| Data last updated |
December 3, 2024 |
| Metadata last updated |
December 3, 2024 |
| Created |
June 24, 2024 |
| Format |
CSV |
| License |
Open Data Commons Attribution License
|
| Additional info | nan |
|---|
| Data extraction page | https://indiawris.gov.in/wris/#/rainfall |
|---|
| Data insights | District-Level Rainfall Trends: Analysis of actual rainfall over the years can reveal patterns, seasonal variations, and long-term trends in rainfall for each district in Himachal Pradesh.
Identification of Rainfall Anomalies: By comparing actual rainfall with normal rainfall, we can identify districts and periods with significant deviations, indicating droughts or unusually wet periods.
Assessment of Rainfall Storage: Comparing rainfall storage data with actual rainfall can provide insights into the efficiency of water storage systems in each district.
Impact of Climate Change: Long-term data analysis can help assess the impact of climate change on rainfall patterns at the district level in Himachal Pradesh.
Agricultural Planning and Management: Insights from district-level rainfall patterns and deviations can aid in agricultural planning, helping farmers make informed decisions on crop selection and irrigation needs.
Disaster Preparedness and Management: Understanding periods of excessive or deficient rainfall can help in disaster preparedness and management, such as planning for floods or droughts at the district level.
Water Resource Management: The data can support effective water resource management, ensuring sustainable use of water resources in each district of Himachal Pradesh.
Micro-Climate Analysis: Detailed district-wise data allows for micro-climate analysis, helping to understand local climate conditions and their variations within the state.
Policy Formulation and Evaluation: Policymakers can use the data to formulate and evaluate policies related to water management, agriculture, and disaster management based on accurate and granular data.
Historical Comparison: Comparing historical rainfall data with recent data can provide insights into how rainfall patterns have changed over time in each district. |
|---|
| Data last updated | 2024-08-01 00:00:00 |
|---|
| Data retreival date | 2024-08-15 00:00:00 |
|---|
| Datastore active | True |
|---|
| District no | 12 |
|---|
| Frequency | Daily |
|---|
| Granularity | District |
|---|
| Has views | True |
|---|
| Id | ccfddbe2-326d-46df-af6b-413232e9da15 |
|---|
| Idp ready | True |
|---|
| Methodology | Actual rainfall data used in this module are from different sources, i.e., IMD Grid (0.25 x 0.25-degree size), NRSC (0.05 X 0.05-degree size), APWRIMS (Station wise rainfall) & different state/ central agencies (Station wise rainfall). |
|---|
| Mimetype | text/csv |
|---|
| No indicators | 4 |
|---|
| Package id | 4e75d3fb-2bec-4a0a-8144-ab73da1415cc |
|---|
| Position | 2 |
|---|
| Size | 4.9 MiB |
|---|
| Sku | wris-daily_rainfall_district-dt-dl-him |
|---|
| State | active |
|---|
| States uts no | 1 |
|---|
| Tags | ['rainfall', 'weather', 'district level', 'climate', 'high value'] |
|---|
| Url type | upload |
|---|
| Years covered | 2009-2024 |
|---|
| Methodology |
Actual rainfall data used in this module are from different sources, i.e., IMD Grid (0.25 x 0.25-degree size), NRSC (0.05 X 0.05-degree size), APWRIMS (Station wise rainfall) & different state/ central agencies (Station wise rainfall). |
| Similar Resources |
|
| Granularity Level |
District |
| Data Extraction Page |
https://indiawris.gov.in/wris/#/rainfall |
| Data Retreival Date |
2024-08-15 00:00:00 |
| Data Last Updated |
2024-08-01 00:00:00 |
| Sku |
wris-daily_rainfall_district-dt-dl-him |
| Dataset Frequency |
Daily |
| Years Covered |
2009-2024 |
| No of States/UT(s) |
1 |
| No of Districts |
12 |
| No of Tehsils/blocks |
|
| No of Gram Panchayats |
|
| Additional Information |
nan |
| Number of Indicators |
4 |
| Insights from the dataset |
District-Level Rainfall Trends: Analysis of actual rainfall over the years can reveal patterns, seasonal variations, and long-term trends in rainfall for each district in Himachal Pradesh.
Identification of Rainfall Anomalies: By comparing actual rainfall with normal rainfall, we can identify districts and periods with significant deviations, indicating droughts or unusually wet periods.
Assessment of Rainfall Storage: Comparing rainfall storage data with actual rainfall can provide insights into the efficiency of water storage systems in each district.
Impact of Climate Change: Long-term data analysis can help assess the impact of climate change on rainfall patterns at the district level in Himachal Pradesh.
Agricultural Planning and Management: Insights from district-level rainfall patterns and deviations can aid in agricultural planning, helping farmers make informed decisions on crop selection and irrigation needs.
Disaster Preparedness and Management: Understanding periods of excessive or deficient rainfall can help in disaster preparedness and management, such as planning for floods or droughts at the district level.
Water Resource Management: The data can support effective water resource management, ensuring sustainable use of water resources in each district of Himachal Pradesh.
Micro-Climate Analysis: Detailed district-wise data allows for micro-climate analysis, helping to understand local climate conditions and their variations within the state.
Policy Formulation and Evaluation: Policymakers can use the data to formulate and evaluate policies related to water management, agriculture, and disaster management based on accurate and granular data.
Historical Comparison: Comparing historical rainfall data with recent data can provide insights into how rainfall patterns have changed over time in each district. |
| IDP Ready |
Yes |