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Cholera, an acute diarrheal disease spread by lack of hygiene and contaminated water, is a major public health risk in many countries. As cholera is triggered by environmental conditions influenced by climatic variables, establishing a correlation between cholera incidence and climatic variables would provide an opportunity to develop a cholera forecasting model. Considering the auto-regressive nature and the seasonal behavioral patterns of cholera, a seasonal-auto-regressive-integrated-moving-average (SARIMA) model was used for time-series analysis during 2000–2013. As both rainfall (r = 0.43) and maximum temperature (r = 0.56) have the strongest influence on the occurrence of cholera incidence, single-variable (SVMs) and multi-variable SARIMA models (MVMs) were developed, compared and tested for evaluating their relationship with cholera incidence. A low relationship was found with relative humidity (r = 0.28), ENSO (r = 0.21) and SOI (r = −0.23). Using SVM for a 1 °C increase in maximum temperature at one-month lead time showed a 7% increase of cholera incidence (p < 0.001). However, MVM (AIC = 15, BIC = 36) showed better performance than SVM (AIC = 21, BIC = 39). An MVM using rainfall and monthly mean daily maximum temperature with a one-month lead time showed a better fit (RMSE = 14.7, MAE = 11) than the MVM with no lead time (RMSE = 16.2, MAE = 13.2) in forecasting. This result will assist in predicting cholera risks and better preparedness for public health management in the future.

The arid and semi-arid regions of the world are facing limited freshwater resources, minimal amounts of rainfall, and increasing population pressure and water demands. These resources, often groundwater, are vulnerable to both natural variability and anthropogenic interventions. Here, we develop and apply an integrated approach using geophysical, geochemical, and remote sensing observations to quantify the recharge rates of arid region aquifers that are witnessing rapid groundwater depletion. Focusing on the Saq aquifer system in the Arabian Peninsula, our study was three-fold: (1) to examine the areal extent of aquifer recharge domains using geologic, climatic, and remote sensing data; (2) to investigate the origin of, and modern contributions to, the aquifer system by examining the isotopic composition of groundwater samples; and (3) to estimate the magnitude of modern recharge to the aquifer utilizing estimates from the Gravity Recovery and Climate Experiment (GRACE) data and a continuous rainfall-runoff model, the Soil and Water Assessment Tool (SWAT) model. Isotopic examination of the groundwater from Saq revealed that deeper aquifers (fossil water) are more depleted compared to shallow reserves (alluvial aquifer; mixed waters). Analysis of GRACE-derived terrestrial and groundwater storage estimates indicates that the aquifer system received an annual recharge rate of 5.21 ± 0.10 km3/yr (11.85 ± 0.22 mm/yr), equal to about half of the withdrawal rate from wells during the investigation period (2002–2016). Analysis of SWAT results indicates that, from 1998 to 2014, the investigated watersheds received an average annual precipitation of 19.20 km3, of which approximately 51% is partitioned as potential recharge. The temporal variations in storage, depletion and recharge are related to the changes in regional groundwater extraction, rainfall, and extent of irrigated areas.

A forecasting lead time of 5–10 days is desired to increase the flood response and preparedness for large river basins. Large uncertainty in observed and forecasted rainfall appears to be a key bottleneck in providing reliable flood forecasting. Significant efforts continue to be devoted to developing mechanistic hydrological models and statistical and satellite-driven methods to increase the forecasting lead time without exploring the functional utility of these complicated methods. This paper examines the utility of a data-based modeling framework with requisite simplicity that identifies key variables and processes and develops ways to track their evolution and performance. Findings suggest that models with requisite simplicity—relying on flow persistence, aggregated upstream rainfall, and travel time—can provide reliable flood forecasts comparable to relatively more complicated methods for up to 10 days lead time for the Ganges, Brahmaputra, and upper Meghna (GBM) gauging locations inside Bangladesh. Forecasting accuracy improves further by including weather-model-generated forecasted rainfall into the forecasting scheme. The use of water level in the model provides equally good forecasting accuracy for these rivers. The findings of the study also suggest that large-scale rainfall patterns captured by the satellites or weather models and their “predictive ability” of future rainfall are useful in a data-driven model to obtain skillful flood forecasts up to 10 days for the GBM basins. Ease of operationalization and reliable forecasting accuracy of the proposed framework is of particular importance for large rivers, where access to upstream gauge-measured rainfall and flow data are limited, and detailed modeling approaches are operationally prohibitive and functionally ineffective.

In the era of global warning, the insight of future climate and their changing extremes is critical for climate-vulnerable regions of the world. In this study, we have conducted a robust assessment of Regional Climate Model (RCM) results in a monsoon-dominated region within the new Coupled Model Intercomparison Project Phase 5 (CMIP5) and the latest Representative Concentration Pathways (RCP) scenarios. We have applied an advanced bias correction approach to five RCM simulations in order to project future climate and associated extremes over Bangladesh, a critically climate-vulnerable country with a complex monsoon system. We have also generated a new gridded product that performed better in capturing observed climatic extremes than existing products. The bias-correction approach provided a notable improvement in capturing the precipitation extremes as well as mean climate. The majority of projected multi-model RCMs indicate an increase of rainfall, where one model shows contrary results during the 2080s (2071–2100) era. The multi-model mean shows that nighttime temperatures will increase much faster than daytime temperatures and the average annual temperatures are projected to be as hot as present-day summer temperatures. The expected increase of precipitation and temperature over the hilly areas are higher compared to other parts of the country. Overall, the projected extremities of future rainfall are more variable than temperature. According to the majority of the models, the number of the heavy rainy days will increase in future years. The severity of summer-day temperatures will be alarming, especially over hilly regions, where winters are relatively warm. The projected rise of both precipitation and temperature extremes over the intense rainfall-prone northeastern region of the country creates a possibility of devastating flash floods with harmful impacts on agriculture. Moreover, the effect of bias-correction, as presented in probable changes of both bias-corrected and uncorrected extremes, can be considered in future policy making.

The association of cholera and climate has been extensively documented. However, determining the effects of changing climate on the occurrence of disease remains a challenge. Bimodal peaks of cholera in Bengal Delta are hypothesized to be linked to asymmetric flow of the Ganges and Brahmaputra rivers. Spring cholera is related to intrusion of bacteria-laden coastal seawater during low flow seasons, while autumn cholera results from cross-contamination of water resources when high flows in the rivers cause massive inundation. Coarse resolution of General Circulation Model (GCM) output (usually at 100 – 300 km)cannot be used to evaluate variability at the local scale(10–20 km),hence the goal of this study was to develop a framework that could be used to understand impacts of climate change on occurrence of cholera. Instead of a traditional approach of downscaling precipitation, streamflow of the two rivers was directly linked to GCM outputs, achieving reasonable accuracy (R2 = 0.89 for the Ganges and R2 = 0.91 for the Brahmaputra)using machine learning algorithms (Support Vector Regression-Particle Swarm Optimization). Copula methods were used to determine probabilistic risks of cholera under several discharge conditions. Key results, using model outputs from ECHAM5, GFDL, andHadCM3for A1B and A2 scenarios, suggest that the combined low flow of the two rivers may increase in the future, with high flows increasing for first half of this century, decreasing thereafter. Spring and autumn cholera, assuming societal conditions remain constant e.g., at the current rate, may decrease. However significant shifts were noted in the magnitude of river discharge suggesting that cholera dynamics of the delta may well demonstrate an uncertain predictable pattern of occurrence over the next century.

Diarrheal diseases, notably cholera, have been shown to be related to episodic seasonal variability in river discharge, predominantly low flows, in regions where water and sanitation infras...

In August 2017, South Asia was affected by devastating floods that caused widespread death and destruction in Nepal, India, and Bangladesh. The Brahmaputra River experienced a record flow in Bangladesh. However, the prediction of a 200-year flood from an international forecasting agency did not materialize. In fact, the evolution and dynamics of 2017 flood was very different from previous floods in the region—localized in nature and short-lived in duration. We examine the hydrometeorology of the Ganges, Brahmaputra, and Meghna River basins to show how the 2017 August flood emerged differently from past major floods in the region. Assessment of available rainfall datasets reveals the usefulness of satellite sources, such as TMPA, GPM, ERA-Interim, CPC Global Precipitation, and WRF to capture the extreme events and highlights the poor spatial coverage of in-situ measurements. We then present a data-driven Requisitely Simple (ReqSim) flood forecast model. Findings from the real-time application of ReqSim during the 2017 flood provide encouraging results with accurate predictions of peak flood timing and magnitude for several locations. For example, the ReqSim provides 10-day forecasts with Nash Sutcliffe efficiency (NSE) values 0.89–0.97 and Mean Absolute Errors (MAE) 0.33–0.54 m for the Ganges River system inside Bangladesh. For the Brahmaputra River system, NSE value ranges 0.49–0.68 and MAE 0.32–0.48 m for 10-day forecasts and NSE 0.62–0.78 and MAE 0.36–0.68 m for 7-day forecasts. Comparison of 2017 flood with past major regional floods suggests that—it is not the intensity of rainfall or severity of flood in either the Ganges or the Brahmaputra basin—but the lack of peak flow synchronization of these two rivers may help explain why 2017 flood did not become as severe as the floods of 1998 and 2007 in Bangladesh. Ease of operationalization and reliable forecasting accuracy of ReqSim for 2017 flood is of particular relevance for large rivers, where access to upstream gauge-measured rainfall and flow data are limited, and detailed modeling approaches are operationally prohibitive and functionally ineffective. Plain language summary Vast areas of South Asia—across the border regions of Nepal, India, and Bangladesh—were flooded by intense rainfall and overflowing rivers in August 2017. This record flood event killed over a thousand people and affected over 40 million people. However, forecasts of a 200-year flood event, issued by an international agency and covered widely in local media outlets, did not materialize. It was found that the evolution and dynamics of 2017 flood was very different from previous major floods in the region—localized in nature and short-lived in duration. We examine the rainfall and flood generating processes before and during the floods and the performance of a simple flood forecast model in detail to identify the mechanisms that led to these apparent false forecasts including the nature and timing of the peak rainfall events and subsequent floods.

Rotavirus is the most common cause of diarrheal disease among children under 5. Especially in South Asia, rotavirus remains the leading cause of mortality in children due to diarrhea. As climatic extremes and safe water availability significantly influence diarrheal disease impacts in human populations, hydroclimatic information can be a potential tool for disease preparedness. In this study, we conducted a multivariate temporal and spatial assessment of 34 climate indices calculated from ground and satellite Earth observations to examine the role of temperature and rainfall extremes on the seasonality of rotavirus transmission in Bangladesh. We extracted rainfall data from the Global Precipitation Measurement and temperature data from the Moderate Resolution Imaging Spectroradiometer sensors to validate the analyses and explore the potential of a satellite‐based seasonal forecasting model. Our analyses found that the number of rainy days and nighttime temperature range from 16°C to 21°C are particularly influential on the winter transmission cycle of rotavirus. The lower number of wet days with suitable cold temperatures for an extended time accelerates the onset and intensity of the outbreaks. Temporal analysis over Dhaka also suggested that water logging during monsoon precipitation influences rotavirus outbreaks during a summer transmission cycle. The proposed model shows lag components, which allowed us to forecast the disease outbreaks 1 to 2 months in advance. The satellite data‐driven forecasts also effectively captured the increased vulnerability of dry‐cold regions of the country, compared to the wet‐warm regions., Key Points Rotavirus shows strong mortality and morbidity, as well as strong spatial and temporal variability in South AsiaStrong winter and weak monsoon transmission cycles dominate South Asia, modulated by regional climatic extremesSatellite‐derived hydroclimatic information has potential to help forecasting of rotavirus risk over Bengal Delta

Cholera remains one of the most prevalent water-related infections in many tropical regions of the world. Macro-environmental processes provide a natural ecological niche for Vibrio cholerae and because powerful evidence of new biotypes is emerging, it is unlikely that the bacteria will be fully eradicated. Consequently, to develop effective intervention and mitigation strategies, it is necessary to develop cholera prediction models with several months' lead time. Almost all cholera outbreaks originate near the coastal areas and cholera bacteria exhibit a strong relationship with coastal plankton. Using chlorophyll as a surrogate for plankton bloom in coastal areas, recent studies have postulated a relationship between chlorophyll and cholera incidence. Here, we show that seasonal cholera outbreaks in the Bengal Delta can be predicted two to three months in advance with an overall prediction accuracy of over 75% by using satellite-derived chlorophyll and air temperature data. Such high prediction accuracy is achievable because the two seasonal peaks of cholera are predicted using two separate models representing distinctive macro-scale environmental processes. We have shown that interannual variability of pre-monsoon cholera outbreaks can be satisfactorily explained with coastal plankton blooms and a cascade of hydro-coastal processes. Post-monsoon cholera outbreaks, on the other hand, are related to macro-scale monsoon processes and subsequent breakdown of sanitary conditions. Our results demonstrate that satellite data over a range of space and time scales are effective in developing a cholera prediction model for the Bengal Delta with several months' lead time. We anticipate our modeling framework and findings will provide the impetus to explore the utility of satellite derived macro-scale variables for cholera prediction in other cholera endemic regions. Two seasonal peaks of Bengal cholera can be predicted using two separate models.Two to three months prediction lead time achievable.Prediction accuracy exceeds 75% using combinations of chlorophyll and air temperature data.Cascade of hydro-coastal processes may result in cholera outbreaks.

Cholera bacteria exhibit strong association with coastal plankton. Characterization of space–time variability of chlorophyll, a surrogate for plankton abundance, in the northern Bay of Bengal is an essential first step to develop any methodology for predicting cholera outbreaks in the Bengal Delta region using remote sensing. This study quantifies the space–time distribution of chlorophyll, using the data from SeaWiFS, in the Bay of Bengal region using 10 years of satellite data. Variability of chlorophyll at daily scale, irrespective of spatial averaging, resembles white noise. At a monthly scale, chlorophyll shows distinct seasonality and chlorophyll values are significantly higher close to the coast than in the offshore regions. At pixel level (9 km) on monthly scale, on the other hand, chlorophyll does not exhibit much persistence in time. With increased spatial averaging, temporal persistence of chlorophyll increases and lag 1 autocorrelation stabilizes around 0.60 for 1296 km 2 or larger areal averages. In contrast to the offshore regions, spatial analyses of chlorophyll suggest that only coastal region has a stable correlation length of 100 km. Presence (absence) of correlation length in the coastal (offshore) regions indicate that the two regions may have two separate processes controlling the production of phytoplankton. This study puts a lower limit on space–time averaging of satellite measured plankton at 1296 km 2 monthly scale to establish relationships with cholera incidence in Bengal Delta.

20 and the Delaware, 21 Po, 22 Abstract. Phytoplankton abundance is inversely related to sea surface temperature (SST). However, a positive rela- tionship is observed between SST and phytoplankton abundance in coastal waters of Bay of Bengal. This has led to an assertion that in a warming climate, rise in SST may increase phytoplankton blooms and, therefore, cholera outbreaks. Here, we explain why a positive SST-phytoplankton relationship exists in the Bay of Bengal and the implications of such a relationship on cholera dynamics. We found clear evidence of two independent physical drivers for phytoplankton abun- dance. The first one is the widely accepted phytoplankton blooming produced by the upwelling of cold, nutrient-rich deep ocean waters. The second, which explains the Bay of Bengal findings, is coastal phytoplankton blooming during high river discharges with terrestrial nutrients. Causal mechanisms should be understood when associating SST with phytoplankton and subsequent cholera outbreaks in regions where freshwater discharge are a predominant mechanism for phytoplank- ton production.

Outbreaks of diarrheal diseases, including cholera, are related to floods and droughts in regions where water and sanitation infrastructure are inadequate or insufficient. However, availability of data on water scarcity and abundance in transnational basins, are a prerequisite for developing cholera forecasting systems. With more than a decade of terrestrial water storage (TWS) data from the Gravity Recovery and Climate Experiment, conditions favorable for predicting cholera occurrence may now be determined. We explored lead-lag relationships between TWS in the Ganges-Brahmaputra-Meghna basin and endemic cholera in Bangladesh. Since bimodal seasonal peaks in cholera in Bangladesh occur during spring and autumn seasons, two separate logistical models between TWS and disease time series (2002-2010) were developed. TWS representing water availability showed an asymmetrical, strong association with cholera prevalence in the spring (τ = -0.53; P < 0.001) and autumn (τ = 0.45; P < 0.001) up to 6 months in advance. One unit (centimeter of water) decrease in water availability in the basin increased odds of above normal cholera by 24% (confidence interval [CI] = 20-31%; P < 0.05) in the spring, while an increase in regional water by 1 unit, through floods, increased odds of above average cholera in the autumn by 29% (CI = 22-33%; P < 0.05).