Statistical Downscaling Model Research Articles

Analyzing the future climate change of Upper Blue Nile River basin using statistical downscaling techniques

Abstract. Climate change is becoming one of the most threatening issues for the world today in terms of its global context and its response to environmental and socioeconomic drivers. However, large uncertainties between different general circulation models (GCMs) and coarse spatial resolutions make it difficult to use the outputs of GCMs directly, especially for sustainable water management at regional scale, which introduces the need for downscaling techniques using a multimodel approach. This study aims (i) to evaluate the comparative performance of two widely used statistical downscaling techniques, namely the Long Ashton Research Station Weather Generator (LARS-WG) and the Statistical Downscaling Model (SDSM), and (ii) to downscale future climate scenarios of precipitation, maximum temperature (Tmax) and minimum temperature (Tmin) of the Upper Blue Nile River basin at finer spatial and temporal scales to suit further hydrological impact studies. The calibration and validation result illustrates that both downscaling techniques (LARS-WG and SDSM) have shown comparable and good ability to simulate the current local climate variables. Further quantitative and qualitative comparative performance evaluation was done by equally weighted and varying weights of statistical indexes for precipitation only. The evaluation result showed that SDSM using the canESM2 CMIP5 GCM was able to reproduce more accurate long-term mean monthly precipitation but LARS-WG performed best in capturing the extreme events and distribution of daily precipitation in the whole data range. Six selected multimodel CMIP3 GCMs, namely HadCM3, GFDL-CM2.1, ECHAM5-OM, CCSM3, MRI-CGCM2.3.2 and CSIRO-MK3 GCMs, were used for downscaling climate scenarios by the LARS-WG model. The result from the ensemble mean of the six GCM showed an increasing trend for precipitation, Tmax and Tmin. The relative change in precipitation ranged from 1.0 to 14.4 % while the change for mean annual Tmax may increase from 0.4 to 4.3 ∘C and the change for mean annual Tmin may increase from 0.3 to 4.1 ∘C. The individual result of the HadCM3 GCM has a good agreement with the ensemble mean result. HadCM3 from CMIP3 using A2a and B2a scenarios and canESM2 from CMIP5 GCMs under RCP2.6, RCP4.5 and RCP8.5 scenarios were downscaled by SDSM. The result from the two GCMs under five different scenarios agrees with the increasing direction of three climate variables (precipitation, Tmax and Tmin). The relative change of the downscaled mean annual precipitation ranges from 2.1 to 43.8 % while the change for mean annual Tmax and Tmin may increase in the range from 0.4 to 2.9 ∘C and from 0.3 to 1.6 ∘C respectively.

Evaluation of Statistical-Downscaling/Bias-Correction Methods to Predict Hydrologic Responses to Climate Change in the Zarrine River Basin, Iran

Modeling the hydrologic responses to future changes of climate is important for improving adaptive water management. In the present application to the Zarrine River Basin (ZRB), with the major reach being the main inflow source of Lake Urmia (LU), firstly future daily temperatures and precipitation are predicted using two statistical downscaling methods: the classical statistical downscaling model (SDSM), augmented by a trend-preserving bias correction, and a two-step updated quantile mapping (QM) method. The general circulation models (GCM) input to SDSM are climate predictors of the Canadian Earth System Model (CanESM2) GCM under the representative concentration pathway (RCP) emission scenarios, RCP45 and RCP85, whereas that to the QM is provided by the most suitable of several Climate Model Intercomparison Project Phase 5 (CMIP5) GCMs under RCP60, in addition. The performances of the two downscaling methods are compared to each other for a past “future” period (2006–2016) and the QM is found to be better and so is selected in the subsequent ZR streamflow simulations by means of the Soil and Water Assessment Tool (SWAT) hydrological model, calibrated and validated for the reference period (1991–2012). The impacts of climate change on the hydrologic response of the river basin, specifically the inflow to the Boukan Reservoir, the reservoir-dependable water release (DWR), are then compared for the three RCPs in the near- (2020–2038), middle- (2050–2068) and far- (2080–2098) future periods assuming (1) the “current” consumptive demand to be continued in the future, and (2) a more conservative “recommended” demand. A systematic future shortage of the available water is obtained for case (1) which can be mitigated somewhat for (2). Finally, the SWAT-predicted ZRB outflow is compared with the Montana-based estimated environmental flow of the ZR. The latter can successfully be sustained at good and fair levels for the near- and middle-future periods, but not so for the summer months of the far-future period, particularly, for RCP85.

Rainfall variation prediction using SD technology based on temperature model for Weihe River basin

Abstract Global warming has exerted a significant influence on the water cycle in global drainage basins, involving various aspects associated with ecology, society, economy and human activity. Therefore, accurate forecast and analysis of climatic change trends are the prerequisite to disaster alleviation and prevention. This paper selects a large-scale climate predictor by modeling steps and evaluation indicators for a statistical downscaling (SD) model and establishes a statistical relation between atmospheric circulation factor and precipitation and air temperature at eight stations in Wei River basin in Shaanxi Province for a global climate model output based on hydrological data for Wei River basin in Shaanxi Province. A study was made of the simulated precipitation and air temperature in Wei River basin by principal component analysis and multiple linear regression and achieved favorable results. The approach is better in physical significance and is characterized by accurate climate simulation, simple calculation for statistical downscaling and is convenient for application in various climate models.

Ensembling Downscaling Techniques and Multiple GCMs to Improve Climate Change Predictions in Cryosphere Scarcely-Gauged Catchment

Future projections of climate variables are the key for the development of mitigation and adaptation strategy to changing climate. However, such projections are often subjected to large uncertainties which make implementation of climate change strategies on water resources system a challenging job. Major uncertainty sources are General Circulation models (GCMs), post-processing and climate heterogeneity based on catchment characteristics (e.g. scares data and high-altitude). Here we presents the comparisons between different GCMs, statistical downscaling and bias correction approaches and finally climate projections, with the integration of gridded and converted (monthly to daily) data for a high-altitude, scarcely-gauged Jhelum River basin, Pakistan. Current study relies on climate projections obtained from factorial combination of 5-GCMs, 2 statistical downscaling and 2 bias correction methods. In addition, we applied bias corrected APHRODITE, converted daily data using MODAWEC model and observed data. Further, five GCMs (CGCM3, HadCM3, CCSM3, ECHAM5 and CSIRO-MK3.5) were tested to scrutinize two suitable GCMs integrated with Statistical Downscaling Model (SDSM) and Smooth Support Vector Machine (SSVM). Results illustrate that the CGCM3 and HadCM3 were suitable GCMs for selected study basin. Both downscaling techniques are able to simulate precipitation, however, SSVM performed slightly better than SDSM. We found that the integration of CGCM3 with SSVM (SSVM-CGCM3) generates precipitation and temperature better than the CGCM3 (SDSM-CGCM3) and HadCM3 (SDSM-HadCM3) with SDSM. Furthermore, the low elevation stations were influenced by monsoon, significantly prone to rise in precipitation and temperature, while high-altitude stations were influenced by westerlies circulations, less prone to climate change. The projections indicated rise in basin-wide annual precipitation by 25.51, 36.76 and 45.52 mm and temperature by 0.64, 1.47 and 2.79 °C, during 2030s, 2060s and 2090s, respectively. The methods and results of this study can be adopted to evaluate climate change implications in the catchments of characteristics similar to Jhelum River basin.

Estimating the effect of climate change on water resources: Integrated use of climate and hydrological models in the Werii watershed of the Tekeze river basin, Northern Ethiopia

This paper presents the effect of climate change on water resources in the Werii watershed (1797 km2) using climate (SDSM) and hydrological (WetSpa) models. A fully distributed model (WetSpa) was used to simulate the water resources of the base (2004–2010) and future (2015–2050) periods. The digital elevation model (DEM), land-use, soil and hydro-meteorological features of the Werii watershed were used as inputs to the WetSpa model. Likewise, the statistical downscaling model (SDSM) was used to downscale climate projections from the regional climate model (REMO) which in turn would be used as input for the WetSpa model for future water resources simulation based on A1B and B1 special report for emission scenarios (SRES). Simulations of the SDSM and WetSpa models showed that rainfall will be increased by 24% under A1B and 25.3% under B1. The minimum and maximum temperatures will also increment by 0.17 and 0.09 °C, respectively, under A1B and by 0.16 and 0.07 °C under B1, respectively. Similarly, for A1B and B1, positive changes are likely to occur for baseflow by 14% and 8%, respectively, for recharge by 5% and 2%, respectively, and for evapotranspiration by 15% and 18%, respectively. However, the surface runoff would decrease by 13% and 14%, respectively, under similar trends from the base period. This implies that a positive change is likely in the future water balance components of the watershed with the exception of runoff. As a result, increased exploitation of the water resources comparable to the resources increment is advised. However, optimized water resources allocation is worthwhile, providing it is in a sustainable way.

Statistical downscaling of rainfall under transitional climate in Limbang River Basin by using SDSM

Climate change is a global phenomenon that has affected hundreds of people around the globe. In transitional climatic patterns, it is essential to compute the severity of rainfall in the regions prone to hydro-meteorological disasters. Therefore, the main aim of this study is to assess the severity of rainfall under three Representative Concentration Pathways (RCPs) from Global Climate Model data of CanESM2 in Limbang River basin. Furthermore, the objective is to check the capability of Statistical Downscaling Model (SDSM) in the tropical region. The historical data of nine weather stations were used for the period of 30 years (1976 - 2005) and Global Climate Model data of CanESM2 under RCPs of RCP2.6, RCP4.5 and RCP8.5 for the period of 2071-2100. The model was calibrated for the period of 1976-1995 and validated for the period of 1996-2005. After successful calibration and validation of SDSM, the future rainfall was simulated separately for all the three scenarios of RCPs. The obtained results have shown the values of R2 and RMSE for the model calibration and validation ranged between 0.58 – 0.86 and between 1.49 and 4.7, respectively for all stations. The obtained future rainfall data from 2071 – 2100 was then compared with the base period rainfall from 1976 - 2005. It was shown that under RCP2.6 scenario there will be an increase of 8.13%, while 14.7% rise in the RCP4.5 scenario during the period of 2071- 2100. An abrupt increase of about 40.6% was observed under the robust scenario of RCP8.5. Therefore, it is concluded that future pattern of rainfall in Limbang River basin under all the scenarios is constantly increasing due to the climate change.

Assessing the implementation of bias correction in the climate prediction

An issue of the climate changes nowadays becomes trigger and irregular. The increment of the greenhouse gases (GHGs) emission into the atmospheric system day by day gives huge impact to the fluctuated weather and global warming. It becomes significant to analyse the changes of climate parameters in the long term. However, the accuracy in the climate simulation is always be questioned to control the reliability of the projection results. Thus, the Linear Scaling (LS) as a bias correction method (BC) had been applied to treat the gaps between observed and simulated results. About two rainfall stations were selected in Pahang state there are Station Lubuk Paku and Station Temerloh. Statistical Downscaling Model (SDSM) used to perform the relationship between local weather and atmospheric parameters in projecting the long term rainfall trend. The result revealed the LS was successfully to reduce the error up to 3% and produced better climate simulated results.

Development of a time‐varying downscaling model considering non‐stationarity using a Bayesian approach

Stationarity in the relationship between causal variables and target variables is the fundamental assumption of statistical downscaling models. However, we hypothesize that this assumption may not be valid in a changing climate. This study develops a downscaling technique in which the relationship between causal and target variables is considered to be time‐varying rather than static. The proposed time‐varying downscaling model (TVDM) is utilized to downscale monthly precipitation over India to 0.25 × 0.25° gridded scale using the large‐scale outputs from multiple general circulation models (GCMs), namely the Hadley Centre Coupled Model version 3 (HadCM3), coupled Hadley Centre Global Environmental Model version 2‐Earth System model (HadGEM2‐ES) and Canadian Earth System Model version 2 (CanESM2). Observed precipitation data are obtained from the India Meteorological Department (IMD), Pune. For future projection, the temporal evolution of each of the TVDM parameters is investigated using its deterministic (trend and periodicity) and stochastic components. TVDM is found to outperform the most commonly used statistical downscaling model (SDSM) and regional climate model (RCM) output at all the locations. The Regional Climate Model version 4 (RegCM4) precipitation data (RCM outputs) are obtained from the Coordinated Regional Climate Downscaling Experiment (CORDEX) data portal supplied by Indian Institute of Tropical Meteorology (IITM), Pune. The proposed model (TVDM) differs from the existing stationarity assumption‐based approaches in updating the relationship between causal and target variables over time. It is understood that parameter uncertainty is the major issue in consideration of non‐stationarity. Still, the TVDM is found to be very useful in the context of climate change due to its time‐varying component.

Impacts of climate change on reference evapotranspiration in the Qilian Mountains of China: Historical trends and projected changes

Global climate change is likely to affect reference evapotranspiration (ET0) and the use of water resources for vegetation management. Our goals were to identify spatio‐temporal characteristics of ET0 and factors controlling the change in ET0 and to project spatio‐temporal changes in the Qilian Mountains of China under the future climate conditions. Changes in ET0 were estimated by the Penman–Monteith method for 22 meteorological stations from 1960 to 2015. We quantified the attributions of climatic factors with the differentiation equation method. Then, we assessed the spatio‐temporal changes in projected ET0 with CanESM2 model outputs and statistical downscaling model for three representative concentration pathways (RCP) scenarios for years 2016–2100. We found that annual ET0 averaged across the region was 1001.5 mm, with an insignificant decrease of −0.43 mm/year during 1960–2015. The lowest values were present in the alpine region in the central area, while the highest ET0 was detected in the western region. An annual and seasonal “evaporation paradox” existed in the Qilian Mountains during the past few decades. Mean daily air temperature measured (Tmean) and wind speed (U2) were the dominant factors in ET0 change. However, the decreasing trend in ET0 may be due to a diminished effect of Tmean triggered by short‐wave radiation (R s), actual vapour pressure (e a), and wind speed (U2), but especially by the substantial reduction in U2 at most stations. Compared with the baseline, ET0 is likely to increase by 6.31–7.20, 6.11–10.41, and 6.58–17.66%, respectively, under RCP scenarios of 2.6 (very low forcing scenario), 4.5 (medium stabilization scenario), and 8.5 (very high emission scenario), but RCP2.6 ET0 rates level off and even decline after 2050 while RCP4.5 rates climb only marginally after 2050. Thus, ET0 projected with the CanESM2 model displayed an upwards trend in the Qilian Mountains, especially the central alpine region.

Modeling Climate Change Projections for Ferozpur Sub-catchment of Jhelum Sub-basin of Kashmir Valley

Aims: The study aimed at modeling the climate change projections for Ferozpur subcatchment of Jhelum sub-basin of Kashmir Valley using the SDSM model.
 Study Design: The study was carried out in three different time slices viz Baseline (1985-2015), Mid-century (2030-2059) and End-century (2070-2099).
 Place and Duration of Study: Division of Agricultural Engineering, SKUAST-K, Shalimar between August 2015 and July 2016.
 Methodology: Statistical downscaling model (SDSM) was applied in downscaling weather files (Tmax, Tminand precipitation). The study includes the calibration of the SDSM model by using Observed daily climate data (Tmax, Tmin and precipitation) of thirty one years and large scale atmospheric variables encompassing National Centers for Environmental Prediction (NCEP) reanalysis data, the validation of the model, and the outputs of downscaled scenario A2 of the Global Climate Model (GCM) data of Hadley Centre Coupled Model, Version 3 (HadCM3) model for the future. Daily Climate (Tmax, Tmin and precipitation) scenarios were generated from 1961 to 2099 under A2 defined by Intergovernmental Panel on Climate Change (IPCC).
 Results: The results showed that temperature and precipitation would increase by 0.29°C, 255.38 mm (30.97%) in MC (Mid-century) (2030-2059); and 0.67oC and 233.28 mm (28.29%) during EC (End-century) (2070-2099), respectively.
 Conclusion: The climate projections for 21st century under A2 scenario indicated that both mean annual temperature and precipitation are showing an increasing trend.

Evaluation of statistical downscaling models using pattern and dependence structure in the monsoon‐dominated region of Pakistan

AbstractThe uphill topography and sudden spatial variation in the climate of the monsoon‐dominated region of Pakistan render it difficult to make predictions with dynamical climate models. This study evaluates the use of two statistical models for the downscaling of climate variables: the Multivariate Multiple Linear Regression Model (MMLRM) and the Multisite Multivariate Statistical Downscaling Model (MMSDSM). A randomisation technique was implemented to establish the extreme values and spatial dependence structure for a number of variables by adding correlated noise to the predictand data for multiple sites, generated from a multivariate normal distribution. In the case of precipitation, probability mapping techniques were used to make adjustments based on a gamma distribution. To cope with the dimensionality and multicollinearity problems of the input data (predictors), empirical orthogonal functions were used. The models were calibrated using the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) predictors for the period 1960–1990 and validated for 1990–2000. To investigate the efficacy of the proposed models, different statistical measures – such as root mean square error, mean bias error and analysis of how closely the observed variability was reproduced – were used. The results show mixed support for both models: MMLRM underestimates the variability, and MMSDSM has the ability to predict the extreme values and spatial dependence structure. The results of logistic regression analysis show that the precipitation probability values generated by the logistic model closely reproduced the patterns of observed precipitation – there is an increase in the occurrence of precipitation events during the monsoon season over the target area.

Climate change impact assessment of a river basin using CMIP5 climate models and the VIC hydrological model

ABSTRACTClimate change has significant impacts on water availability in larger river basins. The present study evaluates the possible impacts of projected future daily rainfall (2011–2099) on the hydrology of a major river basin in peninsular India, the Godavari River Basin, (GRB), under RCP4.5 and RCP8.5 scenarios. The study highlights a criteria-based approach for selecting the CMIP5 GCMs, based on their fidelity in simulating the Indian Summer Monsoon rainfall. The nonparametric kernel regression based statistical downscaling model is employed to project future daily rainfall and the variable infiltration capacity (VIC) macroscale hydrological model is used for hydrological simulations. The results indicate an increase in future rainfall without significant change in the spatial pattern of hydrological variables in the GRB. The climate-change-induced projected hydrological changes provide a crucial input to define water resource policies in the GRB. This methodology can be adopted for the climate change impacts assessment of larger river basins worldwide.

Analysis of reference evapotranspiration (ET0) trends under climate change in Bangladesh using observed and CMIP5 data sets

ET0 is an important hydro-meteorological phenomenon, which is influenced by changing climate like other climatic parameters. This study investigates the present and future trends of ET0 in Bangladesh using 39 years’ historical and downscaled CMIP5 daily climatic data for the twenty-first century. Statistical Downscaling Model (SDSM) was used to downscale the climate data required to calculate ET0. Penman–Monteith formula was applied in ET0 calculation for both the historical and modelled data. To analyse ET0 trends and trend changing patterns, modified Mann–Kendall and Sequential Mann–Kendall tests were, respectively, done. Spatial variations of ET0 trends are presented by inverse distance weighting interpolation using ArcGIS 10.2.2. Results show that RCP8.5 (2061–2099) will experience the highest amount of ET0 totals in comparison to the historical and all other scenarios in the same time span of 39 years. Though significant positive trends were observed in the mid and last months of year from month-wise trend analysis of representative concentration pathways, significant negative trends were also found for some months using historical data in similar analysis. From long-term annual trend analysis, it was found that major part of the country represents decreasing trends using historical data, but increasing trends were observed for modelled data. Theil–Sen estimations of ET0 trends in the study depict a good consistency with the Mann–Kendall test results. The findings of the study would contribute in irrigation water management and planning of the country and also in furthering the climate change study using modelled data in the context of Bangladesh.

Modeling of extreme risk in river water quality under climate change

Abstract A river water quality management model under average climatic conditions may not be able to account for the extreme risk of low water quality which is more prominent under an increase in river water temperature and altered river flows. A modeling framework is developed to assess the risk of river low water quality extremes by integrating a statistical downscaling model based on Canonical Correlation Analysis, risk quantification model based on Frank Archimedean Copula function and multiple logistic regression model integrated with a river water quality simulation model, QUAL2 K. The results reveal that the combination of predicted decrease in low flows of approximately 57% and increase in maximum river water temperatures of approximately 1.2°C has shown an increase of about 46% in risk of low water quality conditions for the future scenarios along Tunga-Bhadra River, India. The extreme risk of low water quality is observed to increase by 50.6% for the period 2020–2040 when compared with the current extreme conditions of 4.5% and average risk conditions of about 3% for the period 1988–2005. The study captured the occurrence of extremes of low water quality with evidence of a strong link between climate and water quality impairment events.

Optimal Selection of Predictor Variables in Statistical Downscaling Models of Precipitation

Two screening methods aimed at selection of predictor variables for use in a statistical downscaling (SD) model developed for precipitation are proposed and evaluated in this study. The SD model developed in this study relies heavily on appropriate predictors chosen and accurate relationships between site-specific predictand (i.e. precipitation) and general circulation model (GCM)-scale predictors for providing future projections at different spatial and temporal scales. Methods to characterize these relationships via rigid and flexible functional forms of relationships using mixed integer nonlinear programming (MINLP) formulation with binary variables, and artificial neural network (ANN) methods respectively are developed and evaluated in this study. The proposed methods and three additional methods based on the correlations between predictors and predictand, stepwise regression (SWR) and principal component analysis (PCA) are evaluated in this study. The screening methods are evaluated by employing them in conjunction with an SD model at 22 rain gauge locations in south Florida, USA. The predictor variables that are selected by different predictor selection methods are used in a statistical downscaling model developed in this study to downscale precipitation at a monthly temporal scale. Results suggest that optimal selection of variables using MINLP and ANN provided improved performance and error measures compared to two other models that did not use these methods for screening the variables. Results from application and evaluations of screening methods indicate improved downscaling of precipitation is possible by SD models when an optimal set of predictors are used and the selection of the predictors is site-specific.

Assessment of future climate change impacts on nonpoint source pollution in snowmelt period for a cold area using SWAT

The source area of Liao River is a typical cold region in northeastern China, which experiences serious problems with agricultural nonpoint source pollution (NPS), it is important to understand future climate change impacts on NPS in the watershed. This issue has been investigated by coupling semi distributed hydrological model (SWAT), statistical downscaling model (SDSM) and global circulation model (GCMs). The results show that annual average temperature would rise by 2.1 °C (1.3 °C) in the 2080 s under scenario RCP8.5 (RCP4.5), and annual precipitation would increase by 67 mm (33 mm). The change in winter temperature and precipitation is most significant with an increase by 0.23 °C/10a (0.17 °C/10a) and 1.94 mm/10a (2.78 mm/10a). The future streamflow, TN and TP loads would decrease by 19.05% (10.59%), 12.27% (8.81%) and 10.63% (6.11%), respectively. Monthly average streamflow, TN and TP loads would decrease from March to November, and increase from December to February. This is because the increased precipitation and temperature in winter, which made the spring snowpack melting earlier. These study indicate the trends of nonpoint source pollution during the snowmelt period under climate change conditions, accordingly adaptation measures will be necessary.

Analysis of the evolution of precipitation in the Haihe river basin of China under changing environment

Precipitation is one of the important factors of water cycle and main sources of regional water resources. It is of great significance to analyze the evolution of precipitation under changing environment for identifying the evolution law of water resources, thus can provide a scientific reference for the sustainable utilization of water resources and the formulation of related policies and measures. Generally, analysis of the evolution of precipitation consists of three levels: analysis the observed precipitation change based on measured data, explore the possible factors responsible for the precipitation change, and estimate the change trend of precipitation under changing environment. As the political and cultural centre of China, the climatic conditions in the Haihe river basin have greatly changed in recent decades. This study analyses the evolution of precipitation in the basin under changing environment based on observed meteorological data, GCMs and statistical methods. Firstly, based on the observed precipitation data during 1961-2000 at 26 meteorological stations in the basin, the actual precipitation change in the basin is analyzed. Secondly, the observed precipitation change in the basin is attributed using the fingerprint-based attribution method, and the causes of the observed precipitation change is identified. Finally, the change trend of precipitation in the basin under climate change in the future is predicted based on GCMs and a statistical downscaling model. The results indicate that: 1) during 1961-2000, the precipitation in the basin showed a decreasing trend, and the possible mutation time was 1965; 2) natural variability may be the factor responsible for the observed precipitation change in the basin; 3) under climate change in the future, precipitation in the basin will slightly increase by 4.8% comparing with the average, and the extremes will not vary significantly.

Climate Change and Landslide Risk Assessment in Uttaradit Province, Thailand

The incidents of sudden landslides in Thailand during the past decade have occurred frequently and more severely. The rain-triggered landslide hazard analysis is the focus of this research. The combination of geotechnical and hydrological data is used to determine permeability, conductivity, bedding orientation, overburden and presence of loose blocks. The regional landslide hazard mapping is developed using the Slope Stability Index SINMAP model supported on Arc GIS software version 10.1. The geological data can indicate the shear strength and the angle of friction values for soils above given rock types, which leads to the general applicability of the approach for landslide hazard analysis. In terms of hydrological data, the millimetres/twenty-four hours of average rainfall data are used to assess the rain triggered landslide hazard analysis in slope stability mapping. The Statistical Downscaling Model (SDSM) version 4.2, is used to assess the simulation scenario of future change; the study area is Uttaradit province, northern Thailand. The landslide hazard mapping will be compared and shown by areas (km 2 ) for both present and future conditions under climate simulation scenarios A2 and B2 in Uttaradit province. The identified risk areas largely lie in hilly and mountainous terrain; which areas can be given additional protection during land use planning in order to reduce the risk of slope failure and the associated impacts on human activities.

Comparative Assessment of Two Objective Forecast Models for Cases of Persistent Extreme Precipitation Events in the Yangtze–Huai River Valley in Summer 2016

Abstract Two persistent extreme precipitation events (PEPEs) that caused severe flooding in the Yangtze–Huai River valley in summer 2016 presented a significant challenge to operational forecasters. To provide forecasters with useful references, the capacity of two objective forecast models in predicting these two PEPEs is investigated. The objective models include a numerical weather prediction (NWP) model from the European Centre for Medium-Range Weather Forecasts (ECMWF), and a statistical downscaling model, the Key Influential Systems Based Analog Model (KISAM). Results show that the ECMWF ensemble provides a skillful spectrum of solutions for determining the location of the daily heavy precipitation (≥25 mm day−1) during the PEPEs, despite its general underestimation of heavy precipitation. For lead times longer than 3 days, KISAM outperforms the ensemble mean and nearly one-half or more of all the ensemble members of ECMWF. Moreover, at longer lead times, KISAM generally performs better in reproducing the meridional location of accumulated rainfall over the two PEPEs compared to the ECMWF ensemble mean and the control run. Further verification of the vertical velocity that affects the production of heavy rainfall in ECMWF and KISAM implies the quality of the depiction of ascending motion during the PEPEs has a dominating influence on the models’ performance in predicting the meridional location of the PEPEs at all lead times. The superiority of KISAM indicates that statistical downscaling techniques are effective in alleviating the deficiency of global NWP models for PEPE forecasts in the medium range of 4–10 days.

Future risk assessment by estimating historical heat wave trends with projected heat accumulation using SimCLIM climate model in Pakistan

Climate change has adverse effects at global, regional and local level. Heat wave events have serious contribution for global warming and natural hazards in Pakistan. Historical (1997–2015) heat wave were analyzed over different provinces (Punjab, Sindh and Baluchistan) of Pakistan to identify the maximum temperature trend. Heat accumulation in Pakistan were simulated by the General Circulation Model (GCM) combined with 3 GHG (Green House Gases) Representative Concentration Pathways (RCPs) (RCP-4.5, 6.0, and 8.5) by using SimCLIM model (statistical downscaling model for future trend projections). Heat accumulation was projected for year 2030, 2060, and 2090 for seasonal and annual analysis in Pakistan. Heat accumulation were projected to increase by the baseline year (1995) was represented in percentage change. Projection shows that Sindh and southern Punjab was mostly affected by heat accumulation. This study identified the rising trend of heat wave over the period (1997–2015) for Punjab, Sindh and Baluchistan (provinces of Pakistan), which identified that most of the meteorological stations in Punjab and Sindh are highly prone to heat waves. According to model projection; future trend of annual heat accumulation, in 2030 was increased 17%, 26%, and 32% but for 2060 the trends were reported by 54%, 49%, and 86% for 2090 showed highest upto 62%, 75%, and 140% for RCP-4.5, RCP-6.0, and RCP-8.5, respectively. While seasonal trends of heat accumulation were projected to maximum values for monsoon and followed by pre-monsoon and post monsoon. Heat accumulation in monsoon may affect the agricultural activities in the region under study.