Validation Time Period Research Articles

The Combined Power of Double Mass Curves and Bias Correction for the Maximisation of the Accuracy of an Ensemble Satellite-Based Precipitation Estimate Product

Precise estimation of the spatial and temporal characteristics of rainfall is essential for producing the reliable catchment response needed for proper management of water resources. However, in most parts of the world, gauged rainfall stations are sparsely distributed and fail to properly capture the spatial variability of rainfall. Furthermore, the gauged rainfall data can sometimes be of short length or require validation. Following this, we present a procedure that enhances the trustworthiness of gauged rainfall data and the accuracy of the rainfall estimations of five satellite-based precipitation estimate (SPE) products by validating them using the 1779 gauged rainfall stations across Thailand. The five SPE products considered include CMORPH-BLD; TRMM-3B42; CHIRPS; CHIRPS-PL; and TRMM-3B42RT. Prior to validation, the gauged rainfall dataset was verified using double mass curve (DMC) analysis to eliminate questionable and inconsistent readings. This led to the improvement of the Nash–Sutcliffe Efficiency (NSE) between the station of interest and its surroundings by 13.9% (0.758–0.863), together with an average 11.8% increase with SPE products, whilst dropping only 7% of questionable dataset. Three different bias correction (BC) procedures were applied to correct SPE products using gauge-based gridded rainfall (GGR). Once DMC and BC procedures were implemented together, the performance of the SPE products was found to increase significantly. Finally, the application of the ensemble weighted average of the three best-performing bias-corrected SPE products (Bias-CMORPH-BLD, Bias-TRMM-3B42, and Bias-CHIRPS) further enhanced the NSE to 0.907 and 0.880 in calibration and validation time periods, respectively. The proposed DMC-based correction SPE and the weighting procedure of multiple SPE products allows for an easy means of obtaining daily rainfall in remote locations with sufficient accuracy.

Choosing an arbitrary calibration period for hydrologic models: How much does it influence water balance simulations?

AbstractThe selection of calibration and validation time periods in hydrologic modelling is often done arbitrarily. Nonstationarity can lead to an optimal parameter set for one period which may not accurately simulate another. However, there is still much to be learned about the responses of hydrologic models to nonstationary conditions. We investigated how the selection of calibration and validation periods can influence water balance simulations. We calibrated Soil and Water Assessment Tool hydrologic models with observed streamflow for three United States watersheds (St. Joseph River of Indiana/Michigan, Escambia River of Florida/Alabama, and Cottonwood Creek of California), using time period splits for calibration/validation. We found that the choice of calibration period (with different patterns of observed streamflow, precipitation, and air temperature) influenced the parameter sets, leading to dissimilar simulations of water balance components. In the Cottonwood Creek watershed, simulations of 50‐year mean January streamflow varied by 32%, because of lower winter precipitation and air temperature in earlier calibration periods on calibrated parameters, which impaired the ability for models calibrated to earlier periods to simulate later periods. Peaks of actual evapotranspiration for this watershed also shifted from April to May due to different parameter values depending on the calibration period's winter air temperatures. In the St. Joseph and Escambia River watersheds, adjustments of the runoff curve number parameter could vary by 10.7% and 20.8%, respectively, while 50‐year mean monthly surface runoff simulations could vary by 23%–37% and 169%–209%, depending on the observed streamflow and precipitation of the chosen calibration period. It is imperative that calibration and validation time periods are chosen selectively instead of arbitrarily, for instance using change point detection methods, and that the calibration periods are appropriate for the goals of the study, considering possible broad effects of nonstationary time series on water balance simulations. It is also crucial that the hydrologic modelling community improves existing calibration and validation practices to better include nonstationary processes.

Assessment of climate change impact on the Gomti River basin in India under different RCP scenarios

Climate change can adversely affect the elements of water balance in river basins. This paper studies the effects of climate change on hydro-climatological components of the Gomti River basin using SWAT hydrological model. Model performance has been evaluated through sensitivity analysis, calibration, and validation. During calibration and validation time period, the Nash-Sutcliffe efficiency (NSE) and coefficient of determination (R2) values were obtained as 0.86, 0.87, and 0.79, 0.83, respectively. From the sensitivity analysis, it was found that SCS runoff curve number for antecedent moisture condition II (CN2) is the most sensitive parameter, followed by saturated hydraulic conductivity (SOL_K) and then available water capacity of the soil layer (SOL_AWC) etc. To assess the future climate change effects on hydrological variables, MIROC-ESM, MIROC-ESM-CHEM, MIROC_MIROC5, and MOHC_HADGEM2_ES Global Climate Models (GCMs) under RCP 4.5 and RCP 8.5 were applied in the calibrated SWAT model. The following trends were observed after applying the scenarios—the temperature is rising under RCP 4.5 (8.5) scenarios, near-century by + 0.24% (+ 0.65), mid-century by + 2.8% (+ 4.62), and end-century by + 5.25% (+ 10.98), respectively. Precipitation is declining under RCP 4.5 (8.5) by − 29.17% (− 29.65%), − 28.69% (− 28.88%), and − 23.82% (− 20.11%), respectively. Evapotranspiration under RCP 4.5 (RCP 8.5) is increasing by + 3.62% (+ 3.95%), + 4.30% (+ 5.38%), and + 5.49% (+ 6.03%), respectively. Consequently, streamflow under RCP 4.5 (RCP 8.5) is declining by − 31.27% (− 23.99%), − 27.45% (− 28.96%), and − 24.30% (− 18.03%), respectively in near, mid, and end century. The findings will be beneficial for managing future water resources in the Gomti River basin.

Rain event detection in commercial microwave link attenuation data using convolutional neural networks

Abstract. Quantitative precipitation estimation with commercial microwave links (CMLs) is a technique developed to supplement weather radar and rain gauge observations. It is exploiting the relation between the attenuation of CML signal levels and the integrated rain rate along a CML path. The opportunistic nature of this method requires a sophisticated data processing using robust methods. In this study we focus on the processing step of rain event detection in the signal level time series of the CMLs, which we treat as a binary classification problem. This processing step is particularly challenging, because even when there is no rain, the signal level can show large fluctuations similar to that during rainy periods. False classifications can have a high impact on falsely estimated rainfall amounts. We analyze the performance of a convolutional neural network (CNN), which is trained to detect rainfall-specific attenuation patterns in CML signal levels, using data from 3904 CMLs in Germany. The CNN consists of a feature extraction and a classification part with, in total, 20 layers of neurons and 1.4×105 trainable parameters. With a structure inspired by the visual cortex of mammals, CNNs use local connections of neurons to recognize patterns independent of their location in the time series. We test the CNN's ability to recognize attenuation patterns from CMLs and time periods outside the training data. Our CNN is trained on 4 months of data from 800 randomly selected CMLs and validated on 2 different months of data, once for all CMLs and once for the 3104 CMLs not included in the training. No CMLs are excluded from the analysis. As a reference data set, we use the gauge-adjusted radar product RADOLAN-RW provided by the German meteorological service (DWD). The model predictions and the reference data are compared on an hourly basis. Model performance is compared to a state-of-the-art reference method, which uses the rolling standard deviation of the CML signal level time series as a detection criteria. Our results show that within the analyzed period of April to September 2018, the CNN generalizes well to the validation CMLs and time periods. A receiver operating characteristic (ROC) analysis shows that the CNN is outperforming the reference method, detecting on average 76 % of all rainy and 97 % of all nonrainy periods. From all periods with a reference rain rate larger than 0.6 mm h−1, more than 90 % was detected. We also show that the improved event detection leads to a significant reduction of falsely estimated rainfall by up to 51 %. At the same time, the quality of the correctly estimated rainfall is kept at the same level in regards to the Pearson correlation with the radar rainfall. In conclusion, we find that CNNs are a robust and promising tool to detect rainfall-induced attenuation patterns in CML signal levels from a large CML data set covering all of Germany.

Optimization of the Hydrological Tank Model by Downhill Simplex Method

The Hydrological Tank model was used in this study to simulate the rainfall-runoff relationship in the Flint River basin near Carsonville, Georgia, USA. The model’s structure consisted of two tanks (upper and lower), and it depended primarily on a set of eight parameters to simulate daily stream flow components (surface, subsurface, and base flows) from daily precipitation while taking into account the effects of daily evapotranspiration, the water storage height, and the infiltration rate. The parameter set was calibrated, optimized, and validated with data spanning a period of 10 years (Oct. 1st, 2007–Sep. 30th, 2017). Model calibration and validation were performed by using the first and second 5-year periods of water flow data, respectively. The model performance was verified using the coefficient of determination (R2) and the Nash–Sutcliffe efficiency (NSE) coefficient. The results of one- and two-parameter sensitivity analyses showed that the model performance was highly dependent on the parameters Ko and K1 (the coefficients of the surface and subsurface flows, respectively). The Downhill Simplex Method (DSM) was used to find the optimum parameter set via the maximization of R2 and NSE. The optimum parameter set produced by maximizing the NSE values was adopted in this study because it produced a very good correlation between the simulated and observed stream flows for both the calibration and validation time periods. Using the adopted optimum parameter set, the maximum values of R2 and NSE were 0.818 and 0.812 in the calibration time period, respectively, and 0.756 and 0.750 in the validation time period, respectively. Moreover, the DSM has proven to be very reliable in locating global optimums and requires reasonable computational efforts in the calibration of a hydrological model.

Improving Princeton Forcing Dataset over Iran Using the Delta-Ratio Method

In this study, we corrected the bias in the Princeton forcing dataset, i.e., precipitation, maximum and minimum temperatures, and wind speed, by adjusting its long-term mean monthly climatology to match observations for the period 1988–2012 using the delta-ratio method. To this end, we collected meteorological data from 97 stations covering the domain of Iran. We divided Iran into three climatic zones based on the De Martonne classification, i.e., Arid, Humid, and Per-Humid zones, and then applied the delta-ratio method for each climatic zone separately to adjust the bias. After adjustment, the new datasets were compared to the observations in 1958–1987. Results based on four skill scores, including the Nash Sutcliffe efficiency (NSE), percent bias (PBIAS), root-mean-square error (RMSE), and R2, indicate that the adjustment greatly improved the quality of the gridded dataset, specifically, precipitation, maximum temperature, and wind speed. For example, NSE for annual precipitation during the validation time period increased from −0.03 to 0.72, PBIAS reduced from 29.2% to 6.6%, RMSE decreased by 182.44 mm, and R2 increased from 0.06 to 0.75. Assessing the results in different climatic zones of Iran reveals that precipitation improved more significantly in the Per-Humid zone followed by the Humid zone, while maximum temperature improved better in the Arid areas. For wind speed, the values improved comparably in the three climate zones. However, the delta values for monthly minimum temperature calculated during the adjustment time period cannot be applied in the validation time period, due to the fact that the Princeton climate data cannot follow the behavior of minimum temperature during the validation phase. In short, we showed that a simple bias adjustment approach, along with minimum observed station data, can significantly improve the performance of global gridded datasets.

Predicting the temporal transferability of model parameters through a hydrological signature analysis

Attention has recently increased on the use of hydrological signatures as a potential tool for assessing the fidelity of model structures and providing insights into the transfer of model parameters. The utility of hydrological signatures as model performance/reliability indicators in a calibration-validation testing scenario (i.e., the temporal transfer of model parameters) is the focus of this study. The Probability Distributed Model, a flexible conceptual hydrological model, is used to test the approach across a number of catchments included in the MOPEX data set. We explore the change in model performance across calibration and validation time periods and contrast it to the corresponding change in several hydrological signatures to assess signature worth. Results are explored in finer detail by utilizing a moving window approach to calibration and validation time periods. The results of this study indicated that the most informative signature can vary, both spatially and temporally, based on physical and climatic characteristics and their interaction to the model parameterization. Thus, one signature could not adequately illustrate complex watershed behaviors nor predict model performance in new analysis periods.

Benchmarking CME Arrival Time and Impact: Progress on Metadata, Metrics, and Events

AbstractAccurate forecasting of the arrival time and subsequent geomagnetic impacts of coronal mass ejections (CMEs) at Earth is an important objective for space weather forecasting agencies. Recently, the CME Arrival and Impact working team has made significant progress toward defining community‐agreed metrics and validation methods to assess the current state of CME modeling capabilities. This will allow the community to quantify our current capabilities and track progress in models over time. First, it is crucial that the community focuses on the collection of the necessary metadata for transparency and reproducibility of results. Concerning CME arrival and impact we have identified six different metadata types: 3‐D CME measurement, model description, model input, CME (non)arrival observation, model output data, and metrics and validation methods. Second, the working team has also identified a validation time period, where all events within the following two periods will be considered: 1 January 2011 to 31 December 2012 and January 2015 to 31 December 2015. Those two periods amount to a total of about 100 hit events at Earth and a large amount of misses. Considering a time period will remove any bias in selecting events and the event set will represent a sample set that will not be biased by user selection. Lastly, we have defined the basic metrics and skill scores that the CME Arrival and Impact working team will focus on.

A Lightweight Continuous Authentication Protocol for the Internet of Things.

Modern societies are moving toward an information-oriented environment. To gather and utilize information around people’s modern life, tiny devices with all kinds of sensing devices and various sizes of gateways need to be deployed and connected with each other through the Internet or proxy-based wireless sensor networks (WSNs). Within this kind of Internet of Things (IoT) environment, how to authenticate each other between two communicating devices is a fundamental security issue. As a lot of IoT devices are powered by batteries and they need to transmit sensed data periodically, it is necessary for IoT devices to adopt a lightweight authentication protocol to reduce their energy consumption when a device wants to authenticate and transmit data to its targeted peer. In this paper, a lightweight continuous authentication protocol for sensing devices and gateway devices in general IoT environments is introduced. The concept of valid authentication time period is proposed to enhance robustness of authentication between IoT devices. To construct the proposed lightweight continuous authentication protocol, token technique and dynamic features of IoT devices are adopted in order to reach the design goals: the reduction of time consumption for consecutive authentications and energy saving for authenticating devices through by reducing the computation complexity during session establishment of continuous authentication. Security analysis is conducted to evaluate security strength of the proposed protocol. In addition, performance analysis has shown the proposed protocol is a strong competitor among existing protocols for device-to-device authentication in IoT environments.

Misleading prioritizations from modelling range shifts under climate change

AbstractAimConservation planning requires the prioritization of a subset of taxa and geographical locations to focus monitoring and management efforts. Integration of the threats and opportunities posed by climate change often relies on predictions from species distribution models, particularly for assessments of vulnerability or invasion risk for multiple taxa. We evaluated whether species distribution models could reliably rank changes in species range size under climate and land use change.LocationConterminous U.S.A.Time period1977–2014.Major taxa studiedPasserine birds.MethodsWe estimated ensembles of species distribution models based on historical North American Breeding Bird Survey occurrences for 190 songbirds, and generated predictions to recent years given c. 35 years of observed land use and climate change. We evaluated model predictions using standard metrics of discrimination performance and a more detailed assessment of the ability of models to rank species vulnerability to climate change based on predicted range loss, range gain, and overall change in range size.ResultsSpecies distribution models yielded unreliable and misleading assessments of relative vulnerability to climate and land use change. Models could not accurately predict range expansion or contraction, and therefore failed to anticipate patterns of range change among species. These failures occurred despite excellent overall discrimination ability and transferability to the validation time period, which reflected strong performance at the majority of locations that were either always or never occupied by each species.Main conclusionsModels failed for the questions and at the locations of greatest interest to conservation and management. This highlights potential pitfalls of multi‐taxa impact assessments under global change; in our case, models provided misleading rankings of the most impacted species, and spatial information about range changes was not credible. As modelling methods and frameworks continue to be refined, performance assessments and validation efforts should focus on the measures of risk and vulnerability useful for decision‐making.

Influence of calibration data on hydrological model prediction

Hydrological model performance depends on the information content and hydrological variability in the calibration data. The hypothesis for this study was that a priori predictability of the model performance can be made based on knowledge about the data used for calibration. If the validation time period has similar hydrological variability to the calibration period where the model is expected to perform better and it is termed as the 'interpolation case', otherwise it is 'extrapolation', where a better performance cannot be expected. In this study a geometrical property of data was used to discriminate between the cases of interpolation or extrapolation. The methodology was demonstrated using WaSiM-ETH model in Rems catchment. The results show that the relative residual is very high at extrapolation case as compared to the interpolation case. The result of this study can be used to make a priori estimate of the error in prediction without running the model.

Evaluating uncertainties in modelling the snow hydrology of the Fraser River Basin, British Columbia, Canada

Abstract. This study evaluates predictive uncertainties in the snow hydrology of the Fraser River Basin (FRB) of British Columbia (BC), Canada, using the Variable Infiltration Capacity (VIC) model forced with several high-resolution gridded climate datasets. These datasets include the Canadian Precipitation Analysis and the thin-plate smoothing splines (ANUSPLIN), North American Regional Reanalysis (NARR), University of Washington (UW) and Pacific Climate Impacts Consortium (PCIC) gridded products. Uncertainties are evaluated at different stages of the VIC implementation, starting with the driving datasets, optimization of model parameters, and model calibration during cool and warm phases of the Pacific Decadal Oscillation (PDO). The inter-comparison of the forcing datasets (precipitation and air temperature) and their VIC simulations (snow water equivalent – SWE – and runoff) reveals widespread differences over the FRB, especially in mountainous regions. The ANUSPLIN precipitation shows a considerable dry bias in the Rocky Mountains, whereas the NARR winter air temperature is 2 °C warmer than the other datasets over most of the FRB. In the VIC simulations, the elevation-dependent changes in the maximum SWE (maxSWE) are more prominent at higher elevations of the Rocky Mountains, where the PCIC-VIC simulation accumulates too much SWE and ANUSPLIN-VIC yields an underestimation. Additionally, at each elevation range, the day of maxSWE varies from 10 to 20 days between the VIC simulations. The snow melting season begins early in the NARR-VIC simulation, whereas the PCIC-VIC simulation delays the melting, indicating seasonal uncertainty in SWE simulations. When compared with the observed runoff for the Fraser River main stem at Hope, BC, the ANUSPLIN-VIC simulation shows considerable underestimation of runoff throughout the water year owing to reduced precipitation in the ANUSPLIN forcing dataset. The NARR-VIC simulation yields more winter and spring runoff and earlier decline of flows in summer due to a nearly 15-day earlier onset of the FRB springtime snowmelt. Analysis of the parametric uncertainty in the VIC calibration process shows that the choice of the initial parameter range plays a crucial role in defining the model hydrological response for the FRB. Furthermore, the VIC calibration process is biased toward cool and warm phases of the PDO and the choice of proper calibration and validation time periods is important for the experimental setup. Overall the VIC hydrological response is prominently influenced by the uncertainties involved in the forcing datasets rather than those in its parameter optimization and experimental setups.

Privacy Preserving Data-Sharing Scheme in Content-Centric Networks Against Collusion Name Guessing Attacks

Content-centric networks are designed as potential candidates for future 5G networks and the Internet. In these kinds of networks, contents are queried, searched, and routed on names that people are interested in. Collecting names that a person queries in a content-centric network can violate his/her privacy. As more and more people are concerned about their privacy in daily life, it is desirable to present privacy-preserving protocols for content-centric networks. Currently, many schemes are designed to protect people’s privacy but few of them consider the malicious behaviors of the transmitting routers, especially when the routers collude with a certain user. We discuss a kind of attack called collusion name guessing attack where intermediate routers collude with a certain user to perform a name guessing attack in order to expose people’s privacy. It is shown that present schemes cannot resist such kind of attack, which will be a new challenge for content-centric networks. A new scheme with anonymous user identity and limited key validation time is designed to fight against the collusion name guessing attack. In the scheme, the users are anonymous and the shared keys are valid within a specified time period so the adversary does not know whose packets should be collected and it is infeasible to precompute the name matching datasets during the valid time period of the key. Moreover, slow matching for all users and all time periods needs enormous storage and will last a long time, which will make the attack cost-ineffective.

The ALERT scale: an observational study of early prediction of adverse hospital outcome for medical patients

ObjectivesSome medical patients are at greater risk of adverse outcomes than others and may benefit from higher observation hospital units. We constructed and validated a model predicting adverse hospital outcome...

Estimating wave height using the difference in percentile coastal sound level

This study investigated the correlation between sound level and wave height using field experiments conducted from July 14, 2009, to November 2, 2009, at the Hasaki coast in Ibaraki, Japan. We modeled wave height using the observed data from the first half of the study period to calibrate our model, and validated it using wave height from the latter half of the study period. Based on our results, we propose a model for estimating wave height using sound level and differences in sound level. The model predicted wave height with good accuracy (R=0.87), and accurately described wave heights during the validation time period (R=0.87). By comparing field data and each of the parameters utilized, it is clear that percentile sound level is a more accurate parameter for estimating wave height.

Linking spatial inundation indicators and hydrological modelling to improve assessment of inundation extent

Environmental water requirements (EWRs), including past inundation regimes, provide useful information for the conservation and management of wetlands systems. However, determining past inundation regimes is difficult as very few wetlands have historical records of water level or flow to determine EWRs. Generally, two approaches have been used to reconstruct wetland inundation regimes; remote sensing or hydrological modelling. Remote sensing methods have applied spectral indices of inundation to multi-temporal archival satellite imagery and developed simple empirical models to link inundation extent to rainfall or stream flow records. Hydrological models use a water balance approach and simple relationships between volume and area based on the wetland bathymetry to derive an inundation regime. While remote sensing approaches can provide a high spatial resolution of information, the temporal resolution is limited to an image every few weeks, and the usability of each image is subject to cloud cover. Hydrological models require some data in the region of interest for calibration, and often the model is not sensitive to the inundation variables of interest when calibrating to the data available. A study is presented where this is the case, where good results are achieved by a hydrological model when compared to downstream flow data, but very different wetland inundation regimes can be simulated. Due to this limitation, the remotely-sensed inundation regime was required as another source of information to calibrate the hydrological model, constraining the parameters involved in the modelled wetland storages. The remote-sensing constrained hydrological model provided greater confidence in the representation of processes occurring in the simulated wetland storages, and hence could be considered appropriate to be applied to a validation time period and for scenario testing. The findings of this research support the combination of remotely sensed indicators of inundation and hydrological modelling to increase confidence in the spatial representation of the dynamics of inundation. Understanding wetland inundation dynamics is critical to the understanding and management of threatened wetland ecosystems. Hence, the methods outlined in paper will ultimately assist in informing better management of wetlands.

An Implementation of Bitemporal Relational Database Management Systems

This paper presents an implementation of a nested bitemporal relational data model that allows more than one level of nesting. The fundamental construct for representing temporal data is a Bitemporal Atom (BTA) that consists of five parts: valid time period, the time periods' lower and upper bounds this data is recorded in the database and a value. Bitemporal data is attached to attributes and applied as an abstract data type. A number of examples of the management of nested bitemporal data model and queries to demonstrate the features of the model are provided. A prototype implementation has been completed in an object-relational database system to demonstrate the feasibility of the model.

Estimation of Satellite Rainfall Error Variance Using Readily Available Geophysical Features

The present study addresses the estimation of error variance (mean square error, MSE) of three satellite rainfall products: i) Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product of 3B42RT; ii) Climate Prediction Center (CPC) Morph (CMORPH); and iii) Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Nonlinear regression model is used to fit the response variable (satellite rainfall error variance) with explanatory variable (satellite rainfall rate) by grouping them as function of three key geophysical features: topography, climate, and season. The results of the study suggest that the error variance of a rainfall product is strongly correlated with rainfall rate and can be expressed as a power-law function. The geophysical feature based error classification analysis helps in achieving superior accuracy for prognostic error variance quantification in the absence of ground truth data. The multiple correlation coefficients between the estimated and observed error variance over an independent validation region (Upper Mississippi River basin) and time period (2007-2010) are found to be 0.75, 0.86, and 0.87 for 3B42RT, CMORPH, and PERSIANN-CCS products, respectively. In another validation region (Arkansas-Red River basin), the correlation coefficients are 0.59, 0.89, and 0.92 for the same products, respectively. Results of the assessment of error variance models reveal that the type of error component present in a satellite rainfall product directly impacts the accuracy of estimated error variance. The model estimates the error variance more accurately when the precipitation error components are mostly hit bias or false precipitation, while for a product with extensive missed precipitation, the accuracy of estimated error variance is significantly compromised. The study clearly demonstrates the feasibility of quantifying the error variance of satellite rainfall products in a spatially and temporally varying manner using readily available geophysical features and rainfall rate. The study is a path finder to a globally applicable and operationally feasible methodology for error variance estimation at high spatial and temporal scales for advancing satellite rainfall applications in ungauged basins.

Accounting for Backwater Effects in Flow Routing by the Discrete Linear Cascade Model

Flow-routing at a tributary (Koros River) of the Tisza River in Hungary was achieved by relating the storage coefficient (k )o f the state-space formulated discrete linear cascade model (DLCM) to the concurrent discharge rate of the Tisza. As ar esult, the root mean square error of the 1-day forecasts decreased from 25 m 3 ·s −1 (k ¼ 1.7 days −1 and the number of storage elements is 2) with the corresponding Nash-Sutcliffe-type performance value of 0.95 to 11 m 3 ·s −1 in the calibration period and to 15 m 3 ·s −1 in the val- idation period (the corresponding Nash-Sutcliff-type performance values are 0.99 and 0.98, respectively). During floods of the Tisza, the k value decreased to as little as0.35 days −1 , indicating a significant slowdown of the tributary flood-wave because of the resulting backwater effect. Subsequent stage-forecasts were aided by a coupled autoregressive moving-average (1,1) model of the DLCM error sequence and the application of the Jones formula in addition to a conveyance curve, the latter yielding the most accurate 1-day forecasts with a root mean square error of 28 cm and Nash-Sutcliff-type performance value of 0.99 for the combined (validation and calibration) time periods. The method requires no significant change in the mathematical structure of the original DLCM and thus is well-posed for inclusion of existing operational streamflow-forecasting schemes. DOI: 10.1061/(ASCE)HE.1943-5584.0000771. ©2 014 American Society of Civil Engineers.

Case study comparison between litigated and voluntary nutrient management strategies

<h3>Abstract</h3> <h3>Objective</h3> To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. <h3>Design</h3> Population based cohort study. <h3>Setting and participants</h3> QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. <h3>Main outcome measures</h3> The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. <h3>Results</h3> 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R²); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell’s C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. <h3>Conclusion</h3> The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.