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Multivariate bias correction (BC) models are well-known to correct more statistical attributes in climate model simulations. However, their inherent complexity and excessive parameters can introduce higher uncertainty into future climate simulations. In contrast, univariate BC models, with fewer parameters, are limited to correcting certain attributes. An issue that has not been investigated in-depth is the impact of an increased number of variables in the multivariate BC has on the bias-corrected climate models’ stability. This study compares the performance of a multivariate BC approach, Multivariate Recursive Nested Bias Correction (MRNBC), and a univariate BC approach, Continuous Wavelet-based Bias Correction (CWBC), as the number of variables to be corrected increases, known as the “curse of dimensionality” (CoD). The analysis uses high-resolution climate model outputs for both current and future simulations of sea surface temperature and precipitation in the Niño 3.4 region. Results show both BC models effectively correct current climate biases. As the number of variables increases, CWBC remains robust and produces sensible future simulations, while MRNBC’s complexity leads to deterioration in standard deviations and spatial cross-correlation. CWBC, based on univariate correction, is relatively unaffected by the CoD.

Quantifying climate change impact on water resources systems at regional or catchment scales is important in water resources planning and management. General circulation models (GCMs) represent our main source of knowledge about future climate change. However, several key limitations restrict the direct use of GCM simulations for water resource assessments. In particular, the presence of systematic bias and the need for its correction is an essential pre-processing step that improves the quality of GCM simulations, making climate change impact assessments more robust and believable. What exactly is systematic bias? Can systematic bias be quantified if the model is asynchronous with observations or other model simulations? Should model bias be sub-categorized to focus on individual attributes of interest or aggregated to focus on lower moments alone? How would one address bias in multiple attributes without making the correction model complex? How could one be confident that corrected simulations for the yet-to-be-seen future bear a closer resemblance to the truth? How can one meaningfully extrapolate correction to multiple dimensions, without being impacted by the ‘Curse of Dimensionality’? These are some of the questions we attempt to address in the paper. HIGHLIGHTS Importance of procedures for correcting systematic biases is discussed.; Extensive literature is presented on bias correction and its use.; The importance of correcting specific attributes for water resources applications is illustrated.; Challenges in formulating a bias correction alternative are highlighted.; Added information on how correcting these biases is critical before any dynamical or statistical downscaling application.;

Most procedures for redressing systematic bias in climate modeling are calibrated using current climate observations, and perform well. However, their performance in the future climate remains uncertain as no observations exist to compare against. In this context, we use the current and future climate outputs of an ultra-high resolution of Community Earth System Model (UHR-CESM) as the representative truth and bias correct monthly sea surface temperature (SST) simulations of eight Coupled Model Intercomparison Project 6 models over the Niño 3.4 region. A time-frequency bias correction approach is used to correct for bias in distributional, trend, and spectral attributes present in the models. This results in a near perfect power spectrum of the bias corrected current climate model simulations. Considering all correction procedures remain unchanged into the future, the overall representation of the corrected SST simulations shows improvement with consistency across models for the doubled CO _2 scenario, but higher variability and lower consistency in the quadrupled CO _2 concentration scenario.

Design flood is one of the important factors for flood risk assessment and water infrastructures planning and development in a certain location. There are several methods to estimate it, one method which has been commonly and widely use is using flood frequency analysis. This research aims to develop Intensity-Duration-Frequency (IDF) curves in Upper Werba Sub-Watershed, West Papua Province, Indonesia, to estimate design rainfall intensity. The design rainfall intensity is used to estimate peak of flood discharge using Rational Formula in the sub-watershed. Other methods, i.e. Soil Conservation Service and Nakayasu Synthetic Unit Hydrograph are also presented in this paper to provide comparison of the estimated peak of flood discharge. The result shows that the Rational method provide the closest magnitude of estimated flood discharge in Upper Werba Sub-Watershed to the observed streamflow. Therefore, it is suggested that the Rational method can be used for water infrastructure planning and development in the sub-watershed.

Sustainable urban drainage systems (SUDS) or eco-drainage system has been implemented in developed countries. The aims of the construction of the system are: to minimize surface runoff by capturing and temporarily retain it, to improve the quality of surface runoff, to infiltrate stormwater, to increase evapotranspiration, and to enhance landscape aesthetic value. Learning from the success of the implementation of SUDS in developed countries, the paper presents a model of eco-drainage system for residential area in Indonesia, which consists of rainwater harvesting system (RWHS) and retention ponds. A simulation of stormwater and surface runoff utilization is also presented. At 14,602.26 m2 bare land in Mojokerto Regency, East Java, which is transformed into residential area, the model of eco-drainage system could reduce surface runoff volume almost by 70%. It is recommended to adjust the dimension of rainwater storage tank and retention ponds in order to reduce more surface runoff volume.

Climate change impacts have gained great attention to be studied in various fields. In this paper, an investigation of rainfall pattern change is performed using three statistical methods, i.e., simple linear regression, t-test, and Mann-Kendall’s test. The analysis is performed at 10- and 20-year time scales of daily, monthly, and annual rainfall in Flores Island, a dry region in Indonesia. In general, an increasing monthly rainfall trend is detected in the rainy season (October – April) at a 20-year period, using all three methods. Specifically, a significant increasing trend in March 1989 – 2008 is observed, and it contributes to the significant increasing trend of annual rainfall. The findings presented in this paper should be an alert for potential climate change impacts in the region. The positive consideration of having more rainfall in a dry region might turn into a negative reality when adaptation measures are not well-prepared.