Peshawa Mustafa Najmaddin Najmaddin

Increased severity of environmental issues such as climate change, land use, and frequency of wildfires has started causing concerns on a global scale. This research is intended to identify forest wildfire, deforestation, and the carbon exchange process in the Penjween District of the Sulaymaniyah Governorate in Northern Iraq using Remote Sensing Technologies. Three models were employed to evaluate forest cover changes from 2010 to 2020, including the Normalized Difference Vegetation Index (NDVI) and Radar Vegetation Index (RVI), and satellite-based fire alert data. Through NDVI analysis, a notable change in land cover was observed, where there was a decline of ‘barren land’ from 500,000 hectares in 2010 to < 200,000 hectares by 2018, while the shrub and grassland areas increased from 400,000 to 650,000 hectares. The data obtained through RVI showed a strong signal of vegetation (0.9) in Spring 2015. This was followed by declining value trends that RVI data represented. These trends indicated the presence of droughts, changes in land use, and the frequency of land burns. An assessment of burnt areas using MODIS data revealed increased wildfire activity from 2014. Almost two hundred fire incidents had been observed by the year 2020 fires were recorded. July accounted for 113,962 hectares burned. Spatial fire frequency maps showed chronic fire zones, which are likely tied to humans and arid conditions. In any case, the Penjween forests continue to act as a net carbon sink. They release 5.68 ktCO₂e/year but are absorbing 1.03 ktCO₂e/year of the carbon dioxide from the atmosphere

Arid and semi-arid regions have different spectral characteristics from other climatic regions. Therefore, appropriate remotely sensed indicators of land use and land cover types need to be defined for arid and semi-arid lands, as indices developed for other climatic regions may not give plausible results in arid and semi-arid regions. For instance, the normalized difference built-up index (NDBI) and normalized difference bareness index (NDBaI) are unable to distinguish between built-up areas and bare and dry soil that surrounds many cities in dry climates. This paper proposes the application of two newly developed indices, the dry built-up index (DBI) and dry bare-soil index (DBSI) to map built-up and bare areas in a dry climate from Landsat 8. The developed DBI and DBSI were applied to map urban areas and bare soil in the city of Erbil, Iraq. The results show an overall classification accuracy of 93% (κ = 0.86) and 92% (κ = 0.84) for DBI and DBSI, respectively. The results indicate the suitability of the proposed indices to discriminate between urban areas and bare soil in arid and semi-arid climates.

Estimating daily evapotranspiration is challenging when ground observation data are not available or scarce. Remote sensing can be used to estimate the meteorological data necessary for calculating reference evapotranspiration ETₒ. Here, we assessed the accuracy of daily ETₒ estimates derived from remote sensing (ETₒ_-RS) compared with those derived from four ground-based stations (ETₒ_-G) in Kurdistan (Iraq) over the period 2010–2014. Near surface air temperature, relative humidity and cloud cover fraction were derived from the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU), and wind speed at 10 m height from MERRA (Modern-Era Retrospective Analysis for Research and Application). Four methods were used to estimate ETₒ: Hargreaves–Samani (HS), Jensen–Haise (JH), McGuinness–Bordne (MB) and the FAO Penman Monteith equation (PM). ETₒ_-G (PM) was adopted as the main benchmark. HS underestimated ETₒ by 2%–3% (R² = 0.86 to 0.90; RMSE = 0.95 to 1.2 mm day⁻¹ at different stations). JH and MB overestimated ETₒ by 8% to 40% (R²= 0.85 to 0.92; RMSE from 1.18 to 2.18 mm day⁻¹). The annual average values of ETₒ estimated using RS data and ground-based data were similar to one another reflecting low bias in daily estimates. They ranged between 1153 and 1893 mm year⁻¹ for ETₒ_-G and between 1176 and 1859 mm year⁻¹ for ETₒ_-RS for the different stations. Our results suggest that ETₒ_-RS (HS) can yield accurate and unbiased ETₒ estimates for semi-arid regions which can be usefully employed in water resources management.

Rainfall-runoff modelling is a useful tool for water resources management. This study presents a simple daily rainfall-runoff model, based on the water balance equation, which we apply to the 11,630 km² Lesser Zab catchment in northeast Iraq. The model was forced by either observed daily rain gauge data from four stations in the catchment or satellite-derived rainfall estimates from two TRMM Multi-satellite Precipitation Analysis (TMPA) data products (TMPA-3B42 and 3B42RT) based on the Tropical Rainfall Measuring Mission (TRMM) from 2003 to 2014. As well as using raw TMPA data, we used a bias-correction method to adjust TMPA values based on rain gauge data. The uncorrected TMPA data products underestimated observed mean catchment rainfall by −10.1% and −10.7%. Corrected data also slightly underestimated gauged rainfall by −0.7% and −1.6%, respectively. Nash-Sutcliffe Efficiency (NSE) and Pearson’s Correlation Coefficient (r) for the model fit with the observed hydrograph were 0.75 and 0.87, respectively, for a calibration period (2010–2011) using gauged rainfall data. Model validation performance (2012–2014) was best (highest NSE and r; lowest RMSE and bias) using the corrected 3B42 data product and poorest when driven by uncorrected 3B42RT data. Uncertainty and equifinality were also explored. Our results suggest that TRMM data can be used to drive rainfall-runoff modelling in semi-arid catchments, particularly when corrected using rain gauge data.