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Jacob Mardian
,
Catherine Champagne
,
Barrie Bonsal
, and
Aaron Berg

Abstract

Recent advances in artificial intelligence (AI) and explainable AI (XAI) have created opportunities to better predict and understand drought processes. This study uses a machine learning approach for understanding the drivers of drought severity and extent in the Canadian Prairies from 2005 to 2019 using climate and satellite data. The model is trained on the Canadian Drought Monitor (CDM), an extensive dataset produced by expert analysis of drought impacts across various sectors that enables a more comprehensive understanding of drought. Shapley additive explanation (SHAP) is used to understand model predictions during emerging or worsening drought conditions, providing insight into the key determinants of drought. The results demonstrate the importance of capturing spatiotemporal autocorrelation structures for accurate drought characterization and elucidates the drought time scales and thresholds that optimally separate each CDM severity category. In general, there is a positive relationship between the severity of drought and the time scale of the anomalies. However, high-severity droughts are also more complex and driven by a multitude of factors. It was found that satellite-based evaporative stress index (ESI), soil moisture, and groundwater were effective predictors of drought onset and intensification. Similarly, anomalous phases of large-scale atmosphere–ocean dynamics exhibit teleconnections with Prairie drought. Overall, this investigation provides a better understanding of the physical mechanisms responsible for drought in the Prairies, provides data-driven thresholds for estimating drought severity that could improve future drought assessments, and offers a set of early warning indicators that may be useful for drought adaptation and mitigation.

Significance Statement

This work is significant because it identifies drivers of drought onset and intensification in an agriculturally and economically important region of Canada. This information can be used in the future to improve early warning for adaptation and mitigation. It also uses state-of-the-art machine learning techniques to understand drought, including a novel approach called SHAP probability values to improve interpretability. This provides evidence that machine learning models are not black boxes and should be more widely considered for understanding drought and other hydrometeorological phenomena.

Open access
Jianqiao Chen
,
Bo Han
,
Qinghua Yang
,
Hao Luo
,
Zhipeng Xian
,
Yunfei Zhang
,
Xing Li
, and
Xiaobo Zhang

Abstract

Typhoons frequently hit the Pearl River Delta (PRD), threatening the region’s dense population and assets. Typhoon precipitation forecasting in this region is challenging, in part because of the complex hydrometeorological effects over the coast and the scarcity of upstream marine meteorological observations. Typhoon Mun was formed in the South China Sea on 2 July 2019, and it brought heavy rainfall to the PRD when its center moved to the Beibu Gulf. During Typhoon Mun, an additional sounding was conducted offshore in the PRD every 12 h to assess the incremental impact on the skill of precipitation forecasting. A precipitation prediction based on the Weather Research and Forecasting (WRF) Model underestimated the 12-h accumulated precipitation over the PRD by 87%, with the Final Analysis (FNL) data from the National Centers for Environmental Prediction in the United States as initial fields. To address this issue, we implemented a solution by reconstructing the initial field through the assimilation of the additional radiosonde observations using the WRF three-dimensional variational (3D-Var) method. The prediction with the new initial fields reduced the rainfall underestimation by 24%. A difference analysis indicates that the planetary boundary layer scheme used in FNL underestimates the low-level temperature and humidity, especially after the rainfall peak. In contrast, assimilation gives a more realistic lower-tropospheric structure, significantly enhancing the moisture flux convergence around 925 hPa and divergence around 700 hPa around the PRD. Sensitivity experiments show that assimilating atmospheric thermal (i.e., temperature and humidity) profiles is more helpful than dynamic (wind) profiles in improving the rainfall prediction of the typhoon.

Significance Statement

The impact of typhoon-related precipitation in the Pearl River Delta (PRD) is significant. Improving the numerical forecast precision in this region poses challenges, partly due to the influences of land–sea thermal and topographic factors near the boundary layer, as well as the scarcity of upstream observational data. This study proposes a practical method to improve typhoon-related precipitation prediction from a case study of Typhoon Mun. By assimilating additional sounding observations, we obtain a more realistic structure of the lower atmosphere, better spatial patterns of water vapor fluxes, and, ultimately, better precipitation forecasts. The results of our study suggest that a more advanced observing system of vertical atmospheric structure, especially the thermal one, over the South China Sea is important for improving typhoon predictions.

Open access
Vesta Afzali Gorooh
,
Veljko Petković
,
Malarvizhi Arulraj
,
Phu Nguyen
,
Kuo-lin Hsu
,
Soroosh Sorooshian
, and
Ralph R. Ferraro

Abstract

Reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for understanding the Earth’s hydrological cycle. Precipitation estimation over land and coastal regions is necessary for addressing the high degree of spatial heterogeneity of water availability and demand, and for resolving the extremes that modulate and amplify hazards such as flooding and landslides. Advancements in computation power along with unique high spatiotemporal and spectral resolution data streams from passive meteorological sensors aboard geosynchronous Earth-orbiting (GEO) and low Earth-orbiting (LEO) satellites offer exciting opportunities to retrieve information about surface precipitation phenomena using data-driven machine learning techniques. In this study, the capabilities of U-Net–like architecture are investigated to map instantaneous, summertime surface precipitation intensity at the spatial resolution of 2 km. The calibrated brightness temperature products from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) radiometer are combined with multispectral images (visible, near-infrared, and infrared bands) from the Advanced Baseline Imager (ABI) aboard the GOES-R satellites as main inputs to the U-Net–like precipitation algorithm. Total precipitable water and 2-m temperature from the Global Forecast System (GFS) model are also used as auxiliary inputs to the model. The results show that the U-Net–like algorithm can capture fine-scale patterns and intensity of surface precipitation at high spatial resolution over stratiform and convective precipitation regimes. The evaluations reveal the potential of extracting relevant, high spatial features over complex surface types such as mountainous regions and coastlines. The algorithm allows users to interpret the inputs’ importance and can serve as a starting point for further exploration of precipitation systems within the field of hydrometeorology.

Open access
Rasmus Wiuff

Abstract

World extremes in meteorology are important as they can be used as indicators for climate change. This was one of the main reasons for the creation of the World Meteorological Organization’s World Weather and Climate Extremes Archive in 2006. In contrast to temperature, for instance, which can be described by a single parameter, point rainfall must be described by two parameters, for example, precipitation depth and duration. This makes it difficult to directly compare different rainfall records. In this article, however, it is shown that the world’s greatest rainfall events, with durations ranging from 1 min to 2 years, all have nearly the same precipitation intensity duration index, a new dimensionless number. As a theoretical consequence, the intensity of all these record rainfalls is inversely proportional to the square root of their duration. This physically based result is consistent with earlier statistically based findings. The last measured record rainfall on the World Meteorological Organization’s record list is the point rainfall with the largest precipitation intensity duration index since 1860. This 4-day rainfall that began on 24 February 2007 on Cratère Commerson, Réunion Island, can be considered the largest point rainfall within documented records.

Significance Statement

Floods resulting from extreme rainstorms can be very costly and deadly; thus, understanding such extreme events is very important. Knowledge of extreme rainstorms is also important in determining how much and how fast our climate is changing. In this article, a new dimensionless number, the precipitation intensity duration index (PID) is presented. The world’s greatest point rainfall events, with durations ranging from 1 min to 2 years, all have nearly the same PID. One rainfall event, however, has a considerably larger PID than all others, namely, a 4-day rainfall that began on 24 February 2007 on Cratère Commerson, Réunion Island. Therefore, this rainfall can be considered the largest point rainfall within documented records.

Open access
Vishal Batchu
,
Grey Nearing
, and
Varun Gulshan

Abstract

We develop a deep learning–based convolutional-regression model that estimates the volumetric soil moisture content in the top ∼5 cm of soil. Input predictors include Sentinel-1 (active radar) and Sentinel-2 (multispectral imagery), as well as geophysical variables from SoilGrids and modeled soil moisture fields from SMAP and GLDAS. The model was trained and evaluated on data from ∼1000 in situ sensors globally over the period 2015–21 and obtained an average per-sensor correlation of 0.707 and ubRMSE of 0.055 m3 m−3, and it can be used to produce a soil moisture map at a nominal 320-m resolution. These results are benchmarked against 14 other soil moisture evaluation research works at different locations, and an ablation study was used to identify important predictors.

Significance Statement

Soil moisture is a key variable in various agriculture and water management systems. Accurate and high-resolution estimates of soil moisture have multiple downstream benefits such as reduced water wastage by better understanding and managing the consumption of water, utilizing smarter irrigation methods and effective canal water management. We develop a deep learning–based model that estimates the volumetric soil moisture content in the top ∼5 cm of soil at a nominal 320-m resolution. Our results demonstrate that machine learning is a useful tool for fusing different modalities with ease, while producing high-resolution models that are not location specific. Future work could explore the possibility of using temporal input sources to further improve model performance.

Open access
Moumouni Djibo
,
Christian Chwala
,
Maximilian Graf
,
Julius Polz
,
Harald Kunstmann
, and
François Zougmoré

Abstract

We present high-resolution rainfall maps from commercial microwave link (CML) data in the city of Ouagadougou, Burkina Faso. Rainfall was quantified based on data from 100 CMLs along unique paths and interpolated to achieve rainfall maps with a 5-min temporal and 0.55-km spatial resolution for the monsoon season of 2020. Established processing methods were combined with newly developed filtering methods, minimizing the loss of data availability. The rainfall maps were analyzed qualitatively both at a 5-min and aggregated daily scales. We observed high spatiotemporal variability on the 5-min scale that cannot be captured with any existing measurement infrastructure in West Africa. For the quantitative evaluation, only one rain gauge with a daily resolution was available. Comparing the gauge data with the corresponding CML rainfall map pixel showed a high agreement, with a Pearson correlation coefficient > 0.95 and an underestimation of the CML rainfall maps of ∼10%. Because the CMLs closest to the gauge have the largest influence on the map pixel at the gauge location, we thinned out the CML network around the rain gauge synthetically in several steps and repeated the interpolation. The performance of these rainfall maps dropped only when a radius of 5 km was reached and approximately one-half of all CMLs were removed. We further compared ERA5 and GPM IMERG data with the rain gauge and found that they had much lower correlation than data from the CML rainfall maps. This clearly highlights the large benefit that CML data can provide in the data-scarce but densely populated African cities.

Significance Statement

In this study, we investigate the possibility of deriving accurate high-resolution rainfall maps from commercial microwave link (CML) data in West Africa. The main challenges are the lack of reference data in this area and the adoption of existing processing tools without reference data. We show CML rainfall maps for Ouagadougou, Burkina Faso, with a resolution of 5 min and 0.55 km, which is unprecedented in this region. The comparison with the only available rain gauge, which provides data only at a daily resolution, yields a Pearson correlation of >0.95. An analysis of synthetically thinned-out networks shows that this accuracy is valid for the whole domain. Comparing reanalysis and satellite data with the rain gauge and CML data showed a poor performance of these gridded reference datasets. Also, a high coincidence of temporal dynamics between CML rainfall maps and satellite products was observed. Overall, these findings support the potential of CMLs for future hydrometeorological applications in West Africa.

Open access
Ebrahim Ghaderpour
,
Mohamed Sherif Zaghloul
,
Hatef Dastour
,
Anil Gupta
,
Gopal Achari
, and
Quazi K. Hassan

Abstract

River flow monitoring is a critical task for land management, agriculture, fishery, industry, and other concerns. Herein, a robust least squares triple cross-wavelet analysis is proposed to investigate possible relationships between river flow, temperature, and precipitation in the time–frequency domain. The Athabasca River basin (ARB) in Canada is selected as a case study to investigate such relationships. The historical climate and river flow datasets since 1950 for three homogeneous subregions of the ARB were analyzed using a traditional multivariate regression model and the proposed wavelet analysis. The highest Pearson correlation (0.87) was estimated between all the monthly averaged river flow, temperature, and accumulated precipitation for the subregion between Hinton and Athabasca. The highest and lowest correlations between climate and river flow were found to be during the open warm season and cold season, respectively. Particularly, the highest correlations between temperature, precipitation, and river flow were in May (0.78) for Hinton, July (0.54) for Athabasca, and September (0.44) for Fort McMurray. The new wavelet analysis revealed significant coherency between annual cycles of climate and river flow for the three subregions, with the highest of 33.7% for Fort McMurray and the lowest of 4.7% for Hinton with more coherency since 1991. The phase delay analysis showed that annual and semiannual cycles of precipitation generally led the ones in river flow by a few weeks mainly for the upper and middle ARB since 1991. The climate and river flow anomalies were also demonstrated using the baseline period 1961–90, showing a significant increase in temperature and decrease in precipitation since 1991 for all the three subregions. Unlike the multivariate regression, the proposed wavelet method can analyze any hydrometeorological time series in the time–frequency domain without any need for resampling, interpolation, or gap filling.

Open access
Annie Y.-Y. Chang
,
Konrad Bogner
,
Christian M. Grams
,
Samuel Monhart
,
Daniela I. V. Domeisen
, and
Massimiliano Zappa

Abstract

Across the globe, there has been an increasing interest in improving the predictability of subseasonal hydrometeorological forecasts, as they play a valuable role in medium- to long-term planning in many sectors, such as agriculture, navigation, hydropower, and emergency management. However, these forecasts still have very limited skill at the monthly time scale; hence, this study explores the possibilities for improving forecasts through different pre- and postprocessing techniques at the interface with a Precipitationn–Runoff–Evapotranspiration Hydrological Response Unit Model (PREVAH). Specifically, this research aims to assess the benefit of European weather regime (WR) data within a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of subseasonal hydrometeorological forecasts in Switzerland. The WR data contain information about the large-scale atmospheric circulation in the North Atlantic–European region, and thus allow the hydrological model to exploit potential flow-dependent predictability. Four hydrological variables are investigated: total runoff, baseflow, soil moisture, and snowmelt. The improvements in the forecasts achieved with the pre- and postprocessing techniques vary with catchments, lead times, and variables. Adding WR data has clear benefits, but these benefits are not consistent across the study area or among the variables. The usefulness of WR data is generally observed for longer lead times, e.g., beyond the third week. Furthermore, a multimodel approach is applied to determine the “best practice” for each catchment and improve forecast skill over the entire study area. This study highlights the potential and limitations of using WR information to improve subseasonal hydrometeorological forecasts in a hybrid forecasting system in an operational mode.

Open access
Kathleen D. Holman
,
Kristin M. Mikkelson
, and
Dagmar K. Llewellyn

Abstract

Increasing evaporative demand from storage reservoirs is aggravating water scarcity issues across the American West. In the Rio Grande basin, open water evaporation estimates represent approximately one-fifth of all water losses from the basin. However, most estimates of reservoir evaporation rely on outdated methods, point measurements, or simplistic models. Warming temperatures and increasing atmospheric evaporative demand are stressing overallocated resources, increasing the need for improved evaporation estimates. In response to this need, we develop open water evaporation estimates at Elephant Butte Reservoir (EBR), New Mexico, using three evaporation models and field measurements. Few studies quantify spatial heterogeneity in evaporation rates across large reservoirs; we therefore focus our efforts on using the Weather Research and Forecasting Model coupled to an energy budget lake model, WRF-Lake, to simulate evaporation across EBR over the course of two years. We compare results from WRF-Lake, which simulates lake heat storage, to results from the Complementary Relationship Lake Evaporation (CRLE) model and the Global Lake Evaporation Volume dataset (GLEV). Results indicate that monthly and annual evaporation totals from WRF-Lake and GLEV are similar, while CRLE overestimates annual evaporation totals, with monthly peak evaporation offset compared to WRF-Lake and GLEV. While WRF-Lake and GLEV appear to capture monthly and annual evaporation totals, only WRF-Lake simulates differences in evaporation totals across the reservoir surface. Average annual evaporation at EBR was approximately 1487 mm, yet annual totals differed by up to 545 mm depending on location. This study improves understanding of open water evaporation and elucidates limitations of extrapolating point in situ or bulk evaporation estimates across large reservoirs.

Significance Statement

Changes in climate are amplifying the loss of stored water in reservoirs due to increases in evaporation. Water managers need to account for this water loss, but many current methods do not accurately reflect the temporal and spatial variability in evaporation across large, heterogeneous reservoirs. To address this gap, we use a numerical weather prediction model coupled to a lake model to simulate spatial heterogeneity in reservoir evaporation on a subdaily time step. Our results suggest that bulk evaporation models may be sufficient for estimating evaporation at smaller, more homogeneous reservoirs, but more complex formulations may be more appropriate for estimating evaporation rates at large, complex reservoirs and for better understanding the heat storage affects that influence temporal variability of evaporation.

Open access
Lauren E. L. Lowman
,
Jordan I. Christian
, and
Eric D. Hunt

Abstract

As global mean temperature rises, extreme drought events are expected to increasingly affect regions of the United States that are crucial for agriculture, forestry, and natural ecology. A pressing need is to understand and anticipate the conditions under which extreme drought causes catastrophic failure to vegetation in these areas. To better predict drought impacts on ecosystems, we first must understand how specific drivers, namely, atmospheric aridity and soil water stress, affect land surface processes during the evolution of flash drought events. In this study, we evaluated when vapor pressure deficit (VPD) and soil moisture thresholds corresponding to photosynthetic shutdown were crossed during flash drought events across different climate zones and land surface characteristics in the United States. First, the Dynamic Canopy Biophysical Properties (DCBP) model was used to estimate the thresholds that define reduced photosynthesis by assimilating vegetation phenology data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to a predictive phenology model. Next, we characterized and quantified flash drought onset, intensity, and duration using the standardized evaporative stress ratio (SESR) and NLDAS-2 reanalysis. Once periods of flash drought were identified, we investigated how VPD and soil moisture coevolved across regions and plant functional types. Results demonstrate that croplands and grasslands tend to be more sensitive to soil water limitations than trees across different regions of the United States. We found that whether VPD or soil moisture was the primary driver of plant water stress during drought was largely region specific. The results of this work will help to inform land managers of early warning signals relevant for specific ecosystems under threat of flash drought events.

Open access