersheds not used in calibration to assess the model performance in ungaged basins. The statistics improve significantly with calibration of the global model parameters but even for uncalibrated simulations, the WetSpa model reproduces flow rates of acceptable accuracy for most cases. To evaluate the model performance during calibration and validation periods, an Aggregated Measure (AM) is introduced that measures different aspects of the simulated hydrograph such as shape, size and volume. The statistics for the five calibration basins show that the model produces very good to excellent results for the calibration period. With the exception of Blue River basin, the overall model performance for the validation period remains good to very good, indicating that the model is able to simulate the relevant hydrologic processes in the basins accurately. The performance of the uncalibrated model for the subcatchments is more variable, but the hourly flow rates generally reproduced with reasonable accuracy indicating an encouraging performance of the model. [ABSTRACT FROM AUTHOR]

Summary: This paper aims to investigate how surface soil moisture data assimilation affects each hydrologic process and how spatially varying inputs affect the potential capability of surface soil moisture assimilation at the watershed scale. The Ensemble Kalman Filter (EnKF) is coupled with a watershed scale, semi-distributed hydrologic model, the Soil and Water Assessment Tool (SWAT), to assimilate surface (5cm) soil moisture. By intentionally setting inaccurate precipitation with open loop and EnKF scenarios in a synthetic experiment, the capability of surface soil moisture assimilation to compensate for the precipitation errors were examined. Results show that daily assimilation of surface soil moisture for each HRU improves model predictions especially reducing errors in surface and profile soil moisture estimation. Almost all hydrological processes associated with soil moisture are also improved with decreased root mean square error (RMSE) values through the EnKF scenario. The EnKF does not produce as much a significant improvement in streamflow predictions as compared to soil moisture estimates in the presence of large precipitation errors and the limitations of the infiltration–runoff model mechanism. Distributed errors of the soil water content also show the benefit of surface soil moisture assimilation and the influences of spatially varying inputs such as soil and landuse types. Thus, soil moisture update through data assimilation can be a supplementary way to overcome the errors created by inaccurate rainfall. Even though this synthetic study shows the potential of remotely sensed surface soil moisture measurements for applications of watershed scale water resources management, future studies are necessary that focus on the use of real-time observational data. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a generalization of the age-old paired catchment approach: it enables to simulate streamflow at the daily or hourly time-step, based on the observed streamflow at the outlet of one or several neighbor catchment(s). An extensive assessment is presented, based on a network of 1040 French catchments: it demonstrates that, alone, the proposed neighbor catchment (NC) model is as efficient as a rainfall–runoff model, and that when a rainfall–runoff model and the NC model are combined, an unprecedented level of efficiency is reached. Because it is nonlinear, the NC model holds a wide application spectrum: it can be useful for hydrological applications such as time series extension or reconstitution as well as trend assessment. [ABSTRACT FROM AUTHOR]