Availability Of Rainfall Data Research Articles

Spatial propagation of streamflow data in ungauged watersheds using a lumped conceptual model

Abstract Streamflow data are required for the effective management of flood damage; however, streamflow is rarely measured in small watersheds. In this study, a lumped conceptual model was adopted to generate streamflow values at small, ungauged watersheds using the spatial propagation concept. During the process of spatial propagation, parameters related to physical properties were fixed, while event-based initial conditions were optimized to ensure that error rates on the simulated streamflow data were comparable to those for measured data. Then the concept was validated using data from 21 flood events in the ChungJu Dam (CJD) watershed, Korea, which was divided into 22 small, ungauged watersheds. The propagated peak streamflow data at ungauged cross-validation points have average Nash-Sutcliffe efficiency (NSE) values of 0.91–0.99. Averages of NSE values for volume and time to peak streamflow are over 0.85 which satisfies the model criterion (NSE > 0.5). It is concluded that streamflow data for small, ungauged watersheds located upstream can be generated by one gauged downstream streamflow without an extensive amount of gauged streamflow data from other locations using the proposed concept. For accurate simulation, availability of rainfall data is essential for accurately modeling the spatial propagation of streamflow using a lumped conceptual model.

A probabilistic approach for assessing landslide-triggering event rainfall in Papua New Guinea, using TRMM satellite precipitation estimates

Large and numerous landslides can result in widespread impacts which are felt particularly strongly in the largely subsistence-orientated communities residing in the most landslide-prone areas of Papua New Guinea (PNG). Understanding the characteristics of rainfall preceding these landslide events is essential for the development of appropriate early warning systems and forecasting models. Relationships between rainfall and landslides are frequently complex and uncertainties tend to be amplified by inconsistent and incomplete landslide catalogues and sparse rainfall data availability. To address some of these uncertainties a modified Bayesian technique has been used, in conjunction with the multiple time frames method, to produce thresholds of landslide probability associated with rainfall events of specific magnitude and duration. Satellite-derived precipitation estimates have been used to derive representative rainfall accumulations and intensities over a range of different rainfall durations (5, 10, 15, 30, 45, 60, 75 and 90days) for rainfall events which resulted in landslides and those which did not result in landslides. Of the two parameter combinations (accumulation-duration and intensity-duration) analysed, rainfall accumulation and duration provide the best scope for identifying probabilistic thresholds for use in landslide warning and forecasting in PNG. Analysis of historical events and rainfall characteristics indicates that high accumulation (>250mm), shorter duration (<15days) rainfall events and long duration (>75days), high accumulation (>1200mm) rainfall events are more likely to lead to moderate- to high-impact landslides. This analysis has produced the first proxy probability thresholds for landslides in PNG and their application within an early warning framework has been discussed.

DEVELOPMENT OF WARNING CRITERIA FOR LAHAR FLOW DISASTER IN GENDOL RIVER AREA OF MOUNT MERAPI

The eruption of Mount Merapi in 2010 leads to the lahar flow disaster in the region of the mountain slopes. Due to the impact caused by the lahar flow, it is important to develop warning criteria for lahar flow disaster with a simple method corresponding to the limitation of existing data and parameters. One of the methods is by analyzing rainfall data to predict the occurrence of lahar flow in Gendol River. It applies the setting of standard rainfall for warning and evacuation of sediment disasters based on Guidelines for the Development of Warning and Evacuation System against Sediment Disasters in Developing Countries, published by the Ministry of Land, Infrastructure, and Transport (MLIT) Infrastructure Development Institute - Japan, 2004. This study analyzed the critical line (CL) which can be used to predict the occurrence of lahar flow based on the rain characteristics namely working rainfall and rainfall intensity. Furthermore, it can be established by warning line (WL) and the evacuation line (EL) as a basis for determining the standard rainfall for warning (R1) and standard rainfall for evacuation (R2). The value of R1 obtained ± 6 mm and R2 ± 29 mm. The value of R1 and R2 are strongly influenced by the availability of rainfall data and occurrence of lahar flow. The results of this research were expected to be used as input for the warning criteria development of early warning system lahar flow disaster on the slopes of Mount Merapi, particularly in the area of Gendol River.

SWAT use of gridded observations for simulating runoff – a Vietnam river basin study

Abstract. Many research studies that focus on basin hydrology have applied the SWAT model using station data to simulate runoff. But over regions lacking robust station data, there is a problem of applying the model to study the hydrological responses. For some countries and remote areas, the rainfall data availability might be a constraint due to many different reasons such as lacking of technology, war time and financial limitation that lead to difficulty in constructing the runoff data. To overcome such a limitation, this research study uses some of the available globally gridded high resolution precipitation datasets to simulate runoff. Five popular gridded observation precipitation datasets: (1) Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE), (2) Tropical Rainfall Measuring Mission (TRMM), (3) Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN), (4) Global Precipitation Climatology Project (GPCP), (5) a modified version of Global Historical Climatology Network (GHCN2) and one reanalysis dataset, National Centers for Environment Prediction/National Center for Atmospheric Research (NCEP/NCAR) are used to simulate runoff over the Dak Bla river (a small tributary of the Mekong River) in Vietnam. Wherever possible, available station data are also used for comparison. Bilinear interpolation of these gridded datasets is used to input the precipitation data at the closest grid points to the station locations. Sensitivity Analysis and Auto-calibration are performed for the SWAT model. The Nash-Sutcliffe Efficiency (NSE) and Coefficient of Determination (R2) indices are used to benchmark the model performance. Results indicate that the APHRODITE dataset performed very well on a daily scale simulation of discharge having a good NSE of 0.54 and R2 of 0.55, when compared to the discharge simulation using station data (0.68 and 0.71). The GPCP proved to be the next best dataset that was applied to the runoff modelling, with NSE and R2 of 0.46 and 0.51, respectively. The PERSIANN and TRMM rainfall data driven runoff did not show good agreement compared to the station data as both the NSE and R2 indices showed a low value of 0.3. GHCN2 and NCEP also did not show good correlations. The varied results by using these datasets indicate that although the gauge based and satellite-gauge merged products use some ground truth data, the different interpolation techniques and merging algorithms could also be a source of uncertainties. This entails a good understanding of the response of the hydrological model to different datasets and a quantification of the uncertainties in these datasets. Such a methodology is also useful for planning on Rainfall-runoff and even reservoir/river management both at rural and urban scales.

Spatial Interpolation of Annual Precipitation in Annaba-Algeria - Comparison and Evaluation of Methods

The availability of rainfall data is fundamental for most hydrological analyses and for the design and management of water resources systems. There are many methods of spatial interpolation of rainfall despite their effectiveness and ease of use variables, the choice of one or the other depends on the aim of the study, but also on the territorial context of the area (surface, network density, etc.). The study area is the coastal city of Annaba located in eastern Algeria, and which contains a low rainfall network density. The purpose of this study is to determine the most adequate rainfall interpolation technique. Three interpolation techniques, determinist and stochastic, have been tested by visual observation and cross-validation, the best results were achieved by inverse distance weighting to characterize rainfall distribution.

A review of the Scottish rain-gauge network

A review of the rain-gauge network in Scotland was undertaken on behalf of the UK Met Office and the Scottish Environment Protection Agency (SEPA) to evaluate the existing network against current user requirements for rainfall data, investigate the interaction between the rain-gauge and radar networks and to inform recommendations for improvements to the network and associated rainfall data. The review identified a diverse user requirement from core Met Office requirements for numerical weather prediction, rainfall radar adjustment and climatology, to a wide range of water-related applications including flood estimation, forecasting and warning, water quality, water resources, emergency planning, landslip prediction and a variety of amenity applications. Key findings of the review were a need for expansion of the gauge network in urban, remote and high-altitude areas and for radar adjustment/verification purposes. In addition to improved radar rainfall forecasts, such targeted network expansion would improve rainfall data availability and utility for the wider user community. Significant potential benefits to both the Met Office and SEPA were identified through the real-time sharing of data between organisations, with improved radar rainfall forecasts benefiting a range of applications, in particular flood warning.

Spatio‐temporal rainfall variability in the Amazon basin countries (Brazil, Peru, Bolivia, Colombia, and Ecuador)

AbstractRainfall variability in the Amazon basin (AB) is analysed for the 1964–2003 period. It is based on 756 pluviometric stations distributed throughout the AB countries. For the first time it includes data from Bolivia, Peru, Ecuador, and Colombia. In particular, the recent availability of rainfall data from the Andean countries makes it possible to complete previous studies. The impact of mountain ranges on rainfall is pointed out. The highest rainfall in the AB is observed in low windward regions, and low rainfall is measured in leeward and elevated stations. Additionally, rainfall regimes are more diversified in the Andean regions than in the lowlands. Rainfall spatio‐temporal variability is studied based on a varimax‐rotated principal component analysis (PCA). Long‐term variability with a decreasing rainfall since the 1980s prevails in June–July–August (JJA) and September–October–November (SON). During the rainiest seasons, i.e. December–January–February (DJF) and March–April–May (MAM), the main variability is at decadal and interannual time scales. Interdecadal variability is related to long‐term changes in the Pacific Ocean, whereas decadal variability, opposing the northwest and the south of the AB, is associated with changes in the strength of the low‐level jet (LLJ) along the Andes. Interannual variability characterizes more specifically the northeast of the basin and the southern tropical Andes. It is related to El Niño‐Southern Oscillation (ENSO) and to the sea surface temperature (SST) gradient over the tropical Atlantic. Mean rainfall in the basin decreases during the 1975–2003 period at an annual rate estimated to be − 0.32%. Break tests show that this decrease has been particularly important since 1982. Further insights into this phenomenon will permit to identify the impact of climate on the hydrology of the AB. Copyright © 2008 Royal Meteorological Society

Investigating spatial and temporal variability in runoff and sediment generation using a physically-based model, Thales

Limited within-day rainfall data availability has led to a paradigm of daily hydrological modelling that conflicts with key process timescales, resulting in poor modelling of runoff and particularly erosion. A new hydrological modelling framework that accounts for variation in rainfall, runoff and erosion across the landscape, and within each day using a statistical approach, has been developed. The ultimate aim is to develop a model for estimating runoff and erosion that is consistent with key process time and space scales. Central to this aim is to be able to run the model using daily precipitation data with a distribution function (DF) model to account for sub-daily variations in precipitation and with an algorithm that accounts for routing processes in the catchment. A series of DF models have been developed based on 6-minute interval precipitation data from pluviographs at the point scale. This paper provides insight into the effects of scaling up from hillslopes to whole catchments, with the results to underpin an extension of the existing point-scale models to catchment scale. An analysis framework that allows the various effects to be separated was developed. The Thales physically-based hydrological model was employed with grid-scale (25 m2) runoff and sediment generation processes of varying complexity specified. A series of simulations was performed to investigate both the temporal and spatial effects of rainfall and catchment variability on runoff and erosion generation. The results were analysed and compared across different scales to determine whether the scaling of rainfall in space and time could be linked to the scaling of runoff and erosion. In addition, the results were examined to identify an approach to account for the routing effects that become important in medium-sized catchments.

Designing roof catchment water supply systems using water budgeting methods

This paper describes the use of a water balance model to simulate the performance of a roof‐tank system over many years of historical rainfall data. Repeating this for a wide range of system specifications enables the construction of a set of design curves from which the most economic proportioning of roof and tank can be determined, for a nominated reliability of supply or a nominated cost. Two stations having identical mean annual rainfall but contrasting distributions are used as examples. Accuracy, problems of rainfall data availability, and practical aspects of computation are discussed.