Drought Monitoring in the Ganges Basin using Meterological and Landsat-Data from 2012 - 2022

Drought Monitoring in the Ganges Basin using Meterological and Landsat-Data from 2012 - 2022#

  • Can a trend towards more droughts be detected in the Ganges-Basin? Usage of the Vegetation Health Index, Meterological Drought Index (SPEI) and the Mann-Kendall-Test (✅) 2013-2024 enough

  • To what extent did droughts occur in the years between x and y in the Ganges Basin and how can they be quantified using Landsat-data, CHIRPS, MODIS?

  • Comparison within a year pre and post monsoon as well as across several years (⚡) how to differentiate the 2 comparisons?

possible interpretation: How strong is the correlation between drought and precipitatin and between drought and temperature? Whose influence is more relevant? Do extreme temperatures also lead to droughts despite moderate precipitation?

Vergleih zwischen den Jahren entlang der Zeitreihe

Idea#

Area of Interest: Distrikte Mathura (Uttar Pradesh), Aligarh (Uttar Pradesh), Bharatpur (Rajasthan)

Uttar Pradesh:

  • Uttar Pradesh is one of the poorest & most populous states in India

  • economy still largely dependent on agricultural sector; because soils of the fertile Gangetic plain allow two harvests per year in some places

  • About 70% of India’s sugar comes from Uttar Pradesh. Sugarcane is the most important cash crop as the state is country’s largest producer of sugar.

  • Uttar Pradesh continues to have regional disparities, particularly with the western districts of the state showing higher development indicators such as per capita district development product (PCDDP) and gross district development product (GDDP) compared to other regions.[202] Due to inadequate infrastructure and a dense population, Eastern Uttar Pradesh (Purvanchal) faces notable socio-economic disparities

  • In 2009–10, the tertiary sector of the economy (service industries) was the largest contributor to the gross domestic product of the state, contributing 44.8 per cent of the state domestic product compared to 44 per cent from the primary sector (agriculture, forestry, and tourism) and 11.2 per cent from the secondary sector (industrial and manufacturing) => great dependency on agriculture and unstable socio-economic situation

Aligarh: Distrikt in Uttar Pradesh

  • Aligarh has a hot semi-arid climate (Köppen BSh) a little too dry to be a monsoon-influenced humid subtropical climate

  • The city is an agricultural trade centre

  • https://doi.org/10.21203/rs.3.rs-4016824/v1

  • Notably, crops with high water footprint value, such as Barley and Sugarcane, have seen a decline, whereas crops requiring low water footprint value, like Potato and Rice, have shown an uptick

  • The agricultural sector, being the largest consumer of freshwater, primarily for irrigation purposes, ends itself at the nexus of this complex water-energy-food nexus (Abera et al. 2024; Aggarwal and Kalra 1994)

  • he region’s lifeline, the southwest monsoon, brings much-needed respite and rainfall, averaging between 800-900 mm annually. This monsoonal bounty not only rejuvenates the parched land but also fuels the district’s vibrant agricultural sector (Stanton et al. 2011).

  • water scarcity, soil degradation, and unpredictable weather patterns are constant concerns, demanding innovative solutions and sustainable farming practices (Smith and Wigley 2006; Tao and Zhang 2017). Despite these challenges, Aligarh District remains a beacon of agricultural prowess, contributing significantly to Uttar Pradesh’s agrarian economy while preserving its rich heritage and cultural legacy

Rajasthan

  • Rajasthan’s economy is based on the cultivation of cotton, millet, maize, wheat, pulses and barley. The desert areas are home to cattle breeders who raise sheep, goats and camels.

  • Flora and fauna: very different from those of north-eastern India and the Himalayas. In the east and southeast of the state are fertile plains where the population traditionally practises agriculture and grows cereals, pulses and cotton. The northwest, however, is dominated by the Thar desert, where cattle breeders drive their herds from oasis to oasis and breed sheep, goats and camels.

  • In addition to all the historical and modern splendor, the contrast of a very poor India is particularly evident in the outskirts of the cities, as the abandonment of traditional irrigation systems has led to frequent water shortages in recent decades and the associated impoverishment of the rural population. Fortunately, the efforts of many environmental and aid organizations are now bearing fruit, thanks to the rediscovery of the Johans (small ponds), which store the monsoon’s water and have enabled the groundwater level to rise again.

  • Rajasthan is a very dry state that benefits little from the monsoon. The Aravelli Mountains, which run from southwest to northeast across the entire country, act as a watershed and are largely responsible for the formation of the Thar sand desert in the northwest. –> desertification = vulnerability –> generally very dry

Bharatpur District

  • Agricultural activity in the area is mainly of Kharif crops depending upon monsoon and Rabi crops dependent upon irrigation facility available (Rena et al (2021))

  • Groundwater plays a key role to meet the water demand for various purposes in the study area having no perennial river-system (s. a.)

Data#

For NDVI, LST

  • Landsat 8 via USGS –> Band xx, xx, xx, 10 (Thermal) Band 5 (rot)

  • Landsat 9 (ab 2021) ? => USGS

SPEI: The Standardized Precipitation Evapotranspiration Index (SPEI) is an extension of Standardized Precipitation Index (SPI)

Discussion: Correlation meaningful?

Method/Implementation#

IDEA FINAL MAP: VEGETATION HEALTH INDEX

  • Vegetation Health Index is based on vegetaton signals, such as the Normalized Vegetation Index (NDVI) NDVI Formula

  • the Land Surface Temperature (LST) using the TIR sensors (Top of atmosphere radiances) –> brightness temperatures (via Plank’s law) –> conversion digital number to land surface temperature from digital number to land surface temperature

  • calculation of Vegetation condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI) using the NDVI VCI, TCI, VHI calculation

=> workflow VHI –> Ghaleb, F., Mario, M., Najem Sandra, A. (2015). Regional Landsat-Based Drought Monitoring from 1982 to 2014. Climate. pp. 563-577. https://doi.org/10.3390/cli3030563 see also: Munykaka, J-C.B., Chenal, J., Mabaso, S., Tfwala, S.S., Mandal, A. K. (2024). Geospatial Tools and Remote Sensing Strategies for Timely Humanitarian Response: A Case Study on Drought Monitoring in Eswatini. Sustainability 2024, 16, 409. https://doi.org/10.3390/su16010409.

Ejaz, N., Bahrawi, J., Alghamdi K.M., Rahman K.U., Shang, S. (2023). Drought Monitoring Using Landsat Derived Indices and Google Earth Engine Platform: A Case Study from Al-Lith Watershed, Kingdom of Saudi Arabia. remote sensing, 15, 984. https://doi.org/10.3390/rs15040984.

workflow with SPEI SPEI is the new and comprehensive drought index proposed by Vicentro et al. [6] and n this study to monitor meteorological drought The monthly water balance (WB) equation is given by Equation (2), which is obtained by subtracting the calculated PET form monthly precipitation data:

Water_Balance -> P = Precipitation; PET = Potential Evapotranspiration ; i= month Mostly, the SPEI is computed by first standardizing the differences in precipitation and PET values using the log-logistic probability distribution function

  • mean over three-month-interval

  • Chirps precipitation: daily

  • Modis: PET: every 8 days –> not take Min and Max because the comparison is not quite even; when PET divided by 8 then it would be the same nevertheless –> Median does not make sense because invariate to extreme values –> just looking at precipitation without PET does not make sense because of strong possible insolation

log-logistic distribution function -> alpha = scale; beta = shape, gamma = beginning, x = mean of series of CWB vlaues

SPEI: SPEI-equation -> root of -2Ln(P)= probability of exceeding a determined WB value; Constants are Cs and ds categorisation_SPEI

=> Pearson Correlation Coefficient between meterological drought index (SPEI) and RSDIs (VCI, TCI, and VHI)

Ludwig, C., Walli, A., Schleicher, C., Weichselbaum, J., Riffler (M). (2019). A highly automated algorithm for wetland detection using multi-temporal optical satellite data. Remote Sensing and Environment, S. 333-351. https://doi.org/10.1016/j.rse.2019.01.017.

Normalized Difference Moisture Index (NDMI) NDMI_formula where NDB1B2 is the resulting Normalized Difference Index based on bands B1 and B2

  • high index values indicate high probability of water and wetness, while low values indicate dry land surface

  • quantifies moisture levels in vegetation and exhibits a positive correlation with surface water and soil moisture

  • used for wetland mapping: additional spectral indices that are particularly sensitive to moisture level changes in soils and vegetation

  • to create a composite from March to May using Google Earth Engine: ee.median() –> so that we do not miss draught when it did not strike in April

FOSSGIS tools#

  • QGIS Model Builder

  • Google Earth Engine (fetch data and calculate doposit)

Next steps#

  • precipitation data needs to be “gemittelt” for three months pre and post monsoon

  • PET needs to be “gemittelt”

  • calculation of Water Balance –> Mean Water Balance

  • timeline for PET and precipiation for the entire year

  • correlation Water Balance and VHI

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