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• The adaptive irrigation behaviors might worsen the water scarcity in the CRB. • Clustering identified bold, prudent, and forward-looking farmer classes. • We indicated specific parameters for uncertainty reduction in human systems. The Colorado River Basin (CRB) supports the water supply for seven states and forty million people in the Western United States (US) and has been suffering an extensive drought for more than two decades. As climate change continues to reshape water resources distribution in the CRB, its impact can differ in intensity and location, resulting in variations in human adaptation behaviors. The feedback from human systems in response to the environmental changes and the associated uncertainty is critical to water resources management, especially for water-stressed basins. This paper investigates how human adaptation affects water scarcity uncertainty in the CRB and highlights the uncertainties in human behavior modeling. Our focus is on agricultural water consumption, as approximately 80% of the water consumption in the CRB is used in agriculture. We adopted a coupled agent-based and water resources modeling approach for exploring human-water system dynamics, in which an agent is a human behavior model that simulates a farmer's water consumption decisions. We examined uncertainties at the system, agent, and parameter levels through uncertainty, clustering, and sensitivity analyses. The uncertainty analysis results suggest that the CRB water system may experience 13 to 30 years of water shortage during the 2019–2060 simulation period, depending on the paths of farmers' adaptation. The clustering analysis identified three decision-making classes: bold, prudent, and forward-looking, and quantified the probabilities of an agent belonging to each class. The sensitivity analysis results indicated agents whose decision-making models require further investigation and the parameters with the higher uncertainty reduction potentials. By conducting numerical experiments with the coupled model, this paper presents quantitative and qualitative information about farmers' adaptation, water scarcity uncertainties, and future research directions for improving human behavior modeling. [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]

• 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]

• Literature-based and alternative equations for quick-flow runoff are tested. • A new empirical equation was found to outperform previously used equations. • Monthly maps of quick-flow runoff are produced and evaluated for the CONUS. • Patterns of increased quick-flow runoff in winter-spring months are observed. The quantitative estimation of the quick-flow runoff component of streamflow is required for many hydrologic applications. Estimation at the monthly timescale and national spatial scale would be particularly useful for national water availability modeling. This paper reviews a sample of commonly used equations for quick-flow runoff, including several currently in use in continental-scale models. The review shows the wide range of equation forms or heuristics currently in use to predict quick-flow runoff, the limited spatial scale over which these equations are often developed or calibrated, and the scarcity of well-tested equations available for quick-flow runoff at the monthly timescale. Data were gathered from a set of 1301 gaged watersheds across the United States to test a range of equations from the literature, along with several alternative equations, to assess and compare their performance in predicting quick-flow runoff at the monthly timescale. The highest-performing equation was selected for application to monthly maps of explanatory variables to produce monthly quick-flow runoff water budget contribution maps. This equation is a regression against precipitation, soil saturated hydraulic conductivity, surficial geology type, and slope data. Its application indicates that average quick-flow runoff across the conterminous United States in the winter exceeds that in the summer by up to a factor of three. The monthly maps were explored and evaluated for the timespan of 2000–2015. The comparison of equation forms and produced monthly maps will be useful for a variety of hydrologic modeling and monitoring applications. [ABSTRACT FROM AUTHOR]

• The use of information measures can improve prediction of the time horizon of rivers. • KC and LE are useful measures for detecting the chaos and randomness in streamflow. • The horizon time of the river can be estimated by inverse values of LE and KC. • This time is computed for the monthly streamflow of 1879 rivers from the U.S. • Results show that prediction of the time horizon highly depends on human activities. A streamflow time series encompasses a large amount of hidden information and reliable prediction of its behavior in the future remains a challenge. It seems that the use of information measures can significantly contribute to determining the time horizon of rivers and improving predictability. Using the Kolmogorov complexity (KC) and its derivatives (KC spectrum and its highest value), and Lyapunov exponent (LE), it has previously been shown that the degree of streamflow predictability depends on human activities, environmental factors, and natural characteristics. This paper applied the KC and LE measures to investigate the randomness and chaotic behavior of monthly streamflow of 1879 rivers from the United States for a period of 1950–2015 and evaluated their time horizons via the Lyapunov and Kolmogorov time (LT and KT, respectively). [ABSTRACT FROM AUTHOR]

This paper examines the frequency, distribution tails, and peak-over-threshold (POT) of extreme floods through analysis that centers on the October 2015 flooding in North Carolina (NC) and South Carolina (SC), United States (US). The most striking features of the October 2015 flooding were a short time to peak (T p ) and a multi-hour continuous flood peak which caused intensive and widespread damages to human lives, properties, and infrastructure. The 2015 flooding was produced by a sequence of intense rainfall events which originated from category 4 hurricane Joaquin over a period of four days. Here, the probability distribution and distribution parameters (i.e., location, scale, and shape) of floods were investigated by comparing the upper part of empirical distributions of the annual maximum flood (AMF) and POT with light- to heavy- theoretical tails: Fréchet, Pareto, Gumbel, Weibull, Beta, and Exponential. Specifically, four sets of U.S. Geological Survey (USGS) gauging data from the central Carolinas with record lengths from approximately 65–125 years were used. Analysis suggests that heavier-tailed distributions are in better agreement with the POT and somewhat AMF data than more often used exponential (light) tailed probability distributions. Further, the threshold selection and record length affect the heaviness of the tail and fluctuations of the parent distributions. The shape parameter and its evolution in the period of record play a critical and poorly understood role in determining the scaling of flood response to intense rainfall. [ABSTRACT FROM AUTHOR]

Floodplain mapping using hydrodynamic models is difficult in data scarce regions. Additionally, using hydrodynamic models to map floodplain over large stream network can be computationally challenging. Some of these limitations of floodplain mapping using hydrodynamic modeling can be overcome by developing computationally efficient statistical methods to identify floodplains in large and ungauged watersheds using publicly available data. This paper proposes a geomorphic model to generate probabilistic 100-year floodplain maps for the Conterminous United States (CONUS). The proposed model first categorizes the watersheds in the CONUS into three classes based on the height of the water surface corresponding to the 100-year flood from the streambed. Next, the probability that any watershed in the CONUS belongs to one of these three classes is computed through supervised classification using watershed characteristics related to topography, hydrography, land use and climate. The result of this classification is then fed into a probabilistic threshold binary classifier (PTBC) to generate the probabilistic 100-year floodplain maps. The supervised classification algorithm is trained by using the 100-year Flood Insurance Rated Maps (FIRM) from the U.S. Federal Emergency Management Agency (FEMA). FEMA FIRMs are also used to validate the performance of the proposed model in areas not included in the training. Additionally, HEC-RAS model generated flood inundation extents are used to validate the model performance at fifteen sites that lack FEMA maps. Validation results show that the probabilistic 100-year floodplain maps, generated by proposed model, match well with both FEMA and HEC-RAS generated maps. On average, the error of predicted flood extents is around 14% across the CONUS. The high accuracy of the validation results shows the reliability of the geomorphic model as an alternative approach for fast and cost effective delineation of 100-year floodplains for the CONUS. [ABSTRACT FROM AUTHOR]

The need for predictions of flow time-series persists at ungauged catchments, motivating the research goals of our study. By means of the Sacramento model, this paper explores the use of parameter transfer within homogeneous regions of similar climate and flow characteristics and makes comparisons with predictions from a priori parameters. We assessed the performance using the Nash-Sutcliffe (NS), bias, mean monthly hydrograph and flow duration curve (FDC). The study was conducted on a large dataset of 73 catchments within the eastern US. Two approaches to the parameter transferability were developed and evaluated; (i) the within homogeneous region parameter transfer using one donor catchment specific to each region, (ii) the parameter transfer disregarding the geographical limits of homogeneous regions, where one donor catchment was common to all regions. Comparisons between both parameter transfers enabled to assess the gain in performance from the parameter regionalization and its respective constraints and limitations. The parameter transfer within homogeneous regions outperformed the a priori parameters and led to a decrease in bias and increase in efficiency reaching a median NS of 0.77 and a NS of 0.85 at individual catchments. The use of FDC revealed the effect of bias on the inaccuracy of prediction from parameter transfer. In one specific region, of mountainous and forested catchments, the prediction accuracy of the parameter transfer was less satisfactory and equivalent to a priori parameters. In this region, the parameter transfer from the outsider catchment provided the best performance; less-biased with smaller uncertainty in medium flow percentiles (40%–60%). The large disparity of energy conditions explained the lack of performance from parameter transfer in this region. Besides, the subsurface stormflow is predominant and there is a likelihood of lateral preferential flow, which according to its specific properties further explained the reduced efficiency. Testing the parameter transferability using criteria of similar climate and flow characteristics at ungauged catchments and comparisons with predictions from a priori parameters are a novelty. The ultimate limitations of both approaches are recognized and recommendations are made for future research. [ABSTRACT FROM AUTHOR]

This paper advances the physical understanding of the flow duration curve (FDC) regional variation. It provides a process-based analysis of the interaction between climate and landscape properties to explain disparities in FDC shapes. We used (i) long term measured flow and precipitation data over 73 catchments from the eastern US. (ii) We calibrated the Sacramento model (SAC-SMA) to simulate soil moisture and flow components FDCs. The catchments classification based on storm characteristics pointed to the effect of catchments landscape properties on the precipitation variability and consequently on the FDC shapes. The landscape properties effect was pronounce such that low value of the slope of FDC (SFDC)—hinting at limited flow variability—were present in regions of high precipitation variability. Whereas, in regions with low precipitation variability the SFDCs were of larger values. The topographic index distribution, at the catchment scale, indicated that saturation excess overland flow mitigated the flow variability under conditions of low elevations with large soil moisture storage capacity and high infiltration rates. The SFDCs increased due to the predominant subsurface stormflow in catchments at high elevations with limited soil moisture storage capacity and low infiltration rates. Our analyses also highlighted the major role of soil infiltration rates on the FDC despite the impact of the predominant runoff generation mechanism and catchment elevation. In conditions of slow infiltration rates in soils of large moisture storage capacity (at low elevations) and predominant saturation excess, the SFDCs were of larger values. On the other hand, the SFDCs decreased in catchments of prevalent subsurface stormflow and poorly drained soils of small soil moisture storage capacity. The analysis of the flow components FDCs demonstrated that the interflow contribution to the response was the higher in catchments with large value of slope of the FDC. The surface flow FDC was the most affected by the precipitation as it tracked the precipitation duration curve (PDC). In catchments with low SFDCs, this became less applicable as surface flow FDC diverged from PDC at the upper tail (> 40% of the flow percentile). The interflow and baseflow FDCs illustrated most the filtering effect on the precipitation. The process understanding we achieved in this study is key for flow simulation and assessment in addition to future works focusing on process-based FDC predictions. [ABSTRACT FROM AUTHOR]

The changing climate and the associated future increases in temperature are expected to have impacts on drought characteristics and hydrologic cycle. This paper investigates the projected changes in spatiotemporal characteristics of droughts and their future attributes over the Willamette River Basin (WRB) in the Pacific Northwest U.S. The analysis is performed using two subsets of downscaled CMIP5 global climate models (GCMs) each consisting of 10 models from two future scenarios (RCP4.5 and RCP8.5) for 30 years of historical period (1970–1999) and 90 years of future projections (2010–2099). Hydrologic modeling is conducted using the Precipitation Runoff Modeling System (PRMS) as a robust distributed hydrologic model with lower computational cost compared to other models. Meteorological and hydrological droughts are studied using three drought indices (i.e. Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Standardized Streamflow Index). Results reveal that the intensity and duration of hydrological droughts are expected to increase over the WRB, albeit the annual precipitation is expected to increase. On the other hand, the intensity of meteorological droughts do not indicate an aggravation for most cases. We explore the changes of hydrometeolorogical variables over the basin in order to understand the causes for such differences and to discover the controlling factors of drought. Furthermore, the uncertainty of projections are quantified for model, scenario, and downscaling uncertainty. [ABSTRACT FROM AUTHOR]

• Shape-based time-series clustering of gauged watersheds solely based on stream flow data. • Random Forests classification of ungauged watersheds to flow-regime classes. • Boruta algorithm identifies crucial watershed attributes used in Random Forests. • Flow-regime similarity does not equal to similarity in individual attributes. • Catchment Attributes and Meteorology for Large-Sample Studies Dataset. Watershed classification is considered necessary for various purposes, including improving the transferability of streamflow information and allowing the generalization of hydrologic theories. In this paper, we proposed a classification method using shape-based time-series clustering techniques to define hydrologically homogeneous classes for a set of 638 gauged watersheds in the conterminous United States. We defined 15 distinct flow-regime classes based on standardized weekly-step mean annual hydrographs of the watersheds analyzed. Most classes showed regionality, though to various degrees. Classes in the Atlantic Coast region showed strong geographical contiguity indicating that spatial proximity may be an appropriate indicator of flow-regime similarity; but on the other hand, nearby watersheds may exhibit different intra-annual variabilities and watersheds far apart from one another may exhibit a similar flow regime. Such cases show that spatial proximity may not be used as a universal indicator of flow-regime similarity. To expand the flow-regime classification to ungauged watersheds, where no streamflow data are available, we proposed a physical-characteristic classification method using the Random Forests algorithm based on a set of physical-climatic attributes. Based on the Boruta feature-selection algorithm, attributes related to snowiness, precipitation seasonality, and green vegetation coverage stood out as the most relevant controls on regime-class memberships. By analyzing the within-class variability of the watershed attributes, we found that watersheds of the same flow-regime class may be very different in terms of individual attribute values indicating that flow-regime dis/similarity may not be equated to dis/similarity in individual watershed attributes; instead, it is the interplay among attributes that determines the streamflow behavior. Overall, methods proposed for the classification of gauged and ungauged watersheds are believed to be of value in a range of applications including selecting gauged donor watersheds for estimating historical streamflow records at ungauged watersheds. [ABSTRACT FROM AUTHOR]

The allocation of the water resources of Southeastern United States have given rise to inter-state political conflicts for decades. The limited water resource are not only required to satisfy the demand of rapidly growing urban areas, but also the power industry, recreational needs and the environment. Although water resource variability has been attributed to various teleconnections, knowledge of long-term water resource availability is limited. In this paper historical (1952–2014) precipitation and streamflow data from 18 basins in southeast United States are analyzed for long-term changes in the hydroclimatology, possibly brought by urbanization, climate change and/or changed precipitation patterns. A Pettitt test indicates a change in the precipitation-runoff relationship in the late 1990s. After the shift, basin records indicate decreased precipitation storage and consequently increased streamflow. [ABSTRACT FROM AUTHOR]

Growing demands for food security to feed increasing populations worldwide have intensified the search for improved performance of irrigation, the world’s largest water user. These challenges are raised in the face of climate variability and from growing environmental demands. Adaptation measures in irrigated agriculture include fallowing land, shifting cropping patterns, increased groundwater pumping, reservoir storage capacity expansion, and increased production of risk-averse crops. Water users in the Gila Basin headwaters of the U.S. Lower Colorado Basin have faced a long history of high water supply fluctuations producing low-valued defensive cropping patterns. To date, little research grade analysis has investigated economically viable measures for irrigation development to adjust to variable climate. This gap has made it hard to inform water resource policy decisions on workable measures to adapt to climate in the world’s dry rural areas. This paper’s contribution is to illustrate, formulate, develop, and apply a new methodology to examine the economic performance from irrigation capacity improvements in the Gila Basin of the American Southwest. An integrated empirical optimization model using mathematical programming is developed to forecast cropping patterns and farm income under two scenarios (1) status quo without added storage capacity and (2) with added storage capacity in which existing barriers to development of higher valued crops are dissolved. We find that storage capacity development can lead to a higher valued portfolio of irrigation production systems as well as more sustained and higher valued farm livelihoods. Results show that compared to scenario (1), scenario (2) increases regional farm income by 30%, in which some sub regions secure income gains exceeding 900% compared to base levels. Additional storage is most economically productive when institutional and technical constraints facing irrigated agriculture are dissolved. Along with additional storage, removal of constraints on weak transportation capacity, limited production scale, poor information access, weak risk-bearing capacity, limited management skills, scarce labor supply, low food processing capacity, and absolute scale constraints, all can raise the economic value of additional irrigation capacity development. Our results light a path forward to policy makers, water administrators, and farm managers, who bear the burden of protecting farm income, food and water security, and rural economic development in the world’s dry regions faced by the need to adapt to climate variability. [ABSTRACT FROM AUTHOR]

• Comparison of Budyko-based methods to separate catchment and climate effects on flow. • Probabilistic nature of catchment parameter and its uncertainty considered. • New, improved Two-Stage Decomposition method proposed for separation. • Proposed method considers uncertainty in evaporative index using bootstrap method. • Proposed method more robust than using any single earlier-practiced method. Streamflow variability is affected by both climate change and direct human impacts within the catchment. Quantification of the role of these two factors have significant implications for water resources. In recent years, Budyko-based analytical methods have been widely used for such quantification due to their simplicity and efficacy. This paper presents a comparison of such methods to attribute changes in streamflow in four Model Parameter Estimation Experiment (MOPEX) catchments in the contiguous United States. Further, a new, improved Two-stage Decomposition method to separate the role of climate and catchment changes on streamflow is proposed, considering the uncertainty in the catchment parameter that further leads to a range of evaporative index values within a catchment. We find that climate and catchment effects are sensitive to the choice of Budyko equation, disaggregation algorithm and approximation technique. Further, the catchment parameter can be expressed probabilistically, to account for the uncertainty because of limited hydro-meteorological records. The proposed approach employs a bootstrap analysis to obtain the stochastic range of the catchment parameter, considering different choices of Budyko equations. We argue that rather than relying on a single Budyko-based approach for separation of climate and catchment effects on streamflow, the proposed method offers a more robust way by considering the entire range of uncertainty of the catchment parameter. [ABSTRACT FROM AUTHOR]

• A HB framework is used to consider similarity of adjacent basins to restrain time-varying parameter. • Four scenarios were developed to explore the spatial coherence between adjacent basins. • The proposed method succeeded in reducing the uncertainty of time-varying parameters. • The seasonality of the catchment storage was more coherent between adjacent basins. Hydrological processes became non-stationary under the influences of climate change and human activities. This non-stationarity highlights the need to adopt time-varying parameters in hydrological models. The majority of existing literature quantifies time-varying parameters by incorporating real observations of single basin into a hydrological model. They are limited in their information on catchment characteristics and climatic factors to constrain time-varying parameters. Thus, models are difficult to apply for hydrological predictions outside the calibration periods. This paper formulated the time-varying parameters for a lumped hydrological model as explicit functions of physically-based covariates that captured the catchment characteristics. Then, it used a hierarchical Bayesian framework to incorporate the similarity of adjacent basins to reduce the uncertainty of the assumed functions for time-varying parameters. Four modeling scenarios were developed to explore the spatial coherence between different characteristics of adjacent basins. Five criteria were adopted to evaluate the performance of assumed functional forms. Four spatially adjacent catchments in the central United States were selected as case studies to examine the validity of the proposed method. Results showed that (1) the proposed method succeeded in reducing the uncertainty of time-varying parameters, and (2) the seasonality of the catchment storage was more coherent between adjacent basins, indicated by the largest increase in model prediction performance (i.e., 9%). This study improved the understanding of the spatial coherence of time-varying parameters, which helps improve hydrological predictions in the future. [ABSTRACT FROM AUTHOR]