Is Rainfall Decreasing in Agra? A 101-Year Climate Analysis (1922–2022)

Author

Bob Stanley Gardner

Quick Answer:
Rainfall in Agra has been declining at a rate of -1.63 mm per year over the last century, with a major structural shift around 1967, indicating increasing climate variability. Gardner et. al.

This study analyses 101 years (1922–2022) of rainfall data in Agra district, India, using statistical methods to identify long-term climate trends and variability. Gardner et. al.

What does this study explain?

This research examines long-term rainfall patterns in Agra using statistical techniques like the Mann-Kendall test and the Theil-Sen estimator. It helps determine whether rainfall is increasing, decreasing, or remaining stable over time.

Is rainfall decreasing in Agra?

Yes. The analysis shows a statistically significant declining trend in rainfall over the past century.

The rate of decline is approximately -1.63 mm per year, indicating a gradual but consistent reduction in annual precipitation. Gardner et. al.

Why is the rainfall trend important for climate change?

Rainfall is a key indicator of climate change. A decrease in rainfall in semi-arid regions like Agra can lead to:

• Water scarcity
• Agricultural stress
• Increased drought frequency

These changes highlight the growing impact of climate variability at the regional level.

What is the Mann-Kendall test?

The Mann-Kendall test is a non-parametric statistical method used to detect monotonic trends in time-series data. Because it makes no assumption about the underlying data distribution, it is particularly well-suited to hydroclimatological datasets where rainfall values are rarely normally distributed.

This study used the Modified Mann-Kendall test (Hamed & Ramachandra Rao, 1998), which extends the original test by correcting for autocorrelation in the data — a common feature of long climate records where one year’s rainfall influences the next. This modification reduces the risk of falsely detecting a trend that is actually an artefact of serial correlation.

Applied to 101 years of area-weighted rainfall data for Agra district (1922–2022), the test returned a Kendall’s τ of −0.163, a Z statistic of −2.051, and a p-value of 0.04 — confirming a statistically significant negative trend at the 95% confidence level. Gardner et. al.

What is the Theil-Sen estimator?

The Theil-Sen estimator is a non-parametric method for calculating the magnitude of a trend in time-series data. It works by computing the median slope across all possible pairs of data points, which makes it significantly more robust to outliers than ordinary least squares regression — a critical advantage in rainfall datasets where extreme events like droughts or flood years can distort a simple regression line.

In this study, the Theil-Sen estimator was applied after the Modified Mann-Kendall test confirmed the presence of a statistically significant trend. The estimator calculated a slope of −1.63 mm/year across the 101-year record (1922–2022). Spatially, this decline is not uniform across the district — the magnitude ranges from −1.20 mm/year in the central and western tehsils to −2.35 mm/year in the eastern parts, indicating that eastern Agra is experiencing a faster rate of rainfall decline than the west. Gardner et. al.

What is a change point in climate data?

A change point is a year in a time series where the underlying statistical properties of the data — typically the mean — shift significantly, indicating a structural break in the climate pattern rather than normal year-to-year variability.

This study used four independent tests to detect change points in the 101-year rainfall record: the Standard Normal Homogeneity Test (Alexandersson, 1986), Buishand’s Range Test, Buishand’s U Test (Buishand, 1982), and Pettitt’s Test. All four tests converged on the same result — a statistically significant change point in the year 1967, with p-values ranging from 0.0035 to 0.0073, all below the 0.05 significance threshold. The Pettitt test statistic U* was 986, indicating strong departure from the null hypothesis of no change.

The agreement across four independent methods is particularly significant. When multiple homogeneity tests with different statistical assumptions all identify the same breakpoint, it substantially strengthens confidence that 1967 represents a genuine structural shift in Agra’s rainfall regime rather than a statistical artefact. Rainfall after 1967 shows both a lower mean and higher inter-annual variability compared to the pre-1967 period, a pattern consistent with increasing climate instability across semi-arid northern India during the late 20th century. Gardner et. al.

How was rainfall calculated across the Agra district?

Calculating representative areal rainfall for an administrative district requires more than simply averaging point measurements — it requires accounting for the spatial variation in rainfall across the district and weighting each measurement by the area it represents. This study used a two-stage spatial methodology to achieve this.

Stage 1 — Data acquisition from IMD gridded dataset

Daily precipitation data was downloaded from the Indian Meteorological Department (IMD) servers using the imdlib Python library (Nandi & Patel, 2020), which provides programmatic access to IMD’s binary gridded files. The dataset is available at 0.25° spatial resolution, which at Agra’s latitude translates to approximately 27 km × 28 km per grid cell. At this resolution, 18 grid points fell within or sufficiently near the boundary of Agra district to be included in the analysis. Daily values were aggregated to annual totals using Microsoft Excel before spatial processing.

Stage 2 — Thiessen polygon weighting in QGIS

To convert 18 point measurements into a single district-wide rainfall figure, a Thiessen polygon network was constructed in QGIS 3.28.4 using the administrative boundary shapefile obtained from the Survey of India. Each of the 18 grid points was assigned a Thiessen polygon representing its zone of influence — the area closer to that grid point than to any other. Each polygon was then assigned a weight proportional to its area as a fraction of the total district area (10,863 sq. km).

The resulting weights ranged from 0.000366 (grid point P, covering just 1.56 sq. km) to 0.140453 (grid point J, covering 598.3 sq. km), reflecting the substantial spatial variation in how much of the district each grid point represents. Annual district rainfall was then calculated as the sum of each grid point’s rainfall multiplied by its assigned weight — producing a single area-weighted annual figure for each of the 101 years from 1922 to 2022.

This methodology ensures that grid points covering larger portions of the district contribute proportionally more to the final rainfall figure, preventing the common error of over-representing grid points that happen to cluster near district boundaries. The mean annual rainfall calculated using this method was 671 mm, ranging from 642 mm to 712 mm across different tehsils, with a clear west-to-east and south-to-north gradient in rainfall distribution.

Why are semi-arid regions like Agra more vulnerable?

Why are semi-arid regions like Agra more vulnerable?

Semi-arid regions occupy a climatic threshold — they receive enough rainfall to support rain-fed agriculture and human settlement, but not enough to buffer against interannual variability. This marginal position means that even small systematic declines in precipitation can push a region across critical thresholds for water availability, crop production, and ecosystem function.

The semi-arid classification and what it means for Agra

A region is broadly classified as semi-arid when its aridity index — the ratio of mean annual precipitation to potential evapotranspiration — falls between 0.20 and 0.50 (UNEP, 1992). Agra’s mean annual rainfall of 671 mm, combined with its high summer temperatures and significant evapotranspiration demand, places it firmly within this category. Critically, semi-arid regions are not static — as rainfall declines or temperatures rise, the aridity index shifts, and regions can cross from semi-arid into arid classification. The −1.63 mm/year decline documented in this study represents a directional pressure on that boundary.

Spatial and temporal concentration of rainfall

Agra’s vulnerability is compounded by the extreme seasonal concentration of its rainfall. Approximately 603 mm out of 671 mm — nearly 90% of annual precipitation — falls during the southwest monsoon months of June to September. This means the district has essentially no rainfall buffer outside a four-month window. Any reduction in monsoon intensity or duration has an outsized effect compared to regions where rainfall is distributed more evenly across the year. Studies on Indian monsoon variability have documented increasing inter-annual unpredictability across the Indo-Gangetic plain (Singh & Chudasama, 2021), and the cyclic behaviour identified in this study’s 5–7 year variability pattern is consistent with that broader regional signal.

Groundwater dependency and the declining recharge baseline

In semi-arid regions with highly seasonal rainfall, groundwater recharge is almost entirely dependent on monsoon infiltration. As surface rainfall declines, recharge rates fall, aquifer levels drop, and the cost and energy required for irrigation rises. Agra district sits over the Upper Ganga alluvial aquifer system, one of the most intensively exploited groundwater systems in India. The structural decline in rainfall documented from 1967 onwards coincides with the period of maximum groundwater extraction intensification in Uttar Pradesh, creating a compounding stress — less recharge entering the system at the same time as more is being withdrawn. This study’s 101-year precipitation baseline provides one of the few long-term quantitative anchors for assessing how much the natural recharge potential of this system has declined.

Expanding aridity across India

Agra’s situation is not isolated. Research by Kesava Rao et al. (2013) documented a statistically significant increase in the area classified as arid and semi-arid across India between 1971 and 2004, with northern and western regions showing the strongest expansion. The findings of this study — a century-long declining rainfall trend with a structural break in 1967 and accelerating variability — are consistent with and contribute quantitative district-level evidence to that broader national pattern. Semi-arid regions are also disproportionately home to marginal farmers with limited adaptive capacity, making the human cost of rainfall decline in these zones significantly higher per millimetre of loss than in more humid regions.

What are the key findings of this study?

• Rainfall shows a long-term declining trend
• A structural change occurred around 1967
• The decline rate is -1.63 mm/year
• Rainfall variability shows cyclic behaviour

What are the real-world implications?

The results suggest increasing climate stress in the Agra district, including:

• Higher drought risk
• Water management challenges
• Need for climate adaptation strategies

Implications for Water Security and Agriculture in Agra

The declining rainfall trend of −1.63 mm/year has direct consequences for water security in the Agra district. Agra’s mean annual rainfall of 671 mm places it close to the semi-arid threshold, and a century-long decline of this magnitude translates to a cumulative reduction of approximately 163 mm over 100 years — roughly 24% of the district’s mean annual rainfall. For a region where agriculture depends heavily on monsoon precipitation (June–September contributing ~603 mm), even marginal year-on-year reductions compound into significant crop water deficits, particularly for water-intensive crops like wheat and mustard grown in the Rabi season under residual soil moisture. The structural change point detected in 1967 coincides with the early phases of the Green Revolution in Uttar Pradesh, a period of rising groundwater extraction that may have been partly driven by declining surface water availability. These findings are directly relevant to drought risk assessment, groundwater recharge planning, and climate adaptation policy for the broader Gangetic plain semi-arid zone. Gardner et. al.

Rainfall Decline and the Environmental Stress on the Taj Mahal

The Taj Mahal, a UNESCO World Heritage Site located in Agra district, faces compounding environmental pressures that are closely linked to regional climate patterns. The declining rainfall trend of −1.63 mm/year documented in this study has implications beyond agriculture — reduced precipitation directly affects the Yamuna River’s flow regime, which runs adjacent to the monument. A drying Yamuna reduces the moisture content of the alluvial soil beneath the Taj Mahal’s foundations, which rest on wooden piers that depend on consistent groundwater saturation to prevent decay. Several conservation studies have flagged foundation stress as a long-term structural concern, and the century-long precipitation decline documented here provides quantitative climatic evidence that compounds this risk. Additionally, reduced rainfall reduces the natural washing effect on the monument’s marble surface, potentially increasing the residence time of particulate pollutants and accelerating surface discolouration. This study’s finding of a structural change point in 1967 — the same decade Agra’s industrial growth accelerated — suggests that both atmospheric pollution and hydrological stress on the monument have been intensifying over the same period. Researchers studying heritage site vulnerability, Yamuna river flow trends, or climate-driven foundation risk in alluvial plains may find this dataset directly relevant as a long-term regional precipitation baseline. Gardner et. al.

What data was used in this study?

Rainfall data was obtained from the Indian Meteorological Department (IMD) using high-resolution gridded datasets.

The analysis covers a continuous period of 101 years (1922–2022). Gardner et. al.

How can this research be useful?

This study can support:

• Climate change research
• Policy planning
• Water resource management
• Future rainfall prediction models

Keywords

Rainfall Trend, Climate Change India, Mann-Kendall Test, Theil-Sen Estimator, Change Point Detection, Agra Climate, IMD Rainfall Data