Rain Gauge Data Research Articles

Precipitation Products’ Inter–Comparison over East and Southern Africa 1983–2017

During recent decades East Africa (EA) and Southern Africa (SA) have experienced an intensification of hydrological hazards, such as floods and droughts, which have dramatically affected the population, making these areas two of the regions of the African continent most vulnerable to these hazards. Thus, precipitation monitoring and the evaluation of its variability have become fundamentally important actions through the analysis of long-term data records. In particular, satellite-based precipitation products are often used because they counterbalance the sparsity of the rain gauge networks which often characterize these areas. The aim of this work is to compare and contrast the capabilities of three daily satellite-based products in EA and SA from 1983 to 2017. The selected products are two daily rainfall datasets based on high-resolution thermal infrared observations, TAMSAT version 3 and CHIRPS, and a relatively new global product, MSWEP version 2.2, which merges satellite-based, rain gauge and re-analysis precipitation data. The datasets have been directly intercompared, avoiding the traditional rain gauge validation. This is done by means of pairwise comparison statistics at 0.25° spatial resolution and daily time scale to assess rain–detection and quantitative estimate capabilities. Monthly climatology and spatial distribution of seasonality are analyzed as well. The time evolution of the statistical indexes has been evaluated in order to analyze the stability of the rain detection and estimation performances. Considerable agreement among the precipitation products emerged from the analysis, in spite of the differences occurring in specific situations over complex terrain, such as mountainous and coastal regions and deserts. Moreover, the temporal evolution of the statistical indices has demonstrated that the agreement between the products improved over time, with more stable capabilities in identifying precipitating days and estimating daily precipitation starting in the second half of the 1990s.

Evaluation of quantitative precipitation forecast in five Indian river basins

ABSTRACT Quantitative precipitation forecast (QPF) obtained from a numerical weather prediction model is used for streamflow forecasting. This study systematically evaluates the performance of a deterministic QPF obtained from a national agency, the National Centre for Medium Range Weather Forecasting (NCMRWF), compared with that obtained from three international agencies. For observation/reference datasets, we used satellite, raingauge, and satellite-gauge merged data products. The forecast skill is evaluated for 177 sub-basins in Ganga, Narmada, Mahanadi, Tapti, and Godavari river basins. Our results indicate that the forecast accuracy of NCMRWF is closely comparable with that of QPF obtained from a European agency. We conclude that selecting accurate observation/reference data is critical for forecast performance evaluation. We determine the preferred forecast and reference datasets in the selected river basins. Our results suggest that for estimating the parameters of a post-processor, a comparable dataset can be used for which data are available for a longer duration.

Eight scenarios rapid update cycle effect on weather radar data assimilation WRF toward rainfall prediction in Palembang (cases study of flood events on November 12th, 2018)

Weather Research and Forecasting (WRF) is an open source numerical weather prediction model that can be used for high resolution rainfall predictions. Besides these advantages, WRF output accuracy can be affected by the initial condition. The accuration of WRF model can be improved by data assimilation. Data assimilation is combining observation data with model data to improve the initial state of atmospheric flow. This study aims to investigate the effect of assimilation weather radar in models using WRF for predictions rainfall events in Palembang region on November 12th, 2018. This study uses radar radial velocity data as input data for assimilation. The assimilation technique uses the 3DVAR with rapid update cycle (RUC) procedure 1 hour, 3 hours, 6 hours with spin up 12 and 6 hours. The output of the model verified using Global Satellite Mapping of Precipitation (GSMaP) data and using rain gauge data for point verification. The results of this study indicate that the output of the assimilation model, especially in the spin-up 12 hours skenario implementation of the 1-hour RUC is better than the model without assimilation. From the eight scenario models implemented, it can be concluded that the 12 hours spin up is better than the 6 hours spin up.

Spatiotemporal Rainfall Dynamics in Kosi Basin Using Wavelet Analysis

Spatial and temporal analysis of rainfall data were carried out along with wavelet analysis for seven rain gauge sites of Kosi basin, India during the time period from 1985 to 2017. Wavelet spectrum analysis and wavelet coherence analysis were performed to fully characterize the time-frequency rainfall variability of the rain gauge data in these areas. For all the selected gauge stations during the study period, the peak value of the wavelet power spectrum was identified for the 8-16 month band. The results of wavelet spectrum analysis reveal a good correlation of rainfall data in the rain gauge sites lying in the southwest of the Kosi basin. The spectrum analysis also differentiates the wet and dry periods and it was observed that in the majority of the selected sites, a dry period occurred from the year 2005 onwards. This was again confirmed with breakpoint analysis. The wavelet coherence analysis explicit is a good correlation between the rain gauges in the study area. Overall, the variability of the rainfall parameters was more vivid with the wavelet analysis and this can be extended to other climatological parameters.

Evaluation of CMPAS precipitation products over Sichuan, China

High-quality and high-resolution precipitation data are the basis for mesoscale numerical weather forecasting, model verification, and hydrological monitoring, which play an important role in meteorological and hydrological disaster prevention and mitigation. In this study, high-density rain gauge data are used to evaluate the fusion accuracy of the China Meteorological Administration Multisource Precipitation Analysis System (CMPAS), and four CMPAS products with different spatial and temporal resolution and different data sources are compared, to derive the applicability of CMPAS. Results show that all the CMPAS products show high accuracy in the Sichuan Basin, followed by Panxi Area and the western Sichuan Plateau. The errors of the four products all rise with the increase in precipitation. CMPAS overestimates precipitation in summer and autumn and underestimates it in spring and winter. Overall, the applicability of these fused data in the Sichuan Basin is quite good. Due to the lack of observations and the influence of the terrain and meteorological conditions, the evaluation of CMPAS in the plateau area needs further analysis.摘要高质量和高分辨率的降水产品在天气预报, 数值模式模拟和气象防灾减灾方面起着重要的作用. 本文利用四川地区高密度的地面降水传感器观测数据, 比较CMPAS四种不同时空尺度的降水实况分析产品, 评估CMPAS的融合准确性与在四川地区的适用性. 研究表明:四种CMPAS降水产品都在四川盆地内精度较高, 攀西地区和川西高原次之. 随着降水量的增加, 产品的误差也随之增加. CMPAS在夏秋两季对降水量高估, 在春冬两季对降水量低估. 总而言之, CMPAS在四川盆地内与观测数据的融合准确性较高. 由于高原地区气象观测站点数量不足且观测数据受复杂地形和气候条件影响较大, CMPAS在高原地区的适用性检验仍需进一步研究.

Utilization and uncertainties of satellite precipitation data in flash flood hydrological analysis in ungauged watersheds

<p>Aim of the study is to examine the potential utilization of satellite precipitation data to estimate the peak discharges of flash floods in ungauged Mediterranean watersheds. Cumulative precipitation heights from local rain gauge and the GPM-IMERG were correlated in a scatter plot. The calculated linear equations were used to adjust the uncalibrated GPM-IMERG precipitation data in Thasos island (Northern Greece), to investigate the mechanisms of the flash floods recorded in November 2019 and to evaluate the significance of satellite precipitation data in hydrological modeling. The uncalibrated GPM-IMERG precipitation failed to explain the flash floods phenomena. The rain gauge data are reliable to accurately predict the peak discharges only in cases, where the rain gauges are within the study area. The strong correlation between ground rainfall data and satellite spatiotemporal precipitation data (R2 > 0.65), provides linear regression equations that, through their extrapolation and appliance to the rest of the flooded area, could adjust and correct the satellite data, optimizing the efficiency and accuracy of flash flood analysis, especially in ungauged watersheds. The proposed methodology could highly contribute to the optimization of flood mitigation measures establishment, flood risk assessment, hydrological and hydraulic simulation of flash flood events in ungauged watersheds.</p>

On the Sensitivity of the Simulated Diurnal Cycle of Precipitation to 3-Hourly Radiosonde Assimilation: A Case Study over the Western Maritime Continent

AbstractThe diurnal cycle is the most prominent mode of rainfall variability in the tropics, governed mainly by the strong solar heating and land–sea interactions that trigger convection. Over the western Maritime Continent, complex orographic and coastal effects can also play an important role. Weather and climate models often struggle to represent these physical processes, resulting in substantial model biases in simulations over the region. For numerical weather prediction, these biases manifest themselves in the initial conditions, leading to phase and amplitude errors in the diurnal cycle of precipitation. Using a tropical convective-scale data assimilation system, we assimilate 3-hourly radiosonde data from the pilot field campaign of the Years of Maritime Continent, in addition to existing available observations, to diagnose the model biases and assess the relative impacts of the additional wind, temperature, and moisture information on the simulated diurnal cycle of precipitation over the western coast of Sumatra. We show how assimilating such high-frequency in situ observations can improve the simulated diurnal cycle, verified against satellite-derived precipitation, radar-derived precipitation, and rain gauge data. The improvements are due to a better representation of the sea breeze and increased available moisture in the lowest 4 km prior to peak convection. Assimilating wind information alone was sufficient to improve the simulations. We also highlight how during the assimilation, certain multivariate background error constraints and moisture addition in an ad hoc manner can negatively impact the simulations. Other approaches should be explored to better exploit information from such high-frequency observations over this region.

Evaluation of the Integrated Multi-SatellitE Retrievals for the Global Precipitation Measurement (IMERG) Product in the São Francisco Basin (Brazil)

The São Francisco River basin is one of the largest in the Brazilian territory. This basin has enormous economic, social and cultural importance for the country. Its water is used for human and animal supply, irrigation and energy production. This basin is located in an area with different climatic characteristics (humid and semiarid) and studies related to precipitation are very important in this region. In this scenario, the objective of this investigation is to present an assessment of rainfall estimated through the Integrated Multi-SatellitE Retrievals for Global Precipitation Measurement (IMERG) product compared with rain gauges over the São Francisco river basin in Brazil. For that, a period from of 20 years and 18 surface weather stations were used to evaluate the product. Based on different evaluation techniques, the study found that the IMERG is appropriate to represent precipitation over the basin. According to the results, the performance of the IMERG product depends on the location where the rain occurs. The bias ranged from −1.67 to 0.34 mm, the RMSE ranged from 5.36 to 10.36 mm and the values of the correlation coefficients between the daily data from the IMERG and rain gauge ranged from 0.28 to 0.61. The results obtained by Student t-test, density curves and regression analysis, in general, show that the IMERG is able to satisfactorily represent rain gauge data. The exception is the eastern portion of the basin, where the product, on average, underestimates the precipitation (p-value < 0.05) and presents the worst statistical metrics.

Comparative analysis of two hazard rainstorm processes in Hunan affected by the Southwest China vortex

Based on surface rain-gauge data, Doppler radar products, the National Center for Environmental Prediction (NCEP) reanalysis data and other multi-source datasets, two regional rainstorm processes caused by the Southwest China vortex (SCV) in June, 2015 are diagnosed and analyzed from the aspects of water vapor condition, dynamic and thermodynamic mechanisms, and the causes of the regional rainstorms are discussed. The results show that the two processes belong to the SCV rainstorm, and there is the southwest jet in the middle and lower levels. The high-value area of water vapor flux and the water vapor convergence center can well indicate the rainstorm area. In the first process, two water-vapor-flux transport bands from the Bay of Bengal and the South China Sea converge. In the second process, the water vapor mainly comes from the Bay of Bengal. The extreme values of dynamic and thermodynamic factors all appear near 850 hPa in the rainstorm development stage, and the latitudes of the extreme values are close to the severe rainfall area. The first process has a typical rainstorm vorticity configuration with negative vorticity at upper layer and positive vorticity at lower layer. The second process is mainly driven by the strong positive vorticity at the lower layer. There are strong energy frontal zones near the ground, and the rainstorm area is consistent with the convergence region of cold and warm air. The characteristics of radar echo show that there are mid-level and low-level jet streams in the two rainstorm processes. The first process is dominated by the cumulus precipitation, and the adverse wind region promotes the development of the rainstorm. The second process is dominated by cumuliform-stratiform-mixed cloud precipitation, and the train effect is the main cause of the regional rainstorm.

Accuracy assessment and error cause analysis of GPM (V06) in Xiangjiang river catchment

Abstract Application potential and development prospect of satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) have promising implications. This study discusses causes of spatiotemporal differences on GPM data through the following steps: Initially, calculate bias between satellite-based data and rain gauge data of Xiangjiang river catchment to assess the accuracy of GPM (06E, 06 L, and 06F) products. Second, total errors of satellite precipitation data are divided into hit bias (HBIAS: precipitation detected by both GPM and rain gauge station), missed precipitation (MBIAS: precipitation detected only by rain gauge station), and false precipitation (FBIAS: precipitation detected only by GPM). Third, evaluate the impact of precipitation intensity and total precipitation on accuracy of GPM data and their influence on three error components. Several conclusions are drawn from the results above. (1) Satellite-based precipitation measurements perform better on a larger temporal-spatial scale. (2) The accuracy of TRMM and GPM data displays significant variances on space and time. Season, precipitation intensity, and total precipitation are main factors influencing the accuracy of TRMM and GPM data. (3) The detection capability of satellite products change with seasonal variation and different precipitation intensity level.

A spatiotemporal framework to calibrate high‐resolution global monthly precipitation products: An application to the Urmia Lake Watershed in Iran

AbstractImproving precipitation accuracy over a watershed is one of the highest priorities in water resources studies and management. Several global precipitation datasets are available for estimating precipitation over any region in the world. However, local or regional application of these datasets should account for and correct potential errors in the original products. This article presents a novel spatiotemporal calibration framework to improve the accuracy (bias and correlation) of global precipitation datasets in regional applications. The proposed methodology consists of two steps. First, gridded global precipitation datasets are regressed pointwise against rain gauge data. This yields downscaled and bias‐corrected precipitation values at the point scale. Second, the resulting point‐scale regression parameters are used to build a geostatistical model that predicts the regression parameters across the region of interest, allowing for bias‐correcting the precipitation datasets at the regional scale. The framework is applied to the Urmia Lake Watershed in northwestern Iran. Eight global high‐resolution monthly precipitation datasets (CHIRPS, ERA‐5, IMERG, PERSIANN, PERSIANN‐CCS, PERSIANN‐CDR, TRMM and Terra) are evaluated and three downscaling approaches including linear, Q‐Q and Linear Scaling (LS) regression methods are used to calibrate the precipitation datasets based on a regional network of rain gauge observations. Ordinary kriging is subsequently used to predict the regression parameters at ungauged locations. Out of all combinations (i.e., eight datasets and three methods), downscaled IMERG using linear and Q‐Q regression methods showed the best performance in estimating the spatiotemporal variations of monthly precipitation across the watershed of interest. The original IMERG dataset overestimated the monthly precipitation by approximately 20% compared to the precipitation from rain gauges. After applying the proposed methodology in this article, the IMERG bias was reduced by 93%, with an additional 26% decrease in the RMSE.

Fine-scale characteristics of summer precipitation over Cang Mountain

AbstractFine-scale characteristics of summer precipitation over Cang Mountain, a long and narrow mountain with a quasi-north-south orientation in Southwest China, are studied based on station and radar data. Three kinds of rainfall processes are classified according to the initial stations of regional rainfall events (RREs) by utilizing minute-scale rain gauge data. RREs initiating in the western part of Cang Mountain exhibit eastward evolution and tend to reach their maximum rainfall intensity on the mountaintop. The results indicate differences in the precipitation evolution characteristics between short-duration (1–3 h) and long-duration (at least 6 h) events. Short-duration events begin farther from the mountaintop and then propagate eastward, while long-duration events remain longer around the mountaintop. RREs that initiate from the eastern part of Cang Mountain display westward propagation and frequently reach their maximum rainfall intensity over the eastern slope of the mountain. Among them, short-duration events tend to propagate farther west of Cang Mountain at high speeds, but the westward evolution of long-duration events is mainly confined to the eastern part of Cang Mountain. For mountaintop-originated RREs, precipitation quickly reaches its maximum intensity after it starts and then continues for a long time around the mountaintop during the period from late afternoon to early morning. These findings provide references for the fine-scale prediction of precipitation evolution in small-scale mountainous areas.

High-Resolution Spatiotemporal Trend Analysis of Precipitation Using Satellite-Based Products over the United Arab Emirates

Current water demands are adequately satisfied in the United Arab Emirates (UAE) with the available water resources. However, the changing climate and growing water demand pose a great challenge for water resources managers in the country. Hence, there is a great need for management strategies and policies to use the most accurate information regarding water availability. Understanding the frequency and the short- and long-term trends of the precipitation by employing high-resolution data in both the spatial and temporal domains can provide invaluable information. This study examines the long-term precipitation trends over the UAE using 17 years of data from three of the most highly cited satellite-based precipitation products and rain gauge data observed at 18 stations. The UAE received, on average, 42, 51, and 120 wet hours in a year in the 21st century as recorded by CMORPH, PERSIANN, and IMERG, respectively. The results show that the areal average annual precipitation of the UAE is significantly lower in the early 21st century than that of the late 20th century, even though it shows an increasing trend by all the products. The Mann–Kendall trend test showed positive trends in six rain gauge stations and negative trends in two stations out of 18 stations, all of which are located in the wetter eastern part of the UAE. Results indicate that satellite products have great potential for improving the spatial aspects of rainfall frequency analysis and can complement rain gauge data to develop rainfall intensity–duration–frequency curves in a very dry region, where the installation of dense rain gauge networks is not feasible.

Multiple timescale assessment of wet season precipitation estimation over Taiwan using the PERSIANN family products

This study evaluates the performance of five satellite precipitation products (SPPs) from the Precipitation Remotely Sensed Information using Artificial Neural Networks (PERSIANN) family for depicting precipitation changes in Taiwan over multiple timescales. Rain gauge data provided by the Central Weather Bureau (CWB) of Taiwan were used as a reference for evaluation, which focused on the wet seasons (May to October) during the period 2003–2019. All SPPs were found to have good ability in expressing the temporal phase changes over most of Taiwan on all the timescales examined, with significant temporal correlation coefficients (TCC) observed between the SPPs and the CWB data. We further evaluated the performance of the SPPs in making quantitative precipitation estimates based on the root mean square error (RMSE). For all examined timescales, the comparison between the best and worst performance shows greater normalized differences in quantitative estimates (i.e., RMSE) than in temporal phase depiction (i.e., TCC). In general, all SPPs tend to underestimate precipitation over most of Taiwan; however, two relatively new products (PDIR-Now and PERSIANN-CCS-CDR) have better RMSE performance than other SPPs on different timescales. PDIR-Now is the best product for quantitatively estimating precipitation on interannual, annual, and seasonal timescales, while PERSIANN-CCS-CDR is superior for daily and diurnal timescales. The findings also highlight that the performance of the PERSIANN-family in quantitatively estimating Taiwan precipitation does not depend primarily on the spatiotemporal resolution of SPPs, but may be related to the use of the cloud patch approach and the inclusion of weather station information in producing PDIR-Now and PERSIANN-CCS-CDR.

Estimating Rainfall with Multi-Resource Data over East Asia Based on Machine Learning

The lack of accurate estimation of intense precipitation is a universal limitation in precipitation retrieval. Therefore, a new rainfall retrieval technique based on the Random Forest (RF) algorithm is presented using the Advanced Himawari Imager-8 (Himawari-8/AHI) infrared spectrum data and the NCEP operational Global Forecast System (GFS) forecast information. And the gauge-calibrated rainfall estimates from the Global Precipitation Measurement (GPM) product served as the ground truth to train the model. The two-step RF classification model was established for (1) rain area delineation and (2) precipitation grades’ estimation to improve the accuracy of moderate rain and heavy rain. In view of the imbalance categories’ distribution in the datasets, the resampling technique including the Random Under-sampling algorithm and Synthetic Minority Over-sampling Technique (SMOTE) was implemented throughout the whole training process to fully learn the characteristics among the samples. Among the features used, the contributions of meteorological variables to the trained models were generally greater than those of infrared information; in particular, the contribution of precipitable water was the largest, indicating the sufficient necessity of water vapor conditions in rainfall forecasting. The simulation results by the RF model were compared with the GPM product pixel-by-pixel. To prove the universality of the model, we used independent validation sets which are not used for training and two independent testing sets with different periods from the training set. In addition, the algorithm was validated against independent rain gauge data and compared with GFS model rainfall. Consequently, the RF model identified rainfall areas with a Probability Of Detection (POD) of around 0.77 and a False-Alarm Ratio (FAR) of around 0.23 for validation, as well as a POD of 0.60–0.70 and a FAR of around 0.30 for testing. To estimate precipitation grades, the value of classification was 0.70 in validation and in testing the accuracy was 0.60 despite a certain overestimation. In summary, the performance on the validation and test data indicated the great adaptability and superiority of the RF algorithm in rainfall retrieval in East Asia. To a certain extent, our study provides a meaningful range division and powerful guidance for quantitative precipitation estimation.

Quantitative Precipitation Estimation of Extremes in CONUS With Radar Data

AbstractConstructing an accurate, continental, in‐situ‐based, kilometer‐scale, long‐term record of the precipitation field and its spatiotemporal changes remains a significant challenge. Here, we determine the extreme‐value behavior of the NEXRAD Stage IV radar‐based quantitative precipitation estimate. We find that the climatology of 5‐year daily return values in the contiguous United States East of the Rocky Mountains shows only slight variability on spatial scales smaller than 100 km. In light of this finding, we test whether rain‐gauge‐only daily precipitation data sets can produce accurate extreme‐value behavior at spatial scales finer than the spacing between gauges. We find that the 5‐year daily return values are accurate at locations far from rain gauges only if the interpolation between gauges is carried out appropriately for extremes. Precipitation statistics derived from in‐situ rain gauge data are therefore of sufficient spatial resolution to faithfully capture daily extremes over much of the eastern United States.

Performance evaluation of ERA-5, JRA-55, MERRA-2, and CFS-2 reanalysis datasets, over diverse climate regions of Pakistan

Reanalysis precipitation products (RPPs) are frequently used for studying the water cycle changes from short to long-term scale globally. In the current study, ERA-5 produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japanese 55-year Reanalysis (JRA-55), the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), and the Climate Forecast System version 2 (CFS-2) precipitation products were evaluated with the rain-gauge data as a reference during 1981–2019 over Pakistan. The performance was assessed using statistical error metrics on daily, monthly, and annual timescales. The reanalysis precipitation products (RPPs) captured the precipitation intensities and the extreme precipitation events (75th to 99th percentile) across climatic classes. On a daily scale, the ERA-5 follows rain-gauges very closely (RC: 0.67, R: 0.81, RMSE: 1.69 mm), consistently capturing the precipitation intensities (light to violent) and extreme precipitation events (95th percentile), followed by CFS-2. The MERRA-2 captured precipitation intensity but did not detect extreme precipitation events in some regions. The JRA-55 produced good results in the central area while overestimated the precipitation in the northern and southern parts of the study area. On a monthly time scale, ERA-5 performed well as compared to the rest of RPPs, with regression coefficient values of 0.91, correlation coefficient (0.96), and a lower value of RMSE (11.09 mm), followed by JRA-55, MERRA-2, and CFS-2. All the RPPs performed better in winter, pre-monsoon, and post-monsoon seasons with slight deviations/differences, but in monsoon season, the ERA-5 and JRA-55 (MERRA-2, CFS-2) overestimated (underestimated) precipitation mean. The findings can help the researchers select reliable datasets for bias correction of the projections and real-time application in flood, drought estimation, and prediction.

Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales

Study region: The study region is Germany and two sub-regions in Germany, i.e. the state of Rhineland-Palatinate and the city of Reutlingen.Study focus: Opportunistic rainfall sensors, namely personal weather stations and commercial microwave links, together with rain gauge data from the German Weather Service, were used in different combinations to derive rainfall maps with a geostatistical interpolation framework for Germany. This kriging type framework considered the uncertainty of opportunistic sensors and the line structure of commercial microwave links. The resulting rainfall maps were compared to two gauge-adjusted radar products and evaluated to three reference gauge datasets in the respective study regions on both daily and hourly basis.New Hydrological Insights for the Region: The interpolated rainfall products from opportunistic sensors provided good agreement to the reference rain gauges. The dataset combinations including information from the opportunistic sensors performed best. The addition of rain gauges from the German Weather Service did not consistently lead to an improvement of the interpolated rainfall maps. On the country-wide, daily scale the interpolated rainfall maps performed well, but the gauge-adjusted radar products were closer to the reference. For the regional and local scale in Rhineland-Palatinate and Reutlingen with an hourly resolution, the interpolated rainfall maps outperformed the interpolated product from DWD rain gauges and showed a similar agreement to the reference as the radar products.

Estimation of flood influencing characteristics of watershed and their impact on flooding in data-scarce region

ABSTRACT The research is focused on the integrated use of satellite remote sensing, Geographic Information System (GIS), and extensive field observation techniques for a better understanding of the impacts of watershed characteristics on hydrological processes and floods. It aims to create a methodology for assessing flood hazards and risk on a regional and local scale so that protective measures can be designed. Floods have occurred in the study area for many years, causing serious damage to infrastructure and civic structures. The present study evaluates the linear, aerial and relief morphometric parameters using the Cartosat-1 digital elevation model (30 metres) along with the curve number for assessing the flood influencing characteristics of the Vishwamitri River’s sub-watersheds. The study prioritizes five sub-watersheds as high, medium, and low based on their flood influencing characteristics and compound value, as a result, needs the highest priority for flood mitigation measures. The sub-watersheds I and IV of Vishwamitri watershed have been categorized into high priority, sub-watersheds II and V into moderate priority, and sub-watershed III into low priority. The geologic stage of development and erosion proneness of the watershed is quantified by hypsometric integral bearing value as 0.04, indicating the landscape to be in monadnock phase in landscape evolution indicative of a marked old stage in the basin’s evolution. Moreover, the ability of the rain-on-grid model at the watershed scale to simulate flood events and predict flood-prone areas, considering multiple rain gauge data, which will facilitate more accurate flood inundation where ground-based observational data are unavailable is shown.

Performance of daily satellite-based rainfall in groundwater basin of Merapi Aquifer System, Yogyakarta

Evaluation of the performance of daily satellite-based rainfall (CMORPH, CHIRPS, GPM IMERG, and TRMM) was done to obtain applicable satellite rainfall estimates in the groundwater basin of the Merapi Aquifer System (MAS). Performance of satellite data was assessed by applying descriptive statistics, categorical statistics, and bias decomposition on the basis of daily rainfall intensity classification. This classification is possible to measure the performance of daily satellite-based rainfall in much detail. CM (CMORPH) has larger underestimation compared to other satellite-based rainfall assessments. This satellite-based rainfall also mostly has the largest RMSE, while CHR (CHIRPS) has the lowest. CM has a good performance to detect no rain, while IMR (GPM IMERG) has the worst performance. IMR and CHR have a good performance to detect light and moderate rain. Both of them have larger H frequencies and lower MB values compared to other satellite products. CHR mostly has a good performance compared to TR (TRMM), especially on wet periods. CM, IMR, and TR mostly have a good performance on dry periods, while CHR on wet periods. CM mostly has the largest MB and lowest AHB values. CM and CHR have better accuracy to estimate rain amount compared to IMR and TR. All in all, all 4 satellite-based rainfall assessments have large discrepancy compared with rain gauge data along mountain range where orographic rainfall usually occurs in wet periods. Hence, it is recommended to evaluate satellite-based rainfall with time series of streamflow simulation in hydrological modeling framework by merging rain gauge data with more than one satellite-based rainfall than to merge both IMR and TR together.