Use LEFT and RIGHT arrow keys to navigate between flashcards;
Use UP and DOWN arrow keys to flip the card;
H to show hint;
A reads text to speech;
13 Cards in this Set
- Front
- Back
|
SDSM |
statistical downscaling model |
|
|
study area |
•tunga bhadra river is formed at the union of the tunga and bhadra river, this was the upper section of the defined catchment area. •the catchment area ends at the tungabhadra dam •surrounding land is used mainly for farming and covers 15,600 square kilometers •1024 mm of annual precipitation |
|
|
background |
•projected effects of climate change from other studies indicate stress on hydrologic processes
•These projections use general circulation models and offer little value due to the resolution of GCMs
•researchers focuses on this area because there are very few studies on hydrologic modeling of the tunga bhadra basin |
|
|
data |
•Land use and soil type data both used to drive information such as hydraulic conductivity and imperviousness
•climatic data included: daily precipitation, max/min temperature and solar radiation °precipitation records obtained from recording gauges and historical precip data
•observed atmospheric predictors incorporate the natural variability of the climate at the study time
•researchers used the A2 and B2 scenarios of HadCM3 an atmosphere-ocean GCM
•standardized predictors were used as inputs for the SDSM and are supplied on a grid basis that changes by the year |
|
|
method |
HecHMS -predictions divided into three time periods °2011-2040 °2041-2070 °2071-2099 SDSM •creates a way to smooth out and properly use GCM for sub-grid hydrological studies •establishes empirical relationship between GCM and the local climate |
|
|
SDSM |
•used to downscale daily max/min temperatures and daily precip •after downscaling, statistical analysis performed on observed &predicted data using precipitation mean, max, min, variance,percent wet days, dry day spell lengths to evaluate performance of downscaling model •HadCM3 GCM has year lengths of 360 days. modified SDSM output outside of the model to convert this to 365 days |
|
|
Hec Model setup |
•based on different characteristics of soil type and land use pattern •deficit and constant loss model used to compute losses from watershed •historic data split from 1973-1992 for calibration and 1992-2002 for validation •no change in soil cover or land use pattern is assumed to ensure prediction is entirely based in climate change scenarios |
|
|
timeline |
shows the different time periods of each section of the study |
|
|
observed and simulated daily flows of model |
•high flows are underpredicted by the model
•downscaled inputs cause an overestimation in mean streamflows while observation based inputs result in underestimation |
|
|
difference of projected change btwn A2 n b2 |
scenarios |
|
|
boxplots |
compare baseline values to predicted values of A2 and B2 scenarios |
|
|
conc |
1 due to the lack of extreme event modeling capabilities 2. these poor predictions highlight the difficulty of downscaling local daily precipitation values from regional scale predictor values. |
|
|
Studies cited |
•continuation of the presented research but adding bias correction and integrating possible land use changes •a similar study that uses the same methods of model calibration and validation for the Kunhar river basin in Pakistan |
