Gauge Rainfall Research Articles

A Review of Radar‐Rain Gauge Data Merging Methods and Their Potential for Urban Hydrological Applications

AbstractRadar‐rain gauge merging techniques have been widely used to improve the applicability of radar and rain gauge rainfall estimates by combining their advantages, while partially overcoming their individual weaknesses. Despite significant research in this area, guidance on the suitability of and factors affecting merging techniques at the fine spatial‐temporal resolutions required for urban hydrological applications is still insufficient. In this paper, an in‐depth review of radar‐rain gauge merging techniques is conducted, with a focus on their potential for urban hydrological applications. An overview is first given of existing merging techniques and an application‐oriented categorization is proposed: (1) radar bias adjustment methods, (2) rain gauge interpolation methods using radar spatial association as additional information, and (3) radar‐rain gauge integration methods. A detailed review is given of studies focusing on the evaluation and intercomparison of merging methods, based upon which the most widely used and best performing techniques from each category are identified. These are mean field bias adjustment, kriging with external drift, and Bayesian merging. Climatological, operational, and methodological factors affecting merging performance are then reviewed and their relevance for urban applications discussed. Based on this review, conclusions on merging potential for urban applications are drawn and research gaps are identified, which should be addressed to provide further guidance on the use of merging techniques for urban hydrological applications.

Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River basin

Abstract. Satellite rainfall estimates (SREs) are prone to bias as they are indirect derivatives of the visible, infrared, and/or microwave cloud properties, and hence SREs need correction. We evaluate the influence of elevation and distance from large-scale open water bodies on bias for Climate Prediction Center-MORPHing (CMORPH) rainfall estimates in the Zambezi basin. The effectiveness of five linear/non-linear and time–space-variant/-invariant bias-correction schemes was evaluated for daily rainfall estimates and climatic seasonality. The schemes used are spatio-temporal bias (STB), elevation zone bias (EZ), power transform (PT), distribution transformation (DT), and quantile mapping based on an empirical distribution (QME). We used daily time series (1998–2013) from 60 gauge stations and CMORPH SREs for the Zambezi basin. To evaluate the effectiveness of the bias-correction schemes spatial and temporal cross-validation was applied based on eight stations and on the 1998–1999 CMORPH time series, respectively. For correction, STB and EZ schemes proved to be more effective in removing bias. STB improved the correlation coefficient and Nash–Sutcliffe efficiency by 50 % and 53 %, respectively, and reduced the root mean squared difference and relative bias by 25 % and 33 %, respectively. Paired t tests showed that there is no significant difference (p < 0.05) in the daily means of CMORPH against gauge rainfall after bias correction. ANOVA post hoc tests revealed that the STB and EZ bias-correction schemes are preferable. Bias is highest for very light rainfall (< 2.5 mm d−1), for which most effective bias reduction is shown, in particular for the wet season. Similar findings are shown through quantile–quantile (q–q) plots. The spatial cross-validation approach revealed that most bias-correction schemes removed bias by > 28 %. The temporal cross-validation approach showed effectiveness of the bias-correction schemes. Taylor diagrams show that station elevation has an influence on CMORPH performance. Effects of distance > 10 km from large-scale open water bodies are minimal, whereas effects at shorter distances are indicated but are not conclusive for a lack of rain gauges. Findings of this study show the importance of applying bias correction to SREs.

Spatiotemporal precipitation variability and potential drivers during 1961–2015 over the Yellow River Basin, China

Precipitation is a crucial element in the study of climate change and is a key component of climate change processes and the hydrological cycle. Changes in precipitation therefore have wide ranging implications across various fields, including agriculture, ecology and economic development. Known as the ’Mother River of China’, the Yellow River is the sixth longest river in the world and the second‐longest in the country, and has a unique political, economic and cultural role in China. Flowing through arid, semi‐arid and semi‐humid regions, it is well known for its high sand content and limited water resources. Precipitation in the Yellow River Basin (YRB) directly affects not only water security, but also eco‐system health and socio‐economic development. As a climatic phenomenon with characteristics that vary across both space and time, precipitation pattern analysis requires the examination of spatiotemporal changes. This study made use of interpolation grids in the analysis of data from all rainfall gauges associated with the National Meteorological Information Center and China Meteorological Administration; this constitutes a new and more representative method of assessing the spatiotemporal distribution of precipitation over the entire Yellow River basin. Moreover, the precipitation records for the last decade – in which significant variation has been found – were reanalysed to assess changes in precipitation patterns over the region. The results showed a slight increase in precipitation in the upper reaches of the river and the northern part of the basin, along with a slight decrease in the southern part and a significant increasing trend from 1990 to 2009 for areal average annual precipitation. The locations of annual precipitation maxima shifted to the west, especially for the 400mm isoline. Relative to the 1956–2000 series, the new updated series showed a reverse movement for the 400mm precipitation contours. The Hurst exponent indicated the presence of a random series for the longitudinal movement data and a sustained state for the latitude data. The potential drivers of variation in precipitation over the YRB were also explored, and an increase in the extent of vegetation cover was identified as the dominant influencing factor. However, given the huge uncertainty in projections of future global warming, climate change impacts should also be considered; for example, rapid temperature increases might result in the degradation of vegetation cover. The results of the study indicate that there is the potential for an increased risk of both urban and rural flooding in the YRB, with its unique water–sediment conditions, along with negative outcomes for agriculture and the eco‐environment.

Multi-frequency effect towards High Conductivity Overburden Responses using Horizontal Coplanar (HCP) coil configuration for Landslide Hazard Instrumentation

The instrumentation for the sensing of natural disaster phenomena as like as landslide instrumentation has acquired great importance for the instrumentation community. The novel instrumentation for landslide application has been made and calibrated using Horizontal Coplanar (HCP) coil orientation. The early instrumentation for landslide slopes was gradually equipped with standard and automatic instrumentation consisting of three the piezometric cells, two deep-seated steel wire extensometers, four in clinometric tubes, a rainfall gauge, a snow gauge and an air thermometer. This novel instrumentation works in electromagnetic methods similar with electromagnetic methods in geophysics. HCP coil orientation is a coplanar array where both coils are horizontal. In HCP, the depth of sounding is about 1.5 times of the coils separation which have maximum coupled configuration (HCP, VCP, and Vertical Coaxial configuration). The overburden response had inverted the percent value of maximum negative response then there were three maximum response amplitude peaks. Measurement and data processing were performed using a computer through Delphi program. The diagonal cross sections of the overburden as the centre were the maximum value of inverted response of anomaly positions. In aluminium thin plate overburden, the diagonal cross section point as the overburden centre was the maximum value of inverting value of the highest minimum response amplitude. The measurement graphic in the computer screen with several types of overburdens would be displayed. As the result, that the overburden widths for overburden were determined by subtracted of two turning points positions. The overburden positions were accurate for Type P overburden. The master curve had a high anomaly contrasts with two of positive anomaly peaks. Otherwise, the anomaly positions were between two of the negative anomaly peaks.

Rainfall Intensity-Duration-Frequency Relationships for Risk Analysis in the Region of Matopiba, Brazil

Abstract The region of study was MATOPIBA, located in the north of Brazilian Savanna biome (Cerrado), encompassing part of north/northeast of Brazil. The region has been gaining prominence in the last years due to the expansion of agricultural over this area. The aims of this study were: to adjust parameters for rainfall intensity-duration-frequency; and to identify the most vulnerable agricultural areas to erosion based on erosivity and erodibility. The rainfall intensity-duration-frequency function were adjusted using series of maximum annual rainfall event from 105 rainfall gauges. Gumbel model was the most efficient to simulate the maximum rainfall intensity, where these data were used to adjusted the rainfall intensity-duration-frequency model based on K, a, b and c parameters. The most rainfall gauges showed intensity between 51 and 80 mm h-1 and 81 and 120 mm h-1, respectively, for return period of 2 and 100 years with rainfall duration of 30 minutes. The higher rainfall intensity was observed mainly in the central-north of the region associated with rainfall systems. The rainfall intensity showed a huge capacity to cause soil erosion based on the erosivity energy, while the moderate erodibility was observed for areas with Ferralsols and Leptosols and low erodibility for areas with Arenosols.

Characterizing precipitation events leading to surface water flood damage over large regions of complex terrain

Surface water floods (SWFs) that lead to household losses are mainly localized phenomena. Research on describing the associated precipitation characteristics has previously been based on case studies and on the derivation of local rainfall thresholds, but no approaches have yet been presented on the national scale. Here, we propose a new way to overcome this scaling problem. We linked a gridded precipitation dataset based on both rainfall gauges and radar data with geolocated insurance claims for all of Switzerland. We show that the absolute thresholds vary markedly over complex terrain, and we thus propose basing early warning systems for predicting damage-relevant SWF events on local quantiles of maximum intensity and the total sum of event precipitation. A threshold model based on these two parameters is able to classify rainfall events potentially leading to damage-relevant SWF events over large areas of complex terrain, including high mountains and lowland areas, and a variety of geological conditions. Our approach is an important step towards the development of impact-based early warning systems. Weather warning agencies or insurance companies can build upon these findings to find workarounds for issuing user-targeted warnings at national scale or for nowcasting purposes.

Rainfall Trend Analysis in Uma Oya Basin, Sri Lanka, and Future Water Scarcity Problems in Perspective of Climate Variability

The effect of climate variability on the rainfall pattern is canvassed on the Uma Oya river basin, Sri Lanka, consisting of 5 rainfall gauging stations. The Uma Oya basin (720 km2) is given utmost precedence due to environmental concerns seen in the ongoing Uma Oya multipurpose development project (529 million USD worth) which is expected to divert water to the southeast dry zone of the country while adding 231 GWh/year electricity to the national grid. The rainfall data for a period of 26 years (1992–2017) were analysed using Mann–Kendall’s test and Sen’s slope estimator test to identify the rainfall trends. Both of these trend analysis test results depict only one negative trend for Hilpankandura Estate for the month of June; however, the seasonal trend analysis and annual trend analysis do not support this observation. Nevertheless, Mann–Kendall’s test showed potential positive trends for the 3 rainfall gauging stations Kirklees Estate, Ledgerwatte Estate, and Welimada Group only in the 1st intermediate period (March-April), and this is well supported by the monthly trend analysis. Other than these trends, the results do not show any significant negative trends in the Uma Oya catchment. Therefore, the results vividly explain that there is no threat of water scarcity to the catchment area being resistant to changing global climate for the past 26 years.

Joint treatment of point measurement, sampling and neighborhood uncertainty in space-time rainfall mapping

The importance of representing the spatial structure of rainfall accurately has been emphasized in various hydrological studies. It has also been widely acknowledged that there is a need to account for uncertainty in rainfall input. Common approaches focus on accounting for either point measurement or sampling uncertainty in rainfall estimation. We present a method that jointly considers three sources of uncertainty affecting the space-time mapping of rainfall: point measurement, sampling and neighborhood uncertainty. To our knowledge, neighborhood uncertainty has not been included in any prior rainfall uncertainty analysis. We generated an ensemble of 400 realizations of daily rainfall fields at a 2 km × 2 km spatial resolution for a catchment in Western Denmark (1055 km2). At the core of our method is the sequential Gaussian simulation (SGS) technique. Results indicate that our approach is able to reproduce key statistical features of the rainfall distribution. We examined the impact of different spatial (grid and catchment) and temporal supports (one day, one month, 5-year period) on the overall uncertainty. We also quantified the effect of each uncertainty source on rainfall field uncertainty. Finally, we compared our simulation results with those of a parallel expert elicitation study. We found that the expert elicitation uncertainty for average catchment rainfall in a 5-year period was considerably larger than quantified in our study (CV of 1.1% vs. 5%). An even larger discrepancy was found for the 5-year average of gauge rainfall, where expert elicitation resulted in a value that was an order of magnitude higher (CV of 0.2% vs. 2%). Possible reasons for this gap are discussed.

Characteristics of Pore-Water Pressure Response in Slopes During Rainfall

Field measurements of residual soil slopes are valuable for the assessment of slope stability during rainfall. Several residual soil slopes, which are predominantly unsaturated due to their deep groundwater table, were instrumented with tensiometer and rainfall gauge in order to study the response characteristics of the slopes during rainfall. Comprehensive field measurement results involving a large number of rainfall events are presented in the paper. The results show that the pore-water pressures in all instrumented slopes increased in a similar fashion during rainfall, but at different magnitudes depending on soil type. The pore-water pressure response characteristics can then be used to estimate the variation in factor of safety of the slope during rainfall.

Análise Hidrometeorológica Associada a Dois Eventos Extremos de Cheia Ocorridos na Bacia do Rio Pavuna, Estado do Rio de Janeiro, Brasil

Hydrometeorological extreme events are responsible for large socioeconomic losses worldwide. The impacts related to these events can be mitigated through preventive measures derived mainly from scientific research aimed at providing basic tools for operational forecasting and alert systems. In this context, this work sought to characterize the atmospheric conditions leading to the formation of rainfall associated with two extreme events of Pavuna River flooding (Rio de Janeiro State), occurred on December 30th, 2013 and January 16th, 2014, as well as to evaluate the spatial pattern of clouds over Pavuna River basin and the accumulated rainfall related to the changes of water level observed. The dynamic and thermodynamic atmospheric indicators showed a concomitant presence of energy, moisture and dynamic triggers at different levels characterizing environment scenarios favorable to the formation of precipitation in both events. From the radar images and rainfall gauge data it was possible to verify that the precipitation occurred spatially more distributed over the Pavuna River basin for the event of December 2013. For the event of January 2014, however, there was a more localized distribution of rainfall was observed over the basin, as well as higher values of reflectivity and rainfall rates over the common outlet. Using the rainfall data measured at three points in the Pavuna River basin, it was possible to verify that, despite the higher rainfall rates observed over common outlet exudation in the second hydrometeorological event, the highest rainfall accumulations were observed in the first event analyzed, favoring a higher observed.

Hydrological appraisal of rainfall estimates from radar, satellite, raingauge and satellite–gauge combination on the Qinhuai River Basin, China

ABSTRACTMultisource rainfall products can be used to overcome the absence of gauged precipitation data for hydrological applications. This study aims to evaluate rainfall estimates from the Chinese S-band weather radar (CINRAD-SA), operational raingauges, multiple satellites (CMORPH, ERA-Interim, GPM, TRMM-3B42RT) and the merged satellite–gauge rainfall products, CMORPH-GC, as inputs to a calibrated probability distribution model (PDM) on the Qinhuai River Basin in Nanjing, China. The Qinhuai is a middle-sized catchment with an area of 799 km2. All sources used in this study are capable of recording rainfall at high spatial and temporal resolution (3 h). The discrepancies between satellite and radar data are analysed by statistical comparison with raingauge data. The streamflow simulation results from three flood events suggest that rainfall estimates using CMORPH-GC, TRMM-3B42RT and S-band radar are more accurate than those using the other rainfall sources. These findings indicate the potential to use satellite and radar data as alternatives to raingauge data in hydrological applications for ungauged or poorly gauged basins.

Improving Spatial Rainfall Estimates at Mt. Merapi Area Using Radar-Rain Gauge Conditional Merging

Rainfall monitoring is important for providing early warning of lahar flow around Mt. Merapi. The X-band multi-parameter radar developed to support these warning systems provides rainfall information with high spatial and temporal resolution. However, this method underestimates the rainfall compared with rain gauge measurements. Herein, we performed conditional radar-rain gauge merging to obtain the optimal rainfall value distribution. By using the cokriging interpolation method, kriged gauge rainfall, and kriged radar rainfall data were obtained, which were then combined with radar rainfall data to yield the adjusted spatial rainfall. Radar-rain gauge conditional merging with cokriging interpolation provided reasonably well-adjusted spatial rainfall pattern.

Typical Rainfall Distribution Pattern of Flood Event Caused by Tropical Cyclone at Bima City, West Nusa Tenggara, Indonesia

Tropical cyclones are the most serious meteorological phenomena that hit Bima city in December 2016. The strong winds and heavy precipitation associated with a typhoon significantly affect the weather in this city. The impact of a tropical cyclone on precipitation variability in Bima is studied using rainfall data for analyzing hourly rainfall distribution pattern during the event. Depend on the geographic situation and climate characteristic, the hourly rainfall distribution pattern of one area is different to others area. The research aims to analyze hourly rainfall distribution pattern in the form of the rainfall intensity distribution. This research is conducted using one automatic rainfall gauge in Bima city, West Nusa Tenggara province that obtained from Regional Disaster Management Agency (BPBD). The results showed that two events of rainfall were recorded. The first rainfall event was on 20th to 21st December 2016 with a total rainfall 191.4 mm. The second rainfall event occurred on 22nd to 23rd December 2016 with a total rainfall 126.2 mm. The rainfall distribution pattern has rainfall intensity peak at 45% of duration with cumulative rainfall reached 70%. It was found there is no common pattern of temporal rainfall distribution for rainfall induced by tropical cyclones.

Investigating the performance of satellite and reanalysis rainfall products at monthly timescales across different rainfall regimes of Ethiopia

ABSTRACTContinuous availability of a variety of satellite and reanalysis rainfall products have triggered the use of such products as an alternate source of rainfall data in sparsely gauge networked areas. However, before utilizing them a detailed validation of these datasets are essential to have some level of guarantee. In many parts of Africa in general and most parts of Ethiopia particularly in the lowland areas, gauge stations are very sparse and unevenly distributed. In addition, due to the nature of complex topography and geographical location, Ethiopian rainfall shows high variability both temporally and spatially. In view of the above, the present study is aimed at statistically evaluating such rainfall products across different rainfall regimes (regions with different rainfall characteristics as defined by National Meteorological Agency (NMA) of Ethiopia). In the current study, five satellite and two reanalysis rainfall products such as African Rainfall Climatology version 2 (ARC2), Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT), Tropical Rainfall Measuring Mission-3B43 version 7 (TRMM 3B43v7), Climate Prediction Center Morphing Technique (CMORPH), Climate Hazards Group Infrared Precipitation with Stations version 2 (CHIRPSv2), the Climate Forecast System Reanalysis (CFSR) and the European Center for Medium Range Weather Forecast Reanalysis (ERA-Interim) are considered based on their spatial coverage, spatial resolution, temporal resolution, latency period and length of data records. Evaluation is done at monthly and seasonal time scales against the observed gauge rainfall data provided by the National Meteorological Agency of Ethiopia across entire Ethiopia in two different manners, first by considering the entire country as one homogeneous unit and secondly in a distributed manner across the three rainfall regimes of Ethiopia. The obtained results show that: (i) CHIRPSv2 and TRMM 3B43v7 show better performance during June to September (the main rainfall season) and during February to May (the smaller rainfall season) in regimes 1 and 2. (ii) In regime 3 these products show good performance from October to November (smaller rainy season of this regime) and March to May (main rainy season of this regime); (iii).CMORPH, TAMSAT and ARC2 show moderate performance in all three regimes; (iv) CFSR and ERA-Interim exhibit poor performance in all rainfall regimes. Overall, the detailed analysis of statistical evaluation results of the rainfall products at monthly timescale shows that CHIRPSv2 performs comparatively better than the other tested rainfall products across all rainfall regimes. However, the best performance of CHIRPSv2 is obtained in regime 2 followed by regime 1 and regime 3.

Ensemble flood forecasting considering dominant runoff processes – Part 1: Set-up and application to nested basins (Emme, Switzerland)

Abstract. Flash floods evolve rapidly during and after heavy precipitation events and represent a potential risk for society. To predict the timing and magnitude of a peak runoff, it is common to couple meteorological and hydrological models in a forecasting chain. However, hydrological models rely on strong simplifying assumptions and hence need to be calibrated. This makes their application difficult in catchments where no direct observation of runoff is available. To address this gap, a flash-flood forecasting chain is presented based on (i) a nowcasting product which combines radar and rain gauge rainfall data (CombiPrecip); (ii) meteorological data from state-of-the-art numerical weather prediction models (COSMO-1, COSMO-E); (iii) operationally available soil moisture estimations from the PREVAH hydrological model; and (iv) a process-based runoff generation module with no need for calibration (RGM-PRO). This last component uses information on the spatial distribution of dominant runoff processes from the so-called maps of runoff types, which can be derived with different mapping approaches with increasing involvement of expert knowledge. RGM-PRO is event-based and parametrised a priori based on the results of sprinkling experiments. This prediction chain has been evaluated using data from April to September 2016 in the Emme catchment, a medium-sized flash-flood-prone basin in the Swiss Prealps. Two novel forecasting chains were set up with two different maps of runoff types, which allowed sensitivity of the forecast performance to the mapping approaches to be analysed. Furthermore, special emphasis was placed on the predictive power of the new forecasting chains in nested subcatchments when compared with a prediction chain including an original version of the runoff generation module of PREVAH calibrated for one event. Results showed a low sensitivity of the predictive power to the amount of expert knowledge included for the mapping approach. The forecasting chain including a map of runoff types with high involvement of expert knowledge did not guarantee more skill. In the larger basins of the Emme region, process-based forecasting chains revealed comparable skill to a prediction system including a conventional hydrological model. In the small nested subcatchments, although the process-based forecasting chains outperformed the original runoff generation module, no forecasting chain showed satisfying skill in the sense that it could be useful for decision makers. Despite the short period available for evaluation, preliminary outcomes of this study show that operational flash-flood predictions in ungauged basins can benefit from the use of information on runoff processes, as no long-term runoff measurements are needed for calibration.

Modification of Manual Raindrops Type Observatory Ombrometer with Ultrasonic Sensor HC-SR04

Water, in any way it comes, is important for the life of all living things. Indonesia is an area of tropical equatorial with a variation of rain, which is quite high. The regularity of the distribution of rainfall is one of the aspects most important to the activity of the community. As the development of technology, the intensity of rainfall can be measured manually using Ombrometer Observatory tool. The manual tool for measuring the rain precipitation, Ombrometer Observatorium, is used to take data manually. Samples should be taken at 7.00 a.m. everyday using a measuring cup to know the height of the water contained. However, the type is prone to error at the high rainfall intensity, since the drainage of the samples is conducted every 24 hours. Therefore, much water is wasted. To solve the problem, a modification of a rainfall gauge was made, that is Ombrometer Observatory with ultrasonic sensor HC-SR04. The height of the water in the container is sent through a server of which the data is stored in the database every ten minutes to reduce the risk of evaporation. It also minimizes the error in measuring the rainfall intensity. The results have been compared to the ones by BMKG (Meteorology, Climatology, and Geophysics Agency). The correlation value of the measurement ratio reached 0.9739 or 97.39%.

A Step towards Integrating CMORPH Precipitation Estimation with Rain Gauge Measurements

Accurate daily rainfall estimation is required in several applications such as in hydrology, hydrometeorology, water resources management, geomorphology, civil protection, and agriculture, among others. CMORPH daily rainfall estimations were integrated with rain gauge measurements in Brazil between 2000 and 2015, in order to reduce daily rainfall estimation errors by means of the statistical objective analysis scheme (SOAS). Early comparisons indicated high discrepancies between daily rain gauge rainfall measurements and respective CMORPH areal rainfall accumulation estimates that tended to be reduced with accumulation time span (e.g., yearly accumulation). Current results show CMORPH systematically underestimates daily rainfall accumulation along the coastal areas. The normalized error variance (NEXERVA) is higher in sparsely gauged areas at Brazilian North and Central-West regions. Monthly areal rainfall averages and standard deviation were obtained for eleven Brazilian watersheds. While an overall negative tendency (3 mm·h−1) was estimated, the Amazon watershed presented a long-term positive tendency. Monthly areal mean precipitation and respective spatial standard deviation closely follow a power-law relationship for data-rich watersheds, i.e., with denser rain gauge networks. Daily SOAS rainfall accumulation was also used to calculate the spatial distribution of frequencies of 3-day rainfall episodes greater than 100 mm. Frequencies greater than 3% were identified downwind of the Peruvian Andes, the Bolivian Amazon Basin, and the La Plata Basin, as well as along the Brazilian coast, where landslides are recurrently triggered by precipitation.

Formulation of Flood Inundation Map in Jamalpur District by Mathematical Modeling using HEC Packages

Bangladesh lies in the convergence zone of three large rivers of the world-Ganges, Brahmaputra, Meghna, which is called the GBM basin. The Ganges, Brahmaputra and Meghna (GBM) river basins occupy about 1.75 x 106 km2 of the Himalayan region. More than half a billion people in Nepal, India, Bhutan and Bangladesh are directly or indirectly dependent on the water resources of the GBM rivers. Being in this zone, Bangladesh faces heavy rainfall in the monsoon season of June to October, thus a huge amount of water drains out each year through the country. This drain out, along with various other reasons, causes frequent flooding all over Bangladesh. Thus, it is necessary to develop tools for the hydrological study of these three river basins or prediction tools to understand the consequence of different rainfall patterns. The reason of flood in the northern and mid-northern part of Bangladesh is overflowing of Brahmaputra (Jamuna) river. In this thesis work, different hydrologic and hydrodynamic models have been used to go through a number of steps and ultimately, a flood inundation map of Jamalpur district, which lies in the bank of Jamuna river, has been generated. The hydrologic model, HEC-HMS (Hydrologic Engineering Center-Hydrologic Modeling System) has been used to delineate the whole Brahmaputra watershed which lies mostly in the Indian north-eastern region, on the foothills of the Himalayas. Gridded rainfall gages from NASA (National Aeronautics and Space Administration) were used for precipitation input. The model was calibrated and validated for 2 different years at Bahadurabad point at Jamalpur. The purpose of calibration and validation was to set up a certain set of model parameters for different rainfall patterns. HECRAS (Hydrologic Engineering Center-River Analysis System) mathematical model was used to point out the water level for the resultant discharge. With the help of Arc-GIS (Geographical Information System) and HEC-GeoRAS (Hydrologic Engineering Center-Geographic River Analysis System), the flood inundation map of Jamalpur was generated for some of the highest water levels. Expectedly, the highest amount of inundation was observed in the months of July, August and September, the months of high monsoon rainfall. Some of the shortcomings of the thesis work were not counting on the land use pattern of the Brahmaputra basin for discharge, using arithmetic mean for sub basin rainfall gage weights, using observed water level in the downstream boundary condition for HEC-RAS etc. The flood inundation mapping can be used for future flood hazard management for predicted rainfall and help mitigate the damage and further losses. The hydrologic modeling can be used for further calibration for future precipitation prediction, also can be used for categorizing hydrologic characteristics for Brahmaputra basin.

Statistical and Hydrological Evaluations of Multi-Satellite Precipitation Products over Fujiang River Basin in Humid Southeast China

The purpose of the paper is to evaluate the quality and hydrological utility of four popular satellite precipitation products, including the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product (3B42V7), near real-time product (3B42RT), and the Climate Prediction Center (CPC) MORPHing technique (CMORPH) satellite–gauge merged product (CMORPH BLD) and bias-corrected product (CMORPH CRT) over Fujiang River basin, China. First, we provided a statistical assessment of the four precipitation products at multiple spatiotemporal scales. The results show that: (1) all the products except 3B42RT capture the spatial pattern of annual precipitation fairly well; (2) in general, CMORPH BLD benefits from the application of the probability density function-optimal interpolation (PDF-OI) gauge adjustment algorithm and performs best among all the products with Pearson correlation coefficients (CC) of 0.84 and 0.94, equitable threat score (ETS) of 0.56 and 0.63 in grid and basin scales, respectively, followed by 3B42V7 and CMORPH CRT; whereas 3B42RT performs worst across all the metrics; (3) according to the occurrence frequencies of rainfall, satellite estimates mainly fall into the bin of 0–1 mm/day and tend to underestimate light precipitation. In addition, the performance of all the products in warm season is much better than in cold season in both grid and basin scales. Subsequently, a physically based distributed model is established to further evaluate the hydrological utility of different precipitation products. The results reveal that: (1) the errors in precipitation products mainly propagate into hydrological simulations, resulting in the best hydrological performance in CMORPH BLD in both daily and monthly scales after recalibrating the model, while 3B42RT shows limited skills in reproducing the daily observed hydrograph; (2) after recalibrating the model with the respective satellite data, significant improvements are observed for all the products; (3) CMORPH BLD no longer shows its superiority during near-real-time monitoring of floods. There is still a great challenge for the application of current satellite-based estimates into local flood monitoring. This study could be used as guidance for choosing alternative satellite precipitation products for hydrological applications in a local community, particularly in basins in which rainfall gauges are scarce.

Evaluation and Hydrologic Validation of Three Satellite-Based Precipitation Products in the Upper Catchment of the Red River Basin, China

Satellite-based precipitation products (SPPs) provide alternative precipitation estimates that are especially useful for sparsely gauged and ungauged basins. However, high climate variability and extreme topography pose a challenge. In such regions, rigorous validation is necessary when using SPPs for hydrological applications. We evaluated the accuracy of three recent SPPs over the upper catchment of the Red River Basin, which is a mountain gorge region of southwest China that experiences a subtropical monsoon climate. The SPPs included the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 product, the Climate Prediction Center (CPC) Morphing Algorithm (CMORPH), the Bias-corrected product (CMORPH_CRT), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Climate Data Record (PERSIANN_CDR) products. SPPs were compared with gauge rainfall from 1998 to 2010 at multiple temporal (daily, monthly) and spatial scales (grid, basin). The TRMM 3B42 product showed the best consistency with gauge observations, followed by CMORPH_CRT, and then PERSIANN_CDR. All three SPPs performed poorly when detecting the frequency of non-rain and light rain events (<1 mm); furthermore, they tended to overestimate moderate rainfall (1–25 mm) and underestimate heavy and hard rainfall (>25 mm). GR (Génie Rural) hydrological models were used to evaluate the utility of the three SPPs for daily and monthly streamflow simulation. Under Scenario I (gauge-calibrated parameters), CMORPH_CRT presented the best consistency with observed daily (Nash–Sutcliffe efficiency coefficient, or NSE = 0.73) and monthly (NSE = 0.82) streamflow. Under Scenario II (individual-calibrated parameters), SPP-driven simulations yielded satisfactory performances (NSE >0.63 for daily, NSE >0.79 for monthly); among them, TRMM 3B42 and CMORPH_CRT performed better than PERSIANN_CDR. SPP-forced simulations underestimated high flow (18.1–28.0%) and overestimated low flow (18.9–49.4%). TRMM 3B42 and CMORPH_CRT show potential for use in hydrological applications over poorly gauged and inaccessible transboundary river basins of Southwest China, particularly for monthly time intervals suitable for water resource management.