Search Results (8)

Based on the perceptions of the local farmers, this study aims to assess the effects of socioeconomic factors and climatic change on the groundwater livelihood system, with a particular focus on in situ Persian wheels/dug wells. Farmers’ perceptions of climate change and how it is affecting their way of life in the Soan River Basin have also been evaluated to determine the most appropriate adaptive interventions. Information and literature about dug wells was unavailable, which stressed the need to carry out this survey. A structured close-ended questionnaire was designed and administered with as much quantitative data as possible. Random sampling opted for a 5 km buffer zone across the Soan River and its tributaries. Union councils having more than 50% of their area lying in the buffer zone were surveyed, and data was collected. Fifty UCs fell within this criterion, and six dug wells from each Union Council were surveyed. The results of our survey collecting local farmer’s perceptions determined that about 70% of respondents agreed about climate change in the Soan Basin of Pakistan, and 62% of farmers reported that climate change severely impacted their livelihood by affecting agricultural productivity and water availability. Ninety-two percent reported summer becoming hot, 72% highlighted that winters are becoming less cold, and 96% reported that average annual rainfall has decreased compared to 10 years before. About 72% of respondents indicated that available water in their dug wells had decreased, and 80% of respondents explained that their crop yield had decreased compared to 10 years before. Sixty percent preferred drip and 35% sprinkler irrigation as efficient water management practices to cope with water shortages. Ninety-five percent of farmers were ready to use solar pumps for irrigation to tame high pumping costs. The study recommends integrating solar pumping with drip and sprinkler irrigation systems to enhance farmers’ cropped area and productivity. These vulnerable farmers can enhance their resilience and profitability by adopting high-value agriculture (tunnel farming, off-season vegetables, etc.) instead of conventional crops. Full article

The science of hydrological modeling has continuously evolved under the influence of rapid advancements in software and hardware technologies. Starting from simple rational formulae for estimating peak discharge and developing into sophisticated univariate predictive models, accurate conversion of rainfall into runoff and the assessment of inherent uncertainty has been a prime focus for researchers. Therefore, alternative data-driven methods have gained widespread attention in hydrology. Moreover, scientists often couple conventional machine learning models with data pre-processing techniques, i.e., wavelet transformation (WT), to enhance modelling accuracy. In this context, this research work attempts to explore the latent linkage between rainfall and runoff in Pothohar region of Pakistan by developing a novel linkage of five streamline techniques of machine learning, including single decision tree (SDT), decision tree forest (DTF), tree boost (TB), multilayer perceptron (MLP), and gene expression modeling (GEP), with a more sophisticated variant of WT, i.e., maximal overlap discrete wavelet transformation (MODWT), for boundary correction of the transformed components of timeseries data. This study also implements these machine learning models in a stand-alone mode for a more comprehensive comparative analysis of performances. Furthermore, the study uses a combined-basin approach that divides Pothohar region into two basins to compensate for the complex topographic division of the study area. The results indicate that MODWT-based DTF outperformed other stand-alone and hybrid models in terms of modeling accuracy. In the first scenario, considering the Bunha-Kahan River basin, MODWT-DTF yielded the highest NSE (0.86) and the lowest RMSE (220.45 mm) and R2 (0.92 at lag order 3 (Lo3)) when transformed with daubechies4 (db4) at level three. While in the Soan-Haro River basin, MODWT-DTF produced the highest accuracy modeling at lag order 4 (Lo4) (NSE = 0.88, RMSE = 21.72 m³/s, and R2 = 0.91). The highly accurate performance of 3- and 4-days lagged models reflects the temporal consistency in hydrological response of the study area. The comparison of simple and hybrid model performance indicates up to a 55% increase in modeling accuracy due to data pre-processing with wavelet transformation. Full article

The global hydrological cycle is susceptible to climate change (CC), particularly in underdeveloped countries like Pakistan that lack appropriate management of precious freshwater resources. The study aims to evaluate CC impact on stream flow in the Soan River Basin (SRB). The study explores two general circulation models (GCMs), which involve Access 1.0 and CNRM-CM5 using three metrological stations (Rawalpindi, Islamabad, and Murree) data under two emission scenarios of representative concentration pathways (RCPs), such as RCP-4.5 and RCP-8.5. The CNRM-CM5 was selected as an appropriate model due to the higher coefficient of determination (R²) value for future the prediction of early century (2021–2045), mid-century (2046–2070), and late century (2071–2095) with baseline period of 1991–2017. After that, the soil and water assessment tool (SWAT) was utilized to simulate the stream flow of watersheds at the SRB for selected time periods. For both calibration and validation periods, the SWAT model’s performance was estimated based on the coefficient of determination (R²), percent bias (PBIAS), and Nash Sutcliffe Efficiency (NSE). The results showed that the average annual precipitation for Rawalpindi, Islamabad, and Murree will be decrease by 43.86 mm, 60.85 mm, and 86.86 mm, respectively, while average annual maximum temperature will be increased by 3.73 °C, 4.12 °C, and 1.33 °C, respectively, and average annual minimum temperature will be increased by 3.59 °C, 3.89 °C, and 2.33 °C, respectively, in early to late century under RCP-4.5 and RCP-8.5. Consequently, the average annual stream flow will be decreased in the future. According to the results, we found that it is possible to assess how CC will affect small water regions in the RCPs using small scale climate projections. Full article

The present field and petrographic investigations of the Tortonian to Gelasian Siwalik Group in the core of the Hazara-Kashmir Syntaxis have been carried out to comprehend the middle stage Himalayan orogeny that resulted from the collision of Indian and Asian plates. The Chinji, Nagri, Dhok Pathan, and Soan Formations of the Siwalik Group were deposited by river meandering flood plains, braided rivers, and alluvial fan systems, respectively. The Siwalik Group is classified into seven major facies and many minor facies based on sedimentological properties. According to the petrographic analysis, the Siwalik Group sandstone is classified as litharenite and feldspathic litharenite petrofacies. The sandstone of the Siwalik Group is texturally mature, but compositionally it is immature. The data shown on the tectonic discrimination diagrams point to a recycled orogen provenance field for the Siwalik sandstone. In addition to quartz and feldspar, the sandstone includes clasts of volcanic, metamorphic, and sedimentary rock types. The igneous and metamorphic rock clasts were derived from the Lesser and Higher Himalayas. The sedimentary lithic fragments, on the other hand, are derived from both the earlier molasse and pre-molasse rocks. The presence of lithic fragments of the earlier molasse sandstone in the Siwalik sandstone indicates that the Siwalik Group sandstones were deposited during the Middle Stage of the Himalayan orogeny. The paleoclimatic conditions were semi-arid to semi-humid during the Siwalik Group’s deposition. The presence of clay minerals in the shale reveals the intense chemical weathering processes that occurred during their deposition on the flood plains of the river meandering system. Full article

Management of the freshwater resources in a sustained manner requires the information and understanding of the surface water hydrology and streamflow is of key importance in this nexus. This study evaluates the performance of eight different precipitation products (APHRODITE, CHRS CCS, CHRS CDR, CHIRPS, CPC Global, GPCC, GPCP, and PERSIANN) for streamflow prediction in two sub-catchments (Chirah and Dhoke Pathan) of the data-scarce Soan River Basin (SRB) in Pakistan. A modified version of the hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) known as HBV-light was used to generate streamflow. The model was separately calibrated and validated with observed and estimated precipitation data for streamflow simulation with optimized parameterization. The values of R², NSE, KGE and PBIAS obtained during the calibration (validation) period for the Chirah sub-catchment were 0.64, 0.64, 0.68 and −5.6% (0.82, 0.81, 0.88 and 7.4%). On the other hand, values of R², NSE, KGE, and PBIAS obtained during the calibration (validation) period for the Dhoke Pathan sub-catchment were 0.85, 0.85, 0.87, and −3.4% (0.82, 0.7, 0.73 and 6.9%). Different ranges of values were assigned to multiple efficiency evaluation metrics and the performance of precipitation products was assessed. Generally, we found that the performance of the precipitation products was improved (higher metrics values) with increasing temporal and spatial scale. However, our results showed that APHRODITE was the only precipitation product that outperformed other products in simulating observed streamflow at both temporal scales for both Chirah and Dhoke Pathan sub-catchments. These results suggest that with the long-term availability of continuous precipitation records with fine temporal and spatial resolutions, APHRODITE has the high potential to be used for streamflow prediction in this semi-arid river basin. Other products that performed better were GPCC, GPCP, and CHRS CCS; however, their scope was limited either to one catchment or a specific time scale. These results will also help better understand surface water hydrology and in turn, would be useful for better management of the water resources. Full article

Rainfall–runoff modelling has been at the essence of research in hydrology for a long time. Every modern technique found its way to uncover the dynamics of rainfall–runoff relation for different basins of the world. Different techniques of machine learning have been extensively applied to understand this hydrological phenomenon. However, the literature is still scarce in cases of extensive research work on the comparison of streamline machine learning (ML) techniques and impact of wavelet pre-processing on their performance. Therefore, this study compares the performance of single decision tree (SDT), tree boost (TB), decision tree forest (DTF), multilayer perceptron (MLP), and gene expression programming (GEP) in rainfall–runoff modelling of the Soan River basin, Pakistan. Additionally, the impact of wavelet pre-processing through maximal overlap discrete wavelet transformation (MODWT) on the model performance has been assessed. Through a comprehensive comparative analysis of 110 model settings, we concluded that the MODWT-based DTF model has yielded higher Nash–Sutcliffe efficiency (NSE) of 0.90 at lag order (Lo4). The coefficient of determination for the model was also highest among all the models while least root mean square error (RMSE) value of 23.79 m³/s was also produced by MODWT-DTF at Lo4. The study also draws inter-technique comparison of the model performance as well as intra-technique differentiation of modelling accuracy. Full article

The quantitative description of relationships and propagation between different forms of drought at multiple spatiotemporal scales in various geographical locations is informative for early drought warning systems. This study intends to evaluate the historical hydrometeorological drought from 1984–2015 in the Soan River Basin, which is a critical water source for the Pothwar region of Pakistan. The reconnaissance drought index (RDI) and standardized runoff index (SRI) are used to characterize meteorological and hydrological droughts, respectively. The spatiotemporal variations of the RDI and SRI demonstrated that 2000 and 2010 were extremely dry and wet years, respectively. The results further reveal that the frequency of hydrometeorological drought events was higher in a shorter time scale (3 and 6 months), while durations featured longer timescales (9 and 12 months). The RDI and SRI time series showed a significant decreasing trend in terms of the Mann–Kendal and Sen slope estimator (SSE) results. Cross-correlation analysis for RDI and SRI with a time lag acknowledged the existence of a sequence between the RDI and SRI and a positive relationship between the two indices. The findings of this study could be helpful for better understanding drought variability and water resource management. Full article

The global hydrological cycle is vulnerable to changing climatic conditions, especially in developing regions, which lack abundant resources and management of freshwater resources. This study evaluates the impacts of climate change on the hydrological regime of the Chirah and Dhoke Pathan sub catchments of the Soan River Basin (SRB), in Pakistan, by using the climate models included in the NEX-GDDP dataset and the hydrological model HBV-light. After proper calibration and validation, the latter is forced with NEX-GDDP inputs to simulate a historic and a future (under the RCP 4.5 and RCP 8.5 emission scenarios) streamflow. Multiple evaluation criteria were employed to find the best performing NEX-GDDP models. A different ensemble was produced for each sub catchment by including the five best performing NEX-GDDP GCMs (ACCESS1-0, CCSM4, CESM1-BGC, MIROC5, and MRI-CGCM3 for Chirah and BNU-ESM, CCSM4, GFDL-CM3. IPSL-CM5A-LR and NorESM1-M for Dhoke Pathan). Our results show that the streamflow is projected to decrease significantly for the two sub catchments, highlighting the vulnerability of the SRB to climate change. Full article