Rainfall Estimation using Image Processing and Regression Model on DWR Rainfall Product for Delhi-NCR Region

Observed rainfall is a very essential parameter for the analysis of rainfall,day to day weather forecast and its validation.The observed rainfall data is only available from five observatories of IMD;while no rainfall data is available at various important locations in and around Delhi-NCR.However,the 24-hour rainfall data observed by Doppler Weather Radar(DWR)for entire Delhi and surrounding region(up to 150 km)is readily available in a pictorial form.In this paper,efforts have been made to derive/estimate the rainfall at desired locations using DWR hydrological products.Firstly,the rainfall at desired locations has been estimated from the precipitation accumulation product(PAC)of the DWR using image processing in Python language.After this,a linear regression model using the least square method has been developed in R language.Estimated and observed rainfall data of year 2018(July,August and September)was used to train the model.After this,the model was tested on rainfall data of year 2019(July,August and September)and validated.With the use of linear regression model,the error in mean rainfall estimation reduced by 46.58% and the error in max rainfall estimation reduced by 84.53% for the year 2019.The error in mean rainfall estimation reduced by 81.36% and the error in max rainfall estimation reduced by 33.81%for the year 2018.Thus,the rainfall can be estimated with a fair degree of accuracy at desired locations within the range of the Doppler Weather Radar using the radar rainfall products and the developed linear regression model.

Projected Rainfall Intensity Duration Frequency Relationships under Climate Change:A Case Study Thane City

Climate change is the most important factor to increase in short-duration high-intensity rainfall and consequent flooding.Intensity-Duration-Frequency(IDF)curves are commonly used tools in Stormwater design,so a method to derive future IDF curves including climate change effect could be necessary for the mainstreaming climate change information into storm water planning.The objective of the present study is to define a mechanism to reflect the effect of climate change into the projected rainfall IDF relationships.For this,the continuously observed hourly rainfall data from 1969 to 2018 were divided into five subgroups.Then the IDF curve of each subgroup is defined.The rainfall intensity for the next 30 years was then estimated using a linear regression model.The obtained result indicates that for the same duration and for the same return period,the rainfall intensity is likely to increase over time:17%(2019-2028),25%(2029-2038)and 32%(2039-2048).However,the findings presented in this paper will be useful for local authorities and decision makers in terms of improving stormwater design and future flood damage will be avoided.

EFFECTS OF ENSO ON THE RELATIONSHIP BETWEEN IOD AND SUMMER RAINFALL IN CHINA

Based on the data of 1950 – 1999 monthly global SST from Hadley Center, NCAR/NCEP reanalysis data and rainfall over 160 weather stations in China, investigation is conducted into the difference of summer rainfall in China (hereafter referred to as the "CS rainfall") between the years with the Indian Ocean Dipole (IOD) occurring independently and those with IOD occurring along with ENSO so as to study the effects of El Ni?o - Southern Oscillation (ENSO) on the relationship between IOD and the CS rainfall. It is shown that CS rainfall will be more than normal in South China (centered in Hunan province) in the years of positive IOD occurring independently; the CS rainfall will be less (more) than normal in North China (Southeast China) in the years of positive IOD occurring together with ENSO. The effect of ENSO is offsetting (enhancing) the relationship between IOD and summer rainfall in Southwest China, the region joining the Yangtze River basin with the Huaihe River basin (hereafter referred to as the "Yangtze-Huaihe basin") and North China (Southeast China). The circulation field is also examined for preliminary causes of such an influence.

Temporal and Spatial Scale Dependence of Precipitation Analysis over the Tropical Deep Convective Regime

Data from Goddard cumulus ensemble model experiment are used to study temporal and spatial scale dependence of tropical rainfall separation analysis based on cloud budget during Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere Response Experiment （TOGA COARE）. The analysis shows that the calculations of model domain mean or time-mean grid-scale mean simulation data overestimate the rain rates of the two rainfall types associated with net condensation but they severely underestimate the rain rate of the rainfall type associated with net evaporation and hydrometeor convergence.

Evaluation of Latest TMPA and CMORPH Precipitation Products with Independent Rain Gauge Observation Networks over High-latitude and Low-latitude Basins in China

The Tropical Rainfall Measuring Mission （TRMM） Multi-satellite Precipitation Analysis （TMPA） and National Oceanic and Atmospheric Administration （NOAA） Climate Prediction Center （CPC） morphing technique （CMORPH） are two important multi-satellite precipitation products in TRMM-era and perform important functions in GPM-era. Both TMPA and CMORPH systems simultaneously upgraded their retrieval algorithms and released their latest version of precipitation data in 2013. In this study, the latest TMPA and CMORPH products （i.e., Version-7 real-time TMPA （T-rt） and gauge-adjusted TMPA （T-adj）, and Version- 1.0 real-time CMORPH （C-rt） and Version-l.0 gauge-adjusted CMORPH （C-adj）） are evaluated and intercompared by using independent rain gauge observations for a 12-year （2000--2011） period over two typical basins in China with different geographical and climate conditions. Results indicate that all TMPA and CMORPH products tend to overestimate precipitation for the high-latitude semiarid Laoha River Basin and underestimate it for the low-latitude humid Mishui Basin. Overall, the satellite precipitation products exhibit superior performance over Mishui Basin than that over Laoha River Basin. The C-adj presents the best performance over the high-latitude Laoha River Basin, whereas T-adj showed the best performance over the low-latitude Mishui Basin. The two gauge-adjusted products demonstrate potential in water resource management. However, the accuracy of two real-time satellite precipitation products demonstrates large variability in the two validation basins. The C-rt reaches a similar accuracy level with the gauge-adjusted satellite precipitation products in the high-latitude Laoha River Basin, and T-rt performs well in the low-latitude Mishui Basin. The study also reveals that all satellite precipitation products obviously overestimate light rain amounts and events over Laoha River Basin, whereas they underestimate the amount and events over Mishui Basin. The findings of the precision characteristics associated with the latest TMPA and CMORPH precipitation products at different basins will offer satellite pre- cipitation users an enhanced understanding of the applicability of the latest TMPA and CMORPH for water resource management, hydrologic process simulation, and hydrometeorological disaster prediction in other similar regions in China. The findings will also be useful for IMERG algorithm development and update in GPM-era.

Impacts of precipitation variation and soil and water conservation measures on runoff and sediment yield in the Loess Plateau Gully Region, China

The Loess Plateau of China has experienced a lengthy drought and severe soil erosion.Changes in precipitation and land use largely determine the dynamics of runoff and sediment yield in this region. Trend and mutation analyses were performed on hydrological data(1981–2012) from the Yanwachuan watershed in the Loess Plateau Gully Region to study the evolution characteristics of runoff and sediment yield. A time-series contrasting method also was used to evaluate the effects of precipitation and soil and water conservation(SWC) on runoff and sediment yield. Annual sediment yield declined markedly from 1981 to 2012 although there was no significant change in annual precipitation and annual runoff. Change points of annual runoff and annual sediment yield occurred in 1996 and 1997,respectively. Compared with that in the baseline period(1981–1996), annual runoff and annual sediment yield in the change period(1997–2012)decreased by 17.0% and 76.0%, respectively, but annual precipitation increased by 6.3%. Runoff decreased in the flood season and normal season, but increased in the dry season, while sediment yield significantly declined in the whole study period. The SWC measures contributed significantly to the reduction of annual runoff(137.9%) and annual sediment yield(135%) and were more important than precipitation. Biological measures(forestland and grassland) accounted for 61.04% of total runoff reduction, while engineering measures(terraces and dams) accounted for 102.84% of total sediment yield reduction. Furthermore, SWC measures had positive ecological effects. This study provides a scientific basis for soil erosion control on the Loess Plateau.

Qualitative and quantitative uncertainties in regional rainfall frequency analysis

Uncertainty exists widely in hydrological analysis, and this makes the process of uncertainty assessment very im- portant for making robust decisions. In this study, uncertainty sources in regional rainfall frequency analysis are identified for the first time. The numeral unite spread assessment pedigree （NUSAP） method is introduced and is first employed to quantify qual- itative uncertainty in regional rainfall frequency analysis. A pedigree matrix is particularly designed for regional rainfall frequency analysis, by which the qualitative uncertainty can be quantified. Finally, the qualitative and quantitative uncertainties are com- bined in an uncertainty diagnostic diagram, which makes the uncertainty evaluation results more intuitive. From the integrated diagnostic diagram, it can be determined that the uncertainty caused by the precipitation data is the smallest, and the uncertainty from different grouping methods is the largest. For the downstream sub-region, a generalized extreme value （GEV） distribution is better than a generalized logistic （GLO） distribution; for the south sub-region, a Pearson type III （PE3） distribution is the better choice; and for the north sub-region, GEV is more appropriate.