How is Baseflow Index (BFI) impacted by water resource management practices
TL;DR: In this paper, the authors investigated the relationship between BFI and two sets of covariates (natural covariates only and a combined set of natural and water resource management covariates) using the CAMELS-GB large-sample hydrology dataset.
Abstract: Water resource management (WRM) practices, such as groundwater and surface water abstractions and effluent discharges, may impact baseflow Here the CAMELS-GB large-sample hydrology dataset is used to assess the impacts of such practices on Baseflow Index (BFI) using statistical models of 429 catchments from Great Britain Two complementary modelling schemes, multiple linear regression (LR) and machine learning (random forests, RF), are used to investigate the relationship between BFI and two sets of covariates (natural covariates only and a combined set of natural and WRM covariates) The LR and RF models show good agreement between explanatory covariates In all models, the extent of fractured aquifers, clay soils, non-aquifers, and crop cover in catchments, catchment topography, and aridity are significant or important natural covariates in explaining BFI When WRM terms are included, groundwater abstraction is significant or the most important WRM covariate in both modelling schemes, and effluent discharge to rivers is also identified as significant or influential, although natural covariates still provide the main explanatory power of the models Surface water abstraction is a significant covariate in the LR model but of only minor importance in the RF model Reservoir storage
covariates are not significant or are unimportant in both the LR and RF
models for this large-sample analysis Inclusion of WRM terms improves the
performance of some models in specific catchments The LR models of high BFI catchments with relatively high levels of groundwater abstraction show the greatest improvements, and there is some evidence of improvement in LR
models of catchments with moderate to high effluent discharges However,
there is no evidence that the inclusion of the WRM covariates improves the
performance of LR models for catchments with high surface water abstraction
or that they improve the performance of the RF models These observations
are discussed within a conceptual framework for baseflow generation that
incorporates WRM practices A wide range of schemes and measures are used to manage water resources in the UK These include conjunctive-use and low-flow alleviation schemes and hands-off flow measures Systematic information on such schemes is currently unavailable in CAMELS-GB, and their specific effects on BFI cannot be constrained by the current study Given the significance or importance of WRM terms in the models, it is recommended that information on WRM, particularly groundwater abstraction, should be included where possible in future large-sample hydrological datasets and in the analysis and prediction of BFI and other measures of baseflow
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01 Jan 2006
TL;DR: In this paper, the authors describe the REML-E-BLUP method and illustrate the method with some data on soil water content that exhibit a pronounced spatial trend, which is a special case of the linear mixed model where our data are modelled as the additive combination of fixed effects (e.g. the unknown mean, coefficients of a trend model), random effects (the spatially dependent random variation in the geostatistical context) and independent random error (nugget variation in geostatsistics).
Abstract: Geostatistical estimates of a soil property by kriging are equivalent to the best linear unbiased predictions (BLUPs). Universal kriging is BLUP with a fixed-effect model that is some linear function of spatial coordinates, or more generally a linear function of some other secondary predictor variable when it is called kriging with external drift. A problem in universal kriging is to find a spatial variance model for the random variation, since empirical variograms estimated from the data by method-of-moments will be affected by both the random variation and that variation represented by the fixed effects. The geostatistical model of spatial variation is a special case of the linear mixed model where our data are modelled as the additive combination of fixed effects (e.g. the unknown mean, coefficients of a trend model), random effects (the spatially dependent random variation in the geostatistical context) and independent random error (nugget variation in geostatistics). Statisticians use residual maximum likelihood (REML) to estimate variance parameters, i.e. to obtain the variogram in a geostatistical context. REML estimates are consistent (they converge in probability to the parameters that are estimated) with less bias than both maximum likelihood estimates and method-of-moment estimates obtained from residuals of a fitted trend. If the estimate of the random effects variance model is inserted into the BLUP we have the empirical BLUP or E-BLUP. Despite representing the state of the art for prediction from a linear mixed model in statistics, the REML-E-BLUP has not been widely used in soil science, and in most studies reported in the soils literature the variogram is estimated with methods that are seriously biased if the fixed-effect structure is more complex than just an unknown constant mean (ordinary kriging). In this paper we describe the REML-E-BLUP and illustrate the method with some data on soil water content that exhibit a pronounced spatial trend.
237 citations
01 Apr 2017
TL;DR: In this article, the authors investigated the physical controls on spatial patterns of pan-European flow signatures, taking advantage of large open datasets for catchment classification and comparative hydrology, and found that a 15 to 33% improvement in regression model skills when combined with catchment classifications versus simply using all catchments at once.
Abstract: . This study contributes to better understanding the physical controls on spatial patterns of pan-European flow signatures – taking advantage of large open datasets for catchment classification and comparative hydrology. Similarities in 16 flow signatures and 35 catchment descriptors were explored for 35 215 catchments and 1366 river gauges across Europe. Correlation analyses and stepwise regressions were used to identify the best explanatory variables for each signature. Catchments were clustered and analyzed for similarities in flow signature values, physiography and the combination of the two. We found the following. (i) A 15 to 33 % (depending on the classification used) improvement in regression model skills when combined with catchment classification versus simply using all catchments at once. (ii) Twelve out of 16 flow signatures were mainly controlled by climatic characteristics, especially those related to average and high flows. For the baseflow index, geology was more important and topography was the main control for the flashiness of flow. For most of the flow signatures, the second most important descriptor is generally land cover (mean flow, high flows, runoff coefficient, ET, variability of reversals). (iii) Using a classification and regression tree (CART), we further show that Europe can be divided into 10 classes with both similar flow signatures and physiography. The most dominant separation found was between energy-limited and moisture-limited catchments. The CART analyses also separated different explanatory variables for the same class of catchments. For example, the damped peak response for one class was explained by the presence of large water bodies for some catchments, while large flatland areas explained it for other catchments in the same class. In conclusion, we find that this type of comparative hydrology is a helpful tool for understanding hydrological variability, but is constrained by unknown human impacts on the water cycle and by relatively crude explanatory variables.
88 citations
01 Apr 2015
TL;DR: In this paper, Monte Carlo sampling is used to calculate signature uncertainties in rainfall and flow data and demonstrate it in two catchments for common signatures including rainfall runoff thresholds, recession analysis and basic descriptive signatures of flow distribution and dynamics.
Abstract: Information about rainfall-runoff processes is essential for hydrological analyses, modelling and water- management applications. A hydrological, or diagnostic, sig- nature quantifies such information from observed data as an index value. Signatures are widely used, e.g. for catchment classification, model calibration and change detection. Un- certainties in the observed data - including measurement in- accuracy and representativeness as well as errors relating to data management - propagate to the signature values and re- duce their information content. Subjective choices in the cal- culation method are a further source of uncertainty. We review the uncertainties relevant to different signa- tures based on rainfall and flow data. We propose a generally applicable method to calculate these uncertainties based on Monte Carlo sampling and demonstrate it in two catchments for common signatures including rainfall-runoff thresholds, recession analysis and basic descriptive signatures of flow distribution and dynamics. Our intention is to contribute to awareness and knowledge of signature uncertainty, including typical sources, magnitude and methods for its assessment. We found that the uncertainties were often large (i.e. typ- ical intervals of 10-40 % relative uncertainty) and highly variable between signatures. There was greater uncertainty in signatures that use high-frequency responses, small data subsets, or subsets prone to measurement errors. There was lower uncertainty in signatures that use spatial or temporal averages. Some signatures were sensitive to particular uncer- tainty types such as rating-curve form. We found that sig- natures can be designed to be robust to some uncertainty sources. Signature uncertainties of the magnitudes we found have the potential to change the conclusions of hydrological and ecohydrological analyses, such as cross-catchment com- parisons or inferences about dominant processes.
35 citations
TL;DR: In this article , a large-scale analysis of hydrologic signatures was conducted in the U.S., Great Britain, Australia, and Brazil, and in Critical Zone Observatory (CZO) watersheds.
Abstract: Dominant processes in a watershed are those that most strongly control hydrologic function and response. Estimating dominant processes enables hydrologists to design physically realistic streamflow generation models, design management interventions, and understand how climate and landscape features control hydrologic function. A recent approach to estimating dominant processes is through their link to hydrologic signatures, which are metrics that characterize the streamflow timeseries. Previous authors have used results from experimental watersheds to link signature values to underlying processes, but these links have not been tested on large scales. This paper fills that gap by testing signatures in large sample data sets from the U.S., Great Britain, Australia, and Brazil, and in Critical Zone Observatory (CZO) watersheds. We found that most inter-signature correlations are consistent with process interpretations, that is, signatures that are supposed to represent the same process are correlated, and most signature values are consistent with process knowledge in CZO watersheds. Some exceptions occurred, such as infiltration and saturation excess processes that were often misidentified by signatures. Signature distributions vary by country, emphasizing the importance of regional context in understanding signature-process links and in classifying signature values as “high” or “low.” Not all signatures were easily transferable from single, small watersheds to large sample studies, showing that visual or process-based assessment of signatures is important before large-scale use. We provide a summary table with information on the reliability of each signature for process identification. Overall, our results provide a reference for future studies that seek to use signatures to identify hydrological processes.
4 citations
TL;DR: In this paper , a suite of hydrological signatures designed to detect the impacts of reservoirs on the flow regime at large-scales from downstream flow records was collected and compared to identify 40 catchments significantly impacted by the presence of reservoirs.
Abstract: Reservoirs play a vital role in the supply and management of water resources and their operation can significantly alter downstream flow. Despite this, reservoirs are frequently excluded or poorly represented in large-scale hydrological models, which can be partly attributed to a lack of open-access data describing reservoir operations, inflow and storage. To help inform the development of reservoir operation schemes, we collate a suite of hydrological signatures designed to detect the impacts of reservoirs on the flow regime at large-scales from downstream flow records only. By removing the need for pre-and-post-reservoir flow timeseries (a requirement of many pre-existing techniques), these signatures facilitate the assessment of flow alteration across a much wider range of catchments. To demonstrate their application, we calculate the signatures across Great Britain in 111 benchmark (i.e. near-natural) catchments and 186 reservoir catchments (where at least one upstream reservoir is present). We find that abstractions from water resource reservoirs induce deficits in the water balance, and that pre-defined flow releases (e.g. the compensation flow) reduce variability in the downstream flow duration curve and in intra-annual low flows. By comparing signatures in benchmark and reservoir catchments, we define thresholds above which the influence of reservoirs can be distinguished from natural variability and identify 40 catchments significantly impacted by the presence of reservoirs. The signatures also provide insights into local reservoir operations, which can inform the development of tailored reservoir operation schemes, and identify locations where current modelling practices (which lack reservoir representation) will be insufficient.
1 citations
References
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TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Abstract: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
79,257 citations
TL;DR: A new reproducibility index is developed and studied that is simple to use and possesses desirable properties and the statistical properties of this estimate can be satisfactorily evaluated using an inverse hyperbolic tangent transformation.
Abstract: A new reproducibility index is developed and studied. This index is the correlation between the two readings that fall on the 45 degree line through the origin. It is simple to use and possesses desirable properties. The statistical properties of this estimate can be satisfactorily evaluated using an inverse hyperbolic tangent transformation. A Monte Carlo experiment with 5,000 runs was performed to confirm the estimate's validity. An application using actual data is given.
6,916 citations
TL;DR: In this article, Naiman et al. pointed out that harnessing of streams and rivers comes at great cost: Many rivers no longer support socially valued native species or sustain healthy ecosystems that provide important goods and services.
Abstract: H umans have long been fascinated by the dynamism of free-flowing waters. Yet we have expended great effort to tame rivers for transportation, water supply, flood control, agriculture, and power generation. It is now recognized that harnessing of streams and rivers comes at great cost: Many rivers no longer support socially valued native species or sustain healthy ecosystems that provide important goods and services (Naiman et al. 1995, NRC 1992).
5,799 citations
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01 Jan 1990TL;DR: In this paper, the authors propose a method of maximum likelihood estimation method of least squares estimation for generalized linear models for simple linear regression with Poisson responses GLIM, which is based on the MINITAB program.
Abstract: Part 1 Background scope notation distributions derived from normal distribution. Part 2 Model fitting: plant growth sample birthweight sample notation for linear models exercises. Part 3 Exponential family of distributions and generalized linear models: exponential family of distributions generalized linear models. Part 4 Estimation: method of maximum likelihood method of least squares estimation for generalized linear models example of simple linear regression for Poisson responses MINITAB program for simple linear regression with Poisson responses GLIM. Part 5 Inference: sampling introduction for scores sampling distribution for maximum likelihood estimators confidence intervals for the model parameters adequacy of a model sampling distribution for the log-likelihood statistic log-likelihood ratio statistic (deviance) assessing goodness of fit hypothesis testing residuals. Part 6 Multiple regression: maximum likelihood estimation least squares estimation log-likelihood ratio statistic multiple correlation coefficient and R numerical example residual plots orthogonality collinearity model selection non-linear regression. Part 7 Analysis of variance and covariance: basic results one-factor ANOVA two-factor ANOVA with replication crossed and nested factors more complicated models choice of constraint equations and dummy variables analysis of covariance. Part 8 Binary variables and logistic regression: probability distributions generalized linear models dose response models general logistic regression maximum likelihood estimation and the log-likelihood ratio statistic other criteria for goodness of fit least squares methods remarks. Part 9 Contingency tables and log-linear models: probability distributions log-linear models maximum likelihood estimation hypothesis testing and goodness of fit numerical examples remarks. Appendices: conventional parametrizations with sum-to-zero constraints corner-point parametrizations three response variables two response variables and one explanatory variable one response variable and two explanatory variables.
2,737 citations