US 2024/0402386 A1 · METHOD AND SYSTEM FOR SITING HEAT WAVE MONITORING STATIONS BASED ON RISK EVALUATION

Abstract

Disclosed is a method for siting heat wave monitoring stations based on risk evaluation, including: acquiring historical meteorological data of a target region, and preprocessing the historical meteorological data to generate a gridded associated meteorological data set; identifying historical high-temperature heat wave events based on the associated meteorological data set, and calculating parameters and summary indexes of heat wave feature of grids; evaluating station building priority of the grids based on spatial distribution features of the summary indexes; acquiring multi-source data, and evaluating a heat wave risk to generate a heat wave risk map; performing iterative computation using an optimization algorithm based on current station building information, temporal-spatial distribution features of meteorological factors and the heat wave risk map to determine alternative station building positions; and acquiring on-site survey information of each alternative station building position, and determining a position where a station is to be built.

A method for siting heat wave monitoring stations based on risk evaluation, comprising:

acquiring historical meteorological data of a target region, and preprocessing the historical meteorological data according to a spatial distribution and a time sequence to generate a gridded associated meteorological data set;
identifying historical high-temperature heat wave events based on the gridded associated meteorological data set, and calculating parameters and summary indexes of heat wave features for each of grids based on occurrence times and daily heat wave magnitudes of the historical high-temperature heat wave events;
evaluating a priority of building a heat wave monitoring station in each of the grids based on spatial distribution features of the summary indexes of heat wave features in the target region;
acquiring multi-source data of each of the grids in the target region, and evaluating a heat wave risk of each of the grids to generate a heat wave risk map;
performing an iterative computation with an optimization algorithm based on current station building information, temporal-spatial distribution features of meteorological factors and the heat wave risk map, to determine information of alternative station building positions;
and
acquiring on-site survey information of each of the alternative station building positions, and determining information of a position where a station is to be built;
wherein the acquiring historical meteorological data of a target region, and preprocessing the historical meteorological data according to a spatial distribution and a time sequence to generate a gridded associated meteorological data set comprises:
acquiring gridded historical meteorological data of the target region, wherein, meteorological factors of the gridded historical meteorological data comprise an air temperature, a relative humidity and an average wind speed, and the gridded historical meteorological data comprises a plurality of authoritative meteorological data sets;
preprocessing the gridded historical meteorological data according to the spatial distribution and the time sequence, wherein the preprocessing comprises data format conversion, abnormal value elimination and missing value filling;
and
acquiring a daily highest temperature of each of the grids in the target region, and humidity data and wind speed data corresponding to the daily highest temperature from the gridded historical meteorological data to generate the gridded associated meteorological data set;
wherein the identifying historical high-temperature heat wave events based on the gridded associated meteorological data set, and calculating parameters and summary indexes of heat wave features for each of grids based on occurrence times and daily heat wave magnitudes of the historical high-temperature heat wave events comprises:
determining a daily high-temperature data set Ad=UysyeUi=d−15d+15 Ty,j of each of the grids based on the gridded associated meteorological data set, defining an N-th percentile in the daily high-temperature data set Ad as a high-temperature threshold, defining a day with a daily highest temperature greater than the high-temperature threshold as a high-temperature day, and defining three continuous high-temperature days as a high-temperature heat wave event;
calculating a sensible temperature ATd=1.07Td+0.2e−0.65V−2.7 of each of the grids based on the gridded associated meteorological data set, wherein
e
=
RH
100
×
6.105
×
exp
⁢
17.27
T
237.7
+
T
;
calculating a daily heat wave magnitude Md of each of the grids based on the gridded associated meteorological data set and the sensible temperature ATd, wherein
M
d
=
{
T
d
-
T
y
⁢
25
⁢
p
T
y
⁢
75
⁢
p
-
T
y
⁢
25
⁢
p
,
if
⁢
T
d
>
T
y
⁢
25
⁢
p
⁢
and
⁢
T
d
≥
AT
d
AT
d
-
T
y
⁢
25
⁢
p
T
y
⁢
75
⁢
p
-
T
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25
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,
if
⁢
T
d
>
T
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25
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p
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and
⁢
T
d
<
AT
d
0
,
if
⁢
T
d
≤
T
y
⁢
25
⁢
p
;
calculating an average heat wave magnitude, a heat wave days and a heat wave frequency corresponding to each of the grids based on the occurrence time and the daily heat wave magnitude of the high-temperature heat wave event of each of the grids in a reference period, to generate the parameters of the heat wave features;
wherein, Uysye is a set of daily highest temperature data in the reference period of years ys to ye, d is a selected target date, Ui=d−15d+15 is a set of daily highest temperature data in a window period of 15 days before and after the selected target date d, and Ty,j is a daily highest temperature of a j-th day of a year y;
Td is a daily highest temperature of the selected target date d, ATd is a highest sensible temperature of the selected target date d, e is a water vapor pressure, V is wind speed, RH is relative humidity, Ty25p and Ty75p are respectively a 25-th percentile and a 75-th percentile of annual highest temperatures of the years ys to ye;
the average heat wave magnitude is an average value of daily heat wave magnitudes Md in one statistical period, the heat wave days is a number of days of the historical high-temperature heat wave events in one statistical period, and the heat wave frequency is a number of the historical high-temperature heat wave events in one statistical period;
wherein, after the calculating an average heat wave magnitude, a heat wave days and a heat wave frequency corresponding to each of the grids based on the occurrence time and the daily heat wave magnitude of the high-temperature heat wave event of each of the grids in a reference period, to generate the parameters of the heat wave features, the method further comprises:
preprocessing the parameters of the heat wave features according to the spatial distribution and the time sequence to generate a heat wave feature data set, and performing whitening preprocessing on the heat wave feature data set to eliminate autocorrelation of the heat wave feature data set;
obtaining a trend degree of the heat wave frequency with a Mann-Kendall test method and a Sen slope estimation method;
performing an abrupt change detection on the heat wave features with the Mann-Kendall test method and a sliding T test method, to determine an abrupt change point and a corresponding abrupt change time point;
and
taking a subsequence after the abrupt change time point as a stable phase, and averaging average heat wave magnitudes and the heat wave days in the stable phase to obtain an annual average heat wave magnitude and an annual average heat wave days of each of the grids in the stable phase respectively;
wherein the evaluating a priority of building a heat wave monitoring station in each of the grids based on spatial distribution features of the summary indexes of heat wave features in the target region comprises:
creating a heat wave intensity map, a heat wave days map and a heat wave frequency trend degree map based on the spatial distribution features of the summary indexes of the heat wave features in the heat wave feature data set after the abrupt change time point in the target region;
determining an annual average heat wave magnitude grade, an annual average heat wave days grade and a heat wave frequency trend degree grade of each of the grids with a natural breakpoint method based on the heat wave intensity map, the heat wave days map and the heat wave frequency trend degree map;
setting a weight for each of the annual average heat wave magnitude, the annual average heat wave days and the trend degree of the heat wave frequency respectively, wherein the weight of the annual average heat wave days>the weight of the annual average heat wave magnitude>the weight of the trend degree of the heat wave frequency;
and
calculating a station building priority of each of the grids based on the annual average heat wave magnitude grade, the annual average heat wave days grade, the heat wave frequency trend degree grade as well as the weights of the annual average heat wave magnitude, the annual average heat wave days and the trend degree of the heat wave frequency of each of the grids, to generate a building priority map;
wherein the acquiring multi-source data of each of the grids in the target region, and evaluating a heat wave risk of each of the grids to generate a heat wave risk map comprises:
acquiring the multi-source data of each of the grids, and performing format conversion, the abnormal value elimination and the missing value filling on the multi-source data;
based on the building priority map, analyzing a probability, intensity and duration of the high-temperature heat wave event of each of the grids to evaluate a harmfulness index of the high-temperature heat wave event of each of the grids;
analyzing population and land utilization data of each of the grids based on the multi-source data to evaluate an exposure index for an influence of the high-temperature heat wave event;
analyzing population data, resource data, environmental data and economic development data of each of the grids based on the multi-source data to evaluate a vulnerability index for the influence of the high-temperature heat wave event;
and
performing weighted average calculation based on the harmfulness index, the exposure index and the vulnerability index of each of the grids to generate a heat wave risk grade of each of the grids, to create the heat wave risk map;
wherein the heat wave risk grade comprises a high risk, a medium risk and a low risk, and the performing an iterative computation with an optimization algorithm based on current station building information, temporal-spatial distribution features of meteorological factors and the heat wave risk map, to determine information of alternative station building positions comprises:
acquiring the current station building information, and determining position information and service range information of an initial monitoring station;
determining a service radius of a heat wave monitoring station to be built in the target region based on a semivariable function fitting result of different meteorological factors;
setting objective functions:
max Ui=1nAi and min (nH+nM+nL), and constraint functions:
Dij≤Dmax, Σn AH_i≥0.95AH, nH:nM≥3 and nH:nL≥3;
and
based on the optimization algorithm, performing iteration to obtain an approximate optimal solution, and determining the information of alternative station building positions;
wherein Ai is a service area of an i-th station;
nH, nM and nL are numbers of heat wave monitoring stations to be built at the high risk, the medium risk and the low risk respectively, Dij is a distance between the i-th station and a j-th station, Dmax is the service radius of the heat wave monitoring station to be built, AH_i is an area of a high risk region covered by the i-th station, and AH is a total area of high risk regions in the target region.

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Method and system for siting heat wave monitoring stations based on risk evaluation

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Method and system for siting heat wave monitoring stations based on risk evaluation

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Abstract

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