Investigating the Impact of Irrigation on Malaria Vector Larval Habitats and Transmission Using a Hydrology-Based Model.

A combination of accelerated population growth and severe droughts has created pressure on food security and driven the development of irrigation schemes across sub-Saharan Africa. Irrigation has been associated with increased malaria risk, but risk prediction remains difficult due to the heterogeneity of irrigation and the environment. While investigating transmission dynamics is helpful, malaria models cannot be applied directly in irrigated regions as they typically rely only on rainfall as a source of water to quantify larval habitats.

Topographic hydro-conditioning to resolve surface depression storage and ponding in a fully distributed hydrologic model.

Land surface depressions play a central role in the transformation of rainfall to ponding, infiltration and runoff, yet digital elevation models (DEMs) used by spatially distributed hydrologic models that resolve land surface processes rarely capture land surface depressions at spatial scales relevant to this transformation. Methods to generate DEMs through processing of remote sensing data, such as optical and light detection and ranging (LiDAR) have favored surfaces without depressions to avoid adverse slopes that are problematic for many hydrologic routing methods. Here we present a new topographic conditioning workflow, Depression-Preserved DEM Processing (D2P) algorithm, which is designed to preserve physically meaningful surface depressions for depression-integrated and efficient hydrologic modeling.

Enhancing Malaria Research, Surveillance, and Control in Endemic Areas of Kenya and Ethiopia.

Malaria control programs in Africa encounter daunting challenges that hinder progressive steps toward elimination of the disease. These challenges include widespread insecticide resistance in mosquito vectors, increasing outdoor malaria transmission, lack of vector surveillance and control tools suitable for outdoor biting vectors, weakness in malaria surveillance, and an inadequate number of skilled healthcare personnel. Ecological and epidemiological changes induced by environmental modifications resulting from water resource development projects pose additional barriers to malaria control.

Unveiling four decades of intensifying precipitation from tropical cyclones using satellite measurements.

Increases in precipitation rates and volumes from tropical cyclones (TCs) caused by anthropogenic warming are predicted by climate modeling studies and have been identified in several high intensity storms occurring over the last half decade. However, it has been difficult to detect historical trends in TC precipitation at time scales long enough to overcome natural climate variability because of limitations in existing precipitation observations. We introduce an experimental global high-resolution climate data record of precipitation produced using infrared satellite imagery and corrected at the monthly scale by a gauge-derived product that shows generally good performance during two hurricane case studies but estimates higher mean precipitation rates in the tropics than the evaluation datasets.

The spatiotemporal variations and propagation of droughts in Plateau Mountains of China.

Drought incidents and the pressure on water resources have increased in recent years, which has threatened sustainable development. Recently, research has been conducted on drought propagation. However, few studies have investigated the characteristics and mechanisms of drought propagation in plateau mountainous regions with complex topography, which limits the efforts to mitigate drought.

PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies.

Accurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution.

PERSIANN Dynamic Infrared-Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset.

This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.

Predicting distribution of malaria vector larval habitats in Ethiopia by integrating distributed hydrologic modeling with remotely sensed data.

Larval source management has gained renewed interest as a malaria control strategy in Africa but the widespread and transient nature of larval breeding sites poses a challenge to its implementation. To address this problem, we propose combining an integrated high resolution (50 m) distributed hydrological model and remotely sensed data to simulate potential malaria vector aquatic habitats. The novelty of our approach lies in its consideration of irrigation practices and its ability to resolve complex ponding processes that contribute to potential larval habitats.

The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data.

The Center for Hydrometeorology and Remote Sensing (CHRS) has created the CHRS Data Portal to facilitate easy access to the three open data licensed satellite-based precipitation datasets generated by our Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) system: PERSIANN, PERSIANN-Cloud Classification System (CCS), and PERSIANN-Climate Data Record (CDR). These datasets have the potential for widespread use by various researchers, professionals including engineers, city planners, and so forth, as well as the community at large. Researchers at CHRS created the CHRS Data Portal with an emphasis on simplicity and the intention of fostering synergistic relationships with scientists and experts from around the world.

Classification and regression tree (CART) analysis for indicator bacterial concentration prediction for a Californian coastal area.

The study used existing indicator bacterial data and a number of physicochemical parameters that can be measured instantaneously to determine if a decision tree approach, especially classification and regression tree, could be used to predict bacterial concentrations in timely manner for beach closure management. Each indicator bacteria showed different tree structures and each had its own significant variables; Dissolved oxygen played an important role for both total coliform and fecal coliform and turbidity was the most important factor to predict concentrations of enterococci for decision tree approaches. Root mean squared error stayed between 5 and 6.

Identification and application of physical and chemical parameters to predict indicator bacterial concentration in a small Californian creek.

This study of Aliso Creek in California aimed to identify physical and chemical parameters that could be measured instantly to be used in a model to serve as surrogates for indicator bacterial concentrations during dry season flow. In this study, a new data smoothing technique and ranking/categorizing analysis was used to reduce variation to allow better delineation of the relationships between adopted variables and concentrations of indicator bacteria. The ranking/categorizing approach clarified overall trends between physico-chemical data and the indicators and suggested sources of the bacteria.

The challenge of predicting flash floods from thunderstorm rainfall.

A major characteristic of the hydrometeorology of semi-arid regions is the occurrence of intense thunderstorms that develop very rapidly and cause severe flooding. In summer, monsoon air mass is often of subtropical origin and is characterized by convective instability. The existing observational network has major deficiencies for those regions in providing information that is important to run-off generation.