In this study several issues with the standard flood frequency analysis are discussed in the context of a changing hydro-climate in the 21st century. Among these issues the loss of statistical equilibrium in the hydro-climate of a studied region during the 21st century has serious implications on the standard frequency analysis that is discussed in some detail. An alternative method to flood frequency analysis within the framework of a changing climate based on ensemble of future climate projections is reported and demonstrated by a numerical application to a target watershed.

Generally, the remoteness of potential sites for small hydropower (SHP) which are mostly located in mountainous regions, and complex hydrological phenomena, remain significant barriers for SHP development. However, hydrological modeling together with the advancement of remote sensing and geospatial technology can be used to assess SHP potential. This study combined geographic information system (GIS) methods with the Soil and Water Assessment Tool (SWAT) hydrological model to assess the potential for SHP development in the Ciwidey subwatershed, Indonesia. Nine potential sites for SHP were identified according to criteria such as head/elevation drop, stream order, and distance between each potential site. The SWAT model reproduced the observed discharge in the watershed accurately producing an acceptable coefficient of determination (R² = 0.75) and Nash-Sutcliffe Efficiency (NSE = 0.67). According to Flow Duration Curve (FDC) analysis at 60, 75, and 90% dependability threshold, a maximum SHP potential total of 1.72 MW can be harnessed in the Ciwidey subwatershed. This study is expected to boost the initiative of promoting renewable energy, mainly SHP, in Indonesia. Based on these results and the goal of increasing renewable energy resources to bolster national energy security, we recommend an initiative promoting SHP in Indonesia.

Flood damage functions form the core of flood risk assessment. This study proposes a method for establishing flood damage functions for agricultural crops in data-scarce regions. The method assumes that the flood damage ratio is a function of inundation depth only and utilizes inundation depth estimated from flood extent information and hydrodynamic simulations. The parameters of the damage functions are calibrated through the SCE-UA method (Shuffled Complex Evolution method developed at The University of Arizona) so that the calculated flood damages match observations compiled in flood disaster statistics. The established three functions show good agreement with actual agricultural damages caused by a rainfall event in 2010 and are validated against another rainfall event in 2009. The results indicate that the established damage functions are capable of estimating flood damage at the district scale, while damage estimations at finer spatial resolution differ between the functions, suggesting that detailed statistical data need to be incorporated to reduce the estimation uncertainty at fine scales.

A method of frequency analysis, Extended Regional Frequency Analysis (ERFA), is proposed for reliable estimates of extreme daily rainfall probabilities for a long return period from relatively short daily rainfall records. The method uses combined data in a wide meteorologically homogeneous region (e.g., all Japan) to ensure a large number (order of 10,000) of data to minimize the effects of statistical sampling error in the frequency analysis. We applied the ERFA to daily rainfall data observed over Japan and to a high resolution atmospheric model simulation data over the meteorologically homogeneous land region of Japan. We found very good agreement between the empirical probability distribution and theoretical distribution estimated by ERFA, suggesting that the method is promising. However, we have noted some problems regarding ERFA: selection of the distribution, selection of the region, and model bias. These problems, along with possible solutions, are discussed.

As many water related disasters occur frequently around the world, proper assessment of future climate change impact on floods and droughts is essential. In this study, we focused on basin-scale climate change impact assessment as necessary information for studying adaptation measures on the basis of integrated water resources management. We used Meteorological Research Institute-Atmospheric General Circulation Model (MRI-AGCM) 3.2S (20km grid super high resolution model) and a series of simulation methods for climate change analysis. We conducted a comparative study on changes in precipitation, flood discharge and inundation in the future during the wet and dry seasons for five target river basins in the Asian monsoon area. We found that regional precipitation outputs from the high resolution model in this study were in good agreement in the point of tendency of their changes in wet and dry monsoon seasons with the regional precipitation analysis in the Fifth Assessment Report (AR5) of Working Group 1 (WG1) of the International Panel on Climate Change (IPCC, 2013). This study illustrated that the proposed methodology can make more detailed descriptions of climate change possible. The study also found the importance of basin-scale runoff and inundation analysis with downscaling especially for basins where floods occur for a short period, suggesting potential differences between flood change and precipitation change from General Circulation Model (GCM) outputs such as maximum 5-day precipitation index in a basin. As a result, this paper confirms the importance of basin-scale discharge and inundation analysis for climate change considering basin characteristics from the viewpoint of river management.

The present study generated nationwide assessment maps for the impact of climate change on agricultural water resources throughout Japan, by using climate scenarios derived from global climate models and DWCM-AgWU, a hydrological model that incorporated irrigation water management. In addition, we analyzed the uncertainty of the assessment maps, investigating the ranges of the assessment indices in 11 climate scenarios used. For the assessment of drought discharge, we generated the assessment maps for two rice growth stages (puddling and heading) that are highly vulnerable to water shortages. The maps generated in this study provide a framework for assessing the impact of climate change on agricultural water resources in Japan and reveal the vulnerable regions to climate change. The uncertainty analysis suggested that the assessment uncertainty depended on the hydrological processes used to calculate assessment indices and on the magnitude of their natural annual variability. For a deeper understanding of the uncertainty in hydrological assessments it is necessary to investigate the impact on the assessment uncertainty of the natural variability of hydrological processes especially relating to extreme events.

Simple explanations for changes in surface soil moisture in the late 21st century under global warming were explored, based on statistical significance and without consideration of complicated mechanisms. The results of a multi-model ensemble (MME) analysis showed significant increases in surface soil moisture in one northwestern inland area, and significant decreases were projected in two inland areas in southern and northern China. Among three water flux variables, precipitation (P), evaporation (E), and total runoff (R), significant changes in E explained only 10% of the total area showing significant changes in surface soil moisture. Among three combinations of two water flux variables, (P – E), (E + R), and (P – R), significant changes in (P – E) were dominant in coastal northeastern China, but this area did not overlap with areas with significant changes in surface soil moisture. Individual analyses revealed that significant increases in E, (P – R), and (E + R) explained 26%, 13%, and 9%, respectively, of the total area showing a significant decrease in the MME mean surface soil moisture. This result indicates that reliance on the MME mean may hinder understanding of the geophysical mechanism linking water flux variables with surface soil moisture.

We have developed a statistical downscaling method for generating probabilistic climate projections using multiple general circulation models (GCMs). A regression model was established so that the combination of coefficients of the GCMs reflects the characteristics of the variation of observations at each grid point. We adopted the elastic net penalty to estimate the regression model, considering model projection similarities. Using an observation system with a high spatial resolution, we conducted statistically downscaled probabilistic climate projections with 20-km horizontal grid spacing. Mean precipitation is generally projected to increase associated with higher temperatures and consequently increased atmospheric moisture content. Weakening of the winter monsoon may cause precipitation decreases in some areas. There is a high probability of a temperature increase in excess of 4 K in most areas of Japan by the end of the 21st century under the CMIP5 RCP8.5 scenario. The estimated probability of monthly precipitation exceeding 300 mm increases along the Pacific coast of Japan during the summer season and along the coast of the Japan Sea during the winter season. Our probabilistic climate projection using statistical methods is expected to provide useful information to stakeholders involved in impact studies and risk assessments.

Future wave climate projection is important for climate impact assessment of the coastal hazards and environment. In this study, monthly averaged wave heights are estimated by a linear multi-regression model using atmospheric data as explanatory variables. The present statistical model considers local atmospheric information (wind speed at 10 m height, sea level pressure) and large scale atmospheric information obtained from principal component analysis (PCA) of the global sea level pressure field. The representation of swell in the lower latitude is greatly improved by introducing the large scale atmospheric information from the PCA. The present statistical model was applied to the results of the Japan Meteorological Research Institute’s Atmospheric General/Global Circulation Model (MRI-AGCM) climate change projection. The future change of wave heights shows an increase in the northern North Pacific Ocean and a decrease in the North Atlantic Ocean, middle latitude and tropics of the Pacific Ocean.

A statistical downscaling method based on regressing precipitation data is introduced and applied to 60-km resolution Atmospheric General Circulation Model (AGCM60km) output for daily precipitation. The method utilizes a regression domain with a 3×3 60-km grid, and the downscaling target is 3×3 20-km grids in the center of the regression domain. By shifting the regression domain one grid by one grid in 60-km resolution, the same form of regression model, but different regression coefficients for each 20-km grid, can be applied to all the downscaling target areas. Based on application tests for the Asian Monsoon region, the statistical downscaling algorithm shows extremely effective results with a certain pattern of regression error. The monthly based downscaled results from AGCM60km output shows a rather good match to the monthly mean precipitation amount of AGCM20km. The downscaled results also show a plausible mimic to the AGCM20km output in the frequency of daily precipitation amounts; however, the results showed noticeable limitations in simulating low rainfall amounts (e.g., less than 5 mm d^–1), especially on land.

To facilitate accurate assessments of the regional impacts of global warming, and make informed decisions about appropriate measures to mitigate them, detailed global warming projections with uncertainties are needed. The Ministry of Environment of Japan and the Japan Meteorological Agency performed 21 different multi-scenario and multi-ensemble experiments in Japan using the regional climate model MRI-NHRCM with a horizontal resolution of 20 km. To estimate the total range of uncertainty due to natural fluctuations and the variety of experimental runs by a single climate model with multi-physics and multi-SST ensembles under each greenhouse gas emission scenario, a unique statistical method that combined a mixture distribution and bootstrap resampling was adopted. Based on three models that adopted the Yoshimura scheme as a cumulus convection parameterization, annual mean temperatures in Japan were projected to rise significantly by 1.1 ± 0.4°C, 2.0 ± 0.4°C, 2.6 ± 0.6°C, and 4.4 ± 0.6°C under the RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios, respectively, at the end of the 21st century relative to the end of the 20th century (ensemble means ± standard deviations). In contrast, changes in future annual precipitation over Japan were projected to be statistically insignificant.

Climate analogues for 17 Australian cities in the current climate (1979–2003) were identified by using a non-parametric climate analogue approach and multi-ensemble future climate projections in the late 21st century (2075–2099) made with the Meteorological Research Institute’s atmospheric general circulation model, version 3.2H under the Special Report on Emissions Scenarios A1B scenario with a horizontal resolution of about 60 km. By using this approach, climate analogue cities could be identified within the uncertainties of the multi-ensemble future climate projections. A similarity score as a metric of climate analogue is evaluated with the threshold as the quantified uncertainties in nonparametric manner. Ten of the identified climate analogue cities were in Australia, even in a global search, and the other seven analogue cities were in other continents: five in Africa, one in Mexico, and one in Argentina. In an in-country search, climate analogues for the seven target cities whose climate analogues were identified in other parts of the world in the global search were identified in Australia, although the similarity scores were low. Very low similarity scores imply that the future climate of the target city will be novel, that is, a climate that no city is currently experiencing.

Suspended sediment concentrations (SSC) and the duration of high SSC are important for river ecology and water resource conservation. Using annual and storm-event datasets, this paper explores the hypothesis that key suspended sediment variables increase along a land-use disturbance gradient in hilly terrain in Sabah (Malaysian Borneo). Five small (1.7–4.6 km²) catchments of increasing disturbance history – primary forest, old growth virgin jungle reserve, twice-logged forest, multiple-logged forest and mature oil palm – were instrumented from late 2011 with dataloggers and sensors to record river stage, turbidity and rainfall. The oil palm catchment had 4–12 times greater mean discharge-weighted SSC (587 mg L^–1), annual sediment yield (1128 t km^–2 y^–1), median event peak SSC, and duration of SSC above 1000 mg L^–1 than in the other catchments. The multiple-logged catchment (last logged around 2004) has SSC characteristics close to values for primary forest, possibly due to increased ground protection against erosion afforded by low understorey regrowth and/or depletion of erodible sediment by multiple logging episodes. Results demonstrate that in hilly terrain even heavily logged rainforest has high value in safeguarding water quality and reducing erosion, whereas oil palm requires careful land management, especially of road runoff and ground cover.

Changes in hydrological processes due to rising temperatures and related effects on the socio-economy and people’s livelihood are major concerns in Bangladesh. A study has been performed to assess the effects of increasing temperature on the groundwater levels and consequent changes in irrigation costs for groundwater-dependent irrigated agriculture in Northwest Bangladesh. A support vector machine (SVM) was used to model the temporal variations in groundwater level from rainfall, evapotranspiration, groundwater abstraction, and agricultural return flow. A multiple linear regression (MLR) model was developed to define the functional relationship between irrigation costs and groundwater levels. The model showed that average groundwater level during the major irrigation period (January–April) decreased by 0.15–2.01 m due to an increase in temperature of 1–5°C, which increased irrigation costs by 0.05–0.54 thousand Bangladesh Taka (BDT) per hector.