Ashok K. Mishra

Long Lead Time Drought Forecasting Using a Wavelet and Fuzzy Logic Combination Model: A Case Study in Texas

AbstractDrought forecasting is important for drought risk management. Considering the El Nino–Southern Oscillation (ENSO) variability and persistence in drought characteristics, this study developed a wavelet and fuzzy logic (WFL) combination model for long lead time drought forecasting. The idea of WFL is to separate each predictor and predictand into their frequency bands and then reconstruct the predictand series by using its predicted bands. The strongest-frequency bands of predictors and predictand were determined from the average wavelet spectra. Applying this combination model to the state of Texas, it was found that WFL had a significant improvement over the fuzzy logic model that did not use wavelet banding. Comparison with an artificial neural network (ANN) model and a coupled wavelet and ANN (WANN) model showed that WFL was more accurate for drought forecasting. Also, it should be noted that the ENSO variability is not a global precursor of drought. For this reason, prior to an application of s...

Multi-scale evaluation of six high-resolution satellite monthly rainfall estimates over a humid region in China with dense rain gauges

This study evaluates and compares the performance of six high-resolution monthly satellite rainfall estimates (SREs), which include TRMM 3B43V6, TRMM 3B42RTV6, CMORPH, GSMaP MWR+, GSMaP MVK+, and PERSIANN, with dense ground rain gauges located in Ganjiang River Basin. The performance was evaluated at multiple spatial scales: the 0.25° × 0.25° grid, sub-catchment, and the whole basin. It was observed that 3B43V6 generally performed well and was able to capture the ground benchmark rainfall with slight overestimation, whereas all of the other SREs suffered large underestimation in the study area. Among the five pure satellite-derived products, 3B42RTV6 and CMORPH performed better, whereas PERSIANN performed the worst. All of the SREs except 3B43V6 showed a strong seasonal signature with much better performance in the wet season than in the dry season. The results also indicate that SREs performed better in the southeast and central regions, whereas poor performance was observed in the western mountains and in the northern plains. Furthermore, the spatial patterns of SREs errors are influenced mainly by the local terrain. The performance of SREs improved when the spatial scale was increased, whereas the performance reduced when the temporal scale was increased from month to year.

Regionalization of Drought Characteristics Using an Entropy Approach

Assessment and understanding of past climate is an important step for drought mitigation and water resources planning. In this study, streamflow simulated using the variable infiltration capacity (VIC) model was used for drought characterization for a time span of 1950-2000, and subsequently, regionalization was done for the state of Texas based on the annual drought severity level and drought duration. Droughts are regional in nature, and hence, identification of homogenous drought regions is important for investigating the drought characteristics within each of these regions. The concept of entropy was used for identification of homogenous regions based on drought severity and duration. A standardized version of mutual information, known as directional information transfer, was used for station grouping. The homogeneity of regions obtained was checked using L-moments. A total of eight regions were formed based on drought severity, and nine based on drought duration. Regions in west Texas were found to be critical in terms of severity, whereas east Texas showed the least severity. The longest drought duration was experienced in south Texas and lower valley zones, whereas the least drought duration was experienced in east Texas and the upper coast. Severely dry and extremely dry droughts were found to be restricted to the western and central parts of Texas. DOI: 10.1061/(ASCE)HE.1943-5584.0000683.

Variability in Canadian Seasonal Streamflow Information and Its Implication for Hydrometric Network Design

AbstractHydrologic extremes such as severe storms, floods, and droughts are inherently seasonal in nature and remain the main concern in designing hydrometric networks. In general, hydrometric networks have been designed without paying particular attention to the effect of seasonal streamflow information (SSI) at gauging stations on the efficiency of the hydrometric networks. This paper evaluates the effect of SSI on streamflow networks based on nonparametric implementation of entropy theory using the kernel density approach for estimating the mutual information between gauging stations on a seasonal basis. Overall, it is shown that the SSI of individual stations is season dependent and the efficiency of the streamflow network is also season dependent, therefore the effect of seasonality should be incorporated in future hydrometric network design. This methodology was applied at five river basins in Canada and its role for network design is discussed.

Water, Environment, Energy, and Population Growth: Implications for Water Sustainability under Climate Change

AbstractForum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Integrated drought causality, hazard, and vulnerability assessment for future socioeconomic scenarios: An information theory perspective

Drought properties and the socioeconomic impact it makes are expected to increase in the coming years due to climate change. Here we review the possible impacts of changes in climate variability on the properties of different drought types. The downscaled and bias-corrected data from five general circulation models (GCMs) were used to produce an ensemble of precipitation, temperature, and wind speed, through a relative entropy approach and were used for drought analysis. A novel Multivariate Drought Index was then employed for an integrated quantification of all physical forms of drought. We studied the spatial patterns of drought properties and performed multivariate frequency analysis for each planning region in Texas to recognize the distribution of potential drought hazard areas under climate change impact by formulating a Drought Hazard Index. A drought vulnerability assessment was also carried out by taking into consideration various socioeconomic factors, leading to the development of socioeconomic Drought Vulnerability Index. A set of composite drought risk maps that combines hazard and vulnerability analysis were developed. This study also explored the cause-effect relationship between the drought events and several hydroclimatic triggers. A transfer entropy measure was used to quantify the causal relationships, thus indicating the predominant future drought triggers. Overall, the findings are expected to help achieve an effective drought mitigation strategy for the state of Texas.

Extraction of information content from stochastic disaggregation and bias corrected downscaled precipitation variables for crop simulation

We applied a simple statistical downscaling procedure for transforming daily global climate model (GCM) rainfall to the scale of an agricultural experimental station in Katumani, Kenya. The transformation made was two-fold. First, we corrected the rainfall frequency bias of the climate model by truncating its daily rainfall cumulative distribution into the station’s distribution based on a prescribed observed wet-day threshold. Then, we corrected the climate model rainfall intensity bias by mapping its truncated rainfall distribution into the station’s truncated distribution. Further improvements were made to the bias corrected GCM rainfall by linking it with a stochastic disaggregation scheme to correct the time structure problem inherent with daily GCM rainfall. Results of the simple and hybridized GCM downscaled precipitation variables (total, probability of occurrence, intensity and dry spell length) were linked with a crop model for a more objective evaluation of their performance using a non-linear measure based on mutual information based on entropy. This study is useful for the identification of both suitable downscaling technique as well as the effective precipitation variables for forecasting crop yields using GCM’s outputs which can be useful for addressing food security problems beforehand in critical basins around the world.

Potential of Intelligent Transportation Systems in Mitigating Adverse Weather Impacts on Road Mobility: A Review

Adverse weather conditions for roads, which cause transportation systems to perform far below capacity, can severely affect societys economic output. As elimination of road weather events is not possible, transportation agencies perform proactive and reactive maintenance activities to minimize adverse impacts to keep roadways in optimum condition. While reactive maintenance activities are conducted to clear roadways after the occurrence of extreme weather events, proactive activities minimize these impacts beforehand. The success of proactive activities solely depends on the availability of accurate road weather information, however. Traditional road weather forecasting techniques rely on governmental weather services, which are not appropriate to predict route-specific road weather conditions. In this paper, the authors reviewed current intelligent transportation systems (ITS)-based solutions for minimizing road weather impacts and possible ITS innovations to incorporate diverse data sources to improve road weather management activities. ITS-based initiatives, such as road weather information system (RWIS), allow transportation agencies obtain accurate road weather assessments. Location-specific infrastructures such as RWIS are cost prohibitive for system-wide deployments. Connected vehicles equipped with weather sensors could enhance mobile road weather data collection. This strategy could improve proactive maintenance programs and reduce adverse effects of weather to the surface transportation system.

Comparison of BIAS correction techniques for GPCC rainfall data in semi-arid climate

Long-term historical precipitation data are important in developing metrics for studying the impacts of past hydrologic events (e.g., droughts) on water resources management. Many geographical regions around the world often witness lack of long term historical observation and to overcome this challenge, Global Precipitation Climatology Center (GPCC) datasets are found to be useful. However, the GPCC data are available at coarser scale (0.5° resolution), therefore bias correction techniques are often applied to generate local scale information before it can be applied for decision making activities. The objective of this study is to evaluate and compare five different bias correction techniques (BCT’s) to correct the GPCC data with respect to rain gauges in Iraq, which is located in a semi-arid climatic zone. The BCT’s included in this study are: Mean Bias-remove (B) technique, Multiplicative Shift (M), Standardized-Reconstruction (S), Linear Regression (R), and Quantile Mapping (Q). It was observed that the Performance Index (PI) of BCT’s differs in space (i.e., precipitation pattern) and temporal scale (i.e., seasonal and monthly). In general, the PI for the Q and B were better compared to other three (M, S and R) bias correction techniques. Comparatively, Q performs better than B during wet season. However, both these techniques performed equally well during average rainy season. This study suggests that instead of using a single bias correction technique at different climatic regimes, multiple BCT’s needs to be evaluated for identifying appropriate methodology that suits local climatology.

Simulating Hydrological Drought Properties at Different Spatial Units in the United States Based on Wavelet-Bayesian Regression Approach

AbstractBecause of their stochastic nature, droughts vary in space and time, and therefore quantifying droughts at different time units is important for water resources planning. The authors investigated the relationship between meteorological variables and hydrological drought properties using the Palmer hydrological drought index (PHDI). Twenty different spatial units were chosen from the unit of a climatic division to a regional unit across the United States. The relationship between meteorological variables and PHDI was investigated using a wavelet–Bayesian regression model, which enhances the modeling strength of a simple Bayesian regression model. Further, the wavelet–Bayesian regression model was tested for the predictability of global climate models (GCMs) to simulate PHDI, which will also help understand their role for downscaling purposes.