Raza, Ali; Fahmeed, Romana; Syed, Neyha Rubab; Katipoğlu, Okan Mert; Zubair, Muhammad; Alshehri, Fahad; Elbeltagi, Ahmed

The Food and Agriculture Organization recommends that the Penman–Monteith Method contains Equation 56 (PMF) as a widely accepted standard for reference evapotranspiration (ETo) calculation. Despite this, the PMF cannot be employed when meteorological variables are constrained; therefore, alternative models for ETo estimation requiring fewer variables must be chosen, which means that they perform at least as well as, if not better than, the PMF in terms of accuracy and efficiency. This study evaluated five machine learning (ML) algorithms to estimate ETo and compared their results with the standardized PMF. For this purpose, ML models were trained using monthly time series climatic data. The created ML models underwent testing to determine ETo under varying meteorological input combinations. The results of ML models were compared to assess their accuracy and validate their performance using several statistical indicators, errors (root-mean-square (RMSE), mean absolute error (MAE)), model efficiency (NSE), and determination coefficient (R2). The process of evaluating ML models involved the utilization of radar charts, Smith graphs, heatmaps, and bullet charts. Based on our findings, satisfactory results have been obtained using RBFFNN based on M12 input combinations (mean temperature (Tmean), mean relative humidity (RHmean), sunshine hours (Sh)) for ETo estimation. The RBFFNN model exhibited the most precise estimation as RMSE obtained values of 0.30 and 0.22 during the training and testing phases, respectively. In addition, during training and testing, the MAE values for this model were recorded as 0.15 and 0.17, respectively. The highest R2 and NSE values were noted as 0.98 and 0.99 for the RBFNN during performance analysis, respectively. The scatter plots and spatial variations of the RBFNN and PMF in the studied region indicated that the RBFNN had the highest efficacy (R2, NSE) and lowest errors (RMSE, MAE) as compared with the other four ML models. Overall, our study highlights the potential of ML models for ETo estimation in the arid region (Jacobabad), providing vital insights for improving water resource management, helping climate change research, and optimizing irrigation scheduling for optimal agricultural water usage in the region.

Haider, Saif; Masood, Muhammad Umer; Rashid, Muhammad; Alshehri, Fahad; Pande, Chaitanya B.; Katipoğlu, Okan Mert; Costache, Romulus

Assessing the impacts of climate change and land use/land cover changes on water resources within a catchment is essential because it helps us understand how these dynamic factors affect the quantity, quality, and availability of freshwater. This knowledge is crucial for making informed decisions about water management, conservation, and adaptation strategies, especially in regions facing increasing environmental uncertainties and challenges to water resource sustainability. In Pakistan’s Kunhar River Basin (KRB), this investigation explores the potential effects of shifting land use/land cover (LULC), and climate on stream flows. The SWAT (Soil and Water Assessment Tool), a semi-distributed hydrological model, and the most recent Coupled Model Intercomparison Project phase 6 (CMIP6) dataset from multiple global climate models (GCMs) were used to evaluate these effects. The temperature and precipitation data were downscaled using the CMhyd software; for both shared socioeconomic pathways (SSP2 and SSP5), the top-performing GCM out of four was required to produce downscaled precipitation and temperature predictions while taking future land use characteristics into account. The output from the chosen GCM indicated that by the conclusion of the 21st century, relative to the reference period (1985–2014), the study area’s average monthly precipitation, highest temperature, and lowest temperature will be increasing. Precipitation is anticipated to increase between 2015 and 2100 by 20.5% and 29.1% according to the SSP2 and SSP5 scenarios, respectively. This study’s findings, which emphasize the need for project planners and managers taking into account the effects of climate and land cover changes in their management techniques, show that climate change can have a significant impact on the changing seasons of flows in the Kunhar River basin.