Ping River Basin Research Articles

Performance Evaluation of Rainfall-Runoff Models for Predictions of Inflows to Bhumibol Reservoir in Thailand

The Bhumibol reservoir in the Ping River basin is the largest reservoir in the Kingdom of Thailand. This reservoir has contributed to economic development of the country by supplying increased electricity and irrigation water demands as well as flood mitigation in riparian areas along the Ping and the Chao Phraya River. The prediction of inflows to the reservoir is crucial for the optimal management of water for irrigation, power generation and flood control. Properly customized rainfall-runoff models of the catchment could provide the basis for predicting the inflows to the reservoir. Hence, five lumped conceptual rainfall-runoff models were developed for the Ping River basin to simulate daily inflows to the Bhumibol reservoir. The rainfall-runoff models are Australian Water Balance Model (AWBM), Sacramento Soil Moisture Accounting Model, Simplified Hydrolog Model (SIMHYD), Soil Moisture Accounting and Routing Model (SMAR) and Tank Model. The evaluation of the performances of these models showed that all models are capable of predicting inflows. However, the SIMHYD, Sacramento, AWBM and Tank models perform better than SMAR model. Hence, these models could be employed for prediction of inflows to the reservoir with acceptable accuracy.

Integrated assessment of water–energy–food nexus: conceptual framework and application to the Ping River basin, Thailand

ABSTRACT Water, energy and food are the three foremost vital resources that need to be protected for human existence and sustainable development. The study develops an indicator-based framework for integrated assessment of the water–energy–food nexus at the river basin scale. The framework is designed to have three successive levels for the assessment. The proposed three-tiered assessment framework can aid concerned resources management authorities to evaluate the water–energy–food nexus and also to identify the action required to enhance resource productivities. The suitability of the framework is demonstrated through its application to the Ping River basin in Thailand.

Using normalised difference infrared index patterns to constrain semi-distributed rainfall–runoff models in tropical nested catchments

Abstract. A parsimonious semi-distributed rainfall–runoff model has been developed for flow prediction. In distribution, attention is paid to both the timing of the runoff and the heterogeneity of moisture storage capacities within sub-catchments. This model is based on the lumped FLEXL model structure, which has proven its value in a wide range of catchments. To test the value of distribution, the gauged upper Ping catchment in Thailand has been divided into 32 sub-catchments, which can be grouped into five gauged sub-catchments at which internal performance is evaluated. To test the effect of timing, first the excess rainfall was calculated for each sub-catchment, using the model structure of FLEXL. The excess rainfall was then routed to its outlet using the lag time from the storm to peak flow (TlagF) and the lag time of recharge from the root zone to the groundwater (TlagS), as a function of catchment size. Subsequently, the Muskingum equation was used to route sub-catchment runoff to the downstream sub-catchment, with the delay time parameter of the Muskingum equation being a function of channel length. Other model parameters of this semi-distributed FLEX-SD model were kept the same as in the calibrated FLEXL model of the entire upper Ping River basin (UPRB), controlled by station P.1 located at the centre of Chiang Mai province. The outcome of FLEX-SD was compared to the (1) observations at the internal stations, (2) calibrated FLEXL model, and (3) the semi-distributed URBS model – another established semi-distributed rainfall–runoff model. FLEX-SD showed better or similar performance during calibration and especially in validation. Subsequently, we tried to distribute the moisture storage capacity by constraining FLEX-SD on patterns of the NDII (normalised difference infrared index). The readily available NDII appears to be a good proxy for moisture stress in the root zone during dry periods. The maximum moisture-holding capacity in the root zone is assumed to be a function of the maximum seasonal range of NDII values and the annual average NDII values to construct two alternative models, namely FLEX-SD-NDIIMaxMin and FLEX-SD-NDIIAvg. The additional constraint on the moisture-holding capacity (Sumax) by NDII, particularly in FLEX-SD-NDIIAvg, improved both the model performance and the realism of its distribution across the UPRB, which corresponds linearly to the percentage of evergreen forests (R2=0.69). To check how well the models represents simulated root zone soil moisture (Sui), the performance of the FLEX-SD-NDII models was compared to the time series of the soil water index (SWI). The correlation between the Sui and the daily SWI appeared to be very good and was even better than the correlation with the NDII, which does not provide good estimates during wet periods. The SWI, which is model-based, was not used for calibration but appeared to be an appropriate index for validation.

Analyzing the Benefit-Cost Ratio of Sediment Resources by Remote Sensing Data in the Ping River Basin, Thailand

Sediment is one of the important natural resources on the Earth. Information on sediment resources is key to making decisions regarding soil resources management and mitigation during sediment hazard events. Thus, this research analyzed and mapped the benefit-cost ratio (BCR) of sediment in the Ping River using a Geographic Information System (GIS). Furthermore, the benefit of sediment was analyzed using a new application of the Revised Universal Soil Loss Equation (RUSLE) with a spatial resolution of 1 km2. The results reveal that the potential of annual soil loss and sediment deposition in the Ping River Basin (PRB) were approximately 825 and 530 m3/km2·y, respectively. In addition, the results indicated that there was a higher BCR in the upstream area of the PRB where there was greater sediment deposition. The average benefit of sediment in the PRB is USD 4280/km2·y. It is expected that the BCR of the sediment resources map analyzed in this research will help policy-makers for decision-making on the benefits of sediment resources in Thailand.

Flood inundation mapping and hazard assessment for mitigation analysis of local adaptation measures in Upper Ping River Basin, Thailand

Flood inundation mapping and hazard assessment for mitigation analysis of local adaptation measures in Upper Ping River Basin, Thailand

Impact of Climate Change on Reservoir Reliability: A Case of Bhumibol Dam in Ping River Basin, Thailand

Bhumibol Dam is the largest dam in the central region of Thailand and it serves as an important water resource. The dam’s operation relies on reservoir operating rules that were developed on the basis of the relationships among rainfall-inflow, water balance, and downstream water demand. However, due to climate change, changing rainfall variability is expected to render the reliability of the rule curves insecure. Therefore, this study investigated the impact of climate change on the reliability of the current reservoir operation rules of Bhumibol Dam. The future scenarios from 2000 to 2099 are based on EC-EARTH under RCP4.5 and RCP8.5 scenarios downscaled by RegCM4. MIKE11 HD was developed for the inflow simulation. The model generates the inflow well (R2=0.70). Generally, the trend of increasing inflow amounts is expected to continue in the dry seasons from 2000-2099, while large fluctuations of inflow are expected to be found in the wet seasons, reflecting high uncertainties. In the case of standard deviations, a larger deviation is predicted under the RCP8.5 scenario. For the reservoir’s operation in a climate change study, standard operating procedures were applied using historical release records to estimate daily reservoir release needed to serve downstream water demand in the future. It can be concluded that there is high risk of current reservoir operating rules towards the operation reliability under RCP4.5 (80% reliability), but the risk is lower under RCP8.5 (87% reliability) due to increased inflow amounts. The unmanageability occurs in the wet season, cautioning the need to redesign the rules.

Gaussian process regression for predicting water quality index: A case study on Ping River basin, Thailand

<abstract> The water quality index (WQI) is an aggregated indicator used to represent the overall quality of water for any intended use. It is typically calculated from several biological, chemical, and physical parameters. Assessment of factors that affect the WQI is then essential. Climate change is expected to impact a wide range of water quality issues; hence, climate variables are likely to be significant factors to evaluate the WQI. We propose three statistical models; multiple linear regression (MLR), artificial neuron network (ANN), and Gaussian process regression (GPR) to assess the WQI using the climate variables. The data is the WQI of Ping River, which flows through the provinces in the north of Thailand. The climate variables are temperature, humidity, total rainfall, and evaporation. A comparison between these models is determined by model prediction accuracy scores. The results show that the total rainfall is the most significant variable to predict the WQI for the Ping River. Although these three methods can predict the WQI relatively good, overall, the GPR model performs better than the MLR and the ANN. Besides, the GPR is more flexible as it can relax some restrictions and assumptions. Therefore, the GPR is appropriate to assess the WQI under the climate variables for the Ping River. </abstract>

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Investigating statistical bias correction with temporal subsample of the upper Ping River Basin, Thailand

Abstract This study aims to investigate different statistical bias correction techniques to improve the output of a regional climate model (RCM) of daily rainfall for the upper Ping River Basin in Northern Thailand. Three subsamples are used for each bias correction method, which are (1) using full calibrated 30-year-period data, (2) seasonal subsampling, and (3) monthly subsampling. The bias correction techniques are classified into three groups, which are (1) distribution-derived transformation, (2) parametric transformation, and (3) nonparametric transformation. Eleven bias correction techniques with three different subsamples are used to derive transfer function parameters to adjust model bias error. Generally, appropriate bias correction methods with optimal subsampling are locally dependent and need to be defined specifically for a study area. The study results show that monthly subsampling would be well established by capturing the monthly mean variation after correcting the model's daily rainfall. The results also give the best-fitted parameter set of the different subsamples. However, applying the full calibrated data and the seasonal subsamples cannot substantially improve internal variability. Thus, the effect of internal climate variability of the study region is greater than the choice of bias correction methods. Of the bias correction approaches, nonparametric transformation performed best in correcting daily rainfall bias error in this study area as evaluated by statistics and frequency distributions. Therefore, using a combination of methods between the nonparametric transformation and monthly subsampling offered the best accuracy and robustness. However, the nonparametric transformation was quite sensitive to the calibration time period.

An Evaluation of Statistical Downscaling Techniques for Simulating Daily Rainfall Occurrences in the Upper Ping River Basin

This study presents an exhaustive evaluation of the performance of three statistical downscaling techniques for generating daily rainfall occurrences at 22 rainfall stations in the upper Ping river basin (UPRB), Thailand. The three downscaling techniques considered are the modified Markov model (MMM), a stochastic model, and two variants of regression models, statistical models, one with single relationship for all days of the year (RegressionYrly) and the other with individual relationships for each of the 366 days (Regression366). A stepwise regression is applied to identify the significant atmospheric (ATM) variables to be used as predictors in the downscaling models. Aggregated wetness state indicators (WIs), representing the recent past wetness state for the previous 30, 90 or 365 days, are also considered as additional potential predictors since they have been effectively used to represent the low-frequency variability in the downscaled sequences. Grouping of ATM and all possible combinations of WI is used to form eight predictor sets comprising ATM, ATM-WI30, ATM-WI90, ATM-WI365, ATM-WI30&amp;90, ATM-WI30&amp;365, ATM-WI90&amp;365 and ATM-WI30&amp;90&amp;365. These eight predictor sets were used to run the three downscaling techniques to create 24 combination cases. These cases were first applied at each station individually (single site simulation) and thereafter collectively at all sites (multisite simulations) following multisite downscaling models leading to 48 combination cases in total that were run and evaluated. The downscaling models were calibrated using atmospheric variables from the National Centers for Environmental Prediction (NCEP) reanalysis database and validated using representative General Circulation Models (GCM) data. Identification of meaningful predictors to be used in downscaling, calibration and setting up of downscaling models, running all 48 possible predictor combinations and a thorough evaluation of results required considerable efforts and knowledge of the research area. The validation results show that the use of WIs remarkably improves the accuracy of downscaling models in terms of simulation of standard deviations of annual, monthly and seasonal wet days. By comparing the overall performance of the three downscaling techniques keeping common sets of predictors, MMM provides the best results of the simulated wet and dry spells as well as the standard deviation of monthly, seasonal and annual wet days. These findings are consistent across both single site and multisite simulations. Overall, the MMM multisite model with ATM and wetness indicators provides the best results. Upon evaluating the combinations of ATM and sets of wetness indicators, ATM-WI30&amp;90 and ATM-WI30&amp;365 were found to perform well during calibration in reproducing the overall rainfall occurrence statistics while ATM-WI30&amp;365 was found to significantly improve the accuracy of monthly wet spells over the region. However, these models perform poorly during validation at annual time scale. The use of multi-dimension bias correction approaches is recommended for future research.

Flood inundation modeling and hazard assessment in Lower Ping River Basin using MIKE FLOOD

Annual fluvial and pluvial floods happened in Lower Ping River Basin, Thailand. A flood hazard assessment approach was performed in the present study to evaluate flood impacts. The coupled 1D-2D hydrodynamic (HD) modeling package was applied for different return periods to investigate and classify the flood hazards. The 1D hydrodynamic model was calibrated for flood year 2011 at discharge and water level stations. The 2D hydrodynamic model was calibrated using observed flood map. Flood hazards were categorized based on critical flood depths for flooding scenarios of 2-, 5-, 10-, 25-, 50-, and 100-year return periods. The results showed that 28.8% of the total basin area (2423.8 km2) was flooded during the 100-year return period, and the maximum flood inundation area was observed in Kamphaengphet and Nakhon Sawan provinces. Mostly, the flooded area under each return period was categorized under high hazard, followed by low and medium, and the least area was classified under a very high hazard level. More than 40% of sub-districts’ area of Kamphaengphet and Nakhon Sawan provinces were flooded, which is located along the Ping River, while less than 20% sub-districts’ area was flooded in Tak Province. The government and local stakeholders need to consider structural and nonstructural measures to reduce flood impacts in three provinces, and priority-based areal flood management approaches should be considered during the implementation of flood mitigation measures.

Long lead-time daily and monthly streamflow forecasting using machine learning methods

Long lead-time streamflow forecasting is of great significance for water resources planning and management in both the short and long terms. Despite of some studies using machine learning methods in streamflow forecasting, only few studies have been conducted to explore long lead-time forecasting capabilities of these methods, and gain an insight into systematic comparison of model forecasting performance in both the short and long terms. In this work, an artificial neural network (ANN) and a long short term memory (LSTM), a powerful tool for learning long-term temporal dependencies and capturing nonlinear relationship, have been adopted to forecast streamflow at daily and monthly scales for a long lead-time period. For long lead-time streamflow forecasting, a recursive forecasting procedure, which takes the last one-step-ahead forecast as a new input for the next-step-ahead forecast, is used in the ANN and LSTM forecasting systems. Two models are trained and validated for streamflow forecasting using the rainfall and runoff datasets collected from the Nan River Basin and Ping River Basin, Thailand, covering the period 1974 to 2014. To further explore the impact of parameter settings on model performance, two parameters, i.e. the length of time lag and the number of maximum epochs, are examined in the ANN and LSTM models. The main findings are highlighted here. First, with an optimal setting up of model parameters, both the ANN and LSTM model can provide accurate daily forecasting (up to 20 days ahead). Second, in comparison to the ANN model, the LSTM model exhibits better model performance in long lead-time daily forecasting, but less satisfactory in multi-monthly forecasting due to lack of large monthly training dataset. Third, the selection of the length of the time lag and number of maximum epochs used in both ANN and LSTM modelling are the key for long lead-time streamflow forecasting at daily and monthly scales. These findings suggest that the LSTM could be advance in daily streamflow forecasting and thus would be helpful to assist in strategy decisions in water resource management.

Quantification of Seasonal Precipitation over the upper Chao Phraya River Basin in the Past Fifty Years Based on Monsoon and El Niño/Southern Oscillation Related Climate Indices

For better water resources management, we proposed a method to estimate basin-scale seasonal rainfall over selected areas of the Chao Phraya River Basin, Thailand, from existing climate indices that represent variations in the Asian summer monsoon, the El Niño/Southern Oscillation, and sea surface temperatures (SST) in the Pacific Ocean. The basin-scale seasonal rainfall between 1965 and 2015 was calculated for the upper Ping River Basin (PRB) and the upper Nan River Basin (NRB) from a gridded rainfall dataset and rainfall data collected at several gauging stations. The corresponding climate indices, i.e., the Equatorial-Southern Oscillation Index (EQ-SOI), Indian Monsoon Index (IMI), and SST-related indices, were examined to quantify seasonal rainfall. Based on variations in the rainfall anomaly and each climate index, we found that IMI is the primary variable that can explain variations in seasonal rainfall when EQ-SOI is negative. Through a multiple regression analysis, we found that EQ-SOI and two SST-related indices, i.e., Pacific Decadal Oscillation Index (PDO) and SST anomalies in the tropical western Pacific (SSTNW), can quantify the seasonal rainfall for years with positive EQ-SOI. The seasonal rainfall calculated for 1975 to 2015 based on the proposed method was highly correlated with the observed rainfall, with correlation coefficients of 0.8 and 0.86 for PRB and NRB, respectively. These results suggest that the existing indices are useful for quantifying basin-scale seasonal rainfall, provided a proper classification and combination of the climate indices are introduced. The developed method could forecast seasonal rainfall over the target basins if well-forecasted climate indices are provided with sufficient leading time.

Improving Runoff Estimates by Increasing Catchment Subdivision Complexity and Resolution of Rainfall Data in the Upper Ping River Basin, Thailand

Improving Runoff Estimates by Increasing Catchment Subdivision Complexity and Resolution of Rainfall Data in the Upper Ping River Basin, Thailand

Projections of future rainfall for the upper Ping River Basin using regression-based downscaling

The objective of this study was to use regression modelling, a form of statistical downscaling technique, to predict the daily rainfall occurrence and rainfall amounts for a small river basin, the upper Ping River Basin (UPRB) in northern Thailand. Daily historic (1960–2005) rainfall and a number of daily reanalysis variables (NCEP/NCAR) were used to create regression models that estimate the probabilities of rainfall occurrence (wet days) and amounts (rainfall depth) at each of 29 rain gauge stations located in and around the UPRB. The regression models were calibrated using historic (1960–1989) data and validated using historic (1990–2005) data. Regression models were later applied to historic (1960–2005) GCM outputs (MPI-ESM-LR model) which were adjusted to correspond to the selected reanalysis variables using the Nested Bias Correction (NBC) technique. Rainfall occurrence and amounts were predicted for the periods 2006–2050 and 2051–2100 for RCP2.6, RCP4.5, RCP8.5 scenarios. Results show that the effects of climate change vary considerably across the catchment, with significantly declines in both the number of wet days and rainfall depth in the wet- and especially the dry-season in the middle of the catchment but obviously increase slightly towards the northern part of the catchment. Since the stepwise regression was used to select the atmospheric variables to form the regression models for simulating rainfall occurrence and amount, different stations have their own predictors and can influence future rainfall to vary significantly between 29 rain gauge stations. If the top three predictors were selected to form the regression models for simulating rainfall occurrence and amount for all stations, the future rainfall characteristics possibly change and can be used to compare with those of presented in this study. It will show either atmospheric predictors or climate change scenarios would have more effect on future rainfall characteristics.

Comparing the Normalized Difference Infrared Index (NDII) with root zone storage in a lumped conceptual model

Abstract. With remote sensing we can readily observe the Earth's surface, but direct observation of the sub-surface remains a challenge. In hydrology, but also in related disciplines such as agricultural and atmospheric sciences, knowledge of the dynamics of soil moisture in the root zone of vegetation is essential, as this part of the vadose zone is the core component controlling the partitioning of water into evaporative fluxes, drainage, recharge, and runoff. In this paper, we compared the catchment-scale soil moisture content in the root zone of vegetation, computed by a lumped conceptual model, with the remotely sensed Normalized Difference Infrared Index (NDII) in the Upper Ping River basin (UPRB) in northern Thailand. The NDII is widely used to monitor the equivalent water thickness (EWT) of leaves and canopy. Satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to determine the NDII over an 8-day period, covering the study area from 2001 to 2013. The results show that NDII values decrease sharply at the end of the wet season in October and reach lowest values near the end of the dry season in March. The values then increase abruptly after rains have started, but vary in an insignificant manner from the middle to the late rainy season. This paper investigates if the NDII can be used as a proxy for moisture deficit and hence for the amount of moisture stored in the root zone of vegetation, which is a crucial component of hydrological models. During periods of moisture stress, the 8-day average NDII values were found to correlate well with the 8-day average soil moisture content (Su) simulated by the lumped conceptual hydrological rainfall–runoff model FLEX for eight sub-catchments in the Upper Ping basin. Even the deseasonalized Su and NDII (after subtracting the dominant seasonal signal) showed good correlation during periods of moisture stress. The results illustrate the potential of the NDII as a proxy for catchment-scale root zone moisture deficit and as a potentially valuable constraint for the internal dynamics of hydrological models. In dry periods, when plants are exposed to water stress, the EWT (reflecting leaf water deficit) decreases steadily, as moisture stress in the leaves is connected to moisture deficits in the root zone. Subsequently, when the soil moisture is replenished as a result of rainfall, the EWT increases without delay. Once leaf water is close to saturation – mostly during the heart of the wet season – leaf characteristics and NDII values are not well correlated. However, for both hydrological modelling and water management, the stress periods are most important, which is why this product has the potential of becoming a highly efficient model constraint, particularly in ungauged basins.

Rain Fed Rice Agriculture under Climate Variability in Southeast Asia: The Case of Thailand

This study was undertaken to analyze historical climate variability and quantify its impacts on rice production in the Ping River Basin, Northern Thailand. Catchment based climate gradient trend showed Upper Ping received the highest rainfall followed by Lower Ping and Middle Ping, respectively. Temperature and evaporation increased with decreasing altitude. The Ping climate exhibited high spatiotemporal variability. Temperature had an increasing trend but no significant trend in rainfall during the period of 1961-2010. Multiple regression models significantly explained 54% (P<0.01) rice yield, 43% (P<0.01) total production, and 47% (P=0.01) rice area temporal variation by climatic factors variability in Ping Basin, respectively. A critical impact assessment of climate variability on rice production showed that highest yield gap of 48%, planted area loss as high as of 55% and yield loss of 32% were observed. These losses in planted area and productivity are of paramount importance from food security perspective opting appropriate adaptation strategy at basin level. Planting of rice could be done Mid May to end of May in Upper and Middle Ping catchments while end of May to first week of June in Lower Ping without waiting until Mid-June and early July. Our findings indicate that there is a need to modify rice crop calendar that would ensure farmers to obtain better yield. This proposed adjustment of crop calendar will encourage farmers to plant early in May and harvest in September to avoid risk of flooding in October as an adaptation strategy in the face of prevailing climate variability.

Sediment Transport Characteristic of the Ping River Basin, Thailand

This study examined the river sediment transport characteristics of the Ping River basin, which is one of the major river basins in Thailand. River surveys of the Ping River were carried out nine times between 2011 and 2013. Survey data included river cross sections, flow velocities, suspended sediment concentration, and bed load transport in the river. Analyzes of these data indicated that suspended transport rates in the Ping River during normal flow conditions in 2012-2013 ranged between 107 and 9,562 metric tons/day (mt/d), but increased to 35,300 mt/d during high flooding conditions (Thailand's Great Flood of 2011). The rate of bed load transport was 1,401 mt/d during the Flood of 2011. However, the measured bed load in 2012-2013 varied between 0 and 482 mt/d. The bed-to-suspended load ratio in the Ping River fluctuated in the broad range of 0-2.0. Estimates of total sediment transport in the Ping River were made using some of the classic equations from the hydrologic literature. The results obtained from the different methods show that the Laursen-Copeland formula gives the best estimate of total sediment transport rate of the Ping River compared to other methods. Results from this study also reveal that the Bhumibol Dam, constructed in 1964, has had a significant effect on suspended sediment load reduction downstream of the dam.

ANALYSIS OF HYDROLOGIC VARIABLE CHANGES RELATED TO LARGE SCALE RESERVOIR OPERATION IN THAILAND

The objective of this study is to investigate the trend of hydrologic variable changes by applying the Mann-Kendall statistical trend test to daily historical record of reservoir operations data such as inflow to dam reservoirs, release from dam reservoirs, dam storage as well as precipitation and temperature. The five large scale dam reservoirs located in the northern, central and western parts of Thailand are selected to analyze the trend of the Ping River basin having the Bhumibol dam, the Nan River basin having the Sirikit dam, the Pasak River basin having the Pasak Jolasid dam and the Mae Klong River basin having the Srinagarind and Vajiralongkorn dams. Through the analysis, we found that the temperature and precipitation show increasing trends except the Ping River basin; water resources availability in terms of inflow to the Bhumibol dam and Pasak Jolasid dam shows decreasing trend during dry season; the inflow of all reservoirs in rainy season had increasing trends; and dam release of the all reservoirs shows significant increasing trends in the dry season. Furthermore, water storage for the Bhumibol dam were found decreasing trends throughout the year while the Srinagarind dam was detected increasing trends. Also, the increasing trends of water shortage in the Bhumibol and Pasak Jolasid dam, and raising trend of excess storage volume in the Sirikit dam and both dams in the Mae Klong River Basin were observed.

Gender relations and water management in different eco-cultural contexts in Northern Thailand

This paper assessed water management by households from three ethnic groups in two contrasting ecological settings (upland and lowland) in the Upper Ping River Basin in Northern Thailand. Important gender differences in the use and management of water were identified. Women are major users of water for agriculture in the uplands, but less so in the lowlands. In the lowlands irrigation is viewed as a masculine activity. In the uplands the role of women is more widely accepted, with women frequently being members of water user groups. Men, however, dominate 'decision-making' positions in community-based and state-led water organisations in both upland and lowland areas. Perceptions of contributions to daily tasks, and behavioural traits important to governance roles, differed between men and women, and sometimes also across eco-cultural contexts, underlining the complexity of factors influencing gender relations in water governance.