Fitting Rainfall Analysis to Your Problem Statement

Precipitation Related Climate Change Analysis


Applications

A] Baseline and total seasonal and monthly changes in slow onset for water resources planning

B] Extreme changes, changes in return period, IDF, or DDF for water infrastructure design

C] Extreme changes in short duration (hourly), changes in return period, IDF, or DDF for urban water infrastructure design  

D] Water resources, flooding, inundation modelling

E] Extreme changes in short duration (hourly), changes in return period, IDF, or DDF for urban water infrastructure design  

Precipitation related climate change analysis type

Annual or monthly mean

Daily precipitation extremes

Subdaily to multiple Intensity Duration Function(IDF), or Density Duration Function (DDF)

Hydrological model input data

Very high resolution RCM precipitation changes for urban water systems

Recommended methodology

Change factor approach (Percentage change per degree, or percentage in different scenarios)

Generalized Extreme Value analysis, multiple distributions fitting testing.

Generalized Extreme Value analysis and IDF curve fitting, multiple distributions fitting and testing. Multiple fitting methods and high temporal resolution for critical urban infrastructure design

Bias correction statistical downscaled with climate change projections conducted by CLIMsystems with data and held by CLIMsystems

1-3 km resolution convective storm modelling using RCM simulations, mainframe computer facilities in partnership with the Institute of Atmospheric Physics, Beijing

Historical data required

Observation based Monthly historical data

Daily observation time series

Subdaily observation data

Subdaily or daily observations

Sub-hourly precipitation observation

GCM/RCM data required

Multiple GCM and RCM monthly mean ensemble results

Multiple GCM daily precipitation based extreme value change patterns

Multiple GCM 3 hourly precipitation output extreme value change patterns

Multiple sources: subdaily or daily GCM or RCM

RCM sub-hourly data

CLIMsystems tool

SimCLIM 4.0 for Desktop monthly pattern scenario generator, rapid assessment, easy training

SimCLIM 4.0 for Desktop GEV tool and in-house tools, moderate intensity, time consuming if many assets need to be assessed. Can be run in a customised form by CLIMsystems for larger areas with a data set generated for later extraction and application.

Subdaily extreme event analysis in-house methods with shape files as outputs for specific time slices and RCPs. Cost increases with number of RCPs and time slices required.

Multiple GCM daily/sub-daily BCSD dataset, moderate labour intensity, can be run for multiple RCPs and time slices but costs increase slightly with additional runs.

WRF specific domain case by case, labour and time intensive, often prohibitively expensive for running multiple RCPs and time slices. Specific model runs decided through close consultation with the client in order to control costs.

Potential linkage to other models

WEAP,DSSAT

Related infrastructure design  models

Related infrastructure design  models

SWAT, DHI, EWater, HECS, SWMM, Flood Modeller, other flood datasets

SWAT, DHI, EWater, HECS, SWMM, other Related infrastructure design  models

Pro/Cons

Quick and easy to generate, but seasonal/monthly average change cannot reflect the extremes which are crucial for certain approaches to water resource management.

Widely used for engineering design, applying daily GCM/RCM output but may under estimate sub-daily changes in extremes, which is important for flooding(Field, 2012; Wuebbles et al., 2014). See methods D] and E].

Widely used for engineering design. GCM/RCM sub-daily data reflects changes in higher temporal resolution (Prein et al., 2016).  Not directly applicable for detailed flood modelling.

Can be directly linked to hydrological flood modelling and cost-effectively for multiple scenarios and multiple time slices (Rudd & Kay, 2015; Peck et al., 2012; Wang et al. 2013).

Can be directly linked to hydrological flood modelling and may reflect some detailed change patterns.  But it is costly and time consuming, therefor this method is not easy to apply to multiple scenarios and time slices experiments over large geographical areas. Limited scenarios means approach integrated statistical approaches for a range of climate change output scenarios (Casanueva et al. 2015; Casanueva et al. 2016; Stocker, 2010).