Predictions In Ungauged Basins Research Articles

Robustness of flood-model calibration using single and multiple events

ABSTRACTLack of discharge data for model calibration is challenging for flood prediction in ungauged basins. Since establishment and maintenance of a permanent discharge station is resource demanding, a possible remedy could be to measure discharge only for a few events. We tested the hypothesis that a few flood-event hydrographs in a tropical basin would be sufficient to calibrate a bucket-type rainfall–runoff model, namely the HBV model, and proposed a new event-based calibration method to adequately predict floods. Parameter sets were chosen based on calibration of different scenarios of data availability, and their ability to predict floods was assessed. Compared to not having any discharge data, flood predictions improved already when one event was used for calibration. The results further suggest that two to four events for calibration may considerably improve flood predictions with regard to accuracy and uncertainty reduction, whereas adding more events beyond this resulted in small performance gains.

Enhancing Predictions in Ungauged Basins Using Machine Learning to Its Full Potential

In ungauged basins, long short-term memory (LSTM) networks provide unparalleled precision in prediction. Using k-fold validation, we trained and tested various LSTMs on 531 basins from the CAMELS data set, allowing us to make predictions in basins with no training data. The training and test data set contained 30 years of daily rainfall-runoff data from US catchments ranging in size from 4 to 2,000 km2, with aridity indexes ranging from 0.22 to 5.20, and 12 of the 13 IGPB vegetated land cover classes. Over a 15-year validation period, this effectively "ungauged" model was compared to the Sacramento Soil Moisture Accounting (SAC-SMA) model as well as the NOAA National Water Model reanalysis. Each basin's SAC-SMA was calibrated separately using 15 years of daily data. Across the 531 basins, the out-of-sample LSTM exhibited greater median Nash-Sutcliffe Efficiencies (0.69) than either the calibrated SAC-SMA (0.64) or the National Water Model (0.64). (0.58). This means that there is usually enough information in available catchment attributes data about similarities and differences between catchment-level rainfall-runoff behaviors to generate out-of-sample simulations that are generally more accurate than current models under ideal (i.e., calibrated) conditions. We discovered evidence that adding physical restrictions to the LSTM models improves simulations, which we believe should be the focus of future physics-guided machine learning research.

A simple topography-driven and calibration-free runoff generation module

Abstract. Reading landscapes and developing calibration-free runoff generation models that adequately reflect land surface heterogeneities remains the focus of much hydrological research. In this study, we report a novel and simple topography-driven runoff generation parameterization – the HAND-based Storage Capacity curve (HSC), which uses a topographic index (HAND, Height Above the Nearest Drainage) to identify hydrological similarity and the extent of saturated areas in catchments. The HSC can be used as a module in any conceptual rainfall–runoff model. Further, coupling the HSC parameterization with the mass curve technique (MCT) to estimate root zone storage capacity (SuMax), we developed a calibration-free runoff generation module, HSC-MCT. The runoff generation modules of HBV and TOPMODEL were used for comparison purposes. The performance of these two modules (HSC and HSC-MCT) was first checked against the data-rich Bruntland Burn (BB) catchment in Scotland, which has a long time series of field-mapped saturation area extent. We found that HSC, HBV and TOPMODEL all perform well to reproduce the hydrograph, but the HSC module performs better in reproducing saturated area variation, in terms of correlation coefficient and spatial pattern. The HSC and HSC-MCT modules were subsequently tested for 323 MOPEX catchments in the US, with diverse climate, soil, vegetation and geological characteristics. In comparison with HBV and TOPMODEL, the HSC performs better in both calibration and validation, particularly in the catchments with gentle topography, less forest cover, and arid climate. Despite having no calibrated parameters, the HSC-MCT module performed comparably well with calibrated modules, highlighting the robustness of the HSC parameterization to describe the spatial distribution of the root zone storage capacity and the efficiency of the MCT method to estimate SuMax. This novel and calibration-free runoff generation module helps to improve the prediction in ungauged basins and has great potential to be generalized at the global scale.

PUB in Québec: A robust geomorphology-based deconvolution-reconvolution framework for the spatial transposition of hydrographs

The flexibility and parsimony of transpositioning hydrographs using geomorphology-based deconvolution-reconvolution frameworks is particularly adapted to prediction in ungauged basins. Although already tested in semi-arid and oceanic-temperate hydro-climates, its predictions must be reproducible in a variety of hydrological contexts. The present study explores the nivo-pluvial hydrological regime using geomorphology-based hydrograph transposition between 21 gauged catchments ranging from 1.1 to 4466.4 km2 in Québec, Canada, and constitutes a case study in prediction in ungauged basins. Three metrics were used to assess model performance for each donor-target pair: Nash Sutcliffe Efficiency (NSE), NSE calculated for the square root of discharge (NSEsqrt), and Volumetric Efficiency (VE). The classic transposition of hydrographs using the specific discharge ratio, used as a reference, was almost always outperformed by the geomorphology-based approach. Good but seasonally variable performance values were obtained for several pairs of catchments, revealing simultaneous structural and circumstancial effects. The difference in size, the physical distance between the gauged donor and its target ungauged catchment, and the season influenced the performance of the geomorphology-based transposition.

Runoff Predictions in Ungauged Arctic Basins Using Conceptual Models Forced by Reanalysis Data

Due to global warming, the problem of assessing water resources and their vulnerability to climate drivers in the Arctic region has become a focus in the recent years. This study is aimed at investigating three lumped hydrological models to predict daily runoff of large-scale Arctic basins in the case of substantial data scarcity. All models were driven only by meteorological forcing reanalysis dataset without any additional information about landscape, soil, or vegetation cover properties of the studied basins. Model parameter regionalization based on transferring the whole parameter set showed good efficiency for predictions in ungauged basins. We run a blind test of the proposed methodology for ensemble runoff predictions on five sub-basins, for which only monthly observations were available, and obtained promising results for current water resources assessment for a broad domain of ungauged basins in the Russian Arctic.

Improved Process Representation in the Simulation of the Hydrology of a Meso-Scale Semi-Arid Catchment

The variability of rainfall and climate, combined with land use and land cover changes, and variation in geology and soils makes it a difficult task to accurately describe the key hydrological processes in a catchment. With the aim to better understand the key hydrological processes and runoff generation mechanisms in the semi-arid meso-scale Kaap catchment in South Africa, a hydrological model was developed using the open source STREAM model. Dominant runoff processes were mapped using a simplified Height Above the Nearest Drainage approach combined with geology. The Prediction in Ungauged Basins (PUB) framework of runoff signatures was used to analyse the model results. Results show that in the headwater sub-catchments of Noordkaap and Suidkaap, plateaus dominate, associated with slow flow processes. Therefore, these catchments have high baseflow components and are likely the main recharge zone for regional groundwater in the Kaap. In the Queens sub-catchment, hillslopes associated with intermediate and fast flow processes dominate. However, this catchment still has a strong baseflow component, but it seems to be more impacted by evaporation depletion, due to different soils and geology, especially in drier years. At the Kaap outlet, the model indicates that hillslopes are important, with intermediate and fast flow processes dominating and most runoff being generated through direct runoff and shallow groundwater components, particularly in wetter months and years. There is a high impact of water abstractions and evaporation during the dry season, affecting low flows in the catchment. Results also indicate that the root zone storage and the parameters of effective rainfall separation (between unsaturated and saturated zone), quickflow coefficient and capillary rise, were very sensitive in the model. The inclusion of capillary rise (feedback from the saturated to unsaturated zone) greatly improved the simulation results.

Transferability of regionalization methods under changing climate

Regionalization methods have been extensively discussed as the solution for runoff predictions in ungauged basins (PUB), especially during the PUB decade (2003–2012). At the same time, research topics relevant to climate change appear to be an essential and attractive field for hydrologists in recent decades, because the availability and quality of water resources are strongly affected by climate change. However, it is still unknown whether regionalization methods can be used to predict hydrological impacts of climate change for ungauged catchments or how much uncertainty of future predictions may result from the use of regionalization methods. Therefore, in this study, we investigate the transferability of regionalization methods (i.e. spatial proximity and physical similarity methods, regression method) under changing climate conditions and compare the uncertainty resulting from regionalization methods with that from using climate models. The investigation is based on 108 catchments in Norway, with large variability in climate conditions and geographic characteristics. The study applies a lumped conceptual rainfall-runoff model (WASMOD) with simple structure and six model parameters. Our result shows that (a) the differences in the predictions by the regionalization methods tend to increase in the future, (b) the physical similarity method with parameter option (i.e. the model parameters from the physically-similar donor catchments are first averaged and then used to run the model for the target catchment) shows higher transferability than other methods, (c) the uncertainty contributions from climate models and regionalization methods to future runoff prediction are basin dependent, and (d) the uncertainty of future runoff prediction due to regionalization methods can be higher than that from climate models in low precipitation areas. This study provides insight to the choice of regionalization methods under changing climate conditions and the role of regionalization methods to the uncertainty contributions in future runoff predictions.

A computer vision-based approach to fusing spatiotemporal data for hydrological modeling

This study develops a novel approach to data-driven hydrological modeling. The approach adopts the feature representation technique in computer vision to effectively exploit spatial information contained in time-variant input data fields and seamlessly fuse multisource information via machine learning. The new approach overcomes a major limitation of existing approaches in which the spatial heterogeneity of input variables cannot be sufficiently accounted for. The approach is applied to predict the streamflow in a watershed on the northern margin of the Qinghai-Tibetan Plateau, and its performance is compared with various data-driven and process-based models. The major findings are as follows. First, the new approach represents a general framework for the fusion of multisource spatiotemporal data for hydrological modeling and demonstrates great potential to incorporate fast-growing environmental big data. Second, the new approach demonstrates satisfactory short-term forecasting, long-term simulation, and transfer learning performances and is promising for addressing predictions in ungauged basins. Third, the predictors, including precipitation, temperature, leaf area index, and historical streamflow, play markedly distinct roles in modeling streamflow with the novel approach. Finally, topographic information is not a necessary model input in the proposed approach because spatial patterns can be well embodied by other inputs (e.g., temperature) that have high similarities with topography. This study represents the first attempt to bring computer vision into data-driven hydrological modeling and may inspire future studies in this promising direction.

Landscape heterogeneity and hydrological processes: a review of landscape-based hydrological models

Landscapes and water are closely linked. Water shapes landscapes, and landscape heterogeneity in turn determines water storage, partitioning, and movement. Understanding hydrological processes from an ecological perspective is an exciting and fast-growing field of research. The motivation of this paper is to review advances in the interaction between landscape heterogeneity and hydrological processes, and propose a framework for synthesizing and moving forward. Landscape heterogeneity, mainly topography and land cover, has been widely incorporated into existing hydrological models, but not in a systematic way. Topography, as one of the most important landscape traits, has been extensively used in hydrological models, but mostly to drive water flow downhill. Land cover heterogeneity, represented mostly by vegetation, is usually linked with evaporation and transpiration rather than runoff generation. Moreover, the proportion of different land cover types is usually the only index involved in hydrological models, leaving the influence of vegetation patterns and structure on hydrologic connectivity still largely unexplored. Additionally, moving from “what heterogeneity exists” to “why-type” questions probably offers us new insights into the nexus of landscape and water. We believe that the principles of self-organization and co-evolution of landscape features shed light on the possibility to infer subsurface heterogeneity from a few observable landscapes, allowing us to simplify complexity to a few quantifiable metrics, and utilizing these metrics in models with sufficient heterogeneity but limited complexity. Landscape-based models can also be beneficial to improve our ability of prediction in ungauged basins and prediction in a changing environment (Panta Rhei, everything flows).

Modelling ungauged catchments using the catchment runoff response similarity

Various types of regionalization approaches have been proposed in the last several decades for predictions in ungauged basins. The most commonly used methods are based on the proximity of catchment centroids and physiographic and/or climatic conditions of the catchments. However, the proximity of the catchment centroids and catchment physical attributes do not necessarily translate into similarities in hydrologic behavior. It is also difficult to identify the key attributes that favor hydrologic similarity. Therefore, in this study, we proposed a new method called catchment runoff-response similarity (CRRS), in the view of reducing the hydrologic process predictive uncertainty and to solve the problem of the key attributes identification that favor hydrologic similarity. The CRRS has a two-step approach: 1) the commonly used regionalization approach is used to temporarily transpose the calibrated model parameter from gauged to ungauged catchments, and 2) the runoff response of each smaller delineated subbasin of the gauged and ungauged basins are obtained based on the parameter value computed in the first step. The similar subbasins of the gauged and ungauged basins are then identified based on their runoff response similarity. The final parameter value in the ungauged subbasins are determined based on the notion that similar subbasins with runoff responses to similar input rainfall could have similar model structure settings. The applicability of the proposed approach was verified for the Geum River Basin (GRB) of South Korea and the Lake Tana Basin (LTB) of Ethiopia. Leave-one-out evaluations of the proposed parameter transfer approach at various test-gauging stations showed that the CRRS approach outperformed the other widely used methods. The CRRS in the GRB and LTB, reached 91% and 77% during the calibration and 86% and 67% during the validation period, respectively, of the at-site calibrated model performance on average over the test stations. The overall worth of the CRRS over the second best commonly used regionalization approach were also found to be 10% and 14% during the calibration and 7% and 4% during the validation period in the GRB and LTB, respectively. In general, it can be concluded from the overall result that the runoff predictive uncertainty in the ungauged catchments of the two study basins was significantly reduced by the CRRS approach. Therefore, the proposed approach can be used as an alternative method for runoff prediction in the ungauged basins worldwide.

Assessing baseflow index vulnerability to variation in dry spell length for a range of catchment and climate properties

AbstractBaseflow index (BFI) prediction in ungauged basins has largely been based on the use of catchment physiographic attributes as dominant variables. In a context where changes in climate are increasingly evident, it is also important to study how the slow component of flow is potentially affected by climate. The aim of this study was to illustrate the impact of climate variability on the baseflow process based on analysis of daily rainfall characteristics and hydrological modelling simulation exercises validated with observed data. Ten catchments were analysed that span southern to northern Europe and range from arid Mediterranean to maritime temperate climate conditions. Additionally, more than 2,000 virtual catchments were modelled that cover an extended gradient of physiographic and climate properties. The relative amounts of baseflow were summarized by the BFI. The catchment slow response delay time (Ks) was assumed to be a measure of catchment effects, and the impact of climate properties was investigated with the dry spell length (d). Well‐drained and poorly‐drained groups were identified based on Ks and d, and their response to an increase or decrease in dry spell length was analysed. Overall, for either well‐ or poorly‐drained groups, an extension in dry spell length appeared to have minor effects on the baseflow compared with a decrease in dry spell length. Under the same dry spell variation, the BFI vulnerability appeared higher for catchments characterized by large initial d values in combination with poorly‐drained systems, but attributing an equal weight to the variations in d both in the case of dry and wet initial conditions, it is in the end concluded that the BFI vulnerability appears higher for systems laying in the transition zone between well‐ and poorly‐drained systems.

Quantifying the roles of single stations within homogeneous regions using complex network analysis

Regionalization and pooling stations to form homogeneous regions or communities are essential for reliable parameter transfer, prediction in ungauged basins, and estimation of missing information. Over the years, several clustering methods have been proposed for regional analysis. Most of these methods are able to quantify the study region in terms of homogeneity but fail to provide microscopic information about the interaction between communities, as well as about each station within the communities. We propose a complex network-based approach to extract this valuable information and demonstrate the potential of our approach using a rainfall network constructed from the Indian gridded daily precipitation data. The communities were identified using the network-theoretical community detection algorithm for maximizing the modularity. Further, the grid points (nodes) were classified into universal roles according to their pattern of within- and between-community connections. The method thus yields zoomed-in details of individual rainfall grids within each community.

Integrating Field Experiments with Modeling to Evaluate the Freshwater Availability at Ungauged Sites: A Case Study of Pingtan Island (China)

Predictions in ungauged basins (PUB) has been always a focus of hydrological research. The problem presented by ungauged basins is how to reasonably estimate water resource availability. To solve the issues of data scale, this study combines field experiments and hydrological models to estimate freshwater availability in a typical ungauged sea island located in southeastern China. The free parameters in the hydrological model were derived from the point-scale rainfall-runoff experiments rather than calibration using river discharge observations. The rainfall-runoff experiments were performed on six sites covering 11 land cover types. Model validation at a sub-catchment showed that the combined method could successfully reproduce monthly streamflow, with a Nash–Sutcliffe Efficiency of 0.82, correlation coefficient of 0.85, and flow volume error of 6.5%. The simulation results indicate high heterogeneity and distinct seasonal dynamics in freshwater availability across the entire island. This pioneering PUB study for Chinese islands could provide reference for planning and management of freshwater in a water shortage area.

A Water Allocation Decision‐Support Model and Tool for Predictions in Ungauged Basins in Northeast British Columbia, Canada

AbstractPressures on water resources due to changing climate, increasing demands, and enhanced recognition of environmental flow needs result in the need for hydrology information to support informed water allocation decisions. However, the absence of hydrometric measurements and limited access to hydrology information in many areas impairs water allocation decision‐making. This paper describes a water balance‐based modeling approach and an innovative web‐based decision‐support hydrology tool developed to address this need. Using high‐resolution climate, vegetation, and watershed data, a simple gridded water balance model, adjusted to account for locational variability, was developed and calibrated against gauged watersheds, to model mean annual runoff. Mean monthly runoff was modeled empirically, using multivariate regression. The modeled annual runoff results are within 20% of the observed mean annual discharge for 78% of the calibration watersheds, with a mean absolute error of 16%. Modeled monthly runoff corresponds well to observed monthly runoff, with a median Nash–Sutcliffe statistic of 0.92 and a median Spearman rank correlation statistic of 0.98. Monthly and annual flow estimates produced from the model are incorporated into a map‐ and watershed‐based decision‐support system referred to as the Northeast Water Tool, to provide critical information to decision makers and others on natural water supply, existing allocations, and the needs of the environment.

An inter-comparison of similarity-based methods for organisation and classification of groundwater hydrographs

Classification and similarity based methods, which have recently received major attention in the field of surface water hydrology, namely through the PUB (prediction in ungauged basins) initiative, have not yet been applied to groundwater systems. However, it can be hypothesised, that the principle of “similar systems responding similarly to similar forcing” applies in subsurface hydrology as well. One fundamental prerequisite to test this hypothesis and eventually to apply the principle to make “predictions for ungauged groundwater systems” is efficient methods to quantify the similarity of groundwater system responses, i.e. groundwater hydrographs. In this study, a large, spatially extensive, as well as geologically and geomorphologically diverse dataset from Southern Germany and Western Austria was used, to test and compare a set of 32 grouping methods, which have previously only been used individually in local-scale studies. The resulting groupings are compared to a heuristic visual classification, which serves as a baseline. A performance ranking of these classification methods is carried out and differences in homogeneity of grouping results were shown, whereby selected groups were related to hydrogeological indices and geological descriptors. This exploratory empirical study shows that the choice of grouping method has a large impact on the object distribution within groups, as well as on the homogeneity of patterns captured in groups. The study provides a comprehensive overview of a large number of grouping methods, which can guide researchers when attempting similarity-based groundwater hydrograph classification.

Application of different clustering approaches to hydroclimatological catchment regionalization in mountainous regions, a case study in Utah State

With respect to the different hydrological responses of catchments, even the adjacent ones, in mountainous regions, there are a great number of motivations for classifying them into homogeneous clusters. These motivations include prediction in ungauged basins (PUB), model parameterization, understanding the potential impact of environmental changes, transferring information from gauged catchments to the ungauged ones. The present study investigated the similarity of catchments through the hydro-climatological pure time-series of a 14-year period from 2001 to 2015. Data sets encompass more than 13,000 month-station streamflow, rainfall, and temperature data obtained from 27 catchments in Utah State as one of the eight mountainous states of the USA. The identification, analysis, and interpretation of homogeneous catchments were investigated by applying the four approaches of clustering, K-means, Ward, and SOM (Self-Organized Map) and a newly proposed Wavelet-Entropy-based (WE-SOM) clustering method. By using two clustering evaluation criteria, 3, 5, and 6 clusters were determined as the best numbers of clusters, depending on the method employed, where each cluster represents different hydro-climatological behaviors. Despite the absence of geographic characteristics in input data matrix, the results indicated a regionalization in agreement with topographic characteristics. Considering the dependency of the hydrological behavior of catchments on the physiographic field aspects and characteristics, WE-SOM method demonstrated a more acceptable performance, compared to the other three conventional clustering methods, by providing more clusters. WE-SOM appears to be a promising approach in catchment clustering. It preserves the topological structure of data which can, as a result, be proofed in a greater number of clusters by dividing data into higher numbers of distinct clusters with similar altitudes of catchments in each cluster. The results showed the aptitude of wavelets to quantify the time-based variability of temperature, rainfall and streamflow, in the way contributing to the regionalization of diverse catchments.

Regionalisation of hydrological model parameters in nested catchmentss

A prediction in ungauged basins is one of the challenging tasks for a hydrologist of this century. Even though the physically based hydrological models can be the more appropriate in ungauged basins but data requirement limit the use. Conceptual hydrological models are simple and easy to use. But these model needs calibration before it can be used. Availability of data at all location in the basin limits the calibration of conceptual hydrological models. In this study, a calibration methodology is presented for discharge series limited condition using upstream and downstream data from nested catchment. It has been found that reasonable model parameters can be estimated for middle catchment using immediate upstream and downstream data. The regionalised parameter at the catchment outlet was tested at several locations inside the catchment to test the suitability of the outlet based model parameter for the interior location along the channel. It has been found that the model parameters obtained at the outlet of the catchment by regionalisation methods can be applied to the neighbouring points along the channel. A conceptual hydrological model, HBV-IWS was used for on upper Neckar catchment to demonstrate the methodology.

On the evolution of hydrological modelling for water resources in Eastern Africa.

Abstract This review provides an assessment of the evolution of hydrological modelling for Eastern Africa. We outline the historical development and perspectives considered as this region, like many regions around the world, sees increasing attention on how water resources can be sustainably developed. We emphasize the spatial scales and modelling approaches that typify the region and how these have changed with time. The review is done in two complementary approaches. The first approach is to explore a practical, real-world example providing context for the Eastern Africa region and the water resource development issues presently faced. We use Tanzania's 34 000 km 2 Kilombero Valley (KV) river basin to explore implications of significant gaps in data and modelling scales. We hypothesize that these gaps limit the application of the current state-of-the-science to inform water management policy and practice under current and estimated future conditions. In our second approach, we investigate possible solutions to bridge these gaps through a review of case studies from other Eastern Africa's basins across a range of sizes. Our result highlight that some applications of the models considered under this review anticipated more recent international developments as indicated in Predictions in Ungauged Basins and Panta Rhei initiatives. Through this review, it is clear that there is a possibility to improve understanding of the hydrological processes relevant at scales such as the KV river basin through the use of (1) global precipitation datasets (e.g. satellite and/or homogenized observed data) as input data; (2) remote sensing datasets as model evaluation variable; (3) regionalization around the transferability of model parameters; (4) modification of model codes/structures to suit local conditions; and (5) understanding and application of uncertainty principles in hydrological modelling. Given that many regions of the world face similar water resource management challenges as Eastern Africa; it is likely that the findings of this review could help guide how we develop the next generation of modelling approaches to leverage information from various scales.

Runoff prediction in ungauged catchments in Norway: comparison of regionalization approaches

Abstract Runoff prediction in ungauged catchments has been a challenging topic over recent decades. Much research have been conducted including the intensive studies of the PUB (Prediction in Ungauged Basins) Decade of the International Association for Hydrological Science. Great progress has been made in the field of regionalization study of hydrological models; however, there is no clear conclusion yet about the applicability of various methods in different regions and for different models. This study made a comprehensive assessment of the strengths and limitations of existing regionalization methods in predicting ungauged stream flows in the high latitudes, large climate and geographically diverse, seasonally snow-covered mountainous catchments of Norway. The regionalization methods were evaluated using the water balance model – WASMOD (Water And Snow balance MODeling system) on 118 independent catchments in Norway, and the results show that: (1) distance-based similarity approaches (spatial proximity, physical similarity) performed better than regression-based approaches; (2) one of the combination approaches (combining spatial proximity and physical similarity methods) could slightly improve the simulation; and (3) classifying the catchments into homogeneous groups did not improve the simulations in ungauged catchments in our study region. This study contributes to the theoretical understanding and development of regionalization methods.

Characterizing Uncertainty in Daily Streamflow Estimates at Ungauged Locations for the Massachusetts Sustainable Yield Estimator

AbstractHydrologic characterization at ungauged locations is one of the quintessential challenges of hydrology. Beyond simulation of historical streamflows, it is similarly important to characterize the level of uncertainty in hydrologic estimates. In tandem with updates to Massachusetts Sustainable Yield Estimator, this work explores the application of global uncertainty estimates to daily streamflow simulations. Expanding on a method developed for deterministic modeling, this approach produces confidence intervals on daily streamflow developed through nonlinear spatial interpolation of daily streamflow using flow duration curves; the 95% confidence is examined. Archived cross‐validations of daily streamflows from 66 watersheds in and around Massachusetts are used to evaluate an approach to uncertainty characterization. Neighboring sites are treated as ungauged, producing relative errors that can be resampled and applied to target sites. The method, with some modification, is found to provide appropriately narrow confidence intervals that contain 95% of the observed streamflows in cross‐validation. Further characterizing uncertainty, multiday means of daily streamflow are evaluated. Working through cross‐validation in Massachusetts, two‐ to three‐month averages of daily streamflow show the best performance. These two approaches to uncertainty characterization inform how streamflow simulation produced for prediction in ungauged basins can be used for water resources management.