Streamflow Time Series Data Research Articles

Hydrographic shifts in coastal waters reflect climate-driven changes in hydrological regimes across Northwestern Patagonia

Climate-driven changes in freshwater inputs have been shown to affect the structure and function of coastal ecosystems. We evaluated changes in the influence of river runoff on coastal systems of Northwestern Patagonia (NWP) over recent decades (1993–2021) by combined analysis of long-term streamflow time series, hydrological simulation, satellite-derived and reanalysis data on sea surface conditions (temperature, turbidity, and salinity). Significant decreases in minimum streamflow across a zone spanning six major river basins were evident at weekly, monthly, and seasonal scales. These changes have been most pronounced in mixed-regime northern basins (e.g., Puelo River) but appear to be progressing southward to rivers characterised by a nival regime. In the adjacent two-layer inner sea, reduced freshwater input corresponds with a shallower halocline and increased surface temperatures across northern Patagonia. Our results underscore the rapidly evolving influence of rivers on adjacent estuarine and coastal waters in NWP. We highlight the need for cross-ecosystem observation, forecasting, mitigation and adaptation strategies in a changing climate, together with corresponding adaptive basin management of systems that supply runoff to the coastal marine waters.

Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers.

Streamflow time series data typically exhibit nonlinear and nonstationary characteristics that complicate precise estimation. Recently, multifactorial machine learning (ML) models have been developed to enhance the performance of streamflow predictions. However, the lack of interpretability within these ML models raises concerns about their inner workings and reliability. This paper introduces an innovative hybrid architecture, the TCN-LSTM-Multihead-Attention model, which combines two layers of temporal convolutional networks (TCN) followed by one layer of long short-term memory (LSTM) units, integrated with a Multihead-Attention mechanism for predicting streamflow with streamflow causation-driven prediction samples (RCDP), employing local and global interpretability studies through Shapley values and partial dependency analysis. The find_peaks method was used to identify peak flow events in the test dataset, validating the model's generality and uncovering the physical causative patterns of streamflow. The results show that (1) compared to the LSTM model with the same hyperparameter settings, the proposed TCN-LSTM-Multihead-Attention hybrid model increased the R2 by 52.9%, 2.5%, 43.1%, and 10.7% respectively at four stations in the test set predictions using RCDP samples. Moreover, comparing the prediction results of the hybrid model under different samples in Hengshan station, the R2 for RCDP increased by 5.06% and 1.22% compared to streamflow autoregressive prediction samples (RAP) and meteorological-soil volumetric water content coupled autoregressive prediction samples (MCSAP) respectively. (2) Historical streamflow data from the preceding 3days predominantly influences predictions due to strong autocorrelation, with flow quantity (Q) typically emerging as the most significant feature alongside precipitation (P), surface soil moisture (SSM), and adjacent station flow data. (3) During periods of low and normal flow, historical data remains the most crucial factor; however, during flood periods, the roles of upstream inflow and precipitation become significantly more pronounced. This model facilitates the identification and quantification of various hydrodynamic impacts on flow predictions, including upstream flood propagation, precipitation, and soil moisture conditions. It also elucidates the model's nonlinear relationships and threshold responses, thereby enhancing the interpretability and reliability of streamflow predictions.

Enhancing spatial streamflow prediction through machine learning algorithms and advanced strategies

Forecasting and extending streamflow is a critical aspect of hydrology, especially where the time series are locally unavailable for a variety of reasons. The necessity of preprocessing, model fine-tuning, feature selection, or sampling to enhance prediction outcomes for streamflow forecasting using ML techniques is evaluated in this study. In this regard, the monthly streamflow at Pol-Chehr station is analyzed using various monthly rainfall and streamflow time series data from different stations. The results of streamflow prediction in the k-folds cross-validator approach are generally better than those of the time series approach, except when raw data with no preprocessing or feature selection is used. Applying the simple SVR model to raw data leads to the weakest result, but using the GA-SVR model on raw data significantly increases the Nash coefficient by about 215% and 72%, decreases the NRMSE by about 48% and 36% in the k-fold and time series approaches, even with no feature selection. On the other hand, standardization produces highly accurate model predictions in both the k-fold and time series approaches, with a minimum Nash coefficient of 0.83 and 0.73 during the test period in the simple SVR model, respectively. Finally, using optimization algorithms like GA to fine-tune ML models and feature selection does not always yield improved prediction accuracy, but it depends on whether raw or preprocessed data is chosen. In conclusion, combining k-fold cross-validator and preprocessing typically yields highly accurate predictive results, with an R value exceeding 93.7% (Nash = 0.83, SI = 0.55, NRMSE = 0.09), without requiring any additional fine-tuning or optimization. Using feature selection is only significant when utilizing the TS approach as well.

A comparative evaluation of streamflow prediction using the SWAT and NNAR models in the Meenachil River Basin of Central Kerala, India.

Reliable and accurate modelling of streamflow is still a challenging task due to their complex behaviour, need for extensive parameter for development as well as lack of complete or accurate data. In this study, the applicability of an emerging data-driven model, specifically a neural network autoregression (NNAR) model, was evaluated for the first time as a substitute to the physically based hydrological model Soil and Water Assessment Tool (SWAT) for predicting streamflow under data-scarce conditions and for immediate high-quality modelling results. The inputs to the NNAR model were the lagged values of the daily streamflow time series data, and the output was the predicted value for the next day. Using streamflow data that was windowed by 20 days, the NNAR model produced the best prediction. The results of the statistical metrics used to evaluate the performance of the NNAR model were satisfactory (R = 0.90, RMSE = 28.27, MAE = 11.92, R2 = 0.83), indicating a high degree of agreement between the predicted and observed streamflow. The NNAR model outputs demonstrated its ability to accurately predict streamflow in the river basin, even without an explicit understanding of the physical processes that govern the system.

Attribution analysis of different driving forces on vegetation and streamflow variation in the Jialing River Basin, China

Abstract As an important tributary of the Yangtze River basin, the Jialing River Basin (JRB) has a variable topography and fragile ecological environment. Under the influence of climate warming and human activities, the runoff and vegetation cover of the area are undergoing significant changes. For evaluating the impact of different factors on vegetation and streamflow variation in the JRB, the univariate linear regression method was employed to analyze the variation trend of streamflow and vegetation, and a heuristic segmentation algorithm was applied for identifying the mutation year of streamflow and NDVI time-series data in the JRB. Then, the influence degree of different factors on streamflow variation in the JRB was calculated by the Budyko equation and elastic coefficient method. Finally, the contribution rates of different factors to vegetation variation in the JRB were calculated by the multiple linear regression method. The results indicated that the mutation year of streamflow and NDVI time series data were 1985 and 2006, respectively. The contribution rates of climate factors and human activities to streamflow change in the JRB were 42.7 and 57.3%, respectively. The contribution rates to vegetation change were 28.89 and 71.11%, respectively. In general, human activities are the main driving force leading to runoff and vegetation changes in the JRB. This study can provide a theoretical basis for the ecological environment protection and water resources management of the JRB.

Unveiling the climatic origin of streamflow persistence through multifractal analysis of hydro-meteorological datasets of India

ABSTRACT Persistence and multifractality are two fundamental properties of hydrological time series. This study aims at understanding the origin of the Hurst exponent of streamflow time series. Thus, we have obtained streamflow, rainfall, and potential evapotranspiration (PET) time series data pertaining to 101 stations in 17 river basins in India. The persistence and multifractal properties of the time series were estimated using multifractal detrended fluctuation analysis (MFDFA). Results suggest that most of the rainfall time series (85%) exhibit short-term persistence while all the PET time series and 59% of the streamflow time series exhibit long-term persistence. The Hurst exponent exhibited by the river flow series simulated by the dynamic Budyko (DB) model that considers only rainfall and PET as inputs displayed a good agreement with the Hurst exponent of observed streamflow, supporting our hypothesis that climatic factors typically shape the value of the Hurst exponent.

A Physically Based Model of a Two‐Pass Digital Filter for Separating Groundwater Runoff From Streamflow Time Series

AbstractGroundwater runoff is an important component of river runoff and is sensitive to climate change. However, it is difficult to differentiate groundwater runoff from total river runoff by current base flow separation methods. Here, we propose a physically based model with a two‐pass digital filter for separating groundwater runoff from streamflow time series. The proposed method is based on the fundamental theory of surface–groundwater interactions under a variable groundwater recharge process that is highly associated with river stages. Our results show that the groundwater flow recession constant and the base flow recession constant can be determined separately by performing variogram analysis on long‐term streamflow time series data. For watersheds with areas of 103–104 km2, the groundwater flow recession constant differs from the typical values of the base flow recession constant by an order of magnitude or more. Therefore, the developed method can be used to quantify groundwater runoff dynamics on a temporal scale spanning tens of years. Unlike other approaches to base flow separation, our proposed method can reveal the effects of climate change on groundwater runoff.

Exploring the Application of Flood Scaling Property in Hydrological Model Calibration

AbstractModel calibration has always been one major challenge in the hydrological community. Flood scaling properties (FS) are often used to estimate the flood quantiles for data-scarce catchments based on the statistical relationship between flood peak and contributing areas. This paper investigates the potential of applying FS and multivariate flood scaling properties [multiple linear regression (MLR)] as constraints in model calibration. Based on the assumption that the scaling property of flood exists in four study catchments in northern China, eight calibration scenarios are designed with adopting different combinations of traditional indicators and FS or MLR as objective functions. The performance of the proposed method is verified by employing a distributed hydrological model, namely, the Soil and Water Assessment Tool (SWAT) model. The results indicate that reasonable performance could be obtained in FS with fewer requirements of observed streamflow data, exhibiting better simulation of flood peaks than the Nash–Sutcliffe efficiency coefficient calibration scenario. The observed streamflow data or regional flood information are required in the MLR calibration scenario to identify the dominant catchment descriptors, and MLR achieves better performance on catchment interior points, especially for the events with uneven distribution of rainfall. On account of the improved performance on hydrographs and flood frequency curve at the watershed outlet, adopting the statistical indicators and flood scaling property simultaneously as model constraints is suggested. The proposed methodology enhances the physical connection of flood peak among subbasins and considers watershed actual conditions and climatic characteristics for each flood event, facilitating a new calibration approach for both gauged catchments and data-scarce catchments.Significance StatementThis paper proposes a new hydrological model calibration strategy that explores the potential of applying flood scaling properties as constraints. The proposed method effectively captures flood peaks with fewer requirements of observed streamflow time series data, providing a new alternative method in hydrological model calibration for ungauged watersheds. For gauged watersheds, adopting flood scaling properties as model constraints could make the hydrological model calibration more physically based and improve the performance at catchment interior points. We encourage this novel method to be adopted in model calibration for both gauged and data-scarce watersheds.

Combining a segmentation procedure and the BaRatin stationary model to estimate nonstationary rating curves and the associated uncertainties

Streamflow time series data are fundamental to hydrological science and water management applications, which are commonly derived from stage-discharge rating curves. The rating curves are usually nonstationary due to temporal characteristics of stream channel and human activities such as indiscriminate dig of sediment. This implies the need for an efficient method to estimate nonstationary rating curves from an adequate dataset. This study employed a shifting method to estimate the nonstationary rating curves and the associated uncertainties. The method includes two steps: a coupled algorithm PELT&CROPS was first performed to divide the gauging samples into homogeneous families by detecting the multiple change points in a time series of a residual indicator. Then, the rating curves were calibrated with the BaRatin stationary model using these homogeneous gaugings within each stationary period. The method was applied at Shijiao Hydrological Station in China. We found that: (1) Set the lower and upper limit of parameter CROPS (changepoints for arangeofpenalties) to 0 and Inf respectively, the whole range of segmentation can be calculated. (2) Parameter Minseglen (minimum segment length) is of great significance to the segmentation result. More changepoints will be detected as the value of Minseglen decreasing. An appropriate Minseglen value should be assigned to find the optimal segmentation. (3) The stage-discharge relationship in the low flow part is more sensitive to the variation of river topography. Ten segmentations are needed to produce an accurate discharge for low flow prediction, while two segmentations for calibration can produce an accurate discharge for high flow prediction. (4) Information redundancy exists in the vast amount of gaugings. The BaRatin model can reduce the use of calibration data by employing stage-discharge function with a precise exponent from the hydraulic knowledge at a hydrological station.

Insights on Streamflow Predictability Across Scales Using Horizontal Visibility Graph Based Networks

Streamflow is a dynamical process that integrates water movement in space and time within basin boundaries. The authors characterize the dynamics associated with streamflow time series data from about seventy-one U.S. Geological Survey (USGS) stream-gauge stations in the state of Iowa. They employ a novel approach called visibility graph (VG). It uses the concept of mapping time series into complex networks to investigate the time evolutionary behavior of dynamical system. The authors focus on a simple variant of VG algorithm called horizontal visibility graph (HVG). The tracking of dynamics and hence, the predictability of streamflow processes, are carried out by extracting two key pieces of information called characteristic exponent, {\lambda} of degree distribution and global clustering coefficient, GC pertaining to HVG derived network. The authors use these two measures to identify whether streamflow process has its origin in random or chaotic processes. They show that the characterization of streamflow dynamics is sensitive to data attributes. Through a systematic and comprehensive analysis, the authors illustrate that streamflow dynamics characterization is sensitive to the normalization, and the time-scale of streamflow time-series. At daily scale, streamflow at all stations used in the analysis, reveals randomness with strong spatial scale (basin size) dependence. This has implications for predictability of streamflow and floods. The authors demonstrate that dynamics transition through potentially chaotic to randomly correlated process as the averaging time-scale increases. Finally, the temporal trends of {\lambda} and GC are statistically significant at about 40% of the total number of stations analyzed. Attributing this trend to factors such as changing climate or land use requires further research.

Spatiotemporal Variability in the Hydrometeorological Time-Series over Upper Indus River Basin of Pakistan

This paper investigates the spatiotemporal variability in hydrometeorological time-series to evaluate the current and future scenarios of water resources availability from upper Indus basin (UIB). Mann–Kendall and Sen’s slope estimator tests were used to analyze the variability in the temperature, precipitation, and streamflow time-series data at 27 meteorological stations and 34 hydrological stations for the period of 1963 to 2014. The time-series data of entire study period were divided into two equal subseries of 26 years each (1963–1988 and 1989–2014) to assess the overlapping aspect of climate change acceleration over UIB. The results showed a warming pattern at low altitude stations, while a cooling tendency was detected at high-altitude stations. An increase in streamflow was detected during winter and spring seasons at all hydrological stations, whereas the streamflow in summer and autumn seasons exhibited decreasing trends. The annual precipitation showed a significant decreasing trend at ten stations, while a significant increasing trend was observed at Kohat station during second subseries of the study period. The most significant winter drying trends were observed at Gupis, Chitral, Garidopatta, and Naran stations of magnitude of 47%, 13%, 25%, and 18%, respectively, during the second subseries. The annual runoff exhibited significant deceasing trends over Jhelum subbasin at Azad Pattan, Chinari, Domel Kohala, Muzaffarabad, and Palote, while within Indus basin at Chahan, Gurriala, Khairabad, Karora, and Kalam in the second time-series. It is believed that the results of this study will be helpful for the decision-makers to develop strategies for planning and development of future water resources projects.

A New Framework for Exploring Process Controls of Flow Duration Curves

AbstractThe flow duration curve (FDC) is effectively the cumulative distribution function of streamflow. For a long time, hydrologists have sought deeper understanding of the process controls on the shape of FDC, which has been a challenge due to contrasting processes controlling the fast flow and slow flow components of streamflow and their interactions. In this paper, we outline a new framework for exploring the process controls of the FDC, which involves studying fast and slow flow components of FDC separately and combining them statistically, explicitly accounting for their dependence. We illustrate the potential of the framework by constructing empirical fast flow duration (FFDC) and slow flow duration (SFDC) curves from the flow components obtained by traditional baseflow separation. Streamflow time series data from 245 MOPEX catchments across the continental United States are used. The dependence of FFDC and SFDC components is captured by the Gumbel copula, the strength of which varies regionally. In catchments where FFDC and SFDC are independent, FDC can be approximated by a simple convolution of FFDC and SFDC. The proposed conceptual framework opens the way for future modeling studies to explore process controls of fast and slow components of streamflow separately, their dependence, and their relative contributions to the shape of the FDC.

Comparative assessment of time series and artificial intelligence models to estimate monthly streamflow: A local and external data analysis approach

River flow rates are important for water resources projects. Given this, the current study explored the use of autoregressive (AR) and moving average (MA) techniques as individual time series models and compared them to the same models hybridized with an autoregressive conditional heteroscedasticity (ARCH) model to estimate monthly streamflow. In addition, two artificial intelligence (AI) approaches, namely, multivariate adaptive regression splines (MARS) and gene expression programming (GEP), were explored. The performance of each of these models in estimating monthly streamflow was compared based on local and external data analyses. Using the local data analysis approach, streamflow data at each target station was estimated using observed streamflow at the same station. The external data analysis approach used neighboring station streamflow data to estimate streamflow data for the target station. The Beinerahe Roodbar and Pole Astaneh stations on the Sefidrood River, Iran, as well as the Port Elgin and Walkerton stations on the Saugeen River, Canada, were used as study areas. Upstream and downstream monthly streamflow time series data were used. The performance of all models was compared using three error metrics, including the root mean square error, mean absolute error, and correlation coefficient. The results showed that the hybrid time series models (i.e., AR-ARCH and MA-ARCH) outperformed the conventional AR and MA models. A comparison of all applied models revealed that the hybrid AR-ARCH and MA-ARCH time series models performed better than the AI techniques, when using a local data analysis approach. The external data analysis approach was more accurate for monthly streamflow estimation than the local data analysis approach. To conclude, based on the outcomes of the AI models under the external data analysis approach, nearby data can be used to estimate streamflow of a target station when the target station streamflow data are not available.

Hydro-meteorological trends in the Gidabo catchment of the Rift Valley Lakes Basin of Ethiopia

The global and regional variability and changes of climate and stream flows are likely to have significant influence on water resource availability. The magnitude and impacts of climate variability and change differs spatially and temporally. This study examines the long term hydroclimatic changes, analyses of the hydro-climate variability and detect whether there exist significant trend or not in the Gidabo catchment, rift valley lakes basin of Ethiopia. Precipitation, temperature and stream flow time series data were used in monthly, seasonal and annual time scales. The precipitation and temperature data span is between 1982 and 2014 and that of stream flow is between 1976 and 2006. To detect trends the analysis were done by using Mann Kendal (MK), Sen's graphical method and to detect change point using the Pettit test. The comparison of trend analysis between MK trend test and Sen graphical method results depict mostly similar pattern. The annual rainfall trends exhibited a significant decrease by about 12 mm per year in the upstream, which is largely driven by the significant decrease in the peak season rainfall. The Pettit test revealed that the years 1997 and 2007 were the change points. It is noted that the rise of temperature over a catchment might have decreased the availability of soil moisture which resulted in less runoff. The temperature analyses also revealed that the catchment was getting warmer; particularly in the upstream. The minimum temperature trend showed a significant increase about 0.08°c per annum. There is generally a decreasing trend in stream flow. The monthly stream flow also exhibited a decreasing trend in February, March and September. The decline in annual and seasonal rainfall and the increase in temperature lead to more evaporation and directly affecting the stream flow negatively. This trend compounded with the growth of population and increasing demand for irrigation water exacerbates the competing demand for water resources. It thus calls for prudence in devising appropriate intervention in the planning and sustainable development of the basin water resources.

Optimized River Stream-Flow Forecasting Model Utilizing High-Order Response Surface Method

Accurate and reliable stream-flow forecasting has a key role in water resources planning and management. Most recently, soft computing approaches have become progressively prevalent in modelling hydrological variables and most specifically stream-flows. This is due to their ability to capture the non-linearity and non-stationarity characteristics of the hydrological variables with minimum information requirements. Despite this, they present several challenges in the modelling architecture, as there is a need to establish a suitable pre-processing method for the stream-flow data and an appropriate optimization model has to be integrated in order re-adjust the weights and biases associated with the model structure. On top of that, artificial intelligent models require “trial and error” procedures in order to be properly tuned (number of hidden layers, number of neurons within the hidden layers and the type of the transfer function). However, soft computing approach experienced several problems while calibration such as over-fitting. In this research, the Response Surface Method (RSM) is improved based on high-order polynomial functions for forecasting the river stream-flow namely; High-Order Response Surface (HORS) method. Several higher orders have been examined, second, third, fourth and fifth polynomial functions in order to figure out the best fit that able to mimic the pattern of stream-flow. In order to demonstrate the effectiveness of the proposed model, monthly stream-flow time series data located in Aswan High Dam (AHD) has been examined. A detailed analysis of the overall statistical indicators revealed that the proposed method showed outstanding performance for monthly stream-flow forecasting at AHD. It could be concluded that the fifth order polynomial function outperforms the other orders of the polynomial functions especially with May model who achieved minimum MAE 0.12, NRMSE 0.07, MSE 0.03 and maximum SF and R2 (0.97, 0.99) respectively.

Characteristics and Multifractal Properties of Daily Streamflow in a Semiarid Environment

Multifractality of daily streamflow time series data was investigated for ten hydrometric stations of the semiarid Karkheh watershed (western Iran). Mann–Kendall test results indicated nonsignificant trend (at 95 % confidence level) in the annual streamflow time series data of all stations. We applied multifractal detrended fluctuation analysis technique to detect characteristics and multifractal properties of daily streamflow time series data. The Hurst exponent (H) values in all of the stations varied between 0.5 and 1, showing long memory (persistence), except Aran Gharb station, which indicated short memory (anti-persistence). Fluctuation function, F q (s), versus s detected crossovers representing streamflow temporal scaling of 310–400 days at the studied stations, which corresponds to 1 year. Results confirmed multifractality of daily streamflow time series data in all stations, as evidenced by the q relationships with h(q), τ(q) and D(q) and also the singularity spectrum f(α) versus α relationships. Analyses of shuffled and surrogated streamflow series demonstrated that multifractality is due to both PDF and long-range correlation properties. According to the results, similar patterns of hydrological characteristics and streamflow generation mechanisms exist across the study area.

Linking the Budyko framework and the Dunne diagram

The spatial and temporal heterogeneity of climate, soils, topography and vegetation control the water and energy balances among catchments. Two well-known hydrological theories underpinning these processes are the Budyko framework and the Dunne diagram. Relating the scaling of water–energy balances (Budyko) and runoff generation mechanisms (Dunne) raises some important catchment comparison questions, namely: (i) how do streamflow characteristics vary according to the annual water and energy balances?; (ii) to what extent do biophysical drivers of runoff explain the observed streamflow variability?; and (iii) are there quantifiable process overlaps between these two approaches, and can they offer insights into the mechanics of catchment co-evolution? This study addresses these questions by analysing daily streamflow and precipitation time series data to quantify hydrological similarity across 355 catchments located along a tropical–temperate climatic gradient in eastern Australia. We used eight hydrological metrics to describe the hydrological response over a 33-year period (1980–2013). Hierarchical cluster, ordination analysis, the Budyko framework, and generalized additive models were used to evaluate hydrological similarity, extract the dominant response, and examine how the landscape and climatic characteristics of catchments influence the dominant streamflow response. The catchments were classified into five clusters based on the analysis of their hydrological characteristics and similarity, which vary along the annual water and energy balances gradient in the Budyko framework. Furthermore, we show that the streamflow similarity is explained by six catchment-specific biophysical factors that overlap with those described by the Dunne diagram for runoff generation, which in this case have the following order of relative importance: (i) Dryness Index; (ii) Fraction of Photosynthetically Active Radiation; (iii) Saturated Hydraulic Conductivity; (iv) Soil Depth; (v) Maximum Slope and (vi) Fraction of Woody Vegetation Cover. The research advances our understanding of the role of biophysical controls on hydrologic similarity and formal process links between the Budyko Framework and Dunne diagram of runoff mechanisms.

A worldwide analysis of trends in water-balance evapotranspiration

Abstract. Climate change is expected to alter the global hydrological cycle, with inevitable consequences for freshwater availability to people and ecosystems. But the attribution of recent trends in the terrestrial water balance remains disputed. This study attempts to account statistically for both trends and interannual variability in water-balance evapotranspiration (ET), estimated from the annual observed streamflow in 109 river basins during "water years" 1961–1999 and two gridded precipitation data sets. The basins were chosen based on the availability of streamflow time-series data in the Dai et al. (2009) synthesis. They were divided into water-limited "dry" and energy-limited "wet" basins following the Budyko framework. We investigated the potential roles of precipitation, aerosol-corrected solar radiation, land use change, wind speed, air temperature, and atmospheric CO2. Both trends and variability in ET show strong control by precipitation. There is some additional control of ET trends by vegetation processes, but little evidence for control by other factors. Interannual variability in ET was overwhelmingly dominated by precipitation, which accounted on average for 54–55% of the variation in wet basins (ranging from 0 to 100%) and 94–95% in dry basins (ranging from 69 to 100%). Precipitation accounted for 45–46% of ET trends in wet basins and 80–84% in dry basins. Net atmospheric CO2 effects on transpiration, estimated using the Land-surface Processes and eXchanges (LPX) model, did not contribute to observed trends in ET because declining stomatal conductance was counteracted by slightly but significantly increasing foliage cover.

Dynamic analysis of stream flow and water chemistry to infer subsurface water and nitrate fluxes in a lowland dairying catchment

Summary The use of process-based, dynamic and spatially-explicit models to describe water and nitrogen fluxes at the catchment-scale is often hampered by a shortage of detailed land use, hydrological and biogeochemical information. Accordingly, such complex models tend to be restricted to a small number of well investigated catchments, often associated with research projects. On the other hand, stream flow and stream water chemistry time series data are available for a much larger number of catchments, e.g. for many catchments that are routinely monitored by government agencies for state-of-the-environment reporting. It was the main aim of this study to provide a spatially lumped model that allows meaningful analysis of catchment-scale water and nitrate fluxes based on such data sets. Based on stream flow time series data, catchment hydrodynamics are often analysed using approaches derived from the linearised Boussinesq equation, which has analytical solutions for dynamic groundwater discharge expressed in terms of eigenvalues and eigenfunctions (eigenmodel approach). Calibrated Boussinesq models generally yield a good reproduction of stream flow dynamics, and stable estimates for aquifer parameters such as hydraulic conductivity and mean aquifer depth. By linking a soil water balance model with two Boussinesq groundwater eigenmodels linked in series, and assuming constant solute concentrations discharging from each source, a dynamic catchment model predicting stream flow and water chemistry at the catchment outlet (“StreamGEM”) was developed. Compared with previous approaches, inclusion of water chemistry in this model both aided hydrological understanding, and allowed assessment of catchmentscale nitrate fluxes. Simultaneous calibration of the model to stream flow and nitrate concentration data from a small lowland dairying catchment yielded good predictions to both variables (Nash–Sutcliffe Model Efficiency of 0.90 and 0.84), and the fitted parameters were able to be used to estimate annual flow and nitrate fluxes through near-surface, shallow groundwater, and deeper groundwater reservoirs conceptually present in the catchment. The calibration was cross-validated using an independent time series from the same catchment. The results support the hypothesis, based on groundwater observations, that stream flow in the catchment is the result of mixed discharge from a shallower, rapidly draining zone of oxidised groundwater carrying relatively high loads of agricultural nitrate, with a relatively deeper and slower draining zone of reduced groundwater that is essentially nitrate free. The proportions of stream flow discharging from the near-surface, shallow groundwater, and deeper groundwater reservoirs were estimated to be 5%, 80% and 15%, respectively. In spite of its small contribution to total stream flow, the deeper groundwater reservoir sustained stream flow during summer and dominated stream water chemistry 61% of the time. By combining the flow and nitrate concentration estimates derived from model calibration, it was estimated that discharge of shallow groundwater was responsible for 91% of the nitrate load entering the stream. However, the predicted nitrate concentration in this reservoir was significantly lower than the predicted nitrate concentration of near-surface flow and root zone leachate concentrations estimated using a nutrient budgeting model. This indicates that denitrification occurs within this reservoir. On the basis of the calibrated model, it was estimated that 36% of the nitrate recharged from the vadose zone gets denitrified within the shallow groundwater reservoir, and up to 9% in the deeper groundwater reservoir.

Scale effects on information theory-based measures applied to streamflow patterns in two rural watersheds

Summary Understanding streamflow patterns in space and time is important for improving flood and drought forecasting, water resources management, and predictions of ecological changes. Objectives of this work include (a) to characterize the spatial and temporal patterns of streamflow using information theory-based measures at two thoroughly-monitored agricultural watersheds located in different hydroclimatic zones with similar land use, and (b) to elucidate and quantify temporal and spatial scale effects on those measures. We selected two USDA experimental watersheds to serve as case study examples, including the Little River experimental watershed (LREW) in Tifton, Georgia and the Sleepers River experimental watershed (SREW) in North Danville, Vermont. Both watersheds possess several nested sub-watersheds and more than 30 years of continuous data records of precipitation and streamflow. Information content measures (metric entropy and mean information gain) and complexity measures (effective measure complexity and fluctuation complexity) were computed based on the binary encoding of 5-year streamflow and precipitation time series data. We quantified patterns of streamflow using probabilities of joint or sequential appearances of the binary symbol sequences. Results of our analysis illustrate that information content measures of streamflow time series are much smaller than those for precipitation data, and the streamflow data also exhibit higher complexity, suggesting that the watersheds effectively act as filters of the precipitation information that leads to the observed additional complexity in streamflow measures. Correlation coefficients between the information-theory-based measures and time intervals are close to 0.9, demonstrating the significance of temporal scale effects on streamflow patterns. Moderate spatial scale effects on streamflow patterns are observed with absolute values of correlation coefficients between the measures and sub-watershed area varying from 0.2 to 0.6 in the two watersheds. We conclude that temporal effects must be evaluated and accounted for when the information theory-based methods are used for performance evaluation and comparison of hydrological models.