A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers

The geophysical and hydrological processes governing river flow formation exhibit persistence at several timescales, which may manifest itself with the presence of positive seasonal correlation of streamflow at several different time lags. We investigate here how persistence propagates along subsequent seasons and affects low and high flows. We define the high-flow season (HFS) and the low-flow season (LFS) as the 3-month and the 1-month periods which usually exhibit the higher and lower river flows, respectively. A dataset of 224 rivers from six European countries spanning more than 50 years of daily flow data is exploited. We compute the lagged seasonal correlation between selected river flow signatures, in HFS and LFS, and the average river flow in the antecedent months. Signatures are peak and average river flow for HFS and LFS, respectively. We investigate the links between seasonal streamflow correlation and various physiographic catchment characteristics and hydro-climatic properties. We find persistence to be more intense for LFS signatures than HFS. To exploit the seasonal correlation in the frequency estimation of high and low flows, we fit a bi-variate meta-Gaussian probability distribution to the selected flow signatures and average flow in the antecedent months in order to condition the distribution of high and low flows in the HFS and LFS, respectively, upon river flow observations in the previous months. The benefit of the suggested methodology is demonstrated by updating the frequency distribution of high and low flows one season in advance in a real-world case. Our findings suggest that there is a traceable physical basis for river memory which, in turn, can be statistically assimilated into high- and low-flow frequency estimation to reduce uncertainty and improve predictions for technical purposes

A Comparison of Univariate and Multivariate Forecasting Models Predicting Emergency Department Patient Arrivals during the COVID-19 Pandemic

The COVID-19 pandemic has heightened the existing concern about the uncertainty surrounding patient arrival and the overutilization of resources in emergency departments (EDs). The prediction of variations in patient arrivals is vital for managing limited healthcare resources and facilitating data-driven resource planning. The objective of this study was to forecast ED patient arrivals during a pandemic over different time horizons. A secondary objective was to compare the performance of different forecasting models in predicting ED patient arrivals. We included all ED patient encounters at an urban teaching hospital between January 2019 and December 2020. We divided the data into training and testing datasets and applied univariate and multivariable forecasting models to predict daily ED visits. The influence of COVID-19 lockdown and climatic factors were included in the multivariable models. The model evaluation consisted of the root mean square error (RMSE) and mean absolute error (MAE) over different forecasting horizons. Our exploratory analysis illustrated that monthly and weekly patterns impact daily demand for care. The Holt–Winters approach outperformed all other univariate and multivariable forecasting models for short-term predictions, while the Long Short-Term Memory approach performed best in extended predictions. The developed forecasting models are able to accurately predict ED patient arrivals and peaks during a surge when tested on two years of data from a high-volume urban ED. These short-and long-term prediction models can potentially enhance ED and hospital resource planning

How Predictable are Temperature-series Undergoing Noise-controlled Dynamics in the Mediterranean

Mediterranean is thought to be sensitive to global climate change, but its future interdecadal variability is uncertain for many climate models. A study was made of the variability of the winter temperature over the Mediterranean Sub-regional Area (MSA), employing a reconstructed temperature series covering the period 1698 to 2010. This paper describes the transformed winter temperature data performed via Empirical Mode Decomposition for the purposes of noise reduction and statistical modeling. This emerging approach is discussed to account for the internal dependence structure of natural climate variability

The role of soil moisture initialization in subseasonal and seasonal streamflow prediction: A case study in Sri Lanka

The two main contributors to streamflow predictability at subseasonal to seasonal timescales in tropical regions are: (i) the predictability of meteorologic (particularly precipitation) anomalies, and (ii) the land surface soil moisture state at the start of the forecast period. Meteorological predictions at subseasonal timescale are usually fraught with error and may not be dependable. The accurate initialization of soil moisture, as obtained through real-time land data analysis, may provide skill in subseasonal to seasonal streamflow prediction, even when the prediction skill for rainfall is small. A series of experiments using the Catchment Land Surface Model (CLSM) is performed to characterize the contribution of accurate soil moisture initialization to the skill of streamflow prediction in Sri Lanka at timescales up to 2 months. We find that at the monthly timescale, accurate soil moisture initialization provides between 10% and 60% of the total runoff prediction skill that could be obtained under a perfect prediction of meteorological forcing. Some contributions to streamflow forecast skill are also found for the second month of forecast

Comparison of modelling techniques for milk-production forecasting

peer-reviewedThe objective of this study was to assess the suitability of 3 different modeling techniques for the prediction of total daily herd milk yield from a herd of 140 lactating pasture-based dairy cows over varying forecast horizons. A nonlinear auto-regressive model with exogenous input, a static artificial neural network, and a multiple linear regression model were developed using 3 yr of historical milk-production data. The models predicted the total daily herd milk yield over a full season using a 305-d forecast horizon and 50-, 30-, and 10-d moving piecewise horizons to test the accuracy of the models over long- and short-term periods. All 3 models predicted the daily production levels for a full lactation of 305 d with a percentage root mean square error (RMSE) of ≤12.03%. However, the nonlinear auto-regressive model with exogenous input was capable of increasing its prediction accuracy as the horizon was shortened from 305 to 50, 30, and 10 d [RMSE (%) = 8.59, 8.1, 6.77, 5.84], whereas the static artificial neural network [RMSE (%) = 12.03, 12.15, 11.74, 10.7] and the multiple linear regression model [RMSE (%) = 10.62, 10.68, 10.62, 10.54] were not able to reduce their forecast error over the same horizons to the same extent. For this particular application the nonlinear auto-regressive model with exogenous input can be presented as a more accurate alternative to conventional regression modeling techniques, especially for short-term milk-yield predictions

Hybrid approach of fractal and linguistic forecasting of winter wheat yields in Southern Rússia

The article investigated and formed the imperatives of the impact of the external natural environment on the grain yield in the south of Russia, forcing to abandon the simplified classical concepts and methods of analysis. The author's research concept defines quantitative risk analysis, as a category, inverse forecast, which is possible only on the basis of economic and mathematical modeling. The modern theory of assessing measures of economic risks, forecasting and managing them is still far from adequate to the real needs of practical agricultural management. This determines the main feature of modern risk, which is its total and comprehensive nature. It is difficult to manage risks in regions with frequent droughts, which are classified as areas of risk farming. The methodology of studying risks in the field of agriculture is based on the study of the dynamics of the natural environment of growing crops, the conjuncture uncertainty of the external economic environment, the variability of land management technologies. Climatic and agrometeorological conditions are becoming an important factor affecting crop yields. The yield series accumulates information about the fluctuation of weather conditions and their influence on the yield, they contain information about certain regularities that synergy relates to the concept of “long-term memory”. The paper describes the features of the spectrum of climatic conditions affecting socio-economic indicators, the growth and yield of grain (winter wheat) in southern Russia, as well as the results of the implementation of the author-hybrid approach to the fractal and linguistic forecasting of winter wheat yield in southern Russia.This work was supported by the RFBR grant № 17-06-00354,19-410-230022р_аinfo:eu-repo/semantics/publishedVersio

Using a scenario-neutral framework to avoid potential maladaptation to future flood risk

This study develops a coherent framework to detect those catchment types associated with ahigh risk of maladaptation to futureflood risk. Using the“scenario‐neutral”approach to impactassessment the sensitivity of Irish catchments tofluvialflooding is examined in the context of nationalclimate change allowances. A predefined sensitivity domain is used to quantifyflood responses to +2 °Cmean annual temperature with incremental changes in the seasonality and mean of the annual precipitationcycle. The magnitude of the 20‐yearflood is simulated at each increment using two rainfall‐runoff models(GR4J, NAM), then concatenated as response surfaces for 35 sample catchments. A typology of catchmentsensitivity is developed using clustering and discriminant analysis of physical attributes. The same attributesare used to classify 215 ungauged/data‐sparse catchments. To address possible redundancies, the exposure ofdifferent catchment types to projected climate is established using an objectively selected subset of theCoupled Model Intercomparison Project Phase 5 ensemble. Hydrological model uncertainty is shown tosignificantly influence sensitivity and have a greater effect than ensemble bias. A nationalflood riskallowance of 20%, considering all 215 catchments is shown to afford protection against ~48% to 98% of theuncertainty in the Coupled Model Intercomparison Project Phase 5 subset (Representative ConcentrationPathway 8.5; 2070–2099), irrespective of hydrological model and catchment type. However, results indicatethat assuming a standard national or regional allowance could lead to local over/under adaptation. Herein,catchments with relatively less storage are sensitive to seasonal amplification in the annual cycle ofprecipitation and warrant special attention

Evaluating the long short-term memory (LSTM) network for discharge prediction under changing climate conditions

Better understanding the predictive capabilities of hydrological models under contrasting climate conditions will enable more robust decision-making. Here, we tested the ability of the long short-term memory (LSTM) for daily discharge prediction under changing conditions using six snow-influenced catchments in Switzerland. We benchmarked the LSTM using the Hydrologiska Byråns Vattenbalansavdelning (HBV) bucket-type model with two parameterizations. We compared the model performance under changing conditions against constant conditions and tested the impact of the time-series size used in calibration on the model performance. When calibrated, the LSTM resulted in a much better fit than the HBV. However, in validation, the performance of the LSTM dropped considerably, and the fit was as good or poorer than the HBV performance in validation. Using longer time series in calibration improved the robustness of the LSTM, whereas HBV needed fewer data to ensure a robust parameterization. When using the maximum number of years in calibration, the LSTM was considered robust to simulate discharges in a drier period than the one used in calibration. Overall, the HBV was found to be less sensitive for applications under contrasted climates than the data-driven model. However, other LSTM modeling setups might be able to improve the transferability between different conditions

Vegetation anomalies caused by antecedent precipitation in most of the world

Quantifying environmental controls on vegetation is critical to predict the net effect of climate change on global ecosystems and the subsequent feedback on climate. Following a non-linear Granger causality framework based on a random forest predictive model, we exploit the current wealth of multi-decadal satellite data records to uncover the main drivers of monthly vegetation variability at the global scale. Results indicate that water availability is the most dominant factor driving vegetation globally: about 61% of the vegetated surface was primarily water-limited during 1981-2010. This included semiarid climates but also transitional ecoregions. Intraannually, temperature controls Northern Hemisphere deciduous forests during the growing season, while antecedent precipitation largely dominates vegetation dynamics during the senescence period. The uncovered dependency of global vegetation on water availability is substantially larger than previously reported. This is owed to the ability of the framework to (1) disentangle the co-linearities between radiation/temperature and precipitation, and (2) quantify non-linear impacts of climate on vegetation. Our results reveal a prolonged effect of precipitation anomalies in dry regions: due to the long memory of soil moisture and the cumulative, nonlinear, response of vegetation, water-limited regions show sensitivity to the values of precipitation occurring three months earlier. Meanwhile, the impacts of temperature and radiation anomalies are more immediate and dissipate shortly, pointing to a higher resilience of vegetation to these anomalies. Despite being infrequent by definition, hydro-climatic extremes are responsible for up to 10% of the vegetation variability during the 1981-2010 period in certain areas, particularly in water-limited ecosystems. Our approach is a first step towards a quantitative comparison of the resistance and resilience signature of different ecosystems, and can be used to benchmark Earth system models in their representations of past vegetation sensitivity to changes in climate