Anticipating Future Hydrological Changes in the Northern River Basins of Pakistan: Insights from the Snowmelt Runoff Model and an Improved Snow Cover Data

Snow‐covered areas (SCAs) are the fundamental source of water for the hydrological cycle for some region. Accurate measurements of river discharge from snowmelt can help manage much needed water required for hydropower generation and irrigation purposes. This study aims to apply the snowmelt runoff model (SRM) in the Upper Indus basin by the Astore River in northern Pakistan for the years 2000 to 2006. The Shuttle Radar Topographic Mission (SRTM) data are used to generate the Digital Elevation Model (DEM) of the region. Various variables (snow cover depletion curves (SCDCs), temperature and precipitation) and parameters (degree‐day factor, recession coefficient, runoff coefficients, time lag, critical temperature and temperature lapse rate) are used as input in the SRM. However, snow cover data are direct and an important input to the SRM. Satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to estimate the SCA. Normalized difference snow index (NDSI) algorithm is applied for snow cover mapping and to differentiate snow from other land features. Nash–Sutcliffe coefficient of determination (R2) and volume difference (DV) are used for quality assessment of the SRM. The results of the current research show that for the study years (2000–2006), the average value of R2 is 0·87 and average volume difference DV is 1·18%. The correlation coefficient between measured and computed runoff is 0·95. The results of the study further show that a high level of accuracy can be achieved during the snowmelt season. The simulation results endorse that the SRM in conjunction with MODIS snow cover product is very useful for water resource management in the Astore River and can be used for runoff forecasts in the Indus River basin in northern Pakistan. Copyright © 2011 John Wiley & Sons, Ltd.

ABSTRACTPakistan is an agriculture-based economy and major proportion of irrigation water for its cultivated lands is abstracted from the Upper Indus Basin (UIB). UIB water supplies are mostly contributed from the high-altitude snow and glacier fields situated in the Hindukush–Karakoram–Himalayan ranges. Any change in the flows of these river catchments due to climate variability may result in the form of catastrophic events like floods and droughts and hence will adversely affect the economy of Pakistan. This study aims to simulate snowmelt runoff in a mountainous sub-catchment (Shyok River basin) of the UIB under climate change scenarios. Snowmelt Runoff Model (SRM) coupled with remotely sensed snow cover product (MOD10A2) is used to simulate the snowmelt runoff under current and future climate scenarios in the study area. The results indicate that (a) SRM has efficiently simulated the flow in Shyok River with average Nash–Sutcliff coefficient value (R2) of 0.8 (0.63–0.93) for all six years (2000–2006) of basin-wide and zone-wise simulations, (b) an increase of 10% (by 2050) and 20% (by 2075) in SCA will result in a flow rise of ∼11% and ∼20%, respectively, and (c) an increase of 1°C (by 2025), 2°C (by 2050), 3°C (by 2075) and 4°C (by 2100) in mean temperature will result in a flow rise of ∼26%, ∼54%, ∼81% and ∼118%, respectively. This study suggests that SRM equipped with remotely sensed snow cover data is an effective tool to estimate snowmelt runoff in high mountain data-scarce environments.

A test of Snowmelt Runoff Model (SRM) for the Gongnaisi River basin in the western Tianshan Mountains, China

Upper Indus Basin (UIB) supplies more than 70% flow to the downstream agricultural areas during summer due to the melting of snow and glacial ice. The estimation of the stream flow under future climatic projections is a pre-requisite to manage water resources properly. This study focused on the simulation of snowmelt-runoff using Snowmelt-Runoff Model (SRM) under the current and future Representative Concentration Pathways (RCP 2.6, 4.5 and 8.5) climate scenarios in the two main tributaries of the UIB namely the Astore and the Hunza River basins. Remote sensing data from Advanced Land Observation Satellite (ALOS) and Moderate Resolution Imaging Spectroradiometer (MODIS) along with in-situ hydro-climatic data was used as input to the SRM. Basin-wide and zone-wise approaches were used in the SRM. For the zone-wise approach, basin areas were sliced into five elevation zones and the mean temperature for the zones with no weather stations was estimated using a lapse rate value of −0.48 °C to −0.76 °C/100 m in both studied basins. Zonal snow cover was estimated for each zone by reclassifying the MODIS snow maps according to the zonal boundaries. SRM was calibrated over 2000–2001 and validated over the 2002–2004 data period. The results implied that the SRM simulated the river flow efficiently with Nash-Sutcliffe model efficiency coefficient of 0.90 (0.86) and 0.86 (0.86) for the basin-wide (zone-wise) approach in the Astore and Hunza River Basins, respectively, over the entire simulation period. Mean annual discharge was projected to increase by 11–58% and 14–90% in the Astore and Hunza River Basins, respectively, under all the RCP mid- and late-21st-century scenarios. Mean summer discharge was projected to increase between 10–60% under all the RCP scenarios of mid- and late-21st century in the Astore and Hunza basins. This study suggests that the water resources of Pakistan should be managed properly to lessen the damage to human lives, agriculture, and economy posed by expected future floods as indicated by the climatic projections.

Investigation of continuous daily streamflow based on both rainfall and snowmelt in a cryosphere catchment is challenging, particularly when climate records are limited or unavailable. This study compares the accuracy of the Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) and the Snowmelt-Runoff Model (SRM) to perform continuous simulation of rainfall and snowmelt-runoff in the scarcely gauged Jhelum River basin of Pakistan under current and potential climate change scenarios. We used Tropical Rainfall Measuring Mission (TRMM) precipitation data and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data to examine the efficiency of both models. Observed streamflow data from 5 years (2000–2005) were used for calibration and from another 3 years (2007–2010) were used for model validation. Good agreement was attained between the simulated and observed streamflow for annual and snowmelt season in the validation period: (0.71, 10.4) and (0.58, 12.4) for HEC-HMS and (0.74, 8.82) and (0.64, 1.74) for SRM [statistic stated as (Nash–Sutcliffe efficiency and difference in volume %)], respectively. Future streamflow was projected for 2095 using potential climate change scenarios based on precipitation, mean temperature, and snow cover area (SCA). The HEC-HMS and SRM indicated variations in annual streamflow from −8 to +14 % and −13 to +35 %, respectively, with a change in temperature from −2 to +4 °C and from −11 to +32 % and 13–42 % with a change in precipitation from −10 to +20 % along a temperature increase from 2 to 4 °C, respectively. Additionally, SRM showed that changes in SCA from −10 to +30 % would contribute to annual streamflow from −4 to +14 %, whereas a temperature increase from 2 to 4 °C along with a 20 % increase in SCA extent would increase the annual streamflow by 34 %. Overall, the results of this study reveal that the SRM model has a high computing efficiency and requires fewer data inputs than HEC-HMS to predict runoff under changing climate conditions in a high-altitude, scarcely gauged basin.

Integration of aspect and slope in snowmelt runoff modeling in a mountain watershed

The Himalayan region, a major source of fresh water, is recognized as a water tower of the world. Many perennial rivers originate from Nepal Himalaya, located in the central part of the Himalayan region. Snowmelt water is essential freshwater for living, whereas it poses flood disaster potential, which is a major challenge for sustainable development. Climate change also largely affects snowmelt hydrology. Therefore, river discharge measurement requires crucial attention in the face of climate change, particularly in the Himalayan region. The snowmelt runoff model (SRM) is a frequently used method to measure river discharge in snow-fed mountain river basins. This study attempts to investigate snowmelt contribution in the overall discharge of the Budhi Gandaki River Basin (BGRB) using satellite remote sensing data products through the application of the SRM model. The model outputs were validated based on station measured river discharge data. The results show that SRM performed well in the study basin with a coefficient of determination (R2) >0.880. Moreover, this study found that the moderate resolution imaging spectroradiometer (MODIS) snow cover data and European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological datasets are highly applicable to the SRM in the Himalayan region. The study also shows that snow days have slightly decreased in the last three years, hence snowmelt contribution in overall discharge has decreased slightly in the study area. Finally, this study concludes that MOD10A2 and ECMWF precipitation and two-meter temperature products are highly applicable to measure snowmelt and associated discharge through SRM in the BGRB. Moreover, it also helps with proper freshwater planning, efficient use of winter water flow, and mitigating and preventive measures for the flood disaster.

Water managers in Pakistan need timely and accurate seasonal flow forecasts like in several areas of the world to allocate water for various kinds of water usages like canal operations for irrigation, reservoir operations, and strategies to respond to extreme cases. Various hydrological models are deployed that include University of British Columbia Watershed Model (UBCWM) and snowmelt runoff model (SRM) to seasonal streamflow forecasts in Pakistan. Here, I assess the approach that employed snow water equivalent (SWE), temperature (T), precipitation (P), and February month flow (Feb) to forecasts Kharif (April–September) season with no need for intensive hydrological modeling in a skillful way for Upper Indus Basin (UIB) at Tarbela Dam. For Indus, I compare this approach results with Indus River System Authority (IRSA), UBCWM, and SRM. This approach uses multiple linear regression (MLR) to develop regression function for forecast seasonal flow volume. This regression approach provides much skillful flow forecasts that is consistent in uncertainty spread for the Indus. The regression forecast accuracy with a mean absolute percentage error (MAPE) is 7.95% with regard to statistical approach used by IRSA, UBCWM, and SRM as 10.25%, 11.05%, and 8.91%, respectively. This approach even allows improvement of 1% (volume ~ 0.6 km3) and is very simple to apply with requirement of four input data that are easy to procure or download and process. With this information in hand, a regression function can generate seasonal forecast for the authorities in Pakistan.

ABSTRACT: The snowmelt‐runoff model (SRM) was used to produce accurate simulations of streamfiow during the snowmelt period (April‐September) for ten years on the Rio Grande Basin (3419 km2) near Del Norte, Colorado, U.S.A. In order to use SRM in the forecast situation, it was necessary to develop a family of snow cover depletion curves for each elevation zone based on accumulated snow water equivalent on April 1. Selection of an appropriate curve for a particular year from snow course measurements allows input of the daily snow cover extent to SRM for forecast purposes. Data from three years (1980, 1981, and 1985) were used as a quasi‐forecast test of the procedure. In these years forecasted snow cover extent data were input to SRM, but observed temperature and precipitation data were used. The resulting six‐month hydrographs were very similar to the hydrographs in the ten simulation years previously tested based on comparisons of performance evaluation criteria. Based on this result, the Soil Conservation Service (SCS) requested SRM forecasts for 1987 on the Rio Grande. Using the same procedure but with SCS estimated temperature and precipi‐tation data, SRM produced a forecast hydrograph that had a r2= 0.82 and difference in seasonal volume of 4.4 percent. To approximate actual operational conditions, SRM computed daily flows were updated every seven days with measured flows. The resulting forecast hydrograph had a R2= 0.90 and a difference in volume of 3.5 percent. The method developed needs to be refined and tested on additional years and basins, but the approach appears to be applicable to operational runoff forecasting using remote sensing data.

The simulation of snowmelt runoff in alpine mountainous areas is of great significance not only for the risk assessment of snowmelt flood in spring and summer, but also for the development and management of water resources in the basin. An improved snowmelt runoff model (SRM) is constructed based on the analysis of change characteristics of climate, runoff, and snow and ice cover in the middle and upper reaches of the Taxkorgan River in Xinjiang Province, China. Because of the large evaporation in the study basin, the evaporation loss is added to the model. The SRM and the improved SRM are calibrated and verified by using data such as temperature, precipitation, water vapor pressure, and snow-covered area (SCA) ratio in the study basin from 2002 to 2012. The results show that, compared with the SRM, the average Nash-Sutcliffe coefficient (NSE) of annual runoff simulation increases from 0.80 to 0.86 in the calibration and increases from 0.74 to 0.83 in the validation through the improved model, and the average runoff error reduces from - 12.8 to 1.32% in the calibration and reduces from - 20.0 to - 11.51% in the validation. After adding the measured flow rate for real-time correction, the average NSE of annual runoff simulation increases from 0.91 to 0.93 and the average annual runoff error reduces from - 7.76 to - 3.91% in the calibration. The average NSE increases from 0.85 to 0.89 and the average runoff error reduces from - 12.35 to - 2.76% in the validation. It indicates that the SRM structure with increased evaporation loss is more in line with the actual situation. The short-term simulation effect of the model is greatly improved by adding the measured flow rate for real-time correction. At the same time, the improved SRM and the hypothetical climate change scenario are used to analyze the impact analysis of the snowmelt runoff simulation in the partial wet year. The results show that in the case of rising temperature, the ice and snow ablation period is prolonged, and the annual runoff also changes significantly in time distribution. It is of guiding significance for the influence of climate change on the runoff of recharged rivers with ice-snow meltwater in the other alpine regions.

Observation and model-based studies suggest substantial hydrological flow pattern changes in mountain watershed where hydrology is dominated by cryospheric processes (IPCC 2007). The response of cryospheric processes to warming climate in mountain areas can be analysed by examining the responses in the seasonal and annual hydrologic regimes of rivers where snowmelt contributes significantly to the runoff. This study is carried out in Langtang basin, which aims to assess the impact of potential warming on snowmelt contribution and river discharge utilizes a Snowmelt Runoff Model (SRM), which is one of a very few models in the world today that requires remote sensing derived snow cover data as a model input. In this study, snow cover and hydrometric data were derived from Moderate Resolution Imaging Spectro-radiometer (MODIS) snow product and Snow and Glacier Hydrological Unit (SGHU) of Department of Hydrology and Meteorology, Government of Nepal. The model is calibrated for the year 2006 and validated in 2005. Different climatic scenarios are used (only change in temperature) to run the model in order to understand the impact of changing climate on runoff component and river discharge. In 2006, snow and glacier melt component contributes 35% in winter, 18% in summer and 19% annually in the stream flow. In this study, model predicts that snow and glacier melt contribution in stream flow will increase approximately at the rate of 2% in winter, 5% in summer and 4% in annual flow per 1°C temperature rise. Due to increase in snowmelt contribution, river discharge will also increase at the rate of 2% in winter, 6% in summer and 5% in annual flow under the projected temperature rise of 1°C.Journal of Hydrology and Meteorology, Vol. 9(1) 2015, p.74-84

This multidisciplinary study focuses on projecting and adapting to future hydrological changes in the Mekong – an international river of global significance in terms of rapidly increasing human pressures and climate-change vulnerability. A modelling framework was developed to project future changes in both the river flow regime and hydrological extremes (i.e. high/low flows and floods), under multiple scenarios of climate change, irrigation and hydropower developments. Furthermore, we developed a combined quantitative-qualitative approach to develop suitable adaptation measures and strategies to future floods in the Mekong Delta being a key vulnerability hotspot. Results show that the Mekong’s future flow regime is subjected to substantial changes under climate change and human developments. Climate change will intensify the hydrological cycle, resulting in increasing average river flows (between +5 % and +16%, annually), and more frequent and extreme high flows during the wet season. Flow regime shows substantial alterations in the seasonal flow distributions under the combined impacts of climate change, irrigation expansions and hydropower developments. While dry season flows increase strongly (monthly changes up to +150%), wet season flows show contrasting changes with reductions during June - October (up to -25%) and substantial increases during November – December (up to 36%). A follow-up modelling assessment for the Mekong Delta shows substantial increases in flood hazards under climate change and sea level rise, shown by higher flood frequencies and flood depths across the whole delta. Increasing flood hazards therefore represents a key issue to be addressed in terms of future adaptation. The adaptation appraisal study further shows that effective adaptation requires looking beyond sole infrastructural investments. Instead, technological innovations for flood risk management combined with improved governance and institutional capacities offer ample opportunities to adapt to future hydrological changes. This study projects substantial future hydrological changes under future climate change and accelerating socioeconomic developments and shows potentially serious consequences for water related safety and sustainable water resources uses and allocations. Furthermore, this study demonstrates amble opportunities to manage future changes through strategic development planning and through adaptive interventions. Insights from this study address the needs for quantified future hydrological changes and emphasize adequate adaptation to the associated risks in an important international river experiencing climate change and rapid socioeconomic developments.

Abstract. Snow and glacial melt runoff are the major sources of water contribution from the high mountainous terrain of the Indus River upstream of the Tarbela reservoir. A reliable forecast of seasonal water availability for the Kharif cropping season (April–September) can pave the way towards better water management and a subsequent boost in the agro-economy of Pakistan. The use of degree-day models in conjunction with satellite-based remote-sensing data for the forecasting of seasonal snow and ice melt runoff has proved to be a suitable approach for data-scarce regions. In the present research, the Snowmelt Runoff Model (SRM) has not only been enhanced by incorporating the glacier (G) component but also applied for the forecast of seasonal water availability from the Upper Indus Basin (UIB). Excel-based SRM+G takes account of separate degree-day factors for snow and glacier melt processes. All-year simulation runs with SRM+G for the period 2003–2014 result in an average flow component distribution of 53, 21, and 26 % for snow, glacier, and rain, respectively. The UIB has been divided into Upper and Lower parts because of the different climatic conditions in the Tibetan Plateau. The scenario approach for seasonal forecasting, which like the Ensemble Streamflow Prediction method uses historic meteorology as model forcings, has proven to be adequate for long-term water availability forecasts. The accuracy of the forecast with a mean absolute percentage error (MAPE) of 9.5 % could be slightly improved compared to two existing operational forecasts for the UIB, and the bias could be reduced to −2.0 %. However, the association between forecasts and observations as well as the skill in predicting extreme conditions is rather weak for all three models, which motivates further research on the selection of a subset of ensemble members according to forecasted seasonal anomalies.

Comprehensive evaluation of 0.25° precipitation datasets combined with MOD10A2 snow cover data in the ice-dominated river basins of Pakistan

In contrast to widespread glacier retreat evidenced globally, glaciers in the Karakoram region have exhibited positive mass balances and general glacier stability over the past decade. Snow and glacier meltwater from the Karakoram and the western Himalayas, which supplies the Indus River Basin, provide an essential source of water to more than 215 million people, either directly, as potable water, or indirectly, through hydroelectric generation and irrigation for crops. This study focuses on water resources in the Upper Indus Basin (UIB) which combines the ranges of the Hindukush, Karakoram and Himalaya (HKH). Specifically, we focus on the Gilgit River Basin (GRB) to inform more sustainable water use policy at the sub-basin scale. We employ two degree-day approaches, the Spatial Processes in Hydrology (SPHY) and Snowmelt Runoff Model (SRM), to simulate runoff in the GRB during 2001–2012. The performance of SRM was poor during July and August, the period when glacier melt contribution typically dominates runoff. Consequently, SPHY outperformed SRM, likely attributable to SPHY’s ability to discriminate between glacier, snow, and rainfall contributions to runoff during the ablation period. The average simulated runoff revealed the prevalent snowmelt contribution as 62%, followed by the glacier melt 28% and rainfall 10% in GRB. We also assessed the potential impact of climate change on future water resources, based on two Representative Concentration Pathways (RCP) (RCP 4.5 and RCP 8.5). We estimate that summer flows are projected to increase by between 5.6% and 19.8% due to increased temperatures of between 0.7 and 2.6 °C over the period 2039–2070. If realized, increased summer flows in the region could prove beneficial for a range of sectors, but only over the short to medium term and if not associated with extreme events. Long-term projections indicate declining water resources in the region in terms of snow and glacier melt.