Flow prediction in data scarce catchments: a case study of Northern Thailand

Flow time-series data are crucial for water resources and floods management. In catchments where flow observations are not available or data are of poor quality, a method for modelling flow time-series is needed. The overall objective of this study is to assess the applicability of recent regionalisation methods to predict flows in tropical monsoon-dominated catchments where hydrological response is particularly variable over the seasons. This PhD thesis addresses six primary challenges in the context of such catchments: 1) data quality, 2) rainfall estimation in mountainous catchments, 3) using regression for regionalising rainfall-flow response indices, 4) catchment non-stationarity, 5) conditioning rainfall-runoff models and 6) uncertainty analysis. The main novel contributions are: developing a data quality scoring system and exploring its effects on modelling; comparing a customised technique for rain gauge interpolation and using satellite products for spatial rainfall estimation; demonstrating practical difficulties in predicting land use change impacts; assessing the performance of recent conditioning methods and estimating prediction uncertainty in monsoonal areas. The main practical outcomes are: 1) the most in-depth study yet published of methods for predicting flows in northern Thailand for water resources planning; 2) recommendations towards improving data support for water resources estimation in Thailand. Using data from 44 gauged sub-catchments of the upper Ping catchment in northern Thailand from the period 1995-2006, three relevant flow response indices (runoff coefficient, base flow index and seasonal flow elasticity) were regionalised by regression against 14 available catchment properties. The runoff coefficient was the most successfully regionalised, followed by base flow index and lastly the seasonal elasticity of flow. The non-stationarity (represented by the differences between two six-year sub-periods) was significant both in the flow response indices and in land use indices; however relationships between the two sets of indices were weak. The regression equations were not helpful in predicting the non-stationarity in the flow indices except somewhat for the runoff coefficient. Rainfall estimation errors from two different estimation methods were large and believed to significantly contribute to uncertainty in regionalised flow response indices and modelled flow time-series. The three regionalised flow response indices were used individually and in combination to condition the IHACRES rainfall-runoff model using a Bayesian approach. The runoff coefficient was the most informative index. This is followed by the base flow index and lastly the seasonal flow elasticity. Using the variance of the regression coefficients and of the regression residuals had limited success in estimating the flow uncertainty intervals because uncertainty from the IHACRES model structure is not sufficiently represented by the variance of the regression. The regionalised model was considered to be too imprecise at the daily time scale but offers good support to water resources planning at the monthly and seasonal time scales. A partly subjective data quality scoring system showed the clear influence of rainfall and flow data quality on regionalisation uncertainty. Recommendations include developing more relevant soils databases, improved records of abstractions and investment in the gauge network.Open Acces

Optimal Multi-Reservoir Operation for Hydropower Production in the Nam Ngum River Basin

This research aims to investigate optimal hydropower production of multi-reservoirs in Lao PDR and develop optimal reservoir rule curves. The Nam Ngum 1 and 2 (NN1 and NN2, respectively) reservoirs in the Nam Ngum River basin (NNRB), which is located in the middle of Laos, are selected as study areas. Mixed integer nonlinear programming (MINLP) is developed as an optimization model to maximize the hydropower production of joint reservoir operation of NN1 and NN2. The optimal operation rule curves are established by using the storage level estimated by the optimization model. Given the limited sideflow data, an integrated flood analysis system (IFAS) and water balance equation are used to simulate the sideflow into NN1 reservoir. A good fit is observed between the monthly streamflow simulated by IFAS and that calculated by the water balance equation. Compared with the observed data, the MINLP model can increase the annual and monthly hydropower production by 20.22% (6.01% and 14.21% for NN1 and NN2, respectively). The water storage level estimated by the MINLP model is used to build the operation rule curves. Results show that the MINLP model of multi-reservoir is a useful and effective approach for multi-reservoir operations and is expected to hold high application value for similar reservoirs in NNRB

Which factors determine adaptation to drought amongst farmers in Northern Thailand? Investigating farmers’ appraisals of risk and adaptation and their exposure to drought information communications as determinants of their adaptive responses

Drought communications constitute an important source of learning about climate risks and responses that can assist adaptation decision-making amongst those whose livelihoods are threatened by drought. This paper applies Protection Motivation Theory to explore associations between drought communications and attitudes towards drought risk and adaptation amongst farmers in Northern Thailand. The analysis reveals links between drought communications, farmers’ adaptation appraisal, and their adaptation decisions, whilst links with risk appraisal are minimal. The results highlight positive feedbacks between adaptation experience and appraisal and reveal a weak negative relationship between risk appraisal and adaptation appraisal. The findings imply benefits to framing drought communications in terms of the efficacy and attainability of suitable adaptations, rather than simply highlighting drought risks or providing drought warnings, to best enable farmers to build drought resilience

The contribution of a catchment-scale advice network to successful agricultural drought adaptation in Northern Thailand

The intensification of drought affects agricultural production, leading to economic losses, environmental degradation and social impacts. To move toward more resilient system configurations requires understanding the processes that shape farmers' adaptation amidst complex institutional contexts. Social networks are an important part of collective action for supporting adaptive capacity and there are continuing calls to strengthen network connectivity for agricultural governance under the impacts of climate change. Through a survey of 176 farmers in northern Thailand, we explore the extent to which the characteristics of information shared in a catchment advice network are associated with adaptations. Statistical analyses reveal the perceived efficacy of communications as well as farmers’ relative closeness in the advice network to be positively associated with adaptation to drought. We identify a capacity for local actors to bridge information bottlenecks in the network and opportunities for institutions to enhance their dissemination of information to reach less networked farmers. We find that not all adaptations are perceived as effective against future drought and infer opportunities to support engagement with extension services, encourage the sharing of local knowledge and experience and devise policy and interventions to strengthen advice networks for more resilient agricultural systems

Indicator-to-impact links to help improve agricultural drought preparedness in Thailand

Droughts in Thailand are becoming more severe due to climate change. Developing a reliable Drought Monitoring and Early Warning System (DMEWS) is essential to strengthen a country&rsquo;s resilience to droughts. However, for a DMEWS to be valuable, the drought indicators it provides stakeholders must have relevance to tangible impacts on the ground. Here, we analyse drought indicator-to-impact relationships in Thailand, using a combination of correlation analysis and machine learning techniques (random forest). In the correlation analysis, we study the link between meteorological drought indicators and high-resolution remote sensing vegetation indices used as proxies for crop-yield and forest-growth impacts. Our analysis shows that this link varies depending on land use, season, and region. The random forest models built to estimate regional crop productivity allow a more in-depth analysis of the crop-/region-specific importance of different drought indicators. The results highlight seasonal patterns of drought vulnerability for individual crops, usually linked to their growing season, although the effects are somewhat attenuated in irrigated regions. Integration of the approaches provides new detailed knowledge of crop-/region-specific indicator-to-impact links, which can form the basis of targeted mitigation actions in an improved DMEWS in Thailand, and could be applied in other parts of Southeast Asia and beyond.</p

Quantifying surface water supplies under changing climate and land use

This chapter addresses the challenge of quantifying water security under climate and land use change, focusing on surface water supplies. The objective is to illustrate selected quantification challenges and modeling approaches using case studies. The chapter focuses on the supply-demand balance in zones that represent the aggregations of supply to and demand from many thousands of people (or equivalent water demand from agriculture, industry, and other uses). It selects two particular areas of challenge: understanding the impacts of land use change on river flows and incorporating climate change uncertainty into reservoir storage analysis. The chapter also illustrates this focus within the range of topics related to water security. Finally, the chapter discusses how water resources systems have traditionally dealt with uncertainties in inflows and what alternatives are being developed to accommodate for the bigger uncertainties posed by the changing climate

Projection of Hydro-Climatic Extreme Events under Climate Change in Yom and Nan River Basins, Thailand

Due to a continuous increase in global temperature, the climate has been changing without sign of alleviation. An increase in the air temperature has caused changes in the hydrologic cycle, which have been followed by several emergencies of natural extreme events around the world. Thailand is one of the countries that has incurred a huge loss in assets and lives from the extreme flood and drought events, especially in the northern part. Therefore, the purpose of this study was to assess the hydrological regime in the Yom and Nan River basins, affected by climate change as well as the possibility of extreme floods and droughts. The hydrological processes of the study areas were generated via the physically-based hydrological model, namely the Soil and Water Assessment Tool (SWAT) model. The projected climate conditions were dependent on the outputs of the Global Climate Models (GCMs) as the Representative Concentration Pathways (RCPs) 2.6 and 8.5 between 2021 and 2095. Results show that the average air temperature, annual rainfall, and annual runoff will be significantly increased in the intermediate future (2046–2070) onwards, especially under RCP 8.5. According to the Flow Duration Curve and return period of peak discharge, there are fluctuating trends in the occurrence of extreme floods and drought events under RCP 2.6 from the future (2021–2045) to the far future (2071–2095). However, under RCP 8.5, the extreme flood and drought events seem to be more severe. The probability of extreme flood remains constant from the reference period to the near future, then rises dramatically in the intermediate and the far future. The intensity of extreme droughts will be increased in the near future and decreased in the intermediate future due to high annual rainfall, then tending to have an upward trend in the far future

Projection of Hydro-Climatic Extreme Events under Climate Change in Yom and Nan River Basins, Thailand

Due to a continuous increase in global temperature, the climate has been changing without sign of alleviation. An increase in the air temperature has caused changes in the hydrologic cycle, which have been followed by several emergencies of natural extreme events around the world. Thailand is one of the countries that has incurred a huge loss in assets and lives from the extreme flood and drought events, especially in the northern part. Therefore, the purpose of this study was to assess the hydrological regime in the Yom and Nan River basins, affected by climate change as well as the possibility of extreme floods and droughts. The hydrological processes of the study areas were generated via the physically-based hydrological model, namely the Soil and Water Assessment Tool (SWAT) model. The projected climate conditions were dependent on the outputs of the Global Climate Models (GCMs) as the Representative Concentration Pathways (RCPs) 2.6 and 8.5 between 2021 and 2095. Results show that the average air temperature, annual rainfall, and annual runoff will be significantly increased in the intermediate future (2046–2070) onwards, especially under RCP 8.5. According to the Flow Duration Curve and return period of peak discharge, there are fluctuating trends in the occurrence of extreme floods and drought events under RCP 2.6 from the future (2021–2045) to the far future (2071–2095). However, under RCP 8.5, the extreme flood and drought events seem to be more severe. The probability of extreme flood remains constant from the reference period to the near future, then rises dramatically in the intermediate and the far future. The intensity of extreme droughts will be increased in the near future and decreased in the intermediate future due to high annual rainfall, then tending to have an upward trend in the far future