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The trend analysis approach is used to estimate changing climate and its impact on the environment, agriculture and water resources. Innovative polygonal trend analyses are qualitative methods applied to detect changes in the environment. In this study, the Innovative Trend Pivot Analysis Method (ITPAM) and Trend Polygon Star Concept Method were applied for temperature trend detection in Soan River Basin (SRB), Potohar region, Pakistan. The average monthly temperature data (1995–2020) for 11 stations were used to create polygon graphics. Trend length and slope were calculated separately for arithmetic mean and standard deviation. The innovative methods produced useful scientific information, with the identification of monthly shifts and trend behaviors of temperature data at different stations. Some stations showed an increasing trend and others showed decreasing behavior. This increasing and decreasing variability is the result of climate change. The winter season temperature is increasing, and the months of December to February are getting warmer. Summer is expanding and pushing autumn towards winter, swallowing the early period of the cold season. The monthly polygonal trends with risk graphs depicted a clear picture of climate change in the Potohar region of Pakistan. The phenomena of observed average temperature changes, indicated by both qualitative methods, are interesting and have the potential to aid water managers’ understanding of the cropping system of the Potohar region.

The trend analysis approach is adopted for the prediction of future climatological behavior and climate change impact on agriculture, the environment, and water resources. In this study, the innovative trend pivot analysis method (ITPAM) and trend polygon star concept method were applied for precipitation trend detection at 11 stations located in the Soan River basin (SRB), Potohar region, Pakistan. Polygon graphics of total monthly precipitation data were created and trends length and slope were calculated separately for arithmetic mean and standard deviation. As a result, the innovative methods produced useful scientific information and helped in identifying, interpreting, and calculating monthly shifts under different trend behaviors, that is, increase in some stations and decrease in others of precipitation data. This increasing and decreasing variability emerges from climate change. The risk graphs of the total monthly precipitation and monthly polygonal trends appear to show changes in the trend of meteorological data in the Potohar region of Pakistan. The monsoonal rainfall of all stations shows a complex nature of behavior, and monthly distribution is uneven. There is a decreasing trend of rainfall in high land stations of SRB with a significant change between the first dataset and the second dataset in July and August. It was examined that monsoon rainfall is increasing in lowland stations indicating a shifting pattern of monsoonal rainfall from highland to lowland areas of SRB. The increasing and decreasing trends in different periods with evidence of seasonal variations may cause irregular behavior in the water resources and agricultural sectors. Significance Statement The monthly polygonal trends with risk graphs of total monthly precipitation data depicted a clear picture of climate change effects in the Potohar region of Pakistan. The monsoonal rainfall showed a significant decreasing trend in highland stations and an increasing trend in lowland stations, indicating a shifting pattern of monsoonal rainfall from highland to lowland areas.

Pakistan faces water scarcity and high operational costs for traditional irrigation systems, hindering agricultural productivity. Solar-powered irrigation systems (SPIS) can potentially provide a sustainable and affordable solution, but face technical, financial and policy barriers to adoption. A comprehensive study is needed to examine feasibility and identify barriers. Therefore, a comprehensive review study is conducted to identify the potential for solar irrigation, key issues and challenges related to its implementation in Pakistan. The analysis is based on published studies, technical reports and a survey of solar-powered drip irrigation systems. The use of SPIS in Pakistan is becoming a cost-effective and sustainable option for irrigation, particularly in remote and off-grid areas. However, these systems also have their challenges, such as high initial costs, maintenance and repairs, limited access to spare parts, lack of government policies and regulations, lack of technical expertise, lack of financing options and social acceptance. The most pressing issue is the risk of groundwater exploitation by using SPIS. Based on the analysis of the energy and water situation in Pakistan, it is important to sustainably use both solar energy and groundwater resources, through the implementation of effective management strategies and policies. With the right policies and investment in research and development of SPIS and groundwater, farmers can benefit by increasing crop yields, conserving water resources, reducing the cost of energy, increasing productivity and improving the standard of living and access to electricity in remote and off-grid areas. It is recommended that the adoption of solar energy be promoted to run high efficiency irrigation systems (HEIS) with urgent capacity improvement among farmers, advisors and system installers to sustainably manage water resources in SPIS. This would not only help to reduce the consumption of fossil fuels and associated environmental impacts, but also increase farmers’ income and reduce their operational costs. Moreover, the use of SPIS can improve crop yields, leading to food security and poverty reduction. Thus, the government and policymakers should consider implementing policies and incentives to encourage the large-scale adoption of solar energy in the agricultural sector.

Streamflow recession analysis is widely used for estimating catchment-scale hydrogeological parameters in gauged catchments because of its low cost, ease of use, and efficiency in terms of results....

Reliable hourly flood forecasting using weather radar rainfall data for early warning system is essential for reducing natural disaster risk during extreme typhoon events. This study proposed a novel approach integrated with physics-based WASH123D and HEC-HMS models to forecast 1 h ahead flood level in the Fengshan Creek basin, northern Taiwan. The comparison was done with data-driven support vector machine (SVM) model, and performances were assessed by using statistical indicators (root mean square error, correlation coefficient, the error of time to peak flood level, the error of peak flood). Four typhoons and two plum rain events (with 620 data sets) were selected for the process of model calibration and validation. The model performs better when it used quantitative precipitation estimate radar data rather than rain gauge data. Results of using 1 h ahead quantitative precipitation forecast (QPF) as input for flood forecasting were encouraging but not feasible to use directly for early flood warning system due to errors in peak flood levels and timing. Therefore, the improvement in accuracy of 1 h ahead flood forecasting was done using physics-based approach and SVM model. The systematic comparison revealed that the SVM model is an attractive way out to improve the accuracy of QPF forecasted flood levels but unable to fully describe the flood level patterns in terms of timings and flood peaks, while the results obtained by the physics-based approach were accurate and much better than the SVM model. The approach fully described the physics of hydrograph patterns and outputs have exactly the same 1 h ahead predictions, in excellent agreement with observations. The reliable and accurate reflections of timing and amount of flood peaks in all selected typhoons by a newly developed physics-based approach with its operational nature are recommended to use by the government in the future for early warning to reduce the flood impacts during typhoon events.

The investigations of groundwater hydrograph reasonably reflect the aquifer response to recharge–discharge phenomenon and its characteristics. A better understanding of aquifer characteristics such as regional aquifer classification, recharge and discharge patterns, aquifer geology and flow patterns are the surface indicators that may be more effective and less costly for interpreting basic regional hydrogeological conditions and assessments. This study deals with the application of Hierarchical Clustering Analysis to understand the groundwater spatio-temporal patterns and to visualize/classify the nature of the aquifer in the regional area of Kaohsiung City, Taiwan. Groundwater level fluctuation patterns and slopes of rising and recession limbs are used to identify the pumping effects and classify aquifers. The results of clustering analysis show that the groundwater observation wells in the study area can be divided into five major characteristics along with the upstream to downstream of Kaoping River. The clusters are consistent with basic lithology distribution and age of sedimentary, which represents the characteristics of groundwater level fluctuation. The identified groundwater hydrographs patterns provide newer insights related to aquifer response to recharge–discharge phenomenon, types of aquifers and their behaviors. The knowledge of water level fluctuations in the observation wells provides a piece of prior information about the abstraction of groundwater. The proposed aquifer classification and pumping effect have great potential for applied use in groundwater management e.g., save drilling cost.

This research was undertaken for the evaluation of soil erosion using the semi-distributed basin scale SWAT model for four subcatchments of the Dhrabi River Catchment (DRC), which is located in the Pothwar Plateau region. Two subcatchments (catchment-25 and -31) are characterized by gullies while the other two (catchment-27 and -32) are managed with terraced landuse system. The performance of the model was satisfactory with coefficient of determination (R2) = 0.67 to 0.91 and Nash-Sutcliffe efficiency (ENS) = 0.54 to 0.85 for both surface runoff and sediment yield during the calibration (2009- 2010) and validation (2011) periods. The PUSLE factor was found to be the most sensitive parameter during model calibration. It was observed that all of the rainfall- runoff events occurred during the monsoon season (June to September). The estimated annual sediment loss ranged from 2.6 t/hm2 to 31.1 t/hm2 over the duration of the simulation period for the non-terraced catchments, in response to annual precipitation amounts that were between 194.8 mm to 579.3 mm. In contrast, the predicted annual sediment levels for the terraced catchments ranged from 0.52 t/hm2 to 10.10 t/hm2 due to similar precipitation amounts. The terraced catchments resulted in 4 to 5 times lower sediment yield as compared to non-terraced catchments. The results suggest that there is a huge potential for terraces to reduce soil erosion in the DRC specifically and Pothwar area generally, which have proven to be an efficient approach to establishing soil and water conservation structures in this region. Keywords: SWAT modeling, sediment yield, Modified version of Universal Soil Loss Equation (MUSLE), calibration, validation, parameter evaluation, small watersheds DOI: 10.25165/j.ijabe.20191201.3769 Citation: Hussain F, Nabi G, Wu R-S, Hussain B, Abbas T. Parameter evaluation for soil erosion estimation on small watersheds using SWAT model. Int J Agric & Biol Eng, 2019; 12(1): 96–108.

Hydraulic conductivity is the key and one of the most uncertain parameters in groundwater modeling. The grid based numerical simulation require spatial distribution of sampled hydraulic conductivity at un-sampled locations in the study area. This spatial interpolation has been routinely performed using variogram based models (two-point geostatistics methods). These traditional techniques fail to capture the complex geological structures, provides smoothing effects and ignore the higher order moments of subsurface heterogeneities. In this work, a multiple-point geostatistics (MPS) method is applied to interpolate hydraulic conductivity data which will be further used in WASH123D numerical groundwater simulation model for regional smart groundwater management. To do this, MPS need ‘training images (TIs) as a key input. TI is a conceptual model of subsurface geological heterogeneity which was developed by using concept of ages, topographic slope as an index criteria and knowledge of geologist. After considerations of full physics of study area, an example shows the advantages of using multiple-point geostatistics compared with the traditional two-point geostatistics methods (such as Kriging) for the interpolation of hydraulic conductivity data in a complex geological formation.

Samarinda’s flooding issue is threatening future city development. As the most populated city in Kalimantan, Samarinda (the municipality of East Borneo) plays a role-model in disaster management for a neighboring city. This paper introduces current flood disaster handling in this city. History of disaster management in Indonesia is started from the earlier of Indonesian independence. Year 2008, after hit by severe Tsunami in Aceh and its surrounding, Government of Republic of Indonesia form special agency to manage disaster specifically, namely National Board for Disaster Management (in Bahasa called: Badan Penanggulangan Bencana Nasional [BNPB]) and follows by the regional and local government to form similar agency in provincial and local scale (including Samarinda), called Regional Board for Disaster Management (in Bahasa: Badan Penanggulangan Bencana Daerah[BPBD]) which is formed in 2009 and 2011, respectively. The aim of this paper is to explain flood management in Samarinda where is flood hazard increase gradually and need to be a priority. Descriptive analysis is used in this study including secondary data and interviewed stakeholders. Finally, the finding of study obtains found five constraints related to Samarinda’s flood management including administrative and policy, social, economic, environment and technical and knowledge constraint. This study also promotes several schemes of non-structural approach to enlarge alternative perspective in flood management.

Flood issue in Samarinda almost surrounding every year, especially in the rainy season (October – April). Its issue depends on the several factors such as drainage condition,topographical area and also the problems of Karang Mumus river. This paper focused on the river problem. This river has decreasing capacity caused by constriction. Its affected by a lot of people live on the riverbank area and the worst of it is impossible to relocate all of them. In this study, we tried to analyze how to reduce the discharge. The analysis covered by hydrological analysis by using several scenarios. Most of the results exhibit the discharge of the river decrease than the current condition. The discharge decreased until 59,2% after scenario was applied.

This study evaluates the spatiotemporal rainfall variability over the semimountainous Soan River Basin (SRB) of sub-Himalayan Pothwar region, Pakistan. The temporal rainfall trend analysis of sixteen rain gauges was performed on annual basis with long-term (1981–2016) data. The results depicted that there is substantial year-to-year and season-to-season variability in rainfall patterns, and rainfall patterns are generally erratic in nature. The results highlight that most of the highland rainfall stations showed decreasing trends on annual basis. The central and lowland stations of the study area recorded an increasing trend of rainfall except for Talagang station. The average annual rainfall of the study area ranges between 492 mm and 1710 mm in lowland and high-altitude areas, respectively. Of the whole year’s rainfall, about 70 to 75% fall during the monsoon season. The rainfall spatial distribution maps obtained using the inverse distance weighting (IDW) method, through the GIS software, revealed the major rainfall range within the study area. There is a lack of water during postmonsoon months (November–February) and great differences in rainfall amounts between the mountainous areas and the lowlands. There is a need for the rational management of mountainous areas using mini and check dams to increase water production and stream regulation for lowland areas water availability. The spatiotemporal rainfall variability is crucial for better water resource management schemes in the study area of Pothwar region, Pakistan.

The reliability of weather radar data in real-time flood forecasting and early warning system remain ambivalent due to high uncertainty in Quantitative Precipitation Forecasts (QPF). In this study, a methodology is presented with the objective to improve the flood forecasting results with the application of radar rainfall calculated in three different ways. The QPF radar rainfall forecast data of four typhoon events in Fèngshān River Basin, Taiwan, were simulated using the WASH123D numerical model. The simulated results were corrected using a physical real-time correction technique and compared with direct simulation without correction for all three QPF calculation methods. According to model performance evaluation criteria, in the third method of QPF calculation, flood peak error was the lowest in all three methods, indicating better results for flood forecasting and can be used for flood early warning systems. The impact of the real-time correction technique was assessed using mass balance analysis. It was found that flow change is between 16% and 42% from direct simulation, indicating being on the safe side in case of a flood warning. However, the impact of the real-time physical correction on the water level itself is in a reasonable range. Still, QPF rainfall correction/calculation is more important to obtain accurate results for flood forecasting. Therefore, the application of real-time correction to correct the model water level has a certain degree of credibility, which is the mass balance of the model. This approach is recommended for flood forecasting early warning systems.

The Guatemala’s rural population have limited resources, high vulnerability to climate change, traditional agriculture practices and adversely affected by water scarcity. These problems engender the need for further economic development and imposed pressure on the existing water resources. In response, Rain Water Harvest (RWH) is the measure as an alternative source towards water shortage problem and a decrease in groundwater extraction. However, the identification of optimal sites for RWH is an important step to maximize the amount of water harvested and minimize the ecological impact. In this study, an Analytical Hierarchy Process (AHP) was used to determinate optimal sites using Geographic Information Systems (GIS) in order to integrate spatial information. Physical and socio-economic features were main decision criteria along with six sub-criteria: potential runoff; land use; soil texture; slope; distance from agricultural land; and distance from roads. In the investigation, several different criteria with different AHP structures were utilized to assess the flexibility of structures. The result maps with respect to different criteria and AHP structurs are overlaid in a systematic scheme to identify the most suitable site for RWH project. The results identified four sites as optimally suitable and eight as highly suitable. The total 424,070.81 m3 volume of water can be potentially harvested from these optimally and highly suitable sites. The study area comprised of 770.61 km2 of Guatemala northeastern region and it is suggested that the RWH system for agriculture purpose should be promoted through government and multistakeholder co-operations as an alternative water resource.

This study devised a practical solution to mitigate urban inundation and artificial recharge of groundwater using recharge wells which is the most viable surface runoff rainwater harvesting (RWH) technique in urban areas. The Rainwater Harvesting Pilot Project at Gaddafi Stadium Lahore was established to deal with urban flooding, artificial recharge of groundwater, and to avoid the mixing of rainwater in municipal sewerage. The study showed that Lahore city has great RWH potential from critical ponding roads that can be utilized to recharge the Lahore aquifer. With that ratio of recharge, the groundwater level can rise to 3.54 ft after every monsoon period if the same recharge wells structure are used, which is a key to groundwater sustainability in Lahore city. Moreover, the maximum recharging capacity of wells was 29.32 m3/h with satisfactory performance. Both recharge wells cleared the ponding volume within 3 to 3.5 h after the rainfall stopped. The filter media performance was also favorable with 25%&ndash, 30% removal of contamination. All the water quality parameters were within the permissible limit against prescribed standards except coliform count that indicated the presence of sewage. In such a case the mixing of charcoal is recommended to shut up the coliform signals. This study identified that RWH using recharge wells is an alternative freshwater supply source for sustainable development of Lahore city and this technique should be the part of Master Planning and Policy Decision of Lahore as a suggestion.

This study evaluated parameters of soil erosion and optimization of micro watersheds by applying a semidistributed basin-scale Soil and Water Assessment Tool (SWAT) model in various small watersheds of the Chakwal and Attock districts of Pothwar, Pakistan. The model was calibrated and validated on a daily basis for a small catchment (Catchment-25) of the Dhrabi watershed without any soil conservation structures. Statistical measures (R2 and EN-S) were used to evaluate model performance; the model performed satisfactorily well for both surface runoff and sediment yield estimations, with the R2 and EN-S values both being greater than 0.75, during calibration (2009–2010) and validation (2011). The model was applied to various small watershed sites in the Chakwal and Attock districts after successful calibration and validation. Soil erosion estimation was performed at these sites having loose stone soil and water conservation structures and being under various slope gradient and vegetation cover conditions. The structures had significant effects, and the average sediment yield reduction engendered by the loose stone structures at the various sites varied from 54 to 98 %. The sediment yield and erosion reductions were also compared under conditions involving vegetation cover change. Agricultural land with winter wheat crops had a higher sediment yield level than did fallow land with crop residue, which facilitated sediment yield reduction along with the soil conservation structures. Analyzing various slope gradients revealed that all selected sites had a maximum slope area of less than 5 %; stone structures were installed at these sites to reduce sediment yield. Based on slope classification analysis, the model was upscaled for the whole districts of Chakwal and Attock. The results indicated that 60 % of Chakwal (4095 km2) and Attock (3918 km2) by area lies in a slope range of 0–4 %; this thus implies that considerable potential exists for implementing soil conservation measures by installing stone structures. Estimates revealed that minimum sediment yield reductions of 122,850 t year−1 in Chakwal District and 117,540 t year−1 in Attock District could be achieved by installing loose stone structures in 60 % of the agricultural areas of both districts having a slope of 0–4 %; these findings can serve as a reference for policymakers and planners. The overarching findings of this study show that the SWAT model provides reliable results for sediment yield and soil erosion estimation, which can be used in rocky mountainous watersheds for erosion control and watershed management.

This study involved developing a natural disaster risk assessment framework based on the consideration of three phases: a pre-disaster phase, disaster impact phase, and post-disaster recovery phase. The exposure of natural disasters exhibits unique characteristics. The interactions of numerous factors should be considered in risk assessment as well as in monitoring environment to provide natural disaster warnings. In each phase, specific factors indicate the relative status in the area subjected to risk assessment. Three types of natural disaster were assessed, namely debris flows, floods, and droughts. The Chishan basin in Taiwan was used as a case study and the adequacy of the relocation of Xiaolin village was evaluated. Incorporating resilience into the assessment revealed that the higher the exposure is, the higher the resilience becomes. This is because highly populated areas are typically allocated enough resources to respond to disasters. In addition, highly populated areas typically exhibit high resilience. The application of this analysis in the policy of relocation of damaged village after disaster provides valuable information for decision makers to achieve the sustainability of land use planning.

This paper proposes a method to utilize weather and land cover models to generate future environmental scenarios, and presents the watershed models to simulate the hydrological impact on watershed-scale hydrology. The Weather Generator model and General Circulation Model were applied to produce rainfall and local temperature under different climate conditions, and the Conservation and Land Use and its Effects model was incorporated to simulate future land cover variability. The circumstances of future climate and land cover changes were used as inputs to drive the HEC-HMS rainfall runoff model for obtaining surface runoff in a mountainous area. The WASH123D model was then utilized for the entire watershed simulation. Modeling results were then examined to discuss hydrological impacts on three different time periods: near future (2020-2039), future (2050-2069), and distant future (2080-2099). The Fengshan Creek basin in northern Taiwan was selected as study site. Simulations results indicated that the influence of climate change revealed more relevant effects when compared to local land cover changes. The ground water levels tended to diminish as the land cover area changed. In addition, both river and groundwater levels reveal that it is drier in dry season and wetter in wet season in future.

Flooding issues in Samarinda have high depending on the capacity of Karang Mumus river. Considering the ability of Karang Mumus river to drain off flood discharge, there wore evidence that the constriction of River will drive to flooding issues, especially in rainy/wet season (October-April). The constriction of river happens because many people build nonpermanent houses and building on the river and river banks. Flooding potentially damages to the houses, roads, and other public facilities increasingly. To cope the issue, Government of Samarinda has tried many solutions to overcome the issue by building The Benanga dam and it has been planned to build multiple Dams in Karang Mumus sub-Watershed. This paper aims to analyze the effectiveness of flood control effort using multiple dams scenario in Karang Mumus Sub-Watershed. Analyzing process including hydrology simulation, the relationship between hydrographs and rise of water level simulation in Karang Mumus River. The result of this paper shows water level when peak discharge flows out existing river bank. Analyzing result also shows that flood control scenario is effective to reducing flood discharge until fifty percent compared without existing conditions.

Uncertainty is inherent in modeling studies. However, the quantification of uncertainties associated with a model is a challenging task. Furthermore, snowmelt estimation is a crucial part of the Soil and Water Assessment Tool (SWAT) model in watersheds where spring runoff is strongly affected by melting snow. The SWAT model for the snow dependent Kunhar basin in Himalayan watershed was calibrated (2001 - 2005) and validated (2006 - 2009) using Sequential Uncertainty Fitting Algorithm (SUFI-2). For the model uncertainty, two indices P-factor and R-factor along with frequently used objective functions R2, NSE, PBIAS, were taken into consideration. For calibration, multisite daily and monthly simulation results of SUFI-2 revealed that percentage of data enveloped by 95% confidence interval was 85% (monthly) to 87% (daily) at upstream calibration point and 63% (monthly) to 73% (daily) data at the downstream calibration point. Model validation by the usage of elevation bands indicated better model performance, enveloping 15 - 20% more observed data than the validation without elevation bands together with the other statistical standards. Equifinality in the model parameters was observed, and it was discovered that the model uncertainty lie inside the model parameters. It is recommended that critical model parameters correspondence with the watershed characteristics should be checked. The calibrated version of the model could be further used for the analysis and impacts of climate and land use changes on stream flows, water quality and sediment yield.