Uncertainties in the Hydrological Modelling Using Remote Sensing Data over the Himalayan Region

Himalayas the “roof of the world” are the source of water supply for major South Asian Rivers and fulfill the demand of almost one sixth of world’s humanity. Hydrological modeling poses a big challenge for Himalayan River Basins due to complex topography, climatology and lack of quality input data. In this study, hydrological uncertainties arising due to remotely sensed inputs, input resolution and model structure has been highlighted for a Himalayan Gandak River Basin. Firstly, spatial input DEM (Digital Elevation Model) from two sources SRTM (Shuttle Radar Topography Mission) and ASTER (Advanced Space borne Thermal Emission and Reflection Radiometer) with resolutions 30m, 90m and 30m respectively has been evaluated for their delineation accuracy. The result reveals that SRTM 90m has best performance in terms of least area delineation error (13239.28 km2) and least stream network delineation error. The daily satellite precipitation estimates TRMM 3B42 V7 (Tropical Rainfall Monitoring Mission) and CMORPH (Climate Prediction Center MORPHing Technique) are evaluated for their feasibly over these terrains. Evaluation based on various scores related to visual verification method, Yes/no dichotomous, and continuous variable verification method reveal that TRMM 3B42 V7 has better scores than CMORPH. The effect of DEM resolution on the SWAT (Soil Water Assessment Tool) model outputs has been demonstrated using sixteen DEM grid sizes (40m-1000m). The analysis reveals that sediment and flow are greatly affected by the DEM resolutions (for DEMs>300m). The amount of total nitrogen (TN) and total phosphorous (TP) are found affected via slope and volume of flow for DEM grid size ≥150m. The T-test results are significant for SWAT outputs for grid size >500m at a yearly time step. The SWAT model is accessed for uncertainty during various hydrological processes modeling with different setups/structure. The results reflects that the use of elevation band modeling routine (with six to eight elevation bands) improves the streamflow statistics and water budgets from upstream to downstream gauging sites. Also, the SWAT model represents a consistent pattern of spatiotemporal snow cover dynamics when compared with MODIS data. At the end, the uncertainty in the stream flow simulation for TRMM 3B42 V7 for various rainfall intensity has been accessed with the statistics Percentage Bias (PBIAS) and RSR (RMSE-observations Standard Deviation Ratio). The results found that TRMM simulated streamflow is suitable for moderate (7.5 to 35.4 mm/day) to heavy rainfall intensities (35.5 to 124.4 mm/day). The finding of the present work can be useful for TRMM based studies for water resources management over the similar parts of the world

An appraisal of precipitation distribution in the high-altitude catchments of the Indus basin

Scarcity of in-situ observations coupled with high orographic influences has prevented a comprehensive assessment of precipitation distribution in the high-altitude catchments of Indus basin. Available data are generally fragmented and scattered with different organizations and mostly cover the valleys. Here, we combine most of the available station data with the indirect precipitation estimates the accumulation zones of major glaciers to analyse altitudinal dependency of precipitation in the high-altitude Indus basin. The available observations signified the importance of orography in each sub-hydrological basin but could not infer an accurate distribution of precipitation with altitude. We used Kriging with External Drift (KED) interpolation scheme with elevation as a predictor to appraise spatiotemporal distribution of mean monthly, seasonal and annual precipitation for the period of 1998-2012. The KED-based annual precipitation estimates are verified by the corresponding basin-wide observed specific runoffs, which show good agreement. In contrast to earlier studies, our estimates reveal substantially higher precipitation in most of the sub-basins indicating two distinct rainfall maxima; 1st along southern and lower most slopes of Chenab, Jhelum, Indus main and Swat basins, and 2nd around north-west corner of Shyok basin in the central Karakoram. The study demonstrated that the selected gridded precipitating products covering this region are prone to significant errors. In terms of quantitative estimates, ERA-Interim is relatively close to the observations followed by WFDEI and TRMM, while APHRODITE gives highly underestimated precipitation estimates in the study area Basin-wide seasonal and annual correction factors introduced for each gridded dataset can be useful for lumped hydrological modelling studies, while the estimated precipitation distribution can serve as a basis for bias correction of any gridded precipitation products for the study area

Geoinformatic and Hydrologic Analysis using Open Source Data for Floods Management in Pakistan

There is being observed high variability in the spatial and temporal rainfall patterns under changing climate, enhancing both the intensity and frequency of the natural disasters like floods. Pakistan, a country which is highly prone to climate change, is recently facing the challenges of both flooding and severe water shortage as the surface water storage capacity is too limited to cope with heavy flows during rainy months. Thus, an effective and timely predication and management of high flows is a dire need to address both flooding and long term water shortage issues. The work of this thesis was aimed at developing and evaluating different open source data based methodologies for floods detection and analysis in Pakistan. Specifically, the research work was conducted for developing and evaluating a hydrologic model being able to run in real time based on satellite rainfall data, as well as to perform flood hazard mapping by analyzing seasonality of flooded areas using MODIS classification approach. In the first phase, TRMM monthly rainfall data (TMPA 3B43) was evaluated for Pakistan by comparison with rain gauge data, as well as by further focusing on its analysis and evaluation for different time periods and climatic zones of Pakistan. In the next phase, TRMM rainfall data and other open source datasets like digital soil map and global land cover map were utilized to develop and evaluate an event-based hydrologic model using HEC-HMS, which may be able to be run in real time for predicting peak flows due to any extreme rainfall event. Finally, to broaden the study canvas from a river catchment to the whole country scale, MODIS automated water bodies classification approach with MODIS daily surface reflectance products was utilized to develop a historical archive of reference water bodies and perform seasonal analysis of flooded areas for Pakistan. The approach was found well capable for its application for floods detection in plain areas of Pakistan. The open source data based hydrologic modeling approach devised in this study can be helpful for conducting similar rainfall-runoff modeling studies for the other river catchments and predicting peak flows at a river catchment scale, particularly in mountainous topography. Similarly, the outcomes of MODIS classification analysis regarding reference and seasonal water and flood hazard maps may be helpful for planning any management interventions in the flood prone areas of Pakistan

Catchment-scale spatial targeting of flood management measures to reduce flood hazard: An end-to-end modelling approach applied to the East Rapti catchment, Nepal

Globally, practical approaches to managing flood hazards are moving away from mitigation solely at the point of the impact, and towards an integrated catchment-scale approach which considers flood source areas, flow pathways of flood waters and impacted communities. The current method for managing the fluvial flood risk in Nepal, however, generally involves localised structural interventions in affected areas using a static and reactive approach. This method does not create long term resilience to the hazards. There is therefore the need to rely less on these large-scale structural measures and focus instead on sustainable and non-structural measures for flood mitigation that allow the catchments and communities within them to be more resilient. The three-stage, end-to-end approach developed in this thesis provides a process to help shift towards an integrated catchment management for flood hazard reduction in Nepal. The approach centres on identifying flood water source areas within the catchment and spatially targeting flood management measures in these locations. Consideration is also given to the potential impact of future, flow magnitude increasing, land cover change such as deforestation and the abandonment of terraced agriculture that is evident in many Nepali catchments. Stage 1 adopts SCIMAP-Flood, a catchment-scale decision support framework that identifies critical source areas for flood waters. The framework uses maps flood water generating areas based on spatial rainfall patterns and land cover, the incorporation of travel times across a catchment, and modelling of hydrological connectivity. Outputs are used to create catchment-scale flood management scenarios which target flood source areas; tested flood management measures include targeted afforestation, check dams in key sub-catchments and abandoned terrace restoration. In Stage 2 the flood management scenarios are assessed using CRUM3, a physically-based, spatially distributed, catchment-scale hydrological model. The impact of the flood management measures can be evaluated throughout the catchment using the modelled change in discharge. Stage 3 uses LISFLOOD-FP, a 2D flood inundation model, to establish the change flood inundation patterns at key flood impacted communities within the catchment from the created flood management scenarios. Stage 2 and Stage 3 utilise a coupled hydrological-hydraulic modelling approach with the results from the CRUM3 model entering the LISFLOOD-FP model as inflow hydrographs. The approach is applied to the East Rapti catchment, a 3,084 km2 sub-catchment of the Nayarani River in southern central Nepal. The catchment contains three river flow gauges (Lothar Khola [catchment area - 169 km2], Manahari Khola [427 km2] and Rapti River [471 km2]) placed within the main sub-catchments and eight rainfall gauges. Additional data used to drive the approach was attained from global datasets and acquired during fieldwork. This thesis has researched the potential effectiveness of the implementation of flood management interventions at the catchment-scale and evidences an alternative approach to flood management that is applicable in both Nepal and the wider Himalayan Region. Based on the integrated modelling approach, the results predict that the high flow magnitudes in the East Rapti catchment can be reduced through a catchment-scale approach. However, even with a combined approach of large scale spatially targeted afforestation and check dam implementation (Q99.9 decrease of <=5.3%), the use of solely catchment-scale flood management approaches to combat flood hazard might not be effective at reducing the flood impact to at-risk communities. A significant outcome from the catchment-scale modelling work was that there is a far greater potential for land use change to increase, rather than reduce through mitigation, flow magnitudes in the East Rapti catchment. The model results suggest that any land within the East Rapti catchment that is altered from existing forest will contribute to increasing the flow magnitude (Q99.9 increase of up to 48.2%)

Evaluation of Seasonal, Drought, and Wet Condition Effects on Performance of Satellite-Based Precipitation Data over Different Climatic Conditions in Iran

The Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) are the most important and widely used data sources in several applications—e.g., forecasting drought and flood, and managing water resources—especially in the areas with sparse or no other robust sources. This study explored the accuracy and precision of satellite data products over a span of 18 years (2000–2017) using synoptic ground station data for three regions in Iran with different climates, namely (a) humid and high rainfall, (b) semi-arid, and (c) arid. The results show that the monthly precipitation products of GPM and TRMM overestimate the rainfall. On average, they overestimated the precipitation amount by 11% in humid, by 50% in semi-arid, and by 43% in arid climate conditions compared to the ground-based data. This study also evaluated the satellite data accuracy in drought and wet conditions based on the standardized precipitation index (SPI) and different seasons. The results showed that the accuracy of satellite data varies significantly under drought, wet, and normal conditions and different timescales, being lowest under drought conditions, especially in arid regions. The highest accuracy was obtained on the 12-month timescale and the lowest on the 3-month timescale. Although the accuracy of the data is dependent on the season, the seasonal effects depend on climatic conditions.Peer Reviewe

Climate change over Leh (Ladakh), India

Mountains over the world are considered as the indicators of climate change. The Himalayas are comprised of five ranges, viz., Pir Panjal, Great Himalayas, Zanskar, Ladhak, and Karakorum. The Ladakh region lies in the northernmost state of India, Jammu and Kashmir, in the Ladhak range. It has a unique cold-arid climate and lies immediately south of the Karakorum range. With scarce water resources, such regions show high sensitivity and vulnerability to the change in climate and need urgent attention. The objective of this study is to understand the climate of the Ladakh region and to characterize its changing climate. Using different temperature and precipitation datasets over Leh and surrounding regions, we statistically analyze the current trends of climatic patterns over the region. The study shows that the climate over Leh shows a warming trend with reduced precipitation in the current decade. The reduced average seasonal precipitation might also be associated with some indications of reducing number of days with higher precipitation amounts over the region

Identification of floodwater source areas in Nepal using SCIMAP‐Flood

Practical approaches for managing flooding from fluvial sources are moving away from mitigation solely at the point of impact and towards integrated catchment management. This considers the source areas, flow pathways of floodwaters and the locations and exposure to the risk of communities. For a field site in southern Nepal, we analyse catchment response to a range of simulated rainfall events, which when evaluated collectively can help guide potential flood management solutions. This is achieved through the adoption of SCIMAP-Flood, a decision support framework that works at the catchment-scale to identify critical source areas for floodwaters. The SCIMAP-Flood Fitted inverse modelling approach has been applied to the East Rapti catchment, Nepal. For multiple flood impact locations throughout the catchment, SCIMAP-Flood effectively identifies locations where flood management measures would have the most positive effects on risk reduction. The results show that the spatial targeting of mitigation measures in areas of irrigated and rainfed agriculture and the prevention of deforestation or removal of shrubland would be the most effective approaches. If these actions were in the upper catchment above Hetauda or upstream of Manahari they would have the most effective reduction in the flood peak

Seasonal forecasting of reservoir inflows in data sparse regions

Management of large, transboundary river systems can be politically and strategically problematic. Accurate flow forecasting based on public domain data offers the potential for improved resource allocation and infrastructure management. This study investigates the scope for reservoir inflow forecasting in data sparse regions using public domain information. Four strategically important headwater reservoirs in Central Asia are used to pilot forecasting methodologies (Toktogul, Andijan and Kayrakkum in Kyrgyzstan and Nurek in Tajikistan). Two approaches are developed. First, statistical forecasting of monthly inflow is undertaken using relationships with satellite precipitation estimates as well as reanalysis precipitation and temperature products. Second, mean summer inflows to reservoirs are conditioned on the tercile of preceding winter large scale climate modes (El Niño Southern Oscillation, North Atlantic Oscillation, or Indian Ocean Dipole). The transferability of both approaches is evaluated through implementation to a basin in Morocco. A methodology for operationalising seasonal forecasts of inflows to Nurek reservoir in Tajikistan is also presented. The statistical models outperformed the long-term average mean monthly inflows into Toktogul and Andijan reservoirs at lead times of 1-4 months using operationally available predictors. Stratifying models to forecast monthly inflows for only summer months (April-September) improved skill over long term average mean monthly inflows. Individual months Niño 3.4 during October-January were significantly (p < 0.01) correlated to following mean summer inflows Toktogul, Andijan and Nurek reservoirs during the period 1941-1980. Significant differences (p < 0.01) occurred in summer inflows into all reservoirs following opposing phases of winter Niño 3.4 during the period 1941-1980. Over the period 1941-2016 (1993-1999 missing), there exists only a 22% chance of positive summer inflow anomalies into Nurek reservoir following November-December La Niña conditions. Cross validated model skill assessed using the Heidke Hit Proportion outperforms chance, with a hit rate of 51-59% depending upon the period of record used. This climate mode forecasting approach could be extended to natural hazards (e.g. avalanches and mudflows) or to facilitate regional electricity hedging (between neighbouring countries experiencing reduced/increased demand). Further research is needed to evaluate the potential for forecasting winter energy demand, potentially reducing the impact of winter energy crises across the region