Continuous urbanization over the last few years has led to the increase in impervious surfaces and stormwater runoff. Low Impact Development (LID) is currently receiving increased attention as a promising strategy for surface runoff management. To analyze the performance of LID practices for surface runoff management, a long-term hydrological modeling from 2001 to 2015 along with a cost-effectiveness analysis were carried out on a campus in Dresden, Germany. Seven LID practices and six precipitation scenarios were designed and simulated in a Storm Water Management Model (SWMM). A cost-effectiveness analysis was conducted by calculating the life-cycle costs and runoff removal rate of LID practices. Results demonstrated that the LID practices significantly contributed to surface runoff mitigation in the study area. The LID performance was primarily affected by the length of the precipitation scenarios and LID implementing schemes. The runoff removal rate of the LID practices fluctuated significantly when the rainfall scenario was shorter than 12 months. When the rainfall scenario exceeded 1 year the effects on the runoff removal rate was constant. The combination of an infiltration trench, permeable pavement, and rain barrel (IT ​+ ​PP ​+ ​RB), was the best runoff control capacity with a removal rate ranging from 23.2% to 27.4%. Whereas, the rain barrel was the most cost-effective LID option with a cost-effectiveness (C/E) ratio ranged from 0.34 to 0.41. The modeling method was improved in this study by conducting long-term hydrological simulations with different durations rather than short-term simulations with single storms. In general, the methods and results of this study provided additional improvements and guidance for decision-making process regarding the implementation of appropriate LID practices.

High-quality data availability is a major problem for climate and hydrological studies, especially in basins with complex topography where gauge network is typically limited and unevenly distributed. This study investigates the performance of fourteen precipitation products: seven Satellite-Based (SPPs), two Gauge-Based (GPPs) and five Reanalysis Products (RPPs) against the ground observations (1998-2007) in the transboundary Jhelum River basin (33,397 km²). Among the seven SPPs (bias corrected), five have demonstrated significantly high correlation coefficient (CC>0.7) with observed rainfall. However, most of the products tended to underestimate the seasonal precipitation amount, particularly in Winter and Spring. Likewise, Asian Precipitation–Highly-Resolved Observational Data Integration Towards Evaluation of the Water Resources APHRODITE (GPPs) and Japanese 55-year Reanalysis JRA55 (RPPs) are best performing products in daily streamflow predictions with Nash–Sutcliffe efficiency (0.68 & 0.62), whilst MSWEP, AgMERRA and CHIRPS have also good potential in flow prediction. Generally, our results indicate that APHRODITE and JRA55 could be used as alternative source of precipitation data in Himalayas region.

Water resources planning and management depend on the quality of climatic data, particularly rainfall data, for reliable hydrological modeling. This can be very problematic in transboundary rivers with limited disclosing of data among the riparian countries. Satellite precipitation products are recognized as a promising source to substitute the ground-based observations in these conditions. This research aims to assess the feasibility of using a satellite-based precipitation product for better hydrological modeling in an ungauged and riparian river in Pakistan, i.e., the Chenab River. A semidistributed hydrological model of The soil and water assessment tool (SWAT) was set up and two renowned satellite precipitation products, i.e., global precipitation mission (GPM) IMERG-F v6 and tropical rainfall measuring mission (TRMM) 3B42 v7, were selected to assess the runoff pattern in Chenab River. The calibration was done from 2001–2006 with two years of a warmup period. The validation (2007–2010) results exhibit higher correlation between observed and simulated discharges at monthly timescale simulations, IMERG-F (R² = 0.89, NSE = 0.82), 3B42 (R² = 0.85, NSE = 0.72), rather than daily timescale simulations, IMERG-F (R² = 0.66, NSE = 0.61), 3B42 (R² = 0.64, NSE = 0.54). Moreover, the comparison between IMERG-F and 3B42, shows that IMERG-F is superior to 3B42 by indicating higher R², NSE and lower percent bias (PBIAS) at both monthly and daily timescale. The results are strengthened by Taylor diagram statistics, which represent a higher correlation (R) and less RMS error between observed and simulated values for IMERG-F. IMERG-F has great potential utility in the Chenab River catchment as it outperformed the 3B42 precipitation in this study. However, its poor skill of capturing peaks at daily timescale remains, leaving a room for IMERG-F to improve its algorithm in the upcoming release.

Pakistan is among the countries that are highly vulnerable to climate change. The country has experienced severe floods and droughts during recent decades. The agricultural sector in Pakistan is adversely affected by climate change. This systematic review paper set out to analyze the existing literature on adaptation measures at the farm level toward climate change in Pakistan. Adopting a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, a total of 62 articles were identified from the Web of Science and Scopus databases. The review paper indicates that the main adaptation strategies adopted by farmers are as follows: changing cropping practices, changing farm management techniques, advanced land use management practices, and nonagriculture livelihood options. Further, this review shows the factors influencing the farmer’s adaptation measures to climate change. Influencing factors were examined and classified into three groups: demographic, socioeconomic, and resources and institutional. Barriers hindering farmers’ adaptive capacity were identified as lack of access to information and knowledge, lack of access to extension services, lack of access to credit facility, and lack of farm resources.

Sewer pipelines often leak due to physical, operational, and environmental deterioration factors. Due to the hidden infrastructure of the sewer systems, leakage detection is often costly, challenging, and crucial at the city scale. Various sewer inspection methods (SIMs) have been developed and implemented at this time. This study evaluates the existing SIMs and categorizes them based on their area of impact (AoI) into three classes. Tier-one (T-I) methods, such as deterioration models and hotspot mapping, tend to grasp a broader and reliable understanding of the sewer systems' structural health and pinpoint the network sections that are more prone to leakage. As an intermediate solution, Tier-two (T-II) non-destructive methods, such as aerial thermal imagery (ATI) and electrical resistivity tomography (ERT), inspect the potential pipe clusters regardless of their material and visualize the leaked plume generated from defects and cracks. Tier-three (T-III) methods include in-pipe SIMs, such as visual and multi-sensory inspections, that can provide an in-depth understanding of the pipe and its deterioration stage. In this study, we suggest that a sustainable sewer inspection plan should include at least two SIMs belonging to different tiers to provide a dual investigation of precision and AoI, a balance between cost and time as well as an equilibrium between self-sufficiency and decentralization.

Remote sensing precipitation or precipitation from numerical weather prediction (NWP) is considered to be the best substitute for in situ ground observations for flood simulations in transboundary, data-scarce catchments. This research was aimed to evaluate the possibility of using a combination of a satellite precipitation product and NWP precipitation for better flood forecasting in the transboundary Chenab River Basin (CRB) in Pakistan. The gauge-calibrated satellite precipitation product, i.e., Global Satellite Mapping of Precipitation (GSMaP_Gauge), was selected to calibrate the Integrated Flood Analysis System (IFAS) model for the 2016 flood event in the Chenab River at the Marala Barrage gauging site in Pakistan. Precipitation from the Global Forecast System (GFS) NWP, with nine different lead times up to 4 days, was used in the calibrated IFAS model to predict the flood hydrograph in the Chenab River. The hydrologic simulations, with global GFS forecasts, were unable to predict the flood peak for all lead times. Then, the Weather Research and Forecasting (WRF) model was used to downscale the precipitation forecasts with one-way and two-way nesting approaches. In the WRF model, the CRB was centered in two domains of 25 km and 5 km resolutions. The downscaled precipitation forecasts were subsequently supplied to the IFAS model, and the predicted simulations were compared to obtain the optimal flood peak simulation in the Chenab River. It was found in this study that the simulated hydrographs, at different lead times, from the precipitation of two-way WRF nesting exhibited superior performance with the highest R² and Nash–Sutcliffe efficiency (NSE) and the lowest percent bias (PBIAS) compared with one-way nesting. Moreover, it was concluded that the combination of GFS forecast and two-way WRF nesting can provide high-quality precipitation prediction to simulate flood hydrographs with a remarkable lead time of 96 h when applying coupled hydrometeorological flow simulation.