Considerable controversy exists among researchers over the behavior of glaciers in the Upper Indus Basin (UIB) with regard to climate change. Glacier monitoring studies using the Geographic Information System (GIS) and remote sensing techniques have given rise to contradictory results for various reasons. This uncertain situation deserves a thorough examination of the statistical trends of temperature and streamflow at several gauging stations, rather than relying solely on climate projections. Planning for equitable distribution of water among provinces in Pakistan requires accurate estimation of future water resources under changing flow regimes. Due to climate change, hydrological parameters are changing significantly; consequently the pattern of flows are changing. The present study assesses spatial and temporal flow variability and identifies drought and flood periods using flow data from the Indus River. Trends and variations in river flows were investigated by applying the Mann-Kendall test and Sen’s method. We divide the annual water cycle into two six-month and four three-month seasons based on the local water cycle pattern. A decile indices technique is used to determine drought and flood periods. Overall, the analysis indicates that flow and temperature variabilities are greater seasonally than annually. At the Tarbela Dam, Indus River, annual mean, maximum, and minimum flows decreased steeply from 1986–2010 compared to the 1961–1985 period. Seasonal flow analysis unveils a more complex flow regime: Winter (October–March), (December–February), and spring (March–May) seasons demonstrate increasing flows along with increasing maximum temperature, whereas summer (April–September), (June–August) and autumn (September–November) showed decreasing trends in the flow. Spatial analysis shows that minimum discharge increased at the higher elevation gauging station (Kharmong, 2542 m.a.s.l.) and decreased at the lower elevation gauging station (Tarbela). Over the same timeframe, maximum and mean discharges decreased more substantially at lower elevations than at higher elevations. Drought and flood analysis revealed 2000–2004 to be the driest period in the Indus Basin for this record.

This article explores the water policy narrative in Pakistan, and identifies its historical trajectories and influences, as well as the impact of the global agenda setting of water for 2030. For this purpose, water sector reforms in Pakistan are examined as a case study. The National Water Policy (NWP) 2018 and Participatory Irrigation Management (PIM) reforms are critically evaluated and loopholes identified, in terms of both theoretical aspects and constraints in their practical implementation. The overall analysis reveals that the engineering narrative is dominant in policy circles and large-scale infrastructure construction is seen as an exceptional measure to overcome the current loss of storage potential due to sedimentation. On the other hand, the adoption of the Integrated Water Resource Management (IWRM) framework reflects the desire of state institutions to imbue water policy reform with international credibility. The IWRM framework has been adopted as isomorphic mimicry to appease international financing institutions and donors. PIM reform is thus far from delivering the desired results due to ideological battles among new (i.e., the Participatory Farmers Institution) and traditional bureaucratic irrigation institutions. As adopted, the global agenda setting of water, in the shape of IWRM, is a repackaging of existing activities, and prevents alternative thinking in the setting of water priorities according to developmental needs.

In this study, a model was created with the Water Evaluation and Planning (WEAP) System and used to explore the benefits of altering the operations of Tarbela Dam in terms of reliability, resilience, and vulnerability (RRV) for the three objectives of irrigation supply, hydropower generation, and flood control. Sensitivity analysis and logical reasoning with operators identified a feasible operational rule curve for testing using the integrated performance analysis. The reservoir performance for the altered operations was compared to the baseline performance following current operations for both historical and projected future climate and water demand conditions. Key simulation results show that the altered operations strategy tested under historical climate and water demand conditions would increase RRV by 17%, 67%, and 7%, respectively, for the water supply objective and 34%, 346%, and 22%, respectively, for hydropower generation. For projected future conditions, the proposed operations strategy would increase RRV by 7%, 219%, and 11%, respectively, for water supply and 19%, 136%, and 13% for hydropower generation. Synthesis of the results suggests significant benefits for reliability and resilience of water supply and hydropower are possible with slight operational adjustments. Overall, the integrated performance analysis supports the need to develop an optimized operations rule for Tarbela to adapt to projected climate and demand scenarios.

As sensor measurements emerge in urban water systems, data-driven unsupervised machine learning algorithms have drawn tremendous interest in event detection and hydraulic water level and flow prediction recently. However, most of them are applied in water distribution systems and few studies consider using unsupervised cluster analysis to group the time-series hydraulic-hydrologic data in stormwater urban drainage systems. To improve the understanding of how cluster analysis contributes to flooding location detection, this study compared the performance of K-means clustering, agglomerative clustering, and spectral clustering in uncovering time-series water depth dissimilarity. In this work, the water depth datasets are simulated by an urban drainage model and then formatted for a clustering problem. Three standard performance evaluation metrics, namely the silhouette coefficient index, Calinski–Harabasz index, and Davies–Bouldin index are employed to assess the clustering performance in flooding detection under various storms. The results show that silhouette coefficient index and Davies–Bouldin index are more suitable for assessing the performance of K-means and agglomerative clustering, while the Calinski–Harabasz index only works for spectral clustering, indicating these clustering algorithms are metric-dependent flooding indicators. The results also reveal that the agglomerative clustering performs better in detecting short-duration events while K-means and spectral clustering behave better in detecting long-duration floods. The findings of these investigations can be employed in urban stormwater flood detection at the specific junction-level sites by using the occurrence of anomalous changes in water level of correlated clusters as flood early warning for the local neighborhoods.