The hydrometeorology of the Amazon basin in the ERA-40 reanalysis for 1958–2001 is compared with observations of precipitation, temperature, and streamflow. After 1979, the reanalysis over the Amazon has a small cool bias of the order of 0.35 K, and a small low bias of precipitation of the order of 0.3 mm day . In the early years (1958–72), there is a large upward drift in reanalysis precipitat...

The main goal of this study is to use "top-down"? approach to simulate catchment scale hydrologic processes, in terms of water partitioning, runoff generation, storage dynamics and human/water interaction, and therefore to obtain a comprehensive understanding on intra-annual water balance of watersheds. An evaporation model at seasonal scale is developed based on Budyko framework. A runoff gene...

1 Runoff production in blanket peat catchments of the northern Pennine hills, UK was measured through monitoring and experimentation at the plot, hillslope and catchment scale. Water flow from soil pipes was measured in one of the study catchments and overland flow, throughflow and water table were measured in runoff plots; rainfall simulation and tension-infiltrometry provided information on i...

In this study, an integrated system for the comprehensive analysis of climate change, runoff, reservoir operation, and power generation was developed. In order to apply reliable climate change scenarios to the Geum River Basin, we applied representative concentration pathway (RCP) scenarios adopted by the Intergovernmental Panel on Climate Change for its fifth assessment report in 2014 to the s...

Recent studies have shown that Bayesian Neural Networks (BNNs) are powerful tools for providing reliable hydrologic prediction and quantifying the prediction uncertainty. The reasonable estimation of the prediction uncertainty, a valuable for decision making to address water resources management and design problems, is influenced by the techniques used to deal with different uncertainty sources...

The interpretation of deep learning (DL) hydrological models is a key challenge in data-driven modeling streamflow, as the DL are often seen “black box” despite outperforming process-based streamflow prediction. Here we explore interpretability convolutional long short-term memory network (CNN-LSTM) previously trained to successfully predict at 226 stream gauge stations across southwestern Cana...

A method for estimation of mean baseflow residence time in watersheds from hydrograph runoff recession characteristics was developed. Runoff recession characteristics were computed for the period 1993–96 in the 2 km2 Winnisook watershed, Catskill Mountains, southeastern New York, and were used to derive mean values of subsurface hydraulic conductivity and the storage coefficient. These values w...

Design flood estimation is often required in engineering practice such as design of hydrologic structures, flood plain management, river ecological studies and flood insurance studies. The national guideline for design flood estimation in Australia known as Australian Rainfall and Runoff (ARR) recommends the Design Event Approach as the preferred method of rainfallbased design flood estimation ...