Onboarding World Resources Institute's thermal power plant physical c…

…limate vulnerability factors Onboarding World Resources Institute's thermal power plant physical climate vulnerability factors Signed-off-by: EglantineGiraud <[email protected]>
os-climate · Nov 2, 2023 · ef6bfb4 · ef6bfb4
1 parent d490a0e
commit ef6bfb4
Showing 1 changed file with 17 additions and 11 deletions.
diff --git a/src/physrisk/vulnerability_models/thermal_power_generation_models.py b/src/physrisk/vulnerability_models/thermal_power_generation_models.py
@@ -104,16 +104,16 @@ def get_distributions(
         assert isinstance(response_baseline, HazardEventDataResponse)
 
         baseline_curve = ExceedanceCurve(1.0 / response_baseline.return_periods, response_baseline.intensities)
-        inundation_protection_level = (
+        protection_depth = (
             0.0
             if len(response_baseline.intensities) == 0
             else baseline_curve.get_value(1.0 / asset.get_inundation_protection_return_period())
         )
 
         intensity_curve = ExceedanceCurve(1.0 / response_scenario.return_periods, response_scenario.intensities)
-        if intensity_curve.values[0] < inundation_protection_level:
-            if inundation_protection_level < intensity_curve.values[-1]:
-                intensity_curve = intensity_curve.add_value_point(inundation_protection_level)
+        if intensity_curve.values[0] < protection_depth:
+            if protection_depth < intensity_curve.values[-1]:
+                intensity_curve = intensity_curve.add_value_point(protection_depth)
 
         intensities, probs = intensity_curve.get_probability_bins()
         probs = np.insert(probs, 0, intensity_curve.probs[0])
@@ -129,14 +129,20 @@ def get_distributions(
         elif asset.turbine in self.vuln_curves_by_type:
             curves = self.vuln_curves_by_type[asset.turbine]
 
-        impacts = [
-            np.max([np.interp(intensity, curve.intensity, curve.impact_mean) for curve in curves])
-            if inundation_protection_level < intensity and 0 < len(curves)
-            else 0.0
-            for intensity in intensities
-        ]
+        if 0 < len(curves):
+            impacts = [
+                np.max([np.interp(intensity, curve.intensity, curve.impact_mean) for curve in curves])
+                for intensity in intensities
+            ]
+        else:
+            impacts = [0.0 for _ in intensities]
+
+        # keep all bins, but make use of vulnerability matrix to apply protection level
+        # for improved performance we could truncate (and treat identity matrix as a special case)
+        # but this general version allows model uncertainties to be added
+        protection = np.where(protection_depth < intensities, 1.0, 0.0)
 
-        vul = VulnerabilityDistrib(self.hazard_type, intensities, impacts, np.eye(len(impacts)))
+        vul = VulnerabilityDistrib(self.hazard_type, intensities, impacts, np.diag(protection))
         event = HazardEventDistrib(self.hazard_type, intensities, probs)
         return vul, event