Rice yields in Thailand are among the lowest in Asia. In northeast Thailand where about 90% of rice cultivation is rain-fed, climate variability and change affect rice yields. Understanding climate ...characteristics and their impacts on the rice yield is important for establishing proper adaptation and mitigation measures to enhance productivity. In this paper, we investigate climatic conditions of the past 30years (1984–2013) and assess the impacts of the recent climate trends on rice yields in the Mun River Basin in northeast Thailand. We also analyze the relationship between rice yield and a drought indicator (Standardized Precipitation and Evapotranspiration Index, SPEI), and the impact of SPEI trends on the yield. Our results indicate that the total yield losses due to past climate trends are rather low, in the range of <50kg/ha per decade (3% of actual average yields). In general, increasing trends in minimum and maximum temperatures lead to modest yield losses. In contrast, precipitation and SPEI-1, i.e. SPEI based on one monthly data, show positive correlations with yields in all months, except in the wettest month (September). If increasing trends of temperatures during the growing season persist, a likely climate change scenario, there is high possibility that the yield losses will become more serious in future. In this paper, we show that the drought index SPEI-1 detects soil moisture deficiency and crop stress in rice better than precipitation or precipitation based indicators. Further, our results emphasize the importance of spatial and temporal resolutions in detecting climate trends and impacts on yields.
Display omitted
•Analysis of impacts of past climate trends on rice yield in the Mun River Basin.•The analysis also includes relationships between rice yield and SPEI.•Increasing Tmax and Tmin cause damage to rice production in the area.•1-month SPEI has stronger relationship with rice yield than other timescales and rainfall.•The rice yield impacts due to climate trends in the basin were rather low.
The Upper Indus Basin (UIB) heavily depends on its frozen water resources, and an accelerated melt due to the projected climate change may significantly alter future water availability. The future ...hydro-climatic regime and water availability of the Hunza basin (a sub-basin of UIB) were analysed using the newly released Coupled Model Intercomparison Project Phase 6 (CMIP6) climate projections. A data and parameter parsimonious precipitation-runoff model, the Distance Distribution Dynamics (DDD) model, was used with energy balance-based subroutines for snowmelt, glacier melt and evapotranspiration. The DDD model was set up for baseline (1991-2010), mid-century (2041-2060) and end-century (2081-2100) climates projections from two global circulation models (GCM), namely EC-Earth3 and MPI-ESM. The projections indicate a substantial increase in temperature (1.1-8.6 °C) and precipitation (12-32%) throughout the twenty-first century. The simulations show the future flow increase between 23-126% and the future glacier melt increase between 30-265%, depending on the scenarios and GCMs used. Moreover, the simulations suggest an increasing glacier melt contribution from all elevations with a significant increase from the higher elevations. The findings provide a basis for planning and modifying reservoir operation strategies with respect to hydropower generation, irrigation withdrawals, flood control, and drought management.
This study applies the soil and water assessment tool (SWAT), with climate (precipitation and temperature) outputs from four general circulation models (GCMs) and a regional circulation model ...(PRECIS), to evaluate (1) the impacts of climate change on reservoir sedimentation and (2) the impacts of climate change and reservoir development on sediment outflow in the Nam Ou River Basin located in northern Laos. Three reservoir–density scenarios, namely one reservoir (1R), three reservoirs in series (3R), and five reservoirs in series (5R), were evaluated for both no climate change and climate change conditions. The results show that under no climate change conditions, by 2070, around 17, 14, and 15% of the existing reservoir storage volume in the basin will be lost for 1R, 3R, and 5R scenarios, respectively. Notably, under climate change scenario with highest changes in erosion and sediment outflux from the basin, the additional reduction in reservoir storage capacity due to sedimentation is estimated to be nearly 26% for 1R, 21% for 3R, and 23% for 5R. Climate change alone is projected to change annual sediment outflux from the basin by −20 to 151%. In contrast, the development of reservoirs in the basin will reduce the annual sediment outflux from the basin varying from 44 to 80% for 1R, 44–81% for 3R, and 66–89% for 5R, considering climate change. In conclusion, climate change is expected to increase the sediment yield of the Nam Ou Basin, resulting in faster reduction of the reservoir’s storage capacity. Sediment yield from the Nam Ou River Basin is likely to decrease significantly due to the trapping of sediment by planned reservoirs. The impact of reservoirs is much more significant than the impact of climate change on the sediment outflow of the basin. Hence, it is necessary to investigate appropriate reservoir sediment management strategies.
Conventional calibration methods adopted in hydrological modelling are based on streamflow data measured at certain river sections. However, streamflow measurements are usually sparse and, in such ...instances, remote-sensing-based products may be used as an additional dataset(s) in hydrological model calibration. This study compares two main calibration approaches: (a) single variable calibration with streamflow and evapotranspiration separately, and (b) multi-variable calibration with both variables together. Here, we used remote sensing-based evapotranspiration data from Global Land Evaporation: the Amsterdam Model (GLEAM ET), and measured streamflow at four stations to calibrate a Soil and Water Assessment Tool (SWAT) and evaluate the performances for Chindwin Basin, Myanmar. Our results showed that when one variable (either streamflow or evapotranspiration) is used for calibration, it led to good performance with respect to the calibration variable but resulted in reduced performance in the other variable. In the multi-variable calibration using both streamflow and evapotranspiration, reasonable results were obtained for both variables. For example, at the basin outlet, the best NSEs (Nash-Sutcliffe Efficiencies) of streamflow and evapotranspiration on monthly time series are, respectively, 0.98 and 0.59 in the calibration with streamflow alone, and 0.69 and 0.73 in the calibration with evapotranspiration alone. Whereas, in the multi-variable calibration, the NSEs at the basin outlet are 0.97 and 0.64 for streamflow and evapotranspiration, respectively. The results suggest that the GLEAM ET data, together with streamflow data, can be used for model calibration in the study region as the simulation results show reasonable performance for streamflow with an NSE > 0.85. Results also show that many different sets of parameter values (‘good parameter sets’) can produce results comparable to the best parameter set.
Three statistical downscaling methods are compared with regard to their ability to downscale summer (June–September) daily precipitation at a network of 14 stations over the Yellow River source ...region from the NCEP/NCAR reanalysis data with the aim of constructing high-resolution regional precipitation scenarios for impact studies. The methods used are the Statistical Downscaling Model (SDSM), the Generalized LInear Model for daily CLIMate (GLIMCLIM), and the non-homogeneous Hidden Markov Model (NHMM). The methods are compared in terms of several statistics including spatial dependence, wet- and dry spell length distributions and inter-annual variability. In comparison with other two models, NHMM shows better performance in reproducing the spatial correlation structure, inter-annual variability and magnitude of the observed precipitation. However, it shows difficulty in reproducing observed wet- and dry spell length distributions at some stations. SDSM and GLIMCLIM showed better performance in reproducing the temporal dependence than NHMM. These models are also applied to derive future scenarios for six precipitation indices for the period 2046–2065 using the predictors from two global climate models (GCMs; CGCM3 and ECHAM5) under the IPCC SRES A2, A1B and B1scenarios. There is a strong consensus among two GCMs, three downscaling methods and three emission scenarios in the precipitation change signal. Under the future climate scenarios considered, all parts of the study region would experience increases in rainfall totals and extremes that are statistically significant at most stations. The magnitude of the projected changes is more intense for the SDSM than for other two models, which indicates that climate projection based on results from only one downscaling method should be interpreted with caution. The increase in the magnitude of rainfall totals and extremes is also accompanied by an increase in their inter-annual variability.
•Flow regime alterations in 2009–2015 are more pronounced than in 1993–2008.•Dams in 1993–2008 are unable to increase dry flows and reduce flood flows.•Dam effects on flow regimes in 2009–2015 are ...greater than climate change effects.•Riverbed incision causes decreased dry water levels in the Vietnamese Mekong Delta.•Decreased dry water levels has increased salinity intrusion.
The Mekong basin, where climate change and anthropogenic interventions (e.g., dams, sand mining, and sluice gates) have intensified in the recent decades affecting the pristine flow regime and salinity intrusion.
This paper aims at quantifying the flow regime alterations in the entire Mekong from 1980 to 2015 and linking with the controlling drivers of alterations. In this regard, various indicators, analytical methods, and a semi two-dimensional hydrodynamic and advection-dispersion model were used.
The flow regime alterations in the high-dam development period (2009–2015) are more pronounced than in the low-dam development period (1993–2008), compared to the no-dam development period (1980–1992), based on most of the indicators analyzed. In the high-dam development period all existing dams with large reservoir capacity seemed to have cumulatively reduced the flood pulses and frequency and increased the low-flow discharge along the entire Mekong through reservoir operations, exceeding climate change effect. In the recent years the water levels in the low-flow season in the Vietnamese Mekong Delta (VMD) have decreased, possibly because of increased riverbed incision caused by reduced sediment supply and increased sand mining. The reduced water levels together with the increased number of the sluice gates constructed seemed to have increased salinity intrusion in the VMD which may be partly reduced by early emergency water release from upstream dams.
The Upper Blue Nile (UBN) basin is less-explored in terms of drought studies as compared to other parts of Ethiopia and lacks a basin-specific drought monitoring system. This study compares six ...drought indices: Standardized Precipitation Index (SPI), Standardized Precipitation Evaporation Index (SPEI), Evapotranspiration Deficit Index (ETDI), Soil Moisture Deficit Index (SMDI), Aggregate Drought Index (ADI), and Standardized Runoff-discharge Index (SRI), and evaluates their performance with respect to identifying historic drought events in the UBN basin. The indices were calculated using monthly time series of observed precipitation, average temperature, river discharge, and modeled evapotranspiration and soil moisture from 1970 to 2010. The Pearson’s correlation coefficients between the six drought indices were analyzed. SPI and SPEI at 3-month aggregate period showed high correlation with ETDI and SMDI (r > 0.62), while SPI and SPEI at 12-month aggregate period correlate better with SRI. The performance of the six drought indices in identifying historic droughts: 1973–1974, 1983–1984, 1994–1995, and 2003–2004 was analyzed using data obtained from Emergency Events Database (EM-DAT) and previous studies. When drought onset dates indicated by the six drought indices are compared with that in the EM-DAT. SPI, and SPEI showed early onsets of drought events, except 2003–2004 drought for which the onset date was unavailable in EM-DAT. Similarly, ETDI, SMDI and SRI-3 showed early onset for two drought events and late onsets in one-drought event. In contrast, ADI showed late onsets for two drought events and early onset for one drought event. None of the six drought indices could individually identify the onsets of all the selected historic drought events; however, they may identify the onsets when combined by considering several input variables at different aggregate periods.
•We assessed the effects of four precipitation inputs on streamflow simulation using SWAT model in the Irrawaddy Basin.•The interpolated in-situ gauge precipitation data gave the best streamflow ...simulation.•PERSIANN-CDR and CHIRPS also showed reasonably good streamflow simulation in most cases in terms of NSE and R2.•The choice of precipitation inputs influenced on parameter estimation and model uncertainty.
The Irrawaddy River Basin, Myanmar.
Precipitation is the most important input variable to numerically simulate the hydrological responses of a river basin. Nowadays, a number of precipitation data products with different spatial and temporal resolutions are available. However, the accuracy of these products may vary greatly and the variations may themselves differ in different river basins. Such differences have direct implications on the use of these datasets in hydrological modelling. Here, using a hydrological model, we investigated the effects of four precipitation datasets (in-situ gauge precipitation with and without interpolation, PERSIANN-CDR, and CHIRPS) on streamflow simulations in the Irrawaddy Basin in Myanmar.
We identified considerable differences in streamflow simulation with the use of different precipitation inputs. The four datasets showed varied annual and seasonal precipitation values over the basin. Although the gauge density within the study area is very low, streamflow simulations forced with interpolated gauge data outperformed the models forced with other datasets. However, simulations forced with CHIRPS and PERSIANN-CDR also showed good results in most cases in terms of Nash Efficiency and R2, but mostly with high biases. In calibration, the four precipitation inputs resulted in varied best-fitted parameter values and ranges. All the above observations indicate that the selection of suitable precipitation input(s) is necessary for an accurate investigation of the hydrological responses of any given basin.
PDF
