Innovatory rainfall simulator design – A concept of moving storm automation

We developed an advanced design programmable rainfall simulator (RS) to simulate a moving storm rainfall condition. The RS consists of an automated nozzle control system coupled with a pressure regulator mechanism for an operating range of 50 kPa to 180 kPa at a drop height of 2000 mm above the soil flume surface. Additionally, a programmable mobile application was developed to regulate all RS valves. Near natural rainfall conditions were simulated at varying spatial and temporal resolutions in a controlled environment. A soil flume of 2500 mm × 1400 mm × 500 mm was fabricated to conduct different hydrological experiments. The flume was designed to record overland, subsurface, and base flows simultaneously. This study focused on a detailed analysis of moving storms and their impact on hydrograph characteristics. Experimental results showed a considerable difference in terms of time to peak (tp), peak discharge (Qp), and hydrograph recession for two different storm movement directions (upstream and downstream). Two multiple regression models indicate a statistically significant relationship between the dependent variable (tp or Qp) and the independent variables (i.e. storm movement direction, storm velocity, and bed slope gradient) at a 5 % level of significance. Further, the impact of these moving storm phenomena reduces with the increase in the storm movement velocity.

Dynamical Downscaling Projections of Late 21st Century U.S. Landfalling Hurricane Activity

U.S. landfalling tropical cyclone (TC) activity was projected for late 21st century conditions using a two-step dynamical downscaling framework. A regional atmospheric model, run for 27 seasons, generated tropical storm cases. Each storm case was re-simulated (up to 15 days) using the higher resolution GFDL hurricane model. Thirteen CMIP3 or CMIP5 modeled climate change projections were explored as scenarios. Robustness of projections was assessed using statistical significance tests and comparing the sign of changes derived from different models. The proportion of TCs (tropical storms and hurricanes) making U.S. landfall increases for the warming scenarios (by order 50% or more). For category 1-3 hurricane frequency, a robust decrease is projected (basin-wide), but robust changes are not projected for U.S. landfalling cases. A relatively robust increase in U.S. landfalling category 4-5 hurricane frequency is projected, averaging about +400% across the models; 10 of 13 models/ensembles project an increase (statistically significant in three individual models), while three models projected no change. The most robust projections overall for U.S. landfalling TC activity are for increased near-storm rainfall rates: these increases average +18% (all tropical storms and hurricanes), +26% (all hurricanes), and +37% (major hurricanes). Landfalling hurricane wind speed intensities show no robust signal, in contrast to a ~5% increase in basin-averaged TC intensity; basin-wide Power Dissipation Index (PDI) is projected to decrease, partly due to decreased duration. TC translation speed increases a few percent in most simulations. A caveat is the framework’s low correlation of modeled U.S. TC landfalls vs. observed interannual variations (1980-2016).

The impact of land use and rainfall patterns on the soil loss of the hillslope

In order to discuss the effect of rainfall patterns and land use types on soil erosion, the experiment is carried out under natural rainfall events on different kinds of runoff plots in Zhangjiachong watershed. Based on the observed data of 44 individual rainfall events including moderate, heavy and storm rainfall, the differences of erosion modulus among hedgerows plots, terrace plots, and slope plots under different rainfall patterns are analyzed. And the effects of hedgerow and terrace patterns on control of soil loss are revealed by RUSLE. Wilcoxon signed rank test is applied to analyze the significant difference of erosion modulus in different plots and the coefficient of variation is used to compare the characteristics of erosion modulus under different rainfall patterns. The results show that the soil erosion modulus of earth banked terrace has the highest value and the lowest soil erosion modulus occurs in the slope land with hedgerows. The coefficients of variation for soil erosion modulus under heavy and storm rainfall are larger than that of moderate rainfall. Hedgerow pattern can effectively control soil erosion under moderate and heavy rainfall while the effect of hedgerow is considerably weakened under storm rainfall. Earth banked terraces own the highest erosion modulus followed by slope land and stone dike terraces.

Flood Stage Forecasting at the Gurye-Gyo Station in Sumjin River Using LSTM-Based Deep Learning Models

Instances of flood damage caused by extreme storm rainfall due to climate change and variability have been showing an increasing trend. Particularly, a flood forecasting and warning system has been recognized as an important nonstructural measure for flood damage reduction, including loss of life. Flood forecasting and warning have been performed by the forecasts of flood discharge and flood stage using the physically based rainfall-runoff models. However, recently, studies involving the application of a machine learning-based flood forecasting models, which addresses the limitations of extant physically based flood stage forecasting models, have been performed. We may require various case studies to determine more accurate methods. Therefore, this study performed the real-time forecasting of the river water level or stage at the Gurye station of the Sumjin river with lead times of 1, 3, and 6 h by applying a long short-term memory (LSTM)-based deep learning model. In addition, the applicability of the LSTM model was evaluated by comparing the results with those from widely used models based on support vector machine and multilayer perceptron. Consequently, we noted that the LSTM model exhibited a relatively better forecasting performance. Therefore, the applicability of the LSTM model should be extensively studied for flood forecasting applications.

Seasonal Changes in the Drivers of Water Physico-Chemistry Variability of a Small Freshwater Tidal River

Where rivers meet the sea, tides can exert a physical and chemical influence on the lower reaches of a river. How tidal dynamics in these tidal river reaches interact with upstream hydrological drivers such as storm rainfall, which ultimately determines the quantity and composition of material transferred from watersheds to estuaries, is currently unknown. We monitored a small freshwater tidal river in the Pacific Northwest, United States in high resolution over 1 year to evaluate the relative importance of tides vs. upstream hydrological flows (i.e., base flow and precipitation events) on basic physico-chemical parameters (pH, dissolved oxygen, turbidity, specific conductivity, and temperature), and how these interactions relate to the downstream estuary. Tidal variability and diurnal cycles (i.e., solar radiation) dominated water physico-chemical variability in the summer, but the influence of these drivers was overshadowed by storm-driven sharp pulses in river physico-chemistry during the remainder of the year. Within such events, we found incidences of counterclockwise hysteresis of pH, counterclockwise hysteresis of dissolved oxygen, and clockwise hysteresis of turbidity, although systematic trends were not observed across events. The dominance of storm rainfall in the river’s physico-chemistry dynamics, and similar pulses of decreased pH observed in adjacent estuarine waters, suggest that the linkage between tidal streams and the broader system is variable throughout the year. High-frequency monitoring of tidal river biogeochemistry is therefore crucial to enable the assessment of how the relative strength of these drivers may change with future sea level rise and altered precipitation patterns to modulate biogeochemical dynamics across the land-ocean-atmosphere continuum.

Monthly extreme rainfall risk envelope graph method development and application in Algeria

Rainfall patterns are bound to change as a result of global warming and climate change impacts. Rainfall events are dependent on geographic location, geomorphology, coastal area closeness and general circulation air movements. Accordingly, there are increases and decreases at different meteorology station time-series records leading to extreme events such as droughts and floods. This paper suggests a methodology in terms of envelope curves for monthly extreme rainfall event occurrences at a set of risk levels or return periods that may trigger the extreme occurrences at meteorology station catchments. Generally, in many regions, individual storm rainfall records are not available for intensity–duration–frequency (IDF) curve construction. The main purpose of this paper is, in the absence of individual storm rainfall records, to suggest monthly envelope curves, which provide a relationship between return period and monthly extreme rainfall values. The first step is to identify each monthly extreme rainfall records probability distribution function (PDF) for risk level and return period calculations. Subsequently, the return period rainfall amount relationships are presented on double-logarithmic graphs with the best power model as a set of envelope curves. The applications of these methodologies are implemented for three Hodna drainage basin meteorology station rainfall records in northern Algeria. It is concluded that the most extreme rainfall risk months are June, August and September, which may lead to floods or flash floods in the study area. A new concept is presented for the possible extreme value triggering months through the envelope curves as ‘low’, ‘medium’ and ‘high’ class potentials.

FORMATION CONDITIONS OF WARM-SEASON STORM RAINFALL IN PERM REGION

The article deals with the issue of the formation conditions and possibility of predicting storm rainfall in the territory of Perm region using atmospheric instability indices. In the course of the study, the spatial and temporal distribution of storm rainfall cases was assessed, as well as the most favorable values of meteorological parameters and synoptic situations contributing to rainfall were determined. The instability indices were calculated based on the ERA-Interim reanalysis of the spectral model of the European Center for Medium-Range Weather Forecasts (ECMWF) and the reanalysis of the American hydrodynamic model Climate Forecast System (CFS). On the basis of correlation analysis, the dependence of instability indices on the amount and average intensity of storm rainfall was estimated. The critical values of the considered instability indices were determined and corrected in relation to the studied territory. The study showed that the most frequent storm rainfall is observed in June and July in the afternoon and evening. It was found that storm rainfall is mainly associated with the passage of cold fronts. The assessment of the quality of instability indices has shown the feasibility of their use for the forecast and diagnosis of storm precipitation, including in the gradation of this dangerous phenomenon.

Groundwater fluctuations during a debris flow event in Western Norway – triggered by rain and snowmelt

Pore pressure is crucial in triggering debris slides and flows. Here we present measurements of ground water pore pressure and temperature recorded by a piezometer 1.6 m below the surface on a slope susceptible to debris flows in Western Norway. One of the largest oscillations in data collected over four years coincided with a debris flow event on the slope that occurred during storm Hilde on 15–16 November 2013. More than 100 landslides were registered during the storm. Rainfall totalled about 80–100 mm in 24 hours, locally up to 129 mm, and an additional trigger factor for the slides was a rapid rise in air temperature that caused snowmelt. On 15 November, the groundwater level in the hillslope rose by 10 cm per hour and reached 44 cm below the surface. At the same time, air temperature rose from 0 °C to over 8 °C, and the groundwater temperature dropped by 1.5 °C. The debris flow probably occurred late in the evening of 15 November, when the groundwater level reached its peak. Measurements of the groundwater in the hillslope in the period 2010–2013 show that the event in 2013 was not exceptional. Storm Dagmar on 25–26 December 2011 caused a similar rise in groundwater level, but did not trigger any failures. The data suggest that during heavy rainstorms the slope is in a critical state for a slide to be triggered for a short time – about 4–5 hours.

A Hydro-Informatic Approach For Estimation Of Design Flash-Flood In Bargi Dam Cross-Section Of Narmada River, India

Estimation of design flood is imperative for hydraulic designs of spillways and various other water resources development projects as well as very essential for flood risk assessment. The objective of the present study is to apply Geographical Information System (GIS) supported hydro informatics approach for estimation of design flash-flood in Bargi dam cross-section. A criterion used for estimation of design flash flood is validated by central water commission (CWC). A hydrologic modelling software (HEC-GeoHMS) is used for the delineation of basin characterises for simulation of the precipitation-runoff process of the dendritic basin system. The SUH (Synthetic Unit Hydrograph) and flood hydrographs for 25, 50 and 100 year return periods are computed along with time distribution curve which can be used to derive the time distribution co-efficient of storm rainfall in the sub-basins for the rainstorm of any duration. It is observed in this research that the peak characteristics of the design flash-flood are more perceptive to the various design storm pattern. It is demonstrated that flood hydrographs are important in flood-risk management. The results attained exhibit the capability of the flood hydrograph to describe the effects of different hydraulic systems.