Gridded Rainfall Data Research Articles

Evaluating the Performance of CHIRPS Satellite Rainfall Data for Streamflow Forecasting

Streamflow forecasting can offer valuable information for optimal management of water resources, flood mitigation, and drought warning. This research aims in evaluating the effectiveness of CHIRPS satellite rainfall data in comparison with IMD gridded Rainfall Data and development of various flow forecasting models. Daily rainfall data for three decades (1983–2012) over the Nethravathi Basin, Karnataka, India is used for analysis. The analysis is carried out for the monsoon season (June–September), out of which 70% data considered for training the model and remaining for testing. Different input combinations are developed, and soft-computing methods like ANFIS, GRNN, PSO-ANN, and ELM are applied for flow forecasting on a temporal scale. The model performance is evaluated using various statistical indices like NNSE, RRMSE, and MAE. The results indicate that CHIRPS rainfall showed better performance in comparison with IMD data. ELM expressed an enhanced effect when compared to all other methods. The usefulness and effectiveness of CHIRPS data compared to IMD data has been explored.

The Role of the Spatial Distribution of Radar Rainfall on Hydrological Modeling for an Urbanized River Basin in Japan

Recently, the use of gridded rainfall data with high spatial resolutions in hydrological applications has greatly increased. Various types of radar rainfall data with varying spatial resolutions are available in different countries worldwide. As a result of the variety in spatial resolutions of available radar rainfall data, the hydrological community faces the challenge of selecting radar rainfall data with an appropriate spatial resolution for hydrological applications. In this study, we consider the impact of the spatial resolution of radar rainfall on simulated river runoff to better understand the impact of radar resolution on hydrological applications. Very high-resolution polarimetric radar rainfall (XRAIN) data are used as input for the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) to simulate runoff from the Tsurumi River Basin, Japan. A total of 20 independent rainfall events from 2012–2015 were selected and categorized into isolated/convective and widespread/stratiform events based on their distribution patterns. First, the hydrological model was established with basin and model parameters that were optimized for each individual rainfall event; then, the XRAIN data were rescaled at various spatial resolutions to be used as input for the model. Finally, we conducted a statistical analysis of the simulated results to determine the optimum spatial resolution for radar rainfall data used in hydrological modeling. Our results suggest that the hydrological response was more sensitive to isolated or convective rainfall data than it was to widespread rain events, which are best simulated at ≤1 km and ≤5 km, respectively; these results are applicable in all sub-basins of the Tsurumi River Basin, except at the river outlet.

Symmetrical uncertainty and random forest for the evaluation of gridded precipitation and temperature data

Selection of appropriate gridded rainfall and temperature data is a key problem for hydro-climatic studies, particularly in regions where long-term reliable and dense observations are not available. The ability of two intelligent algorithms, symmetrical uncertainty (SU) and random forest (RF), to assess the degree of similarity or the distance between two time series was utilized in this study for the evaluation of gridded climate data. In this study, the performances of seven widely used gridded rainfall datasets and five temperature datasets were evaluated against the available station data in Egypt. Monthly rainfall and mean temperature data recorded at 57 locations for the period 1979–2014 were used for this purpose. The results revealed the better performance of Global Precipitation Climatology Centre (GPCC) gridded rainfall and University of Delaware (Udel) gridded temperature data in replicating observed rainfall and mean temperature, respectively, in most of the locations in Egypt. Validation of the results using conventional statistical metrics revealed the better performance of different datasets in term of different metrics at different locations. However, the mean values of all the metrics support the results obtained using SU and RF. The study indicates that SU and RF can be used for the selection of appropriate gridded rainfall and temperature data by avoiding confusion arise from contradictory results obtained using various statistical metrics.

Assessment of spatial uncertainty of heavy rainfall at catchment scale using a dense gauge network

Abstract. Hydrology and remote-sensing communities have made use of dense rain-gauge networks for studying rainfall uncertainty and variability. However, in most regions, these dense networks are only available at small spatial scales (e.g., within remote-sensing subpixel areas) and over short periods of time. Just a few studies have applied a similar approach, i.e., employing dense gauge networks to catchment-scale areas, which limits the verification of their results in other regions. Using 10-year rainfall measurements from a network of 150 rain gauges, WegenerNet (WEGN), we assess the spatial uncertainty in observed heavy rainfall events. The WEGN network is located in southeastern Austria over an area of 20 km × 15 km with moderate orography. First, the spatial variability in rainfall in the region was characterized using a correlogram at daily and sub-daily scales. Differences in the spatial structure of rainfall events between warm and cold seasons are apparent, and we selected heavy rainfall events, the upper 10 % of wettest days during the warm season, for further analyses because of their high potential for causing hazards. Secondly, we investigated the uncertainty in estimating mean areal rainfall arising from a limited gauge density. The average number of gauges required to obtain areal rainfall with errors less than a certain threshold (≤20 % normalized root-mean-square error – RMSE – is considered here) tends to increase, roughly following a power law as the timescale decreases, while the errors can be significantly reduced by establishing regularly distributed gauges. Lastly, the impact of spatial aggregation on extreme rainfall was examined, using gridded rainfall data with various horizontal grid spacings. The spatial-scale dependence was clearly observed at high intensity thresholds and high temporal resolutions; e.g., the 5 min extreme intensity increases by 44 % for the 99.9th and by 25 % for the 99th percentile, with increasing horizontal resolution from 0.1 to 0.01∘. Quantitative uncertainty information from this study can guide both data users and producers to estimate uncertainty in their own observational datasets, consequently leading to the sensible use of the data in relevant applications. Our findings could be transferred to midlatitude regions with moderate topography, but only to a limited extent, given that regional factors that can affect rainfall type and process are not explicitly considered in the study.

A classification of synoptic weather patterns linked to extreme rainfall over the Limpopo River Basin in southern Africa

In the last few decades, the Limpopo River Basin (LRB) has experienced a number of extreme rainfall events which were responsible for considerable socio-economic and environmental impacts. Most of the population here is poor and dependent on rain-fed agriculture. In order to better understand these events over the LRB, CHIRPS, 0.05° gridded rainfall data are used to identify the daily extreme events, analyse their interannual variability and examine relationships with large scale climate modes over the 1981–2016 period. Analysis of the top 20 events suggests a pattern with rainfall generally decreasing from the eastern to western parts of the basin. Typically, the highest rainfall amounts occur over the regions where there are steep topographical gradients between the mountainous regions of northeastern South Africa and the Mozambican floodplains. Almost half of the top 200 extreme events are associated with tropical extra-tropical cloud bands (48%), with tropical low-pressure systems (28%), Mesoscale Convective Systems (14%), and cut-off lows (10%) in the mid-upper atmosphere, also making sizeable contributions. The monthly distribution of the events showed that most of the events occurred during the late summer months (January–March) when tropical lows and cloud bands are more common. On interannual time-scales, most of the summers with above average number of events coincide with La Nina conditions and, to lesser extent, a positive subtropical South Indian Ocean Dipole.

MOS guidance using a neural network for the rainfall forecast over India

In the present study, a model output statistics (MOS) guidance model was developed by using the neural network technique for a bias-corrected rainfall forecast. The model was developed over the Indian window (0–\(40{^{\circ }}\hbox {N}\) and 60–\(100{^{\circ }}\hbox {E}\)) by using the observed and global forecast system (GFS) T-1534 model output (up to 5 days) at a \(0.125{^{\circ }} \times \,0.125{^{\circ }}\) regular grid during the summer monsoon (June–September) 2016. The skill of the developed MOS model forecast against the observed \(0.125{^{\circ }} \times 0.125{^{\circ }}\) grid rainfall data is obtained for the summer monsoon (June–September) 2017. The skill of the MOS model rainfall forecast is found to show good improvement over the T-1534 model’s direct forecast over the Indian window. In general, the T-1534 model’s direct forecast shows high skill but the forecast obtained by using the MOS model shows better skill than the direct model’s forecast, although a major improvement is seen for the Day 1 forecast at the national level. So the skill of the bias-corrected rainfall forecast by using the MOS guidance and the T-1534 model output is high and has the potential of being used as an operational forecast over the Indian region.

A conceptually superior variant of Shepard's method with modified neighbourhood selection for precipitation interpolation

AbstractThe accuracy of gridded precipitation data depends on the availability of a uniformly spaced rain gauge network and an appropriate spatial interpolation method that considers the rainfall variability and other factors that influence the precipitation patterns in the region of interest. In the current study, conceptually superior variants of a widely used spatial interpolation algorithm, Shepard's method, are proposed, formulated and evaluated to overcome one of the major limitations in neighbourhood selection, that is, arbitrary selection of rain gauges. The variants provide mechanisms to objectively select the rain gauges (control points) based on correlation (variant 1), distribution similarity (variant 2) and a combination of both (variant 3). The improved variants were used in the development of gridded rainfall data at a resolution of 5 km over the Kabini River basin in south India, and in the state of Kentucky, United States. Results from multiple experiments using the original Shepard's method and its variants indicate improvements in the accuracy of precipitation estimates. Also, these variants have preserved the site‐specific statistics and distributional characteristics of the rainfall data. A variant 1 that uses a correlation‐based neighbourhood selection criterion performed better for daily and monthly data compared to others and is suitable for generation of gridded rainfall data. The variant 1 when used with information from clustering of sites for selection of the neighbours has led to improvement in gridded precipitation data estimates. The proposed variant 1 can also be used for point data estimation useful for filling missing data at any site.

Some Aspects of the Vagaries in the Pattern of Meteorological Parameters in the State of Rajasthan in Association with the LULC Change

Present work examined the role of the change in land use and land cover (LULC) in altering the rainfall and temperature pattern over the western part of the Rajasthan, India. For this purpose, the high-resolution gridded rainfall and surface temperature data sets prepared by IMD (India Meteorological Department) and Climatological Research Unit Time Series (CRU TS 3.21) have been utilized. An increase in surface temperature and decrease in rainfall for the principal monsoon season (JJAS) during the period 1969–2012 is observed over the study region. Statistically robust significance test establish the fact that the pattern of rainfall and surface temperature has changed significantly during the past decades. Comparative analysis of the satellite observations from Landsat 5 TM (1992) and Landsat 8 OLI/TIRS (2012) revealed a considerable LULC changes in terms of the increase in vegetated land and urban settlement areas, whereas sand and water bodies have witnessed a decrease in area. Remotely sensed images from the OLS sensor (mounted on DMSP) indicate the escalation in the urbanization signature over the study region. Present analysis reveals that the correlation between the rainfall/surface temperature and the parameters which are more influenced by the local forcing (such as relative humidity/specific humidity/upward long-wave radiation) is not significant. Therefore, it is proposed that the changes observed in the rainfall and temperature pattern over the study area is much more influenced by the large scale forcing and moderately could be associated with the LULC changes and variations in the land-atmosphere fluxes. Keywords: IMD, CRU TS 3.2, JJAS, cumulative distribution, LULC, statistically robust, significant

Modulation of active-break spell of Indian summer monsoon by Madden Julian Oscillation

The Madden Julian Oscillation (MJO) is the major fluctuation in tropical weather on weekly to monthly time scale and a major driver of Indian summer monsoon (ISM). In this study, using Indian Meteorological Department (IMD) high resolution daily gridded rainfall data and Wheeler-Hendon MJO indices, the daily rainfall distribution over India associated with various phases of eastward propagating MJO was examined to understand the MJO–monsoon rainfall relationship. The present study reveal that the onset of break and active events over India and the duration of these events are strongly related to the phase and strength of the MJO. The break events were relatively better associated with the strong MJO phases than the active events. About 80% of the break events were found to be set in during the phases 1, 2, 7 and 8 of MJO with maximum during phase 1 (34%). On the other hand, about 58% of the active events were set in during the MJO phases 3–6 with maximum during phase 6 (21%). The results of this study indicate an opportunity for using the real time information and skillful prediction of MJO phases for the extended range prediction of break and active conditions.

Comparison of different satellite-derived rainfall products with IMD gridded data over Indian meteorological subdivisions during Indian Summer Monsoon (ISM) 2016 at weekly temporal resolution

In this study, effort is made to estimate weekly rainfall over India at 36 meteorological subdivisions during monsoon 2016 (June to September) by using several precipitation products from different satellites which include Indian National Satellite System (INSAT) INSAT-3D derived Hydro Estimate (HE), INSAT Multi-Spectral Rainfall Algorithm (IMSRA) rainfall products, Integrated Multi-satellite Retrievals for GPM (IMERG) derived from JPL, NASA, and National Center for Medium Range Weather Forecasting (NCMRWF) Merged Satellite Gauge (NMSG) of IMD. Different satellite estimates, and rain gauge satellite merged products have been compared with gridded rainfall products prepared by India Meteorological Department (IMD). HE at 4 km pixel resolution and IMSRA at 0.100 × 0.100 resolution are derived rainfall products from Indian satellite INSAT-3D.GPM (IMERG) product is derived at 0.100 × 0.100 resolution from Global Precipitation Mission (GPM) satellite jointly operated by JAXA (Japan) and NASA (USA). NCMRWF Merged Satellite-Gauge (NMSG) rainfall data has been generated at a 0.25° × 0.25° grid resolution by IMD & NCMRWF. The data is analyzed preliminarily for the southwest monsoon season of 2016 at weekly temporal resolution. The overall comparison shows that GPM (IMERG) estimates better rainfall over the coastal and plain region, however, HE estimates better rainfall over the hilly region, in general, and for Kerala region, particularly. NMSG product needs improvement over the hilly region and considerable improvement is required in IMSRA product. For all 36 meteorological subdivisions, NMSG product has 0.56 average Nash-Sutcliffe efficiencies (NSE) for 17 weeks with IMD Gridded rainfall data. However, for satellite-derived rainfall by GPM (IMERG), HE and IMSRA have 0.29, 0.1 and −0.8 average NSE at 17 weeks, respectively. Based on this study, it is concluded that NMSG daily rainfall product is best, however, amongst satellite-derived rainfall, overall GPM gives a better result with a few exceptions and IMSRA need more modifications.

Evaluation of the Weather Research and Forecasting Model Forecasts for Indian Summer Monsoon Rainfall of 2014 Using Ground Based Observations

The present study focuses on evaluating the ability of short-range rainfall forecasts from the Weather Research and Forecasting (WRF) model against observed gridded rainfall data from the India Meteorological Department (IMD) available over the Indian landmass during Indian summer monsoon (ISM) viz. June to September for the year 2014. The spatial distribution of the WRF predicted rainfall matches well with the IMD observed rainfall and indicates systematic rainfall biases over the Indian landmass. In general, precipitation is underestimated in dry, low elevation areas and overestimated in wet, high elevation areas by the WRF model. Based on rainfall verification scores, it was found that low and moderate rainfall was forecasted reasonably well by the WRF model compared to heavy rainfall. Moreover, extremal dependency score (EDS) has been used to verify heavy rainfall forecasts, as the traditional threat scores saturates towards high rainfall thresholds. Further, the separate analysis was carried out over five different homogeneous rainfall zones of India. Results show that the WRF model was able to predict rainfall reasonably well with higher correlation coefficient, lower bias and root mean square deviation in most of the zones. Moreover, the WRF rainfall forecasts at high spatial resolution (5 km) were also examined over Karnataka, India using dense rain gauge network.

Uncertainty in Estimated Trends Using Gridded Rainfall Data: A Case Study of Bangladesh

This study assessed the uncertainty in the spatial pattern of rainfall trends in six widely used monthly gridded rainfall datasets for 1979–2010. Bangladesh is considered as the case study area where changes in rainfall are the highest concern due to global warming-induced climate change. The evaluation was based on the ability of the gridded data to estimate the spatial patterns of the magnitude and significance of annual and seasonal rainfall trends estimated using Mann–Kendall (MK) and modified MK (mMK) tests at 34 gauges. A set of statistical indices including Kling–Gupta efficiency, modified index of agreement (md), skill score (SS), and Jaccard similarity index (JSI) were used. The results showed a large variation in the spatial patterns of rainfall trends obtained using different gridded datasets. Global Precipitation Climatology Centre (GPCC) data was found to be the most suitable rainfall data for the assessment of annual and seasonal rainfall trends in Bangladesh which showed a JSI, md, and SS of 22%, 0.61, and 0.73, respectively, when compared with the observed annual trend. Assessment of long-term trend in rainfall (1901–2017) using mMK test revealed no change in annual rainfall and changes in seasonal rainfall only at a few grid points in Bangladesh over the last century.

Evaluation of soil moisture prediction for Gopalkheda sub-catchment, India

ABSTRACT In present study, station-based daily rainfall data and gridded rainfall (0.25° × 0.25°) from India Meteorological Department (IMD) with lumped soil parameters have been used to simulate the soil moisture content within top 90-cm vertical soil profile at an interval depth of 15 cm using 1D numerical unsaturated flow module of coupled MIKE SHE/MIKE 11 for the Gopalkheda sub-catchment, India. The unsaturated flow parameters have been calibrated for the years 1991–1998 and validated for the years 1999–2004. The model has simulated stream flow at sub-catchment outlet and soil moisture content in the sub-catchment using both station-based rainfall and gridded rainfall datasets. The simulated results are compared with observed stream flows and available soil moisture data using statistical performance indices, i.e. Nash-Sutcliffe Efficiency (NSE), Root mean square error (RMSE), coefficient of determination (R 2). The results of calibrated model indicated that performance of hydrological model is highly dependent on nature of rainfall inputs into the model. The overall analysis indicated that gridded rainfall data are promising for stream flow and soil moisture predictions within the sub-catchment. The application of distributed hydrological model would be significant to water resource planner and agronomist in monitoring the water availability and soil moisture within their response sub-catchment.

Characteristics of Observed Meteorological Drought and its Linkage with Low-Level Easterly Wind Over India

Meteorological drought in India arises due to significant deficiency of rainfall for abnormal periods over an area. The large spatial and temporal variability of Indian summer monsoon rainfall (ISMR) over the Eastern Gangetic Plain (EGP) of India triggers meteorological drought (further leading to agricultural and hydrological drought), with widespread effects on both agricultural production and water resources over the area. To assess meteorological drought over agro-climatic zones of the states of Uttar Pradesh (UP), Bihar and West Bengal (WB) in the EGP, high-resolution gridded rainfall data (1961–2013) at resolution of 0.25° × 0.25° of India Meteorological Department (IMD) and u, v wind at 850 hPa at resolution of 0.25 × 0.25° of ERA-40 (1961–2002) of the European Centre for Medium-Range Weather Forecasts (ECMWF) is considered. Over the agro-climatic zones, the seasonality index (SI) of summer monsoon rainfall, spatial and temporal distribution of the 4-month Standardized Precipitation Index (SPI-4), frequency and probability of drought occurrence is estimated. The severe drought-prone zones are found to be over agro-climatic zones 6, 8 and 10 of UP; 1, 2 and 3B of Bihar with more than 50% probability of drought occurrence. At a 95% confidence level, a significant decrease in rainfall (for the period 1961–2013) is found over these zones. Over the EGP, a low-level easterly wind at 850 hPa in July is shifted towards foothills of the Himalaya, while in August it is weakened during drought conditions. This low-level easterly wind may be responsible for less moisture incursion over the Gangetic Plain from the Bay of Bengal, and may be the probable cause of less rainfall over the EGP, leading to meteorological drought.

ИЗОТОПНЫЙ СОСТАВ АТМОСФЕРНЫХ ОСАДКОВ В ПРЕДГОРЬЯХ АЛТАЯ: ДАННЫЕ НАБЛЮДЕНИЙ И ИНТЕРПОЛЯЦИИ

Актуальность. В последние годы соотношения стабильных изотопов кислорода и водорода в атмосферных осадках, полученных на станциях сети Global Network of Isotopes in Precipitation (GNIP), активно используются в качестве надежных маркеров современных, а также прогнозируемых климатических и гидрологических изменений. Однако для обширной территории России наборы таких данных существенно ограничены не только в пространстве, но и по времени. Одним из наиболее перспективных районов исследования в данном направлении является предгорье Алтая – территория, на которой отмечаются значительные климатические и гидрологические изменения в последние десятилетия. Цель: оценить в предгорьях Алтая изменение изотопного состава атмосферных осадков, отобранных в исследуемый период в соответствие с рекомендациями GNIP, и сравнить их с данными интерполяции на основе раннее полученных результатов по сети GNIP для сопредельных территорий. Методика. Отбор проб атмосферных осадков осуществлялся в соответствии с критериями GNIP. Синоптический анализ проводился с учетом данных метеонаблюдений ближайшей метеостанции Росгидромета. Изотопный анализ атмосферных осадков был выполнен методом лазерной абсорбционной ИК-спектрометрии на приборе PICARRO L2130-i. Полученные результаты сравнивали с интерполированными данными GNIP для предгорной зоны Алтая, рассчитанными с помощью ISOtopic GRIdded Rainfall Data Software (ISOGRIDS) и Isoscapes Modeling, Analysis and Prediction (IsoMAP). Результаты. Анализ изотопного состава атмосферных осадков, отобранных в 2016 г. в предгорьях Алтая в соответствие с рекомендациями GNIP, показал их существенное варьирование. Сравнение изотопного состава атмосферных осадков, одновременно отбиравшихся на изучаемой территории по двум схемам (индивидуальный отбор и ежемесячный), показало высокую степень согласованности, что позволяет при ограниченных условиях применять только их ежемесячный (не событийный) отбор. Сравнение средневзвешенных месячных значений изотопного состава отобранных атмосферных осадков и рассчитанных в IsoMAP и ISOGRIDS показало максимальные отличия в феврале и сентябре. Выявленные отличия связаны с особенностями синоптических условий 2016 г., а именно с устойчивыми положительными температурами в феврале, что нетипично для исследуемой территории, и небольшой повторяемостью осадков в сентябре.

Spatial distribution of the trends in precipitation and precipitation extremes in the sub-Himalayan region of Pakistan

The northern sub-Himalayan region is the primary source of water for a large part of Pakistan. Changes in precipitation and precipitation extremes in the area may have severe impacts on water security and hydrology of Pakistan. The objective of the study is to evaluate the spatial characteristics of the trends in annual and seasonal precipitation and precipitation extremes in Gilgit Baltistan, the northern administrative territory of Pakistan surrounded by Hindu Kush, Karakoram, and the Himalayan regions. The daily gridded rainfall data (1951–2007) of Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) at 0.25° spatial resolution was used for evaluating the trends. The novelty of the present study is the application of the modified Mann-Kendall (MMK) trend for the evaluation of the significance of precipitation trends in order to differentiate the secular trends from climate natural variability. Besides, cumulative distribution function (CDF) plots of daily rainfall for the early (1951–1980) and later (1981–2007) periods were used to show the changes in extremes. The results revealed no significant change in annual precipitation but increase in summer rainfall in the range of 0.25 to 1.25 mm/year in the upper part and decrease in winter precipitation from 0 to − 0.25 mm/year in the west part of the region. Annual number of rainy days was also found to decrease in winter up to − 1.33 days/decade where the region receives a major portion of total precipitation. The decrease in winter rainfall and rainy days caused an increase in continuous dry days (around 0.27 days/year) and decrease in continuous wet days (up to − 0.26 days/year). Trend analysis and CDF plot revealed that though the numbers of rainy days are decreasing, the numbers of extreme rainfall days are increasing, which indicates rainfall become more erratic and intense in the region. The increases in both continuous dry days and extreme rainfall days indicate more droughts and floods may have adverse impacts on the hydrology of Pakistan.

SEASONAL COMPARISON OF ERA-INTERIM PRECIPITATION DATASET FOR ENTIRE INDIAN REGION

Abstract. Era-Interim (ECMWF Re- analysis) is a global reanalysis atmospheric product which is being continuously updated in real time since 1979. It is also termed as third generation reanalysis product. The Era–Interim gives meteorological products like precipitation, temperature, etc. In the present work, 3-hourly Era–Interim product for the entire India is compared with gridded data provided by IMD for period 1979–2013 and APHRODITE data for period 1979–2007, respectively. The comparison is done on seasonal basis and the seasons are taken based on the pattern of rainfall, hence, the four seasons selected are DJF (December, January & February), MAM (March, April & May), JJAS (June, July, August & September) and ON (October & November). In the methodology the Era-Interim 3-hourly products are converted into the daily products and then it is used to form seasonal images for each year. All the images are then taken to form four images as outcome for the entire study period which represents the average rainfall (mm/day) for the entire region. This is being done for the IMD and for the APHRODITE Data. All the four images are then taken for the comparison with the reference images of the IMD 0.5º × 0.5º gridded rainfall data and with the APHRODITE 0.5º × 0.5º gridded rainfall data. The correlation coefficient and the RMSE for each season is calculated. The mean value is compared with the mean of IMD and APHRODITE rainfall products, respectively and a bias in mean is also calculated along with the scatter plots of Era-Interim with the reference datasets. The Era – Interim data came out with suitable comparative parameters with high correlation coefficient and low RMSE value in certain regions and in specific seasons. Scatter plots have also given good correlation in all the seasons. Bias maps have also shown very less bias in specific seasons for certain regions. The suitability maps prepared for the study region also shows that most of the region lies in most suitable range and very less in unsuitable range.

Assessment of SWAT Model Performance in Simulating Daily Streamflow under Rainfall Data Scarcity in Pacific Island Watersheds

Evaluating the performance of watershed models is essential for a reliable assessment of water resources, particularly in Pacific island watersheds, where modeling efforts are challenging due to their unique features. Such watersheds are characterized by low water residence time, highly permeable volcanic rock outcrops, high topographic and rainfall spatial variability, and lack of hydrological data. The Soil and Water Assessment Tool (SWAT) model was used for hydrological modeling of the Nuuanu area watershed (NAW) and Heeia watershed on the Island of Oahu (Hawaii). The NAW, which had well-distributed rainfall gauging stations within the watershed, was used for comparison with the Heeia watershed that lacked recoded rainfall data within the watershed. For the latter watershed, daily rain gauge data from the neighboring watersheds and spatially interpolated 250 m resolution rainfall data were used. The objectives were to critically evaluate the performance of SWAT under rain gauge data scarce conditions for small-scale watersheds that experience high rainfall spatial variability over short distances and to determine if spatially interpolated gridded rainfall data can be used as a remedy in such conditions. The model performance was evaluated by using the Nash–Sutcliffe efficiency (NSE), the percent bias (PBIAS), and the coefficient of determination (R2), including model prediction uncertainty at 95% confidence interval (95PCI). Overall, the daily observed streamflow hydrographs were well-represented by SWAT when well-distributed rain gauge data were used for NAW, yielding NSE and R2 values of > 0.5 and bracketing > 70% of observed streamflows at 95PCI. However, the model showed an overall low performance (NSE and R2 ≤ 0.5) for the Heeia watershed compared to the NAW’s results. Although the model showed low performance for Heeia, the gridded rainfall data generally outperformed the rain gauge data that were used from outside of the watershed. Thus, it was concluded that finer resolution gridded rainfall data can be used as a surrogate for watersheds that lack recorded rainfall data in small-scale Pacific island watersheds.

Unobserved component modeling for seasonal rainfall patterns in Rayalaseema region, India 1951–2015

Rainfall is the significant parameter for climate change, meteorological and hydrological process. The present paper describes the seasonal rainfall patterns in the Rayalaseema region of Andhra Pradesh state, India using the unobserved component model (UCM) with the hidden components like trend, seasonal, cyclical and irregular. The seasonal rainfall data were provided by India Meteorological Department (IMD), using daily gridded rainfall data with 10 automatic weather stations spread over the Rayalaseema region and the study deals with four seasons of rainfall as classified by IMD, namely winter, pre-monsoon, southwest monsoon and northeast monsoon. Basic Structural Model (BSM) with the components of constant trend, deterministic trigonometric seasonal, deterministic cycle and irregular is selected from the parsimonious models of UCM based on Akaike’s information criteria (AIC), Bayesian information criteria (BIC), significant tests and statistical fit. The model parameters are obtained using maximum likelihood method; the adequacy of the selected model is determined through correlation and normal diagnostics. The forecast of the seasonal rainfall patterns during the years 2016–2018 has been noticed with the help of selected UCM. From the model forecast, it is observed that the pre-monsoon season receives rainfall of 96.7 mm in the years 2016 and 2018, whereas 71.3 mm in the year 2017; the southwest monsoon season receives the rainfall of 396.0 mm in the year 2016, 424.2 mm in 2017 and 419.2 mm in 2018; the northeast monsoon season receives the rainfall of 286.8 mm in 2016, 261.3 mm in 2017 and 286.8 mm in 2018.

Usability of estimated soil moisture derived from gridded rainfall in agricultural operations

Soil moisture information has not been used extensively under PAN India mode operationally in the country due to sparse network of soil moisture measuring stations. Effort has been made to estimate the soil moisture using gridded rainfall data using soil water balance (SWB) model and subsequently to compare quantitatively with insitu soil moisture measured by gravimetric method. It is observed from the quantitative validation with ground observations that during monsoon season the accuracy with the observed was found to be fairly accurate from the early part of the July to the end of monsoon when the soil was almost recharged with the rainfall. One of the limiting factors is that during dry period and in the low rainfall areas accuracy is relatively low. Thus, the soil moisture information generated using gridded rainfall data can be used from the second month (July) of monsoon season to the end of the monsoon season on PAN India mode for irrigation scheduling and determination of sowing dates and for contingency crop planning etc.