Probable maximum precipitation over Tamil Nadu -A generalised approach

In this paper to estimate areal probable maximum precipitation (PMP) for 1000, 5000 and 10,000 km2 for 1-day duration over Tamil Nadu, three generalised charts depicting variation of areal PMP for these three areas have been prepared. The study showed that the areal PMP estlm1tes for 1000, 5000 and 10000 km2 over Tamil Nadu seems to vary between 48-32, 38-26 and 32-22 cm respectively for l-day duration .

1 to 10 days extreme rainfall studies for Kerala state

The daily rainfall data for 80 years from 98 stations in Kerala region have been analysed to arrive at the Probable Maximum Precipitation (PMP) estimates for rainfall durations or 1 to 10 days. Hershfield's statistical technique has been adopted for the estimation of PMP from annual maximum data. The study will be useful in the estimation of extreme precipitation for computation of design floods, required for design of spillways of dams and other major hydraulic structures in the Kerala state.

Estimation of probable maximum precipitation of a high-mountain basin in a changing climate

To estimate the probable maximum precipitation (PMP) in a changing climate, this study proposes a new PMP estimation framework based on weather research forecasting (WRF) initialed with temperature (predicted by post-processing) for changing climate conditions. First, in order to determine temperature disturbance influencing PMP under climate change, a random forest (RF) model considering error correction is introduced to predict the temperature in the future. Results show that the revised RF model could improve accuracy in temperature prediction. Furthermore, numerical experiments of disturbance amplification of three factors (humidity, wind speed, and temperature) using the WRF model are conducted. This new scheme could consider the effect of three elements (horizontal range, vertical layer, and ratio) of influencing factors’ maximization on PMP. Results indicate that for the most unfavorable precipitation scenario of each factor magnification, the combination of three elements is different. Then, the joint amplification numerical experiments of three factors proved the existence of their interactions when multi-factors changed simultaneously. Finally, this method was tested in a high-mountain basin, the Upper Nujiang River Basin. Results showed that the increase of wind speed plays a leading role in rainfall enhancement, and the rising of relative humidity and temperature has a certain disturbance effect on rainfall.

Probable maximum precipitation for the catchment of Koyna dam

Estimates of Probable Maximum Precipitation (PMP) for different durations were made for the catchment above Koyna dam on the Koyna river. The catchment spans an area of 892 km2 and the PMP estimates were made for a range of durations of 1 to 3 days. The PMP estimates for Koyna dam were found to be 48, 87 and 117 cm by the physical method and 54, 89 and 124 cm by statistical method for 1, 2 and 3 day respectively. These estimates can be used to check the existing spillway design flood of Koyna dam.

The Influence of Landcover and Climate Change on the Hydrology of the Minjiang River Watershed

Changes in the climate and landcover are the two most important factors that influence terrestrial hydrological systems. Today, watershed-scale hydrological models are widely used to estimate the individual impacts of changes in the climate and landcover on watershed hydrology. The Minjiang river watershed is an ecologically and economically important, humid, subtropical watershed, located in south-eastern China. Several studies are available on the impacts of recent climate change on the watershed; however, no efforts have been made to separate the individual contributions of climate and landcover changes. This study is an attempt to separate the individual impacts of recent (1989–2018) climate and landcover changes on some of the important hydrological components of the watershed, and highlight the most influential changes in climate parameters and landcover classes. A calibrated soil and water assessment tool (SWAT) was employed for the study. The outcomes revealed that, during the study period, water yield decreased by 6.76%, while evapotranspiration, surface runoff and sediment yield increased by 1.08%, 24.11% and 33.85% respectively. The relative contribution of climate change to landcover change for the decrease in the water yield was 95%, while its contribution to the increases in evapotranspiration, surface runoff and sediment yield was 56%, 77% and 51%, respectively. The changes in climate parameters that were most likely responsible for changes in ET were increasing solar radiation and temperature and decreasing wind speed, those for changes in the water yield were decreasing autumn precipitation and increasing solar radiation and temperature, those for the increase in surface runoff were increasing summer and one-day maximum precipitation, while those for the increasing sediment yield were increasing winter and one-day maximum precipitation. Similarly, an increase in the croplands at the expense of needle-leaved forests was the landcover change that was most likely responsible for a decrease in the water yield and an increase in ET and sediment yield, while an increase in the amount of urban land at the expense of broadleaved forests and wetlands was the landcover change that was most likely responsible for increasing surface runoff. The findings of the study can provide support for improving management and protection of the watershed in the context of landcover and climate change.

Depth–Duration–Frequency Relationship Model of Extreme Precipitation in Flood Risk Assessment in the Upper Vistula Basin

The Upper Vistula Basin is a flood-prone region in the summer season (May–October) due to intensive rainfall. From the point of view of water management, it is particularly important to assess the variability in this main factor of flood risk, as well as to establish the depth–duration–frequency (DDF) relationship for maximum precipitation, this having not yet been derived for the region. The analysis of a 68-year (1951–2018) data series of summer maximum precipitation collected by 11 meteorological stations showed the series’ stationarity, which supports the conclusion that there is no increase in the risk of rainfall floods due to the intensification of extreme precipitation. A new approach is proposed for the determination of the DDF relationship, where the best-fitted distribution for each station is selected from among the set of candidate distributions, instead of adopting one fixed distribution for all stations. This approach increases the accuracy of the DDF relationships for individual stations as compared to the commonly used approach. In particular, the traditionally used Gumbel distribution turns out to be not well fitted to the investigated data series, and the advantage of the recently popular GEV distribution is not significant.

Monitoring and Prediction of Climate Change Impact on 24-h Probable Maximum Precipitation in the Southeast of Caspian Sea

Due to the impacts of climate change on Probable Maximum Precipitation (PMP), and its importance in designing hydraulic structures, PMP estimation is crucial. In this study, the effect of climate change on 24-h probable maximum precipitation (PMP24) was investigated in a part of the Qareh-Su basin located in the Southeast of Caspian Sea. So far, there are no studies emphasizing on climate change impact on hydrological (physical) PMP values have been conducted in the study area. For this purpose, the climatic data were applied during the years 1988–2017. To generate future data, the outputs of the CanESM2 (Second Generation Canadian Earth System Model) model as a general circulation model (GCM) under optimistic (RCP2.6), middle (RCP4.5), and pessimistic (RCP8.5) emission scenarios, and statistical downscaling model (SDSM) were used in the near (2019-2048) and the far (2049-2078) future periods. The PMP24 values were estimated using a physical method in the baseline and future periods under the three scenarios. The PMP24 value was estimated at 143 mm for the baseline-period, using a physical approach. These values were 98, 105, and 109 for the near-future and 129, 122, and 126mm for the far-future period. The results showed that the physical approach's PMP24 values tend to fall at 14-38%. Overall, the PMP24 values decrease in the future, and the rate of decrease in the near-future was more than the rate of the far-future. The spatial distribution maps of PMP24 in the baseline and future-periods showed that the PMP24 values decreased from west to east.

Estimation of Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) Using GSSHA Model (Case Study Area Upper Citarum Watershed)

Probable Maximum Flood (PMF) used in the design of hydrological structures reliabilities and safety which its value is obtained from the Probable Maximum Precipitation (PMP). The objectives of this study are to estimate PMP and PMF value in Upper Citarum Watershed and understand the impact from different PMP value to PMF value with two scenarios those are Scenario A and B. Scenario A will calculate the PMP value from each Global Satellite Mapping of Precipitation (GSMaP) rainfall data grid and Scenario B calculate the PMP value from the mean area rainfall. PMP value will be obtained by the statistical Hershfield method, and the PMF will be obtained by employed the PMP value as the input data in Gridded Surface Subsurface Hydrologic Analysis (GSSHA) hydrologic model. Model simulation results for PMF hydrographs from both scenarios show that spatial distribution of rainfall in the Upper Citarum watershed will affect the calculated discharge and whether Scenario A or B can be applied in the study area for PMP duration equal or higher than 72 hours. PMF peak discharge for Scenario A is averagely 13,12% larger than Scenario B.